instance_id,db_id,db_type,question,gold_sql,gold_columns,n_gold_columns,gold_derivation,snow_instance_id,n_schema_columns
sf001,GLOBAL_WEATHER__CLIMATE_DATA_FOR_BI,snowflake,"Assuming today is April 1, 2024, I would like to know the daily snowfall amounts greater than 6 inches for each U.S. postal code during the week ending after the first two full weeks of the previous year. Show the postal code, date, and snowfall amount.","WITH timestamps AS
(   
    SELECT
        DATE_TRUNC(year,DATEADD(year,-1,DATE '2024-08-29')) AS ref_timestamp,
        LAST_DAY(DATEADD(week,2 + CAST(WEEKISO(ref_timestamp) != 1 AS INTEGER),ref_timestamp),week) AS end_week,
        DATEADD(day, day_num - 7, end_week) AS date_valid_std
    FROM
    (   
        SELECT
            ROW_NUMBER() OVER (ORDER BY SEQ1()) AS day_num
        FROM
            TABLE(GENERATOR(rowcount => 7))
    ) 
)
SELECT
    country,
    postal_code,
    date_valid_std,
    tot_snowfall_in 
FROM 
    GLOBAL_WEATHER__CLIMATE_DATA_FOR_BI.standard_tile.history_day
NATURAL INNER JOIN
    timestamps
WHERE
    country='US' AND
    tot_snowfall_in > 6.0 
ORDER BY 
    postal_code,date_valid_std
;",HISTORY_DAY.COUNTRY; HISTORY_DAY.DATE_VALID_STD; HISTORY_DAY.POSTAL_CODE; HISTORY_DAY.TOT_SNOWFALL_IN,4,lite_sql,sf001,215
sf002,FINANCE__ECONOMICS,snowflake,"As of December 31, 2022, list the top 10 active large banks, each with assets over $10 billion, that have the highest percentage of uninsured assets based on quarterly estimates. Provide the names of these banks and their respective percentages of uninsured assets.","WITH big_banks AS (
    SELECT id_rssd
    FROM FINANCE__ECONOMICS.CYBERSYN.financial_institution_timeseries
    WHERE variable = 'ASSET'
      AND date = '2022-12-31'
      AND value > 1E10
)
SELECT name
FROM FINANCE__ECONOMICS.CYBERSYN.financial_institution_timeseries AS ts
INNER JOIN FINANCE__ECONOMICS.CYBERSYN.financial_institution_attributes AS att ON (ts.variable = att.variable)
INNER JOIN FINANCE__ECONOMICS.CYBERSYN.financial_institution_entities AS ent ON (ts.id_rssd = ent.id_rssd)
INNER JOIN big_banks ON (big_banks.id_rssd = ts.id_rssd)
WHERE ts.date = '2022-12-31'
  AND att.variable_name = '% Insured (Estimated)'
  AND att.frequency = 'Quarterly'
  AND ent.is_active = True
ORDER BY (1 - value) DESC
LIMIT 10;",FINANCIAL_INSTITUTION_ENTITIES.ID_RSSD; FINANCIAL_INSTITUTION_ENTITIES.IS_ACTIVE; FINANCIAL_INSTITUTION_ENTITIES.NAME; FINANCIAL_INSTITUTION_TIMESERIES.DATE; FINANCIAL_INSTITUTION_TIMESERIES.ID_RSSD; FINANCIAL_INSTITUTION_TIMESERIES.VALUE; FINANCIAL_INSTITUTION_TIMESERIES.VARIABLE,7,snow_sql_near_exact,sf002,441
sf011,CENSUS_GALAXY__ZIP_CODE_TO_BLOCK_GROUP_SAMPLE,snowflake,"Determine the population distribution within each block group relative to its census tract in New York State using 2021 ACS data. Include block group ID, census value, state county tract ID, total tract population, and the population ratio of each block group.","WITH TractPop AS (
    SELECT
        CG.""BlockGroupID"",
        FCV.""CensusValue"",
        CG.""StateCountyTractID"",
        CG.""BlockGroupPolygon""
    FROM
        CENSUS_GALAXY__ZIP_CODE_TO_BLOCK_GROUP_SAMPLE.PUBLIC.""Dim_CensusGeography"" CG
    JOIN
        CENSUS_GALAXY__ZIP_CODE_TO_BLOCK_GROUP_SAMPLE.PUBLIC.""Fact_CensusValues_ACS2021"" FCV
        ON CG.""BlockGroupID"" = FCV.""BlockGroupID""
    WHERE
        CG.""StateAbbrev"" = 'NY'
        AND FCV.""MetricID"" = 'B01003_001E'
),

TractGroup AS (
    SELECT
        CG.""StateCountyTractID"",
        SUM(FCV.""CensusValue"") AS ""TotalTractPop""
    FROM
        CENSUS_GALAXY__ZIP_CODE_TO_BLOCK_GROUP_SAMPLE.PUBLIC.""Dim_CensusGeography"" CG
    JOIN
        CENSUS_GALAXY__ZIP_CODE_TO_BLOCK_GROUP_SAMPLE.PUBLIC.""Fact_CensusValues_ACS2021"" FCV
        ON CG.""BlockGroupID"" = FCV.""BlockGroupID""
    WHERE
        CG.""StateAbbrev"" = 'NY'
        AND FCV.""MetricID"" = 'B01003_001E'
    GROUP BY
        CG.""StateCountyTractID""
)

SELECT
    TP.""BlockGroupID"",
    TP.""CensusValue"",
    TP.""StateCountyTractID"",
    TG.""TotalTractPop"",
    CASE WHEN TG.""TotalTractPop"" <> 0 THEN TP.""CensusValue"" / TG.""TotalTractPop"" ELSE 0 END AS ""BlockGroupRatio""
FROM
    TractPop TP
JOIN
    TractGroup TG
    ON TP.""StateCountyTractID"" = TG.""StateCountyTractID"";",Dim_CensusGeography.BlockGroupID; Dim_CensusGeography.BlockGroupPolygon; Dim_CensusGeography.StateAbbrev; Dim_CensusGeography.StateCountyTractID; Fact_CensusValues_ACS2021.BlockGroupID; Fact_CensusValues_ACS2021.CensusValue; Fact_CensusValues_ACS2021.MetricID,7,lite_sql,sf011,57
sf012,WEATHER__ENVIRONMENT,snowflake,What were the total amounts of building and contents damage reported under the National Flood Insurance Program in the City of New York for each year from 2010 to 2019?,"SELECT 
    YEAR(claims.date_of_loss)               AS year_of_loss,
    claims.nfip_community_name,
    SUM(claims.building_damage_amount) AS total_building_damage_amount,
    SUM(claims.contents_damage_amount) AS total_contents_damage_amount
FROM WEATHER__ENVIRONMENT.CYBERSYN.fema_national_flood_insurance_program_claim_index claims
WHERE 
    claims.nfip_community_name = 'City Of New York' 
    AND year_of_loss >=2010 AND year_of_loss <=2019
GROUP BY year_of_loss, claims.nfip_community_name
ORDER BY year_of_loss, claims.nfip_community_name;",FEMA_NATIONAL_FLOOD_INSURANCE_PROGRAM_CLAIM_INDEX.BUILDING_DAMAGE_AMOUNT; FEMA_NATIONAL_FLOOD_INSURANCE_PROGRAM_CLAIM_INDEX.CONTENTS_DAMAGE_AMOUNT; FEMA_NATIONAL_FLOOD_INSURANCE_PROGRAM_CLAIM_INDEX.DATE_OF_LOSS; FEMA_NATIONAL_FLOOD_INSURANCE_PROGRAM_CLAIM_INDEX.NFIP_COMMUNITY_NAME,4,snow_sql_near_exact,sf012,313
sf014,CENSUS_GALAXY__AIML_MODEL_DATA_ENRICHMENT_SAMPLE,snowflake,"What is the New York State ZIP code with the highest number of commuters traveling over one hour, according to 2021 ACS data? Include the zip code, the total commuters, state benchmark for this duration, and state population.","WITH Commuters AS (
    SELECT
        GE.""ZipCode"",
        SUM(CASE WHEN M.""MetricID"" = 'B08303_013E' THEN F.""CensusValueByZip"" ELSE 0 END +
            CASE WHEN M.""MetricID"" = 'B08303_012E' THEN F.""CensusValueByZip"" ELSE 0 END) AS ""Num_Commuters_1Hr_Travel_Time""
    FROM
        CENSUS_GALAXY__AIML_MODEL_DATA_ENRICHMENT_SAMPLE.PUBLIC.""LU_GeographyExpanded"" GE
    JOIN
        CENSUS_GALAXY__AIML_MODEL_DATA_ENRICHMENT_SAMPLE.PUBLIC.""Fact_CensusValues_ACS2021_ByZip"" F
        ON GE.""ZipCode"" = F.""ZipCode""
    JOIN
        CENSUS_GALAXY__AIML_MODEL_DATA_ENRICHMENT_SAMPLE.PUBLIC.""Dim_CensusMetrics"" M
        ON F.""MetricID"" = M.""MetricID""
    WHERE
        GE.""PreferredStateAbbrev"" = 'NY'
        AND (M.""MetricID"" = 'B08303_013E' OR M.""MetricID"" = 'B08303_012E') -- Metric IDs for commuters with 1+ hour travel time
    GROUP BY
        GE.""ZipCode""
),

StateBenchmark AS (
    SELECT
        SB.""StateAbbrev"",
        SUM(SB.""StateBenchmarkValue"") AS ""StateBenchmark_Over1HrTravelTime"",
        SB.""TotalStatePopulation""
    FROM
        CENSUS_GALAXY__AIML_MODEL_DATA_ENRICHMENT_SAMPLE.PUBLIC.""Fact_StateBenchmark_ACS2021"" SB
    WHERE
        SB.""MetricID"" IN ('B08303_013E', 'B08303_012E')
        AND SB.""StateAbbrev"" = 'NY'
    GROUP BY
        SB.""StateAbbrev"", SB.""TotalStatePopulation""
)

SELECT
    C.""ZipCode"",
    SUM(C.""Num_Commuters_1Hr_Travel_Time"") AS ""Total_Commuters_1Hr_Travel_Time"",
    SB.""StateBenchmark_Over1HrTravelTime"",
    SB.""TotalStatePopulation"",
FROM
    Commuters C
CROSS JOIN
    StateBenchmark SB
GROUP BY
    C.""ZipCode"", SB.""StateBenchmark_Over1HrTravelTime"", SB.""TotalStatePopulation""
ORDER BY
    ""Total_Commuters_1Hr_Travel_Time"" DESC
LIMIT 1;",Dim_CensusMetrics.MetricID; Fact_CensusValues_ACS2021_ByZip.CensusValueByZip; Fact_CensusValues_ACS2021_ByZip.MetricID; Fact_CensusValues_ACS2021_ByZip.ZipCode; Fact_StateBenchmark_ACS2021.MetricID; Fact_StateBenchmark_ACS2021.StateAbbrev; Fact_StateBenchmark_ACS2021.StateBenchmarkValue; Fact_StateBenchmark_ACS2021.TotalStatePopulation; LU_GeographyExpanded.PreferredStateAbbrev; LU_GeographyExpanded.ZipCode,10,snow_sql_near_exact,sf014,57
sf018,BRAZE_USER_EVENT_DEMO_DATASET,snowflake,"Examine user engagement with push notifications within a specified one-hour window on June 1, 2023.","WITH push_send AS (
    SELECT
        id,
        app_group_id,
        user_id,
        campaign_id,
        message_variation_id,
        platform,
        ad_tracking_enabled,
        TO_TIMESTAMP(TIME) AS ""TIME"",
        'Send' AS ""EVENT_TYPE""
    FROM
        BRAZE_USER_EVENT_DEMO_DATASET.PUBLIC.USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW
    WHERE
        TO_TIMESTAMP(TIME) BETWEEN '2023-06-01 08:00:00' AND '2023-06-01 09:00:00'
),
push_bounce AS (
    SELECT
        id,
        app_group_id,
        user_id,
        campaign_id,
        message_variation_id,
        platform,
        ad_tracking_enabled,
        TO_TIMESTAMP(TIME) AS ""TIME"",
        'Bounce' AS ""EVENT_TYPE""
    FROM
        BRAZE_USER_EVENT_DEMO_DATASET.PUBLIC.USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW
    WHERE
        TO_TIMESTAMP(TIME) BETWEEN '2023-06-01 08:00:00' AND '2023-06-01 09:00:00'
),
push_open AS (
    SELECT
        id,
        app_group_id,
        user_id,
        campaign_id,
        message_variation_id,
        platform,
        ad_tracking_enabled,
        TO_TIMESTAMP(TIME) AS ""TIME"",
        'Open' AS ""EVENT_TYPE"",
        carrier,
        browser,
        device_model
    FROM
        BRAZE_USER_EVENT_DEMO_DATASET.PUBLIC.USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW
    WHERE
        TO_TIMESTAMP(TIME) BETWEEN '2023-06-01 08:00:00' AND '2023-06-01 09:00:00'
),
push_open_influence AS (
    SELECT
        id,
        app_group_id,
        user_id,
        campaign_id,
        message_variation_id,
        platform,
        TO_TIMESTAMP(TIME) AS ""TIME"",
        'Influenced Open' AS ""EVENT_TYPE"",
        carrier,
        browser,
        device_model
    FROM
        BRAZE_USER_EVENT_DEMO_DATASET.PUBLIC.USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW
    WHERE
        TO_TIMESTAMP(TIME) BETWEEN '2023-06-01 08:00:00' AND '2023-06-01 09:00:00'
)
SELECT
    ps.app_group_id,
    ps.campaign_id,
    ps.user_id,
    ps.time,
    po.time push_open_time,
    ps.message_variation_id,
    ps.platform,
    ps.ad_tracking_enabled,
    po.carrier,
    po.browser,
    po.device_model,
    COUNT(
        DISTINCT ps.id
    ) push_notification_sends,
    COUNT(
        DISTINCT ps.user_id
    ) unique_push_notification_sends,
    COUNT(
        DISTINCT pb.id
    ) push_notification_bounced,
    COUNT(
        DISTINCT pb.user_id
    ) unique_push_notification_bounced,
    COUNT(
        DISTINCT po.id
    ) push_notification_open,
    COUNT(
        DISTINCT po.user_id
    ) unique_push_notification_opened,
    COUNT(
        DISTINCT poi.id
    ) push_notification_influenced_open,
    COUNT(
        DISTINCT poi.user_id
    ) unique_push_notification_influenced_open
FROM
    push_send ps
    LEFT JOIN push_bounce pb
    ON ps.message_variation_id = pb.message_variation_id
    AND ps.user_id = pb.user_id
    AND ps.app_group_id = pb.app_group_id
    LEFT JOIN push_open po
    ON ps.message_variation_id = po.message_variation_id
    AND ps.user_id = po.user_id
    AND ps.app_group_id = po.app_group_id
    LEFT JOIN push_open_influence poi
    ON ps.message_variation_id = poi.message_variation_id
    AND ps.user_id = poi.user_id
    AND ps.app_group_id = poi.app_group_id
GROUP BY
    1,2,3,4,5,6,7,8,9,10,11;",USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_BOUNCE_VIEW.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_INFLUENCEDOPEN_VIEW.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_OPEN_VIEW.USER_ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.AD_TRACKING_ENABLED; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.APP_GROUP_ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.CAMPAIGN_ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.ID; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.PLATFORM; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.TIME; USERS_MESSAGES_PUSHNOTIFICATION_SEND_VIEW.USER_ID,27,lite_sql,sf018,1780
sf040,US_ADDRESSES__POI,snowflake,"Find the top 10 northernmost addresses in Florida's largest zip code area. What are their address numbers, street names, and types?","WITH zip_areas AS (
    SELECT
        geo.geo_id,
        geo.geo_name AS zip,
        states.related_geo_name AS state,
        countries.related_geo_name AS country,
        ST_AREA(TRY_TO_GEOGRAPHY(value)) AS area
    FROM US_ADDRESSES__POI.CYBERSYN.geography_index AS geo
    JOIN US_ADDRESSES__POI.CYBERSYN.geography_relationships AS states
        ON (geo.geo_id = states.geo_id AND states.related_level = 'State')
    JOIN US_ADDRESSES__POI.CYBERSYN.geography_relationships AS countries
        ON (geo.geo_id = countries.geo_id AND countries.related_level = 'Country')
    JOIN US_ADDRESSES__POI.CYBERSYN.geography_characteristics AS chars
        ON (geo.geo_id = chars.geo_id AND chars.relationship_type = 'coordinates_geojson')
    WHERE geo.level = 'CensusZipCodeTabulationArea'
),

zip_area_ranks AS (
    SELECT
        *,
        ROW_NUMBER() OVER (PARTITION BY country, state ORDER BY area DESC, geo_id) AS zip_area_rank
    FROM zip_areas
)

SELECT addr.number, addr.street, addr.street_type
FROM US_ADDRESSES__POI.CYBERSYN.us_addresses AS addr
JOIN zip_area_ranks AS areas
    ON (addr.id_zip = areas.geo_id)
WHERE addr.state = 'FL' AND areas.country = 'United States' AND areas.zip_area_rank = 1
ORDER BY LATITUDE DESC
LIMIT 10;",GEOGRAPHY_CHARACTERISTICS.GEO_ID; GEOGRAPHY_CHARACTERISTICS.RELATIONSHIP_TYPE; GEOGRAPHY_CHARACTERISTICS.VALUE; GEOGRAPHY_INDEX.GEO_ID; GEOGRAPHY_INDEX.GEO_NAME; GEOGRAPHY_INDEX.LEVEL; GEOGRAPHY_RELATIONSHIPS.GEO_ID; GEOGRAPHY_RELATIONSHIPS.RELATED_GEO_NAME; GEOGRAPHY_RELATIONSHIPS.RELATED_LEVEL; US_ADDRESSES.ID_ZIP; US_ADDRESSES.LATITUDE; US_ADDRESSES.NUMBER; US_ADDRESSES.STATE; US_ADDRESSES.STREET; US_ADDRESSES.STREET_TYPE,15,lite_sql,sf040,61
sf044,FINANCE__ECONOMICS,snowflake,"What was the percentage change in post-market close prices for the Magnificent 7 tech companies from January 1 to June 30, 2024?","WITH ytd_performance AS (
  SELECT
    ticker,
    MIN(date) OVER (PARTITION BY ticker) AS start_of_year_date,
    FIRST_VALUE(value) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS start_of_year_price,
    MAX(date) OVER (PARTITION BY ticker) AS latest_date,
    LAST_VALUE(value) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS latest_price
  FROM FINANCE__ECONOMICS.CYBERSYN.stock_price_timeseries
  WHERE
    ticker IN ('AAPL', 'MSFT', 'AMZN', 'GOOGL', 'META', 'TSLA', 'NVDA')
    AND date BETWEEN DATE '2024-01-01' AND DATE '2024-06-30'  -- Adjusted to cover only from the start of 2024 to the end of June 2024
    AND variable_name = 'Post-Market Close'
)
SELECT
  ticker,
  (latest_price - start_of_year_price) / start_of_year_price * 100 AS percentage_change_ytd
FROM
  ytd_performance
GROUP BY
  ticker, start_of_year_date, start_of_year_price, latest_date, latest_price
ORDER BY percentage_change_ytd DESC;",STOCK_PRICE_TIMESERIES.DATE; STOCK_PRICE_TIMESERIES.TICKER; STOCK_PRICE_TIMESERIES.VALUE; STOCK_PRICE_TIMESERIES.VARIABLE_NAME,4,lite_sql,sf044,441
bq001,ga360,bigquery,"I wonder how many days between the first transaction and the first visit both in Feburary 2017 for each transacting visitor, along with the device used in the transaction.","DECLARE start_date STRING DEFAULT '20170201';
DECLARE end_date STRING DEFAULT '20170228';

WITH visit AS (
    SELECT
        fullvisitorid,
        MIN(date) AS date_first_visit
    FROM
        `bigquery-public-data.google_analytics_sample.ga_sessions_*`
    WHERE
       _TABLE_SUFFIX BETWEEN start_date AND end_date
    GROUP BY fullvisitorid
),

transactions AS (
    SELECT
        fullvisitorid,
        MIN(date) AS date_transactions
    FROM
        `bigquery-public-data.google_analytics_sample.ga_sessions_*` AS ga,
        UNNEST(ga.hits) AS hits
    WHERE
        hits.transaction.transactionId IS NOT NULL
        AND
        _TABLE_SUFFIX BETWEEN start_date AND end_date
    GROUP BY fullvisitorid
),

device_transactions AS (
    SELECT DISTINCT
        fullvisitorid,
        date,
        device.deviceCategory
    FROM
        `bigquery-public-data.google_analytics_sample.ga_sessions_*` AS ga,
        UNNEST(ga.hits) AS hits
    WHERE
        hits.transaction.transactionId IS NOT NULL
        AND
        _TABLE_SUFFIX BETWEEN start_date AND end_date
),

visits_transactions AS (
    SELECT
        visit.fullvisitorid,
        date_first_visit,
        date_transactions,
        device_transactions.deviceCategory AS device_transaction
    FROM
        visit
        JOIN transactions
        ON visit.fullvisitorid = transactions.fullvisitorid
        JOIN device_transactions
        ON visit.fullvisitorid = device_transactions.fullvisitorid 
        AND transactions.date_transactions = device_transactions.date
)

SELECT
       fullvisitorid,
       DATE_DIFF(PARSE_DATE('%Y%m%d', date_transactions), PARSE_DATE('%Y%m%d', date_first_visit), DAY) AS time,
       device_transaction
FROM visits_transactions
ORDER BY fullvisitorid;",ga_sessions_*.date; ga_sessions_*.device; ga_sessions_*.fullVisitorId; ga_sessions_*.hits,4,lite_sql,sf_bq001,16
bq002,ga360,bigquery,"What's the maximum monthly, weekly, and daily product revenues (in millions) generated by the top-performing traffic source in the first half of 2017?","DECLARE start_date STRING DEFAULT '20170101';
DECLARE end_date STRING DEFAULT '20170630';

WITH daily_revenue AS (
    SELECT
        trafficSource.source AS source,
        date,
        SUM(productRevenue) / 1000000 AS revenue
    FROM
        `bigquery-public-data.google_analytics_sample.ga_sessions_*`,
        UNNEST (hits) AS hits,
        UNNEST (hits.product) AS product
    WHERE
        _table_suffix BETWEEN start_date AND end_date
    GROUP BY
        source, date
),
weekly_revenue AS (
    SELECT
        source,
        CONCAT(EXTRACT(YEAR FROM (PARSE_DATE('%Y%m%d', date))), 'W', EXTRACT(WEEK FROM (PARSE_DATE('%Y%m%d', date)))) AS week,
        SUM(revenue) AS revenue
    FROM daily_revenue
    GROUP BY source, week
),
monthly_revenue AS (
    SELECT
        source,
        CONCAT(EXTRACT(YEAR FROM (PARSE_DATE('%Y%m%d', date))),'0', EXTRACT(MONTH FROM (PARSE_DATE('%Y%m%d', date)))) AS month,
        SUM(revenue) AS revenue
    FROM daily_revenue
    GROUP BY source, month
),

top_source AS (
    SELECT source, SUM(revenue) AS total_revenue
    FROM daily_revenue
    GROUP BY source
    ORDER BY total_revenue DESC
    LIMIT 1
),

max_revenues AS (
    (
      SELECT
        'Daily' AS time_type,
        date AS time,
        source,
        MAX(revenue) AS max_revenue
      FROM daily_revenue
      WHERE source = (SELECT source FROM top_source)
      GROUP BY source, date
      ORDER BY max_revenue DESC
      LIMIT 1
    )

    UNION ALL

    (
      SELECT
        'Weekly' AS time_type,
        week AS time,
        source,
        MAX(revenue) AS max_revenue
      FROM weekly_revenue
      WHERE source = (SELECT source FROM top_source)
      GROUP BY source, week
      ORDER BY max_revenue DESC
      LIMIT 1
    )

    UNION ALL

    (
      SELECT
          'Monthly' AS time_type,
          month AS time,
          source,
          MAX(revenue) AS max_revenue
      FROM monthly_revenue
      WHERE source = (SELECT source FROM top_source)
      GROUP BY source, month
      ORDER BY max_revenue DESC
      LIMIT 1
    )
)

SELECT
    max_revenue
FROM max_revenues
ORDER BY max_revenue DESC;",ga_sessions_*.date; ga_sessions_*.hits; ga_sessions_*.trafficSource,3,lite_sql,sf_bq002,16
bq003,ga360,bigquery,Compare the average pageviews per visitor between purchase and non-purchase sessions for each month from April to July in 2017.,"WITH cte1 AS (
    SELECT
        CONCAT(EXTRACT(YEAR FROM (PARSE_DATE('%Y%m%d', date))), '0',
            EXTRACT(MONTH FROM (PARSE_DATE('%Y%m%d', date)))) AS month,
        SUM(totals.pageviews) / COUNT(DISTINCT fullVisitorId) AS avg_pageviews_non_purchase
    FROM
        `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`,
        UNNEST (hits) AS hits,
        UNNEST (hits.product) AS product
    WHERE
        _table_suffix BETWEEN '0401' AND '0731'
        AND totals.transactions IS NULL
        AND product.productRevenue IS NULL
    GROUP BY month
),
cte2 AS (
    SELECT
        CONCAT(EXTRACT(YEAR FROM (PARSE_DATE('%Y%m%d', date))), '0',
            EXTRACT(MONTH FROM (PARSE_DATE('%Y%m%d', date)))) AS month,
        SUM(totals.pageviews) / COUNT(DISTINCT fullVisitorId) AS avg_pageviews_purchase
    FROM
        `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`,
        UNNEST (hits) AS hits,
        UNNEST (hits.product) AS product
    WHERE
        _table_suffix BETWEEN '0401' AND '0731'
        AND totals.transactions >= 1
        AND product.productRevenue IS NOT NULL
    GROUP BY month
)
SELECT
    month, avg_pageviews_purchase, avg_pageviews_non_purchase
FROM cte1 INNER JOIN cte2
USING(month)
ORDER BY month;",ga_sessions_*.date; ga_sessions_*.fullVisitorId; ga_sessions_*.hits; ga_sessions_*.totals,4,lite_sql,sf_bq003,16
bq004,ga360,bigquery,What's the most popular other purchased product in July 2017 with consumers who bought products relevant to YouTube?,"with product_and_quatity AS (
    SELECT 
        DISTINCT v2ProductName AS other_purchased_products,
        SUM(productQuantity) AS quatity
    FROM
        `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`,
        UNNEST(hits) AS hits,
        UNNEST(hits.product) AS product
    WHERE
        _table_suffix BETWEEN '0701' AND '0731'
        AND NOT REGEXP_CONTAINS(LOWER(v2ProductName), 'youtube')
        AND fullVisitorID IN (
            SELECT 
            DISTINCT fullVisitorId
            FROM
                `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`,
                UNNEST(hits) AS hits,
                UNNEST(hits.product) AS product
            WHERE
                _table_suffix BETWEEN '0701' AND '0731'
                AND REGEXP_CONTAINS(LOWER(v2ProductName), 'youtube')
        )
    GROUP BY v2ProductName
)
SELECT other_purchased_products
FROM product_and_quatity
ORDER BY quatity DESC
LIMIT 1;",ga_sessions_*.fullVisitorId; ga_sessions_*.hits,2,lite_sql,sf_bq004,16
bq006,austin,bigquery,What is the date with the second highest Z-score for daily counts of 'PUBLIC INTOXICATION' incidents in Austin for the year 2016? List the date in the format of '2016-xx-xx'.,"WITH incident_stats AS (
  SELECT 
    COUNT(descript) AS total_pub_intox
  FROM 
    `bigquery-public-data.austin_incidents.incidents_2016` 
  WHERE 
    descript = 'PUBLIC INTOXICATION' 
  GROUP BY 
    date
),
average_and_stddev AS (
  SELECT 
    AVG(total_pub_intox) AS avg, 
    STDDEV(total_pub_intox) AS stddev 
  FROM 
    incident_stats
),
daily_z_scores AS (
  SELECT 
    date, 
    COUNT(descript) AS total_pub_intox, 
    ROUND((COUNT(descript) - a.avg) / a.stddev, 2) AS z_score
  FROM 
    `bigquery-public-data.austin_incidents.incidents_2016`,
    (SELECT avg, stddev FROM average_and_stddev) AS a
  WHERE 
    descript = 'PUBLIC INTOXICATION'
  GROUP BY 
    date, avg, stddev
)

SELECT 
  date
FROM 
  daily_z_scores
ORDER BY 
  z_score DESC
LIMIT 1
OFFSET 1",incidents_*.date; incidents_*.descript,2,lite_sql,sf_bq006,81
bq008,ga360,bigquery,"What's the most common next page for visitors who were part of ""Data Share"" campaign and after they accessed the page starting with '/home' in January 2017. And what's the maximum duration time (in seconds) when they visit the corresponding home page?","with page_visit_sequence AS (
    SELECT
        fullVisitorID,
        visitID,
        pagePath,
        LEAD(timestamp, 1) OVER (PARTITION BY fullVisitorId, visitID order by timestamp) - timestamp AS page_duration,
        LEAD(pagePath, 1) OVER (PARTITION BY fullVisitorId, visitID order by timestamp) AS next_page,
        RANK() OVER (PARTITION BY fullVisitorId, visitID order by timestamp) AS step_number
    FROM (
        SELECT
            pages.fullVisitorID,
            pages.visitID,
            pages.pagePath,
            visitors.campaign,
            MIN(pages.timestamp) timestamp
        FROM (
            SELECT
                fullVisitorId,
                visitId,
                trafficSource.campaign campaign
            FROM
                `bigquery-public-data.google_analytics_sample.ga_sessions_*`,
                UNNEST(hits) as hits
            WHERE
                _TABLE_SUFFIX BETWEEN '20170101' AND '20170131'
                AND hits.type='PAGE'
                AND REGEXP_CONTAINS(hits.page.pagePath, r'^/home')
                AND REGEXP_CONTAINS(trafficSource.campaign, r'Data Share')
        ) AS visitors
        JOIN(
            SELECT
                fullVisitorId,
                visitId,
                visitStartTime + hits.time / 1000 AS timestamp,
                hits.page.pagePath AS pagePath
            FROM
                `bigquery-public-data.google_analytics_sample.ga_sessions_*`,
                UNNEST(hits) as hits
            WHERE
                _TABLE_SUFFIX BETWEEN '20170101' AND '20170131'
        ) as pages
        ON
            visitors.fullVisitorID = pages.fullVisitorID
            AND visitors.visitID = pages.visitID
        GROUP BY 
            pages.fullVisitorID, visitors.campaign, pages.visitID, pages.pagePath
        ORDER BY 
            pages.fullVisitorID, pages.visitID, timestamp
    )
    ORDER BY fullVisitorId, visitID, step_number
),
most_common_next_page AS (
    SELECT
        next_page,
        COUNT(next_page) as page_count
    FROM page_visit_sequence
    WHERE
        next_page IS NOT NULL
        AND REGEXP_CONTAINS(pagePath, r'^/home')
    GROUP BY next_page
    ORDER BY page_count DESC
    LIMIT 1
),
max_page_duration AS (
    SELECT MAX(page_duration) as max_duration
    FROM page_visit_sequence
    WHERE
        page_duration IS NOT NULL
        AND REGEXP_CONTAINS(pagePath, r'^/home')
)
SELECT
    next_page,
    max_duration
FROM
    most_common_next_page,
    max_page_duration;",ga_sessions_*.fullVisitorId; ga_sessions_*.hits; ga_sessions_*.trafficSource; ga_sessions_*.visitId; ga_sessions_*.visitStartTime,5,lite_sql,sf_bq008,16
bq009,ga360,bigquery,"Which traffic source receives the top revenue in 2017 and what is the difference (millions, rounded to two decimal places) between its highest and lowest revenue months?","WITH MONTHLY_REVENUE AS (
    SELECT 
        FORMAT_DATE(""%Y%m"", PARSE_DATE(""%Y%m%d"", date)) AS month,
        trafficSource.source AS source,
        ROUND(SUM(totals.totalTransactionRevenue) / 1000000, 2) AS revenue
    FROM `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`
    GROUP BY 1, 2
),

YEARLY_REVENUE AS (
    SELECT
        source,
        SUM(revenue) AS total_revenue
    FROM MONTHLY_REVENUE
    GROUP BY source
),

TOP_SOURCE AS (
    SELECT 
        source
    FROM YEARLY_REVENUE
    ORDER BY total_revenue DESC
    LIMIT 1
),

SOURCE_MONTHLY_REVENUE AS (
    SELECT
        month,
        source,
        revenue
    FROM MONTHLY_REVENUE
    WHERE source IN (SELECT source FROM TOP_SOURCE)
),

REVENUE_DIFF AS (
    SELECT 
        source,
        ROUND(MAX(revenue), 2) AS max_revenue,
        ROUND(MIN(revenue), 2) AS min_revenue,
        ROUND(MAX(revenue) - MIN(revenue), 2) AS diff_revenue
    FROM SOURCE_MONTHLY_REVENUE
    GROUP BY source
)

SELECT 
    source,
    diff_revenue
FROM REVENUE_DIFF;",ga_sessions_*.date; ga_sessions_*.totals; ga_sessions_*.trafficSource,3,lite_sql,sf_bq009,16
bq010,ga360,bigquery,"Find the top-selling product among customers who bought 'Youtube Men’s Vintage Henley' in July 2017, excluding itself.","WITH GET_CUS_ID AS (
    SELECT 
        DISTINCT fullVisitorId as Henley_CUSTOMER_ID
    FROM 
        `bigquery-public-data.google_analytics_sample.ga_sessions_201707*`,
        UNNEST(hits) AS hits,
        UNNEST(hits.product) as product
    WHERE
        product.v2ProductName = ""YouTube Men's Vintage Henley""
        AND product.productRevenue IS NOT NULL
    )

SELECT
    product.v2ProductName AS other_purchased_products
FROM
    `bigquery-public-data.google_analytics_sample.ga_sessions_201707*` TAB_A 
    RIGHT JOIN GET_CUS_ID
    ON GET_CUS_ID.Henley_CUSTOMER_ID=TAB_A.fullVisitorId,
    UNNEST(hits) AS hits,
    UNNEST(hits.product) as product
WHERE
    TAB_A.fullVisitorId IN (
        SELECT * FROM GET_CUS_ID
    )
    AND product.v2ProductName <> ""YouTube Men's Vintage Henley""
    AND product.productRevenue IS NOT NULL
GROUP BY
    product.v2ProductName
ORDER BY
    SUM(product.productQuantity) DESC
LIMIT 1;",ga_sessions_*.fullVisitorId; ga_sessions_*.hits,2,lite_sql,sf_bq010,16
bq011,ga4,bigquery,"How many pseudo users were active in the last 7 days but inactive in the last 2 days as of January 7, 2021?","SELECT
  COUNT(DISTINCT MDaysUsers.user_pseudo_id) AS n_day_inactive_users_count
FROM
  (
    SELECT
      user_pseudo_id
    FROM
      `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*` AS T
    CROSS JOIN
      UNNEST(T.event_params) AS event_params
    WHERE
      event_params.key = 'engagement_time_msec' AND event_params.value.int_value > 0
      AND event_timestamp > UNIX_MICROS(TIMESTAMP_SUB(TIMESTAMP('2021-01-07 23:59:59'), INTERVAL 7 DAY))
      AND _TABLE_SUFFIX BETWEEN '20210101' AND '20210107'
  ) AS MDaysUsers
LEFT JOIN
  (
    SELECT
      user_pseudo_id
    FROM
      `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*` AS T
    CROSS JOIN
      UNNEST(T.event_params) AS event_params
    WHERE
      event_params.key = 'engagement_time_msec' AND event_params.value.int_value > 0
      AND event_timestamp > UNIX_MICROS(TIMESTAMP_SUB(TIMESTAMP('2021-01-07 23:59:59'), INTERVAL 2 DAY))
      AND _TABLE_SUFFIX BETWEEN '20210105' AND '20210107'
  ) AS NDaysUsers
ON MDaysUsers.user_pseudo_id = NDaysUsers.user_pseudo_id
WHERE
  NDaysUsers.user_pseudo_id IS NULL;",events_*.event_params; events_*.event_timestamp; events_*.user_pseudo_id,3,lite_sql,sf_bq011,23
sf_bq012,ETHEREUM_BLOCKCHAIN,snowflake,"What is the average balance of the top 10 addresses with the most balance on the Ethereum blockchain, considering both incoming and outgoing transactions with valid addresses, but only those recorded as used on receipt, as well as transaction fees? Only keep successful transactions with no call type or where the call type is 'call'. The average balance, expressed in quadrillions (10^15), is rounded to two decimal places.","WITH double_entry_book AS (
  -- Debits
  SELECT 
    ""to_address"" AS ""address"",
    ""value"" AS ""value""
  FROM ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TRACES""
  WHERE ""to_address"" IS NOT NULL
    AND ""status"" = 1
    AND (""call_type"" NOT IN ('delegatecall', 'callcode', 'staticcall') OR ""call_type"" IS NULL)
  
  UNION ALL
  
  -- Credits
  SELECT 
    ""from_address"" AS ""address"",
    - ""value"" AS ""value""
  FROM ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TRACES""
  WHERE ""from_address"" IS NOT NULL
    AND ""status"" = 1
    AND (""call_type"" NOT IN ('delegatecall', 'callcode', 'staticcall') OR ""call_type"" IS NULL)
  
  UNION ALL
  
  -- Transaction fees debits
  SELECT 
    ""miner"" AS ""address"",
    SUM(CAST(""receipt_gas_used"" AS NUMBER) * CAST(""gas_price"" AS NUMBER)) AS ""value""
  FROM ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TRANSACTIONS"" AS ""transactions""
  JOIN ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""BLOCKS"" AS ""blocks""
    ON ""blocks"".""number"" = ""transactions"".""block_number""
  GROUP BY ""blocks"".""miner""
  
  UNION ALL
  
  -- Transaction fees credits
  SELECT 
    ""from_address"" AS ""address"",
    -(CAST(""receipt_gas_used"" AS NUMBER) * CAST(""gas_price"" AS NUMBER)) AS ""value""
  FROM ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TRANSACTIONS""
),
top_10_balances AS (
  SELECT
    ""address"",
    SUM(""value"") AS ""balance""
  FROM double_entry_book
  GROUP BY ""address""
  ORDER BY ""balance"" DESC
  LIMIT 10
)
SELECT 
    ROUND(AVG(""balance"") / 1e15, 2) AS ""average_balance_trillion""
FROM top_10_balances;",BLOCKS.hash; BLOCKS.miner; TRACES.call_type; TRACES.from_address; TRACES.status; TRACES.to_address; TRACES.trace_type; TRACES.value; TRANSACTIONS.block_hash; TRANSACTIONS.from_address; TRANSACTIONS.gas_price; TRANSACTIONS.receipt_gas_used; TRANSACTIONS.receipt_status,13,snow_sql_near_exact,sf_bq012,88
sf_bq017,GEO_OPENSTREETMAP,snowflake,"What are the five longest types of highways within the multipolygon boundary of Denmark (as defined by Wikidata ID 'Q35') by total length,  analyzed through planet features?","WITH bounding_area AS (
    SELECT ""geometry"" AS geometry
    FROM GEO_OPENSTREETMAP.GEO_OPENSTREETMAP.PLANET_FEATURES,
    LATERAL FLATTEN(INPUT => planet_features.""all_tags"") AS ""tag""
    WHERE ""feature_type"" = 'multipolygons'
      AND ""tag"".value:""key"" = 'wikidata'
      AND ""tag"".value:""value"" = 'Q35'
),

highway_info AS (
    SELECT 
        SUM(ST_LENGTH(
                ST_GEOGRAPHYFROMWKB(planet_features.""geometry"")
            )
        ) AS highway_length,
        ""tag"".value:""value"" AS highway_type
    FROM 
        GEO_OPENSTREETMAP.GEO_OPENSTREETMAP.PLANET_FEATURES AS planet_features,
        bounding_area
    CROSS JOIN LATERAL FLATTEN(INPUT => planet_features.""all_tags"") AS ""tag""
    WHERE ""tag"".value:""key"" = 'highway'
    AND ""feature_type"" = 'lines'
    AND ST_DWITHIN(
        ST_GEOGFROMWKB(planet_features.""geometry""), 
        ST_GEOGFROMWKB(bounding_area.geometry),
        0.0
    ) 
    GROUP BY highway_type
)

SELECT 
  REPLACE(highway_type, '""', '') AS highway_type
FROM
  highway_info
ORDER BY 
  highway_length DESC
LIMIT 5;",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry,3,lite_sql,sf_bq017,86
bq018,covid19_open_data,bigquery,Which day in March and April had the highest COVID-19 confirmed case growth rate in the United States? The format is MM-DD.,"WITH us_cases_by_date AS (
  SELECT
    date,
    SUM( cumulative_confirmed ) AS cases
  FROM
    `bigquery-public-data.covid19_open_data.covid19_open_data`
  WHERE
    country_name=""United States of America""
    AND date between '2020-03-01' and '2020-04-30'
  GROUP BY
    date
  ORDER BY
    date ASC
 )

, us_previous_day_comparison AS
(SELECT
  date,
  cases,
  LAG(cases) OVER(ORDER BY date) AS previous_day,
  cases - LAG(cases) OVER(ORDER BY date) AS net_new_cases,
  (cases - LAG(cases) OVER(ORDER BY date))*100/LAG(cases) OVER(ORDER BY date) AS percentage_increase
FROM us_cases_by_date
)
SELECT
  FORMAT_DATE('%m-%d', Date) 
FROM
  us_previous_day_comparison
ORDER BY  
  percentage_increase
DESC
LIMIT 1",covid19_open_data.country_name; covid19_open_data.cumulative_confirmed; covid19_open_data.date,3,lite_sql,sf_bq018,701
bq021,new_york,bigquery,"For the top 20 Citi Bike routes in 2016, which route is faster than yellow taxis and among those, which one has the longest average bike duration? Please provide the start station name of this route. The coordinates are rounded to three decimals.","WITH top20route AS (
SELECT
  start_station_name, end_station_name, avg_bike_duration, avg_taxi_duration
FROM (
  SELECT
    start_station_name,
    end_station_name,
    ROUND(start_station_latitude, 3) AS ss_lat,
    ROUND(start_station_longitude, 3) AS ss_long,
    ROUND(end_station_latitude, 3) AS es_lat,
    ROUND(end_station_longitude, 3) AS es_long,
    AVG(tripduration) AS avg_bike_duration,
    COUNT(*) AS bike_trips
  FROM
    `bigquery-public-data.new_york.citibike_trips`
  WHERE 
  EXTRACT(YEAR from starttime) = 2016 AND
    start_station_name != end_station_name
  GROUP BY
    start_station_name, end_station_name, ss_lat, ss_long, es_lat, es_long
  ORDER BY
    bike_trips DESC
  LIMIT
    20
) a
JOIN (
  SELECT
    ROUND(pickup_latitude, 3) AS pu_lat,
    ROUND(pickup_longitude, 3) AS pu_long,
    ROUND(dropoff_latitude, 3) AS do_lat,
    ROUND(dropoff_longitude, 3) AS do_long,
    AVG(UNIX_SECONDS(dropoff_datetime)-UNIX_SECONDS(pickup_datetime)) AS avg_taxi_duration,
    COUNT(*) AS taxi_trips
  FROM
    `bigquery-public-data.new_york.tlc_yellow_trips_2016`
  GROUP BY
    pu_lat, pu_long, do_lat, do_long
) b
ON
  a.ss_lat = b.pu_lat AND
  a.es_lat = b.do_lat AND
  a.ss_long = b.pu_long AND
  a.es_long = b.do_long
)

SELECT start_station_name
FROM top20route
WHERE avg_bike_duration < avg_taxi_duration
ORDER BY
avg_bike_duration
DESC
LIMIT 1",citibike_trips.end_station_latitude; citibike_trips.end_station_longitude; citibike_trips.end_station_name; citibike_trips.start_station_latitude; citibike_trips.start_station_longitude; citibike_trips.start_station_name; citibike_trips.starttime; citibike_trips.tripduration; tlc_yellow_trips_*.dropoff_datetime; tlc_yellow_trips_*.dropoff_latitude; tlc_yellow_trips_*.dropoff_longitude; tlc_yellow_trips_*.pickup_datetime; tlc_yellow_trips_*.pickup_latitude; tlc_yellow_trips_*.pickup_longitude,14,lite_sql,sf_bq021,252
bq022,chicago,bigquery,"Given the taxi trip data in Chicago, partition the trips that last no more than 1 hour into 6 quantiles based on trip duration. Please provide the minimum/maximum trip duration (rounded-off to integer minutes), total trips, and average fare for each quantile.","SELECT
  ROUND(MIN(trip_seconds) / 60, 0) AS min_minutes,
  ROUND(MAX(trip_seconds) / 60, 0) AS max_minutes,
  COUNT(*) AS total_trips,
  AVG(fare) AS average_fare
FROM (
  SELECT
    trip_seconds,
    NTILE(6) OVER (ORDER BY trip_seconds) AS quantile,
    fare
  FROM
    `bigquery-public-data.chicago_taxi_trips.taxi_trips`
  WHERE
    trip_seconds BETWEEN 0 AND 3600
)
GROUP BY
  quantile
ORDER BY
  min_minutes, max_minutes;",taxi_trips.fare; taxi_trips.trip_seconds,2,lite_sql,sf_bq022,45
bq025,census_bureau_international,bigquery,"Provide a list of the top 10 countries for the year 2020, ordered by the highest percentage of their population under 20 years old. For each country, include the total population under 20 years old, the total midyear population, and the percentage of the population that is under 20 years old.","SELECT
  age.country_name,
  SUM(age.population) AS under_25,
  pop.midyear_population AS total,
  ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25
FROM (
  SELECT
    country_name,
    population,
    country_code
  FROM
    `bigquery-public-data.census_bureau_international.midyear_population_agespecific`
  WHERE
    year =2020
    AND age < 20) age
INNER JOIN (
  SELECT
    midyear_population,
    country_code
  FROM
    `bigquery-public-data.census_bureau_international.midyear_population`
  WHERE
    year = 2020) pop
ON
  age.country_code = pop.country_code
GROUP BY
  1,
  3
ORDER BY
  4 DESC
/* Remove limit for visualization */
LIMIT
  10",midyear_population.country_code; midyear_population.midyear_population; midyear_population.year; midyear_population_agespecific.age; midyear_population_agespecific.country_code; midyear_population_agespecific.country_name; midyear_population_agespecific.population; midyear_population_agespecific.year,8,lite_sql,sf_bq025,165
sf_bq028,DEPS_DEV_V1,snowflake,"Considering only the latest release versions of NPM package, which packages are the top 8 most popular based on the Github star number, as well as their versions?","WITH HighestReleases AS (
    SELECT
        HR.""Name"",
        HR.""Version""
    FROM (
        SELECT
            ""Name"",
            ""Version"",
            ROW_NUMBER() OVER (
                PARTITION BY ""Name""
                ORDER BY 
                    TO_NUMBER(PARSE_JSON(""VersionInfo""):""Ordinal"") DESC
            ) AS RowNumber
        FROM
            DEPS_DEV_V1.DEPS_DEV_V1.PACKAGEVERSIONS
        WHERE
            ""System"" = 'NPM'
            AND TO_BOOLEAN(PARSE_JSON(""VersionInfo""):""IsRelease"") = TRUE
    ) AS HR
    WHERE HR.RowNumber = 1
),
PVP AS (
    SELECT
        PVP.""Name"", 
        PVP.""Version"", 
        PVP.""ProjectType"", 
        PVP.""ProjectName""
    FROM
        DEPS_DEV_V1.DEPS_DEV_V1.PACKAGEVERSIONTOPROJECT AS PVP
    JOIN
        HighestReleases AS HR
    ON
        PVP.""Name"" = HR.""Name""
        AND PVP.""Version"" = HR.""Version""
    WHERE
        PVP.""System"" = 'NPM'
        AND PVP.""ProjectType"" = 'GITHUB'
)
SELECT
    PVP.""Name"", 
    PVP.""Version""
FROM
    PVP
JOIN
    DEPS_DEV_V1.DEPS_DEV_V1.PROJECTS AS P
ON
    PVP.""ProjectType"" = P.""Type"" 
    AND PVP.""ProjectName"" = P.""Name""
ORDER BY 
    P.""StarsCount"" DESC
LIMIT 8;",PACKAGEVERSIONS.Name; PACKAGEVERSIONS.System; PACKAGEVERSIONS.Version; PACKAGEVERSIONS.VersionInfo; PACKAGEVERSIONTOPROJECT.Name; PACKAGEVERSIONTOPROJECT.ProjectName; PACKAGEVERSIONTOPROJECT.ProjectType; PACKAGEVERSIONTOPROJECT.System; PACKAGEVERSIONTOPROJECT.Version; PROJECTS.Name; PROJECTS.StarsCount; PROJECTS.Type,12,lite_sql,sf_bq028,78
bq031,noaa_data,bigquery,"Show me the daily weather data (temperature, precipitation, and wind speed) in Rochester for the first season of year 2019, converted to Celsius, centimeters, and meters per second, respectively. Also, include the moving averages (window size = 8) and the differences between the moving averages for up to 8 days prior (all values rounded to one decimal place, sorted by date in ascending order, and records starting from 2019-01-09).","WITH transrate AS (
    SELECT
        DATE(CAST(year AS INT64), CAST(mo AS INT64), CAST(da AS INT64)) AS observation_date
        , ROUND((temp - 32.0) / 1.8, 1) AS temp_mean_c -- using Celsius instead of Fahrenheit
        , ROUND(prcp * 2.54, 1) AS prcp_cm -- from inches to centimeters
        , ROUND(CAST(wdsp AS FLOAT64) * 1.852 / 3.6, 1) AS wdsp_ms -- from knots to meters per second
    FROM `bigquery-public-data.noaa_gsod.gsod*`
    WHERE _TABLE_SUFFIX = ""2019""
        AND CAST(mo AS INT64) <= 3
        AND stn in (SELECT usaf FROM `bigquery-public-data.noaa_gsod.stations` WHERE name = ""ROCHESTER"")
),

moving_avg AS (
    SELECT
        observation_date
        , temp_mean_c
        , prcp_cm
        , wdsp_ms
        , AVG(temp_mean_c) OVER (ORDER BY observation_date ROWS 7 PRECEDING) AS temp_moving_avg
        , AVG(prcp_cm) OVER (ORDER BY observation_date ROWS 7 PRECEDING) AS prcp_moving_avg
        , AVG(wdsp_ms) OVER (ORDER BY observation_date ROWS 7 PRECEDING) AS wdsp_moving_avg
    FROM transrate
),

lag_moving_avg AS (
    SELECT
        observation_date
        , temp_mean_c
        , prcp_cm
        , wdsp_ms
        , LAG(temp_moving_avg, 1) OVER (ORDER BY observation_date) AS lag1_temp_moving_avg
        , LAG(prcp_moving_avg, 1) OVER (ORDER BY observation_date) AS lag1_prcp_moving_avg
        , LAG(wdsp_moving_avg, 1) OVER (ORDER BY observation_date) AS lag1_wdsp_moving_avg

        , LAG(temp_moving_avg, 2) OVER (ORDER BY observation_date) AS lag2_temp_moving_avg
        , LAG(prcp_moving_avg, 2) OVER (ORDER BY observation_date) AS lag2_prcp_moving_avg
        , LAG(wdsp_moving_avg, 2) OVER (ORDER BY observation_date) AS lag2_wdsp_moving_avg

        , LAG(temp_moving_avg, 3) OVER (ORDER BY observation_date) AS lag3_temp_moving_avg
        , LAG(prcp_moving_avg, 3) OVER (ORDER BY observation_date) AS lag3_prcp_moving_avg
        , LAG(wdsp_moving_avg, 3) OVER (ORDER BY observation_date) AS lag3_wdsp_moving_avg

        , LAG(temp_moving_avg, 4) OVER (ORDER BY observation_date) AS lag4_temp_moving_avg
        , LAG(prcp_moving_avg, 4) OVER (ORDER BY observation_date) AS lag4_prcp_moving_avg
        , LAG(wdsp_moving_avg, 4) OVER (ORDER BY observation_date) AS lag4_wdsp_moving_avg

        , LAG(temp_moving_avg, 5) OVER (ORDER BY observation_date) AS lag5_temp_moving_avg
        , LAG(prcp_moving_avg, 5) OVER (ORDER BY observation_date) AS lag5_prcp_moving_avg
        , LAG(wdsp_moving_avg, 5) OVER (ORDER BY observation_date) AS lag5_wdsp_moving_avg

        , LAG(temp_moving_avg, 6) OVER (ORDER BY observation_date) AS lag6_temp_moving_avg
        , LAG(prcp_moving_avg, 6) OVER (ORDER BY observation_date) AS lag6_prcp_moving_avg
        , LAG(wdsp_moving_avg, 6) OVER (ORDER BY observation_date) AS lag6_wdsp_moving_avg

        , LAG(temp_moving_avg, 7) OVER (ORDER BY observation_date) AS lag7_temp_moving_avg
        , LAG(prcp_moving_avg, 7) OVER (ORDER BY observation_date) AS lag7_prcp_moving_avg
        , LAG(wdsp_moving_avg, 7) OVER (ORDER BY observation_date) AS lag7_wdsp_moving_avg

        , LAG(temp_moving_avg, 8) OVER (ORDER BY observation_date) AS lag8_temp_moving_avg
        , LAG(prcp_moving_avg, 8) OVER (ORDER BY observation_date) AS lag8_prcp_moving_avg
        , LAG(wdsp_moving_avg, 8) OVER (ORDER BY observation_date) AS lag8_wdsp_moving_avg
    FROM moving_avg
)

SELECT
    observation_date
    , temp_mean_c
    , prcp_cm
    , wdsp_ms

    , ROUND(lag1_temp_moving_avg, 1) AS lag1_temp_moving_avg
    , ROUND(lag1_prcp_moving_avg, 1) AS lag1_prcp_moving_avg
    , ROUND(lag1_wdsp_moving_avg, 1) AS lag1_wdsp_moving_avg
    
    , ROUND(lag1_temp_moving_avg - lag2_temp_moving_avg, 1) AS diff2_temp_moving_avg
    , ROUND(lag1_prcp_moving_avg - lag2_prcp_moving_avg, 1) AS diff2_prcp_moving_avg
    , ROUND(lag1_wdsp_moving_avg - lag2_wdsp_moving_avg, 1) AS diff2_wdsp_moving_avg
    , ROUND(lag2_temp_moving_avg, 1) AS lag2_temp_moving_avg
    , ROUND(lag2_prcp_moving_avg, 1) AS lag2_prcp_moving_avg
    , ROUND(lag2_wdsp_moving_avg, 1) AS lag2_wdsp_moving_avg
    
    , ROUND(lag2_temp_moving_avg - lag3_temp_moving_avg, 1) AS diff3_temp_moving_avg
    , ROUND(lag2_prcp_moving_avg - lag3_prcp_moving_avg, 1) AS diff3_prcp_moving_avg
    , ROUND(lag2_wdsp_moving_avg - lag3_wdsp_moving_avg, 1) AS diff3_wdsp_moving_avg
    , ROUND(lag3_temp_moving_avg, 1) AS lag3_temp_moving_avg
    , ROUND(lag3_prcp_moving_avg, 1) AS lag3_prcp_moving_avg
    , ROUND(lag3_wdsp_moving_avg, 1) AS lag3_wdsp_moving_avg
    
    , ROUND(lag3_temp_moving_avg - lag4_temp_moving_avg, 1) AS diff4_temp_moving_avg
    , ROUND(lag3_prcp_moving_avg - lag4_prcp_moving_avg, 1) AS diff4_prcp_moving_avg
    , ROUND(lag3_wdsp_moving_avg - lag4_wdsp_moving_avg, 1) AS diff4_wdsp_moving_avg
    , ROUND(lag4_temp_moving_avg, 1) AS lag4_temp_moving_avg
    , ROUND(lag4_prcp_moving_avg, 1) AS lag4_prcp_moving_avg
    , ROUND(lag4_wdsp_moving_avg, 1) AS lag4_wdsp_moving_avg
    
    , ROUND(lag4_temp_moving_avg - lag5_temp_moving_avg, 1) AS diff5_temp_moving_avg
    , ROUND(lag4_prcp_moving_avg - lag5_prcp_moving_avg, 1) AS diff5_prcp_moving_avg
    , ROUND(lag4_wdsp_moving_avg - lag5_wdsp_moving_avg, 1) AS diff5_wdsp_moving_avg
    , ROUND(lag5_temp_moving_avg, 1) AS lag5_temp_moving_avg
    , ROUND(lag5_prcp_moving_avg, 1) AS lag5_prcp_moving_avg
    , ROUND(lag5_wdsp_moving_avg, 1) AS lag5_wdsp_moving_avg
    
    , ROUND(lag5_temp_moving_avg - lag6_temp_moving_avg, 1) AS diff6_temp_moving_avg
    , ROUND(lag5_prcp_moving_avg - lag6_prcp_moving_avg, 1) AS diff6_prcp_moving_avg
    , ROUND(lag5_wdsp_moving_avg - lag6_wdsp_moving_avg, 1) AS diff6_wdsp_moving_avg
    , ROUND(lag6_temp_moving_avg, 1) AS lag6_temp_moving_avg
    , ROUND(lag6_prcp_moving_avg, 1) AS lag6_prcp_moving_avg
    , ROUND(lag6_wdsp_moving_avg, 1) AS lag6_wdsp_moving_avg
    
    , ROUND(lag6_temp_moving_avg - lag7_temp_moving_avg, 1) AS diff7_temp_moving_avg
    , ROUND(lag6_prcp_moving_avg - lag7_prcp_moving_avg, 1) AS diff7_prcp_moving_avg
    , ROUND(lag6_wdsp_moving_avg - lag7_wdsp_moving_avg, 1) AS diff7_wdsp_moving_avg
    , ROUND(lag7_temp_moving_avg, 1) AS lag7_temp_moving_avg
    , ROUND(lag7_prcp_moving_avg, 1) AS lag7_prcp_moving_avg
    , ROUND(lag7_wdsp_moving_avg, 1) AS lag7_wdsp_moving_avg
    
    , ROUND(lag7_temp_moving_avg - lag8_temp_moving_avg, 1) AS diff8_temp_moving_avg
    , ROUND(lag7_prcp_moving_avg - lag8_prcp_moving_avg, 1) AS diff8_prcp_moving_avg
    , ROUND(lag7_wdsp_moving_avg - lag8_wdsp_moving_avg, 1) AS diff8_wdsp_moving_avg
    , ROUND(lag8_temp_moving_avg, 1) AS lag8_temp_moving_avg
    , ROUND(lag8_prcp_moving_avg, 1) AS lag8_prcp_moving_avg
    , ROUND(lag8_wdsp_moving_avg, 1) AS lag8_wdsp_moving_avg
FROM lag_moving_avg
WHERE
  lag8_temp_moving_avg IS NOT NULL
ORDER BY observation_date;
-- all result rounded to 1 decimal place",gsod_*.da; gsod_*.mo; gsod_*.prcp; gsod_*.stn; gsod_*.temp; gsod_*.wdsp; gsod_*.year; stations.name; stations.usaf,9,lite_sql,sf_bq031,660
bq032,noaa_data,bigquery,Can you provide the latitude of the final coordinates for the hurricane that traveled the second longest distance in the North Atlantic during 2020?,"WITH hurricane_geometry AS (
  SELECT
    * EXCEPT (longitude, latitude),
    ST_GEOGPOINT(longitude, latitude) AS geom,
    MAX(usa_wind) OVER (PARTITION BY sid) AS max_wnd_speed
  FROM
    `bigquery-public-data.noaa_hurricanes.hurricanes`
  WHERE
    season = '2020'
    AND basin = 'NA'
    AND name != 'NOT NAMED'
),
dist_between_points AS (
  SELECT
    sid,
    name,
    season,
    iso_time,
    max_wnd_speed,
    geom,
    ST_DISTANCE(geom, LAG(geom, 1) OVER (PARTITION BY sid ORDER BY iso_time ASC)) / 1000 AS dist
  FROM
    hurricane_geometry
),
total_distances AS (
  SELECT
    sid,
    name,
    season,
    iso_time,
    max_wnd_speed,
    geom,
    SUM(dist) OVER (PARTITION BY sid ORDER BY iso_time ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_distance,
    SUM(dist) OVER (PARTITION BY sid) AS total_dist
  FROM
    dist_between_points
),
ranked_hurricanes AS (
  SELECT
    *,
    DENSE_RANK() OVER (ORDER BY total_dist DESC) AS dense_rank
  FROM
    total_distances
)

SELECT
  ST_Y(geom)
FROM
  ranked_hurricanes
WHERE
  dense_rank = 2
ORDER BY
cumulative_distance
DESC
LIMIT 1
;",hurricanes.basin; hurricanes.iso_time; hurricanes.latitude; hurricanes.longitude; hurricanes.name; hurricanes.season; hurricanes.sid; hurricanes.usa_wind,8,lite_sql,sf_bq032,660
sf_bq033,PATENTS,snowflake,"How many U.S. publications related to IoT (where the abstract includes the phrase 'internet of things') were filed each month from 2008 to 2022, including months with no filings?","WITH Patent_Matches AS (
    SELECT
      TO_DATE(CAST(ANY_VALUE(patentsdb.""filing_date"") AS STRING), 'YYYYMMDD') AS Patent_Filing_Date,
      patentsdb.""application_number"" AS Patent_Application_Number,
      MAX(abstract_info.value:""text"") AS Patent_Title,
      MAX(abstract_info.value:""language"") AS Patent_Title_Language
    FROM
      PATENTS.PATENTS.PUBLICATIONS AS patentsdb,
      LATERAL FLATTEN(input => patentsdb.""abstract_localized"") AS abstract_info
    WHERE
      LOWER(abstract_info.value:""text"") LIKE '%internet of things%'
      AND patentsdb.""country_code"" = 'US'
    GROUP BY
      Patent_Application_Number
),

Date_Series_Table AS (
    SELECT
        DATEADD(day, seq4(), DATE '2008-01-01') AS day,
        0 AS Number_of_Patents
    FROM
        TABLE(
            GENERATOR(
                ROWCOUNT => 5479
            )
        )
    ORDER BY
        day
)

SELECT
  TO_CHAR(Date_Series_Table.day, 'YYYY-MM') AS Patent_Date_YearMonth,
  COUNT(Patent_Matches.Patent_Application_Number) AS Number_of_Patent_Applications
FROM
  Date_Series_Table
  LEFT JOIN Patent_Matches
    ON Date_Series_Table.day = Patent_Matches.Patent_Filing_Date
WHERE
    Date_Series_Table.day < DATE '2023-01-01'
GROUP BY
  TO_CHAR(Date_Series_Table.day, 'YYYY-MM')
ORDER BY
  Patent_Date_YearMonth;",PUBLICATIONS.abstract_localized; PUBLICATIONS.application_number; PUBLICATIONS.country_code; PUBLICATIONS.filing_date,4,lite_sql,sf_bq033,79
bq034,ghcn_d,bigquery,"I want to know the IDs, names of weather stations within a 50 km straight-line distance from the center of Chicago (41.8319°N, 87.6847°W)","WITH params AS (
  SELECT ST_GeogPoint(-87.6847, 41.8319) AS center,
         50 AS maxdist_km
),
distance_from_center AS (
  SELECT
    id,
    name,
    state,
    ST_GeogPoint(longitude, latitude) AS loc,
    ST_Distance(ST_GeogPoint(longitude, latitude), params.center) AS dist_meters
  FROM
    `bigquery-public-data.ghcn_d.ghcnd_stations`,
    params
  WHERE ST_DWithin(ST_GeogPoint(longitude, latitude), params.center, params.maxdist_km*1000)
),
nearest_stations AS (
  SELECT 
    *, 
    RANK() OVER (ORDER BY dist_meters ASC) AS rank
  FROM 
    distance_from_center
),
nearest_nstations AS (
  SELECT 
    station.* 
  FROM 
    nearest_stations AS station, params
)
SELECT * from nearest_nstations",ghcnd_stations.id; ghcnd_stations.latitude; ghcnd_stations.longitude; ghcnd_stations.name; ghcnd_stations.state,5,lite_sql,sf_bq034,37
bq035,san_francisco,bigquery,What is the total distance traveled by each bike in the San Francisco Bikeshare program? Use data from bikeshare trips and stations to calculate this.,"SELECT
  bike_number, 
  AVG(dist_in_m) AS avg_dist_m, 
  SUM(dist_in_m) AS total_dist_m
FROM (
  SELECT
    ST_DISTANCE(
      ST_GEOGPOINT(start_lon, start_lat),
      ST_GEOGPOINT(end_lon, end_lat)
    ) AS dist_in_m,
    starts.bike_number
  FROM (
    SELECT 
      latitude AS start_lat,
      longitude AS start_lon,
      bike_number,
      trip_id
    FROM `bigquery-public-data.san_francisco.bikeshare_trips` trips
    LEFT JOIN `bigquery-public-data.san_francisco.bikeshare_stations` stations
      ON trips.start_station_id = stations.station_id
  ) starts
  LEFT JOIN (
    SELECT 
      latitude AS end_lat,
      longitude AS end_lon,
      bike_number,
      trip_id
    FROM `bigquery-public-data.san_francisco.bikeshare_trips` trips
    LEFT JOIN `bigquery-public-data.san_francisco.bikeshare_stations` stations
      ON trips.end_station_id = stations.station_id
  ) ends ON ends.trip_id = starts.trip_id
)
GROUP BY bike_number
ORDER BY total_dist_m DESC",bikeshare_stations.latitude; bikeshare_stations.longitude; bikeshare_stations.station_id; bikeshare_trips.bike_number; bikeshare_trips.end_station_id; bikeshare_trips.start_station_id; bikeshare_trips.trip_id,7,lite_sql,sf_bq035,118
sf_bq037,HUMAN_GENOME_VARIANTS,snowflake,"About the refined human genetic variations collected in phase 3 on 2015-02-20, I want to know the minimum and maximum start positions as well as the proportions of these two respectively for reference bases 'AT' and 'TA'.","WITH A AS (
    SELECT
        ""reference_bases"",
        ""start_position""
    FROM
        ""HUMAN_GENOME_VARIANTS"".""HUMAN_GENOME_VARIANTS"".""_1000_GENOMES_PHASE_3_OPTIMIZED_SCHEMA_VARIANTS_20150220""
    WHERE
        ""reference_bases"" IN ('AT', 'TA')
),
B AS (
    SELECT
        ""reference_bases"",
        MIN(""start_position"") AS ""min_start_position"",
        MAX(""start_position"") AS ""max_start_position"",
        COUNT(1) AS ""total_count""
    FROM
        A
    GROUP BY
        ""reference_bases""
),
min_counts AS (
    SELECT
        A.""reference_bases"",  -- Explicitly referencing the column from table A
        A.""start_position"" AS ""min_start_position"",
        COUNT(1) AS ""min_count""
    FROM
        A
    INNER JOIN B 
        ON A.""reference_bases"" = B.""reference_bases""
    WHERE
        A.""start_position"" = B.""min_start_position""
    GROUP BY
        A.""reference_bases"", A.""start_position""
),
max_counts AS (
    SELECT
        A.""reference_bases"",  -- Explicitly referencing the column from table A
        A.""start_position"" AS ""max_start_position"",
        COUNT(1) AS ""max_count""
    FROM
        A
    INNER JOIN B
        ON A.""reference_bases"" = B.""reference_bases""
    WHERE
        A.""start_position"" = B.""max_start_position""
    GROUP BY
        A.""reference_bases"", A.""start_position""
)
SELECT
    B.""reference_bases"",  -- Explicitly referencing the column from table B
    B.""min_start_position"",
    CAST(min_counts.""min_count"" AS FLOAT) / B.""total_count"" AS ""min_position_ratio"",
    B.""max_start_position"",
    CAST(max_counts.""max_count"" AS FLOAT) / B.""total_count"" AS ""max_position_ratio""
FROM
    B
LEFT JOIN
    min_counts ON B.""reference_bases"" = min_counts.""reference_bases"" AND B.""min_start_position"" = min_counts.""min_start_position""
LEFT JOIN
    max_counts ON B.""reference_bases"" = max_counts.""reference_bases"" AND B.""max_start_position"" = max_counts.""max_start_position""
ORDER BY
    B.""reference_bases"";",_1000_GENOMES_PHASE_3_OPTIMIZED_SCHEMA_VARIANTS_20150220.reference_bases; _1000_GENOMES_PHASE_3_OPTIMIZED_SCHEMA_VARIANTS_20150220.start_position,2,snow_sql_near_exact,sf_bq037,202
bq039,new_york_plus,bigquery,"Which are the top 10 taxi trips in New York City from July 1 to July 7, 2016, with more than 5 passengers, a trip distance of at least 10 miles, and a positive fare, ranked by total fare amount? Display the pickup and dropoff zones, trip duration, driving speed in miles per hour, and tip rate. Note that you should avoid invalid items.","SELECT 
    tz.zone_name AS pickup_zone,
    tz1.zone_name AS dropoff_zone, 
    time_duration_in_secs,
    driving_speed_miles_per_hour,
    tip_rate
FROM
(
SELECT *,
    TIMESTAMP_DIFF(dropoff_datetime,pickup_datetime,SECOND) as time_duration_in_secs,
    ROUND(trip_distance / (TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, SECOND) / 3600), 2) AS driving_speed_miles_per_hour,
    (CASE WHEN total_amount=0 THEN 0
          ELSE (tip_amount*100/total_amount) END) as tip_rate
FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2016`
) t
INNER JOIN `bigquery-public-data.new_york_taxi_trips.taxi_zone_geom` tz
ON t.pickup_location_id = tz.zone_id
INNER JOIN `bigquery-public-data.new_york_taxi_trips.taxi_zone_geom` tz1
ON t.dropoff_location_id = tz1.zone_id
WHERE 
    pickup_datetime BETWEEN '2016-07-01' AND '2016-07-07' 
    AND dropoff_datetime BETWEEN '2016-07-01' AND '2016-07-07'
    AND TIMESTAMP_DIFF(dropoff_datetime,pickup_datetime,SECOND) > 0
    AND passenger_count > 5
    AND trip_distance >= 10
    AND tip_amount >= 0 
    AND tolls_amount >= 0 
    AND mta_tax >= 0 
    AND fare_amount >= 0
    AND total_amount >= 0
ORDER BY total_amount DESC
LIMIT 10;",taxi_zone_geom.zone_id; taxi_zone_geom.zone_name; tlc_yellow_trips_*.dropoff_datetime; tlc_yellow_trips_*.dropoff_location_id; tlc_yellow_trips_*.fare_amount; tlc_yellow_trips_*.mta_tax; tlc_yellow_trips_*.passenger_count; tlc_yellow_trips_*.pickup_datetime; tlc_yellow_trips_*.pickup_location_id; tlc_yellow_trips_*.tip_amount; tlc_yellow_trips_*.tolls_amount; tlc_yellow_trips_*.total_amount; tlc_yellow_trips_*.trip_distance,13,lite_sql,sf_bq039,360
bq042,noaa_data,bigquery,"Help me analyze the weather conditions (including temperature, wind speed and precipitation) at NYC's airport LaGuardia for June 12, year over year, starting from 2011 to 2020.","SELECT
  -- Create a timestamp from the date components.
  TIMESTAMP(CONCAT(year,""-"",mo,""-"",da)) AS timestamp,
  -- Replace numerical null values with actual null
  AVG(IF (temp=9999.9,
      null,
      temp)) AS temperature,
  AVG(IF (wdsp=""999.9"",
      null,
      CAST(wdsp AS Float64))) AS wind_speed,
  AVG(IF (prcp=99.99,
      0,
      prcp)) AS precipitation
FROM
  `bigquery-public-data.noaa_gsod.gsod20*`
WHERE
  CAST(YEAR AS INT64) > 2010
  AND CAST(YEAR AS INT64) < 2021
  AND CAST(MO AS INT64) = 6
  AND CAST(DA AS INT64) = 12
  AND stn = ""725030"" -- La Guardia
GROUP BY
  timestamp
ORDER BY
  timestamp ASC;",gsod_*.da; gsod_*.mo; gsod_*.prcp; gsod_*.stn; gsod_*.temp; gsod_*.wdsp; gsod_*.year,7,lite_sql,sf_bq042,660
sf_bq043,TCGA,snowflake,"What are the RNA expression levels of the genes MDM2, TP53, CDKN1A, and CCNE1, along with associated clinical information, in bladder cancer patients with CDKN2A mutations in the 'TCGA-BLCA' project?  Use clinical data from the Genomic Data Commons Release 39, data about somatic mutations derived from the hg19 human genome reference in Feb 2017.","SELECT
  genex.""case_barcode"" AS ""case_barcode"",
  genex.""sample_barcode"" AS ""sample_barcode"",
  genex.""aliquot_barcode"" AS ""aliquot_barcode"",
  genex.""HGNC_gene_symbol"" AS ""HGNC_gene_symbol"",
  clinical_info.""Variant_Type"" AS ""Variant_Type"",
  genex.""gene_id"" AS ""gene_id"",
  genex.""normalized_count"" AS ""normalized_count"",
  genex.""project_short_name"" AS ""project_short_name"",
  clinical_info.""demo__gender"" AS ""gender"",
  clinical_info.""demo__vital_status"" AS ""vital_status"",
  clinical_info.""demo__days_to_death"" AS ""days_to_death""
FROM ( 
  SELECT
    case_list.""Variant_Type"" AS ""Variant_Type"",
    case_list.""case_barcode"" AS ""case_barcode"",
    clinical.""demo__gender"",
    clinical.""demo__vital_status"",
    clinical.""demo__days_to_death""
  FROM
    (SELECT
      mutation.""case_barcode"",
      mutation.""Variant_Type""
    FROM
      ""TCGA"".""TCGA_VERSIONED"".""SOMATIC_MUTATION_HG19_DCC_2017_02"" AS mutation
    WHERE
      mutation.""Hugo_Symbol"" = 'CDKN2A'
      AND mutation.""project_short_name"" = 'TCGA-BLCA'
    GROUP BY
      mutation.""case_barcode"",
      mutation.""Variant_Type""
    ORDER BY
      mutation.""case_barcode""
    ) AS case_list /* end case_list */
  INNER JOIN
    ""TCGA"".""TCGA_VERSIONED"".""CLINICAL_GDC_R39"" AS clinical
  ON
    case_list.""case_barcode"" = clinical.""submitter_id"" /* end clinical annotation */ ) AS clinical_info
INNER JOIN
  ""TCGA"".""TCGA_VERSIONED"".""RNASEQ_HG19_GDC_2017_02"" AS genex
ON
  genex.""case_barcode"" = clinical_info.""case_barcode""
WHERE
  genex.""HGNC_gene_symbol"" IN ('MDM2', 'TP53', 'CDKN1A','CCNE1')
ORDER BY
  ""case_barcode"",
  ""HGNC_gene_symbol"";",SOMATIC_MUTATION_HG19_DCC_2017_02.Hugo_Symbol; SOMATIC_MUTATION_HG19_DCC_2017_02.case_barcode; SOMATIC_MUTATION_HG19_DCC_2017_02.project_short_name,3,lite_sql,sf_bq043,1279
bq045,noaa_data,bigquery,Which weather stations in Washington State had more than 150 rainy days in 2023 but fewer rainy days than in 2022? Define a 'rainy day' as any day where the precipitation recorded is more than 0 millimeters.,"WITH WashingtonStations2023 AS 
    (
        SELECT 
            weather.stn AS station_id,
            ANY_VALUE(station.name) AS name
        FROM
            `bigquery-public-data.noaa_gsod.stations` AS station
        INNER JOIN
            `bigquery-public-data.noaa_gsod.gsod2023` AS weather
        ON
            station.usaf = weather.stn
        WHERE
            station.state = 'WA' 
            AND 
            station.usaf != '999999'
        GROUP BY
            station_id
    ),
prcp2023 AS (
SELECT
    washington_stations.name,
    (
        SELECT 
            COUNT(*)
        FROM
            `bigquery-public-data.noaa_gsod.gsod2023` AS weather
        WHERE
            washington_stations.station_id = weather.stn
            AND
            prcp > 0
            AND
            prcp !=99.99
    )
    AS rainy_days
FROM 
    WashingtonStations2023 AS washington_stations
ORDER BY
    rainy_days DESC
),
WashingtonStations2022 AS 
    (
        SELECT 
            weather.stn AS station_id,
            ANY_VALUE(station.name) AS name
        FROM
            `bigquery-public-data.noaa_gsod.stations` AS station
        INNER JOIN
            `bigquery-public-data.noaa_gsod.gsod2022` AS weather
        ON
            station.usaf = weather.stn
        WHERE
            station.state = 'WA' 
            AND 
            station.usaf != '999999'
        GROUP BY
            station_id
    ),
prcp2022 AS (
SELECT
    washington_stations.name,
    (
        SELECT 
            COUNT(*)
        FROM
            `bigquery-public-data.noaa_gsod.gsod2022` AS weather
        WHERE
            washington_stations.station_id = weather.stn
            AND
            prcp > 0
            AND
            prcp != 99.99
    )
    AS rainy_days
FROM 
    WashingtonStations2022 AS washington_stations
ORDER BY
    rainy_days DESC
)

SELECT prcp2023.name
FROM prcp2023
JOIN prcp2022
on prcp2023.name = prcp2022.name
WHERE prcp2023.rainy_days > 150
AND prcp2023.rainy_days < prcp2022.rainy_days",gsod_*.prcp; gsod_*.stn; stations.name; stations.state; stations.usaf,5,lite_sql,sf_bq045,660
bq049,iowa_liquor_sales_plus,bigquery,"Display the monthly per capita Bourbon Whiskey sales in 2022 for the zip code with the third-highest total sales in Dubuque County, considering only the population aged 21 and over.","WITH DUBUQUE_LIQUOR_CTE AS (
SELECT
  CASE
      WHEN UPPER(category_name) LIKE 'BUTTERSCOTCH SCHNAPPS' THEN 'All Other' --Edge case is not a scotch
      WHEN UPPER(category_name) LIKE '%WHISKIES' 
            AND UPPER(category_name) NOT LIKE '%RYE%'
            AND UPPER(category_name) NOT LIKE '%BOURBON%'
            AND UPPER(category_name) NOT LIKE '%SCOTCH%'     THEN 'Other Whiskey'
      WHEN UPPER(category_name) LIKE '%RYE%'                 THEN 'Rye Whiskey'
      WHEN UPPER(category_name) LIKE '%BOURBON%'             THEN 'Bourbon Whiskey'
      WHEN UPPER(category_name) LIKE '%SCOTCH%'              THEN 'Scotch Whiskey'
      ELSE 'All Other'
  END                              AS category_group,
  EXTRACT(MONTH FROM date)         AS month,    -- At the time of this query, there is only data until month 6.
  LEFT(CAST(zip_code AS string),5) AS zip_code, -- Casting to string necessary because zip_code has a mix of int & str types.
  ROUND(SUM(sale_dollars), 2)      AS sale_dollars_sum,

FROM 
  bigquery-public-data.iowa_liquor_sales.sales

WHERE
  UPPER(county)               = 'DUBUQUE'
  AND EXTRACT(YEAR FROM date) = 2022

GROUP BY
  category_group,
  month,
  zip_code
  
ORDER BY 
  category_group,
  month,
  zip_code
),

DUBUQUE_POPULATION_CTE AS (
SELECT
  zipcode,
  SUM(population) AS population_sum
FROM bigquery-public-data.census_bureau_usa.population_by_zip_2010
WHERE 
  minimum_age >= 21
GROUP BY 
  zipcode
),
MONTH_INFO AS (
SELECT 
  l.month,
  l.zip_code,
  l.sale_dollars_sum,
  ROUND(sale_dollars_sum/p.population_sum, 2) AS dollars_per_capita
FROM 
  DUBUQUE_LIQUOR_CTE AS l
  LEFT JOIN 
  DUBUQUE_POPULATION_CTE AS p
  ON l.zip_code = p.zipcode
WHERE
  category_group = 'Bourbon Whiskey'
GROUP BY 
  category_group,
  zip_code,
  month,
  sale_dollars_sum,
  zipcode,
  population_sum
ORDER BY
  zip_code,
  month
),
zip_code_sales AS (
    SELECT
        zip_code,
        SUM(sale_dollars_sum) AS total_sale_dollars_sum
    FROM MONTH_INFO
    GROUP BY zip_code
),
ranked_zip_codes AS (
    SELECT
        zip_code,
        total_sale_dollars_sum,
        ROW_NUMBER() OVER (ORDER BY total_sale_dollars_sum DESC) AS rank
    FROM zip_code_sales
)
SELECT
    t.month,
    t.zip_code,
    t.dollars_per_capita
FROM MONTH_INFO t
JOIN ranked_zip_codes r
ON t.zip_code = r.zip_code
WHERE r.rank = 3
ORDER BY t.month;",population_by_zip_*.minimum_age; population_by_zip_*.population; population_by_zip_*.zipcode; sales.category_name; sales.county; sales.date; sales.sale_dollars; sales.zip_code,8,lite_sql,sf_bq049,30
sf_bq050,NEW_YORK_CITIBIKE_1,snowflake,"Help me look at the total number of bike trips, average trip duration (in minutes), average daily temperature, wind speed, and precipitation when trip starts (rounded to 1 decimal), as well as the month with the most trips (e.g., `4`), categorized by different starting and ending neighborhoods in New York City for the year 2014.","WITH data AS (
    SELECT
        ""ZIPSTARTNAME"".""borough"" AS ""borough_start"",
        ""ZIPSTARTNAME"".""neighborhood"" AS ""neighborhood_start"",
        ""ZIPENDNAME"".""borough"" AS ""borough_end"",
        ""ZIPENDNAME"".""neighborhood"" AS ""neighborhood_end"",
        CAST(""TRI"".""tripduration"" / 60 AS NUMERIC) AS ""trip_minutes"",
        ""WEA"".""temp"" AS ""temperature"",
        CAST(""WEA"".""wdsp"" AS NUMERIC) AS ""wind_speed"",
        ""WEA"".""prcp"" AS ""precipitation"",
        EXTRACT(MONTH FROM DATE(""TRI"".""starttime"")) AS ""start_month""
    FROM
        ""NEW_YORK_CITIBIKE_1"".""NEW_YORK_CITIBIKE"".""CITIBIKE_TRIPS"" AS ""TRI""
    INNER JOIN
        ""NEW_YORK_CITIBIKE_1"".""GEO_US_BOUNDARIES"".""ZIP_CODES"" AS ""ZIPSTART""
        ON ST_WITHIN(
            ST_POINT(""TRI"".""start_station_longitude"", ""TRI"".""start_station_latitude""),
            ST_GEOGFROMWKB(""ZIPSTART"".""zip_code_geom"")
        )
    INNER JOIN
        ""NEW_YORK_CITIBIKE_1"".""GEO_US_BOUNDARIES"".""ZIP_CODES"" AS ""ZIPEND""
        ON ST_WITHIN(
            ST_POINT(""TRI"".""end_station_longitude"", ""TRI"".""end_station_latitude""),
            ST_GEOGFROMWKB(""ZIPEND"".""zip_code_geom"")
        )
    INNER JOIN
        ""NEW_YORK_CITIBIKE_1"".""NOAA_GSOD"".""GSOD2014"" AS ""WEA""
        ON TO_DATE(CONCAT(""WEA"".""year"", LPAD(""WEA"".""mo"", 2, '0'), LPAD(""WEA"".""da"", 2, '0')), 'YYYYMMDD') = DATE(""TRI"".""starttime"")
    INNER JOIN
        ""NEW_YORK_CITIBIKE_1"".""CYCLISTIC"".""ZIP_CODES"" AS ""ZIPSTARTNAME""
        ON ""ZIPSTART"".""zip_code"" = CAST(""ZIPSTARTNAME"".""zip"" AS STRING)
    INNER JOIN
        ""NEW_YORK_CITIBIKE_1"".""CYCLISTIC"".""ZIP_CODES"" AS ""ZIPENDNAME""
        ON ""ZIPEND"".""zip_code"" = CAST(""ZIPENDNAME"".""zip"" AS STRING)
    WHERE
        ""WEA"".""wban"" = (
            SELECT ""wban"" 
            FROM ""NEW_YORK_CITIBIKE_1"".""NOAA_GSOD"".""STATIONS""
            WHERE
                ""state"" = 'NY'
                AND LOWER(""name"") LIKE LOWER('%New York Central Park%')
            LIMIT 1
        )
        AND EXTRACT(YEAR FROM DATE(""TRI"".""starttime"")) = 2014
),
agg_data AS (
    SELECT
        ""borough_start"",
        ""neighborhood_start"",
        ""borough_end"",
        ""neighborhood_end"",
        COUNT(*) AS ""num_trips"",
        ROUND(AVG(""trip_minutes""), 1) AS ""avg_trip_minutes"",
        ROUND(AVG(""temperature""), 1) AS ""avg_temperature"",
        ROUND(AVG(""wind_speed""), 1) AS ""avg_wind_speed"",
        ROUND(AVG(""precipitation""), 1) AS ""avg_precipitation""
    FROM data
    GROUP BY
        ""borough_start"",
        ""neighborhood_start"",
        ""borough_end"",
        ""neighborhood_end""
),
most_common_months AS (
    SELECT
        ""borough_start"",
        ""neighborhood_start"",
        ""borough_end"",
        ""neighborhood_end"",
        ""start_month"",
        ROW_NUMBER() OVER (
            PARTITION BY ""borough_start"", ""neighborhood_start"", ""borough_end"", ""neighborhood_end"" 
            ORDER BY COUNT(*) DESC
        ) AS ""row_num""
    FROM data
    GROUP BY
        ""borough_start"",
        ""neighborhood_start"",
        ""borough_end"",
        ""neighborhood_end"",
        ""start_month""
)

SELECT
    a.*,
    m.""start_month"" AS ""most_common_month""
FROM
    agg_data a
JOIN
    most_common_months m
    ON a.""borough_start"" = m.""borough_start"" 
    AND a.""neighborhood_start"" = m.""neighborhood_start"" 
    AND a.""borough_end"" = m.""borough_end"" 
    AND a.""neighborhood_end"" = m.""neighborhood_end"" 
    AND m.""row_num"" = 1
ORDER BY 
    a.""neighborhood_start"", 
    a.""neighborhood_end"";",CITIBIKE_TRIPS.end_station_latitude; CITIBIKE_TRIPS.end_station_longitude; CITIBIKE_TRIPS.start_station_latitude; CITIBIKE_TRIPS.start_station_longitude; CITIBIKE_TRIPS.starttime; CITIBIKE_TRIPS.tripduration; GSOD_*.da; GSOD_*.mo; GSOD_*.prcp; GSOD_*.temp; GSOD_*.wban; GSOD_*.wdsp; GSOD_*.year; ZIP_CODES.borough; ZIP_CODES.neighborhood; ZIP_CODES.state_code; ZIP_CODES.zip; ZIP_CODES.zip_code; ZIP_CODES.zip_code_geom,19,snow_sql_near_exact,sf_bq050,245
sf_bq052,PATENTSVIEW,snowflake,"I wonder which patents within CPC subsection 'C05' or group 'A01G' in the USA have at least one forward or backward citations within one month of their application dates. Give me the ids, titles, application date, forward/backward citation counts and summary texts.","SELECT
    app.""patent_id"" AS ""patent_id"",
    patent.""title"",
    app.""date"" AS ""application_date"",
    filterData.""bkwdCitations_1"",
    filterData.""fwrdCitations_1"",
    summary.""text"" AS ""summary_text""
FROM
    PATENTSVIEW.PATENTSVIEW.BRF_SUM_TEXT AS summary
JOIN
    PATENTSVIEW.PATENTSVIEW.PATENT AS patent
    ON summary.""patent_id"" = patent.""id""
JOIN
    PATENTSVIEW.PATENTSVIEW.APPLICATION AS app
    ON app.""patent_id"" = summary.""patent_id""
JOIN (
    SELECT DISTINCT
        cpc.""patent_id"",
        IFNULL(citation_1.""bkwdCitations_1"", 0) AS ""bkwdCitations_1"",
        IFNULL(citation_1.""fwrdCitations_1"", 0) AS ""fwrdCitations_1""
    FROM
        PATENTSVIEW.PATENTSVIEW.CPC_CURRENT AS cpc
    JOIN (
        SELECT
            b.""patent_id"",
            b.""bkwdCitations_1"",
            f.""fwrdCitations_1""
        FROM (
            SELECT
                cited.""patent_id"",
                COUNT(*) AS ""fwrdCitations_1""
            FROM
                PATENTSVIEW.PATENTSVIEW.USPATENTCITATION AS cited
            JOIN
                PATENTSVIEW.PATENTSVIEW.APPLICATION AS apps
                ON cited.""patent_id"" = apps.""patent_id""
            WHERE
                apps.""country"" = 'US'
                AND cited.""date"" >= apps.""date""
                AND TRY_CAST(cited.""date"" AS DATE) <= DATEADD(MONTH, 1, TRY_CAST(apps.""date"" AS DATE)) -- Citation within 1 month
            GROUP BY
                cited.""patent_id""
        ) AS f
        JOIN (
            SELECT
                cited.""patent_id"",
                COUNT(*) AS ""bkwdCitations_1""
            FROM
                PATENTSVIEW.PATENTSVIEW.USPATENTCITATION AS cited
            JOIN
                PATENTSVIEW.PATENTSVIEW.APPLICATION AS apps
                ON cited.""patent_id"" = apps.""patent_id""
            WHERE
                apps.""country"" = 'US'
                AND cited.""date"" < apps.""date""
                AND TRY_CAST(cited.""date"" AS DATE) >= DATEADD(MONTH, -1, TRY_CAST(apps.""date"" AS DATE)) -- Citation within 1 month before
            GROUP BY
                cited.""patent_id""
        ) AS b
        ON b.""patent_id"" = f.""patent_id""
        WHERE
            b.""bkwdCitations_1"" IS NOT NULL
            AND f.""fwrdCitations_1"" IS NOT NULL
            AND (b.""bkwdCitations_1"" > 0 OR f.""fwrdCitations_1"" > 0)
    ) AS citation_1
    ON cpc.""patent_id"" = citation_1.""patent_id""
    WHERE
        cpc.""subsection_id"" = 'C05'
        OR cpc.""group_id"" = 'A01G'
) AS filterData
ON app.""patent_id"" = filterData.""patent_id""
ORDER BY app.""date"";",APPLICATION.country; APPLICATION.date; APPLICATION.patent_id; BRF_SUM_TEXT.patent_id; BRF_SUM_TEXT.text; CPC_CURRENT.group_id; CPC_CURRENT.patent_id; CPC_CURRENT.subsection_id; PATENT.id; PATENT.title; USPATENTCITATION.date; USPATENTCITATION.patent_id,12,lite_sql,sf_bq052,304
bq053,new_york,bigquery,"How has the number of trees of each fall color in New York City changed from 1995 to 2015, considering only trees that were still alive in 2015 and excluding those marked as dead in 1995?","SELECT
  c.fall_color,
  SUM(d.count_growth) AS change
FROM (
  SELECT
    fall_color,
    UPPER(species_scientific_name) AS latin
  FROM
    `bigquery-public-data.new_york.tree_species`)c
JOIN (
  SELECT
    IFNULL(a.upper_latin,
      b.upper_latin) AS latin,
    (IFNULL(count_2015,
        0)-IFNULL(count_1995,
        0)) AS count_growth
  FROM (
    SELECT
      UPPER(spc_latin) AS upper_latin,
      spc_common,
      COUNT(*) AS count_2015
    FROM
      `bigquery-public-data.new_york.tree_census_2015`
    WHERE
      status=""Alive""
    GROUP BY
      spc_latin,
      spc_common)a
  FULL OUTER JOIN (
    SELECT
      UPPER(spc_latin) AS upper_latin,
      COUNT(*) AS count_1995
    FROM
      `bigquery-public-data.new_york.tree_census_1995`
    WHERE
      status !=""Dead""
    GROUP BY
      spc_latin)b
  ON
    a.upper_latin=b.upper_latin
  ORDER BY
    count_growth DESC)d
ON
  d.latin=c.latin
GROUP BY
  fall_color
ORDER BY
  change DESC",tree_census_*.spc_common; tree_census_*.spc_latin; tree_census_*.status; tree_species.fall_color; tree_species.species_scientific_name,5,lite_sql,sf_bq053,252
sf_bq057,CRYPTO,snowflake,"Which month (e.g., 3) in 2021 witnessed the highest percent of Bitcoin volume that took place in CoinJoin transactions? Also give me the percentage of CoinJoins transactions, the average input and output UTXOs ratio, and the proportion of CoinJoin transaction volume for that month (all 1 decimal).","WITH totals AS (
    -- Aggregate monthly totals for Bitcoin txs, input/output UTXOs,
    -- and input/output values (UTXO stands for Unspent Transaction Output)
    SELECT
        ""txs_tot"".""block_timestamp_month"" AS tx_month,
        COUNT(""txs_tot"".""hash"") AS tx_count,
        SUM(""txs_tot"".""input_count"") AS tx_inputs,
        SUM(""txs_tot"".""output_count"") AS tx_outputs,
        SUM(""txs_tot"".""input_value"") / 100000000 AS tx_input_val,
        SUM(""txs_tot"".""output_value"") / 100000000 AS tx_output_val
    FROM CRYPTO.CRYPTO_BITCOIN.TRANSACTIONS AS ""txs_tot""
    WHERE ""txs_tot"".""block_timestamp_month"" BETWEEN CAST('2021-01-01' AS DATE) AND CAST('2021-12-31' AS DATE)
    GROUP BY ""txs_tot"".""block_timestamp_month""
    ORDER BY ""txs_tot"".""block_timestamp_month"" DESC
),
coinjoinOuts AS (
    -- Builds a table where each row represents an output of a 
    -- potential CoinJoin tx, defined as a tx that had more 
    -- than two outputs and had a total output value less than its
    -- input value, per Adam Fiscor's description in this article: 
    SELECT 
        ""txs"".""hash"",
        ""txs"".""block_number"",
        ""txs"".""block_timestamp_month"",
        ""txs"".""input_count"",
        ""txs"".""output_count"",
        ""txs"".""input_value"",
        ""txs"".""output_value"",
        ""o"".value:""value"" AS ""outputs_val""
    FROM CRYPTO.CRYPTO_BITCOIN.TRANSACTIONS AS ""txs"", 
         LATERAL FLATTEN(INPUT => ""txs"".""outputs"") AS ""o""
    WHERE ""txs"".""output_count"" > 2 
      AND ""txs"".""output_value"" <= ""txs"".""input_value""
      AND ""txs"".""block_timestamp_month"" BETWEEN CAST('2021-01-01' AS DATE) AND CAST('2021-12-31' AS DATE)
    ORDER BY ""txs"".""block_number"", ""txs"".""hash"" DESC
),
coinjoinTxs AS (
    -- Builds a table of just the distinct CoinJoin tx hashes
    -- which had more than one equal-value output.
    SELECT 
        ""coinjoinouts"".""hash"" AS ""cjhash"",
        ""coinjoinouts"".""outputs_val"" AS outputVal,
        COUNT(*) AS cjOuts
    FROM coinjoinOuts AS ""coinjoinouts""
    GROUP BY ""coinjoinouts"".""hash"", ""coinjoinouts"".""outputs_val""
    HAVING COUNT(*) > 1
),
coinjoinsD AS (
    -- Filter out all potential CoinJoin txs that did not have
    -- more than one equal-value output. Do not list the
    -- outputs themselves, only the distinct tx hashes and
    -- their input/output counts and values.
    SELECT DISTINCT 
        ""coinjoinouts"".""hash"", 
        ""coinjoinouts"".""block_number"", 
        ""coinjoinouts"".""block_timestamp_month"",
        ""coinjoinouts"".""input_count"",
        ""coinjoinouts"".""output_count"",
        ""coinjoinouts"".""input_value"",
        ""coinjoinouts"".""output_value""
    FROM coinjoinOuts AS ""coinjoinouts""
    INNER JOIN coinjoinTxs AS ""coinjointxs"" 
        ON ""coinjoinouts"".""hash"" = ""coinjointxs"".""cjhash""
),
coinjoins AS (
    -- Aggregate monthly totals for CoinJoin txs, input/output UTXOs,
    -- and input/output values
    SELECT 
        ""cjs"".""block_timestamp_month"" AS cjs_month,
        COUNT(""cjs"".""hash"") AS cjs_count,
        SUM(""cjs"".""input_count"") AS cjs_inputs,
        SUM(""cjs"".""output_count"") AS cjs_outputs,
        SUM(""cjs"".""input_value"") / 100000000 AS cjs_input_val,
        SUM(""cjs"".""output_value"") / 100000000 AS cjs_output_val
    FROM coinjoinsD AS ""cjs""
    GROUP BY ""cjs"".""block_timestamp_month""
    ORDER BY ""cjs"".""block_timestamp_month"" DESC
)
SELECT EXTRACT(MONTH FROM tx_month) AS month,
    -- Calculate resulting CoinJoin percentages:
    -- tx_percent = percent of monthly Bitcoin txs that were CoinJoins
    ROUND(coinjoins.cjs_count / totals.tx_count * 100, 1) AS tx_percent,
    
    -- utxos_percent = percent of monthly Bitcoin utxos that were CoinJoins
    ROUND((coinjoins.cjs_inputs / totals.tx_inputs + coinjoins.cjs_outputs / totals.tx_outputs) / 2 * 100, 1) AS utxos_percent,
    
    -- value_percent = percent of monthly Bitcoin volume that took place
    -- in CoinJoined transactions
    ROUND(coinjoins.cjs_input_val / totals.tx_input_val * 100, 1) AS value_percent
FROM totals
INNER JOIN coinjoins
    ON totals.tx_month = coinjoins.cjs_month
ORDER BY value_percent DESC
LIMIT 1;",TRANSACTIONS.block_number; TRANSACTIONS.block_timestamp_month; TRANSACTIONS.hash; TRANSACTIONS.input_count; TRANSACTIONS.input_value; TRANSACTIONS.output_count; TRANSACTIONS.output_value; TRANSACTIONS.outputs,8,lite_sql,sf_bq057,286
bq059,san_francisco_plus,bigquery,"What is the highest average speed (rounded to 1 decimal, in metric m/s) for bike trips in Berkeley with trip distance greater than 1000 meters?","WITH stations AS (
  SELECT station_id
  FROM
    `bigquery-public-data.san_francisco_bikeshare.bikeshare_station_info` AS stainfo
  WHERE stainfo.region_id = (
    SELECT region.region_id
    FROM `bigquery-public-data.san_francisco_bikeshare.bikeshare_regions` AS region
    WHERE region.name = ""Berkeley""
  )
),
meta_data AS (
    SELECT
        round(st_distance(start_station_geom, end_station_geom), 1) as distancia_metros,
        round(st_distance(start_station_geom, end_station_geom) / duration_sec, 1) as velocidade_media
    FROM
        `bigquery-public-data.san_francisco_bikeshare.bikeshare_trips` AS trips
    WHERE
        cast(trips.start_station_id as string) IN (SELECT station_id FROM stations)
        AND cast(trips.end_station_id as string) IN (SELECT station_id FROM stations)
        AND start_station_latitude IS NOT NULL
        AND start_station_longitude IS NOT NULL
        AND end_station_latitude IS NOT NULL
        AND end_station_longitude IS NOT NULL
        AND st_distance(start_station_geom, end_station_geom) > 1000
    ORDER BY
        velocidade_media DESC
    LIMIT 1
)

SELECT velocidade_media as max_velocity
FROM meta_data;",bikeshare_regions.name; bikeshare_regions.region_id; bikeshare_station_info.region_id; bikeshare_station_info.station_id; bikeshare_trips.duration_sec; bikeshare_trips.end_station_geom; bikeshare_trips.end_station_id; bikeshare_trips.end_station_latitude; bikeshare_trips.end_station_longitude; bikeshare_trips.start_station_geom; bikeshare_trips.start_station_id; bikeshare_trips.start_station_latitude; bikeshare_trips.start_station_longitude,13,lite_sql,sf_bq059,278
bq060,census_bureau_international,bigquery,Which top 3 countries had the highest net migration in 2017 among those with an area greater than 500 square kilometers? And what are their migration rates?,"WITH results AS (
    SELECT
        growth.country_name,
        growth.net_migration,
        CAST(area.country_area as INT64) as country_area
    FROM (
        SELECT
            country_name,
            net_migration,
            country_code
        FROM
            `bigquery-public-data.census_bureau_international.birth_death_growth_rates`
        WHERE
            year = 2017
    ) growth
    INNER JOIN (
        SELECT
            country_area,
            country_code
        FROM
            `bigquery-public-data.census_bureau_international.country_names_area`
        WHERE
            country_area > 500
    ) area
    ON
        growth.country_code = area.country_code
    ORDER BY
        net_migration DESC
    LIMIT 3
)
SELECT country_name, net_migration
FROM results;",birth_death_growth_rates.country_code; birth_death_growth_rates.country_name; birth_death_growth_rates.net_migration; birth_death_growth_rates.year; country_names_area.country_area; country_names_area.country_code,6,lite_sql,sf_bq060,165
bq061,census_bureau_acs_1,bigquery,Which census tract has witnessed the largest increase in median income between 2015 and 2018 in California? Tell me the tract code.,"WITH acs_2018 AS (
    SELECT
      geo_id,
      median_income AS median_income_2018
    FROM
      `bigquery-public-data.census_bureau_acs.censustract_2018_5yr` 
),
acs_2015 AS (
    SELECT
      geo_id,
      median_income AS median_income_2015
    FROM
      `bigquery-public-data.census_bureau_acs.censustract_2015_5yr` ),
acs_diff AS (
    SELECT
      a18.geo_id,
      a18.median_income_2018,
      a15.median_income_2015,
      (a18.median_income_2018 - a15.median_income_2015) AS median_income_diff,
    FROM
      acs_2018 a18
    JOIN
      acs_2015 a15
    ON
      a18.geo_id = a15.geo_id
),
max_geo_id AS (
    SELECT
      geo_id
    FROM
      acs_diff
    WHERE
      median_income_diff IS NOT NULL
      AND acs_diff.geo_id in (
        SELECT
          geo_id
        FROM
          `bigquery-public-data.geo_census_tracts.census_tracts_california`
      )
    ORDER BY
      median_income_diff DESC
    LIMIT 1
)
SELECT
    tracts.tract_ce as tract_code
FROM
    max_geo_id
JOIN
    `bigquery-public-data.geo_census_tracts.census_tracts_california` AS tracts
ON
    max_geo_id.geo_id = tracts.geo_id;",census_tracts_california.geo_id; census_tracts_california.tract_ce; censustract_*.geo_id; censustract_*.median_income,4,lite_sql,sf_bq061,4373
bq064,census_bureau_acs_1,bigquery,"Could you calculate the population and average individual income (both rounded to 1 decimal) for each zip code based on U.S. census tract data in 2017? Only include those zip codes within a 5-mile radius of a specific geographic point (47.685833°N, -122.191667°W) in Washington and sort the results according to income in descending order.","WITH all_zip_tract_join AS (
  SELECT 
    zips.zip_code, 
    zips.functional_status as zip_functional_status,
    tracts.tract_ce, 
    tracts.geo_id as tract_geo_id, 
    tracts.functional_status as tract_functional_status,
    ST_Area(ST_Intersection(tracts.tract_geom, zips.zip_code_geom))
        / ST_Area(tracts.tract_geom) as tract_pct_in_zip_code
  FROM  
    `bigquery-public-data.geo_census_tracts.us_census_tracts_national` tracts,
    `bigquery-public-data.geo_us_boundaries.zip_codes` zips
  WHERE 
    ST_Intersects(tracts.tract_geom, zips.zip_code_geom)
),
zip_tract_join AS (
  SELECT * FROM all_zip_tract_join WHERE tract_pct_in_zip_code > 0
),
census_totals AS (
  -- convert averages to additive totals
  SELECT 
    geo_id,
    total_pop,
    total_pop * income_per_capita AS total_income 
  FROM 
    `bigquery-public-data.census_bureau_acs.censustract_2017_5yr` 
),
joined AS ( 
  -- join with precomputed census/zip pairs,
  -- compute zip's share of tract
  SELECT 
    zip_code, 
    total_pop * tract_pct_in_zip_code    AS zip_pop,
    total_income * tract_pct_in_zip_code AS zip_income
  FROM census_totals c
  JOIN zip_tract_join ztj
  ON c.geo_id = ztj.tract_geo_id
),
sums AS ( 
  -- aggregate all ""pieces"" of zip code
  SELECT
    zip_code, 
    SUM(zip_pop) AS zip_pop,
    SUM(zip_income) AS zip_total_inc
  FROM joined 
  GROUP BY zip_code
),
zip_pop_income AS (
    SELECT 
        zip_code, zip_pop, 
        -- convert to averages
        zip_total_inc / zip_pop AS income_per_capita
    FROM sums
),
zipcodes_within_distance as (
    SELECT 
        zip_code, zip_code_geom
    FROM 
        `bigquery-public-data.geo_us_boundaries.zip_codes`
    WHERE
        state_code = 'WA'  -- Washington state code
        AND
        ST_DWithin(
            ST_GeogPoint(-122.191667, 47.685833),
            zip_code_geom,
            8046.72
        )
)
select 
  stats.zip_code,
  ROUND(stats.zip_pop, 1) as zip_population,
  ROUND(stats.income_per_capita, 1) as average_income
from 
  zipcodes_within_distance area
join 
  zip_pop_income stats
on area.zip_code = stats.zip_code
ORDER BY
    average_income DESC;",censustract_*.geo_id; censustract_*.income_per_capita; censustract_*.total_pop; us_census_tracts_national.geo_id; us_census_tracts_national.tract_geom; zip_codes.state_code; zip_codes.zip_code; zip_codes.zip_code_geom,8,lite_sql,sf_bq064,4373
bq066,sdoh,bigquery,"Could you assess the relationship between the poverty rates from the previous year's census data and the percentage of births without maternal morbidity for the years 2016 to 2018? Use only data for births where no maternal morbidity was reported and for each year, use the 5-year census data from the year before to compute the Pearson correlation coefficient","WITH poverty_and_natality AS (
  SELECT
    EXTRACT(YEAR FROM n.Year) AS data_year,
    p.geo_id AS county_fips,
    (p.poverty / p.pop_determined_poverty_status) * 100 AS poverty_rate,
    SUM(n.Births) AS total_births,
    SUM(CASE WHEN n.Maternal_Morbidity_YN = 0 THEN n.Births ELSE 0 END) AS births_without_morbidity
  FROM
    `bigquery-public-data.census_bureau_acs.county_2015_5yr` p
  JOIN
    `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_maternal_morbidity` n
  ON p.geo_id = n.County_of_Residence_FIPS
  WHERE
    p.pop_determined_poverty_status > 0 AND
    EXTRACT(YEAR FROM n.Year) = 2016
  GROUP BY
    p.geo_id, p.poverty, p.pop_determined_poverty_status, EXTRACT(YEAR FROM n.Year)
  UNION ALL
  SELECT
    EXTRACT(YEAR FROM n.Year) AS data_year,
    p.geo_id AS county_fips,
    (p.poverty / p.pop_determined_poverty_status) * 100 AS poverty_rate,
    SUM(n.Births) AS total_births,
    SUM(CASE WHEN n.Maternal_Morbidity_YN = 0 THEN n.Births ELSE 0 END) AS births_without_morbidity
  FROM
    `bigquery-public-data.census_bureau_acs.county_2016_5yr` p
  JOIN
    `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_maternal_morbidity` n
  ON p.geo_id = n.County_of_Residence_FIPS
  WHERE
    p.pop_determined_poverty_status > 0 AND
    EXTRACT(YEAR FROM n.Year) = 2017
  GROUP BY
    p.geo_id, p.poverty, p.pop_determined_poverty_status, EXTRACT(YEAR FROM n.Year)
  UNION ALL
  SELECT
    EXTRACT(YEAR FROM n.Year) AS data_year,
    p.geo_id AS county_fips,
    (p.poverty / p.pop_determined_poverty_status) * 100 AS poverty_rate,
    SUM(n.Births) AS total_births,
    SUM(CASE WHEN n.Maternal_Morbidity_YN = 0 THEN n.Births ELSE 0 END) AS births_without_morbidity
  FROM
    `bigquery-public-data.census_bureau_acs.county_2017_5yr` p
  JOIN
    `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality_by_maternal_morbidity` n
  ON p.geo_id = n.County_of_Residence_FIPS
  WHERE
    p.pop_determined_poverty_status > 0 AND
    EXTRACT(YEAR FROM n.Year) = 2018
  GROUP BY
    p.geo_id, p.poverty, p.pop_determined_poverty_status, EXTRACT(YEAR FROM n.Year)
)

SELECT
  data_year,
  CORR(poverty_rate, (births_without_morbidity / total_births) * 100) AS correlation_coefficient
FROM
  poverty_and_natality
GROUP BY
  data_year",county_*.geo_id; county_*.pop_determined_poverty_status; county_*.poverty; county_natality_by_maternal_morbidity.Births; county_natality_by_maternal_morbidity.County_of_Residence_FIPS; county_natality_by_maternal_morbidity.Maternal_Morbidity_YN; county_natality_by_maternal_morbidity.Year,7,lite_sql,sf_bq066,4068
sf_bq068,CRYPTO,snowflake,What are the maximum and minimum balances across all addresses for different address types on Bitcoin Cash during March 2014?,"WITH double_entry_book AS (
    -- debits
    SELECT
        ARRAY_TO_STRING(""inputs"".value:addresses, ',') AS ""address"",  -- Use the correct JSON path notation
        ""inputs"".value:type AS ""type"",
        - ""inputs"".value:value AS ""value""
    FROM CRYPTO.CRYPTO_BITCOIN_CASH.TRANSACTIONS,
         LATERAL FLATTEN(INPUT => ""inputs"") AS ""inputs""
    WHERE TO_TIMESTAMP(""block_timestamp"" / 1000000) >= '2014-03-01' 
      AND TO_TIMESTAMP(""block_timestamp"" / 1000000) < '2014-04-01'

    UNION ALL
 
    -- credits
    SELECT
        ARRAY_TO_STRING(""outputs"".value:addresses, ',') AS ""address"",  -- Use the correct JSON path notation
        ""outputs"".value:type AS ""type"",
        ""outputs"".value:value AS ""value""
    FROM CRYPTO.CRYPTO_BITCOIN_CASH.TRANSACTIONS, 
         LATERAL FLATTEN(INPUT => ""outputs"") AS ""outputs""
    WHERE TO_TIMESTAMP(""block_timestamp"" / 1000000) >= '2014-03-01' 
      AND TO_TIMESTAMP(""block_timestamp"" / 1000000) < '2014-04-01'
),
address_balances AS (
    SELECT 
        ""address"",
        ""type"",
        SUM(""value"") AS ""balance""
    FROM double_entry_book
    GROUP BY ""address"", ""type""
),
max_min_balances AS (
    SELECT
        ""type"",
        MAX(""balance"") AS max_balance,
        MIN(""balance"") AS min_balance
    FROM address_balances
    GROUP BY ""type""
)
SELECT
    REPLACE(""type"", '""', '') AS ""type"",  -- Replace double quotes with nothing
    max_balance,
    min_balance
FROM max_min_balances
ORDER BY ""type"";",INPUTS.addresses; INPUTS.block_timestamp; INPUTS.type; INPUTS.value; OUTPUTS.addresses; OUTPUTS.block_timestamp; OUTPUTS.type; OUTPUTS.value,8,snow_sql_near_exact,sf_bq068,286
sf_bq070,IDC,snowflake,Could you construct a structured clean dataset from `dicom_all` for me? It should retrieve digital slide microscopy (SM) images from the TCGA-LUAD and TCGA-LUSC datasets and meet the requirements in `dicom_dataset_selection.md`. The target labels are tissue type and cancer subtype.,"WITH
  sm_images AS (
    SELECT
      ""SeriesInstanceUID"" AS ""digital_slide_id"", 
      ""StudyInstanceUID"" AS ""case_id"",
      ""ContainerIdentifier"" AS ""physical_slide_id"",
      ""PatientID"" AS ""patient_id"",
      ""TotalPixelMatrixColumns"" AS ""width"", 
      ""TotalPixelMatrixRows"" AS ""height"",
      ""collection_id"",
      ""crdc_instance_uuid"",
      ""gcs_url"", 
      CAST(
        ""SharedFunctionalGroupsSequence""[0].""PixelMeasuresSequence""[0].""PixelSpacing""[0] AS FLOAT
      ) AS ""pixel_spacing"", 
      CASE ""TransferSyntaxUID""
          WHEN '1.2.840.10008.1.2.4.50' THEN 'jpeg'
          WHEN '1.2.840.10008.1.2.4.91' THEN 'jpeg2000'
          ELSE 'other'
      END AS ""compression""
    FROM
      IDC.IDC_V17.DICOM_ALL
    WHERE
      ""Modality"" = 'SM' 
      AND ""ImageType""[2] = 'VOLUME'
  ),

  tissue_types AS (
    SELECT DISTINCT *
    FROM (
      SELECT
        ""SeriesInstanceUID"" AS ""digital_slide_id"",
        CASE ""steps_unnested2"".value:""CodeValue""::STRING
            WHEN '17621005' THEN 'normal' -- meaning: 'Normal' (i.e., non-neoplastic)
            WHEN '86049000' THEN 'tumor'  -- meaning: 'Neoplasm, Primary'
            ELSE 'other'                 -- meaning: 'Neoplasm, Metastatic'
        END AS ""tissue_type""
      FROM
        IDC.IDC_V17.DICOM_ALL
        CROSS JOIN
          LATERAL FLATTEN(input => ""SpecimenDescriptionSequence""[0].""PrimaryAnatomicStructureSequence"") AS ""steps_unnested1""
        CROSS JOIN
          LATERAL FLATTEN(input => ""steps_unnested1"".value:""PrimaryAnatomicStructureModifierSequence"") AS ""steps_unnested2""
    )
  ),

  specimen_preparation_sequence_items AS (
    SELECT DISTINCT *
    FROM (
      SELECT
        ""SeriesInstanceUID"" AS ""digital_slide_id"",
        ""steps_unnested2"".value:""ConceptNameCodeSequence""[0].""CodeMeaning""::STRING AS ""item_name"",
        ""steps_unnested2"".value:""ConceptCodeSequence""[0].""CodeMeaning""::STRING AS ""item_value""
      FROM
        IDC.IDC_V17.DICOM_ALL
        CROSS JOIN
          LATERAL FLATTEN(input => ""SpecimenDescriptionSequence""[0].""SpecimenPreparationSequence"") AS ""steps_unnested1""
        CROSS JOIN
          LATERAL FLATTEN(input => ""steps_unnested1"".value:""SpecimenPreparationStepContentItemSequence"") AS ""steps_unnested2""
    )
  )

SELECT
  a.*,
  b.""tissue_type"",
  REPLACE(REPLACE(a.""collection_id"", 'tcga_luad', 'luad'), 'tcga_lusc', 'lscc') AS ""cancer_subtype""
FROM 
  sm_images AS a
  JOIN tissue_types AS b 
    ON b.""digital_slide_id"" = a.""digital_slide_id""
  JOIN specimen_preparation_sequence_items AS c 
    ON c.""digital_slide_id"" = a.""digital_slide_id""
WHERE
  (a.""collection_id"" = 'tcga_luad' OR a.""collection_id"" = 'tcga_lusc')
  AND a.""compression"" != 'other'
  AND (b.""tissue_type"" = 'normal' OR b.""tissue_type"" = 'tumor')
  AND (c.""item_name"" = 'Embedding medium' AND c.""item_value"" = 'Tissue freezing medium')
ORDER BY 
  a.""crdc_instance_uuid"";",DICOM_ALL.ImageType; DICOM_ALL.Modality; DICOM_ALL.PatientID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SpecimenDescriptionSequence; DICOM_ALL.StudyInstanceUID; DICOM_ALL.collection_id; DICOM_ALL.gcs_url,8,lite_sql,sf_bq070,2100
sf_bq072,DEATH,snowflake,"Please tell me the total and Black deaths due to vehicle-related incidents and firearms separately, for each age from 12 to 18.","WITH BlackRace AS (
    SELECT CAST(""Code"" AS INT) AS CODE
    FROM DEATH.DEATH.RACE
    WHERE LOWER(""Description"") LIKE '%black%'
)
SELECT 
    v.""Age"", 
    v.""Total"" AS ""Vehicle_Total"", 
    v.""Black"" AS ""Vehicle_Black"",
    g.""Total"" AS ""Gun_Total"", 
    g.""Black"" AS ""Gun_Black""
FROM (
    SELECT 
        ""Age"", 
        COUNT(*) AS ""Total"", 
        COUNT_IF(""Race"" IN (SELECT CODE FROM BlackRace)) AS ""Black""
    FROM DEATH.DEATH.DEATHRECORDS d
    JOIN (
        SELECT 
            DISTINCT e.""DeathRecordId"" AS ""id""
        FROM DEATH.DEATH.ENTITYAXISCONDITIONS e
        JOIN (
            SELECT * 
            FROM DEATH.DEATH.ICD10CODE 
            WHERE LOWER(""Description"") LIKE '%vehicle%'
        ) c 
        ON e.""Icd10Code"" = c.""Code""
    ) f
    ON d.""Id"" = f.""id""
    WHERE ""Age"" BETWEEN 12 AND 18
    GROUP BY ""Age""
) v  -- Vehicle

JOIN (
    SELECT 
        ""Age"", 
        COUNT(*) AS ""Total"", 
        COUNT_IF(""Race"" IN (SELECT CODE FROM BlackRace)) AS ""Black""
    FROM DEATH.DEATH.DEATHRECORDS d
    JOIN (
        SELECT 
            DISTINCT e.""DeathRecordId"" AS ""id""
        FROM DEATH.DEATH.ENTITYAXISCONDITIONS e
        JOIN (
            SELECT 
                ""Code"", ""Description"" 
            FROM DEATH.DEATH.ICD10CODE
            WHERE ""Description"" LIKE '%firearm%'
        ) c 
        ON e.""Icd10Code"" = c.""Code""
    ) f
    ON d.""Id"" = f.""id""
    WHERE ""Age"" BETWEEN 12 AND 18
    GROUP BY ""Age""
) g
ON g.""Age"" = v.""Age"";",DEATHRECORDS.Age; DEATHRECORDS.Id; DEATHRECORDS.Race; ENTITYAXISCONDITIONS.DeathRecordId; ENTITYAXISCONDITIONS.Icd10Code; ICD10CODE.Code; ICD10CODE.Description; RACE.Code; RACE.Description,9,lite_sql,sf_bq072,91
bq074,sdoh,bigquery,"Count the number of counties that experienced an increase in unemployment from 2015 to 2018, using 5-year ACS data, and a decrease in dual-eligible enrollee counts between December 1, 2015, and December 1, 2018.","WITH acs_2018 AS (
  SELECT geo_id, unemployed_pop AS unemployed_2018  
  FROM `bigquery-public-data.census_bureau_acs.county_2018_5yr` 
),
 
acs_2015 AS (
  SELECT geo_id, unemployed_pop AS unemployed_2015  
  FROM `bigquery-public-data.census_bureau_acs.county_2015_5yr` 
),
 
unemployed_change AS (
  SELECT
    u18.unemployed_2018, u18.geo_id, u15.unemployed_2015,
    (u18.unemployed_2018 - u15.unemployed_2015) AS u_change
  FROM acs_2018 u18
  JOIN acs_2015 u15
  ON u18.geo_id = u15.geo_id
),
 
duals_Jan_2018 AS (
  SELECT Public_Total AS duals_2018, County_Name, FIPS 
  FROM `bigquery-public-data.sdoh_cms_dual_eligible_enrollment.dual_eligible_enrollment_by_county_and_program` 
  WHERE Date = '2018-12-01'
),

duals_Jan_2015 AS (
  SELECT Public_Total AS duals_2015, County_Name, FIPS
  FROM `bigquery-public-data.sdoh_cms_dual_eligible_enrollment.dual_eligible_enrollment_by_county_and_program` 
  WHERE Date = '2015-12-01'
),

duals_change AS (
  SELECT
    d18.FIPS, d18.County_Name, d18.duals_2018, d15.duals_2015,
    (d18.duals_2018 - d15.duals_2015) AS total_duals_diff
  FROM duals_Jan_2018 d18
  JOIN duals_Jan_2015 d15
  ON d18.FIPS = d15.FIPS
),
 
corr_tbl AS (
  SELECT unemployed_change.geo_id, duals_change.County_Name, unemployed_change.u_change, duals_change.total_duals_diff
  FROM unemployed_change
  JOIN duals_change
  ON unemployed_change.geo_id = duals_change.FIPS
)


SELECT COUNT(*)
FROM corr_tbl
WHERE
u_change >0
AND
corr_tbl.total_duals_diff < 0",county_*.geo_id; county_*.unemployed_pop; dual_eligible_enrollment_by_county_and_program.County_Name; dual_eligible_enrollment_by_county_and_program.Date; dual_eligible_enrollment_by_county_and_program.FIPS; dual_eligible_enrollment_by_county_and_program.Public_Total,6,lite_sql,sf_bq074,4068
bq076,chicago,bigquery,Which month generally has the greatest number of motor vehicle thefts in 2016?,"SELECT
  incidents AS highest_monthly_thefts
FROM (
  SELECT
    year,
    EXTRACT(MONTH FROM date) AS month,
    COUNT(1) AS incidents,
    RANK() OVER (PARTITION BY year ORDER BY COUNT(1) DESC) AS ranking
  FROM
    `bigquery-public-data.chicago_crime.crime`
  WHERE
    primary_type = 'MOTOR VEHICLE THEFT'
    AND year = 2016
  GROUP BY
    year,
    month
)
WHERE
  ranking = 1
ORDER BY
  year DESC
LIMIT 1;",crime.date; crime.primary_type; crime.year,3,lite_sql,sf_bq076,45
bq077,chicago,bigquery,"For each year from 2010 to 2016, what is the highest number of motor thefts in one month?","SELECT
  year,
  incidents
FROM (
  SELECT
    year,
    EXTRACT(MONTH
    FROM
      date) AS month,
    COUNT(1) AS incidents,
    RANK() OVER (PARTITION BY year ORDER BY COUNT(1) DESC) AS ranking
  FROM
    `bigquery-public-data.chicago_crime.crime`
  WHERE
    primary_type = 'MOTOR VEHICLE THEFT'
    AND year BETWEEN 2010 AND 2016
  GROUP BY
    year,
    month )
WHERE
  ranking = 1
ORDER BY
  year ASC",crime.date; crime.primary_type; crime.year,3,lite_sql,sf_bq077,45
bq078,open_targets_platform_2,bigquery,Retrieve the approved symbol of target genes with the highest overall score that are associated with the disease 'EFO_0000676' from the data source 'IMPC'.,"SELECT
  T1.targetId AS target_id,
  T1.datasourceId,
  targets.approvedSymbol AS approved_symbol,
  overall_associations.score AS overall_score
FROM
  `bigquery-public-data.open_targets_platform.associationByDatasourceDirect` as T1
JOIN
  `bigquery-public-data.open_targets_platform.targets` AS targets
ON
  targetId = targets.id
JOIN
  `bigquery-public-data.open_targets_platform.associationByOverallDirect` AS overall_associations
ON
  T1.targetId = overall_associations.targetId
WHERE
  overall_associations.diseaseId = 'EFO_0000676' AND datasourceId = 'impc'
ORDER BY
  overall_associations.score DESC
LIMIT
  1;",associationByDatasourceDirect.datasourceId; associationByDatasourceDirect.targetId; associationByOverallDirect.diseaseId; associationByOverallDirect.score; associationByOverallDirect.targetId; targets.approvedSymbol; targets.id,7,lite_sql,sf_bq078,351
bq081,san_francisco_plus,bigquery,"Find the latest ride data for each region between 2014 and 2017. I want to know the name of each region, the trip ID of this ride, the ride duration, the start time, the starting station, and the gender of the rider.","SELECT t1.*
  FROM 
  (SELECT Trips.trip_id TripId,
               Trips.duration_sec TripDuration,
               Trips.start_date TripStartDate,
               Trips.start_station_name TripStartStation,
               Trips.member_gender Gender,
               Regions.name RegionName
          FROM `bigquery-public-data.san_francisco_bikeshare.bikeshare_trips` Trips
         INNER JOIN `bigquery-public-data.san_francisco_bikeshare.bikeshare_station_info` StationInfo
            ON CAST(Trips.start_station_id AS STRING) = CAST(StationInfo.station_id AS STRING)
         INNER JOIN `bigquery-public-data.san_francisco_bikeshare.bikeshare_regions` Regions
            ON StationInfo.region_id = Regions.region_id
         WHERE (EXTRACT(YEAR from Trips.start_date)) BETWEEN 2014 AND 2017
           ) 
           t1
 RIGHT JOIN (SELECT MAX(start_date) TripStartDate,
                   Regions.name RegionName
              FROM `bigquery-public-data.san_francisco_bikeshare.bikeshare_station_info` StationInfo
             INNER JOIN `bigquery-public-data.san_francisco_bikeshare.bikeshare_trips` Trips
                ON CAST(StationInfo.station_id AS STRING) = CAST(Trips.start_station_id AS STRING)
             INNER JOIN `bigquery-public-data.san_francisco_bikeshare.bikeshare_regions` Regions
                ON Regions.region_id = StationInfo.region_id
                 WHERE (EXTRACT(YEAR from Trips.start_date) BETWEEN 2014 AND 2017
           AND Regions.name IS NOT NULL)
             GROUP BY RegionName) 
             t2
    ON t1.RegionName = t2.RegionName AND t1.TripStartDate = t2.TripStartDate",bikeshare_regions.name; bikeshare_regions.region_id; bikeshare_station_info.region_id; bikeshare_station_info.station_id; bikeshare_trips.duration_sec; bikeshare_trips.member_gender; bikeshare_trips.start_date; bikeshare_trips.start_station_id; bikeshare_trips.start_station_name; bikeshare_trips.trip_id,10,lite_sql,sf_bq081,278
sf_bq083,CRYPTO,snowflake,"What is the daily change in the total market value (formatted as a string in USD currency format) of the USDC token (with a target address of ""0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48"") in 2023 , considering both Mint (the input starts with 0x42966c68) and Burn (the input starts with 0x40c10f19) transactions?","SELECT 
  TO_DATE(TO_TIMESTAMP_NTZ(""block_timestamp"" / 1000000)) AS ""Date"",  -- 将时间戳转换为日期格式，除以1000000
  TO_CHAR(SUM(
      CASE
          WHEN ""input"" LIKE '0x40c10f19%' THEN 1
          ELSE -1
      END * 
      CAST(CONCAT('0x', LTRIM(SUBSTRING(""input"", 
                                       CASE 
                                           WHEN ""input"" LIKE '0x40c10f19%' THEN 75
                                           ELSE 11
                                       END, 64), '0')) AS FLOAT) / 1000000)
  , '$999,999,999,999') AS ""Δ Total Market Value""
FROM 
  ""CRYPTO"".""CRYPTO_ETHEREUM"".""TRANSACTIONS""
WHERE 
  TO_DATE(TO_TIMESTAMP_NTZ(""block_timestamp"" / 1000000)) BETWEEN '2023-01-01' AND '2023-12-31'
  AND ""to_address"" = '0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48'  -- USDC Token
  AND (""input"" LIKE '0x42966c68%' -- Burn
       OR ""input"" LIKE '0x40c10f19%' -- Mint
  )
GROUP BY 
  TO_DATE(TO_TIMESTAMP_NTZ(""block_timestamp"" / 1000000))
ORDER BY 
  ""Date"" DESC;",TRANSACTIONS.block_timestamp; TRANSACTIONS.input; TRANSACTIONS.to_address,3,snow_sql_near_exact,sf_bq083,286
bq085,covid19_jhu_world_bank,bigquery,"Could you provide the total number of confirmed COVID-19 cases and the number of cases per 100,000 people, based on the 2020 population, on April 20, 2020, for the US, France, China, Italy, Spain, Germany, and Iran?","SELECT
  c.country,
  c.total_confirmed_cases,
  (c.total_confirmed_cases / p.population) * 100000 AS cases_per_100k
FROM
  (
    SELECT
      CASE
        WHEN country_region = 'US' THEN 'United States'
        WHEN country_region = 'Iran' THEN 'Iran, Islamic Rep.'
        ELSE country_region
      END AS country,
      SUM(confirmed) AS total_confirmed_cases
    FROM
      `bigquery-public-data.covid19_jhu_csse.summary`
    WHERE
      date = '2020-04-20'
      AND country_region IN ('US', 'France', 'China', 'Italy', 'Spain', 'Germany', 'Iran')
    GROUP BY
      country
  ) AS c
JOIN
  (
    SELECT
      country_name AS country,
      SUM(value) AS population
    FROM
      `bigquery-public-data.world_bank_wdi.indicators_data`
    WHERE
      indicator_code = 'SP.POP.TOTL'
      AND year = 2020
    GROUP BY
      country_name
  ) AS p
ON
  c.country = p.country
ORDER BY
  cases_per_100k DESC",indicators_data.country_name; indicators_data.indicator_code; indicators_data.value; indicators_data.year; summary.confirmed; summary.country_region; summary.date,7,lite_sql,sf_bq085,3610
bq086,covid19_open_world_bank,bigquery,"What percentage of each country’s population was confirmed to have COVID-19 as of June 30, 2020?","WITH
  country_pop AS (
  SELECT
    country_code AS iso_3166_1_alpha_3,
    year_2018 AS population_2018
  FROM
    `bigquery-public-data.world_bank_global_population.population_by_country`)
SELECT
  country_code,
  country_name,
  cumulative_confirmed AS june_confirmed_cases,
  population_2018,
  ROUND(cumulative_confirmed/population_2018 * 100,2) AS case_percent
FROM
  `bigquery-public-data.covid19_open_data.covid19_open_data`
JOIN
  country_pop
USING
  (iso_3166_1_alpha_3)
WHERE
  date = '2020-06-30'
  AND aggregation_level = 0
ORDER BY
  case_percent DESC",covid19_open_data.aggregation_level; covid19_open_data.country_code; covid19_open_data.country_name; covid19_open_data.cumulative_confirmed; covid19_open_data.date; covid19_open_data.iso_3166_1_alpha_3; population_by_country.country_code; population_by_country.year_2018,8,lite_sql,sf_bq086,854
bq087,covid19_symptom_search,bigquery,Can you assess the collective percentage change in average search frequency for Anosmia symptoms across the five major boroughs of New York City from 2019 to 2020?,"SELECT
  table_2019.avg_symptom_Anosmia_2019,
  table_2020.avg_symptom_Anosmia_2020,
  ((table_2020.avg_symptom_Anosmia_2020 - table_2019.avg_symptom_Anosmia_2019) / table_2019.avg_symptom_Anosmia_2019) * 100 AS avg_increase
FROM (
  SELECT
    AVG(SAFE_CAST(symptom_Anosmia AS FLOAT64)) AS avg_symptom_Anosmia_2020
  FROM
    `bigquery-public-data.covid19_symptom_search.symptom_search_sub_region_2_weekly`
  WHERE
    sub_region_1 = ""New York""
    AND sub_region_2 IN (""Bronx County"", ""Queens County"", ""Kings County"", ""New York County"", ""Richmond County"")
    AND date >= '2020-01-01'
    AND date < '2021-01-01'
) AS table_2020,
(
  SELECT
    AVG(SAFE_CAST(symptom_Anosmia AS FLOAT64)) AS avg_symptom_Anosmia_2019
  FROM
    `bigquery-public-data.covid19_symptom_search.symptom_search_sub_region_2_weekly`
  WHERE
    sub_region_1 = ""New York""
    AND sub_region_2 IN (""Bronx County"", ""Queens County"", ""Kings County"", ""New York County"", ""Richmond County"")
    AND date >= '2019-01-01'
    AND date < '2020-01-01'
) AS table_2019",symptom_search_sub_region_*.date; symptom_search_sub_region_*.sub_region_1; symptom_search_sub_region_*.sub_region_2; symptom_search_sub_region_*.symptom_anosmia,4,lite_sql,sf_bq087,1710
bq088,covid19_symptom_search,bigquery,"Can you provide the average levels of anxiety and depression symptoms from the weekly country data in the United States for the years 2019 and 2020, and calculate the percentage increase in these symptoms from 2019 to 2020?","SELECT
  table_2019.avg_symptom_Anxiety_2019,
  table_2020.avg_symptom_Anxiety_2020,
  ((table_2020.avg_symptom_Anxiety_2020 - table_2019.avg_symptom_Anxiety_2019)/table_2019.avg_symptom_Anxiety_2019) * 100 AS percent_increase_anxiety,
  table_2019.avg_symptom_Depression_2019,
  table_2020.avg_symptom_Depression_2020,
  ((table_2020.avg_symptom_Depression_2020 - table_2019.avg_symptom_Depression_2019)/table_2019.avg_symptom_Depression_2019) * 100 AS percent_increase_depression
FROM (
  SELECT
    AVG(CAST(symptom_Anxiety AS FLOAT64)) AS avg_symptom_Anxiety_2020,
    AVG(CAST(symptom_Depression AS FLOAT64)) AS avg_symptom_Depression_2020,
  FROM
    `bigquery-public-data.covid19_symptom_search.symptom_search_country_weekly`
  WHERE
    country_region_code = ""US""
    AND date >= '2020-01-01'
    AND date <'2021-01-01') AS table_2020,
  (
  SELECT
    AVG(CAST(symptom_Anxiety AS FLOAT64)) AS avg_symptom_Anxiety_2019,
    AVG(CAST(symptom_Depression AS FLOAT64)) AS avg_symptom_Depression_2019,
  FROM
    `bigquery-public-data.covid19_symptom_search.symptom_search_country_weekly`
  WHERE
    country_region_code = ""US""
    AND date >= '2019-01-01'
    AND date <'2020-01-01') AS table_2019",symptom_search_country_weekly.country_region_code; symptom_search_country_weekly.date; symptom_search_country_weekly.symptom_anxiety; symptom_search_country_weekly.symptom_depression,4,lite_sql,sf_bq088,1710
bq089,covid19_usa,bigquery,"Given the latest population estimates from the 2018 five-year American Community Survey, what is the number of vaccine sites per 1000 people for counties in California?","WITH
  num_vaccine_sites_per_county AS (
  SELECT
    facility_sub_region_1 AS us_state,
    facility_sub_region_2 AS us_county,
    facility_sub_region_2_code AS us_county_fips,
    COUNT(DISTINCT facility_place_id) AS num_vaccine_sites
  FROM
    bigquery-public-data.covid19_vaccination_access.facility_boundary_us_all
  WHERE
    STARTS_WITH(facility_sub_region_2_code, ""06"")
  GROUP BY
    facility_sub_region_1,
    facility_sub_region_2,
    facility_sub_region_2_code ),
  total_population_per_county AS (
  SELECT
    LEFT(geo_id, 5) AS us_county_fips,
    ROUND(SUM(total_pop)) AS total_population
  FROM
    bigquery-public-data.census_bureau_acs.censustract_2018_5yr
  WHERE
    STARTS_WITH(LEFT(geo_id, 5), ""06"")
  GROUP BY
    LEFT(geo_id, 5) )
SELECT
  * EXCEPT(us_county_fips),
  ROUND((num_vaccine_sites * 1000) / total_population, 2) AS sites_per_1k_ppl
FROM
  num_vaccine_sites_per_county
INNER JOIN
  total_population_per_county
USING
  (us_county_fips)
ORDER BY
  sites_per_1k_ppl ASC
LIMIT
  100;",censustract_*.geo_id; censustract_*.total_pop; facility_boundary_us_all.facility_place_id; facility_boundary_us_all.facility_sub_region_1; facility_boundary_us_all.facility_sub_region_2; facility_boundary_us_all.facility_sub_region_2_code,6,lite_sql,sf_bq089,6066
bq090,CYMBAL_INVESTMENTS,bigquery,How much higher the average intrinsic value is for trades using the feeling-lucky strategy compared to those using the momentum strategy under long-side trades?,"WITH MomentumTrades AS (
  SELECT
    StrikePrice - LastPx AS priceDifference
  FROM
    `bigquery-public-data.cymbal_investments.trade_capture_report`
  WHERE
    SUBSTR(TargetCompID, 0, 4) = 'MOMO'
    AND (SELECT Side FROM UNNEST(Sides)) = 'LONG'
),

FeelingLuckyTrades AS (
  SELECT
    StrikePrice - LastPx AS priceDifference
  FROM
    `bigquery-public-data.cymbal_investments.trade_capture_report`
  WHERE
    SUBSTR(TargetCompID, 0, 4) = 'LUCK'
    AND (SELECT Side FROM UNNEST(Sides)) = 'LONG'
)

SELECT
  AVG(FeelingLuckyTrades.priceDifference) - AVG(MomentumTrades.priceDifference) AS averageDifference 
FROM
  MomentumTrades,
  FeelingLuckyTrades",trade_capture_report.LastPx; trade_capture_report.Sides; trade_capture_report.StrikePrice; trade_capture_report.TargetCompID,4,lite_sql,sf_bq090,14
sf_bq091,PATENTS,snowflake,In which year did the assignee with the most applications in the patent category 'A61' file the most?,"WITH AA AS (
    SELECT 
        FIRST_VALUE(""assignee_harmonized"") OVER (PARTITION BY ""application_number"" ORDER BY ""application_number"") AS assignee_harmonized,
        FIRST_VALUE(""filing_date"") OVER (PARTITION BY ""application_number"" ORDER BY ""application_number"") AS filing_date,
        ""application_number""
    FROM 
        PATENTS.PATENTS.PUBLICATIONS AS pubs
        , LATERAL FLATTEN(input => pubs.""cpc"") AS c
    WHERE 
        c.value:""code"" LIKE 'A61%'
),

PatentApplications AS (
    SELECT 
        ANY_VALUE(assignee_harmonized) as assignee_harmonized,
        ANY_VALUE(filing_date) as filing_date
    FROM AA
    GROUP BY ""application_number""
),

AssigneeApplications AS (
SELECT 
    COUNT(*) AS total_applications,
    a.value::STRING AS assignee_name,
    CAST(FLOOR(filing_date / 10000) AS INT) AS filing_year
FROM 
    PatentApplications
    , LATERAL FLATTEN(input => assignee_harmonized) AS a
GROUP BY 
    a.value::STRING, filing_year
),

TotalApplicationsPerAssignee AS (
    SELECT
        assignee_name,
        SUM(total_applications) AS total_applications
    FROM 
        AssigneeApplications
    GROUP BY 
        assignee_name
    ORDER BY 
        total_applications DESC
    LIMIT 1
),

MaxYearForTopAssignee AS (
    SELECT
        aa.assignee_name,
        aa.filing_year,
        aa.total_applications
    FROM 
        AssigneeApplications aa
    INNER JOIN
        TotalApplicationsPerAssignee tapa ON aa.assignee_name = tapa.assignee_name
    ORDER BY 
        aa.total_applications DESC
    LIMIT 1
)

SELECT filing_year
FROM 
    MaxYearForTopAssignee",PUBLICATIONS.application_number; PUBLICATIONS.assignee_harmonized; PUBLICATIONS.cpc; PUBLICATIONS.filing_date,4,lite_sql,sf_bq091,79
sf_bq093,CRYPTO,snowflake,"Tell me the maximum and minimum net changes in balances for Ethereum Classic addresses on October 14, 2016, considering debits, credits, and gas fees, while excluding internal calls like 'delegatecall', 'callcode', and 'staticcall'.","WITH double_entry_book AS (
    -- Debits
    SELECT 
        ""to_address"" AS ""address"", 
        ""value"" AS ""value""
    FROM 
        CRYPTO.CRYPTO_ETHEREUM_CLASSIC.TRACES
    WHERE 
        ""to_address"" IS NOT NULL
        AND ""status"" = 1
        AND (""call_type"" NOT IN ('delegatecall', 'callcode', 'staticcall') OR ""call_type"" IS NULL)
        AND TO_DATE(TO_TIMESTAMP(""block_timestamp"" / 1000000)) = '2016-10-14'

    UNION ALL
    
    -- Credits
    SELECT 
        ""from_address"" AS ""address"", 
        - ""value"" AS ""value""
    FROM 
        CRYPTO.CRYPTO_ETHEREUM_CLASSIC.TRACES
    WHERE 
        ""from_address"" IS NOT NULL
        AND ""status"" = 1
        AND (""call_type"" NOT IN ('delegatecall', 'callcode', 'staticcall') OR ""call_type"" IS NULL)
        AND TO_DATE(TO_TIMESTAMP(""block_timestamp"" / 1000000)) = '2016-10-14'

    UNION ALL

    -- Transaction Fees Debits
    SELECT 
        ""miner"" AS ""address"", 
        SUM(CAST(""receipt_gas_used"" AS NUMERIC) * CAST(""gas_price"" AS NUMERIC)) AS ""value""
    FROM 
        CRYPTO.CRYPTO_ETHEREUM_CLASSIC.TRANSACTIONS AS ""transactions""
    JOIN 
        CRYPTO.CRYPTO_ETHEREUM_CLASSIC.BLOCKS AS ""blocks"" 
        ON ""blocks"".""number"" = ""transactions"".""block_number""
    WHERE 
        TO_DATE(TO_TIMESTAMP(""block_timestamp"" / 1000000)) = '2016-10-14'
    GROUP BY 
        ""blocks"".""miner""

    UNION ALL
    
    -- Transaction Fees Credits
    SELECT 
        ""from_address"" AS ""address"", 
        -(CAST(""receipt_gas_used"" AS NUMERIC) * CAST(""gas_price"" AS NUMERIC)) AS ""value""
    FROM 
        CRYPTO.CRYPTO_ETHEREUM_CLASSIC.TRANSACTIONS
    WHERE 
        TO_DATE(TO_TIMESTAMP(""block_timestamp"" / 1000000)) = '2016-10-14'
),
net_changes AS (
    SELECT 
        ""address"",
        SUM(""value"") AS ""net_change""
    FROM 
        double_entry_book
    GROUP BY 
        ""address""
)
SELECT 
    MAX(""net_change"") AS ""max_net_change"",
    MIN(""net_change"") AS ""min_net_change""
FROM
    net_changes;",BLOCKS.miner; BLOCKS.number; TRANSACTIONS.block_number; TRANSACTIONS.block_timestamp; TRANSACTIONS.from_address; TRANSACTIONS.gas_price; TRANSACTIONS.hash; TRANSACTIONS.receipt_contract_address; TRANSACTIONS.receipt_gas_used; TRANSACTIONS.receipt_status; TRANSACTIONS.to_address; TRANSACTIONS.value,12,snow_sql_near_exact,sf_bq093,286
bq095,open_targets_platform_1,bigquery,"Generate a list of drugs from the table containing molecular details that have completed clinical trials for pancreatic endocrine carcinoma, disease ID EFO_0007416. Please include each drug's name, the target approved symbol, and links to the relevant clinical trials.","SELECT
  targets.approvedSymbol AS target_symbol,
  drugs.name AS drug_name,
  source_urls.element.url AS clinical_trial_reference_url,
FROM
  `open-targets-prod.platform.evidence` AS evidence,
  UNNEST(evidence.urls.list) AS source_urls
JOIN
  `open-targets-prod.platform.targets` AS targets
ON
  evidence.targetId=targets.id
JOIN
  `open-targets-prod.platform.molecule` AS drugs
ON
  evidence.drugId=drugs.id
WHERE
  datasourceId=""chembl""
  AND diseaseId=""EFO_0007416""
  AND evidence.clinicalStatus = ""Completed""",evidence.clinicalStatus; evidence.datasourceId; evidence.diseaseId; evidence.drugId; evidence.targetId; evidence.urls; molecule.id; molecule.name; targets.approvedSymbol; targets.id,10,lite_sql,sf_bq095,332
bq096,gbif,bigquery,Which year had the first day after January with more than 10 sightings of Sterna paradisaea north of 40 degrees latitude?,"WITH tenplus AS (
  SELECT 
    year, 
    EXTRACT(DAYOFYEAR FROM DATE(eventdate)) AS dayofyear, 
    COUNT(*) AS count
  FROM 
    bigquery-public-data.gbif.occurrences
  WHERE 
    eventdate IS NOT NULL 
    AND species = 'Sterna paradisaea' 
    AND decimallatitude > 40.0 
    AND month > 1
  GROUP BY 
    year, 
    eventdate
  HAVING 
    COUNT(*) > 10
)

SELECT 
  year AS year
FROM 
  tenplus
GROUP BY 
  year
ORDER BY 
  MIN(dayofyear)
LIMIT 1;",occurrences.decimallatitude; occurrences.eventdate; occurrences.month; occurrences.species; occurrences.year,5,lite_sql,sf_bq096,50
bq097,sdoh,bigquery,"What is the increasing amount of the average earnings per job between the years 2012 and 2017 for each geographic region in Massachusetts (indicated by ""MA"" at the end of GeoName)?","WITH bea_2012 AS (
  SELECT GeoFIPS, GeoName, Earnings_per_job_avg AS earnings_2012
  FROM `bigquery-public-data.sdoh_bea_cainc30.fips`
  WHERE Year='2012-01-01' AND ENDS_WITH(GeoName, ""MA"") IS TRUE
),

bea_2017 AS (
  SELECT GeoFIPS, GeoName, Earnings_per_job_avg AS earnings_2017
  FROM `bigquery-public-data.sdoh_bea_cainc30.fips`
  WHERE Year='2017-01-01' AND ENDS_WITH(GeoName, ""MA"") IS TRUE
),

earnings_diff AS (
  SELECT
    bea_2017.GeoFIPS, bea_2017.GeoName, bea_2017.earnings_2017, bea_2012.earnings_2012, 
    (bea_2017.earnings_2017 - bea_2012.earnings_2012) AS earnings_change
   FROM bea_2017 
   JOIN bea_2012
   ON bea_2017.GeoFIPS = bea_2012.GeoFIPS
)
 
SELECT * FROM earnings_diff WHERE earnings_change IS NOT NULL ORDER BY earnings_change DESC",fips.Earnings_per_job_avg; fips.GeoFIPS; fips.GeoName; fips.Year,4,lite_sql,sf_bq097,4068
bq098,new_york_plus,bigquery,"For NYC yellow taxi trips between January 1-7, 2016, could you tell me the percentage of no tips in each borough. Ensure trips where the dropoff occurs after the pickup, the passenger count is greater than 0, and trip distance, tip, tolls, MTA tax, fare, and total amount are non-negative.","WITH t2 AS
(
SELECT 
    t.*,
    t.pickup_location_id as pickup_zone_id,
    tz.borough as pickup_borough
FROM
(
SELECT *,
    TIMESTAMP_DIFF(dropoff_datetime,pickup_datetime,SECOND) as time_duration_in_secs,
    (CASE WHEN total_amount=0 THEN 0
    ELSE (tip_amount*100/total_amount) END) as tip_rate
FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2016`
) t
INNER JOIN `bigquery-public-data.new_york_taxi_trips.taxi_zone_geom` tz
ON t.pickup_location_id = tz.zone_id
WHERE 
    pickup_datetime BETWEEN '2016-01-01' AND '2016-01-07' 
    AND dropoff_datetime BETWEEN '2016-01-01' AND '2016-01-07'
    AND TIMESTAMP_DIFF(dropoff_datetime,pickup_datetime,SECOND) > 0
    AND passenger_count > 0
    AND trip_distance >= 0 
    AND tip_amount >= 0 
    AND tolls_amount >= 0 
    AND mta_tax >= 0 
    AND fare_amount >= 0
    AND total_amount >= 0
),
t3 AS
(SELECT 
pickup_borough,
(CASE 
    WHEN tip_rate = 0 THEN 'no tip'
    WHEN tip_rate <= 5 THEN 'Less than 5%'
    WHEN tip_rate <= 10 THEN '5% to 10%'
    WHEN tip_rate <= 15 THEN '10% to 15%'
    WHEN tip_rate <= 20 THEN '15% to 20%'
    WHEN tip_rate <= 25 THEN '20% to 25%'
    ELSE 'More than 25%' END)as tip_category,
COUNT(*) as no_of_trips
FROM t2
GROUP BY 1,2
ORDER BY pickup_borough ASC),
INFO AS (
SELECT pickup_borough
     , tip_category
     , Sum(no_of_trips) as no_of_trips,
     (CASE 
          WHEN pickup_borough is null THEN (select sum(no_of_trips)
          FROM t3)
          
          WHEN pickup_borough is not null and tip_category is null THEN (select sum(no_of_trips)
          FROM t3)
          
          WHEN pickup_borough is not null and tip_category is not null THEN (select sum(no_of_trips)
          FROM t3
          WHERE pickup_borough = m.pickup_borough)
          END) as parent_sum,
       (
          Sum(no_of_trips)
            /
          (
            CASE 
          WHEN pickup_borough is null THEN (select sum(no_of_trips)
          FROM t3)
          
          WHEN pickup_borough is not null and tip_category is null THEN (select sum(no_of_trips)
          FROM t3)
          
          WHEN pickup_borough is not null and tip_category is not null THEN (select sum(no_of_trips)
          FROM t3
          WHERE pickup_borough = m.pickup_borough)
          END
          )
        ) as percentage
FROM t3 m
GROUP BY ROLLUP(pickup_borough, tip_category)
order by 1, 2
)

SELECT 
    pickup_borough,
    (SUM(CASE WHEN tip_category = 'no tip' THEN no_of_trips ELSE 0 END) * 100.0 / SUM(no_of_trips)) AS percentage_no_tip
FROM t3
GROUP BY pickup_borough
ORDER BY pickup_borough;",taxi_zone_geom.borough; taxi_zone_geom.zone_id; tlc_yellow_trips_*.dropoff_datetime; tlc_yellow_trips_*.fare_amount; tlc_yellow_trips_*.mta_tax; tlc_yellow_trips_*.passenger_count; tlc_yellow_trips_*.pickup_datetime; tlc_yellow_trips_*.pickup_location_id; tlc_yellow_trips_*.tip_amount; tlc_yellow_trips_*.tolls_amount; tlc_yellow_trips_*.total_amount; tlc_yellow_trips_*.trip_distance,12,lite_sql,sf_bq098,360
sf_bq099,PATENTS,snowflake,"For patent class A01B3, I want to analyze the information of the top 3 assignees based on the total number of applications. Please provide the following five pieces of information: the name of this assignee,  total number of applications, the year with the most applications, the number of applications in that year, and the country code with the most applications during that year.","WITH PatentApplications AS (
   SELECT 
        ""assignee_harmonized"" AS assignee_harmonized,
        ""filing_date"" AS filing_date,
        ""country_code"" AS country_code,
        ""application_number"" AS application_number
    FROM 
        PATENTS.PATENTS.PUBLICATIONS AS pubs,
        LATERAL FLATTEN(input => pubs.""cpc"") AS c
    WHERE c.value:""code"" LIKE 'A01B3%'

),

AssigneeApplications AS (
    SELECT 
        COUNT(*) AS year_country_cnt,
        a.value:""name"" AS assignee_name,
        CAST(FLOOR(filing_date / 10000) AS INT) AS filing_year,
        apps.country_code as country_code
    FROM 
        PatentApplications as apps,
        LATERAL FLATTEN(input => assignee_harmonized) AS a
    GROUP BY 
        assignee_name, filing_year, country_code
),

RankedApplications AS (
    SELECT
        assignee_name,
        filing_year,
        country_code,
        year_country_cnt,
        SUM(year_country_cnt) OVER (PARTITION BY assignee_name, filing_year) AS total_cnt,
        ROW_NUMBER() OVER (PARTITION BY assignee_name, filing_year ORDER BY year_country_cnt DESC) AS rn
    FROM
        AssigneeApplications
),

AggregatedData AS (
    SELECT
        total_cnt AS year_cnt,
        assignee_name,
        filing_year,
        country_code
    FROM
        RankedApplications
    WHERE
        rn = 1
)


SELECT 
    total_count,
    REPLACE(assignee_name, '""', '') AS assignee_name,
    year_cnt,
    filing_year,
    country_code
FROM (
    SELECT 
        year_cnt,
        assignee_name,
        filing_year,
        country_code,
        SUM(year_cnt) OVER (PARTITION BY assignee_name) AS total_count,
        ROW_NUMBER() OVER (PARTITION BY assignee_name ORDER BY year_cnt DESC) AS rn
    FROM
        AggregatedData
    ORDER BY assignee_name
) sub
WHERE rn = 1
ORDER BY total_count
DESC
LIMIT 3",PUBLICATIONS.application_number; PUBLICATIONS.assignee_harmonized; PUBLICATIONS.country_code; PUBLICATIONS.cpc; PUBLICATIONS.filing_date,5,snow_sql_near_exact,sf_bq099,79
bq102,gnomAD,bigquery,"Identify which start positions are associated with missense variants in the BRCA1 gene on chromosome 17, where the reference base is 'C' and the alternate base is 'T'. Using data from the gnomAD v2.1.1 version.","WITH gene_region AS (
  SELECT 
    MIN(start_position) AS start_pos, 
    MAX(end_position) AS end_pos
  FROM `bigquery-public-data.gnomAD.v2_1_1_genomes__chr17` AS main_table
  WHERE EXISTS (
    SELECT 1 
    FROM UNNEST(main_table.alternate_bases) AS alternate_bases
    WHERE EXISTS (
      SELECT 1 
      FROM UNNEST(alternate_bases.vep) AS vep
      WHERE vep.SYMBOL = 'BRCA1'
    )
  )
)


SELECT 
  DISTINCT start_position
FROM `bigquery-public-data.gnomAD.v2_1_1_genomes__chr17` AS main_table,
     UNNEST(main_table.alternate_bases) AS alternate_bases,
     UNNEST(alternate_bases.vep) AS vep,
     gene_region
WHERE main_table.start_position >= gene_region.start_pos
  AND main_table.start_position <= gene_region.end_pos
  AND REGEXP_CONTAINS(vep.Consequence, r""missense_variant"")
  AND reference_bases = ""C""
  AND alternate_bases.alt = ""T""",v2_1_1_genomes__chr_*.alternate_bases; v2_1_1_genomes__chr_*.end_position; v2_1_1_genomes__chr_*.reference_bases; v2_1_1_genomes__chr_*.start_position,4,lite_sql,sf_bq102,1150
bq103,gnomAD,bigquery,"Generate summary statistics on genetic variants in the region between positions 55039447 and 55064852 on chromosome 1. This includes the number of variants, the total allele count, the total number of alleles, and distinct gene symbols (using Variant Effect Predictor, VEP, for gene annotation). Additionally, compute the density of mutations by dividing the length of the region by the number of variants.  Using data from the gnomAD v3 version.","WITH summary_stats AS (
  SELECT
    COUNT(1) AS num_variants,
    SUM((SELECT alt.AC FROM UNNEST(alternate_bases) AS alt)) AS sum_AC,
    SUM(AN) AS sum_AN,
    -- Also include some information from Variant Effect Predictor (VEP).
    STRING_AGG(DISTINCT (SELECT annot.symbol FROM UNNEST(alternate_bases) AS alt,
                                               UNNEST(vep) AS annot LIMIT 1), ', ') AS genes
  FROM bigquery-public-data.gnomAD.v3_genomes__chr1 AS main_table
  WHERE start_position >= 55039447 AND start_position <= 55064852
)
SELECT
  ROUND((55064852 - 55039447) / num_variants, 3) AS burden_of_mutation,
  *
FROM summary_stats;",v3_genomes__chr_*.AN; v3_genomes__chr_*.alternate_bases; v3_genomes__chr_*.start_position,3,lite_sql,sf_bq103,1150
sf_bq104,GOOGLE_TRENDS,snowflake,Identify which DMA had the highest search scores for the terms that were top rising one year ago,"WITH LatestWeek AS (
    SELECT
        DATEADD(WEEK, -52, MAX(""week"")) AS ""last_year_week""
    FROM
        GOOGLE_TRENDS.GOOGLE_TRENDS.TOP_RISING_TERMS
),
LatestRefreshDate AS (
    SELECT
        MAX(""refresh_date"") AS ""latest_refresh_date""
    FROM
        GOOGLE_TRENDS.GOOGLE_TRENDS.TOP_RISING_TERMS
),
RankedTerms AS (
    SELECT
        ""term"",
        ""week"",
        CASE WHEN ""score"" IS NULL THEN NULL ELSE ""dma_name"" END AS ""dma_name"",
        ""rank"",
        ""score"",
        ROW_NUMBER() OVER (
            PARTITION BY ""term"", ""week""
            ORDER BY ""score"" DESC
        ) AS rn
    FROM
        GOOGLE_TRENDS.GOOGLE_TRENDS.TOP_RISING_TERMS
    WHERE
        ""week"" = (SELECT ""last_year_week"" FROM LatestWeek)
        AND ""refresh_date"" = (SELECT ""latest_refresh_date"" FROM LatestRefreshDate)
)

SELECT
    ""term""
FROM
    RankedTerms
WHERE
    rn = 1
ORDER BY
    ""rank""
LIMIT 1;",TOP_RISING_TERMS.dma_name; TOP_RISING_TERMS.rank; TOP_RISING_TERMS.refresh_date; TOP_RISING_TERMS.score; TOP_RISING_TERMS.term; TOP_RISING_TERMS.week,6,lite_sql,sf_bq104,34
bq105,nhtsa_traffic_fatalities_plus,bigquery,"How many traffic accidents per 100,000 people, specifically due to driver distraction, were recorded in each state in the years 2015 and 2016? Identify the top five states each year with the highest rates. Exclude accidents where the distraction status of the driver was recorded as 'Not Distracted,' 'Unknown if Distracted,' or 'Not Reported.' Use state population data from the 2010 census for calculating the rate.","SELECT * FROM
(
SELECT
  '2015' AS year,
  COUNT(a.consecutive_number) AS total,
  a.state_name AS state,
  c.state_pop AS population,
  (COUNT(a.consecutive_number) / c.state_pop * 100000) AS rate_per_100000
FROM
  `bigquery-public-data.nhtsa_traffic_fatalities.accident_2015` a
JOIN
  `bigquery-public-data.nhtsa_traffic_fatalities.distract_2015` b
ON
  a.consecutive_number = b.consecutive_number
JOIN (
  SELECT
    SUM(d.population) AS state_pop,
    e.state_name AS state
  FROM
    `bigquery-public-data.census_bureau_usa.population_by_zip_2010` d
  JOIN
    `bigquery-public-data.utility_us.zipcode_area` e
  ON
    d.zipcode = e.zipcode
  GROUP BY
    state ) c
ON
  c.state = a.state_name
WHERE
  b.driver_distracted_by_name NOT IN ('Not Distracted', 'Unknown if Distracted', 'Not Reported')
GROUP BY
  state,
  population,
  c.state_pop
ORDER BY
  rate_per_100000 DESC
LIMIT 5
)
UNION ALL
(
SELECT
  '2016' AS year,
  COUNT(a.consecutive_number) AS total,
  a.state_name AS state,
  c.state_pop AS population,
  (COUNT(a.consecutive_number) / c.state_pop * 100000) AS rate_per_100000
FROM
  `bigquery-public-data.nhtsa_traffic_fatalities.accident_2016` a
JOIN
  `bigquery-public-data.nhtsa_traffic_fatalities.distract_2016` b
ON
  a.consecutive_number = b.consecutive_number
JOIN (
  SELECT
    SUM(d.population) AS state_pop,
    e.state_name AS state
  FROM
    `bigquery-public-data.census_bureau_usa.population_by_zip_2010` d
  JOIN
    `bigquery-public-data.utility_us.zipcode_area` e
  ON
    d.zipcode = e.zipcode
  GROUP BY
    state ) c
ON
  c.state = a.state_name
WHERE
  b.driver_distracted_by_name NOT IN ('Not Distracted', 'Unknown if Distracted', 'Not Reported')
GROUP BY
  state,
  population,
  c.state_pop
ORDER BY
  rate_per_100000 DESC
LIMIT 5
)",accident_*.consecutive_number; accident_*.state_name; distract_*.consecutive_number; distract_*.driver_distracted_by_name; population_by_zip_*.population; population_by_zip_*.zipcode; zipcode_area.state_name; zipcode_area.zipcode,8,lite_sql,sf_bq105,813
bq109,open_targets_genetics_1,bigquery,"Find the average, variance, max-min difference, and the QTL source(right study) of the maximum log2(h4/h3) for data where right gene id is ""ENSG00000169174"", h4 > 0.8, h3 < 0.02, reported trait includes ""lesterol levels"", right biological feature is ""IPSC"", and the variant is '1_55029009_C_T'.","WITH coloc_stats AS (
  SELECT
    coloc.coloc_log2_h4_h3,
    coloc.right_study AS qtl_source
  FROM
    `open-targets-genetics.genetics.variant_disease_coloc` AS coloc
  JOIN
    `open-targets-genetics.genetics.studies` AS studies
  ON
    coloc.left_study = studies.study_id
  WHERE
    coloc.right_gene_id = ""ENSG00000169174""
    AND coloc.coloc_h4 > 0.8
    AND coloc.coloc_h3 < 0.02
    AND studies.trait_reported LIKE ""%lesterol levels%""
    AND coloc.right_bio_feature = 'IPSC'
    AND CONCAT(coloc.left_chrom, '_', coloc.left_pos, '_', coloc.left_ref, '_', coloc.left_alt) = '1_55029009_C_T'
),
max_value AS (
  SELECT
    MAX(coloc_log2_h4_h3) AS max_log2_h4_h3
  FROM
    coloc_stats
)

SELECT
  AVG(coloc_log2_h4_h3) AS average,
  VAR_SAMP(coloc_log2_h4_h3) AS variance,
  MAX(coloc_log2_h4_h3) - MIN(coloc_log2_h4_h3) AS max_min_difference,
  (SELECT qtl_source FROM coloc_stats WHERE coloc_log2_h4_h3 = (SELECT max_log2_h4_h3 FROM max_value)) AS qtl_source_of_max
FROM
  coloc_stats;",studies.study_id; studies.trait_reported; variant_disease_coloc.coloc_h3; variant_disease_coloc.coloc_h4; variant_disease_coloc.coloc_log2_h4_h3; variant_disease_coloc.left_alt; variant_disease_coloc.left_chrom; variant_disease_coloc.left_pos; variant_disease_coloc.left_ref; variant_disease_coloc.left_study; variant_disease_coloc.right_bio_feature; variant_disease_coloc.right_gene_id; variant_disease_coloc.right_study,13,lite_sql,sf_bq109,293
bq110,sdoh,bigquery,What has been the change in the number of homeless veterans in each CoC region of New York between 2012 and 2018?,"WITH homeless_2012 AS (
  SELECT Homeless_Veterans AS Vet12, CoC_Name  
  FROM `bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc` 
  WHERE SUBSTR(CoC_Number,0,2) = ""NY"" AND Count_Year = 2012
),
 
homeless_2018 AS (
  SELECT Homeless_Veterans AS Vet18, CoC_Name  
  FROM `bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc` 
  WHERE SUBSTR(CoC_Number,0,2) = ""NY"" AND Count_Year = 2018
),
 
veterans_change AS (
  SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange
  FROM homeless_2018
  JOIN homeless_2012
  ON homeless_2018.CoC_Name = homeless_2012.CoC_Name
)

SELECT COC_Name, VetChange FROM veterans_change
ORDER BY CoC_Name;",hud_pit_by_coc.CoC_Name; hud_pit_by_coc.CoC_Number; hud_pit_by_coc.Count_Year; hud_pit_by_coc.Homeless_Veterans,4,lite_sql,sf_bq110,4068
bq112,bls,bigquery,"Did the increase on average annual wages for all industries in Allegheny County, Pittsburgh keep pace with inflation of all consumer items between 1998 and 2017? Tell me their growth rates respectively (2 decimals).","WITH geo AS (
  SELECT DISTINCT geo_id
  FROM `bigquery-public-data.geo_us_boundaries.counties`
  WHERE county_name = ""Allegheny"" 
),
avg_wage_1998 AS(
  SELECT
    ROUND(AVG(avg_wkly_wage_10_total_all_industries) * 52, 2) AS wages_1998
  FROM
    `bigquery-public-data.bls_qcew.1998*`
  WHERE
    geoid = (SELECT geo_id FROM geo) --Selecting Allgeheny County
),
    
avg_wage_2017 AS (
  SELECT
    ROUND(AVG(avg_wkly_wage_10_total_all_industries) * 52, 2) AS wages_2017
  FROM
    `bigquery-public-data.bls_qcew.2017*`
  WHERE
    geoid = (SELECT geo_id FROM geo) --Selecting Allgeheny County
),

avg_cpi_1998 AS (
  SELECT
    AVG(value) AS cpi_1998
  FROM
    `bigquery-public-data.bls.cpi_u` c
  WHERE
    year = 1998
    AND item_code in (
      SELECT DISTINCT item_code FROM `bigquery-public-data.bls.cpi_u` WHERE LOWER(item_name) = ""all items""
    )
    AND area_code = (
      SELECT DISTINCT area_code FROM `bigquery-public-data.bls.cpi_u` WHERE area_name LIKE '%Pittsburgh%'
    )
), 
-- A104 is the code for Pittsburgh, PA
-- SA0 is the code for all items
    
avg_cpi_2017 AS(
  SELECT
    AVG(value) AS cpi_2017
  FROM
    `bigquery-public-data.bls.cpi_u` c
  WHERE
    year = 2017
    AND item_code in (
      SELECT DISTINCT item_code FROM `bigquery-public-data.bls.cpi_u` WHERE LOWER(item_name) = ""all items""
    )
    AND area_code = (
      SELECT DISTINCT area_code FROM `bigquery-public-data.bls.cpi_u` WHERE area_name LIKE '%Pittsburgh%'
    )
)
-- A104 is the code for Pittsburgh, PA
-- SA0 is the code for all items

SELECT
  ROUND((wages_2017 - wages_1998) / wages_1998 * 100, 2) AS wages_percent_change,
  ROUND((cpi_2017 - cpi_1998) / cpi_1998 * 100, 2) AS cpi_percent_change
FROM
  avg_wage_2017,
  avg_wage_1998,
  avg_cpi_2017,
  avg_cpi_1998",1990_q1.avg_wkly_wage_10_total_all_industries; 1990_q1.geoid; counties.county_name; counties.geo_id; cpi_u.area_code; cpi_u.area_name; cpi_u.item_code; cpi_u.item_name; cpi_u.value; cpi_u.year,10,lite_sql,sf_bq112,492
bq113,bls,bigquery,Which Utah county has witnessed the greatest percentage increase of construction jobs from 2000 to 2018? And what is the corresponding increase rate?,"WITH utah_code AS (
  SELECT DISTINCT geo_id
  FROM bigquery-public-data.geo_us_boundaries.states
  WHERE state_name = 'Utah'
),
e2000 as(
  SELECT
    AVG(month3_emplvl_23_construction) AS construction_employees_2000,
    geoid
  FROM
    `bigquery-public-data.bls_qcew.2000_*`
  WHERE
    geoid LIKE CONCAT((SELECT geo_id FROM utah_code), '%')
  GROUP BY
    geoid),

e2018 AS (
  SELECT
    AVG(month3_emplvl_23_construction) AS construction_employees_2018,
    geoid,
  FROM
    `bigquery-public-data.bls_qcew.2018_*` e2018
  WHERE
    geoid LIKE CONCAT((SELECT geo_id FROM utah_code), '%')
  GROUP BY
    geoid)

SELECT
  c.county_name AS county,
  (construction_employees_2018 - construction_employees_2000) / construction_employees_2000 * 100 AS increase_rate
FROM
  e2000
JOIN
  e2018 USING (geoid)
JOIN 
  `bigquery-public-data.geo_us_boundaries.counties` c ON c.geo_id = e2018.geoid
WHERE
  c.state_fips_code = (SELECT geo_id FROM utah_code)
ORDER BY
  increase_rate desc
LIMIT 1",1990_q1.geoid; 1990_q1.month3_emplvl_23_construction; counties.county_name; counties.geo_id; counties.state_fips_code; states.geo_id; states.state_name,7,lite_sql,sf_bq113,492
bq114,openaq,bigquery,"What are the top three cities where the difference between the PM2.5 measurements in 1990 from the EPA and in 2020 from OpenAQ is the greatest, given that the locations are matched with latitude and longitude rounded to two decimal places?","SELECT
  aq.city,
  epa.arithmetic_mean,
  aq.value,
  aq.timestamp,
  (epa.arithmetic_mean - aq.value)
FROM
  `bigquery-public-data.openaq.global_air_quality` AS aq
JOIN
  `bigquery-public-data.epa_historical_air_quality.air_quality_annual_summary` AS epa
ON
  ROUND(aq.latitude, 2) = ROUND(epa.latitude, 2)
  AND ROUND(aq.longitude, 2) = ROUND(epa.longitude, 2)
WHERE
  epa.units_of_measure = ""Micrograms/cubic meter (LC)""
  AND epa.parameter_name = ""Acceptable PM2.5 AQI & Speciation Mass""
  AND epa.year = 1990
  AND aq.pollutant = ""pm25""
  AND EXTRACT(YEAR FROM aq.timestamp) = 2020
ORDER BY
  (epa.arithmetic_mean - aq.value) DESC
LIMIT 3",air_quality_annual_summary.arithmetic_mean; air_quality_annual_summary.latitude; air_quality_annual_summary.longitude; air_quality_annual_summary.parameter_name; air_quality_annual_summary.units_of_measure; air_quality_annual_summary.year; global_air_quality.city; global_air_quality.latitude; global_air_quality.longitude; global_air_quality.pollutant; global_air_quality.timestamp; global_air_quality.value,12,lite_sql,sf_bq114,891
bq115,census_bureau_international,bigquery,Which country has the highest percentage of population under the age of 25 in 2017?,"SELECT
country_name
FROM
(SELECT
  age.country_name,
  SUM(age.population) AS under_25,
  pop.midyear_population AS total,
  ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25
FROM (
  SELECT
    country_name,
    population,
    country_code
  FROM
    `bigquery-public-data.census_bureau_international.midyear_population_agespecific`
  WHERE
    year =2017
    AND age < 25) age
INNER JOIN (
  SELECT
    midyear_population,
    country_code
  FROM
    `bigquery-public-data.census_bureau_international.midyear_population`
  WHERE
    year = 2017) pop
ON
  age.country_code = pop.country_code
GROUP BY
  1,
  3
ORDER BY
  4 DESC
)
LIMIT
1",midyear_population.country_code; midyear_population.midyear_population; midyear_population.year; midyear_population_agespecific.age; midyear_population_agespecific.country_code; midyear_population_agespecific.country_name; midyear_population_agespecific.population; midyear_population_agespecific.year,8,lite_sql,sf_bq115,165
bq119,noaa_data,bigquery,"Please show information of the hurricane with the third longest total travel distance in the North Atlantic during 2020, including its travel coordinates, the cumulative travel distance at each point, and the maximum sustained wind speed at those times.","WITH hurricane_geometry AS (
  SELECT
    * EXCEPT (longitude, latitude),
    ST_GEOGPOINT(longitude, latitude) AS geom,
  FROM
    `bigquery-public-data.noaa_hurricanes.hurricanes`
  WHERE
    season = '2020'
    AND basin = 'NA'
    AND name != 'NOT NAMED'
),
dist_between_points AS (
  SELECT
    sid,
    name,
    season,
    iso_time,
    usa_wind,
    geom,
    ST_DISTANCE(geom, LAG(geom, 1) OVER (PARTITION BY sid ORDER BY iso_time ASC)) / 1000 AS dist
  FROM
    hurricane_geometry
),
total_distances AS (
  SELECT
    sid,
    name,
    season,
    iso_time,
    usa_wind,
    geom,
    SUM(dist) OVER (PARTITION BY sid ORDER BY iso_time ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_distance,
    SUM(dist) OVER (PARTITION BY sid) AS total_dist
  FROM
    dist_between_points
),
ranked_hurricanes AS (
  SELECT
    *,
    DENSE_RANK() OVER (ORDER BY total_dist DESC) AS dense_rank
  FROM
    total_distances
)

SELECT
  geom,cumulative_distance,usa_wind
FROM
  ranked_hurricanes
WHERE
  dense_rank = 3
ORDER BY
cumulative_distance;",hurricanes.basin; hurricanes.iso_time; hurricanes.latitude; hurricanes.longitude; hurricanes.name; hurricanes.season; hurricanes.sid; hurricanes.usa_wind,8,lite_sql,sf_bq119,660
bq120,sdoh,bigquery,"What are the top 10 regions with the highest total SNAP participation, along with their respective ratios of households earning under $20,000 to SNAP households, as of 2017?","WITH acs_2017 AS (
  SELECT geo_id, income_less_10000 AS i10, income_10000_14999 AS i15, income_15000_19999 AS i20
  FROM `bigquery-public-data.census_bureau_acs.county_2017_5yr`
 ),

snap_2017_Jan AS (
  SELECT FIPS, SNAP_All_Participation_Households AS snap_total
  FROM `bigquery-public-data.sdoh_snap_enrollment.snap_enrollment`
  WHERE Date = '2017-01-01'
)

SELECT acs_2017.geo_id, snap_2017_Jan.snap_total,
(acs_2017.i10 + acs_2017.i15 + acs_2017.i20) As households_under_20,
(acs_2017.i10 + acs_2017.i15 + acs_2017.i20)/snap_2017_Jan.snap_total As under_20_snap_ratio 
FROM acs_2017
JOIN snap_2017_Jan
ON  acs_2017.geo_id = snap_2017_Jan.FIPS
WHERE snap_2017_Jan.snap_total > 0
ORDER BY snap_2017_Jan.snap_total DESC
LIMIT 10",county_*.geo_id; county_*.income_10000_14999; county_*.income_15000_19999; county_*.income_less_10000; snap_enrollment.Date; snap_enrollment.FIPS; snap_enrollment.SNAP_All_Participation_Households,7,lite_sql,sf_bq120,4068
sf_bq121,STACKOVERFLOW,snowflake,"How do the average reputation and number of badges vary among Stack Overflow users based on the number of complete years they have been members, considering only those who joined on or before October 1, 2021?","WITH sub AS (
  SELECT 
    ""users"".""id"",
    CAST(TO_TIMESTAMP(MAX(""users"".""creation_date"") / 1000000.0) AS DATE) AS ""user_creation_date"",  -- 使用 MAX 聚合 creation_date 并转换为 DATE
    MAX(""users"".""reputation"") AS ""reputation"",  
    SUM(CASE WHEN badges.""user_id"" IS NULL THEN 0 ELSE 1 END) AS ""num_badges""
  FROM ""STACKOVERFLOW"".""STACKOVERFLOW"".""USERS"" ""users""
  LEFT JOIN ""STACKOVERFLOW"".""STACKOVERFLOW"".""BADGES"" badges
    ON ""users"".""id"" = badges.""user_id""
  WHERE CAST(TO_TIMESTAMP(""users"".""creation_date"" / 1000000.0) AS DATE) <= DATE '2021-10-01'
  GROUP BY ""users"".""id""
)

SELECT 
  DATEDIFF(YEAR, ""user_creation_date"", DATE '2021-10-01') AS ""user_tenure"",
  COUNT(1) AS ""Num_Users"",
  AVG(""reputation"") AS ""Avg_Reputation"",
  AVG(""num_badges"") AS ""Avg_Num_Badges""
FROM sub
GROUP BY ""user_tenure""
ORDER BY ""user_tenure"";",BADGES.user_id; USERS.creation_date; USERS.id; USERS.reputation,4,snow_sql_near_exact,sf_bq121,228
bq123,stackoverflow,bigquery,Which day of the week has the third highest percentage of questions answered within an hour? Please tell me the day along with the percentage.,"WITH first_answers AS (
  SELECT
    parent_id AS question_id,
    MIN(creation_date) AS first_answer_date
  FROM
    `bigquery-public-data.stackoverflow.posts_answers`
  GROUP BY
    parent_id
)

SELECT
  FORMAT_DATE('%A', DATE(q.creation_date)) AS question_day,
  SUM(CASE WHEN f.first_answer_date IS NOT NULL AND TIMESTAMP_DIFF(f.first_answer_date, q.creation_date, MINUTE) <= 60 THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS percent_questions
FROM
  `bigquery-public-data.stackoverflow.posts_questions` q
LEFT JOIN
  first_answers f
ON
  q.id = f.question_id
GROUP BY
  question_day
ORDER BY
  percent_questions DESC
LIMIT 1 OFFSET 2",posts_answers.creation_date; posts_answers.parent_id; posts_questions.creation_date; posts_questions.id,4,lite_sql,sf_bq123,228
bq124,fhir_synthea,bigquery,"Can you identify how many alive patients, currently managing chronic conditions such as diabetes or hypertension, are prescribed seven or more medications?","With INFO AS (
SELECT 
  MR.patientId, 
  P.last_name,
  ARRAY_TO_STRING(P.first_name, "" "") AS First_name,
  Condition.Codes, 
  Condition.Conditions,
  MR.med_count AS COUNT_NUMBER
FROM
  (SELECT 
    id, 
    name[safe_offset(0)].family as last_name, 
    name[safe_offset(0)].given as first_name, 
    TIMESTAMP(deceased.dateTime) AS deceased_datetime 
  FROM `bigquery-public-data.fhir_synthea.patient`) AS P
JOIN
  (SELECT  subject.patientId as patientId, 
           COUNT(DISTINCT medication.codeableConcept.coding[safe_offset(0)].code) AS med_count
   FROM    `bigquery-public-data.fhir_synthea.medication_request`
   WHERE   status = 'active'
   GROUP BY 1
   ) AS MR
ON MR.patientId = P.id 
JOIN
  (SELECT 
  PatientId, 
  STRING_AGG(DISTINCT condition_desc, "", "") AS Conditions, 
  STRING_AGG(DISTINCT condition_code, "", "") AS Codes
  FROM(
    SELECT 
      subject.patientId as PatientId, 
              code.coding[safe_offset(0)].code condition_code,
              code.coding[safe_offset(0)].display condition_desc
       FROM `bigquery-public-data.fhir_synthea.condition`
       wHERE 
         code.coding[safe_offset(0)].display = 'Diabetes'
         OR 
         code.coding[safe_offset(0)].display = 'Hypertension' 
    )
  GROUP BY PatientId
  ) AS Condition
ON MR.patientId = Condition.PatientId
WHERE med_count >= 7 
AND P.deceased_datetime is NULL /*only alive patients*/
GROUP BY patientId, last_name, first_name, Condition.Codes, Condition.Conditions, MR.med_count
ORDER BY last_name
)

SELECT COUNT(*) FROM INFO",condition.code; condition.subject; medication_request.status; medication_request.subject; patient.deceased; patient.id; patient.name,7,lite_sql,sf_bq124,456
bq126,the_met,bigquery,"What are the titles, artist names, mediums, and original image URLs of objects with 'Photograph' in their names from the 'Photographs' department, created not by an unknown artist, with an object end date of 1839 or earlier?","SELECT
  o.artist_display_name,
  o.title,
  o.object_end_date,
  o.medium,
  i.original_image_url
FROM (
  SELECT
    object_id,
    title,
    artist_display_name,
    object_end_date,
    medium
  FROM
    `bigquery-public-data.the_met.objects`
  WHERE
    department = ""Photographs""
    AND object_name LIKE ""%Photograph%""
    AND artist_display_name != ""Unknown""
    AND object_end_date <= 1839
) o
INNER JOIN (
  SELECT
    original_image_url,
    object_id
  FROM
    `bigquery-public-data.the_met.images`
) i
ON
  o.object_id = i.object_id
ORDER BY
  o.object_end_date
;",images.object_id; images.original_image_url; objects.artist_display_name; objects.department; objects.medium; objects.object_end_date; objects.object_id; objects.object_name; objects.title,9,lite_sql,sf_bq126,61
sf_bq127,PATENTS_GOOGLE,snowflake,"For each publication family whose earliest publication was first published in January 2015, please provide the earliest publication date, the distinct publication numbers, their country codes, the distinct CPC and IPC codes, distinct families (namely, the ids) that cite and are cited by this publication family. Please present all lists as comma-separated values, sorted by the first letter of the code for clarity.","WITH fam AS (
  SELECT DISTINCT
    ""family_id""
  FROM
    ""PATENTS_GOOGLE"".""PATENTS_GOOGLE"".""PUBLICATIONS""
),

crossover AS (
  SELECT
    ""publication_number"",
    ""family_id""
  FROM
    ""PATENTS_GOOGLE"".""PATENTS_GOOGLE"".""PUBLICATIONS""
),

pub AS (
  SELECT
    ""family_id"",
    MIN(""publication_date"") AS ""publication_date"",
    LISTAGG(""publication_number"", ',') WITHIN GROUP (ORDER BY ""publication_number"") AS ""publication_number"",
    LISTAGG(""country_code"", ',') WITHIN GROUP (ORDER BY ""country_code"") AS ""country_code""
  FROM
    ""PATENTS_GOOGLE"".""PATENTS_GOOGLE"".""PUBLICATIONS"" AS p
  GROUP BY
    ""family_id""
),

tech_class AS (
  SELECT
    p.""family_id"",
    LISTAGG(DISTINCT cpc.value:""code""::STRING, ',') WITHIN GROUP (ORDER BY cpc.value:""code""::STRING) AS ""cpc"",
    LISTAGG(DISTINCT ipc.value:""code""::STRING, ',') WITHIN GROUP (ORDER BY ipc.value:""code""::STRING) AS ""ipc""
  FROM
    ""PATENTS_GOOGLE"".""PATENTS_GOOGLE"".""PUBLICATIONS"" AS p
    CROSS JOIN LATERAL FLATTEN(input => p.""cpc"") AS cpc
    CROSS JOIN LATERAL FLATTEN(input => p.""ipc"") AS ipc
  GROUP BY
    p.""family_id""
),

cit AS (
  SELECT
    p.""family_id"",
    LISTAGG(crossover.""family_id"", ',') WITHIN GROUP (ORDER BY crossover.""family_id"" ASC) AS ""citation""
  FROM
    ""PATENTS_GOOGLE"".""PATENTS_GOOGLE"".""PUBLICATIONS"" AS p
    CROSS JOIN LATERAL FLATTEN(input => p.""citation"") AS citation
    LEFT JOIN
      crossover
    ON
      citation.value:""publication_number""::STRING = crossover.""publication_number""
  GROUP BY
    p.""family_id""
),

tmp_gpr AS (
  SELECT
    ""family_id"",
    LISTAGG(crossover.""publication_number"", ',') AS ""cited_by_publication_number""
  FROM
    ""PATENTS_GOOGLE"".""PATENTS_GOOGLE"".""ABS_AND_EMB"" AS p
    CROSS JOIN LATERAL FLATTEN(input => p.""cited_by"") AS cited_by
    LEFT JOIN
      crossover
    ON
      cited_by.value:""publication_number""::STRING = crossover.""publication_number""
  GROUP BY
    ""family_id""
),

gpr AS (
  SELECT
    tmp_gpr.""family_id"",
    LISTAGG(crossover.""family_id"", ',') WITHIN GROUP (ORDER BY crossover.""family_id"" ASC) AS ""cited_by""
  FROM
    tmp_gpr
    CROSS JOIN LATERAL FLATTEN(input => SPLIT(tmp_gpr.""cited_by_publication_number"", ',')) AS cited_by_publication_number
    LEFT JOIN
      crossover
    ON
      cited_by_publication_number.value::STRING = crossover.""publication_number""
  GROUP BY
    tmp_gpr.""family_id""
)

SELECT
  fam.""family_id"",
  pub.""publication_date"",
  pub.""publication_number"",
  pub.""country_code"",
  tech_class.""cpc"",
  tech_class.""ipc"",
  cit.""citation"",
  gpr.""cited_by""
FROM
  fam
  LEFT JOIN pub ON fam.""family_id"" = pub.""family_id""
  LEFT JOIN tech_class ON fam.""family_id"" = tech_class.""family_id""
  LEFT JOIN cit ON fam.""family_id"" = cit.""family_id""
  LEFT JOIN gpr ON fam.""family_id"" = gpr.""family_id""
WHERE
  pub.""publication_date"" BETWEEN 20150101 AND 20150131;",ABS_AND_EMB.cited_by; PUBLICATIONS.citation; PUBLICATIONS.country_code; PUBLICATIONS.cpc; PUBLICATIONS.family_id; PUBLICATIONS.ipc; PUBLICATIONS.publication_date; PUBLICATIONS.publication_number,8,lite_sql,sf_bq127,87
sf_bq128,PATENTSVIEW,snowflake,"Tell me the patent title and abstract, as well as the publication date, the backward citation and forward citation count within 5 years for those published in January 2014. The detailed requirements are provided in `forward_backward_citation.md`.","SELECT
    patent.""title"",
    patent.""abstract"",
    app.""date"" AS publication_date,
    filterData.""bkwdCitations"",
    filterData.""fwrdCitations_5""
FROM
    ""PATENTSVIEW"".""PATENTSVIEW"".""PATENT"" AS patent
JOIN
    ""PATENTSVIEW"".""PATENTSVIEW"".""APPLICATION"" AS app
    ON app.""patent_id"" = patent.""id""
JOIN (
    SELECT
        DISTINCT cpc.""patent_id"",
        IFNULL(citation_5.""bkwdCitations"", 0) AS ""bkwdCitations"",
        IFNULL(citation_5.""fwrdCitations_5"", 0) AS ""fwrdCitations_5""
    FROM
        ""PATENTSVIEW"".""PATENTSVIEW"".""CPC_CURRENT"" AS cpc
    LEFT JOIN (
        SELECT
            b.""patent_id"",
            b.""bkwdCitations"",
            f.""fwrdCitations_5""
        FROM (
            SELECT 
                cited.""citation_id"" AS ""patent_id"",
                IFNULL(COUNT(*), 0) AS ""fwrdCitations_5""
            FROM 
                ""PATENTSVIEW"".""PATENTSVIEW"".""USPATENTCITATION"" AS cited
            JOIN
                ""PATENTSVIEW"".""PATENTSVIEW"".""APPLICATION"" AS apps
                ON cited.""citation_id"" = apps.""patent_id""
            WHERE
                apps.""country"" = 'US'
                AND cited.""date"" >= apps.""date""
                AND TRY_CAST(cited.""date"" AS DATE) <= DATEADD(YEAR, 5, TRY_CAST(apps.""date"" AS DATE)) -- 5-year citation window
            GROUP BY 
                cited.""citation_id""
        ) AS f
        JOIN (
            SELECT 
                cited.""patent_id"",
                IFNULL(COUNT(*), 0) AS ""bkwdCitations""
            FROM 
                ""PATENTSVIEW"".""PATENTSVIEW"".""USPATENTCITATION"" AS cited
            JOIN
                ""PATENTSVIEW"".""PATENTSVIEW"".""APPLICATION"" AS apps
                ON cited.""patent_id"" = apps.""patent_id""
            WHERE
                apps.""country"" = 'US'
                AND cited.""date"" < apps.""date"" -- backward citation count
            GROUP BY 
                cited.""patent_id""
        ) AS b
        ON b.""patent_id"" = f.""patent_id""
        WHERE
            b.""bkwdCitations"" IS NOT NULL
            AND f.""fwrdCitations_5"" IS NOT NULL
    ) AS citation_5 
    ON cpc.""patent_id"" = citation_5.""patent_id""
    WHERE 
        cpc.""subsection_id"" IN ('C05', 'C06', 'C07', 'C08', 'C09', 'C10', 'C11', 'C12', 'C13')
        OR cpc.""group_id"" IN ('A01G', 'A01H', 'A61K', 'A61P', 'A61Q', 'B01F', 'B01J', 'B81B', 'B82B', 'B82Y', 'G01N', 'G16H')
) AS filterData
ON app.""patent_id"" = filterData.""patent_id""
WHERE
    TRY_CAST(app.""date"" AS DATE) < '2014-02-01' 
    AND TRY_CAST(app.""date"" AS DATE) >= '2014-01-01';",APPLICATION.country; APPLICATION.date; APPLICATION.patent_id; CPC_CURRENT.group_id; CPC_CURRENT.patent_id; CPC_CURRENT.subsection_id; PATENT.abstract; PATENT.id; PATENT.title; USPATENTCITATION.citation_id; USPATENTCITATION.date; USPATENTCITATION.patent_id,12,lite_sql,sf_bq128,304
bq130,covid19_nyt,bigquery,"Analyze daily new COVID-19 case counts from March to May 2020, identifying the top five states by daily increases. Please compile a ranking based on how often each state appears in these daily top fives. Then, examine the state that ranks fourth overall and identify its top five counties based on their frequency of appearing in the daily top five new case counts.","WITH StateCases AS (
    SELECT
        b.state_name,
        b.date,
        b.confirmed_cases - a.confirmed_cases AS daily_new_cases
    FROM 
        (SELECT
            state_name,
            state_fips_code,
            confirmed_cases,
            DATE_ADD(date, INTERVAL 1 DAY) AS date_shift
        FROM
            `bigquery-public-data.covid19_nyt.us_states`
        WHERE
            date >= '2020-02-29' AND date <= '2020-05-30'
        ) a
    JOIN
        `bigquery-public-data.covid19_nyt.us_states` b 
        ON a.state_fips_code = b.state_fips_code AND a.date_shift = b.date
    WHERE
        b.date >= '2020-03-01' AND b.date <= '2020-05-31'
),
RankedStatesPerDay AS (
    SELECT
        state_name,
        date,
        daily_new_cases,
        RANK() OVER (PARTITION BY date ORDER BY daily_new_cases DESC) as rank
    FROM
        StateCases
),
TopStates AS (
    SELECT
        state_name,
        COUNT(*) AS appearance_count
    FROM
        RankedStatesPerDay
    WHERE
        rank <= 5
    GROUP BY
        state_name
    ORDER BY
        appearance_count DESC
),
FourthState AS (
    SELECT
        state_name
    FROM
        TopStates
    LIMIT 1
    OFFSET 3
),
CountyCases AS (
    SELECT
        b.county,
        b.date,
        b.confirmed_cases - a.confirmed_cases AS daily_new_cases
    FROM 
        (SELECT
            county,
            county_fips_code,
            confirmed_cases,
            DATE_ADD(date, INTERVAL 1 DAY) AS date_shift
        FROM
            `bigquery-public-data.covid19_nyt.us_counties`
        WHERE
            date >= '2020-02-29' AND date <= '2020-05-30'
        ) a
    JOIN
        `bigquery-public-data.covid19_nyt.us_counties` b 
        ON a.county_fips_code = b.county_fips_code AND a.date_shift = b.date
    WHERE
        b.date >= '2020-03-01' AND b.date <= '2020-05-31'
        AND b.state_name = (SELECT state_name FROM FourthState)
),
RankedCountiesPerDay AS (
    SELECT
        county,
        date,
        daily_new_cases,
        RANK() OVER (PARTITION BY date ORDER BY daily_new_cases DESC) as rank
    FROM
        CountyCases
),
TopCounties AS (
    SELECT
        county,
        COUNT(*) AS appearance_count
    FROM
        RankedCountiesPerDay
    WHERE
        rank <= 5
    GROUP BY
        county
    ORDER BY
        appearance_count DESC
    LIMIT 5
)
SELECT
    county
FROM
    TopCounties;",us_counties.confirmed_cases; us_counties.county; us_counties.county_fips_code; us_counties.date; us_counties.state_name; us_states.confirmed_cases; us_states.date; us_states.state_fips_code; us_states.state_name,9,lite_sql,sf_bq130,29
bq143,CPTAC_PDC,bigquery,"Use CPTAC proteomics and RNAseq data for Clear Cell Renal Cell Carcinoma to select 'Primary Tumor' and 'Solid Tissue Normal' samples. Join the datasets on sample submitter IDs and gene symbols. Calculate the correlation between protein abundance (log2 ratio) and gene expression levels (log-transformed+1 FPKM) for each gene and sample type. Filter out correlations with an absolute value greater than 0.5, and compute the average correlation for each sample type.","WITH 
quant AS (
    SELECT 
        meta.sample_submitter_id, 
        meta.sample_type, 
        quant.case_id, 
        quant.aliquot_id, 
        quant.gene_symbol, 
        CAST(quant.protein_abundance_log2ratio AS FLOAT64) AS protein_abundance_log2ratio 
    FROM 
        `isb-cgc-bq.CPTAC.quant_proteome_CPTAC_CCRCC_discovery_study_pdc_current` AS quant
    JOIN 
        `isb-cgc-bq.PDC_metadata.aliquot_to_case_mapping_current` AS meta
        ON quant.case_id = meta.case_id
        AND quant.aliquot_id = meta.aliquot_id
        AND meta.sample_type IN ('Primary Tumor', 'Solid Tissue Normal')
),
gexp AS (
    SELECT DISTINCT 
        meta.sample_submitter_id, 
        meta.sample_type, 
        rnaseq.gene_name, 
        LOG(rnaseq.fpkm_unstranded + 1) AS HTSeq__FPKM   -- Confirm the correct column name here
    FROM 
        `isb-cgc-bq.CPTAC.RNAseq_hg38_gdc_current` AS rnaseq
    JOIN 
        `isb-cgc-bq.PDC_metadata.aliquot_to_case_mapping_current` AS meta
        ON meta.sample_submitter_id = rnaseq.sample_barcode
),
correlation AS (
    SELECT 
        quant.gene_symbol, 
        gexp.sample_type, 
        COUNT(*) AS n, 
        CORR(protein_abundance_log2ratio, HTSeq__FPKM) AS corr  -- Confirm the correct column name here
    FROM 
        quant 
    JOIN 
        gexp 
        ON quant.sample_submitter_id = gexp.sample_submitter_id
        AND gexp.gene_name = quant.gene_symbol
        AND gexp.sample_type = quant.sample_type
    GROUP BY 
        quant.gene_symbol, gexp.sample_type
),
pval AS (
    SELECT  
        gene_symbol, 
        sample_type, 
        n, 
        corr
    FROM 
        correlation
    WHERE 
        ABS(corr) <= 0.5
)
SELECT sample_type, AVG(corr)
FROM pval
GROUP BY sample_type;",RNAseq_hg38_gdc_current.fpkm_unstranded; RNAseq_hg38_gdc_current.gene_name; RNAseq_hg38_gdc_current.sample_barcode; aliquot_to_case_mapping_current.aliquot_id; aliquot_to_case_mapping_current.case_id; aliquot_to_case_mapping_current.sample_submitter_id; aliquot_to_case_mapping_current.sample_type; quant_proteome_CPTAC_CCRCC_discovery_study_pdc_current.aliquot_id; quant_proteome_CPTAC_CCRCC_discovery_study_pdc_current.case_id; quant_proteome_CPTAC_CCRCC_discovery_study_pdc_current.gene_symbol; quant_proteome_CPTAC_CCRCC_discovery_study_pdc_current.protein_abundance_log2ratio,11,lite_sql,sf_bq143,1424
bq144,ncaa_insights,bigquery,I would like to merge NCAA basketball historical tournament games outcomes with additional pace and efficiency performance metrics to enable comprehensive analysis of team and opponent dynamics from the 2014 season onwards (2018 included). Please refer to the Query Variable Guide to provide all the data.,"WITH outcomes AS (
SELECT

  season, # 1994
  ""win"" AS label, # our label
  win_seed AS seed, # ranking # this time without seed even
  win_school_ncaa AS school_ncaa,
  lose_seed AS opponent_seed, # ranking
  lose_school_ncaa AS opponent_school_ncaa
FROM `data-to-insights.ncaa.mbb_historical_tournament_games` t
WHERE season >= 2014
UNION ALL

SELECT

  season, # 1994
  ""loss"" AS label, # our label
  lose_seed AS seed, # ranking
  lose_school_ncaa AS school_ncaa,
  win_seed AS opponent_seed, # ranking
  win_school_ncaa AS opponent_school_ncaa
FROM
`data-to-insights.ncaa.mbb_historical_tournament_games` t
WHERE season >= 2014
UNION ALL

SELECT
  season,
  label,
  seed,
  school_ncaa,
  opponent_seed,
  opponent_school_ncaa
FROM
  `data-to-insights.ncaa.2018_tournament_results`
)
SELECT
o.season,
label,
  seed,
  school_ncaa,
  team.pace_rank,
  team.poss_40min,
  team.pace_rating,
  team.efficiency_rank,
  team.pts_100poss,
  team.efficiency_rating,
  opponent_seed,
  opponent_school_ncaa,
  opp.pace_rank AS opp_pace_rank,
  opp.poss_40min AS opp_poss_40min,
  opp.pace_rating AS opp_pace_rating,
  opp.efficiency_rank AS opp_efficiency_rank,
  opp.pts_100poss AS opp_pts_100poss,
  opp.efficiency_rating AS opp_efficiency_rating,
  opp.pace_rank - team.pace_rank AS pace_rank_diff,
  opp.poss_40min - team.poss_40min AS pace_stat_diff,
  opp.pace_rating - team.pace_rating AS pace_rating_diff,
  opp.efficiency_rank - team.efficiency_rank AS eff_rank_diff,
  opp.pts_100poss - team.pts_100poss AS eff_stat_diff,
  opp.efficiency_rating - team.efficiency_rating AS eff_rating_diff
FROM outcomes AS o
LEFT JOIN `data-to-insights.ncaa.feature_engineering` AS team
ON o.school_ncaa = team.team AND o.season = team.season
LEFT JOIN `data-to-insights.ncaa.feature_engineering` AS opp
ON o.opponent_school_ncaa = opp.team AND o.season = opp.season",2018_tournament_results.label; 2018_tournament_results.opponent_school_ncaa; 2018_tournament_results.opponent_seed; 2018_tournament_results.school_ncaa; 2018_tournament_results.season; 2018_tournament_results.seed; feature_engineering.efficiency_rank; feature_engineering.efficiency_rating; feature_engineering.pace_rank; feature_engineering.pace_rating; feature_engineering.poss_40min; feature_engineering.pts_100poss; feature_engineering.season; feature_engineering.team; mbb_historical_tournament_games.lose_school_ncaa; mbb_historical_tournament_games.lose_seed; mbb_historical_tournament_games.season; mbb_historical_tournament_games.win_school_ncaa; mbb_historical_tournament_games.win_seed,19,lite_sql,sf_bq144,552
sf_bq150,TCGA_HG19_DATA_V0,snowflake,"Assess whether different genetic variants affect the log10-transformed TP53 expression levels in TCGA-BRCA samples using sequencing and mutation data. Provide the total number of samples, the number of mutation types, the mean square between groups, the mean square within groups, and the F-statistic.","WITH
cohortExpr AS (
  SELECT
    ""sample_barcode"",
    LOG(10, ""normalized_count"") AS ""expr""
  FROM
    ""TCGA_HG19_DATA_V0"".""TCGA_HG19_DATA_V0"".""RNASEQ_GENE_EXPRESSION_UNC_RSEM""
  WHERE
    ""project_short_name"" = 'TCGA-BRCA'
    AND ""HGNC_gene_symbol"" = 'TP53'
    AND ""normalized_count"" IS NOT NULL
    AND ""normalized_count"" > 0
),
cohortVar AS (
  SELECT
    ""Variant_Type"",
    ""sample_barcode_tumor"" AS ""sample_barcode""
  FROM
    ""TCGA_HG19_DATA_V0"".""TCGA_HG19_DATA_V0"".""SOMATIC_MUTATION_MC3""
  WHERE
    ""SYMBOL"" = 'TP53'
),
cohort AS (
  SELECT
    e.""sample_barcode"" AS ""sample_barcode"",
    v.""Variant_Type"" AS ""group_name"",
    e.""expr""
  FROM
    cohortExpr e
  JOIN
    cohortVar v
  ON
    e.""sample_barcode"" = v.""sample_barcode""
),
grandMeanTable AS (
  SELECT
    AVG(""expr"") AS ""grand_mean""
  FROM
    cohort
),
groupMeansTable AS (
  SELECT
    AVG(""expr"") AS ""group_mean"",
    ""group_name"",
    COUNT(""sample_barcode"") AS ""n""
  FROM
    cohort
  GROUP BY
    ""group_name""
),
ssBetween AS (
  SELECT
    g.""group_name"",
    g.""group_mean"",
    gm.""grand_mean"",
    g.""n"",
    g.""n"" * POW(g.""group_mean"" - gm.""grand_mean"", 2) AS ""n_diff_sq""
  FROM
    groupMeansTable g
  CROSS JOIN
    grandMeanTable gm
),
ssWithin AS (
  SELECT
    c.""group_name"" AS ""group_name"",
    c.""expr"",
    b.""group_mean"",
    b.""n"" AS ""n"",
    POW(c.""expr"" - b.""group_mean"", 2) AS ""s2""
  FROM
    cohort c
  JOIN
    ssBetween b
  ON
    c.""group_name"" = b.""group_name""
),
numerator AS (
  SELECT
    SUM(""n_diff_sq"") / (COUNT(""group_name"") - 1) AS ""mean_sq_between""
  FROM
    ssBetween
),
denominator AS (
  SELECT
    COUNT(DISTINCT ""group_name"") AS ""k"",
    COUNT(""group_name"") AS ""n"",
    SUM(""s2"") / (COUNT(""group_name"") - COUNT(DISTINCT ""group_name"")) AS ""mean_sq_within""
  FROM
    ssWithin
)

SELECT
  ""n"",
  ""k"",
  ""mean_sq_between"",
  ""mean_sq_within"",
  ""mean_sq_between"" / ""mean_sq_within"" AS ""F""
FROM
  numerator,
  denominator;",RNASEQ_GENE_EXPRESSION_UNC_RSEM.HGNC_gene_symbol; RNASEQ_GENE_EXPRESSION_UNC_RSEM.normalized_count; RNASEQ_GENE_EXPRESSION_UNC_RSEM.project_short_name; RNASEQ_GENE_EXPRESSION_UNC_RSEM.sample_barcode; SOMATIC_MUTATION_MC3.SYMBOL; SOMATIC_MUTATION_MC3.Variant_Type; SOMATIC_MUTATION_MC3.sample_barcode_tumor,7,lite_sql,sf_bq150,254
bq151,pancancer_atlas_2,bigquery,"Using TCGA dataset, calculate the chi-squared statistic to evaluate the association between KRAS and TP53 gene mutations in patients diagnosed with pancreatic adenocarcinoma (PAAD). Incorporate clinical follow-up data and high-quality mutation annotations to accurately determine the frequency of patients with co-occurring KRAS and TP53 mutations compared to those with each mutation occurring independently. Ensure that patient records are meticulously matched based on unique identifiers to maintain data integrity. This analysis aims to identify and quantify potential correlations between KRAS and TP53 genetic alterations within the PAAD patient population.","WITH
barcodes AS (
   SELECT bcr_patient_barcode AS ParticipantBarcode
   FROM isb-cgc-bq.pancancer_atlas.Filtered_clinical_PANCAN_patient_with_followup
   WHERE acronym = 'PAAD'
),
table1 AS (
SELECT
   t1.ParticipantBarcode,
   IF(t2.ParticipantBarcode IS NULL, 'NO', 'YES') AS data
FROM
   barcodes AS t1
LEFT JOIN
   (
   SELECT
      ParticipantBarcode AS ParticipantBarcode
   FROM isb-cgc-bq.pancancer_atlas.Filtered_MC3_MAF_V5_one_per_tumor_sample
   WHERE Study = 'PAAD' AND Hugo_Symbol = 'KRAS'
         AND FILTER = 'PASS'
   GROUP BY ParticipantBarcode
   ) AS t2
ON t1.ParticipantBarcode = t2.ParticipantBarcode
),
table2 AS (
SELECT
   t1.ParticipantBarcode,
   IF(t2.ParticipantBarcode IS NULL, 'NO', 'YES') AS data
FROM
   barcodes AS t1
LEFT JOIN
   (
   SELECT
      ParticipantBarcode AS ParticipantBarcode
   FROM isb-cgc-bq.pancancer_atlas.Filtered_MC3_MAF_V5_one_per_tumor_sample
   WHERE Study = 'PAAD' AND Hugo_Symbol = 'TP53'
         AND FILTER = 'PASS'
   GROUP BY ParticipantBarcode
   ) AS t2
ON t1.ParticipantBarcode = t2.ParticipantBarcode
),
summ_table AS (
SELECT
   n1.data AS data1,
   n2.data AS data2,
   COUNT(*) AS Nij
FROM
   table1 AS n1
INNER JOIN
   table2 AS n2
ON
   n1.ParticipantBarcode = n2.ParticipantBarcode
GROUP BY
  data1, data2
),
contingency_table AS (
SELECT
  MAX(IF((data1 = 'YES') AND (data2 = 'YES'), Nij, 0)) AS a,
  MAX(IF((data1 = 'YES') AND (data2 = 'NO'), Nij, 0)) AS b,
  MAX(IF((data1 = 'NO') AND (data2 = 'YES'), Nij, 0)) AS c,
  MAX(IF((data1 = 'NO') AND (data2 = 'NO'), Nij, 0)) AS d,
  (MAX(IF((data1 = 'YES') AND (data2 = 'YES'), Nij, 0)) + MAX(IF((data1 = 'YES') AND (data2 = 'NO'), Nij, 0))) AS row1_total,
  (MAX(IF((data1 = 'NO') AND (data2 = 'YES'), Nij, 0)) + MAX(IF((data1 = 'NO') AND (data2 = 'NO'), Nij, 0))) AS row2_total,
  (MAX(IF((data1 = 'YES') AND (data2 = 'YES'), Nij, 0)) + MAX(IF((data1 = 'NO') AND (data2 = 'YES'), Nij, 0))) AS col1_total,
  (MAX(IF((data1 = 'YES') AND (data2 = 'NO'), Nij, 0)) + MAX(IF((data1 = 'NO') AND (data2 = 'NO'), Nij, 0))) AS col2_total,
  SUM(Nij) AS grand_total
FROM summ_table
)
SELECT
  POWER((a - (row1_total * col1_total) / grand_total), 2) / ((row1_total * col1_total) / grand_total) +
  POWER((b - (row1_total * col2_total) / grand_total), 2) / ((row1_total * col2_total) / grand_total) +
  POWER((c - (row2_total * col1_total) / grand_total), 2) / ((row2_total * col1_total) / grand_total) +
  POWER((d - (row2_total * col2_total) / grand_total), 2) / ((row2_total * col2_total) / grand_total) AS chi_square_statistic
FROM contingency_table
WHERE a IS NOT NULL AND b IS NOT NULL AND c IS NOT NULL AND d IS NOT NULL;",Filtered_MC3_MAF_V5_one_per_tumor_sample.FILTER; Filtered_MC3_MAF_V5_one_per_tumor_sample.Hugo_Symbol; Filtered_MC3_MAF_V5_one_per_tumor_sample.ParticipantBarcode; Filtered_MC3_MAF_V5_one_per_tumor_sample.Study; Filtered_clinical_PANCAN_patient_with_followup.acronym; Filtered_clinical_PANCAN_patient_with_followup.bcr_patient_barcode,6,lite_sql,sf_bq151,1623
sf_bq153,PANCANCER_ATLAS_1,snowflake,Calculate the average log10(normalized_count + 1) expression level of the IGF2 gene for each histology type among LGG patients. Include only patients with valid IGF2 expression data and histology types not enclosed in square brackets. Match gene expression and clinical data using ParticipantBarcode.,"WITH
    table1 AS (
        SELECT 
            ""Symbol"" AS ""symbol"", 
            AVG(LOG(10, ""normalized_count"" + 1)) AS ""data"", 
            ""ParticipantBarcode""
        FROM 
            PANCANCER_ATLAS_1.PANCANCER_ATLAS_FILTERED.EBPP_ADJUSTPANCAN_ILLUMINAHISEQ_RNASEQV2_GENEXP_FILTERED
        WHERE 
            ""Study"" = 'LGG' 
            AND ""Symbol"" = 'IGF2' 
            AND ""normalized_count"" IS NOT NULL
        GROUP BY 
            ""ParticipantBarcode"", ""symbol""
    ),
    table2 AS (
        SELECT
            ""symbol"",
            ""avgdata"" AS ""data"",
            ""ParticipantBarcode""
        FROM (
            SELECT
                'icd_o_3_histology' AS ""symbol"", 
                ""icd_o_3_histology"" AS ""avgdata"",
                ""bcr_patient_barcode"" AS ""ParticipantBarcode""
            FROM 
                PANCANCER_ATLAS_1.PANCANCER_ATLAS_FILTERED.CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED
            WHERE 
                ""acronym"" = 'LGG' 
                AND ""icd_o_3_histology"" IS NOT NULL  
                AND NOT REGEXP_LIKE(""icd_o_3_histology"", '^(\\[.*\\]$)')
        )
    ),
    table_data AS (
        SELECT 
            n1.""data"" AS ""data1"",
            n2.""data"" AS ""data2"",
            n1.""ParticipantBarcode""
        FROM 
            table1 AS n1
        INNER JOIN 
            table2 AS n2
        ON 
            n1.""ParticipantBarcode"" = n2.""ParticipantBarcode""
    ) 

SELECT 
    ""data2"" AS ""Histology_Type"", 
    AVG(""data1"") AS ""Average_Log_Expression""
FROM 
    table_data
GROUP BY 
    ""data2"";",CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.acronym; CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.bcr_patient_barcode; CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.icd_o_3_histology; EBPP_ADJUSTPANCAN_ILLUMINAHISEQ_RNASEQV2_GENEXP_FILTERED.ParticipantBarcode; EBPP_ADJUSTPANCAN_ILLUMINAHISEQ_RNASEQV2_GENEXP_FILTERED.Symbol; EBPP_ADJUSTPANCAN_ILLUMINAHISEQ_RNASEQV2_GENEXP_FILTERED.normalized_count,6,snow_sql_near_exact,sf_bq153,825
sf_bq155,TCGA_HG38_DATA_V0,snowflake,"Help me calculate the t-statistic based on the Pearson correlation coefficient between all possible pairs of gene `SNORA31` in the RNAseq data (Log10 transformation) and unique identifiers in the microRNA data available in TCGA. The cohort for this analysis consists of BRCA patients that are 80 years old or younger at the time of diagnosis and Stage I,II,IIA as pathological state. And only consider samples of size more than 25 and with absolute Pearson correlation at least 0.3, and less than 1.0.","WITH cohort AS (
    SELECT ""case_barcode""
    FROM ""TCGA_HG38_DATA_V0"".""TCGA_BIOCLIN_V0"".""CLINICAL""
    WHERE ""project_short_name"" = 'TCGA-BRCA'
        AND ""age_at_diagnosis"" <= 80
        AND ""pathologic_stage"" IN ('Stage I', 'Stage II', 'Stage IIA')
),
table1 AS (
    SELECT
        ""symbol"",
        ""data"" AS ""rnkdata"",
        ""ParticipantBarcode""
    FROM (
        SELECT
            ""gene_name"" AS ""symbol"", 
            AVG(LOG(10, ""HTSeq__Counts"" + 1)) AS ""data"",
            ""case_barcode"" AS ""ParticipantBarcode""
        FROM ""TCGA_HG38_DATA_V0"".""TCGA_HG38_DATA_V0"".""RNASEQ_GENE_EXPRESSION""
        WHERE ""case_barcode"" IN (SELECT ""case_barcode"" FROM cohort)
            AND ""gene_name"" = 'SNORA31'
            AND ""HTSeq__Counts"" IS NOT NULL
        GROUP BY
            ""ParticipantBarcode"", ""symbol""
    )
),
table2 AS (
    SELECT
        ""symbol"",
        ""data"" AS ""rnkdata"",
        ""ParticipantBarcode""
    FROM (
        SELECT
            ""mirna_id"" AS ""symbol"", 
            AVG(""reads_per_million_miRNA_mapped"") AS ""data"",
            ""case_barcode"" AS ""ParticipantBarcode""
        FROM ""TCGA_HG38_DATA_V0"".""TCGA_HG38_DATA_V0"".""MIRNASEQ_EXPRESSION""
        WHERE ""case_barcode"" IN (SELECT ""case_barcode"" FROM cohort)
            AND ""mirna_id"" IS NOT NULL
            AND ""reads_per_million_miRNA_mapped"" IS NOT NULL
        GROUP BY
            ""ParticipantBarcode"", ""symbol""
    )
),
summ_table AS (
    SELECT 
        n1.""symbol"" AS ""symbol1"",
        n2.""symbol"" AS ""symbol2"",
        COUNT(n1.""ParticipantBarcode"") AS ""n"",
        CORR(n1.""rnkdata"", n2.""rnkdata"") AS ""correlation""
    FROM
        table1 AS n1
    INNER JOIN
        table2 AS n2
    ON
        n1.""ParticipantBarcode"" = n2.""ParticipantBarcode""
    GROUP BY
        ""symbol1"", ""symbol2""
)

SELECT 
    ""symbol1"", 
    ""symbol2"", 
    ABS(""correlation"") * SQRT(( ""n"" - 2 ) / (1 - ""correlation"" * ""correlation"")) AS ""t""
FROM 
    summ_table
WHERE 
    ""n"" > 25 
    AND ABS(""correlation"") >= 0.3 
    AND ABS(""correlation"") < 1.0;",CLINICAL.age_at_diagnosis; CLINICAL.case_barcode; CLINICAL.pathologic_stage; CLINICAL.project_short_name; MIRNASEQ_EXPRESSION.case_barcode; MIRNASEQ_EXPRESSION.mirna_id; MIRNASEQ_EXPRESSION.reads_per_million_miRNA_mapped; RNASEQ_GENE_EXPRESSION.HTSeq__Counts; RNASEQ_GENE_EXPRESSION.case_barcode; RNASEQ_GENE_EXPRESSION.gene_name,10,snow_sql_near_exact,sf_bq155,708
sf_bq158,PANCANCER_ATLAS_1,snowflake,Which top five histological types of breast cancer (BRCA) in the PanCancer Atlas exhibit the highest percentage of CDH1 gene mutations?,"WITH
    table1 AS (
        SELECT
            ""histological_type"" AS ""data1"",
            ""bcr_patient_barcode"" AS ""ParticipantBarcode""
        FROM 
            ""PANCANCER_ATLAS_1"".""PANCANCER_ATLAS_FILTERED"".""CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED""
        WHERE 
            ""acronym"" = 'BRCA' 
            AND ""histological_type"" IS NOT NULL      
    ),
    table2 AS (
        SELECT
            ""Hugo_Symbol"" AS ""symbol"", 
            ""ParticipantBarcode""
        FROM 
            ""PANCANCER_ATLAS_1"".""PANCANCER_ATLAS_FILTERED"".""MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE""
        WHERE 
            ""Study"" = 'BRCA' 
            AND ""Hugo_Symbol"" = 'CDH1'
            AND ""FILTER"" = 'PASS'  
        GROUP BY
            ""ParticipantBarcode"", ""symbol""
    ),
    summ_table AS (
        SELECT 
            n1.""data1"",
            CASE 
                WHEN n2.""ParticipantBarcode"" IS NULL THEN 'NO' 
                ELSE 'YES' 
            END AS ""data2"",
            COUNT(*) AS ""Nij""
        FROM
            table1 AS n1
        LEFT JOIN
            table2 AS n2 
            ON n1.""ParticipantBarcode"" = n2.""ParticipantBarcode""
        GROUP BY
            n1.""data1"", ""data2""
    ),
    percentages AS (
        SELECT
            ""data1"",
            SUM(CASE WHEN ""data2"" = 'YES' THEN ""Nij"" ELSE 0 END) AS ""mutation_count"",
            SUM(""Nij"") AS ""total"",
            SUM(CASE WHEN ""data2"" = 'YES' THEN ""Nij"" ELSE 0 END) / SUM(""Nij"") AS ""mutation_percentage""
        FROM 
            summ_table
        GROUP BY 
            ""data1""
    )
SELECT 
    ""data1"" AS ""Histological_Type""
FROM 
    percentages
ORDER BY 
    ""mutation_percentage"" DESC
LIMIT 5;",CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.acronym; CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.bcr_patient_barcode; CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.histological_type; MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE.Hugo_Symbol; MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE.ParticipantBarcode; MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE.Study,6,snow_sql_near_exact,sf_bq158,825
sf_bq159,PANCANCER_ATLAS_1,snowflake,Calculate the chi-square value to assess the association between histological types and the presence of CDH1 gene mutations in BRCA patients using data from the PanCancer Atlas. Focus on patients with known histological types and consider only reliable mutation entries.  Exclude any histological types or mutation statuses with marginal totals less than or equal to 10. Match clinical and mutation data using ParticipantBarcode,"WITH
    table1 AS (
        SELECT
            ""symbol"",
            ""avgdata"" AS ""data"",
            ""ParticipantBarcode""
        FROM (
            SELECT
                'histological_type' AS ""symbol"", 
                ""histological_type"" AS ""avgdata"",
                ""bcr_patient_barcode"" AS ""ParticipantBarcode""
            FROM 
                ""PANCANCER_ATLAS_1"".""PANCANCER_ATLAS_FILTERED"".""CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED""
            WHERE 
                ""acronym"" = 'BRCA' 
                AND ""histological_type"" IS NOT NULL      
        )
    ),
    table2 AS (
        SELECT
            ""symbol"",
            ""ParticipantBarcode""
        FROM (
            SELECT
                ""Hugo_Symbol"" AS ""symbol"", 
                ""ParticipantBarcode"" AS ""ParticipantBarcode""
            FROM 
                ""PANCANCER_ATLAS_1"".""PANCANCER_ATLAS_FILTERED"".""MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE""
            WHERE 
                ""Study"" = 'BRCA' 
                AND ""Hugo_Symbol"" = 'CDH1'
                AND ""FILTER"" = 'PASS'  
            GROUP BY
                ""ParticipantBarcode"", ""symbol""
        )
    ),
    summ_table AS (
        SELECT 
            n1.""data"" AS ""data1"",
            CASE 
                WHEN n2.""ParticipantBarcode"" IS NULL THEN 'NO' 
                ELSE 'YES' 
            END AS ""data2"",
            COUNT(*) AS ""Nij""
        FROM
            table1 AS n1
        LEFT JOIN
            table2 AS n2 
            ON n1.""ParticipantBarcode"" = n2.""ParticipantBarcode""
        GROUP BY
            n1.""data"", ""data2""
    ),
    expected_table AS (
        SELECT 
            ""data1"", 
            ""data2""
        FROM (     
            SELECT 
                ""data1"", 
                SUM(""Nij"") AS ""Ni""   
            FROM 
                summ_table
            GROUP BY 
                ""data1""
        ) AS Ni_table
        CROSS JOIN ( 
            SELECT 
                ""data2"", 
                SUM(""Nij"") AS ""Nj""
            FROM 
                summ_table
            GROUP BY 
                ""data2""
        ) AS Nj_table
        WHERE 
            Ni_table.""Ni"" > 10 
            AND Nj_table.""Nj"" > 10
    ),
    contingency_table AS (
        SELECT
            T1.""data1"",
            T1.""data2"",
            COALESCE(T2.""Nij"", 0) AS ""Nij"",
            (SUM(T2.""Nij"") OVER (PARTITION BY T1.""data1"")) * 
            (SUM(T2.""Nij"") OVER (PARTITION BY T1.""data2"")) / 
            SUM(T2.""Nij"") OVER () AS ""E_nij""
        FROM
            expected_table AS T1
        LEFT JOIN
            summ_table AS T2
        ON 
            T1.""data1"" = T2.""data1"" 
            AND T1.""data2"" = T2.""data2""
    )
SELECT
    SUM( ( ""Nij"" - ""E_nij"" ) * ( ""Nij"" - ""E_nij"" ) / ""E_nij"" ) AS ""Chi2""
FROM 
    contingency_table;",CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.acronym; CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.bcr_patient_barcode; CLINICAL_PANCAN_PATIENT_WITH_FOLLOWUP_FILTERED.histological_type; MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE.FILTER; MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE.Hugo_Symbol; MC3_MAF_V5_ONE_PER_TUMOR_SAMPLE.ParticipantBarcode,6,snow_sql_near_exact,sf_bq159,825
bq161,pancancer_atlas_2,bigquery,"Calculate the net difference between the number of pancreatic adenocarcinoma (PAAD) patients in TCGA's dataset who are confirmed to have mutations in both KRAS and TP53 genes, and those without mutations in either gene. Utilize patient clinical and follow-up data alongside genomic mutation details from TCGA’s cancer genomics database, focusing specifically on PAAD studies where the mutations have passed quality filters.","WITH
barcodes AS (
   SELECT bcr_patient_barcode AS ParticipantBarcode
   FROM `isb-cgc-bq.pancancer_atlas.Filtered_clinical_PANCAN_patient_with_followup`
   WHERE acronym = 'PAAD'
)
,table1 AS (
SELECT
   t1.ParticipantBarcode,
   IF( t2.ParticipantBarcode is null, 'NO', 'YES') as data
FROM
   barcodes AS t1
LEFT JOIN
   (
   SELECT
      ParticipantBarcode AS ParticipantBarcode
   FROM `isb-cgc-bq.pancancer_atlas.Filtered_MC3_MAF_V5_one_per_tumor_sample`
   WHERE Study = 'PAAD' AND Hugo_Symbol = 'KRAS'
         AND FILTER = 'PASS'
   GROUP BY ParticipantBarcode
   ) AS t2
ON t1.ParticipantBarcode = t2.ParticipantBarcode
)
,table2 AS (
SELECT
   t1.ParticipantBarcode,
   IF( t2.ParticipantBarcode is null, 'NO', 'YES') as data
FROM
   barcodes AS t1
LEFT JOIN
   (
   SELECT
      ParticipantBarcode AS ParticipantBarcode
   FROM `isb-cgc-bq.pancancer_atlas.Filtered_MC3_MAF_V5_one_per_tumor_sample`
   WHERE Study = 'PAAD' AND Hugo_Symbol = 'TP53'
         AND FILTER = 'PASS'
   GROUP BY ParticipantBarcode
   ) AS t2
ON t1.ParticipantBarcode = t2.ParticipantBarcode
),

INFO AS (
SELECT
   n1.data as data1,
   n2.data as data2,
   COUNT(*) as Nij
FROM
   table1 AS n1
INNER JOIN
   table2 AS n2
ON
   n1.ParticipantBarcode = n2.ParticipantBarcode
GROUP BY
  data1, data2
)

SELECT 
(SELECT Nij FROM INFO WHERE data1=""YES"" AND data2=""YES"")
-
(SELECT Nij FROM INFO WHERE data1=""NO"" AND data2=""NO"")",Filtered_MC3_MAF_V5_one_per_tumor_sample.FILTER; Filtered_MC3_MAF_V5_one_per_tumor_sample.Hugo_Symbol; Filtered_MC3_MAF_V5_one_per_tumor_sample.ParticipantBarcode; Filtered_MC3_MAF_V5_one_per_tumor_sample.Study; Filtered_clinical_PANCAN_patient_with_followup.acronym; Filtered_clinical_PANCAN_patient_with_followup.bcr_patient_barcode,6,lite_sql,sf_bq161,1623
sf_bq166,TCGA_MITELMAN,snowflake,"Analyze the largest copy number of chromosomal aberrations including amplifications, gains, homozygous deletions, heterozygous deletions, and normal copy states across cytogenetic bands in TCGA-KIRC kidney cancer samples. Use segment allelic data to identify the maximum copy number aberrations within each chromosomal segment, and report their frequencies, sorted by chromosome and cytoband.","WITH copy AS (
  SELECT 
    ""case_barcode"", 
    ""chromosome"", 
    ""start_pos"", 
    ""end_pos"", 
    MAX(""copy_number"") AS ""copy_number""
  FROM 
    ""TCGA_MITELMAN"".""TCGA_VERSIONED"".""COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23"" 
  WHERE  
    ""project_short_name"" = 'TCGA-KIRC'
  GROUP BY 
    ""case_barcode"", 
    ""chromosome"", 
    ""start_pos"", 
    ""end_pos""
),
total_cases AS (
  SELECT COUNT(DISTINCT ""case_barcode"") AS ""total""
  FROM copy 
),
cytob AS (
  SELECT 
    ""chromosome"", 
    ""cytoband_name"", 
    ""hg38_start"", 
    ""hg38_stop""
  FROM 
    ""TCGA_MITELMAN"".""PROD"".""CYTOBANDS_HG38""
),
joined AS (
  SELECT 
    cytob.""chromosome"", 
    cytob.""cytoband_name"", 
    cytob.""hg38_start"", 
    cytob.""hg38_stop"",
    copy.""case_barcode"",
    copy.""copy_number""  
  FROM 
    copy
  LEFT JOIN cytob
    ON cytob.""chromosome"" = copy.""chromosome"" 
  WHERE 
    (cytob.""hg38_start"" >= copy.""start_pos"" AND copy.""end_pos"" >= cytob.""hg38_start"")
    OR (copy.""start_pos"" >= cytob.""hg38_start"" AND copy.""start_pos"" <= cytob.""hg38_stop"")
),
cbands AS (
  SELECT 
    ""chromosome"", 
    ""cytoband_name"", 
    ""hg38_start"", 
    ""hg38_stop"", 
    ""case_barcode"",
    MAX(""copy_number"") AS ""copy_number""
  FROM 
    joined
  GROUP BY 
    ""chromosome"", 
    ""cytoband_name"", 
    ""hg38_start"", 
    ""hg38_stop"", 
    ""case_barcode""
),
aberrations AS (
  SELECT
    ""chromosome"",
    ""cytoband_name"",
    -- Amplifications: more than two copies for diploid > 4
    SUM( CASE WHEN ""copy_number"" > 3 THEN 1 ELSE 0 END ) AS ""total_amp"",
    -- Gains: at most two extra copies
    SUM( CASE WHEN ""copy_number"" = 3 THEN 1 ELSE 0 END ) AS ""total_gain"",
    -- Homozygous deletions, or complete deletions
    SUM( CASE WHEN ""copy_number"" = 0 THEN 1 ELSE 0 END ) AS ""total_homodel"",
    -- Heterozygous deletions, 1 copy lost
    SUM( CASE WHEN ""copy_number"" = 1 THEN 1 ELSE 0 END ) AS ""total_heterodel"",
    -- Normal for Diploid = 2
    SUM( CASE WHEN ""copy_number"" = 2 THEN 1 ELSE 0 END ) AS ""total_normal""
  FROM 
    cbands
  GROUP BY 
    ""chromosome"", 
    ""cytoband_name""
)
SELECT 
  aberrations.""chromosome"", 
  aberrations.""cytoband_name"",
  total_cases.""total"",  
  100 * aberrations.""total_amp"" / total_cases.""total"" AS ""freq_amp"", 
  100 * aberrations.""total_gain"" / total_cases.""total"" AS ""freq_gain"",
  100 * aberrations.""total_homodel"" / total_cases.""total"" AS ""freq_homodel"", 
  100 * aberrations.""total_heterodel"" / total_cases.""total"" AS ""freq_heterodel"", 
  100 * aberrations.""total_normal"" / total_cases.""total"" AS ""freq_normal""  
FROM 
  aberrations, 
  total_cases
ORDER BY 
  aberrations.""chromosome"", 
  aberrations.""cytoband_name"";",COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.case_barcode; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.chromosome; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.copy_number; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.end_pos; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.project_short_name; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.sample_barcode; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.start_pos; CYTOBANDS_HG38.chromosome; CYTOBANDS_HG38.cytoband_name; CYTOBANDS_HG38.hg38_start; CYTOBANDS_HG38.hg38_stop,11,snow_sql_near_exact,sf_bq166,1444
sf_bq167,META_KAGGLE,snowflake,Please find the giver-and-recipient pair with the most Kaggle forum upvotes. Display their usernames and the respective number of upvotes they gave to each other.,"WITH UserPairUpvotes AS (
  SELECT
    ToUsers.""UserName"" AS ""ToUserName"",
    FromUsers.""UserName"" AS ""FromUserName"",
    COUNT(DISTINCT ""ForumMessageVotes"".""Id"") AS ""UpvoteCount""
  FROM META_KAGGLE.META_KAGGLE.FORUMMESSAGEVOTES AS ""ForumMessageVotes""
  INNER JOIN META_KAGGLE.META_KAGGLE.USERS AS FromUsers
    ON FromUsers.""Id"" = ""ForumMessageVotes"".""FromUserId""
  INNER JOIN META_KAGGLE.META_KAGGLE.USERS AS ToUsers
    ON ToUsers.""Id"" = ""ForumMessageVotes"".""ToUserId""
  GROUP BY
    ToUsers.""UserName"",
    FromUsers.""UserName""
),
TopPairs AS (
  SELECT
    ""ToUserName"",
    ""FromUserName"",
    ""UpvoteCount"",
    ROW_NUMBER() OVER (ORDER BY ""UpvoteCount"" DESC) AS ""Rank""
  FROM UserPairUpvotes
),
ReciprocalUpvotes AS (
  SELECT
    t.""ToUserName"",
    t.""FromUserName"",
    t.""UpvoteCount"" AS ""UpvotesReceived"",
    COALESCE(u.""UpvoteCount"", 0) AS ""UpvotesGiven""
  FROM TopPairs t
  LEFT JOIN UserPairUpvotes u
    ON t.""ToUserName"" = u.""FromUserName"" AND t.""FromUserName"" = u.""ToUserName""
  WHERE t.""Rank"" = 1
)
SELECT
  ""ToUserName"" AS ""UpvotedUserName"",
  ""FromUserName"" AS ""UpvotingUserName"",
  ""UpvotesReceived"" AS ""UpvotesReceivedByUpvotedUser"",
  ""UpvotesGiven"" AS ""UpvotesGivenByUpvotedUser""
FROM ReciprocalUpvotes
ORDER BY ""UpvotesReceived"" DESC, ""UpvotesGiven"" DESC;",FORUMMESSAGEVOTES.FromUserId; FORUMMESSAGEVOTES.Id; FORUMMESSAGEVOTES.ToUserId; USERS.Id; USERS.UserName,5,lite_sql,sf_bq167,237
bq172,cms_data,bigquery,"For the drug with the highest total number of prescriptions in New York State during 2014, could you list the top five states with the highest total claim counts for this drug? Please also include their total claim counts and total drug costs. ","WITH ny_top_drug AS (
  SELECT
    drug_name AS drug_name,
    ROUND(SUM(total_claim_count)) AS total_claim_count
  FROM
    `bigquery-public-data.cms_medicare.part_d_prescriber_2014`
  WHERE
    nppes_provider_state = 'NY'
  GROUP BY
    drug_name
  ORDER BY
    total_claim_count DESC
  LIMIT 1
),
top_5_states AS (
  SELECT
    nppes_provider_state AS state,
    SUM(total_claim_count) AS total_claim_count,
    SUM(total_drug_cost) AS total_drug_cost
  FROM
    `bigquery-public-data.cms_medicare.part_d_prescriber_2014`
  WHERE
    drug_name = (SELECT drug_name FROM ny_top_drug)
  GROUP BY
    state
  ORDER BY
    total_claim_count DESC
  LIMIT 5
)
SELECT
  state,
  total_claim_count,
  total_drug_cost
FROM
  top_5_states;",part_d_prescriber_2014.drug_name; part_d_prescriber_2014.nppes_provider_state; part_d_prescriber_2014.total_claim_count; part_d_prescriber_2014.total_drug_cost,4,lite_sql,sf_bq172,649
sf_bq176,TCGA_MITELMAN,snowflake,"Identify the case barcodes from the TCGA-LAML study with the highest weighted average copy number in cytoband 15q11 on chromosome 15, using segment data and cytoband overlaps from TCGA's genomic and Mitelman databases.","WITH copy AS (
  SELECT 
    ""case_barcode"", 
    ""chromosome"", 
    ""start_pos"", 
    ""end_pos"", 
    MAX(""copy_number"") AS ""copy_number""
  FROM 
    ""TCGA_MITELMAN"".""TCGA_VERSIONED"".""COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23""
  WHERE 
    ""project_short_name"" = 'TCGA-LAML'
  GROUP BY 
    ""case_barcode"", 
    ""chromosome"", 
    ""start_pos"", 
    ""end_pos""
),
total_cases AS (
  SELECT COUNT(DISTINCT ""case_barcode"") AS ""total""
  FROM copy
),
cytob AS (
  SELECT 
    ""chromosome"", 
    ""cytoband_name"", 
    ""hg38_start"", 
    ""hg38_stop""
  FROM 
    ""TCGA_MITELMAN"".""PROD"".""CYTOBANDS_HG38""
),
joined AS (
  SELECT 
    cytob.""chromosome"", 
    cytob.""cytoband_name"", 
    cytob.""hg38_start"", 
    cytob.""hg38_stop"", 
    copy.""case_barcode"",
    (ABS(cytob.""hg38_stop"" - cytob.""hg38_start"") + ABS(copy.""end_pos"" - copy.""start_pos"") 
      - ABS(cytob.""hg38_stop"" - copy.""end_pos"") - ABS(cytob.""hg38_start"" - copy.""start_pos"")) / 2.0 AS ""overlap"", 
    copy.""copy_number""
  FROM 
    copy
  LEFT JOIN 
    cytob
  ON 
    cytob.""chromosome"" = copy.""chromosome""
  WHERE 
    (cytob.""hg38_start"" >= copy.""start_pos"" AND copy.""end_pos"" >= cytob.""hg38_start"")
    OR (copy.""start_pos"" >= cytob.""hg38_start"" AND copy.""start_pos"" <= cytob.""hg38_stop"")
),
INFO AS (
  SELECT 
    ""chromosome"", 
    ""cytoband_name"", 
    ""hg38_start"", 
    ""hg38_stop"", 
    ""case_barcode"",
    ROUND(SUM(""overlap"" * ""copy_number"") / SUM(""overlap"")) AS ""copy_number""
  FROM 
    joined
  GROUP BY 
    ""chromosome"", ""cytoband_name"", ""hg38_start"", ""hg38_stop"", ""case_barcode""
)

SELECT 
  ""case_barcode""
FROM 
  INFO
WHERE 
  ""chromosome"" = 'chr15' 
  AND ""cytoband_name"" = '15q11'
ORDER BY 
  ""copy_number"" DESC
LIMIT 1;",COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.case_barcode; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.chromosome; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.copy_number; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.end_pos; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.project_short_name; COPY_NUMBER_SEGMENT_ALLELIC_HG38_GDC_R23.start_pos; CYTOBANDS_HG38.chromosome; CYTOBANDS_HG38.cytoband_name; CYTOBANDS_HG38.hg38_start; CYTOBANDS_HG38.hg38_stop,10,snow_sql_near_exact,sf_bq176,1444
sf_bq182,GITHUB_REPOS_DATE,snowflake,"Which primary programming languages, determined by the highest number of bytes in each repository, have the sum of over 5 pull requests on January 18, 2023 in all its repositories?","WITH
  event_data AS (
    SELECT
      ""type"",
      EXTRACT(YEAR FROM TO_TIMESTAMP(""created_at"" / 1000000)) AS ""year"",
      EXTRACT(QUARTER FROM TO_TIMESTAMP(""created_at"" / 1000000)) AS ""quarter"",
      REGEXP_REPLACE(
        ""repo""::variant:""url""::string,
        'https:\\/\\/github\\.com\\/|https:\\/\\/api\\.github\\.com\\/repos\\/',
        ''
      ) AS ""name""
    FROM GITHUB_REPOS_DATE.DAY._20230118
  ),

  repo_languages AS (
    SELECT
      ""repo_name"" AS ""name"",
      ""lang""
    FROM (
      SELECT
        ""repo_name"",
        FIRST_VALUE(""language"") OVER (
          PARTITION BY ""repo_name"" ORDER BY ""bytes"" DESC
        ) AS ""lang""
      FROM (
        SELECT
          ""repo_name"",
          ""language"".value:""name"" AS ""language"",
          ""language"".value:""bytes"" AS ""bytes""
        FROM GITHUB_REPOS_DATE.GITHUB_REPOS.LANGUAGES,
        LATERAL FLATTEN(INPUT => ""language"") AS ""language""
      )
    )
    WHERE ""lang"" IS NOT NULL
    GROUP BY ""repo_name"", ""lang""
  ),

  joined_data AS (
    SELECT
      a.""type"" AS ""type"",
      b.""lang"" AS ""language"",
      a.""year"" AS ""year"",
      a.""quarter"" AS ""quarter""
    FROM event_data a
    JOIN repo_languages b
      ON a.""name"" = b.""name""
  ),

  count_data AS (
    SELECT
      ""language"",
      ""year"",
      ""quarter"",
      ""type"",
      COUNT(*) AS ""count""
    FROM joined_data
    GROUP BY ""type"", ""language"", ""year"", ""quarter""
    ORDER BY ""year"", ""quarter"", ""count"" DESC
  )

SELECT
  REPLACE(""language"", '""', '') AS ""language_name"",
  ""count""
FROM count_data
WHERE ""count"" >= 5
  AND ""type"" = 'PullRequestEvent';",LANGUAGES.language; LANGUAGES.repo_name; _20_*.created_at; _20_*.repo; _20_*.type,5,lite_sql,sf_bq182,43
bq185,new_york_plus,bigquery,"What is the average valid trip duration (in minutes) for yellow taxi rides in Brooklyn with more than 3 passengers and a trip distance of at least 10 miles between February 1 and February 7, 2016?","SELECT 
    AVG(TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, SECOND) / 60.0) AS average_trip_duration_in_minutes
FROM
(
    SELECT *
    FROM `bigquery-public-data.new_york_taxi_trips.tlc_yellow_trips_2016` t
    WHERE 
        pickup_datetime BETWEEN '2016-02-01' AND '2016-02-07' AND 
        dropoff_datetime BETWEEN '2016-02-01' AND '2016-02-07' AND
        TIMESTAMP_DIFF(dropoff_datetime, pickup_datetime, SECOND) > 0 AND 
        passenger_count > 3 AND 
        trip_distance >= 10
) t
INNER JOIN `bigquery-public-data.new_york_taxi_trips.taxi_zone_geom` tz
ON t.pickup_location_id = tz.zone_id
INNER JOIN `bigquery-public-data.new_york_taxi_trips.taxi_zone_geom` tz1
ON t.dropoff_location_id = tz1.zone_id
WHERE 
    tz.borough = ""Brooklyn"" AND
    tz1.borough = ""Brooklyn"";",taxi_zone_geom.borough; taxi_zone_geom.zone_id; tlc_yellow_trips_*.dropoff_datetime; tlc_yellow_trips_*.dropoff_location_id; tlc_yellow_trips_*.passenger_count; tlc_yellow_trips_*.pickup_datetime; tlc_yellow_trips_*.pickup_location_id; tlc_yellow_trips_*.trip_distance,8,lite_sql,sf_bq185,360
sf_bq187,ETHEREUM_BLOCKCHAIN,snowflake,"What is the total circulating supply balances of the 'BNB' token for all addresses (excluding the zero address), based on the amount they have received (converted by dividing by 10^18) minus the amount they have sent?","WITH tokenInfo AS (
    SELECT ""address""
    FROM ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TOKENS""
    WHERE ""name"" = 'BNB'
),

receivedTx AS (
    SELECT ""tx"".""to_address"" AS ""addr"", 
           ""tokens"".""name"" AS ""name"", 
           SUM(CAST(""tx"".""value"" AS FLOAT) / POWER(10, 18)) AS ""amount_received""
    FROM ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TOKEN_TRANSFERS"" AS ""tx""
    JOIN tokenInfo ON ""tx"".""token_address"" = tokenInfo.""address""
    JOIN ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TOKENS"" AS ""tokens""
      ON ""tx"".""token_address"" = ""tokens"".""address""
    WHERE ""tx"".""to_address"" <> '0x0000000000000000000000000000000000000000'
    GROUP BY ""tx"".""to_address"", ""tokens"".""name""
),

sentTx AS (
    SELECT ""tx"".""from_address"" AS ""addr"", 
           ""tokens"".""name"" AS ""name"", 
           SUM(CAST(""tx"".""value"" AS FLOAT) / POWER(10, 18)) AS ""amount_sent""
    FROM ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TOKEN_TRANSFERS"" AS ""tx""
    JOIN tokenInfo ON ""tx"".""token_address"" = tokenInfo.""address""
    JOIN ""ETHEREUM_BLOCKCHAIN"".""ETHEREUM_BLOCKCHAIN"".""TOKENS"" AS ""tokens""
      ON ""tx"".""token_address"" = ""tokens"".""address""
    WHERE ""tx"".""from_address"" <> '0x0000000000000000000000000000000000000000'
    GROUP BY ""tx"".""from_address"", ""tokens"".""name""
),

walletBalances AS (
    SELECT r.""addr"",
           COALESCE(SUM(r.""amount_received""), 0) - COALESCE(SUM(s.""amount_sent""), 0) AS ""balance""
    FROM receivedTx AS r
    LEFT JOIN sentTx AS s
      ON r.""addr"" = s.""addr""
    GROUP BY r.""addr""
)

SELECT 
    SUM(""balance"") AS ""circulating_supply""
FROM walletBalances;",TOKEN_TRANSFERS.from_address; TOKEN_TRANSFERS.to_address; TOKEN_TRANSFERS.token_address; TOKEN_TRANSFERS.value,4,snow_sql_near_exact,sf_bq187,88
sf_bq193,GITHUB_REPOS,snowflake,"Retrieve all non-empty, non-commented lines of text from readme.md files in GitHub repositories. Exclude lines that are comments (lines starting with # for Markdown comments and // for code comments), and for each line, provide the frequency of occurrence along with a comma-separated list of programming languages (sorted alphabetically) used in the repository that contains the line.","WITH content_extracted AS (
    SELECT 
        ""D"".""id"" AS ""id"",
        ""repo_name"",
        ""path"",
        SPLIT(""content"", '\n') AS ""lines"",
        ""language_name""
    FROM 
        (
            SELECT 
                ""id"",
                ""content""
            FROM 
                ""GITHUB_REPOS"".""GITHUB_REPOS"".""SAMPLE_CONTENTS""
        ) AS ""D""
    INNER JOIN 
        (
            SELECT 
                ""id"",
                ""C"".""repo_name"" AS ""repo_name"",
                ""path"",
                ""language_name""
            FROM 
                (
                    SELECT 
                        ""id"",
                        ""repo_name"",
                        ""path""
                    FROM 
                        ""GITHUB_REPOS"".""GITHUB_REPOS"".""SAMPLE_FILES""
                    WHERE 
                        LOWER(""path"") LIKE '%readme.md'
                ) AS ""C""
            INNER JOIN 
                (
                    SELECT 
                        ""repo_name"",
                        ""language_struct"".value:""name"" AS ""language_name""
                    FROM 
                        (
                            SELECT 
                                ""repo_name"", 
                                ""language""
                            FROM 
                                ""GITHUB_REPOS"".""GITHUB_REPOS"".""LANGUAGES""
                        )
                    CROSS JOIN 
                        LATERAL FLATTEN(INPUT => ""language"") AS ""language_struct""
                ) AS ""F""
            ON 
                ""C"".""repo_name"" = ""F"".""repo_name""
        ) AS ""E""
    ON 
        ""E"".""id"" = ""D"".""id""
),
non_empty_lines AS (
    SELECT 
        ""line"".value AS ""line_"",
        ""language_name""
    FROM 
        content_extracted,
        LATERAL FLATTEN(INPUT => ""lines"") AS ""line""
    WHERE 
        TRIM(""line"".value) != ''
        AND NOT STARTSWITH(TRIM(""line"".value), '#')
        AND NOT STARTSWITH(TRIM(""line"".value), '//')
),
aggregated_languages AS (
    SELECT 
        ""line_"",
        COUNT(*) AS ""frequency"",
        ARRAY_AGG(""language_name"") AS ""languages""
    FROM 
        non_empty_lines
    GROUP BY 
        ""line_""
)

SELECT 
    REGEXP_REPLACE(""line_"", '^""|""$', '') AS ""line"",
    ""frequency"",
    ARRAY_TO_STRING(ARRAY_SORT(""languages""), ', ') AS ""languages_sorted""
FROM 
    aggregated_languages
ORDER BY 
    ""frequency"" DESC;",LANGUAGES.language; LANGUAGES.repo_name; SAMPLE_CONTENTS.binary; SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.sample_path; SAMPLE_CONTENTS.sample_repo_name,6,snow_sql_near_exact,sf_bq193,34
bq198,ncaa_basketball,bigquery,"What are the top 5 most successful college basketball teams over the seasons from 1900 to 2000, based on the number of times they had the maximum wins in a season?","SELECT
  team_name,
  COUNT(*) AS top_performer_count
FROM (
  SELECT
    DISTINCT c2.season,
    c2.market AS team_name
  FROM (
    SELECT
      season AS a,
      MAX(wins) AS win_max
    FROM
      `bigquery-public-data.ncaa_basketball.mbb_historical_teams_seasons`
    WHERE
      season<=2000
      AND season >=1900
    GROUP BY
      season ),
    `bigquery-public-data.ncaa_basketball.mbb_historical_teams_seasons` c2
  WHERE
    win_max = c2.wins
    AND a = c2.season
    AND c2.market IS NOT NULL
  ORDER BY
    c2.season)
GROUP BY
  team_name
ORDER BY
  top_performer_count DESC,
  team_name
LIMIT
  5",mbb_historical_teams_seasons.market; mbb_historical_teams_seasons.season; mbb_historical_teams_seasons.wins,3,lite_sql,sf_bq198,505
bq199,iowa_liquor_sales,bigquery,"Identify the top 10 liquor categories in Iowa by average price per liter in 2021, and provide their average prices per liter for 2019, 2020, and 2021.","WITH price_2020 AS (
  SELECT 
    category_name AS category, 
    AVG(state_bottle_retail / (bottle_volume_ml / 1000)) AS avg_price_liter_2020
  FROM 
    `bigquery-public-data.iowa_liquor_sales.sales`
  WHERE 
    bottle_volume_ml > 0 
    AND EXTRACT(YEAR FROM date) = 2020
  GROUP BY 
    category
),
price_2019 AS (
  SELECT 
    category_name AS category, 
    AVG(state_bottle_retail / (bottle_volume_ml / 1000)) AS avg_price_liter_2019
  FROM 
    `bigquery-public-data.iowa_liquor_sales.sales`
  WHERE 
    bottle_volume_ml > 0 
    AND EXTRACT(YEAR FROM date) = 2019
  GROUP BY 
    category
),
price_2021 AS (
  SELECT 
    category_name AS category, 
    AVG(state_bottle_retail / (bottle_volume_ml / 1000)) AS avg_price_liter_2021
  FROM 
    `bigquery-public-data.iowa_liquor_sales.sales`
  WHERE 
    bottle_volume_ml > 0 
    AND EXTRACT(YEAR FROM date) = 2021
  GROUP BY 
    category
)
SELECT 
  price_2021.category, 
  price_2019.avg_price_liter_2019, 
  price_2020.avg_price_liter_2020, 
  price_2021.avg_price_liter_2021
FROM 
  price_2021
LEFT JOIN 
  price_2019 ON price_2021.category = price_2019.category
LEFT JOIN 
  price_2020 ON price_2021.category = price_2020.category
ORDER BY 
  price_2021.avg_price_liter_2021 DESC
LIMIT 
  10;",sales.bottle_volume_ml; sales.category_name; sales.date; sales.state_bottle_retail,4,lite_sql,sf_bq199,24
bq203,new_york_plus,bigquery,What percentage of subway stations in each New York borough have at least one ADA-compliant entrance?,"WITH stations_n_entrances AS (
      SELECT borough_name,s.station_name,entry,ada_compliant
      FROM `bigquery-public-data.new_york_subway.stations` s
      JOIN `bigquery-public-data.new_york_subway.station_entrances` se
      ON s.station_name = se.station_name
      )

SELECT se.borough_name, COUNT(DISTINCT se.station_name) num_stations,
      COUNT(DISTINCT adas.station_name) num_stations_w_compliant_entrance, 
      (100*COUNT(DISTINCT adas.station_name))/(COUNT(DISTINCT se.station_name)) percent_compliant_stations
FROM `stations_n_entrances` se
LEFT JOIN `stations_n_entrances` adas
ON se.station_name = adas.station_name
AND adas.entry AND adas.ada_compliant
GROUP BY 1
ORDER BY 4 DESC",station_entrances.ada_compliant; station_entrances.entry; station_entrances.station_name; stations.borough_name; stations.station_name,5,lite_sql,sf_bq203,360
bq204,eclipse_megamovie,bigquery,Find the user with the highest total clicks across all records from all available photo collections.,"SELECT user
FROM (
Select user
      From `bigquery-public-data.eclipse_megamovie.photos_v_0_1`
      UNION ALL
      Select user
      From`bigquery-public-data.eclipse_megamovie.photos_v_0_2`
      UNION ALL
      Select user
      From`bigquery-public-data.eclipse_megamovie.photos_v_0_3`
) 
GROUP BY user 
HAVING COUNT (user)=( 
SELECT MAX(mycount) 
FROM ( 
SELECT user, COUNT(user) mycount 
FROM (
Select user
      From `bigquery-public-data.eclipse_megamovie.photos_v_0_1`
      UNION ALL
      Select user
      From`bigquery-public-data.eclipse_megamovie.photos_v_0_2`
      UNION ALL
      Select user
      From`bigquery-public-data.eclipse_megamovie.photos_v_0_3`
)
GROUP BY user))
ORDER BY COUNT(user) 
LIMIT 1",photos_v_*.user,1,lite_sql,sf_bq204,81
sf_bq209,PATENTS,snowflake,Can you find how many utility patents granted in 2010 have exactly one forward citation within the ten years following their application date?,"WITH patents_sample AS (
    SELECT
        t1.""publication_number"",
        t1.""application_number""
    FROM
        PATENTS.PATENTS.PUBLICATIONS t1
    WHERE
        TO_DATE(
            CASE
                WHEN t1.""grant_date"" != 0 THEN TO_CHAR(t1.""grant_date"")
                ELSE NULL
            END, 
            'YYYYMMDD'
        ) BETWEEN TO_DATE('20100101', 'YYYYMMDD') AND TO_DATE('20101231', 'YYYYMMDD')
),
forward_citation AS (
    SELECT
        patents_sample.""publication_number"",
        COUNT(DISTINCT t3.""citing_application_number"") AS ""forward_citations""
    FROM
        patents_sample
        LEFT JOIN (
            SELECT
                x2.""publication_number"",
                TO_DATE(
                    CASE
                        WHEN x2.""filing_date"" != 0 THEN TO_CHAR(x2.""filing_date"")
                        ELSE NULL
                    END,
                    'YYYYMMDD'
                ) AS ""filing_date""
            FROM
                PATENTS.PATENTS.PUBLICATIONS x2
            WHERE
                x2.""filing_date"" != 0
        ) t2
            ON t2.""publication_number"" = patents_sample.""publication_number""
        LEFT JOIN (
            SELECT
                x3.""publication_number"" AS ""citing_publication_number"",
                x3.""application_number"" AS ""citing_application_number"",
                TO_DATE(
                    CASE
                        WHEN x3.""filing_date"" != 0 THEN TO_CHAR(x3.""filing_date"")
                        ELSE NULL
                    END,
                    'YYYYMMDD'
                ) AS ""joined_filing_date"",
                cite.value:""publication_number""::STRING AS ""cited_publication_number""
            FROM
                PATENTS.PATENTS.PUBLICATIONS x3,
                LATERAL FLATTEN(INPUT => x3.""citation"") cite
            WHERE
                x3.""filing_date"" != 0
        ) t3
            ON patents_sample.""publication_number"" = t3.""cited_publication_number""
            AND t3.""joined_filing_date"" BETWEEN t2.""filing_date"" AND DATEADD(YEAR, 10, t2.""filing_date"")
    GROUP BY
        patents_sample.""publication_number""
)

SELECT
    COUNT(*)
FROM
    forward_citation
WHERE
    ""forward_citations"" = 1;",PUBLICATIONS.application_number; PUBLICATIONS.citation; PUBLICATIONS.filing_date; PUBLICATIONS.grant_date; PUBLICATIONS.publication_number,5,lite_sql,sf_bq209,79
sf_bq210,PATENTS,snowflake,How many US B2 patents granted between 2008 and 2018 contain claims that do not include the word 'claim'?,"WITH patents_sample AS (
  SELECT 
    t1.""publication_number"" AS publication_number,
    claim.value:""text"" AS claims_text
  FROM 
    PATENTS.PATENTS.PUBLICATIONS t1,
    LATERAL FLATTEN(input => t1.""claims_localized"") AS claim
  WHERE 
    t1.""country_code"" = 'US'
    AND t1.""grant_date"" BETWEEN 20080101 AND 20181231
    AND t1.""grant_date"" != 0
    AND t1.""publication_number"" LIKE '%B2%'
),
Publication_data AS (
  SELECT
    publication_number,
    COUNT_IF(claims_text NOT LIKE '%claim%') AS nb_indep_claims
  FROM
    patents_sample
  GROUP BY
    publication_number
)

SELECT COUNT(nb_indep_claims)
FROM Publication_data
WHERE nb_indep_claims != 0",PUBLICATIONS.claims_localized; PUBLICATIONS.country_code; PUBLICATIONS.grant_date; PUBLICATIONS.publication_number,4,lite_sql,sf_bq210,79
sf_bq213,PATENTS,snowflake,What is the most common 4-digit IPC code among US B2 utility patents granted from June to August in 2022?,"WITH interim_table as(
SELECT 
    t1.""publication_number"", 
    SUBSTR(ipc_u.value:""code"", 0, 4) as ipc4
FROM 
    PATENTS.PATENTS.PUBLICATIONS t1,
    LATERAL FLATTEN(input => t1.""ipc"") AS ipc_u
WHERE
""country_code"" = 'US'  
AND ""grant_date"" between 20220601 AND 20220831
  AND ""grant_date"" != 0
  AND ""publication_number"" LIKE '%B2%'  
GROUP BY 
    t1.""publication_number"", 
    ipc4
) 
SELECT 
ipc4
FROM 
interim_table 
GROUP BY ipc4
ORDER BY COUNT(""publication_number"") DESC
LIMIT 1",PUBLICATIONS.country_code; PUBLICATIONS.grant_date; PUBLICATIONS.ipc; PUBLICATIONS.kind_code; PUBLICATIONS.publication_number,5,snow_sql_near_exact,sf_bq213,79
sf_bq216,PATENTS_GOOGLE,snowflake,Identify the top five patents filed in the same year as `US-9741766-B2` that are most similar to it based on technological similarities. Please provide the publication numbers.,"WITH patents_sample AS (
    SELECT 
        ""publication_number"", 
        ""application_number""
    FROM
        PATENTS_GOOGLE.PATENTS_GOOGLE.PUBLICATIONS
    WHERE
        ""publication_number"" = 'US-9741766-B2'
),
flattened_t5 AS (
    SELECT
        t5.""publication_number"",
        f.value AS element_value,
        f.index AS pos
    FROM
        PATENTS_GOOGLE.PATENTS_GOOGLE.ABS_AND_EMB t5,
        LATERAL FLATTEN(input => t5.""embedding_v1"") AS f
),
flattened_t6 AS (
    SELECT
        t6.""publication_number"",
        f.value AS element_value,
        f.index AS pos
    FROM
        PATENTS_GOOGLE.PATENTS_GOOGLE.ABS_AND_EMB t6,
        LATERAL FLATTEN(input => t6.""embedding_v1"") AS f
),
similarities AS (
    SELECT
        t1.""publication_number"" AS base_publication_number,
        t4.""publication_number"" AS similar_publication_number,
        SUM(ft5.element_value * ft6.element_value) AS similarity
    FROM
        (SELECT * FROM patents_sample LIMIT 1) t1
    LEFT JOIN (
        SELECT 
            x3.""publication_number"",
            EXTRACT(YEAR, TO_DATE(CAST(x3.""filing_date"" AS STRING), 'YYYYMMDD')) AS focal_filing_year
        FROM 
            PATENTS_GOOGLE.PATENTS_GOOGLE.PUBLICATIONS x3
        WHERE 
            x3.""filing_date"" != 0
    ) t3 ON t3.""publication_number"" = t1.""publication_number""
    LEFT JOIN (
        SELECT 
            x4.""publication_number"",
            EXTRACT(YEAR, TO_DATE(CAST(x4.""filing_date"" AS STRING), 'YYYYMMDD')) AS filing_year
        FROM 
            PATENTS_GOOGLE.PATENTS_GOOGLE.PUBLICATIONS x4
        WHERE 
            x4.""filing_date"" != 0
    ) t4 ON
        t4.""publication_number"" != t1.""publication_number""
        AND t3.focal_filing_year = t4.filing_year
    LEFT JOIN flattened_t5 AS ft5 ON ft5.""publication_number"" = t1.""publication_number""
    LEFT JOIN flattened_t6 AS ft6 ON ft6.""publication_number"" = t4.""publication_number""
    AND ft5.pos = ft6.pos  -- Align vector positions
    GROUP BY
        t1.""publication_number"", t4.""publication_number""
)
SELECT
    s.similar_publication_number,
    s.similarity
FROM (
    SELECT
        s.*,
        ROW_NUMBER() OVER (PARTITION BY s.base_publication_number ORDER BY s.similarity DESC) AS seqnum
    FROM
        similarities s
) s
WHERE
    seqnum <= 5;",ABS_AND_EMB.embedding_v1; ABS_AND_EMB.publication_number; PUBLICATIONS.filing_date; PUBLICATIONS.publication_number,4,snow_sql_near_exact,sf_bq216,87
bq218,iowa_liquor_sales,bigquery,What are the top 5 items with the highest year-over-year growth percentage in total sales revenue for the year 2023?,"WITH AnnualSales AS (
  SELECT
    item_description,
    EXTRACT(YEAR FROM date) AS year,
    SUM(sale_dollars) AS total_sales_revenue,
    COUNT(DISTINCT invoice_and_item_number) AS unique_purchases
  FROM
    `bigquery-public-data.iowa_liquor_sales.sales`
  WHERE
    EXTRACT(YEAR FROM date) IN (2022, 2023)
    AND item_description IS NOT NULL
    AND sale_dollars IS NOT NULL
  GROUP BY
    item_description, year
),
YoYGrowth AS (
  SELECT
    curr.item_description,
    curr.year,
    curr.total_sales_revenue,
    curr.unique_purchases,
    LAG(curr.total_sales_revenue) OVER(PARTITION BY curr.item_description ORDER BY curr.year) AS prev_year_sales_revenue,
    (curr.total_sales_revenue - LAG(curr.total_sales_revenue) OVER(PARTITION BY curr.item_description ORDER BY curr.year)) / LAG(curr.total_sales_revenue) OVER(PARTITION BY curr.item_description ORDER BY curr.year) * 100 AS yoy_growth_percentage
  FROM
    AnnualSales curr
),
total_info AS (
SELECT
  item_description,
  year,
  total_sales_revenue,
  unique_purchases,
  prev_year_sales_revenue,
  yoy_growth_percentage
FROM
  YoYGrowth
WHERE
  year = 2023
  AND prev_year_sales_revenue IS NOT NULL -- Exclude rows where there's no previous year data to calculate YoY growth
ORDER BY
  year, total_sales_revenue 
DESC
)

SELECT item_description
FROM total_info
order by yoy_growth_percentage
DESC
LIMIT 5",sales.date; sales.invoice_and_item_number; sales.item_description; sales.sale_dollars,4,lite_sql,sf_bq218,24
sf_bq219,IOWA_LIQUOR_SALES,snowflake,"Which two liquor categories, each contributing an average of at least 1% to monthly sales volume over 24 months, have the lowest Pearson correlation coefficient in their sales percentages?","WITH
MonthlyTotals AS
(
  SELECT
    TO_CHAR(""date"", 'YYYY-MM') AS ""month"",
    SUM(""volume_sold_gallons"") AS ""total_monthly_volume""
  FROM
    IOWA_LIQUOR_SALES.IOWA_LIQUOR_SALES.""SALES""
  WHERE
    ""date"" >= '2022-01-01' 
    AND TO_CHAR(""date"", 'YYYY-MM') < TO_CHAR(CURRENT_DATE(), 'YYYY-MM')
  GROUP BY
    TO_CHAR(""date"", 'YYYY-MM')
),

MonthCategory AS
(
  SELECT
    TO_CHAR(""date"", 'YYYY-MM') AS ""month"",
    ""category"",
    ""category_name"",
    SUM(""volume_sold_gallons"") AS ""category_monthly_volume"",
    CASE 
      WHEN ""total_monthly_volume"" != 0 THEN (SUM(""volume_sold_gallons"") / ""total_monthly_volume"") * 100
      ELSE NULL
    END AS ""category_pct_of_month_volume""
  FROM
    IOWA_LIQUOR_SALES.IOWA_LIQUOR_SALES.""SALES"" AS Sales
  LEFT JOIN
    MonthlyTotals ON TO_CHAR(Sales.""date"", 'YYYY-MM') = MonthlyTotals.""month""
  WHERE
    Sales.""date"" >= '2022-01-01' 
    AND TO_CHAR(Sales.""date"", 'YYYY-MM') < TO_CHAR(CURRENT_DATE(), 'YYYY-MM')
  GROUP BY
    TO_CHAR(Sales.""date"", 'YYYY-MM'), ""category"", ""category_name"", ""total_monthly_volume""
),

middle_info AS 
(
  SELECT
    Category1.""category"" AS ""category1"",
    Category1.""category_name"" AS ""category_name1"",
    Category2.""category"" AS ""category2"",
    Category2.""category_name"" AS ""category_name2"",
    COUNT(DISTINCT Category1.""month"") AS ""num_months"",
    CORR(Category1.""category_pct_of_month_volume"", Category2.""category_pct_of_month_volume"") AS ""category_corr_across_months"",
    AVG(Category1.""category_pct_of_month_volume"") AS ""category1_avg_pct_of_month_volume"",
    AVG(Category2.""category_pct_of_month_volume"") AS ""category2_avg_pct_of_month_volume""
  FROM
    MonthCategory Category1
  INNER JOIN
    MonthCategory Category2 
    ON Category1.""month"" = Category2.""month""
  GROUP BY
    Category1.""category"", Category1.""category_name"", Category2.""category"", Category2.""category_name""
  HAVING
    ""num_months"" >= 24
    AND ""category1_avg_pct_of_month_volume"" >= 1
    AND ""category2_avg_pct_of_month_volume"" >= 1
)

SELECT 
  ""category_name1"", 
  ""category_name2""
FROM 
  middle_info
ORDER BY 
  ""category_corr_across_months""
LIMIT 1;",SALES.category; SALES.category_name; SALES.date; SALES.volume_sold_gallons,4,lite_sql,sf_bq219,24
sf_bq221,PATENTS,snowflake,"Identify the CPC technology areas with the highest exponential moving average of patent filings each year (smoothing factor 0.2), and provide the full title and the best year for each CPC group at level 5.","WITH patent_cpcs AS (
    SELECT
        cd.""parents"",
        CAST(FLOOR(""filing_date"" / 10000) AS INT) AS ""filing_year""
    FROM (
        SELECT
            MAX(""cpc"") AS ""cpc"", MAX(""filing_date"") AS ""filing_date""
        FROM
            PATENTS.PATENTS.PUBLICATIONS
        WHERE 
            ""application_number"" != ''
        GROUP BY
            ""application_number""
    ) AS publications
    , LATERAL FLATTEN(INPUT => ""cpc"") AS cpcs
    JOIN
        PATENTS.PATENTS.CPC_DEFINITION cd ON cd.""symbol"" = cpcs.value:""code""
    WHERE 
        cpcs.value:""first"" = TRUE
          AND ""filing_date"" > 0

),
yearly_counts AS (
    SELECT
        ""cpc_group"",
        ""filing_year"",
        COUNT(*) AS ""cnt""
    FROM (
        SELECT
            cpc_parent.value::STRING AS ""cpc_group"",
            ""filing_year""
        FROM patent_cpcs,
             LATERAL FLATTEN(input => patent_cpcs.""parents"") AS cpc_parent
    )
    GROUP BY ""cpc_group"", ""filing_year""
),
ordered_counts AS (
    SELECT
        ""cpc_group"",
        ""filing_year"",
        ""cnt"",
        ROW_NUMBER() OVER (PARTITION BY ""cpc_group"" ORDER BY ""filing_year"" ASC) AS rn
    FROM yearly_counts
),
recursive_ema AS (
    -- Anchor member: first year per cpc_group
    SELECT
        ""cpc_group"",
        ""filing_year"",
        ""cnt"",
        ""cnt"" * 0.2 + 0 * 0.8 AS ""ema"",
        rn
    FROM ordered_counts
    WHERE rn = 1

    UNION ALL

    -- Recursive member: subsequent years
    SELECT
        oc.""cpc_group"",
        oc.""filing_year"",
        oc.""cnt"",
        oc.""cnt"" * 0.2 + re.""ema"" * 0.8 AS ""ema"",
        oc.rn
    FROM ordered_counts oc
    JOIN recursive_ema re
        ON oc.""cpc_group"" = re.""cpc_group""
       AND oc.rn = re.rn + 1
),
max_ema AS (
    SELECT
        ""cpc_group"",
        ""filing_year"",
        ""ema""
    FROM recursive_ema
),
ranked_ema AS (
    SELECT
        me.""cpc_group"",
        me.""filing_year"",
        me.""ema"",
        ROW_NUMBER() OVER (
            PARTITION BY me.""cpc_group"" 
            ORDER BY me.""ema"" DESC, me.""filing_year"" DESC
        ) AS rn_rank
    FROM max_ema me
)
SELECT 
    c.""titleFull"",
    REPLACE(r.""cpc_group"", '""', '') AS ""cpc_group"",
    r.""filing_year"" AS ""best_filing_year""
FROM ranked_ema r
JOIN ""PATENTS"".""PATENTS"".""CPC_DEFINITION"" c 
    ON r.""cpc_group"" = c.""symbol""
WHERE 
    c.""level"" = 5
    AND r.rn_rank = 1
ORDER BY 
    c.""titleFull"", 
    ""cpc_group"" ASC;",CPC_DEFINITION.level; CPC_DEFINITION.parents; CPC_DEFINITION.symbol; CPC_DEFINITION.titleFull; PUBLICATIONS.application_number; PUBLICATIONS.cpc; PUBLICATIONS.filing_date,7,snow_sql_near_exact,sf_bq221,79
sf_bq222,PATENTS,snowflake,"Find the CPC technology areas in Germany with the highest exponential moving average of patent filings each year (smoothing factor 0.1) for patents granted in December 2016. Show me the full title, CPC group and the best year for each CPC group at level 4.","WITH patent_cpcs AS (
    SELECT
        cd.""parents"",
        CAST(FLOOR(""filing_date"" / 10000) AS INT) AS ""filing_year""
    FROM (
        SELECT MAX(""cpc"") AS ""cpc"", MAX(""filing_date"") AS ""filing_date""
        FROM ""PATENTS"".""PATENTS"".""PUBLICATIONS""
        WHERE ""application_number"" != ''
          AND ""country_code"" = 'DE'
          AND ""grant_date"" >= 20161201
          AND ""grant_date"" <= 20161231
        GROUP BY ""application_number""
    ), LATERAL FLATTEN(INPUT => ""cpc"") AS cpcs
    JOIN ""PATENTS"".""PATENTS"".""CPC_DEFINITION"" cd ON cd.""symbol"" = cpcs.value:""code""
    WHERE cpcs.value:""first"" = TRUE
      AND ""filing_date"" > 0
),
yearly_counts AS (
    SELECT
        ""cpc_group"",
        ""filing_year"",
        COUNT(*) AS ""cnt""
    FROM (
        SELECT
            cpc_parent.VALUE AS ""cpc_group"",  -- Corrected reference to flattened ""parents""
            ""filing_year""
        FROM patent_cpcs,
             LATERAL FLATTEN(INPUT => ""parents"") AS cpc_parent  -- Corrected reference to flattened ""parents""
    )
    GROUP BY ""cpc_group"", ""filing_year""
),
moving_avg AS (
    SELECT
        ""cpc_group"",
        ""filing_year"",
        ""cnt"",
        AVG(""cnt"") OVER (PARTITION BY ""cpc_group"" ORDER BY ""filing_year"" ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS ""moving_avg""
    FROM yearly_counts
)
SELECT 
    c.""titleFull"",  -- Ensure correct column name (check case)
    REPLACE(""cpc_group"", '""', '') AS ""cpc_group"",
    MAX(""filing_year"") AS ""best_filing_year""
FROM moving_avg
JOIN ""PATENTS"".""PATENTS"".""CPC_DEFINITION"" c ON ""cpc_group"" = c.""symbol""
WHERE c.""level"" = 4
GROUP BY c.""titleFull"", ""cpc_group""
ORDER BY c.""titleFull"", ""cpc_group"" ASC;",CPC_DEFINITION.level; CPC_DEFINITION.parents; CPC_DEFINITION.symbol; CPC_DEFINITION.titleFull; PUBLICATIONS.application_number; PUBLICATIONS.country_code; PUBLICATIONS.cpc; PUBLICATIONS.filing_date; PUBLICATIONS.grant_date,9,lite_sql,sf_bq222,79
sf_bq223,PATENTS,snowflake,"Which assignees, excluding DENSO CORP itself, have cited patents assigned to DENSO CORP, and what are the titles of the primary CPC subclasses associated with these citations? Provide the name of each citing assignee, the full title of the CPC subclass, and the count of citations grouped by the assignee and the CPC subclass title. Please focus specifically on the main categories of the CPC codes,","SELECT
    REPLACE(citing_assignee, '""', '') AS citing_assignee,
    cpcdef.""titleFull"" AS cpc_title,
    COUNT(*) AS number
FROM (
    SELECT
        pubs.""publication_number"" AS citing_publication_number,
        cite.value:""publication_number"" AS cited_publication_number,
        citing_assignee_s.value:""name"" AS citing_assignee,
        SUBSTR(cpcs.value:""code"", 1, 4) AS citing_cpc_subclass
    FROM 
        PATENTS.PATENTS.PUBLICATIONS AS pubs
    , LATERAL FLATTEN(input => pubs.""citation"") AS cite
    , LATERAL FLATTEN(input => pubs.""assignee_harmonized"") AS citing_assignee_s
    , LATERAL FLATTEN(input => pubs.""cpc"") AS cpcs
    WHERE
        cpcs.value:""first"" = TRUE
) AS pubs
JOIN (
    SELECT
        ""publication_number"" AS cited_publication_number,
        cited_assignee_s.value:""name"" AS cited_assignee
    FROM
        PATENTS.PATENTS.PUBLICATIONS
    , LATERAL FLATTEN(input => ""assignee_harmonized"") AS cited_assignee_s
) AS refs
    ON pubs.cited_publication_number = refs.cited_publication_number
JOIN
    PATENTS.PATENTS.CPC_DEFINITION AS cpcdef
    ON cpcdef.""symbol"" = pubs.citing_cpc_subclass
WHERE
    refs.cited_assignee = 'DENSO CORP'
    AND pubs.citing_assignee != 'DENSO CORP'
GROUP BY
    citing_assignee, cpcdef.""titleFull""",CPC_DEFINITION.symbol; CPC_DEFINITION.titleFull; PUBLICATIONS.assignee_harmonized; PUBLICATIONS.citation; PUBLICATIONS.cpc; PUBLICATIONS.publication_number,6,lite_sql,sf_bq223,79
sf_bq224,GITHUB_REPOS_DATE,snowflake,"Which repository with an approved license in `licenses.md` had the highest combined total of forks, issues, and watches in April 2022?","WITH allowed_repos AS (
    SELECT 
        ""repo_name"",
        ""license""
    FROM 
        GITHUB_REPOS_DATE.GITHUB_REPOS.LICENSES
    WHERE 
        ""license"" IN (
            'gpl-3.0', 'artistic-2.0', 'isc', 'cc0-1.0', 'epl-1.0', 'gpl-2.0',
            'mpl-2.0', 'lgpl-2.1', 'bsd-2-clause', 'apache-2.0', 'mit', 'lgpl-3.0'
        )
),
watch_counts AS (
    SELECT 
        TRY_PARSE_JSON(""repo""):""name""::STRING AS ""repo"",
        COUNT(DISTINCT TRY_PARSE_JSON(""actor""):""login""::STRING) AS ""watches""
    FROM 
        GITHUB_REPOS_DATE.MONTH._202204
    WHERE 
        ""type"" = 'WatchEvent'
    GROUP BY 
        TRY_PARSE_JSON(""repo""):""name""
),
issue_counts AS (
    SELECT 
        TRY_PARSE_JSON(""repo""):""name""::STRING AS ""repo"",
        COUNT(*) AS ""issue_events""
    FROM 
        GITHUB_REPOS_DATE.MONTH._202204
    WHERE 
        ""type"" = 'IssuesEvent'
    GROUP BY 
        TRY_PARSE_JSON(""repo""):""name""
),
fork_counts AS (
    SELECT 
        TRY_PARSE_JSON(""repo""):""name""::STRING AS ""repo"",
        COUNT(*) AS ""forks""
    FROM 
        GITHUB_REPOS_DATE.MONTH._202204
    WHERE 
        ""type"" = 'ForkEvent'
    GROUP BY 
        TRY_PARSE_JSON(""repo""):""name""
)
SELECT 
    ar.""repo_name""
FROM 
    allowed_repos AS ar
INNER JOIN 
    fork_counts AS fc ON ar.""repo_name"" = fc.""repo""
INNER JOIN 
    issue_counts AS ic ON ar.""repo_name"" = ic.""repo""
INNER JOIN 
    watch_counts AS wc ON ar.""repo_name"" = wc.""repo""
ORDER BY 
    (fc.""forks"" + ic.""issue_events"" + wc.""watches"") DESC
LIMIT 1;",LICENSES.license; LICENSES.repo_name; _20_*.actor; _20_*.repo; _20_*.type,5,lite_sql,sf_bq224,43
bq227,london,bigquery,"Could you provide the annual percentage shares, rounded to two decimal places, of the top 5 minor crime categories from 2008 in London's total crimes, with each year displayed in one row?","WITH top5_categories AS (
  SELECT minor_category
  FROM `bigquery-public-data.london_crime.crime_by_lsoa`
  WHERE year = 2008
  GROUP BY minor_category
  ORDER BY SUM(value) DESC
  LIMIT 5
),

total_crimes_per_year AS (
  SELECT 
    year, 
    SUM(value) AS total_crimes_year
  FROM `bigquery-public-data.london_crime.crime_by_lsoa`
  GROUP BY year
),

top5_crimes_per_year AS (
  SELECT
    year,
    minor_category,
    SUM(value) AS total_crimes_category_year
  FROM `bigquery-public-data.london_crime.crime_by_lsoa`
  WHERE minor_category IN (SELECT minor_category FROM top5_categories)
  GROUP BY year, minor_category
)

SELECT
  t.year,
  ROUND(SUM(CASE WHEN t.minor_category = (SELECT minor_category FROM top5_categories LIMIT 1 OFFSET 0) THEN t.total_crimes_category_year ELSE 0 END) / y.total_crimes_year * 100, 2) AS `Category 1`,
  ROUND(SUM(CASE WHEN t.minor_category = (SELECT minor_category FROM top5_categories LIMIT 1 OFFSET 1) THEN t.total_crimes_category_year ELSE 0 END) / y.total_crimes_year * 100, 2) AS `Category 2`,
  ROUND(SUM(CASE WHEN t.minor_category = (SELECT minor_category FROM top5_categories LIMIT 1 OFFSET 2) THEN t.total_crimes_category_year ELSE 0 END) / y.total_crimes_year * 100, 2) AS `Category 3`,
  ROUND(SUM(CASE WHEN t.minor_category = (SELECT minor_category FROM top5_categories LIMIT 1 OFFSET 3) THEN t.total_crimes_category_year ELSE 0 END) / y.total_crimes_year * 100, 2) AS `Category 4`,
  ROUND(SUM(CASE WHEN t.minor_category = (SELECT minor_category FROM top5_categories LIMIT 1 OFFSET 4) THEN t.total_crimes_category_year ELSE 0 END) / y.total_crimes_year * 100, 2) AS `Category 5`
FROM
  top5_crimes_per_year t
JOIN
  total_crimes_per_year y ON t.year = y.year
GROUP BY
  t.year, y.total_crimes_year
ORDER BY
  t.year;",crime_by_lsoa.minor_category; crime_by_lsoa.value; crime_by_lsoa.year,3,lite_sql,sf_bq227,39
bq228,london,bigquery,"Please provide a list of the top three major crime categories in the borough of Barking and Dagenham, along with the number of incidents in each category.","WITH ranked_crimes AS (
    SELECT
        borough,
        major_category,
        RANK() OVER(PARTITION BY borough ORDER BY SUM(value) DESC) AS rank_per_borough,
        SUM(value) AS no_of_incidents
    FROM
        `bigquery-public-data.london_crime.crime_by_lsoa`
    GROUP BY
        borough,
        major_category
)

SELECT
    borough,
    major_category,
    rank_per_borough,
    no_of_incidents
FROM
    ranked_crimes
WHERE
    rank_per_borough <= 3
AND 
    borough = 'Barking and Dagenham'
ORDER BY
    borough,
    rank_per_borough;",crime_by_lsoa.borough; crime_by_lsoa.major_category; crime_by_lsoa.value,3,lite_sql,sf_bq228,39
bq232,london,bigquery,Could you provide the total number of 'Other Theft' incidents within the 'Theft and Handling' category for each year in the Westminster borough?,"WITH borough_data AS (
    SELECT 
        year, 
        month, 
        borough, 
        major_category, 
        minor_category, 
        SUM(value) AS total,
    CASE 
        WHEN 
            major_category = 'Theft and Handling' 
        THEN 
            'Theft and Handling'
        ELSE 
            'Other' 
    END AS major_division,
    CASE 
        WHEN 
            minor_category = 'Other Theft' THEN minor_category
        ELSE 
            'Other'
    END AS minor_division,
    FROM 
        bigquery-public-data.london_crime.crime_by_lsoa
    GROUP BY 1,2,3,4,5
    ORDER BY 1,2
)

SELECT year, SUM(total) AS year_total
FROM borough_data
WHERE 
    borough = 'Westminster'
AND
    major_division != 'Other'
AND 
    minor_division != 'Other'
GROUP BY year, major_division, minor_division
ORDER BY year;",crime_by_lsoa.borough; crime_by_lsoa.major_category; crime_by_lsoa.minor_category; crime_by_lsoa.month; crime_by_lsoa.value; crime_by_lsoa.year,6,lite_sql,sf_bq232,39
sf_bq233,GITHUB_REPOS,snowflake,Can you find the imported Python modules and R libraries from the GitHub sample files and list them along with their occurrence counts? Please sort the results by language and then by the number of occurrences in descending order.,"WITH extracted_modules AS (
SELECT 
    el.""file_id"" AS ""file_id"", 
    el.""repo_name"", 
    el.""path"" AS ""path_"", 
    REPLACE(line.value, '""', '') AS ""line_"",
    CASE
        WHEN ENDSWITH(el.""path"", '.py') THEN 'python'
        WHEN ENDSWITH(el.""path"", '.r') THEN 'r'
        ELSE NULL
    END AS ""language"",
    CASE
        WHEN ENDSWITH(el.""path"", '.py') THEN
            ARRAY_CAT(
                ARRAY_CONSTRUCT(REGEXP_SUBSTR(line.value, '\\bimport\\s+(\\w+)', 1, 1, 'e')),
                ARRAY_CONSTRUCT(REGEXP_SUBSTR(line.value, '\\bfrom\\s+(\\w+)', 1, 1, 'e'))
            )
        WHEN ENDSWITH(el.""path"", '.r') THEN
            ARRAY_CONSTRUCT(REGEXP_SUBSTR(line.value, 'library\\s*\\(\\s*([^\\s)]+)\\s*\\)', 1, 1, 'e'))
        ELSE ARRAY_CONSTRUCT()
    END AS ""modules""
FROM (
    SELECT
        ct.""id"" AS ""file_id"", 
        fl.""repo_name"" AS ""repo_name"", 
        fl.""path"", 
        SPLIT(REPLACE(ct.""content"", '\n', ' \n'), '\n') AS ""lines""
    FROM 
        GITHUB_REPOS_DATE.GITHUB_REPOS.SAMPLE_FILES AS fl
    JOIN 
        GITHUB_REPOS_DATE.GITHUB_REPOS.SAMPLE_CONTENTS AS ct 
        ON fl.""id"" = ct.""id""
) AS el,
LATERAL FLATTEN(input => el.""lines"") AS line 
WHERE
    (
        ENDSWITH(""path_"", '.py') 
        AND 
        (
            ""line_"" LIKE 'import %' 
            OR 
            ""line_"" LIKE 'from %'
        )
    )
    OR
    (
        ENDSWITH(""path_"", '.r') 
        AND 
        ""line_"" LIKE 'library%('
    )

),
module_counts AS (
    SELECT 
        em.""language"",
        f.value::STRING AS ""module"",
        COUNT(*) AS ""occurrence_count""
    FROM 
        extracted_modules AS em,
        LATERAL FLATTEN(input => em.""modules"") AS f
    WHERE 
        em.""modules"" IS NOT NULL
        AND f.value IS NOT NULL
    GROUP BY 
        em.""language"", 
        f.value
),
python AS (
    SELECT 
        ""language"",
        ""module"",
        ""occurrence_count""
    FROM 
        module_counts
    WHERE 
        ""language"" = 'python'
),
rlanguage AS (
    SELECT 
        ""language"",
        ""module"",
        ""occurrence_count""
    FROM 
        module_counts AS mc_inner
    WHERE 
        ""language"" = 'r'
)
SELECT 
    *
FROM 
    python
UNION ALL
SELECT 
    *
FROM 
    rlanguage
ORDER BY 
    ""language"", 
    ""occurrence_count"" DESC;",SAMPLE_CONTENTS.binary; SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.id; SAMPLE_CONTENTS.sample_path; SAMPLE_FILES.id; SAMPLE_FILES.path,6,snow_sql_near_exact,sf_bq233,34
bq234,cms_data,bigquery,What is the most prescribed medication in each state in 2014?,"SELECT
  A.state,
  drug_name,
  total_claim_count
FROM (
  SELECT
    generic_name AS drug_name,
    nppes_provider_state AS state,
    ROUND(SUM(total_claim_count)) AS total_claim_count,
    ROUND(SUM(total_day_supply)) AS day_supply,
    ROUND(SUM(total_drug_cost)) / 1e6 AS total_cost_millions
  FROM
    `bigquery-public-data.cms_medicare.part_d_prescriber_2014`
  GROUP BY
    state,
    drug_name) A
INNER JOIN (
  SELECT
    state,
    MAX(total_claim_count) AS max_total_claim_count
  FROM (
    SELECT
      nppes_provider_state AS state,
      ROUND(SUM(total_claim_count)) AS total_claim_count
    FROM
      `bigquery-public-data.cms_medicare.part_d_prescriber_2014`
    GROUP BY
      state,
      generic_name)
  GROUP BY
    state) B
ON
  A.state = B.state
  AND A.total_claim_count = B.max_total_claim_count;",part_d_prescriber_2014.generic_name; part_d_prescriber_2014.nppes_provider_state; part_d_prescriber_2014.total_claim_count; part_d_prescriber_2014.total_day_supply; part_d_prescriber_2014.total_drug_cost,5,lite_sql,sf_bq234,649
bq235,cms_data,bigquery,Can you tell me which healthcare provider incurs the highest combined average costs for both outpatient and inpatient services in 2014?,"SELECT
  Provider_Name
FROM
(
SELECT
  OP.provider_state AS State,
  OP.provider_city AS City,
  OP.provider_id AS Provider_ID,
  OP.provider_name AS Provider_Name,
  ROUND(OP.average_OP_cost) AS Average_OP_Cost,
  ROUND(IP.average_IP_cost) AS Average_IP_Cost,
  ROUND(OP.average_OP_cost + IP.average_IP_cost) AS Combined_Average_Cost
FROM (
  SELECT
    provider_state,
    provider_city,
    provider_id,
    provider_name,
    SUM(average_total_payments*outpatient_services)/SUM(outpatient_services) AS average_OP_cost
  FROM
    `bigquery-public-data.cms_medicare.outpatient_charges_2014`
  GROUP BY
    provider_state,
    provider_city,
    provider_id,
    provider_name ) AS OP
INNER JOIN (
  SELECT
    provider_state,
    provider_city,
    provider_id,
    provider_name,
    SUM(average_medicare_payments*total_discharges)/SUM(total_discharges) AS average_IP_cost
  FROM
    `bigquery-public-data.cms_medicare.inpatient_charges_2014`
  GROUP BY
    provider_state,
    provider_city,
    provider_id,
    provider_name ) AS IP
ON
  OP.provider_id = IP.provider_id
  AND OP.provider_state = IP.provider_state
  AND OP.provider_city = IP.provider_city
  AND OP.provider_name = IP.provider_name
ORDER BY
  combined_average_cost DESC
LIMIT
  1
);",inpatient_charges_*.average_medicare_payments; inpatient_charges_*.provider_city; inpatient_charges_*.provider_id; inpatient_charges_*.provider_name; inpatient_charges_*.provider_state; inpatient_charges_*.total_discharges; outpatient_charges_*.average_total_payments; outpatient_charges_*.outpatient_services; outpatient_charges_*.provider_city; outpatient_charges_*.provider_id; outpatient_charges_*.provider_name; outpatient_charges_*.provider_state,12,lite_sql,sf_bq235,649
sf_bq236,NOAA_DATA_PLUS,snowflake,What are the top 5 zip codes of the areas in the United States that have experienced the most hail storm events in the past 10 years?,"SELECT
  CONCAT(""city"", ', ', ""state_name"") AS ""city"",
  ""zip_code"",
  COUNT(""event_id"") AS ""count_storms""
FROM (
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2014
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2015
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2016
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2017
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2018
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2019
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2020
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2021
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2022
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2023
    UNION ALL
    SELECT *
    FROM NOAA_DATA_PLUS.NOAA_HISTORIC_SEVERE_STORMS.STORMS_2024
) AS storms
JOIN NOAA_DATA_PLUS.GEO_US_BOUNDARIES.ZIP_CODES
  ON ST_WITHIN(ST_GEOGFROMWKB(storms.""event_point""), ST_GEOGFROMWKB(""zip_code_geom""))
WHERE
   LOWER(storms.""event_type"") = 'hail'
GROUP BY
  ""zip_code"", 
  ""city"", 
  ""state_name""
ORDER BY
  ""count_storms"" DESC
LIMIT 5;",STORMS_*.event_begin_time; STORMS_*.event_latitude; STORMS_*.event_longitude; STORMS_*.event_type; ZIP_CODES.zip_code; ZIP_CODES.zip_code_geom,6,snow_sql_near_exact,sf_bq236,807
sf_bq246,PATENTSVIEW,snowflake,Can you figure out the number of forward citations within 1 years from the application date for the patent that has the most backward citations within 1 years from application among all U.S. patents?,"SELECT filterData.""fwrdCitations_3""
FROM
  PATENTSVIEW.PATENTSVIEW.APPLICATION AS app
JOIN (
  SELECT DISTINCT 
    cpc.""patent_id"", 
    IFNULL(citation_3.""bkwdCitations_3"", 0) AS ""bkwdCitations_3"", 
    IFNULL(citation_3.""fwrdCitations_3"", 0) AS ""fwrdCitations_3""
  FROM
    PATENTSVIEW.PATENTSVIEW.CPC_CURRENT AS cpc
  LEFT JOIN (
    SELECT 
      b.""patent_id"", 
      b.""bkwdCitations_3"", 
      f.""fwrdCitations_3""
    FROM 
      (SELECT 
         cited.""patent_id"",
         COUNT(*) AS ""fwrdCitations_3""
       FROM 
         PATENTSVIEW.PATENTSVIEW.USPATENTCITATION AS cited
       JOIN
         PATENTSVIEW.PATENTSVIEW.APPLICATION AS apps
         ON cited.""patent_id"" = apps.""patent_id""
       WHERE
         apps.""country"" = 'US'
         AND cited.""date"" >= apps.""date""
         AND TRY_CAST(cited.""date"" AS DATE) <= DATEADD(YEAR, 1, TRY_CAST(apps.""date"" AS DATE)) -- Citation within 1 year
       GROUP BY 
         cited.""patent_id""
      ) AS f
    JOIN (
      SELECT 
        cited.""patent_id"",
        COUNT(*) AS ""bkwdCitations_3""
      FROM 
        PATENTSVIEW.PATENTSVIEW.USPATENTCITATION AS cited
      JOIN
        PATENTSVIEW.PATENTSVIEW.APPLICATION AS apps
        ON cited.""patent_id"" = apps.""patent_id""
      WHERE
        apps.""country"" = 'US'
        AND cited.""date"" < apps.""date""
        AND TRY_CAST(cited.""date"" AS DATE) >= DATEADD(YEAR, -1, TRY_CAST(apps.""date"" AS DATE)) -- Citation within 1 year before
      GROUP BY 
        cited.""patent_id""
    ) AS b
    ON b.""patent_id"" = f.""patent_id""
    WHERE 
      b.""bkwdCitations_3"" IS NOT NULL
      AND f.""fwrdCitations_3"" IS NOT NULL
  ) AS citation_3 
  ON cpc.""patent_id"" = citation_3.""patent_id""
) AS filterData
ON app.""patent_id"" = filterData.""patent_id""
ORDER BY filterData.""bkwdCitations_3"" DESC
LIMIT 1;",APPLICATION.country; APPLICATION.date; APPLICATION.patent_id; CPC_CURRENT.patent_id; USPATENTCITATION.date; USPATENTCITATION.patent_id,6,lite_sql,sf_bq246,304
sf_bq248,GITHUB_REPOS,snowflake,"What is the proportion of files whose paths include 'readme.md' that contain the phrase 'Copyright (c)', among all repositories that do not use any programming language with 'python' in its name","WITH requests AS (
    SELECT 
        D.""id"",
        D.""content"",
        E.""repo_name"",
        E.""path""
    FROM 
        (
            SELECT 
                ""id"",
                ""content""
            FROM 
                GITHUB_REPOS.GITHUB_REPOS.SAMPLE_CONTENTS
            GROUP BY 
                ""id"", ""content""
        ) AS D
    INNER JOIN 
        (
            SELECT 
                C.""id"",
                C.""repo_name"",
                C.""path""
            FROM 
                (
                    SELECT 
                        ""id"",
                        ""repo_name"",
                        ""path""
                    FROM 
                        GITHUB_REPOS.GITHUB_REPOS.SAMPLE_FILES
                    WHERE 
                        LOWER(""path"") LIKE '%readme.md'
                    GROUP BY 
                        ""path"", ""id"", ""repo_name""
                ) AS C
            INNER JOIN 
                (
                    SELECT 
                        ""repo_name"",
                        language_struct.value:""name""::STRING AS ""language_name""
                    FROM 
                        GITHUB_REPOS.GITHUB_REPOS.LANGUAGES,
                        LATERAL FLATTEN(input => ""language"") AS language_struct
                    WHERE 
                        LOWER(language_struct.value:""name""::STRING) NOT LIKE '%python%'
                    GROUP BY 
                        ""language_name"", ""repo_name""
                ) AS F
            ON 
                C.""repo_name"" = F.""repo_name""
        ) AS E
    ON 
        D.""id"" = E.""id""
)
SELECT 
    (SELECT COUNT(*) FROM requests WHERE ""content"" LIKE '%Copyright (c)%') / COUNT(*) AS ""proportion""
FROM 
    requests;",LANGUAGES.language; LANGUAGES.repo_name; SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.id; SAMPLE_FILES.id; SAMPLE_FILES.path; SAMPLE_FILES.repo_name,7,lite_sql,sf_bq248,34
sf_bq250,GEO_OPENSTREETMAP_WORLDPOP,snowflake,"What is the total population living on the geography grid which is the farthest from any hospital in Singapore, based on the most recent population data before 2023? Note that geographic grids and distances are calculated based on geospatial data and GIS related functions. Note to use planet layer in openstreetmap.","WITH country_name AS (
  SELECT 'Singapore' AS value
),

last_updated AS (
  SELECT
    MAX(""last_updated"") AS value
  FROM GEO_OPENSTREETMAP_WORLDPOP.WORLDPOP.POPULATION_GRID_1KM AS pop
    INNER JOIN country_name ON (pop.""country_name"" = country_name.value)
  WHERE ""last_updated"" < '2023-01-01'
),

aggregated_population AS (
  SELECT
    ""geo_id"",
    SUM(""population"") AS sum_population,
    ST_POINT(""longitude_centroid"", ""latitude_centroid"") AS centr  -- 计算每个 geo_id 的中心点
  FROM
    GEO_OPENSTREETMAP_WORLDPOP.WORLDPOP.POPULATION_GRID_1KM AS pop
    INNER JOIN country_name ON (pop.""country_name"" = country_name.value)
    INNER JOIN last_updated ON (pop.""last_updated"" = last_updated.value)
  GROUP BY ""geo_id"", ""longitude_centroid"", ""latitude_centroid""
),

population AS (
  SELECT
    SUM(sum_population) AS sum_population,
    ST_ENVELOPE(ST_UNION_AGG(centr)) AS boundingbox  -- 使用 ST_ENVELOPE 来代替 ST_CONVEXHULL
  FROM aggregated_population
),

hospitals AS (
  SELECT
    layer.""geometry""
  FROM
    GEO_OPENSTREETMAP_WORLDPOP.GEO_OPENSTREETMAP.PLANET_LAYERS AS layer
    INNER JOIN population ON ST_INTERSECTS(population.boundingbox, ST_GEOGFROMWKB(layer.""geometry""))
  WHERE
    layer.""layer_code"" IN (2110, 2120)
),

distances AS (
  SELECT
    pop.""geo_id"",
    pop.""population"",
    MIN(ST_DISTANCE(ST_GEOGFROMWKB(pop.""geog""), ST_GEOGFROMWKB(hospitals.""geometry""))) AS distance
  FROM
    GEO_OPENSTREETMAP_WORLDPOP.WORLDPOP.POPULATION_GRID_1KM AS pop
    INNER JOIN country_name ON pop.""country_name"" = country_name.value
    INNER JOIN last_updated ON pop.""last_updated"" = last_updated.value
    CROSS JOIN hospitals
  WHERE pop.""population"" > 0
  GROUP BY ""geo_id"", ""population""
)

SELECT
  SUM(pd.""population"") AS population
FROM
  distances pd
CROSS JOIN population p
GROUP BY distance
ORDER BY distance DESC
LIMIT 1;",PLANET_LAYERS.geometry; PLANET_LAYERS.layer_code; POPULATION_GRID_1KM.country_name; POPULATION_GRID_1KM.geo_id; POPULATION_GRID_1KM.geog; POPULATION_GRID_1KM.last_updated; POPULATION_GRID_1KM.latitude_centroid; POPULATION_GRID_1KM.longitude_centroid; POPULATION_GRID_1KM.population,9,lite_sql,sf_bq250,94
sf_bq252,GITHUB_REPOS,snowflake,"Could you please find the name of the repository that contains the most copied non-binary Swift file in the dataset, ensuring each file is uniquely identified by its ID?","WITH selected_repos AS (
  SELECT
    f.""id"",
    f.""repo_name"" AS ""repo_name"",
    f.""path"" AS ""path""
  FROM
    GITHUB_REPOS.GITHUB_REPOS.SAMPLE_FILES AS f
),
deduped_files AS (
  SELECT
    f.""id"",
    MIN(f.""repo_name"") AS ""repo_name"",
    MIN(f.""path"") AS ""path""
  FROM
    selected_repos AS f
  GROUP BY
    f.""id""
)
SELECT
  f.""repo_name""
FROM
  deduped_files AS f
  JOIN GITHUB_REPOS.GITHUB_REPOS.SAMPLE_CONTENTS AS c 
  ON f.""id"" = c.""id""
WHERE
  NOT c.""binary""
  AND f.""path"" LIKE '%.swift'
ORDER BY c.""copies"" DESC
LIMIT 1;",SAMPLE_CONTENTS.binary; SAMPLE_CONTENTS.copies; SAMPLE_CONTENTS.id; SAMPLE_FILES.id; SAMPLE_FILES.path; SAMPLE_FILES.repo_name,6,lite_sql,sf_bq252,34
sf_bq254,GEO_OPENSTREETMAP,snowflake,"Can you find the names of the multipolygons with valid ids that rank in the top two in terms of the number of points within their boundaries, among those multipolygons that do not have a Wikidata tag but are located within the same geographic area as the multipolygon associated with Wikidata item Q191, analyzed through planet features?","WITH bounding_area AS (
    SELECT ""geometry"" AS geometry
    FROM GEO_OPENSTREETMAP.GEO_OPENSTREETMAP.PLANET_FEATURES,
    LATERAL FLATTEN(INPUT => ""all_tags"") AS tag
    WHERE ""feature_type"" = 'multipolygons'
      AND tag.value:""key"" = 'wikidata'
      AND tag.value:""value"" = 'Q191'
),
bounding_area_features AS (
    SELECT 
        planet_features.""osm_id"", 
        planet_features.""feature_type"", 
        planet_features.""geometry"", 
        planet_features.""all_tags""
    FROM GEO_OPENSTREETMAP.GEO_OPENSTREETMAP.PLANET_FEATURES AS planet_features,
         bounding_area
    WHERE ST_DWITHIN(
        ST_GEOGFROMWKB(planet_features.""geometry""), 
        ST_GEOGFROMWKB(bounding_area.geometry), 
        0.0
    )
),
osm_id_with_wikidata AS (
    SELECT DISTINCT
        baf.""osm_id""
    FROM bounding_area_features AS baf,
         LATERAL FLATTEN(INPUT => baf.""all_tags"") AS tag
    WHERE tag.value:""key"" = 'wikidata'
),

polygons_wo_wikidata AS (
    SELECT 
        baf.""osm_id"",
        tag.value:""value"" as name,
        baf.""geometry"" as geometry
    FROM bounding_area_features AS baf
    LEFT JOIN osm_id_with_wikidata AS wd
      ON baf.""osm_id"" = wd.""osm_id"",
    LATERAL FLATTEN(INPUT => ""all_tags"") AS tag
    WHERE wd.""osm_id"" IS NULL
    AND baf.""osm_id"" IS NOT NULL
    AND baf.""feature_type"" = 'multipolygons'
    AND tag.value:""key"" = 'name'
)

SELECT 
    TRIM(pww.name) as name
FROM bounding_area_features AS baf
JOIN polygons_wo_wikidata AS pww
    ON ST_DWITHIN(
        ST_GEOGFROMWKB(baf.""geometry""), 
        ST_GEOGFROMWKB(pww.geometry), 
        0.0
    )
LEFT JOIN osm_id_with_wikidata AS wd
    ON baf.""osm_id"" = wd.""osm_id""
WHERE wd.""osm_id"" IS NOT NULL
  AND baf.""feature_type"" = 'points'
GROUP BY pww.name
ORDER BY COUNT(baf.""osm_id"") DESC
LIMIT 2",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry; PLANET_FEATURES.osm_id,4,lite_sql,sf_bq254,86
sf_bq255,GITHUB_REPOS,snowflake,"How many commit messages are there in repositories that use the 'Shell' programming language and 'apache-2.0' license, where the length of the commit message is more than 5 characters but less than 10,000 characters, and the messages do not start with the word 'merge', 'update' or 'test'?","SELECT
  COUNT(commits_table.""message"") AS ""num_messages""
FROM (
  SELECT
    L.""repo_name"",
    language_struct.value:""name""::STRING AS ""language_name""
  FROM
    GITHUB_REPOS.GITHUB_REPOS.LANGUAGES AS L,
    LATERAL FLATTEN(input => L.""language"") AS language_struct
) AS lang_table
JOIN 
  GITHUB_REPOS.GITHUB_REPOS.LICENSES AS license_table
ON 
  license_table.""repo_name"" = lang_table.""repo_name""
JOIN (
  SELECT
    *
  FROM
    GITHUB_REPOS.GITHUB_REPOS.SAMPLE_COMMITS
) AS commits_table
ON 
  commits_table.""repo_name"" = lang_table.""repo_name""
WHERE
  license_table.""license"" LIKE 'apache-2.0'
  AND lang_table.""language_name"" LIKE 'Shell'
  AND LENGTH(commits_table.""message"") > 5
  AND LENGTH(commits_table.""message"") < 10000
  AND LOWER(commits_table.""message"") NOT LIKE 'update%'
  AND LOWER(commits_table.""message"") NOT LIKE 'test%'
  AND LOWER(commits_table.""message"") NOT LIKE 'merge%';",LANGUAGES.language; LANGUAGES.repo_name; LICENSES.license; LICENSES.repo_name; SAMPLE_COMMITS.message; SAMPLE_COMMITS.repo_name,6,snow_sql_near_exact,sf_bq255,34
sf_bq260,THELOOK_ECOMMERCE,snowflake,"Find the total number of youngest and oldest users separately for each gender in the e-commerce platform created from January 1, 2019, to April 30, 2022.","WITH filtered_users AS (
    SELECT 
        ""first_name"", 
        ""last_name"", 
        ""gender"", 
        ""age"",
        CAST(TO_TIMESTAMP(""created_at"" / 1000000.0) AS DATE) AS ""created_at""
    FROM 
        ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS""
    WHERE 
        CAST(TO_TIMESTAMP(""created_at"" / 1000000.0) AS DATE) BETWEEN '2019-01-01' AND '2022-04-30'
),
youngest_ages AS (
    SELECT 
        ""gender"", 
        MIN(""age"") AS ""age""
    FROM 
        filtered_users
    GROUP BY 
        ""gender""
),
oldest_ages AS (
    SELECT 
        ""gender"", 
        MAX(""age"") AS ""age""
    FROM 
        filtered_users
    GROUP BY 
        ""gender""
),
youngest_oldest AS (
    SELECT 
        u.""first_name"", 
        u.""last_name"", 
        u.""gender"", 
        u.""age"", 
        'youngest' AS ""tag""
    FROM 
        filtered_users u
    JOIN 
        youngest_ages y
    ON 
        u.""gender"" = y.""gender"" AND u.""age"" = y.""age""
    
    UNION ALL
    
    SELECT 
        u.""first_name"", 
        u.""last_name"", 
        u.""gender"", 
        u.""age"", 
        'oldest' AS ""tag""
    FROM 
        filtered_users u
    JOIN 
        oldest_ages o
    ON 
        u.""gender"" = o.""gender"" AND u.""age"" = o.""age""
)
SELECT 
    ""tag"", 
    ""gender"", 
    COUNT(*) AS ""num""
FROM 
    youngest_oldest
GROUP BY 
    ""tag"", ""gender""
ORDER BY 
    ""tag"", ""gender"";",USERS.age; USERS.created_at; USERS.gender,3,snow_sql_near_exact,sf_bq260,73
sf_bq263,THELOOK_ECOMMERCE,snowflake,"Produce a 2023 monthly report for the 'Sleep & Lounge' category detailing total sales, costs, completed order counts, profits, and profit margins, ensuring accurate cost alignment with sales data.","WITH d AS (
    SELECT
        a.""order_id"", 
        TO_CHAR(TO_TIMESTAMP(a.""created_at"" / 1000000.0), 'YYYY-MM') AS ""month"",  -- 格式化为年月
        TO_CHAR(TO_TIMESTAMP(a.""created_at"" / 1000000.0), 'YYYY') AS ""year"",  -- 格式化为年份
        b.""product_id"", b.""sale_price"", c.""category"", c.""cost""
    FROM 
        ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDERS"" AS a
    JOIN 
        ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDER_ITEMS"" AS b
        ON a.""order_id"" = b.""order_id""
    JOIN 
        ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""PRODUCTS"" AS c
        ON b.""product_id"" = c.""id""
    WHERE 
        a.""status"" = 'Complete'
        AND TO_TIMESTAMP(a.""created_at"" / 1000000.0) BETWEEN TO_TIMESTAMP('2023-01-01') AND TO_TIMESTAMP('2023-12-31')
        AND c.""category"" = 'Sleep & Lounge'
),

e AS (
    SELECT 
        ""month"", 
        ""year"", 
        ""sale_price"", 
        ""category"", 
        ""cost"",
        SUM(""sale_price"") OVER (PARTITION BY ""month"", ""category"") AS ""TPV"",
        SUM(""cost"") OVER (PARTITION BY ""month"", ""category"") AS ""total_cost"",
        COUNT(DISTINCT ""order_id"") OVER (PARTITION BY ""month"", ""category"") AS ""TPO"",
        SUM(""sale_price"" - ""cost"") OVER (PARTITION BY ""month"", ""category"") AS ""total_profit"",
        SUM((""sale_price"" - ""cost"") / ""cost"") OVER (PARTITION BY ""month"", ""category"") AS ""Profit_to_cost_ratio""
    FROM 
        d
)

SELECT DISTINCT 
    ""month"", 
    ""category"", 
    ""TPV"", 
    ""total_cost"", 
    ""TPO"", 
    ""total_profit"", 
    ""Profit_to_cost_ratio""
FROM 
    e
ORDER BY 
    ""month"";",ORDERS.created_at; ORDERS.order_id; ORDERS.status; ORDER_ITEMS.order_id; ORDER_ITEMS.product_id; ORDER_ITEMS.sale_price; PRODUCTS.category; PRODUCTS.cost; PRODUCTS.id,9,snow_sql_near_exact,sf_bq263,73
sf_bq264,THELOOK_ECOMMERCE,snowflake,"Identify the difference in the number of the oldest and youngest users registered between January 1, 2019, and April 30, 2022, from our e-commerce platform data.","WITH youngest AS (
    SELECT
        ""gender"", 
        ""id"", 
        ""first_name"", 
        ""last_name"", 
        ""age"", 
        'youngest' AS ""tag""
    FROM 
        ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS""
    WHERE 
        ""age"" = (SELECT MIN(""age"") FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS"")
        AND TO_TIMESTAMP(""created_at"" / 1000000.0) BETWEEN TO_TIMESTAMP('2019-01-01') AND TO_TIMESTAMP('2022-04-30')
    GROUP BY 
        ""gender"", ""id"", ""first_name"", ""last_name"", ""age""
    ORDER BY 
        ""gender""
),

oldest AS (
    SELECT
        ""gender"", 
        ""id"", 
        ""first_name"", 
        ""last_name"", 
        ""age"", 
        'oldest' AS ""tag""
    FROM 
        ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS""
    WHERE 
        ""age"" = (SELECT MAX(""age"") FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS"")
        AND TO_TIMESTAMP(""created_at"" / 1000000.0) BETWEEN TO_TIMESTAMP('2019-01-01') AND TO_TIMESTAMP('2022-04-30')
    GROUP BY 
        ""gender"", ""id"", ""first_name"", ""last_name"", ""age""
    ORDER BY 
        ""gender""
),

TEMP_record AS (
    SELECT * FROM youngest
    UNION ALL
    SELECT * FROM oldest
)

SELECT 
    SUM(CASE WHEN ""age"" = (SELECT MAX(""age"") FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS"") THEN 1 END) - 
    SUM(CASE WHEN ""age"" = (SELECT MIN(""age"") FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS"") THEN 1 END) AS ""diff""
FROM 
    TEMP_record;",USERS.age; USERS.created_at,2,snow_sql_near_exact,sf_bq264,73
sf_bq265,THELOOK_ECOMMERCE,snowflake,"Can you provide me with the emails of the top 10 users who have the highest average order value, considering only those users who registered in 2019 and made purchases within the same year?","WITH
  main AS (
    SELECT
      ""id"" AS ""user_id"",
      ""email"",
      ""gender"",
      ""country"",
      ""traffic_source""
    FROM
      ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS""
    WHERE
      TO_TIMESTAMP(""created_at"" / 1000000.0) BETWEEN TO_TIMESTAMP('2019-01-01') AND TO_TIMESTAMP('2019-12-31')
  ),

  daate AS (
    SELECT
      ""user_id"",
      ""order_id"",
      CAST(TO_TIMESTAMP(""created_at"" / 1000000.0) AS DATE) AS ""order_date"",
      ""num_of_item""
    FROM
      ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDERS""
    WHERE
      TO_TIMESTAMP(""created_at"" / 1000000.0) BETWEEN TO_TIMESTAMP('2019-01-01') AND TO_TIMESTAMP('2019-12-31')
  ),

  orders AS (
    SELECT
      ""user_id"",
      ""order_id"",
      ""product_id"",
      ""sale_price"",
      ""status""
    FROM
      ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDER_ITEMS""
    WHERE
      TO_TIMESTAMP(""created_at"" / 1000000.0) BETWEEN TO_TIMESTAMP('2019-01-01') AND TO_TIMESTAMP('2019-12-31')
  ),

  nest AS (
    SELECT
      o.""user_id"",
      o.""order_id"",
      o.""product_id"",
      d.""order_date"",
      d.""num_of_item"",
      ROUND(o.""sale_price"", 2) AS ""sale_price"",
      ROUND(d.""num_of_item"" * o.""sale_price"", 2) AS ""total_sale""
    FROM
      orders o
    INNER JOIN
      daate d
    ON
      o.""order_id"" = d.""order_id""
    ORDER BY
      o.""user_id""
  ),

  type AS (
    SELECT
      ""user_id"",
      MIN(nest.""order_date"") AS ""cohort_date"",
      MAX(nest.""order_date"") AS ""latest_shopping_date"",
      DATEDIFF(MONTH, MIN(nest.""order_date""), MAX(nest.""order_date"")) AS ""lifespan_months"",
      ROUND(SUM(""total_sale""), 2) AS ""ltv"",
      COUNT(""order_id"") AS ""no_of_order""
    FROM
      nest
    GROUP BY
      ""user_id""
  ),

  kite AS (
    SELECT
      m.""user_id"",
      m.""email"",
      m.""gender"",
      m.""country"",
      m.""traffic_source"",
      EXTRACT(YEAR FROM n.""cohort_date"") AS ""cohort_year"",
      n.""latest_shopping_date"",
      n.""lifespan_months"",
      n.""ltv"",
      n.""no_of_order"",
      ROUND(n.""ltv"" / n.""no_of_order"", 2) AS ""avg_order_value""
    FROM
      main m
    INNER JOIN
      type n
    ON
      m.""user_id"" = n.""user_id""
  )

SELECT
  ""email""
FROM
  kite
ORDER BY
  ""avg_order_value"" DESC
LIMIT 10;",ORDERS.created_at; ORDERS.num_of_item; ORDERS.order_id; ORDERS.user_id; ORDER_ITEMS.created_at; ORDER_ITEMS.order_id; ORDER_ITEMS.product_id; ORDER_ITEMS.sale_price; ORDER_ITEMS.status; ORDER_ITEMS.user_id; USERS.country; USERS.created_at; USERS.email; USERS.gender; USERS.id; USERS.traffic_source,16,lite_sql,sf_bq265,73
bq268,ga360,bigquery,Identify the longest number of days between the first visit and the last recorded event (either the last visit or the first transaction) for a user where the last recorded event was associated with a mobile device.,"WITH 

visit AS (
SELECT fullvisitorid, MIN(date) AS date_first_visit, MAX(date) AS date_last_visit 
FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*` GROUP BY fullvisitorid),

device_visit AS (
SELECT DISTINCT fullvisitorid, date, device.deviceCategory
FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*`),

transactions AS (
SELECT fullvisitorid, MIN(date) AS date_transactions, 1 AS transaction
FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*` AS ga, UNNEST(ga.hits) AS hits
WHERE  hits.transaction.transactionId IS NOT NULL GROUP BY fullvisitorid),

device_transactions AS (
SELECT DISTINCT fullvisitorid, date, device.deviceCategory
FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*` AS ga, UNNEST(ga.hits) AS hits
WHERE hits.transaction.transactionId IS NOT NULL),

visits_transactions AS (
SELECT visit.fullvisitorid, date_first_visit, date_transactions, date_last_visit , 
       device_visit.deviceCategory AS device_last_visit, device_transactions.deviceCategory AS device_transaction, 
       IFNULL(transactions.transaction,0) AS transaction
FROM visit LEFT JOIN transactions ON visit.fullvisitorid = transactions.fullvisitorid
LEFT JOIN device_visit ON visit.fullvisitorid = device_visit.fullvisitorid 
AND visit.date_last_visit = device_visit.date

LEFT JOIN device_transactions ON visit.fullvisitorid = device_transactions.fullvisitorid 
AND transactions.date_transactions = device_transactions.date ),

mortality_table AS (
SELECT fullvisitorid, date_first_visit, 
       CASE WHEN date_transactions IS NULL THEN date_last_visit ELSE date_transactions  END AS date_event, 
       CASE WHEN device_transaction IS NULL THEN device_last_visit ELSE device_transaction END AS device, transaction
FROM visits_transactions )

SELECT DATE_DIFF(PARSE_DATE('%Y%m%d',date_event), PARSE_DATE('%Y%m%d', date_first_visit),DAY) AS time 
FROM mortality_table
WHERE device = 'mobile'
ORDER BY DATE_DIFF(PARSE_DATE('%Y%m%d',date_event), PARSE_DATE('%Y%m%d', date_first_visit),DAY) DESC
LIMIT 1",ga_sessions_*.date; ga_sessions_*.device; ga_sessions_*.fullVisitorId; ga_sessions_*.hits,4,lite_sql,sf_bq268,16
bq269,ga360,bigquery,Compute the average pageviews per visitor for non-purchase events and purchase events each month between June 1st and July 31st in 2017.,"WITH visitor_pageviews AS (
  SELECT
    FORMAT_DATE('%Y%m', PARSE_DATE('%Y%m%d', date)) AS month,
    CASE WHEN totals.transactions > 0 THEN 'purchase' ELSE 'non_purchase' END AS purchase_status,
    fullVisitorId,
    SUM(totals.pageviews) AS total_pageviews
  FROM
    `bigquery-public-data.google_analytics_sample.ga_sessions_*`
  WHERE
    _TABLE_SUFFIX BETWEEN '20170601' AND '20170731'
    AND totals.pageviews IS NOT NULL
  GROUP BY
    month, purchase_status, fullVisitorId
),
avg_pageviews AS (
  SELECT
    month,
    purchase_status,
    AVG(total_pageviews) AS avg_pageviews_per_visitor
  FROM
    visitor_pageviews
  GROUP BY
    month, purchase_status
)
SELECT
  month,
  MAX(CASE WHEN purchase_status = 'purchase' THEN avg_pageviews_per_visitor END) AS avg_pageviews_purchase,
  MAX(CASE WHEN purchase_status = 'non_purchase' THEN avg_pageviews_per_visitor END) AS avg_pageviews_non_purchase
FROM
  avg_pageviews
GROUP BY
  month
ORDER BY
  month",ga_sessions_*.date; ga_sessions_*.fullVisitorId; ga_sessions_*.totals,3,lite_sql,sf_bq269,16
bq270,ga360,bigquery,"What were the monthly add-to-cart and purchase conversion rates, calculated as a percentage of pageviews on product details, from January to March 2017?","WITH
  cte1 AS
    (SELECT
      CONCAT(EXTRACT(YEAR FROM (PARSE_DATE('%Y%m%d', date))),'0',
                EXTRACT(MONTH FROM (PARSE_DATE('%Y%m%d', date)))) AS month,
      COUNT(hits.eCommerceAction.action_type) AS num_product_view
    FROM `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`,
      UNNEST(hits) AS hits
    WHERE _table_suffix BETWEEN '0101' AND '0331'
      AND hits.eCommerceAction.action_type = '2'
    GROUP BY month),
  cte2 AS
    (SELECT
      CONCAT(EXTRACT(YEAR FROM (PARSE_DATE('%Y%m%d', date))),'0',
                EXTRACT(MONTH FROM (PARSE_DATE('%Y%m%d', date)))) AS month,
      COUNT(hits.eCommerceAction.action_type) AS num_addtocart
    FROM `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`,
      UNNEST(hits) AS hits
    WHERE _table_suffix BETWEEN '0101' AND '0331'
      AND hits.eCommerceAction.action_type = '3'
    GROUP BY month),
  cte3 AS
    (SELECT
      CONCAT(EXTRACT(YEAR FROM (PARSE_DATE('%Y%m%d', date))),'0',
                EXTRACT(MONTH FROM (PARSE_DATE('%Y%m%d', date)))) AS month,
      COUNT(hits.eCommerceAction.action_type) AS num_purchase
    FROM `bigquery-public-data.google_analytics_sample.ga_sessions_2017*`,
      UNNEST(hits) AS hits,
      UNNEST(hits.product) AS product
    WHERE _table_suffix BETWEEN '0101' AND '0331'
      AND hits.eCommerceAction.action_type = '6'
      AND product.productRevenue IS NOT NULL
    GROUP BY month)
SELECT 
  ROUND((num_addtocart/num_product_view * 100),2) AS add_to_cart_rate,
  ROUND((num_purchase/num_product_view * 100),2) AS purchase_rate
FROM cte1
  LEFT JOIN cte2
  USING(month) 
  LEFT JOIN cte3
  USING(month)
ORDER BY month;",ga_sessions_*.date; ga_sessions_*.hits,2,lite_sql,sf_bq270,16
sf_bq271,THELOOK_ECOMMERCE,snowflake,"Could you generate a report that, for each month in 2021, provides the number of orders, number of unique purchasers, and profit (calculated as total product retail price minus total cost) grouped by country, product department, and product category?","WITH
orders_x_order_items AS (
  SELECT orders.*,
         order_items.""inventory_item_id"",
         order_items.""sale_price""
  FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDERS"" AS orders
  LEFT JOIN ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDER_ITEMS"" AS order_items
  ON orders.""order_id"" = order_items.""order_id""
  WHERE TO_TIMESTAMP_NTZ(orders.""created_at"" / 1000000) BETWEEN TO_TIMESTAMP_NTZ('2021-01-01') AND TO_TIMESTAMP_NTZ('2021-12-31')
),

orders_x_inventory AS (
  SELECT orders_x_order_items.*,
         inventory_items.""product_category"",
         inventory_items.""product_department"",
         inventory_items.""product_retail_price"",
         inventory_items.""product_distribution_center_id"",
         inventory_items.""cost"",
         distribution_centers.""name""
  FROM orders_x_order_items
  LEFT JOIN ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""INVENTORY_ITEMS"" AS inventory_items
  ON orders_x_order_items.""inventory_item_id"" = inventory_items.""id""
  LEFT JOIN ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""DISTRIBUTION_CENTERS"" AS distribution_centers
  ON inventory_items.""product_distribution_center_id"" = distribution_centers.""id""
  WHERE TO_TIMESTAMP_NTZ(inventory_items.""created_at"" / 1000000) BETWEEN TO_TIMESTAMP_NTZ('2021-01-01') AND TO_TIMESTAMP_NTZ('2021-12-31')
),

orders_x_users AS (
  SELECT orders_x_inventory.*,
         users.""country"" AS ""users_country""
  FROM orders_x_inventory
  LEFT JOIN ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS"" AS users
  ON orders_x_inventory.""user_id"" = users.""id""
  WHERE TO_TIMESTAMP_NTZ(users.""created_at"" / 1000000) BETWEEN TO_TIMESTAMP_NTZ('2021-01-01') AND TO_TIMESTAMP_NTZ('2021-12-31')
)

SELECT 
  DATE_TRUNC('MONTH', TO_DATE(TO_TIMESTAMP_NTZ(orders_x_users.""created_at"" / 1000000))) AS ""reporting_month"",
  orders_x_users.""users_country"",
  orders_x_users.""product_department"",
  orders_x_users.""product_category"",
  COUNT(DISTINCT orders_x_users.""order_id"") AS ""n_order"",
  COUNT(DISTINCT orders_x_users.""user_id"") AS ""n_purchasers"",
  SUM(orders_x_users.""product_retail_price"") - SUM(orders_x_users.""cost"") AS ""profit""
FROM orders_x_users
GROUP BY 1, 2, 3, 4
ORDER BY ""reporting_month"";",INVENTORY_ITEMS.cost; INVENTORY_ITEMS.created_at; INVENTORY_ITEMS.id; INVENTORY_ITEMS.product_category; INVENTORY_ITEMS.product_department; INVENTORY_ITEMS.product_retail_price; ORDERS.created_at; ORDERS.order_id; ORDERS.user_id; ORDER_ITEMS.inventory_item_id; ORDER_ITEMS.order_id; USERS.country; USERS.created_at; USERS.id,14,snow_sql_near_exact,sf_bq271,73
sf_bq273,THELOOK_ECOMMERCE,snowflake,Can you list the top 5 months from August 2022 to November 2023 where the profit from Facebook-sourced completed orders showed the largest month-over-month increase? Calculate profit as sales minus costs.,"WITH 
orders AS (
  SELECT
    ""order_id"", 
    ""user_id"", 
    ""created_at"",
    DATE_TRUNC('MONTH', TO_TIMESTAMP_NTZ(""delivered_at"" / 1000000)) AS ""delivery_month"",  -- Converting to timestamp
    ""status"" 
  FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDERS""
),

order_items AS (
  SELECT 
    ""order_id"", 
    ""product_id"", 
    ""sale_price"" 
  FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""ORDER_ITEMS""
),

products AS (
  SELECT 
    ""id"", 
    ""cost""
  FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""PRODUCTS""
),

users AS (
  SELECT
    ""id"", 
    ""traffic_source"" 
  FROM ""THELOOK_ECOMMERCE"".""THELOOK_ECOMMERCE"".""USERS""
),

filter_join AS (
  SELECT 
    orders.""order_id"",
    orders.""user_id"",
    order_items.""product_id"",
    orders.""delivery_month"",
    orders.""status"",
    order_items.""sale_price"",
    products.""cost"",
    users.""traffic_source""
  FROM orders
  JOIN order_items ON orders.""order_id"" = order_items.""order_id""
  JOIN products ON order_items.""product_id"" = products.""id""
  JOIN users ON orders.""user_id"" = users.""id""
  WHERE orders.""status"" = 'Complete' 
    AND users.""traffic_source"" = 'Facebook'
    AND TO_TIMESTAMP_NTZ(orders.""created_at"" / 1000000) BETWEEN TO_TIMESTAMP_NTZ('2022-07-01') AND TO_TIMESTAMP_NTZ('2023-11-30')  -- Include July for calculation
),

monthly_sales AS (
  SELECT 
    ""delivery_month"",
    ""traffic_source"",
    SUM(""sale_price"") AS ""total_revenue"",
    SUM(""sale_price"") - SUM(""cost"") AS ""total_profit"",
    COUNT(DISTINCT ""product_id"") AS ""product_quantity"",
    COUNT(DISTINCT ""order_id"") AS ""orders_quantity"",
    COUNT(DISTINCT ""user_id"") AS ""users_quantity""
  FROM filter_join
  GROUP BY ""delivery_month"", ""traffic_source""
)

-- Filter to show only 8th month and onwards, but calculate using July
SELECT 
  current_month.""delivery_month"",
  COALESCE(
    current_month.""total_profit"" - previous_month.""total_profit"", 
    0  -- If there is no previous month (i.e. for 8月), return 0
  ) AS ""profit_vs_prior_month""
FROM monthly_sales AS current_month
LEFT JOIN monthly_sales AS previous_month
  ON current_month.""traffic_source"" = previous_month.""traffic_source""
  AND current_month.""delivery_month"" = DATEADD(MONTH, -1, previous_month.""delivery_month"")  -- Correctly join to previous month
WHERE current_month.""delivery_month"" >= '2022-08-01'  -- Only show August and later data, but use July for calculation
ORDER BY ""profit_vs_prior_month"" DESC
LIMIT 5;",ORDERS.created_at; ORDERS.delivered_at; ORDERS.order_id; ORDERS.status; ORDERS.user_id; ORDER_ITEMS.order_id; ORDER_ITEMS.product_id; ORDER_ITEMS.sale_price; PRODUCTS.cost; PRODUCTS.id; USERS.id; USERS.traffic_source,12,lite_sql,sf_bq273,73
bq275,ga360,bigquery,Can you provide a list of visitor IDs for those who made their first transaction on a mobile device on a different day than their first visit?,"WITH 
  visit AS (
    SELECT fullvisitorid, MIN(date) AS date_first_visit
    FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*` 
    GROUP BY fullvisitorid
  ),
  
  transactions AS (
    SELECT fullvisitorid, MIN(date) AS date_transactions, 1 AS transaction
    FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*` AS ga, 
    UNNEST(ga.hits) AS hits 
    WHERE hits.transaction.transactionId IS NOT NULL 
    GROUP BY fullvisitorid
  ),

  device_transactions AS (
    SELECT DISTINCT fullvisitorid, date, device.deviceCategory AS device_transaction
    FROM `bigquery-public-data.google_analytics_sample.ga_sessions_*` AS ga, 
    UNNEST(ga.hits) AS hits 
    WHERE hits.transaction.transactionId IS NOT NULL
  ),

  visits_transactions AS (
    SELECT visit.fullvisitorid, date_first_visit, date_transactions, device_transaction
    FROM visit 
    LEFT JOIN transactions ON visit.fullvisitorid = transactions.fullvisitorid
    LEFT JOIN device_transactions ON visit.fullvisitorid = device_transactions.fullvisitorid 
    AND transactions.date_transactions = device_transactions.date
  )

SELECT fullvisitorid 
FROM visits_transactions
WHERE DATE_DIFF(PARSE_DATE('%Y%m%d', date_transactions), PARSE_DATE('%Y%m%d', date_first_visit), DAY) > 0
AND device_transaction = ""mobile"";",ga_sessions_*.date; ga_sessions_*.device; ga_sessions_*.fullVisitorId; ga_sessions_*.hits,4,lite_sql,sf_bq275,16
bq279,austin,bigquery,Can you provide the number of distinct active and closed bike share stations for each year 2013 and 2014?,"SELECT
    t.year,
    CASE 
        WHEN t.year = 2013 THEN (
                                  SELECT 
                                    COUNT(DISTINCT station_id)
                                  FROM 
                                    `bigquery-public-data.austin_bikeshare.bikeshare_trips` t
                                  INNER JOIN 
                                    `bigquery-public-data.austin_bikeshare.bikeshare_stations` s
                                  ON 
                                    t.start_station_id = s.station_id
                                  WHERE 
                                    s.status = 'active' AND EXTRACT(YEAR FROM start_time) = 2013
                                 ) 
        WHEN t.year = 2014 THEN (
                                  SELECT 
                                    COUNT(DISTINCT station_id)
                                  FROM 
                                    `bigquery-public-data.austin_bikeshare.bikeshare_trips` t
                                  INNER JOIN 
                                    `bigquery-public-data.austin_bikeshare.bikeshare_stations` s
                                  ON 
                                    t.start_station_id = s.station_id
                                  WHERE 
                                    s.status = 'active' AND EXTRACT(YEAR FROM start_time) = 2014
                                 )
    END
    AS number_status_active,
    CASE 
        WHEN t.year = 2013 THEN (
                                  SELECT 
                                   COUNT(DISTINCT station_id)
                                  FROM 
                                  `bigquery-public-data.austin_bikeshare.bikeshare_trips` t
                                  INNER JOIN 
                                  `bigquery-public-data.austin_bikeshare.bikeshare_stations` s
                                  ON 
                                   t.start_station_id = s.station_id
                                  WHERE 
                                   s.status = 'closed' AND EXTRACT(YEAR FROM start_time) = 2013
                                 ) 
        WHEN t.year = 2014 THEN (
                                  SELECT 
                                  COUNT(DISTINCT station_id)
                                  FROM 
                                    `bigquery-public-data.austin_bikeshare.bikeshare_trips` t
                                  INNER JOIN 
                                    `bigquery-public-data.austin_bikeshare.bikeshare_stations` s
                                  ON 
                                    t.start_station_id = s.station_id
                                  WHERE 
                                    s.status = 'closed' AND EXTRACT(YEAR FROM start_time) = 2014
                                 )
    END
    AS number_status_closed
FROM
    (
      SELECT 
         EXTRACT(YEAR FROM start_time) AS year,
         start_station_id
      FROM
         `bigquery-public-data.austin_bikeshare.bikeshare_trips`
    ) 
    AS t
INNER JOIN
    `bigquery-public-data.austin_bikeshare.bikeshare_stations` s
ON
    t.start_station_id = s.station_id
WHERE
    t.year BETWEEN 2013 AND 2014
GROUP BY
    t.year
ORDER BY
    t.year",bikeshare_stations.station_id; bikeshare_stations.status; bikeshare_trips.start_station_id; bikeshare_trips.start_time,4,lite_sql,sf_bq279,81
bq280,stackoverflow,bigquery,"Please provide the display name of the user who has answered the most questions on Stack Overflow, considering only users with a reputation greater than 10.","WITH UserAnswers AS (
  SELECT
    owner_user_id AS answer_owner_id,
    COUNT(id) AS answer_count
  FROM bigquery-public-data.stackoverflow.posts_answers
  WHERE owner_user_id IS NOT NULL
  GROUP BY owner_user_id
),
DetailedUsers AS (
  SELECT
    id AS user_id,
    display_name AS user_display_name,
    reputation
  FROM bigquery-public-data.stackoverflow.users
  WHERE display_name IS NOT NULL AND reputation > 10
),
RankedUsers AS (
  SELECT
    u.user_display_name,
    u.reputation,
    a.answer_count,
    ROW_NUMBER() OVER (ORDER BY a.answer_count DESC) AS rank
  FROM DetailedUsers u
  JOIN UserAnswers a ON u.user_id = a.answer_owner_id
)
SELECT
  user_display_name,
FROM RankedUsers
WHERE rank = 1;",posts_answers.id; posts_answers.owner_user_id; users.display_name; users.id; users.reputation,5,lite_sql,sf_bq280,228
bq281,austin,bigquery,"What is the highest number of electric bike rides lasting more than 10 minutes taken by subscribers with 'Student Membership' in a single day, excluding rides starting or ending at 'Mobile Station' or 'Repair Shop'?","SELECT
  COUNT(1) AS num_rides
FROM
  `bigquery-public-data.austin_bikeshare.bikeshare_trips` 
WHERE 
start_station_name 
    NOT IN ('Mobile Station', 'Repair Shop')
AND
end_station_name 
    NOT IN ('Mobile Station', 'Repair Shop')
AND 
subscriber_type = 'Student Membership'
AND
bike_type = 'electric'
AND
duration_minutes > 10
GROUP BY 
    EXTRACT(YEAR from start_time), 
    EXTRACT(MONTH from start_time), 
    EXTRACT(DAY from start_time)
ORDER BY num_rides DESC
LIMIT 1",bikeshare_trips.bike_type; bikeshare_trips.duration_minutes; bikeshare_trips.end_station_name; bikeshare_trips.start_station_name; bikeshare_trips.start_time; bikeshare_trips.subscriber_type,6,lite_sql,sf_bq281,81
bq282,austin,bigquery,"Can you tell me the numeric value of the active council district in Austin which has the highest number of bike trips that start and end within the same district, but not at the same station?","SELECT 
  district
FROM (
  SELECT
    S.starting_district AS district,
    T.start_station_id,
    T.end_station_id
  FROM
    `bigquery-public-data.austin_bikeshare.bikeshare_trips` AS T
  INNER JOIN (
    SELECT
      station_id,
      council_district AS starting_district
    FROM
      `bigquery-public-data.austin_bikeshare.bikeshare_stations`
    WHERE
      status = ""active""
  ) AS S ON T.start_station_id = S.station_id
  WHERE
    S.starting_district IN (
      SELECT council_district
      FROM `bigquery-public-data.austin_bikeshare.bikeshare_stations`
      WHERE
        status = ""active"" AND
        station_id = SAFE_CAST(T.end_station_id AS INT64)
    )
    AND T.start_station_id != SAFE_CAST(T.end_station_id AS INT64)
) 
GROUP BY district
ORDER BY COUNT(*) DESC
LIMIT 1;",bikeshare_stations.council_district; bikeshare_stations.station_id; bikeshare_stations.status; bikeshare_trips.end_station_id; bikeshare_trips.start_station_id,5,lite_sql,sf_bq282,81
bq284,bbc,bigquery,"Can you provide a breakdown of the total number of articles into different categories and the percentage of those articles that mention ""education"" within each category from the BBC News?","SELECT 
  category,
  COUNT(*) AS number_total_by_category,  
  CASE 
    WHEN category = 'tech' THEN 
          (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE (LOWER(body) LIKE '%education%') AND category = 'tech') * 100 /
                (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE category = 'tech')
    WHEN category = 'sport' THEN 
          (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE (LOWER(body) LIKE '%education%') AND category = 'sport') * 100 /
                (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE category = 'sport')
    WHEN category = 'business' THEN 
          (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE (LOWER(body) LIKE '%education%') AND category = 'business') * 100 /
                (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE category = 'business')
    WHEN category = 'politics' THEN 
          (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE (LOWER(body) LIKE '%education%') AND category = 'politics') * 100 /
                (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE category = 'politics')
    WHEN category = 'entertainment' THEN 
          (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE (LOWER(body) LIKE '%education%') AND category = 'entertainment') * 100 /
                (SELECT count(*)
                FROM `bigquery-public-data.bbc_news.fulltext`
                WHERE category = 'entertainment')
  END AS percent_education
FROM `bigquery-public-data.bbc_news.fulltext`
GROUP BY
  category;",fulltext.body; fulltext.category,2,lite_sql,sf_bq284,4
bq285,fda,bigquery,Could you provide me with the zip code of the location that has the highest number of bank institutions in Florida?,"with _fips AS
    (
        SELECT
            state_fips_code
        FROM
            `bigquery-public-data.census_utility.fips_codes_states`
        WHERE
            state_name = ""Florida""
    )

    ,_zip AS
    (
        SELECT
            z.zip_code,
            z.zip_code_geom,
        FROM
            `bigquery-public-data.geo_us_boundaries.zip_codes` z, _fips u
        WHERE
            z.state_fips_code = u.state_fips_code
    )

    ,locations AS
    (
        SELECT
            COUNT(i.institution_name) AS count_locations,
            l.zip_code
        FROM
            `bigquery-public-data.fdic_banks.institutions` i
        JOIN
            `bigquery-public-data.fdic_banks.locations` l 
        USING (fdic_certificate_number)
        WHERE
            l.state IS NOT NULL
        AND 
            l.state_name IS NOT NULL
        GROUP BY 2
    )

    SELECT
        z.zip_code
    FROM
        _zip z
    JOIN
        locations l 
    USING (zip_code)
    GROUP BY
        z.zip_code
    ORDER BY
        SUM(l.count_locations) DESC
    LIMIT 1;",fips_codes_states.state_fips_code; fips_codes_states.state_name; institutions.fdic_certificate_number; institutions.institution_name; institutions.state_fips_code; zip_codes.state_fips_code; zip_codes.state_name; zip_codes.zip_code,8,lite_sql,sf_bq285,417
bq286,usa_names,bigquery,"Can you tell me the name of the most popular female baby in Wyoming for the year 2021, based on the proportion of female babies given that name compared to the total number of female babies given the same name across all states?","SELECT
  a.name AS name
FROM
  `bigquery-public-data.usa_names.usa_1910_current` a
JOIN (
  SELECT
    name,
    gender,
    year,
    SUM(number) AS total_number
  FROM
    `bigquery-public-data.usa_names.usa_1910_current`
  GROUP BY
    name,
    gender,
    year) b
ON
  a.name = b.name
  AND a.gender = b.gender
  AND a.year = b.year
WHERE 
    a.gender = 'F' AND
    a.state = 'WY' AND
    a.year = 2021
ORDER BY (a.number / b.total_number) DESC
LIMIT 1",usa_1910_current.gender; usa_1910_current.name; usa_1910_current.number; usa_1910_current.state; usa_1910_current.year,5,lite_sql,sf_bq286,10
sf_bq289,GEO_OPENSTREETMAP_CENSUS_PLACES,snowflake,"Can you find the shortest distance between any two amenities (either a library, place of worship, or community center) located within Philadelphia, analyzed through pennsylvania table and planet features points?","WITH philadelphia AS (
    SELECT 
        * 
    FROM 
        GEO_OPENSTREETMAP_CENSUS_PLACES.GEO_US_CENSUS_PLACES.PLACES_PENNSYLVANIA
    WHERE 
        ""place_name"" = 'Philadelphia'
),
amenities AS (
    SELECT 
        features.*, 
        tags.value:""value"" AS amenity
    FROM 
        GEO_OPENSTREETMAP_CENSUS_PLACES.GEO_OPENSTREETMAP.PLANET_FEATURES_POINTS AS features
    CROSS JOIN philadelphia
    -- Use FLATTEN on ""all_tags"" to get the tags and filter by ""key""
    , LATERAL FLATTEN(input => features.""all_tags"") AS tags
    WHERE 
        ST_CONTAINS(ST_GEOGFROMWKB(philadelphia.""place_geom""), ST_GEOGFROMWKB(features.""geometry""))
    AND 
        tags.value:""key"" = 'amenity' 
    AND 
        tags.value:""value"" IN ('library', 'place_of_worship', 'community_centre')
),
joiin AS (
    SELECT 
        a1.*, 
        a2.""osm_id"" AS nearest_osm_id, 
        ST_DISTANCE(ST_GEOGFROMWKB(a1.""geometry""), ST_GEOGFROMWKB(a2.""geometry"")) AS distance, 
        ROW_NUMBER() OVER (PARTITION BY a1.""osm_id"" ORDER BY ST_DISTANCE(ST_GEOGFROMWKB(a1.""geometry""), ST_GEOGFROMWKB(a2.""geometry""))) AS row_num
    FROM amenities a1
    CROSS JOIN amenities a2
    WHERE a1.""osm_id"" < a2.""osm_id""
    ORDER BY a1.""osm_id"", distance
) 
SELECT distance
FROM joiin  
WHERE row_num = 1
ORDER BY distance ASC
LIMIT 1;",PLACES_PENNSYLVANIA.place_geom; PLACES_PENNSYLVANIA.place_name; PLANET_FEATURES_POINTS.all_tags; PLANET_FEATURES_POINTS.geometry; PLANET_FEATURES_POINTS.osm_id,5,snow_sql_near_exact,sf_bq289,1056
bq290,noaa_data,bigquery,"Can you calculate the difference in maximum temperature, minimum temperature, and average temperature between US and UK weather stations for each day in October 2023, excluding records with missing temperature values?","with 

stations_selected as (
  select
    usaf,
    wban,
    country,
    name
  from
    `bigquery-public-data.noaa_gsod.stations`
  where
    country in ('US', 'UK')
),

data_filtered as (
  select
    gsod.*,
    stations.country
  from
    `bigquery-public-data.noaa_gsod.gsod2023` gsod
  join
    stations_selected stations
  on
    gsod.stn = stations.usaf
    and gsod.wban = stations.wban
  where
    date(gsod.date) between '2023-10-01' and '2023-10-31'
    and gsod.temp != 9999.9
),

-- US Metrics
us_metrics as (
  select
    date(date) as metric_date,
    avg(temp) as avg_temp_us,
    min(temp) as min_temp_us,
    max(temp) as max_temp_us
  from
    data_filtered
  where
    country = 'US'
  group by
    metric_date
),

-- UK Metrics
uk_metrics as (
  select
    date(date) as metric_date,
    avg(temp) as avg_temp_uk,
    min(temp) as min_temp_uk,
    max(temp) as max_temp_uk
  from
    data_filtered
  where
    country = 'UK'
  group by
    metric_date
),

-- Temperature Differences
temp_differences as (
  select
    us.metric_date,
    us.max_temp_us - uk.max_temp_uk as max_temp_diff,
    us.min_temp_us - uk.min_temp_uk as min_temp_diff,
    us.avg_temp_us - uk.avg_temp_uk as avg_temp_diff
  from
    us_metrics us
  join
    uk_metrics uk
  on
    us.metric_date = uk.metric_date
)

select 
  metric_date, 
  max_temp_diff, 
  min_temp_diff, 
  avg_temp_diff
from 
  temp_differences
order by
  metric_date;",gsod_*.date; gsod_*.stn; gsod_*.temp; gsod_*.wban; stations.country; stations.name; stations.usaf; stations.wban,8,lite_sql,sf_bq290,660
sf_bq291,NOAA_GLOBAL_FORECAST_SYSTEM,snowflake,"Can you provide a daily weather summary for July 2019 within a 5 km radius of latitude 26.75 and longitude 51.5? I need the maximum, minimum, and average temperatures; total precipitation; average cloud cover between 10 AM and 5 PM; total snowfall (when average temperature is below 32°F); and total rainfall (when average temperature is 32°F or above) for each forecast date. The data should correspond to forecasts created in July 2019 for the following day.","WITH daily_forecasts AS (
    SELECT
        ""TRI"".""creation_time"",

        CAST(DATEADD(hour, 1, TO_TIMESTAMP_NTZ(TO_NUMBER(""forecast"".value:""time"") / 1000000)) AS DATE) AS ""local_forecast_date"",
        MAX(
            CASE 
                WHEN ""forecast"".value:""temperature_2m_above_ground"" IS NOT NULL 
                THEN ""forecast"".value:""temperature_2m_above_ground"" 
                ELSE NULL 
            END
        ) AS ""max_temp"",
        MIN(
            CASE 
                WHEN ""forecast"".value:""temperature_2m_above_ground"" IS NOT NULL 
                THEN ""forecast"".value:""temperature_2m_above_ground"" 
                ELSE NULL 
            END
        ) AS ""min_temp"",
        AVG(
            CASE 
                WHEN ""forecast"".value:""temperature_2m_above_ground"" IS NOT NULL 
                THEN ""forecast"".value:""temperature_2m_above_ground"" 
                ELSE NULL 
            END
        ) AS ""avg_temp"",
        SUM(
            CASE 
                WHEN ""forecast"".value:""total_precipitation_surface"" IS NOT NULL 
                THEN ""forecast"".value:""total_precipitation_surface"" 
                ELSE 0 
            END
        ) AS ""total_precipitation"",
        AVG(
            CASE 
                WHEN CAST(DATEADD(hour, 1, TO_TIMESTAMP_NTZ(TO_NUMBER(""forecast"".value:""time"") / 1000000)    ) AS TIME) BETWEEN '10:00:00' AND '17:00:00'
                     AND ""forecast"".value:""total_cloud_cover_entire_atmosphere"" IS NOT NULL 
                THEN ""forecast"".value:""total_cloud_cover_entire_atmosphere"" 
                ELSE NULL 
            END
        ) AS ""avg_cloud_cover"",
        CASE
            WHEN AVG(""forecast"".value:""temperature_2m_above_ground"") < 32 THEN 
                SUM(
                    CASE 
                        WHEN ""forecast"".value:""total_precipitation_surface"" IS NOT NULL 
                        THEN ""forecast"".value:""total_precipitation_surface"" 
                        ELSE 0 
                    END
                )
            ELSE 0
        END AS ""total_snow"",
        CASE
            WHEN AVG(""forecast"".value:""temperature_2m_above_ground"") >= 32 THEN 
                SUM(
                    CASE 
                        WHEN ""forecast"".value:""total_precipitation_surface"" IS NOT NULL 
                        THEN ""forecast"".value:""total_precipitation_surface"" 
                        ELSE 0 
                    END
                )
            ELSE 0
        END AS ""total_rain""
    FROM
        ""NOAA_GLOBAL_FORECAST_SYSTEM"".""NOAA_GLOBAL_FORECAST_SYSTEM"".""NOAA_GFS0P25"" AS ""TRI""
    CROSS JOIN LATERAL FLATTEN(input => ""TRI"".""forecast"") AS ""forecast""
    WHERE
        TO_TIMESTAMP_NTZ(TO_NUMBER(""TRI"".""creation_time"") / 1000000) BETWEEN '2019-07-01' AND '2021-07-31'  
        AND ST_DWITHIN(
            ST_GEOGFROMWKB(""TRI"".""geography""),
            ST_POINT(26.75, 51.5),
            5000
        )
        AND CAST(TO_TIMESTAMP_NTZ(TO_NUMBER(""forecast"".value:""time"") / 1000000) AS DATE) = DATEADD(day, 1, CAST( TO_TIMESTAMP_NTZ(TO_NUMBER(""TRI"".""creation_time"") / 1000000) AS DATE))
    GROUP BY
        ""TRI"".""creation_time"",
        ""local_forecast_date""
)

SELECT
    TO_TIMESTAMP_NTZ(TO_NUMBER(""creation_time"") / 1000000),
    ""local_forecast_date"" AS ""forecast_date"",
    ""max_temp"",
    ""min_temp"",
    ""avg_temp"",
    ""total_precipitation"",
    ""avg_cloud_cover"",
    ""total_snow"",
    ""total_rain""
FROM
    daily_forecasts
ORDER BY
    ""creation_time"",
    ""forecast_date"";",NOAA_GFS0P25.creation_time; NOAA_GFS0P25.forecast; NOAA_GFS0P25.geography,3,lite_sql,sf_bq291,90
sf_bq294,SAN_FRANCISCO_PLUS,snowflake,"Can you provide the details of the top 5 longest bike share trips that started during the second half of 2017, including the trip ID, duration in seconds, start date, start station name, route (start station to end station), bike number, subscriber type, member's birth year, current age, age classification, gender, and the region name of the start station? Please exclude trips where the start station name, member's birth year, or member's gender is not specified.","SELECT
  ""trip_id"",
  ""duration_sec"",
  DATE(TO_TIMESTAMP_LTZ(""start_date"" / 1000000)) AS ""star_date"",
  ""start_station_name"",
  CONCAT(""start_station_name"", ' - ', ""end_station_name"") AS ""route"",
  ""bike_number"",
  ""subscriber_type"",
  ""member_birth_year"",
  (EXTRACT(YEAR FROM CURRENT_DATE()) - ""member_birth_year"") AS ""age"",
  CASE
    WHEN (EXTRACT(YEAR FROM CURRENT_DATE()) - ""member_birth_year"") < 40 THEN 'Young (<40 Y.O)'
    WHEN (EXTRACT(YEAR FROM CURRENT_DATE()) - ""member_birth_year"") BETWEEN 40 AND 60 THEN 'Adult (40-60 Y.O)'
    ELSE 'Senior Adult (>60 Y.O)'
  END AS ""age_class"",
  ""member_gender"",
  c.""name"" AS ""region_name""
FROM ""SAN_FRANCISCO_PLUS"".""SAN_FRANCISCO_BIKESHARE"".""BIKESHARE_TRIPS"" a
LEFT JOIN ""SAN_FRANCISCO_PLUS"".""SAN_FRANCISCO_BIKESHARE"".""BIKESHARE_STATION_INFO"" b 
  ON a.""start_station_id"" = b.""station_id""
LEFT JOIN ""SAN_FRANCISCO_PLUS"".""SAN_FRANCISCO_BIKESHARE"".""BIKESHARE_REGIONS"" c 
  ON b.""region_id"" = c.""region_id""
WHERE TO_TIMESTAMP_LTZ(""start_date"" / 1000000) BETWEEN '2017-07-01' AND '2017-12-31'
  AND b.""station_id"" IS NOT NULL
  AND ""member_birth_year"" IS NOT NULL
  AND ""member_gender"" IS NOT NULL
ORDER BY ""duration_sec"" DESC
LIMIT 5;",BIKESHARE_REGIONS.name; BIKESHARE_REGIONS.region_id; BIKESHARE_STATION_INFO.region_id; BIKESHARE_STATION_INFO.station_id; BIKESHARE_TRIPS.bike_number; BIKESHARE_TRIPS.duration_sec; BIKESHARE_TRIPS.end_station_name; BIKESHARE_TRIPS.member_birth_year; BIKESHARE_TRIPS.member_gender; BIKESHARE_TRIPS.start_date; BIKESHARE_TRIPS.start_station_id; BIKESHARE_TRIPS.start_station_name; BIKESHARE_TRIPS.subscriber_type; BIKESHARE_TRIPS.trip_id,14,snow_sql_near_exact,sf_bq294,278
sf_bq295,GITHUB_REPOS_DATE,snowflake,"Among the repositories from the GitHub Archive which include a Python file with less than 15,000 bytes in size and a keyword 'def' in the content, find the top 3 that have the highest number of watch events in 2017?","WITH watched_repos AS (
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201701
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201702
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201703
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201704
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201705
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201706
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201707
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201708
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201709
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201710
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201711
    WHERE
        ""type"" = 'WatchEvent'
    UNION ALL
    SELECT
        PARSE_JSON(""repo""):""name""::STRING AS ""repo""
    FROM 
        GITHUB_REPOS_DATE.MONTH._201712
    WHERE
        ""type"" = 'WatchEvent'
),

repo_watch_counts AS (
    SELECT
        ""repo"",
        COUNT(*) AS ""watch_count""
    FROM
        watched_repos
    GROUP BY
        ""repo""
)

SELECT
    REPLACE(r.""repo"", '""', '') AS ""repo"",
    r.""watch_count""
FROM
    GITHUB_REPOS_DATE.GITHUB_REPOS.SAMPLE_FILES AS f
JOIN
    GITHUB_REPOS_DATE.GITHUB_REPOS.SAMPLE_CONTENTS AS c
    ON f.""id"" = c.""id""
JOIN 
    repo_watch_counts AS r
    ON f.""repo_name"" = r.""repo""
WHERE
    f.""path"" LIKE '%.py' 
    AND c.""size"" < 15000 
    AND POSITION('def ' IN c.""content"") > 0
GROUP BY
    r.""repo"", r.""watch_count""
ORDER BY
    r.""watch_count"" DESC
LIMIT 
    3;",SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.sample_path; SAMPLE_CONTENTS.sample_repo_name; SAMPLE_CONTENTS.size; _20_*.repo; _20_*.type,6,snow_sql_near_exact,sf_bq295,43
bq300,stackoverflow,bigquery,"What is the highest number of answers received for a single Python 2 specific question on Stack Overflow, excluding any discussions that involve Python 3?","WITH
  python2_questions AS (
    SELECT
      q.id AS question_id,
      q.title,
      q.body AS question_body,
      q.tags
    FROM
      `bigquery-public-data.stackoverflow.posts_questions` q
    WHERE
      (LOWER(q.tags) LIKE '%python-2%'
      OR LOWER(q.tags) LIKE '%python-2.x%'
      OR (
        LOWER(q.title) LIKE '%python 2%'
        OR LOWER(q.body) LIKE '%python 2%'
        OR LOWER(q.title) LIKE '%python2%'
        OR LOWER(q.body) LIKE '%python2%'
      ))
      AND (
        LOWER(q.title) NOT LIKE '%python 3%'
        AND LOWER(q.body) NOT LIKE '%python 3%'
        AND LOWER(q.title) NOT LIKE '%python3%'
        AND LOWER(q.body) NOT LIKE '%python3%'
      )
  )

SELECT
  COUNT(*) AS count_number
FROM
  python2_questions q
LEFT JOIN
  `bigquery-public-data.stackoverflow.posts_answers` a
ON
  q.question_id = a.parent_id
GROUP BY q.question_id
ORDER BY count_number DESC
LIMIT 1",posts_answers.parent_id; posts_questions.body; posts_questions.id; posts_questions.tags; posts_questions.title,5,lite_sql,sf_bq300,228
bq301,stackoverflow,bigquery,"Retrieve details of accepted answers related to JavaScript security topics such as XSS, cross-site scripting, exploits, and cybersecurity, for questions posted in January 2016 on Stack Overflow. For each accepted answer, include the answer's ID, the answerer's reputation, score, and comment count, along with the associated question's tags, score, answer count, the asker's reputation, view count, and comment count.","SELECT
    answer.id AS a_id,
    (SELECT users.reputation FROM `bigquery-public-data.stackoverflow.users` users
        WHERE users.id = answer.owner_user_id) AS a_user_reputation,
    answer.score AS a_score,
    answer.comment_count AS answer_comment_count,
    questions.tags as q_tags,
    questions.score AS q_score,  
    questions.answer_count AS answer_count, 
    (SELECT users.reputation FROM `bigquery-public-data.stackoverflow.users` users
        WHERE users.id = questions.owner_user_id) AS q_user_reputation,
    questions.view_count AS q_view_count,
    questions.comment_count AS q_comment_count
FROM
   `bigquery-public-data.stackoverflow.posts_answers` AS answer 
LEFT JOIN
   `bigquery-public-data.stackoverflow.posts_questions` AS questions
      ON answer.parent_id = questions.id
WHERE
    answer.id = questions.accepted_answer_id
    AND 
    (
        questions.tags LIKE '%javascript%' AND
        (questions.tags LIKE '%xss%' OR
        questions.tags LIKE '%cross-site%' OR
        questions.tags LIKE '%exploit%' OR
        questions.tags LIKE '%cybersecurity%')
    )
    AND DATE(questions.creation_date) BETWEEN '2016-01-01' AND '2016-01-31'
    AND DATE(answer.creation_date) BETWEEN '2016-01-01' AND '2016-01-31'",posts_answers.comment_count; posts_answers.creation_date; posts_answers.id; posts_answers.owner_user_id; posts_answers.parent_id; posts_answers.score; posts_questions.accepted_answer_id; posts_questions.answer_count; posts_questions.comment_count; posts_questions.creation_date; posts_questions.id; posts_questions.owner_user_id; posts_questions.score; posts_questions.tags; posts_questions.view_count; users.id; users.reputation,17,lite_sql,sf_bq301,228
bq302,stackoverflow,bigquery,What is the monthly proportion of Stack Overflow questions tagged with 'python' in the year 2022?,"WITH
-- Get recent data
RecentData AS (
    SELECT
        FORMAT_TIMESTAMP('%Y%m', creation_date) AS month_index,
        tags
    FROM
        `bigquery-public-data.stackoverflow.posts_questions`
    WHERE
        EXTRACT(YEAR FROM DATE(creation_date)) = 2022
),

-- Monthly number of questions posted
MonthlyQuestions AS (
    SELECT
        month_index,
        COUNT(*) AS num_questions
    FROM
        RecentData
    GROUP BY
        month_index
),

-- Monthly number of questions posted with specific tags
TaggedQuestions AS (
    SELECT
        month_index,
        tag,
        COUNT(*) AS num_tags
    FROM
        RecentData,
        UNNEST(SPLIT(tags, '|')) AS tag
    WHERE
        tag IN ('python')
    GROUP BY
        month_index, tag
)

SELECT
    a.month_index,
    a.num_tags / b.num_questions AS proportion
FROM
    TaggedQuestions a
LEFT JOIN
    MonthlyQuestions b ON a.month_index = b.month_index
ORDER BY
    a.month_index, proportion DESC;",posts_questions.creation_date; posts_questions.tags,2,lite_sql,sf_bq302,228
bq303,stackoverflow,bigquery,"What are the user IDs and tags for comments, answers, and questions posted by users with IDs between 16712208 and 18712208 on Stack Overflow during July to December 2019?","SELECT u_id, tags
FROM (
    -- select comments with tags from the post
    SELECT cm.u_id, cm.creation_date, cm.text, pq.tags, ""comment"" as type
    FROM (
            SELECT a.parent_id as q_id, c.user_id as u_id, c.creation_date as creation_date, c.text as text
            FROM `bigquery-public-data.stackoverflow.comments` as c
            INNER JOIN `bigquery-public-data.stackoverflow.posts_answers` as a ON (a.id = c.post_id)
            WHERE c.user_id BETWEEN 16712208 AND 18712208
              AND DATE(c.creation_date) BETWEEN '2019-07-01' AND '2019-12-31'
            
            UNION ALL 
            
            SELECT q.id as q_id, c.user_id as u_id, c.creation_date as creation_date, c.text as text
            FROM `bigquery-public-data.stackoverflow.comments` as c
            INNER JOIN `bigquery-public-data.stackoverflow.posts_questions` as q ON (q.id = c.post_id)
            WHERE c.user_id BETWEEN 16712208 AND 18712208
              AND DATE(c.creation_date) BETWEEN '2019-07-01' AND '2019-12-31'
        ) as cm
    INNER JOIN `bigquery-public-data.stackoverflow.posts_questions` as pq ON (pq.id = cm.q_id)
        
    UNION ALL
    -- select answers with tags related to the post
    SELECT pa.owner_user_id as u_id, pa.creation_date as creation_date, pa.body as text, pq.tags as tags, ""answer"" as type
    FROM `bigquery-public-data.stackoverflow.posts_answers` as pa
    LEFT OUTER JOIN `bigquery-public-data.stackoverflow.posts_questions` as pq ON pq.id = pa.parent_id
    WHERE pa.owner_user_id BETWEEN 16712208 AND 18712208
      AND DATE(pa.creation_date) BETWEEN '2019-07-01' AND '2019-12-31'
    
    UNION ALL
    -- select posts
    SELECT pq.owner_user_id as u_id, pq.creation_date as creation_date, pq.body as text, pq.tags as tags, ""question"" as type
    FROM `bigquery-public-data.stackoverflow.posts_questions` as pq
    WHERE pq.owner_user_id BETWEEN 16712208 AND 18712208
      AND DATE(pq.creation_date) BETWEEN '2019-07-01' AND '2019-12-31'
)
ORDER BY u_id, creation_date;",comments.creation_date; comments.post_id; comments.text; comments.user_id; posts_answers.body; posts_answers.creation_date; posts_answers.id; posts_answers.owner_user_id; posts_answers.parent_id; posts_questions.body; posts_questions.creation_date; posts_questions.id; posts_questions.owner_user_id; posts_questions.tags,14,lite_sql,sf_bq303,228
bq304,stackoverflow,bigquery,"What are the top 50 most viewed 'how' questions for each of the following Android-related tags on StackOverflow: 'android-layout', 'android-activity', 'android-intent', 'android-edittext', 'android-fragments', 'android-recyclerview', 'listview', 'android-actionbar', 'google-maps', and 'android-asynctask'? Ensure that each tag has at least 50 questions and exclude any questions containing terms typically associated with troubleshooting, such as 'fail', 'problem', 'error', 'wrong', 'fix', 'bug', 'issue', 'solve', or 'trouble'.","WITH
tags_to_use AS (
    SELECT tag, idx
    FROM UNNEST([
        'android-layout', 
        'android-activity', 
        'android-intent', 
        'android-edittext', 
        'android-fragments', 
        'android-recyclerview', 
        'listview', 
        'android-actionbar', 
        'google-maps', 
        'android-asynctask'
    ]) AS tag WITH OFFSET idx
),
android_how_to_questions AS (
    SELECT
        PQ.*
    FROM
        bigquery-public-data.stackoverflow.posts_questions PQ
    WHERE
        EXISTS (
            SELECT 1
            FROM UNNEST(SPLIT(PQ.tags, '|')) tag
            WHERE tag IN (SELECT tag FROM tags_to_use)
        )
        AND (LOWER(PQ.title) LIKE '%how%' OR LOWER(PQ.body) LIKE '%how%')
        AND NOT (LOWER(PQ.title) LIKE '%fail%' OR LOWER(PQ.title) LIKE '%problem%' OR LOWER(PQ.title) LIKE '%error%'
                 OR LOWER(PQ.title) LIKE '%wrong%' OR LOWER(PQ.title) LIKE '%fix%' OR LOWER(PQ.title) LIKE '%bug%'
                 OR LOWER(PQ.title) LIKE '%issue%' OR LOWER(PQ.title) LIKE '%solve%' OR LOWER(PQ.title) LIKE '%trouble%')
        AND NOT (LOWER(PQ.body) LIKE '%fail%' OR LOWER(PQ.body) LIKE '%problem%' OR LOWER(PQ.body) LIKE '%error%'
                 OR LOWER(PQ.body) LIKE '%wrong%' OR LOWER(PQ.body) LIKE '%fix%' OR LOWER(PQ.body) LIKE '%bug%'
                 OR LOWER(PQ.body) LIKE '%issue%' OR LOWER(PQ.body) LIKE '%solve%' OR LOWER(PQ.body) LIKE '%trouble%')
),
questions_with_tag_rankings AS (
    SELECT
        T.id AS tag_id,
        TTU.idx AS tag_offset,
        T.tag_name,
        T.wiki_post_id AS tag_wiki_post_id,
        Q.id AS question_id,
        Q.title,
        Q.tags,
        Q.view_count,
        RANK() OVER (PARTITION BY T.id ORDER BY Q.view_count DESC) AS question_view_count_rank,
        COUNT(*) OVER (PARTITION BY T.id) AS total_valid_questions
    FROM
        bigquery-public-data.stackoverflow.tags T
    INNER JOIN
        tags_to_use TTU ON T.tag_name = TTU.tag
    INNER JOIN
        android_how_to_questions Q ON T.tag_name IN UNNEST(SPLIT(Q.tags, '|'))
)
SELECT
    question_id
FROM
    questions_with_tag_rankings
WHERE
    question_view_count_rank <= 50 AND total_valid_questions >= 50
ORDER BY
    tag_offset ASC, question_view_count_rank ASC;",posts_questions.body; posts_questions.id; posts_questions.tags; posts_questions.title; posts_questions.view_count; tags.id; tags.tag_name; tags.wiki_post_id,8,lite_sql,sf_bq304,228
bq308,stackoverflow,bigquery,"Show the number of Stack Overflow questions asked each day of the week in 2021, and find out how many and what percentage of those were answered within one hour.","SELECT
  Day_of_Week,
  COUNT(1) AS Num_Questions,
  SUM(answered_in_1h) AS Num_Answered_in_1H,
  ROUND(100 * SUM(answered_in_1h) / COUNT(1),1) AS Percent_Answered_in_1H
FROM
(
  SELECT
    q.id AS question_id,
    EXTRACT(DAYOFWEEK FROM q.creation_date) AS day_of_week,
    MAX(IF(a.parent_id IS NOT NULL AND
           (UNIX_SECONDS(a.creation_date)-UNIX_SECONDS(q.creation_date))/(60*60) <= 1, 1, 0)) AS answered_in_1h
  FROM
    `bigquery-public-data.stackoverflow.posts_questions` q
  LEFT JOIN
    `bigquery-public-data.stackoverflow.posts_answers` a
  ON q.id = a.parent_id
  WHERE EXTRACT(YEAR FROM a.creation_date) = 2020
    AND EXTRACT(YEAR FROM q.creation_date) = 2020
  GROUP BY question_id, day_of_week
)
GROUP BY
  Day_of_Week
ORDER BY
  Day_of_Week;",posts_answers.creation_date; posts_answers.parent_id; posts_questions.creation_date; posts_questions.id,4,lite_sql,sf_bq308,228
bq309,stackoverflow,bigquery,"Show the top 10 longest Stack Overflow questions where the question has an accepted answer or an answer with a score-to-view ratio above 0.01, including the user's reputation, net votes, and badge count.","WITH badge_counts AS (
  SELECT
    c.id,
    COUNT(DISTINCT d.id) AS badge_number
  FROM
    `bigquery-public-data.stackoverflow.users` AS c
  JOIN
    `bigquery-public-data.stackoverflow.badges` AS d
  ON
    c.id = d.user_id
  GROUP BY
    c.id
),
labeled_questions AS (
  SELECT
    a.id,
    IF(
      a.id IN (
        SELECT DISTINCT b.id
        FROM
          `bigquery-public-data.stackoverflow.posts_answers` AS a
        JOIN
          `bigquery-public-data.stackoverflow.posts_questions` AS b
        ON
          a.parent_id = b.id
        WHERE
          b.accepted_answer_id IS NULL
          AND a.score / b.view_count > 0.01
      ) OR accepted_answer_id IS NOT NULL,
      1,
      0
    ) AS label,
    a.owner_user_id,
    LENGTH(a.body) AS body_length
  FROM
    `bigquery-public-data.stackoverflow.posts_questions` AS a
)
SELECT
  lq.id,
  b.reputation,
  b.up_votes - b.down_votes AS net_votes,
  e.badge_number
FROM
  labeled_questions AS lq
JOIN
  `bigquery-public-data.stackoverflow.users` AS b
ON
  lq.owner_user_id = b.id
JOIN
  badge_counts AS e
ON
  b.id = e.id
WHERE
  lq.label = 1
ORDER BY
  lq.body_length DESC
LIMIT
  10;",badges.id; badges.user_id; posts_answers.parent_id; posts_answers.score; posts_questions.accepted_answer_id; posts_questions.body; posts_questions.id; posts_questions.owner_user_id; posts_questions.view_count; users.down_votes; users.id; users.reputation; users.up_votes,13,lite_sql,sf_bq309,228
bq310,stackoverflow,bigquery,"What is the title of the most viewed ""how"" question related to Android development on StackOverflow, across specified tags such as 'android-layout', 'android-activity', 'android-intent', and others","WITH
tags_to_use AS (
    SELECT tag, idx
    FROM UNNEST([
        'android-layout', 
        'android-activity', 
        'android-intent', 
        'android-edittext', 
        'android-fragments', 
        'android-recyclerview', 
        'listview', 
        'android-actionbar', 
        'google-maps', 
        'android-asynctask'
    ]) AS tag WITH OFFSET idx
),
android_how_to_questions AS (
    SELECT
        PQ.*
    FROM
        `bigquery-public-data.stackoverflow.posts_questions` PQ
    WHERE
        EXISTS (
            SELECT 1
            FROM UNNEST(SPLIT(PQ.tags, '|')) tag
            WHERE tag IN (SELECT tag FROM tags_to_use)
        )
        AND (LOWER(PQ.title) LIKE '%how%' OR LOWER(PQ.body) LIKE '%how%')
),
most_viewed_question AS (
    SELECT
        T.id AS tag_id,
        T.tag_name,
        Q.id AS question_id,
        Q.title,
        Q.tags,
        Q.view_count
    FROM
        `bigquery-public-data.stackoverflow.tags` T
    INNER JOIN
        tags_to_use TTU ON T.tag_name = TTU.tag
    INNER JOIN
        android_how_to_questions Q ON T.tag_name IN UNNEST(SPLIT(Q.tags, '|'))
    ORDER BY Q.view_count DESC
    LIMIT 1
)
SELECT
    title
FROM
    most_viewed_question;",posts_questions.body; posts_questions.id; posts_questions.tags; posts_questions.title; posts_questions.view_count; tags.id; tags.tag_name,7,lite_sql,sf_bq310,228
sf_bq320,IDC,snowflake,What is the total count of StudyInstanceUIDs that have a segmented property type of '15825003' and belong to the 'Community' or 'nsclc_radiomics' collections?,"SELECT
  COUNT(*) AS ""total_count""
FROM
  IDC.IDC_V17.DICOM_PIVOT AS ""dicom_pivot""
WHERE
  ""StudyInstanceUID"" IN (
    SELECT
      ""StudyInstanceUID""
    FROM
      IDC.IDC_V17.DICOM_PIVOT AS ""dicom_pivot""
    WHERE
      ""StudyInstanceUID"" IN (
        SELECT
          ""StudyInstanceUID""
        FROM
          IDC.IDC_V17.DICOM_PIVOT AS ""dicom_pivot""
        WHERE
          LOWER(""dicom_pivot"".""SegmentedPropertyTypeCodeSequence"") LIKE LOWER('15825003')
        GROUP BY
          ""StudyInstanceUID""
        INTERSECT
        SELECT
          ""StudyInstanceUID""
        FROM
          IDC.IDC_V17.DICOM_PIVOT AS ""dicom_pivot""
        WHERE
          ""dicom_pivot"".""collection_id"" IN ('Community', 'nsclc_radiomics')
        GROUP BY
          ""StudyInstanceUID""
      )
    GROUP BY
      ""StudyInstanceUID""
  );",DICOM_PIVOT.SegmentedPropertyTypeCodeSequence; DICOM_PIVOT.StudyInstanceUID; DICOM_PIVOT.collection_id,3,snow_sql_near_exact,sf_bq320,2100
sf_bq321,IDC,snowflake,"How many unique StudyInstanceUIDs are there from the DWI, T2 Weighted Axial, Apparent Diffusion Coefficient series, and T2 Weighted Axial Segmentations in the 'qin_prostate_repeatability' collection?","WITH relevant_series AS (
  SELECT 
    DISTINCT ""StudyInstanceUID""
  FROM 
    IDC.IDC_V17.DICOM_ALL
  WHERE 
    ""collection_id"" = 'qin_prostate_repeatability'
    AND ""SeriesDescription"" IN (
      'DWI',
      'T2 Weighted Axial',
      'Apparent Diffusion Coefficient',
      'T2 Weighted Axial Segmentations',
      'Apparent Diffusion Coefficient Segmentations'
    )    
),
t2_seg_lesion_series AS (
  SELECT 
    DISTINCT ""StudyInstanceUID""
  FROM 
    IDC.IDC_V17.DICOM_ALL
  CROSS JOIN LATERAL FLATTEN(input => ""SegmentSequence"") AS segSeq
  WHERE 
    ""collection_id"" = 'qin_prostate_repeatability'
    AND ""SeriesDescription"" = 'T2 Weighted Axial Segmentations'
)

SELECT 
    COUNT(DISTINCT ""StudyInstanceUID"") AS ""total_count""
FROM (
  SELECT 
    ""StudyInstanceUID"" 
  FROM relevant_series
  UNION ALL
  SELECT 
    ""StudyInstanceUID""
  FROM t2_seg_lesion_series
);",DICOM_PIVOT.SeriesDescription; DICOM_PIVOT.StudyInstanceUID; DICOM_PIVOT.collection_id,3,snow_sql_near_exact,sf_bq321,2100
bq327,world_bank,bigquery,"How many debt indicators for Russia have a value of 0, excluding NULL values?","WITH russia_Data AS (
  SELECT DISTINCT 
    id.country_name,
    id.value, -- Format in DataStudio
    id.indicator_name
  FROM (
    SELECT
      country_code,
      region
    FROM
      bigquery-public-data.world_bank_intl_debt.country_summary
    WHERE
      region != """" -- Aggregated countries do not have a region
  ) cs -- Aggregated countries do not have a region
  INNER JOIN (
    SELECT
      country_code,
      country_name,
      value, 
      indicator_name
    FROM
      bigquery-public-data.world_bank_intl_debt.international_debt
    WHERE
      country_code = 'RUS'
  ) id
  ON
    cs.country_code = id.country_code
  WHERE value IS NOT NULL
)
-- Count the number of indicators with a value of 0 for Russia
SELECT 
  COUNT(*) AS number_of_indicators_with_zero
FROM 
  russia_Data
WHERE 
  value = 0;",country_summary.country_code; country_summary.region; international_debt.country_code; international_debt.country_name; international_debt.indicator_name; international_debt.value,6,lite_sql,sf_bq327,153
bq328,world_bank,bigquery,Which region has the highest median GDP (constant 2015 US$) value?,"WITH country_data AS (
  -- CTE for country descriptive data
  SELECT 
    country_code, 
    short_name AS country,
    region, 
    income_group 
  FROM 
    `bigquery-public-data.world_bank_wdi.country_summary`
),

gdp_data AS (
  -- Filter data to only include GDP values
  SELECT 
    data.country_code, 
    country,
    region,
    value AS gdp_value
  FROM 
    `bigquery-public-data.world_bank_wdi.indicators_data` data
  LEFT JOIN country_data
    ON data.country_code = country_data.country_code
  WHERE indicator_code = ""NY.GDP.MKTP.KD"" -- GDP Indicator
    AND country_data.region IS NOT NULL
    AND country_data.income_group IS NOT NULL
),

cal_median_gdp AS (
  -- Calculate the median GDP value for each region
  SELECT 
    region,
    APPROX_QUANTILES(gdp_value, 2)[OFFSET(1)] AS median_gdp
  FROM gdp_data
  GROUP BY region
)
-- Select the regions with their median GDP values
SELECT 
  region
FROM 
  cal_median_gdp
ORDER BY median_gdp DESC
LIMIT 1;",country_summary.country_code; country_summary.income_group; country_summary.region; country_summary.short_name; indicators_data.country_code; indicators_data.indicator_code; indicators_data.value,7,lite_sql,sf_bq328,153
bq330,fda,bigquery,"Which Colorado zip code has the highest concentration of bank locations per block group, based on the overlap between zip codes and block groups?","WITH _fips AS (
    SELECT
        state_fips_code
    FROM
        `bigquery-public-data.census_utility.fips_codes_states`
    WHERE
        state_name = ""Colorado""
),

_bg AS (
    SELECT
        b.geo_id,
        b.blockgroup_geom,
        ST_AREA(b.blockgroup_geom) AS bg_size
    FROM
        `bigquery-public-data.geo_census_blockgroups.us_blockgroups_national` b
    JOIN
        _fips u ON b.state_fips_code = u.state_fips_code
),

_zip AS (
    SELECT
        z.zip_code,
        z.zip_code_geom
    FROM
        `bigquery-public-data.geo_us_boundaries.zip_codes` z
    JOIN
        _fips u ON z.state_fips_code = u.state_fips_code
),

bq_zip_overlap AS (
    SELECT
        b.geo_id,
        z.zip_code,
        ST_AREA(ST_INTERSECTION(b.blockgroup_geom, z.zip_code_geom)) / b.bg_size AS overlap_size,
        b.blockgroup_geom
    FROM
        _zip z
    JOIN
        _bg b ON ST_INTERSECTS(b.blockgroup_geom, z.zip_code_geom)
),

locations AS (
    SELECT
        SUM(overlap_size * count_locations) AS locations_per_bg,
        l.zip_code
    FROM (
        SELECT
            COUNT(CONCAT(institution_name, "" : "", branch_name)) AS count_locations,
            zip_code
        FROM
            `bigquery-public-data.fdic_banks.locations`
        WHERE
            state IS NOT NULL
            AND state_name IS NOT NULL
        GROUP BY
            zip_code
    ) l
    JOIN
        bq_zip_overlap ON l.zip_code = bq_zip_overlap.zip_code
    GROUP BY
        l.zip_code
)

SELECT
    l.zip_code
FROM
    locations l
GROUP BY
    l.zip_code
ORDER BY
    MAX(locations_per_bg) DESC
LIMIT 1;",fips_codes_states.state_fips_code; fips_codes_states.state_name; locations.state_name; us_blockgroups_national.blockgroup_geom; us_blockgroups_national.geo_id; us_blockgroups_national.state_fips_code; zip_codes.state_fips_code; zip_codes.zip_code; zip_codes.zip_code_geom,9,lite_sql,sf_bq330,417
sf_bq334,CRYPTO,snowflake,"In my Bitcoin database, there are discrepancies in transaction records. Can you determine the annual differences in average output values calculated from separate input and output records versus a consolidated transactions table, focusing only on the years common to both calculation methods?","WITH all_transactions AS (
    SELECT 
        TO_TIMESTAMP_NTZ(""block_timestamp"" / 1000000) AS ""timestamp"",  -- 将时间戳转换为日期时间格式
        ""value"",
        'input' AS ""type""
    FROM 
        ""CRYPTO"".""CRYPTO_BITCOIN"".""INPUTS""
    UNION ALL
    SELECT 
        TO_TIMESTAMP_NTZ(""block_timestamp"" / 1000000) AS ""timestamp"",  -- 将时间戳转换为日期时间格式
        ""value"",
        'output' AS ""type""
    FROM 
        ""CRYPTO"".""CRYPTO_BITCOIN"".""OUTPUTS""
),
filtered_transactions AS (
    SELECT
        EXTRACT(YEAR FROM ""timestamp"") AS ""year"",
        ""value""
    FROM 
        all_transactions
    WHERE ""type"" = 'output'
),
average_output_values AS (
    SELECT
        ""year"",
        AVG(""value"") AS ""avg_value""
    FROM 
        filtered_transactions
    GROUP BY ""year""
),
average_transaction_values AS (
    SELECT 
        EXTRACT(YEAR FROM TO_TIMESTAMP_NTZ(""block_timestamp"" / 1000000)) AS ""year"",  -- 同样转换时间戳
        AVG(""output_value"") AS ""avg_transaction_value"" 
    FROM 
        ""CRYPTO"".""CRYPTO_BITCOIN"".""TRANSACTIONS"" 
    GROUP BY ""year"" 
    ORDER BY ""year""
),
common_years AS (
    SELECT
        ao.""year"",
        ao.""avg_value"" AS ""avg_output_value"",
        atv.""avg_transaction_value""
    FROM
        average_output_values ao
    JOIN
        average_transaction_values atv 
        ON ao.""year"" = atv.""year""
)

SELECT
    ""year"",
    ""avg_transaction_value"" - ""avg_output_value"" AS ""difference""
FROM
    common_years
ORDER BY
    ""year"";",INPUTS.block_timestamp; INPUTS.value; OUTPUTS.block_timestamp; OUTPUTS.value; TRANSACTIONS.block_timestamp; TRANSACTIONS.output_value,6,snow_sql_near_exact,sf_bq334,286
bq338,census_bureau_acs_1,bigquery,"Can you find the census tracts in the 36047 area that made both the top 20 lists for biggest population and median income increases from 2011 to 2018, and had over 1000 residents each year?","WITH population_change AS (
    SELECT
        a.geo_id,
        a.total_pop AS pop_2011,
        b.total_pop AS pop_2018,
        ((b.total_pop - a.total_pop) / a.total_pop) * 100 AS population_change_percentage
    FROM
        bigquery-public-data.census_bureau_acs.censustract_2011_5yr a
    JOIN
        bigquery-public-data.census_bureau_acs.censustract_2018_5yr b
    ON
        a.geo_id = b.geo_id
    WHERE 
        a.total_pop > 1000
        AND b.total_pop > 1000
        AND a.geo_id LIKE '36047%'
        AND b.geo_id LIKE '36047%'
    ORDER BY 
        population_change_percentage DESC
    LIMIT 20
),

acs_2018 AS (
    SELECT 
        geo_id, 
        median_income AS median_income_2018
    FROM 
        bigquery-public-data.census_bureau_acs.censustract_2018_5yr  
    WHERE 
        geo_id LIKE '36047%'
        AND total_pop > 1000
),

acs_2011 AS (
    SELECT 
        geo_id, 
        median_income AS median_income_2011
    FROM 
        bigquery-public-data.census_bureau_acs.censustract_2011_5yr 
    WHERE 
        geo_id LIKE '36047%'
    AND total_pop > 1000
),

acs_diff AS (
    SELECT
        a18.geo_id, 
        a18.median_income_2018, 
        a11.median_income_2011,
        (a18.median_income_2018 - a11.median_income_2011) AS median_income_diff
    FROM 
        acs_2018 a18
    JOIN 
        acs_2011 a11
        ON a18.geo_id = a11.geo_id
    WHERE 
        (a18.median_income_2018 - a11.median_income_2011) IS NOT NULL
    ORDER BY 
        (a18.median_income_2018 - a11.median_income_2011) DESC
    LIMIT 20
),

common_geoids AS (
    SELECT population_change.geo_id
    FROM population_change
    JOIN acs_diff ON population_change.geo_id = acs_diff.geo_id
)

SELECT geo_id FROM common_geoids;",censustract_*.geo_id; censustract_*.median_income; censustract_*.total_pop,3,lite_sql,sf_bq338,4373
bq339,san_francisco_plus,bigquery,Which month in 2017 had the largest absolute difference between cumulative bike usage minutes for customers and subscribers?,"WITH monthly_totals AS (
  SELECT
    SUM(CASE WHEN subscriber_type = 'Customer' THEN duration_sec / 60 ELSE NULL END) AS customer_minutes_sum,
    SUM(CASE WHEN subscriber_type = 'Subscriber' THEN duration_sec / 60 ELSE NULL END) AS subscriber_minutes_sum,
    EXTRACT(MONTH FROM end_date) AS end_month
  FROM
    `bigquery-public-data.san_francisco_bikeshare.bikeshare_trips`
  WHERE
    EXTRACT(YEAR FROM end_date) = 2017
  GROUP BY
    end_month
),

cumulative_totals AS (
  SELECT
    end_month,
    SUM(customer_minutes_sum) OVER (ORDER BY end_month ROWS UNBOUNDED PRECEDING) / 1000 AS cumulative_minutes_cust,
    SUM(subscriber_minutes_sum) OVER (ORDER BY end_month ROWS UNBOUNDED PRECEDING) / 1000 AS cumulative_minutes_sub
  FROM
    monthly_totals
),

differences AS (
  SELECT
    end_month,
    ABS(cumulative_minutes_cust - cumulative_minutes_sub) AS abs_diff
  FROM
    cumulative_totals
)

SELECT
  end_month
FROM
  differences
ORDER BY
  abs_diff DESC
LIMIT 1;",bikeshare_trips.duration_sec; bikeshare_trips.end_date; bikeshare_trips.subscriber_type,3,lite_sql,sf_bq339,278
sf_bq341,CRYPTO,snowflake,"Which Ethereum address has the top 3 smallest positive balance from transactions involving the token at address ""0xa92a861fc11b99b24296af880011b47f9cafb5ab""?","WITH transaction_addresses AS (
    SELECT 
        ""from_address"", 
        ""to_address"", 
        CAST(""value"" AS NUMERIC) / 1000000 AS ""value""
    FROM 
        ""CRYPTO"".""CRYPTO_ETHEREUM"".""TOKEN_TRANSFERS""
    WHERE 
        ""token_address"" = '0xa92a861fc11b99b24296af880011b47f9cafb5ab'
),

out_addresses AS (
    SELECT 
        ""from_address"", 
        SUM(-1 * ""value"") AS ""total_value""
    FROM 
        transaction_addresses
    GROUP BY 
        ""from_address""
),

in_addresses AS (
    SELECT 
        ""to_address"", 
        SUM(""value"") AS ""total_value""
    FROM 
        transaction_addresses
    GROUP BY 
        ""to_address""
),

all_addresses AS (
    SELECT 
        ""from_address"" AS ""address"", 
        ""total_value""
    FROM 
        out_addresses

    UNION ALL

    SELECT 
        ""to_address"" AS ""address"", 
        ""total_value""
    FROM 
        in_addresses
)

SELECT 
    ""address""
FROM 
    all_addresses
GROUP BY 
    ""address""
HAVING 
    SUM(""total_value"") > 0
ORDER BY 
    SUM(""total_value"") ASC
LIMIT 3;",TOKEN_TRANSFERS.from_address; TOKEN_TRANSFERS.to_address; TOKEN_TRANSFERS.token_address; TOKEN_TRANSFERS.value,4,snow_sql_near_exact,sf_bq341,286
sf_bq345,IDC,snowflake,"How large are the DICOM image files with SEG or RTSTRUCT modalities and the SOP Class UID ""1.2.840.10008.5.1.4.1.1.66.4"", when grouped by collection, study, and series IDs, if they have no references to other series, images, or sources? Can you also provide a viewer URL formatted as ""https://viewer.imaging.datacommons.cancer.gov/viewer/"" followed by the study ID, and list these sizes in kilobytes, sorted from largest to smallest?","WITH seg_rtstruct AS (
  SELECT
    ""collection_id"",
    ""StudyInstanceUID"",
    ""SeriesInstanceUID"",
    CONCAT('https://viewer.imaging.datacommons.cancer.gov/viewer/', ""StudyInstanceUID"") AS ""viewer_url"",
    ""instance_size""
  FROM
    ""IDC"".""IDC_V17"".""DICOM_ALL""
  WHERE
    ""Modality"" IN ('SEG', 'RTSTRUCT')
    AND ""SOPClassUID"" = '1.2.840.10008.5.1.4.1.1.66.4'
    AND ARRAY_SIZE(""ReferencedSeriesSequence"") = 0
    AND ARRAY_SIZE(""ReferencedImageSequence"") = 0
    AND ARRAY_SIZE(""SourceImageSequence"") = 0
)

SELECT
  seg_rtstruct.""collection_id"",
  seg_rtstruct.""SeriesInstanceUID"",
  seg_rtstruct.""StudyInstanceUID"",
  seg_rtstruct.""viewer_url"",
  SUM(seg_rtstruct.""instance_size"") / 1024 AS ""collection_size_KB""
FROM
  seg_rtstruct
GROUP BY
  seg_rtstruct.""collection_id"",
  seg_rtstruct.""SeriesInstanceUID"",
  seg_rtstruct.""StudyInstanceUID"",
  seg_rtstruct.""viewer_url""
ORDER BY
  ""collection_size_KB"" DESC;",DICOM_ALL.Modality; DICOM_ALL.ReferencedImageSequence; DICOM_ALL.ReferencedSeriesSequence; DICOM_ALL.SOPClassUID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SourceImageSequence; DICOM_ALL.StudyInstanceUID; DICOM_ALL.collection_id; DICOM_ALL.instance_size,9,snow_sql_near_exact,sf_bq345,2100
sf_bq346,IDC,snowflake,"Which five segmentation categories appear most frequently in publicly accessible DICOM SEG data, where the modality is ""SEG"" and the SOPClassUID is ""1.2.840.10008.5.1.4.1.1.66.4""?","WITH
  sampled_sops AS (
    SELECT
      ""collection_id"",
      ""SeriesDescription"",
      ""SeriesInstanceUID"",
      ""SOPInstanceUID"" AS ""seg_SOPInstanceUID"",
      COALESCE(
        ""ReferencedSeriesSequence""[0].""ReferencedInstanceSequence""[0].""ReferencedSOPInstanceUID"",
        ""ReferencedImageSequence""[0].""ReferencedSOPInstanceUID"",
        ""SourceImageSequence""[0].""ReferencedSOPInstanceUID""
      ) AS ""referenced_sop""
    FROM
      ""IDC"".""IDC_V17"".""DICOM_ALL""
    WHERE
      ""Modality"" = 'SEG'
      AND ""SOPClassUID"" = '1.2.840.10008.5.1.4.1.1.66.4'
      AND ""access"" = 'Public'
  ),
  segmentations_data AS (
    SELECT
      dicom_all.""collection_id"",
      dicom_all.""PatientID"",
      dicom_all.""SOPInstanceUID"",
      REPLACE(segmentations.""SegmentedPropertyCategory"":CodeMeaning::STRING, '""', '') AS ""segmentation_category"",
      REPLACE(segmentations.""SegmentedPropertyType"":CodeMeaning::STRING, '""', '') AS ""segmentation_type""
    FROM
      sampled_sops
    JOIN
      ""IDC"".""IDC_V17"".""DICOM_ALL"" AS dicom_all
    ON
      sampled_sops.""referenced_sop"" = dicom_all.""SOPInstanceUID""
    JOIN
      ""IDC"".""IDC_V17"".""SEGMENTATIONS"" AS segmentations
    ON
      segmentations.""SOPInstanceUID"" = sampled_sops.""seg_SOPInstanceUID""
  )
SELECT
  ""segmentation_category"",
  COUNT(*) AS ""count_""
FROM
  segmentations_data
GROUP BY
  ""segmentation_category""
ORDER BY
  ""count_"" DESC
LIMIT 5;",DICOM_ALL.Modality; DICOM_ALL.ReferencedSeriesSequence; DICOM_ALL.SOPClassUID; DICOM_ALL.SOPInstanceUID; DICOM_ALL.SeriesDescription; DICOM_ALL.SeriesInstanceUID; SEGMENTATIONS.SOPInstanceUID; SEGMENTATIONS.SegmentedPropertyCategory; SEGMENTATIONS.SegmentedPropertyType,9,lite_sql,sf_bq346,2100
sf_bq347,IDC,snowflake,"Which modality has the highest count of SOP instances, including MR series with SeriesInstanceUID = ""1.3.6.1.4.1.14519.5.2.1.3671.4754.105976129314091491952445656147"" and all associated segmentation data, along with the total count of instances?","WITH union_mr_seg AS (
  SELECT
    ""dicom_all_mr"".""SOPInstanceUID"",
    '' AS ""segPropertyTypeCodeMeaning"", 
    '' AS ""segPropertyCategoryCodeMeaning""
  FROM
    ""IDC"".""IDC_V17"".""DICOM_ALL"" AS ""dicom_all_mr""
  WHERE
    ""dicom_all_mr"".""SeriesInstanceUID"" IN ('1.3.6.1.4.1.14519.5.2.1.3671.4754.105976129314091491952445656147')
    
  UNION ALL

  SELECT
    ""dicom_all_seg"".""SOPInstanceUID"",
    ""segmentations"".""SegmentedPropertyType"":""CodeMeaning"" AS ""segPropertyTypeCodeMeaning"",
    ""segmentations"".""SegmentedPropertyCategory"":""CodeMeaning"" AS ""segPropertyCategoryCodeMeaning""
  FROM
    ""IDC"".""IDC_V17"".""DICOM_ALL"" AS ""dicom_all_seg""
  JOIN
    ""IDC"".""IDC_V17"".""SEGMENTATIONS"" AS ""segmentations""
  ON
    ""dicom_all_seg"".""SOPInstanceUID"" = ""segmentations"".""SOPInstanceUID""
)

SELECT
  ""dc_all"".""Modality"",
  COUNT(*) AS ""count_""
FROM 
  ""IDC"".""IDC_V17"".""DICOM_ALL"" AS ""dc_all""
INNER JOIN
  union_mr_seg
ON 
  ""dc_all"".""SOPInstanceUID"" = union_mr_seg.""SOPInstanceUID""
GROUP BY
  ""dc_all"".""Modality""
ORDER BY
  ""count_"" DESC
LIMIT 1;",DICOM_ALL.Modality; DICOM_ALL.SeriesInstanceUID; SEGMENTATIONS.SeriesInstanceUID; SEGMENTATIONS.segmented_SeriesInstanceUID,4,snow_sql_near_exact,sf_bq347,2100
sf_bq349,GEO_OPENSTREETMAP,snowflake,"Which OpenStreetMap ID from the planet features table corresponds to an administrative boundary, represented as multipolygons, whose total number of 'amenity'-tagged Points of Interest (POIs), as derived from the planet nodes table, is closest to the median count among all such boundaries?","WITH bounding_area AS (
    SELECT 
        ""osm_id"",
        ""geometry"" AS geometry,
        ST_AREA(ST_GEOGRAPHYFROMWKB(""geometry"")) AS area
    FROM GEO_OPENSTREETMAP.GEO_OPENSTREETMAP.PLANET_FEATURES,
    LATERAL FLATTEN(INPUT => PLANET_FEATURES.""all_tags"") AS ""tag""
    WHERE 
        ""feature_type"" = 'multipolygons'
        AND ""tag"".value:""key"" = 'boundary'
        AND ""tag"".value:""value"" = 'administrative'
),

poi AS (
    SELECT 
        nodes.""id"" AS poi_id,
        nodes.""geometry"" AS poi_geometry,
        tags.value:""value"" AS poitype
    FROM GEO_OPENSTREETMAP.GEO_OPENSTREETMAP.PLANET_NODES AS nodes,
    LATERAL FLATTEN(INPUT => nodes.""all_tags"") AS tags
    WHERE tags.value:""key"" = 'amenity'
),

poi_counts AS (
    SELECT
        ba.""osm_id"",
        COUNT(poi.poi_id) AS total_pois
    FROM bounding_area ba
    JOIN poi
    ON ST_DWITHIN(
        ST_GEOGRAPHYFROMWKB(ba.geometry), 
        ST_GEOGRAPHYFROMWKB(poi.poi_geometry), 
        0.0
    )
    GROUP BY ba.""osm_id""
),

median_value AS (
    SELECT 
        PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY total_pois) AS median_pois
    FROM poi_counts
),

closest_to_median AS (
    SELECT
        ""osm_id"",
        total_pois,
        ABS(total_pois - (SELECT median_pois FROM median_value)) AS diff_from_median
    FROM poi_counts
)

SELECT
    ""osm_id""
FROM closest_to_median
ORDER BY diff_from_median
LIMIT 1;",PLANET_FEATURES.all_tags; PLANET_FEATURES.feature_type; PLANET_FEATURES.geometry; PLANET_FEATURES.osm_id; PLANET_NODES.all_tags; PLANET_NODES.geometry; PLANET_NODES.id,7,lite_sql,sf_bq349,86
bq350,open_targets_platform_1,bigquery,"For the detailed molecule data, Please display the drug id, drug type and withdrawal status for approved drugs with a black box warning and known drug type among 'Keytruda', 'Vioxx', 'Premarin', and 'Humira'","DECLARE
  my_drug_list ARRAY<STRING>;
SET
  my_drug_list = [
  'Keytruda',
  'Vioxx',
  'Humira',
  'Premarin' ];

SELECT
  id AS drug_id,
  tradeNameList.element AS drug_trade_name,
  drugType AS drug_type,
  hasBeenWithdrawn AS drug_withdrawn
FROM
  `open-targets-prod.platform.molecule`,
  UNNEST (tradeNames.list) AS tradeNameList
WHERE
  tradeNameList.element IN UNNEST(my_drug_list)
  AND isApproved = TRUE
  AND blackBoxWarning = TRUE
  AND drugType != 'Unknown';",molecule.blackBoxWarning; molecule.drugType; molecule.hasBeenWithdrawn; molecule.id; molecule.isApproved; molecule.tradeNames,6,lite_sql,sf_bq350,332
bq352,sdoh,bigquery,Please list the average number of prenatal weeks in 2018 for counties in Wisconsin where more than 5% of the employed population had commutes of 45-59 minutes in 2017.,"WITH natality_2018 AS (
  SELECT County_of_Residence_FIPS AS FIPS, Ave_Number_of_Prenatal_Wks AS Vist_Ave, County_of_Residence
  FROM `bigquery-public-data.sdoh_cdc_wonder_natality.county_natality` 
  WHERE SUBSTR(County_of_Residence_FIPS, 0, 2) = ""55"" AND Year = '2018-01-01'
),

acs_2017 AS (
  SELECT geo_id, commute_45_59_mins, employed_pop
  FROM `bigquery-public-data.census_bureau_acs.county_2017_5yr`
),

corr_tbl AS (
  SELECT
    n.County_of_Residence,
    ROUND((a.commute_45_59_mins / a.employed_pop) * 100, 2) AS percent_high_travel,
    n.Vist_Ave
  FROM acs_2017 a
  JOIN natality_2018 n
  ON a.geo_id = n.FIPS
)

SELECT County_of_Residence, Vist_Ave
FROM corr_tbl
WHERE percent_high_travel > 5",county_*.commute_45_59_mins; county_*.employed_pop; county_*.geo_id; county_natality.Ave_Number_of_Prenatal_Wks; county_natality.County_of_Residence; county_natality.County_of_Residence_FIPS; county_natality.Year,7,lite_sql,sf_bq352,4068
bq354,cms_data,bigquery,"Could you provide the percentage of participants for standard acne, atopic dermatitis, psoriasis, and vitiligo defined by the International Classification of Diseases 10-CM(ICD-10-CM), including their subcategories? The ICD-10 codes are: Acne (L70), Atopic dermatitis (L20), Psoriasis (L40), and Vitiligo (L80). ","WITH skin_condition_ICD_concept_ids AS (
    SELECT
        concept_id,
        CASE concept_code
            WHEN 'L70' THEN 'Acne'
            WHEN 'L20' THEN 'Atopic dermatitis'
            WHEN 'L40' THEN 'Psoriasis'
            ELSE 'Vitiligo'
        END AS skin_condition
    FROM
        `bigquery-public-data.cms_synthetic_patient_data_omop.concept`
    WHERE
        concept_code IN ('L70', 'L20', 'L40', 'L80')
        AND vocabulary_id = 'ICD10CM'
),
standard_concept_ids AS (
    SELECT
        concept_id
    FROM
        `bigquery-public-data.cms_synthetic_patient_data_omop.concept`
    WHERE
        standard_concept = 'S'
),
skin_condition_standard_concept_ids AS (
    SELECT
        s.skin_condition,
        r.concept_id_2 AS concept_id
    FROM
        skin_condition_ICD_concept_ids s
    JOIN
        `bigquery-public-data.cms_synthetic_patient_data_omop.concept_relationship` r
    ON
        s.concept_id = r.concept_id_1
    JOIN
        standard_concept_ids sc
    ON
        sc.concept_id = r.concept_id_2
    WHERE
        r.relationship_id = 'Maps to'
),
all_skin_concept_ids AS (
    SELECT DISTINCT
        skin_condition,
        concept_id
    FROM
        skin_condition_standard_concept_ids
),
descendant_concept_ids AS (
    SELECT
        a.skin_condition,
        ca.descendant_concept_id AS concept_id
    FROM
        all_skin_concept_ids a
    JOIN
        `bigquery-public-data.cms_synthetic_patient_data_omop.concept_ancestor` ca
    ON
        a.concept_id = ca.ancestor_concept_id
),
participants_with_condition AS (
    SELECT
        d.skin_condition,
        COUNT(DISTINCT co.person_id) AS nb_of_participants_with_skin_condition
    FROM
        `bigquery-public-data.cms_synthetic_patient_data_omop.condition_occurrence` co
    JOIN
        descendant_concept_ids d
    ON
        co.condition_concept_id = d.concept_id
    GROUP BY
        d.skin_condition
),
total_participants AS (
    SELECT
        COUNT(DISTINCT person_id) AS nb_of_participants
    FROM
        `bigquery-public-data.cms_synthetic_patient_data_omop.person`
)
SELECT
    p.skin_condition,
    100 * p.nb_of_participants_with_skin_condition / t.nb_of_participants AS percentage_of_participants
FROM
    participants_with_condition p,
    total_participants t",concept.concept_code; concept.concept_id; concept.standard_concept; concept.vocabulary_id; concept_ancestor.ancestor_concept_id; concept_ancestor.descendant_concept_id; concept_relationship.concept_id_1; concept_relationship.concept_id_2; concept_relationship.relationship_id; condition_occurrence.condition_concept_id; condition_occurrence.person_id; person.person_id,12,lite_sql,sf_bq354,649
bq355,cms_data,bigquery,Please tell me the percentage of participants not using quinapril and related medications(Quinapril RxCUI: 35208).,"WITH quinapril_concept AS (
    SELECT concept_id
    FROM `bigquery-public-data.cms_synthetic_patient_data_omop.concept`
    WHERE concept_code = ""35208"" AND vocabulary_id = ""RxNorm""
),
quinapril_related_medications AS (
    SELECT DISTINCT descendant_concept_id AS concept_id
    FROM `bigquery-public-data.cms_synthetic_patient_data_omop.concept_ancestor`
    WHERE ancestor_concept_id IN (SELECT concept_id FROM quinapril_concept)
),
participants_with_quinapril AS (
    SELECT COUNT(DISTINCT person_id) AS count
    FROM `bigquery-public-data.cms_synthetic_patient_data_omop.drug_exposure`
    WHERE drug_concept_id IN (SELECT concept_id FROM quinapril_related_medications)
),
total_participants AS (
    SELECT COUNT(DISTINCT person_id) AS count
    FROM `bigquery-public-data.cms_synthetic_patient_data_omop.person`
)
SELECT
    100 - (100 * participants_with_quinapril.count / total_participants.count) AS without_quinapril
FROM
    participants_with_quinapril, total_participants",concept.concept_code; concept.concept_id; concept.vocabulary_id; concept_ancestor.ancestor_concept_id; concept_ancestor.descendant_concept_id; drug_exposure.drug_concept_id; drug_exposure.person_id; person.person_id,8,lite_sql,sf_bq355,649
bq357,noaa_data,bigquery,"What are the latitude and longitude coordinates and dates between 2005 and 2015 with the top 5 highest daily average wind speeds, excluding records with missing wind speed values? Using data from tables start with prefix ""icoads_core"".","WITH DailyAverages AS (
    SELECT 
        year, month, day, latitude, longitude,
        AVG(wind_speed) AS avg_wind_speed,
    FROM 
        `bigquery-public-data.noaa_icoads.icoads_core_*`
    WHERE
        _TABLE_SUFFIX BETWEEN '2005' AND '2015'
    GROUP BY 
        year, month, day, latitude, longitude
)
SELECT 
    year, month, day, latitude, longitude,
    avg_wind_speed,

FROM 
    DailyAverages
WHERE
    avg_wind_speed IS NOT NULL

ORDER BY avg_wind_speed DESC LIMIT 5",icoads_core_*.day; icoads_core_*.latitude; icoads_core_*.longitude; icoads_core_*.month; icoads_core_*.wind_speed; icoads_core_*.year,6,lite_sql,sf_bq357,660
sf_bq358,NEW_YORK_CITIBIKE_1,snowflake,"Can you tell me which bike trip in New York City on July 15, 2015, started and ended in ZIP Code areas with the highest average temperature for that day, as recorded by the Central Park weather station '94728'? If there's more than one trip that meets these criteria, I'd like to know about the one that starts in the smallest ZIP Code and ends in the largest ZIP Code.","SELECT
    ""ZIPSTART"".""zip_code"" AS zip_code_start,
    ""ZIPEND"".""zip_code"" AS zip_code_end
FROM  
    ""NEW_YORK_CITIBIKE_1"".""NEW_YORK_CITIBIKE"".""CITIBIKE_TRIPS"" AS ""TRI""
INNER JOIN
    ""NEW_YORK_CITIBIKE_1"".""GEO_US_BOUNDARIES"".""ZIP_CODES"" AS ""ZIPSTART""
    ON ST_WITHIN(
        ST_POINT(""TRI"".""start_station_longitude"", ""TRI"".""start_station_latitude""),
        ST_GEOGFROMWKB(""ZIPSTART"".""zip_code_geom"")
    )
INNER JOIN
    ""NEW_YORK_CITIBIKE_1"".""GEO_US_BOUNDARIES"".""ZIP_CODES"" AS ""ZIPEND""
    ON ST_WITHIN(
        ST_POINT(""TRI"".""end_station_longitude"", ""TRI"".""end_station_latitude""),
        ST_GEOGFROMWKB(""ZIPEND"".""zip_code_geom"")
    )
INNER JOIN
    ""NEW_YORK_CITIBIKE_1"".""NOAA_GSOD"".""GSOD2015"" AS ""WEA""
    ON TO_DATE(TO_CHAR(""WEA"".""year"") || LPAD(TO_CHAR(""WEA"".""mo""), 2, '0') || LPAD(TO_CHAR(""WEA"".""da""), 2, '0'), 'YYYYMMDD') = DATE_TRUNC('DAY', TO_TIMESTAMP_NTZ(TO_NUMBER(""TRI"".""starttime"") / 1000000))
WHERE
    ""WEA"".""wban"" = '94728'
    AND DATE_TRUNC('DAY', TO_TIMESTAMP_NTZ(TO_NUMBER(""TRI"".""starttime"") / 1000000)) = DATE '2015-07-15'
ORDER BY 
    ""WEA"".""temp"" DESC, ""ZIPSTART"".""zip_code"" ASC, ""ZIPEND"".""zip_code"" DESC
LIMIT 1;",CITIBIKE_TRIPS.end_station_latitude; CITIBIKE_TRIPS.end_station_longitude; CITIBIKE_TRIPS.start_station_latitude; CITIBIKE_TRIPS.start_station_longitude; CITIBIKE_TRIPS.starttime; ZIP_CODES.zip_code; ZIP_CODES.zip_code_geom,7,lite_sql,sf_bq358,245
sf_bq359,GITHUB_REPOS,snowflake,List the repository names and commit counts for the top two GitHub repositories with JavaScript as the primary language and the highest number of commits.,"WITH repositories AS (
    SELECT
        t2.""repo_name"",
        t2.""language""
    FROM (
        SELECT
            t1.""repo_name"",
            t1.""language"",
            RANK() OVER (PARTITION BY t1.""repo_name"" ORDER BY t1.""language_bytes"" DESC) AS ""rank""
        FROM (
            SELECT
                l.""repo_name"",
                lang.value:""name""::STRING AS ""language"",
                lang.value:""bytes""::NUMBER AS ""language_bytes""
            FROM
                GITHUB_REPOS.GITHUB_REPOS.LANGUAGES AS l,
                LATERAL FLATTEN(input => l.""language"") AS lang
        ) AS t1
    ) AS t2
    WHERE t2.""rank"" = 1
),
python_repo AS (
    SELECT
        ""repo_name"",
        ""language""
    FROM
        repositories
    WHERE
        ""language"" = 'JavaScript'
)
SELECT 
    sc.""repo_name"", 
    COUNT(sc.""commit"") AS ""num_commits""
FROM 
    GITHUB_REPOS.GITHUB_REPOS.SAMPLE_COMMITS AS sc
INNER JOIN 
    python_repo 
ON 
    python_repo.""repo_name"" = sc.""repo_name""
GROUP BY 
    sc.""repo_name""
ORDER BY 
    ""num_commits"" DESC
LIMIT 2;",LANGUAGES.language; LANGUAGES.repo_name; SAMPLE_COMMITS.commit; SAMPLE_COMMITS.repo_name,4,snow_sql_near_exact,sf_bq359,34
bq360,nppes,bigquery,"Which of the top 10 most common healthcare provider specializations in Mountain View, CA, has a specialist count closest to the average of these ten specializations?","WITH specialist_counts AS (
  SELECT
    healthcare_provider_taxonomy_1_specialization,
    COUNT(DISTINCT npi) AS number_specialist
  FROM
    `bigquery-public-data.nppes.npi_optimized`
  WHERE
    provider_business_practice_location_address_city_name = ""MOUNTAIN VIEW""
    AND provider_business_practice_location_address_state_name = ""CA""
    AND healthcare_provider_taxonomy_1_specialization > """"
  GROUP BY
    healthcare_provider_taxonomy_1_specialization
),
top_10_specialists AS (
  SELECT
    healthcare_provider_taxonomy_1_specialization,
    number_specialist
  FROM
    specialist_counts
  ORDER BY
    number_specialist DESC
  LIMIT 10
),
average_value AS (
  SELECT
    AVG(number_specialist) AS average_specialist
  FROM
    top_10_specialists
),
closest_to_average AS (
  SELECT
    healthcare_provider_taxonomy_1_specialization,
    number_specialist,
    ABS(number_specialist - (SELECT average_specialist FROM average_value)) AS difference
  FROM
    top_10_specialists
)
SELECT
  healthcare_provider_taxonomy_1_specialization
FROM
  closest_to_average
ORDER BY
  difference
LIMIT 1;",npi_optimized.healthcare_provider_taxonomy_1_specialization; npi_optimized.npi; npi_optimized.provider_business_practice_location_address_city_name; npi_optimized.provider_business_practice_location_address_state_name,4,lite_sql,sf_bq360,822
bq362,chicago,bigquery,Which three companies had the largest increase in trip numbers between two consecutive months in 2018?,"select company from
            (select *,
            row_number() over(partition by company order by month_o_month_calc desc) as rownum
            from
            (select *,
            num_trips - lag(num_trips) over(partition by company order by month) as month_o_month_calc
                from
                (SELECT 
                company,
                format_date(""%Y-%m"", date_sub((cast(trip_start_timestamp as date)), interval 1 month)) as prev_month,
                format_date(""%Y-%m"", cast(trip_start_timestamp as date)) AS month,
                count(1) AS num_trips
                from `bigquery-public-data.chicago_taxi_trips.taxi_trips`
                where extract(YEAR from trip_start_timestamp) = 2018
                group by company, month, prev_month
                order by company,month)
            order by company, month_o_month_calc desc)
            ) 
        where rownum = 1
        order by month_o_month_calc desc, company 
        limit 3",taxi_trips.company; taxi_trips.trip_start_timestamp,2,lite_sql,sf_bq362,45
bq363,chicago,bigquery,"For taxi trips with a duration rounded to the nearest minute, and between 1 and 50 minutes, if the trip durations are divided into 10 quantiles, what are the total number of trips and the average fare for each quantile?","SELECT
  FORMAT('%02.0fm to %02.0fm', min_minutes, max_minutes) AS minutes_range,
  SUM(trips) AS total_trips,
  FORMAT('%3.2f', SUM(total_fare) / SUM(trips)) AS average_fare
FROM (
  SELECT
    MIN(duration_in_minutes) OVER (quantiles) AS min_minutes,
    MAX(duration_in_minutes) OVER (quantiles) AS max_minutes,
    SUM(trips) AS trips,
    SUM(total_fare) AS total_fare
  FROM (
    SELECT
      ROUND(trip_seconds / 60) AS duration_in_minutes,
      NTILE(10) OVER (ORDER BY trip_seconds / 60) AS quantile,
      COUNT(1) AS trips,
      SUM(fare) AS total_fare
    FROM
      `bigquery-public-data.chicago_taxi_trips.taxi_trips`
    WHERE
      ROUND(trip_seconds / 60) BETWEEN 1 AND 50
    GROUP BY
      trip_seconds,
      duration_in_minutes )
  GROUP BY
    duration_in_minutes,
    quantile
  WINDOW quantiles AS (PARTITION BY quantile)
  )
GROUP BY
  minutes_range
ORDER BY
  Minutes_range",taxi_trips.fare; taxi_trips.trip_seconds,2,lite_sql,sf_bq363,45
bq366,the_met,bigquery,"What are the top three most frequently associated labels with artworks from each historical period in The Met's collection, only considering labels linked to 50 or more artworks? Provide me with the period, label, and the associated count.","SELECT period, description, c FROM (
  SELECT 
a.period, 
b.description, 
count(*) c, 
row_number() over (partition by period order by count(*) desc) seqnum 
  FROM `bigquery-public-data.the_met.objects` a
  JOIN (
    SELECT 
        label.description as description, 
        object_id 
    FROM `bigquery-public-data.the_met.vision_api_data`, UNNEST(labelAnnotations) label
  ) b
  ON a.object_id = b.object_id
  WHERE a.period is not null
  group by 1,2
)
WHERE seqnum <= 3
AND c >= 500 # only include labels that have 50 or more pieces associated with it
ORDER BY period, c desc;",objects.object_id; objects.period; vision_api_data.labelAnnotations; vision_api_data.object_id,4,lite_sql,sf_bq366,61
bq374,ga360,bigquery,"Calculates the percentage of new users who, between August 1, 2016, and April 30, 2017, both stayed on the site for more than 5 minutes during their initial visit and made a purchase on a subsequent visit at any later time, relative to the total number of new users in the same period.","WITH initial_visits AS (
    SELECT
        fullVisitorId,
        MIN(visitStartTime) AS initialVisitStartTime
    FROM
        `bigquery-public-data.google_analytics_sample.*`
    WHERE
        totals.newVisits = 1
        AND date BETWEEN '20160801' AND '20170430'
    GROUP BY
        fullVisitorId
),
qualified_initial_visits AS (
  SELECT
    s.fullVisitorId,
    s.visitStartTime AS initialVisitStartTime,
    s.totals.timeOnSite AS time_on_site
  FROM
    `bigquery-public-data.google_analytics_sample.*` s
  JOIN initial_visits i
    ON s.fullVisitorId = i.fullVisitorId
    AND s.visitStartTime = i.initialVisitStartTime
  WHERE
    s.totals.timeOnSite > 300
),
filtered_data AS (
  SELECT
    q.fullVisitorId,
    q.time_on_site,
    IF(COUNTIF(s.visitStartTime > q.initialVisitStartTime AND s.totals.transactions > 0) > 0, 1, 0) AS will_buy_on_return_visit
  FROM
    qualified_initial_visits q
  LEFT JOIN `bigquery-public-data.google_analytics_sample.*` s
    ON q.fullVisitorId = s.fullVisitorId
  GROUP BY
    q.fullVisitorId, q.time_on_site
),
matching_users AS (
  SELECT
    fullVisitorId
  FROM
    filtered_data
  WHERE
    time_on_site > 300 AND will_buy_on_return_visit = 1
),
total_new_users AS (
  SELECT
    COUNT(DISTINCT fullVisitorId) AS total_new_users
  FROM
    `bigquery-public-data.google_analytics_sample.*`
  WHERE
    totals.newVisits = 1
    AND date BETWEEN '20160801' AND '20170430'
),
final_counts AS (
  SELECT
    COUNT(DISTINCT fullVisitorId) AS users_matching_criteria
  FROM
    matching_users
)
SELECT
  (final_counts.users_matching_criteria / total_new_users.total_new_users) * 100 AS percentage_matching_criteria
FROM
  final_counts,
  total_new_users;",ga_sessions_*.date; ga_sessions_*.fullVisitorId; ga_sessions_*.totals; ga_sessions_*.visitStartTime,4,lite_sql,sf_bq374,16
bq376,san_francisco_plus,bigquery,"For each neighborhood in San Francisco, list the number of bike share stations and the total number of crime incidents.","WITH station_neighborhoods AS (
   SELECT
       bs.station_id,
       bs.name AS station_name,
       nb.neighborhood
   FROM `bigquery-public-data.san_francisco.bikeshare_stations` bs
   JOIN
       bigquery-public-data.san_francisco_neighborhoods.boundaries nb
   ON 
       ST_Intersects(ST_GeogPoint(bs.longitude, bs.latitude), nb.neighborhood_geom)
),

neighborhood_crime_counts AS (
   SELECT
       neighborhood,
       COUNT(*) AS crime_count
   FROM (
       SELECT
           n.neighborhood
       FROM
           bigquery-public-data.san_francisco.sfpd_incidents i
       JOIN
           bigquery-public-data.san_francisco_neighborhoods.boundaries n
       ON
           ST_Intersects(ST_GeogPoint(i.longitude, i.latitude), n.neighborhood_geom)
   ) AS incident_neighborhoods
   GROUP BY
       neighborhood
)

SELECT
  sn.neighborhood,
  COUNT(station_name) AS station_number,
  ANY_VALUE(ncc.crime_count) AS crime_number
FROM
  station_neighborhoods sn
JOIN
  neighborhood_crime_counts ncc
ON
  sn.neighborhood = ncc.neighborhood
GROUP BY sn.neighborhood
ORDER BY
  crime_number ASC",boundaries.neighborhood; boundaries.neighborhood_geom; sfpd_incidents.latitude; sfpd_incidents.longitude,4,lite_sql,sf_bq376,278
sf_bq377,GITHUB_REPOS,snowflake,Extract and count the frequency of all package names listed in the require section of JSON-formatted content,"WITH json_files AS (
  SELECT
    c.""id"",
    TRY_PARSE_JSON(c.""content""):""require"" AS ""dependencies""
  FROM
    GITHUB_REPOS.GITHUB_REPOS.SAMPLE_CONTENTS c
),
package_names AS (
  SELECT
    f.key AS ""package_name""
  FROM
    json_files,
    LATERAL FLATTEN(input => ""dependencies"") AS f
)
SELECT
  ""package_name"",
  COUNT(*) AS ""count""
FROM
  package_names
WHERE
  ""package_name"" IS NOT NULL
GROUP BY
  ""package_name""
ORDER BY
  ""count"" DESC;",SAMPLE_CONTENTS.content; SAMPLE_CONTENTS.id,2,lite_sql,sf_bq377,34
bq379,open_targets_platform_1,bigquery,Which target approved symbol has the overall association score closest to the mean score for psoriasis?,"WITH AvgScore AS (
  SELECT
    AVG(associations.score) AS avg_score
  FROM
    `open-targets-prod.platform.associationByOverallDirect` AS associations
  JOIN
    `open-targets-prod.platform.diseases` AS diseases
  ON
    associations.diseaseId = diseases.id
  WHERE
    diseases.name = 'psoriasis'
)
SELECT
  targets.approvedSymbol AS target_approved_symbol
FROM
  `open-targets-prod.platform.associationByOverallDirect` AS associations
JOIN
  `open-targets-prod.platform.diseases` AS diseases
ON
  associations.diseaseId = diseases.id
JOIN
  `open-targets-prod.platform.targets` AS targets
ON
  associations.targetId = targets.id
CROSS JOIN
  AvgScore
WHERE
  diseases.name = 'psoriasis'
ORDER BY
  ABS(associations.score - AvgScore.avg_score) ASC
LIMIT 1",associationByOverallDirect.diseaseId; associationByOverallDirect.score; associationByOverallDirect.targetId; diseases.id; diseases.name; targets.approvedSymbol; targets.id,7,lite_sql,sf_bq379,332
bq383,ghcn_d,bigquery,"Could you provide the highest recorded precipitation, minimum temperature, and maximum temperature from the last 15 days of each year from 2013 to 2016 at weather station USW00094846? Ensure each value represents the peak measurement for that period, with precipitation in millimeters and temperatures in degrees Celsius, including only valid and high-quality data.","WITH data AS (
  SELECT
    EXTRACT(YEAR FROM wx.date) AS year,
    MAX(IF(wx.element = 'PRCP', wx.value/10, NULL)) AS max_prcp,
    MAX(IF(wx.element = 'TMIN', wx.value/10, NULL)) AS max_tmin,
    MAX(IF(wx.element = 'TMAX', wx.value/10, NULL)) AS max_tmax
  FROM
    `bigquery-public-data.ghcn_d.ghcnd_2013` AS wx
  WHERE
    wx.id = 'USW00094846' AND
    wx.qflag IS NULL AND
    wx.value IS NOT NULL AND
    DATE_DIFF(DATE('2013-12-31'), wx.date, DAY) < 15
  GROUP BY
    year

  UNION ALL

  SELECT
    EXTRACT(YEAR FROM wx.date) AS year,
    MAX(IF(wx.element = 'PRCP', wx.value/10, NULL)) AS max_prcp,
    MAX(IF(wx.element = 'TMIN', wx.value/10, NULL)) AS max_tmin,
    MAX(IF(wx.element = 'TMAX', wx.value/10, NULL)) AS max_tmax
  FROM
    `bigquery-public-data.ghcn_d.ghcnd_2014` AS wx
  WHERE
    wx.id = 'USW00094846' AND
    wx.qflag IS NULL AND
    wx.value IS NOT NULL AND
    DATE_DIFF(DATE('2014-12-31'), wx.date, DAY) < 15
  GROUP BY
    year

  UNION ALL

  SELECT
    EXTRACT(YEAR FROM wx.date) AS year,
    MAX(IF(wx.element = 'PRCP', wx.value/10, NULL)) AS max_prcp,
    MAX(IF(wx.element = 'TMIN', wx.value/10, NULL)) AS max_tmin,
    MAX(IF(wx.element = 'TMAX', wx.value/10, NULL)) AS max_tmax
  FROM
    `bigquery-public-data.ghcn_d.ghcnd_2015` AS wx
  WHERE
    wx.id = 'USW00094846' AND
    wx.qflag IS NULL AND
    wx.value IS NOT NULL AND
    DATE_DIFF(DATE('2015-12-31'), wx.date, DAY) < 15
  GROUP BY
    year

  UNION ALL

  SELECT
    EXTRACT(YEAR FROM wx.date) AS year,
    MAX(IF(wx.element = 'PRCP', wx.value/10, NULL)) AS max_prcp,
    MAX(IF(wx.element = 'TMIN', wx.value/10, NULL)) AS max_tmin,
    MAX(IF(wx.element = 'TMAX', wx.value/10, NULL)) AS max_tmax
  FROM
    `bigquery-public-data.ghcn_d.ghcnd_2016` AS wx
  WHERE
    wx.id = 'USW00094846' AND
    wx.qflag IS NULL AND
    wx.value IS NOT NULL AND
    DATE_DIFF(DATE('2016-12-31'), wx.date, DAY) < 15
  GROUP BY
    year
)

SELECT
  year,
  MAX(max_prcp) AS annual_max_prcp,
  MAX(max_tmin) AS annual_max_tmin,
  MAX(max_tmax) AS annual_max_tmax
FROM data
GROUP BY year
ORDER BY year ASC;",ghcnd_*.date; ghcnd_*.element; ghcnd_*.id; ghcnd_*.qflag; ghcnd_*.value,5,lite_sql,sf_bq383,37
bq389,epa_historical_air_quality,bigquery,"Please calculate the monthly average levels of PM10, PM2.5 FRM, PM2.5 non-FRM, volatile organic emissions, SO2 (scaled by a factor of 10), and Lead (scaled by a factor of 100) air pollutants in California for the year 2020.","SELECT
  pm10.month AS month,
  pm10.avg AS pm10,
  pm25_frm.avg AS pm25_frm,
  pm25_nonfrm.avg AS pm25_nonfrm,
  co.avg AS co,
  so2.avg AS so2,
  lead.avg AS lead
FROM
  (SELECT AVG(arithmetic_mean) AS avg, 
          EXTRACT(YEAR FROM date_local) AS year, 
          EXTRACT(MONTH FROM date_local) AS month
   FROM `bigquery-public-data.epa_historical_air_quality.pm10_daily_summary`
   WHERE state_name = 'California' AND EXTRACT(YEAR FROM date_local) = 2020
   GROUP BY year, month) AS pm10
JOIN
  (SELECT AVG(arithmetic_mean) AS avg, 
          EXTRACT(YEAR FROM date_local) AS year, 
          EXTRACT(MONTH FROM date_local) AS month
   FROM `bigquery-public-data.epa_historical_air_quality.pm25_frm_daily_summary`
   WHERE state_name = 'California' AND EXTRACT(YEAR FROM date_local) = 2020
   GROUP BY year, month) AS pm25_frm
ON pm10.year = pm25_frm.year AND pm10.month = pm25_frm.month
JOIN
  (SELECT AVG(arithmetic_mean) AS avg, 
          EXTRACT(YEAR FROM date_local) AS year, 
          EXTRACT(MONTH FROM date_local) AS month
   FROM `bigquery-public-data.epa_historical_air_quality.pm25_nonfrm_daily_summary`
   WHERE state_name = 'California' AND EXTRACT(YEAR FROM date_local) = 2020
   GROUP BY year, month) AS pm25_nonfrm
ON pm10.year = pm25_nonfrm.year AND pm10.month = pm25_nonfrm.month
JOIN
  (SELECT AVG(arithmetic_mean) * 100 AS avg, 
          EXTRACT(YEAR FROM date_local) AS year, 
          EXTRACT(MONTH FROM date_local) AS month
   FROM `bigquery-public-data.epa_historical_air_quality.lead_daily_summary`
   WHERE state_name = 'California' AND EXTRACT(YEAR FROM date_local) = 2020
   GROUP BY year, month) AS lead
ON pm10.year = lead.year AND pm10.month = lead.month
JOIN
  (SELECT AVG(arithmetic_mean) AS avg, 
          EXTRACT(YEAR FROM date_local) AS year, 
          EXTRACT(MONTH FROM date_local) AS month
   FROM `bigquery-public-data.epa_historical_air_quality.voc_daily_summary`
   WHERE state_name = 'California' AND EXTRACT(YEAR FROM date_local) = 2020
   GROUP BY year, month) AS co
ON pm10.year = co.year AND pm10.month = co.month
JOIN
  (SELECT AVG(arithmetic_mean) * 10 AS avg, 
          EXTRACT(YEAR FROM date_local) AS year, 
          EXTRACT(MONTH FROM date_local) AS month
   FROM `bigquery-public-data.epa_historical_air_quality.so2_daily_summary`
   WHERE state_name = 'California' AND EXTRACT(YEAR FROM date_local) = 2020
   GROUP BY year, month) AS so2
ON pm10.year = so2.year AND pm10.month = so2.month
ORDER BY
  month;",lead_daily_summary.arithmetic_mean; lead_daily_summary.date_local; lead_daily_summary.state_name; pm10_daily_summary.arithmetic_mean; pm10_daily_summary.date_local; pm10_daily_summary.state_name; pm25_frm_daily_summary.arithmetic_mean; pm25_frm_daily_summary.date_local; pm25_frm_daily_summary.state_name; pm25_nonfrm_daily_summary.arithmetic_mean; pm25_nonfrm_daily_summary.date_local; pm25_nonfrm_daily_summary.state_name; so2_daily_summary.arithmetic_mean; so2_daily_summary.date_local; so2_daily_summary.state_name; voc_daily_summary.arithmetic_mean; voc_daily_summary.date_local; voc_daily_summary.state_name,18,lite_sql,sf_bq389,879
sf_bq390,IDC,snowflake,"Please provide the study instance UIDs for studies that include both T2-weighted axial magnetic resonance imaging and anatomical structure segmentations of the peripheral zone, in prostate repeatability collection.","WITH
-- Studies that have MR volumes
""mr_studies"" AS (
  SELECT
    ""dicom_all_mr"".""StudyInstanceUID""
  FROM
    ""IDC"".""IDC_V17"".""DICOM_ALL"" AS ""dicom_all_mr""
  WHERE
    ""Modality"" = 'MR'
    AND ""collection_id"" = 'qin_prostate_repeatability'
    AND CONTAINS(""SeriesDescription"", 'T2 Weighted Axial')
),

""seg_studies"" AS (
  SELECT
    ""dicom_all_seg"".""StudyInstanceUID""
  FROM
    ""IDC"".""IDC_V17"".""DICOM_ALL"" AS ""dicom_all_seg""
  JOIN
    ""IDC"".""IDC_V17"".""SEGMENTATIONS"" AS ""segmentations""
  ON
    ""dicom_all_seg"".""SOPInstanceUID"" = ""segmentations"".""SOPInstanceUID""
  WHERE
    ""collection_id"" = 'qin_prostate_repeatability'
    AND CONTAINS(""segmentations"".""SegmentedPropertyType"":""CodeMeaning"", 'Peripheral zone')
    AND ""segmentations"".""SegmentedPropertyCategory"":""CodeMeaning"" = 'Anatomical Structure'
)

SELECT DISTINCT
  ""mr_studies"".""StudyInstanceUID""
FROM
  ""mr_studies""
JOIN
  ""seg_studies""
ON
  ""mr_studies"".""StudyInstanceUID"" = ""seg_studies"".""StudyInstanceUID"";",DICOM_ALL.Modality; DICOM_ALL.SOPInstanceUID; DICOM_ALL.StudyInstanceUID; SEGMENTATIONS.SOPInstanceUID,4,lite_sql,sf_bq390,2100
bq392,noaa_gsod,bigquery,"What are the top 3 dates in October 2009 with the highest average temperature for station number 723758, in the format YYYY-MM-DD?","WITH
  # FIRST CAST EACH YEAR, MONTH, DATE TO STRINGS
  T AS (
    SELECT
      *,
      CAST(year AS STRING) AS year_string,
      CAST(mo AS STRING) AS month_string,
      CAST(da AS STRING) AS day_string
    FROM
      `bigquery-public-data.noaa_gsod.gsod2009`
    WHERE
      stn = ""723758""
  ),

  # SECOND, CONCAT ALL THE STRINGS TOGETHER INTO ONE COLUMN
  TT AS (
    SELECT
      *,
      CONCAT(year_string, ""-"", month_string, ""-"", day_string) AS date_string
    FROM
      T
  ),

  # THIRD, CAST THE DATE STRING INTO A DATE FORMAT
  TTT AS (
    SELECT
      *,
      CAST(date_string AS DATE) AS date_date
    FROM
      TT
  ),

  # FOURTH, CALCULATE THE MEAN TEMPERATURE FOR EACH DATE
  Temp_Avg AS (
    SELECT
      date_date,
      AVG(temp) AS avg_temp
    FROM
      TTT
    WHERE
      date_date BETWEEN '2009-10-01' AND '2009-10-31'
    GROUP BY
      date_date
  )

# FINAL SELECTION OF TOP 3 DATES WITH HIGHEST MEAN TEMPERATURE
SELECT
  date_date AS dates
FROM
  Temp_Avg
ORDER BY
  avg_temp DESC
LIMIT 3;",gsod_*.da; gsod_*.mo; gsod_*.stn; gsod_*.temp; gsod_*.year,5,lite_sql,sf_bq392,44
bq394,noaa_data,bigquery,"What are the top 3 months between 2010 and 2014 with the smallest sum of absolute differences between the average air temperature, wet bulb temperature, dew point temperature, and sea surface temperature, including respective years and sum of differences? Please present the year and month in numerical format.","WITH DailyAverages AS (
    SELECT 
        year, month, day,
        air_temperature,
        wetbulb_temperature,
        dewpoint_temperature,
        sea_surface_temp
    FROM 
        `bigquery-public-data.noaa_icoads.icoads_core_*`
    WHERE
        _TABLE_SUFFIX BETWEEN '2010' AND '2014'
),

MonthlyAverages AS (
    SELECT 
        year,
        month,
        AVG(air_temperature) AS avg_air_temperature,
        AVG(wetbulb_temperature) AS avg_wetbulb_temperature,
        AVG(dewpoint_temperature) AS avg_dewpoint_temperature,
        AVG(sea_surface_temp) AS avg_sea_surface_temp
    FROM 
        DailyAverages
    WHERE
        air_temperature IS NOT NULL
        AND wetbulb_temperature IS NOT NULL
        AND dewpoint_temperature IS NOT NULL
        AND sea_surface_temp IS NOT NULL
    GROUP BY 
        year, month
),

DifferenceSums AS (
    SELECT
        year,
        month,
        (ABS(avg_air_temperature - avg_wetbulb_temperature) +
        ABS(avg_air_temperature - avg_dewpoint_temperature) +
        ABS(avg_air_temperature - avg_sea_surface_temp) +
        ABS(avg_wetbulb_temperature - avg_dewpoint_temperature) +
        ABS(avg_wetbulb_temperature - avg_sea_surface_temp) +
        ABS(avg_dewpoint_temperature - avg_sea_surface_temp)) AS sum_of_differences
    FROM 
        MonthlyAverages
)

SELECT
    year,
    month,
    sum_of_differences
FROM
    DifferenceSums
ORDER BY
    sum_of_differences ASC
LIMIT 3;",icoads_core_*.air_temperature; icoads_core_*.day; icoads_core_*.dewpoint_temperature; icoads_core_*.month; icoads_core_*.sea_surface_temp; icoads_core_*.wetbulb_temperature; icoads_core_*.year,7,lite_sql,sf_bq394,660
bq395,sdoh,bigquery,Which 5 states' percentage change in unsheltered homeless individuals from 2015 to 2018 were top 5 closest to the national average? Please provide the state abbreviation.,"WITH homeless_2015 AS (
  SELECT Unsheltered_Homeless AS U15, SUBSTR(CoC_Number, 0, 2) as State_Abbr
  FROM `bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc`
  WHERE Count_Year = 2015
),
 
homeless_2018 AS (
  SELECT Unsheltered_Homeless AS U18, SUBSTR(CoC_Number, 0, 2) as State_Abbr
  FROM `bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc`
  WHERE Count_Year = 2018
),

unsheltered_change AS (
  SELECT homeless_2018.State_Abbr, 
         SUM(U15) AS Unsheltered_2015, 
         SUM(U18) AS Unsheltered_2018, 
         (SUM(U18) - SUM(U15)) / SUM(U15) * 100 AS Percent_Change
  FROM homeless_2018
  JOIN homeless_2015
  ON homeless_2018.State_Abbr = homeless_2015.State_Abbr
  GROUP BY State_Abbr
),

average_change AS (
  SELECT AVG(Percent_Change) AS Avg_Change
  FROM unsheltered_change
),

closest_to_avg AS (
  SELECT State_Abbr
  FROM unsheltered_change, average_change
  ORDER BY ABS(Percent_Change - Avg_Change)
  LIMIT 5
)

SELECT State_Abbr FROM closest_to_avg;",hud_pit_by_coc.CoC_Number; hud_pit_by_coc.Count_Year; hud_pit_by_coc.Unsheltered_Homeless,3,lite_sql,sf_bq395,4068
bq396,nhtsa_traffic_fatalities,bigquery,Which top 3 states had the largest differences in the number of traffic accidents between rainy and clear weather during weekends in 2016? Please also provide the respective differences for each state.,"WITH weekend_accidents AS (
    SELECT
        state_name,
        CASE
            WHEN atmospheric_conditions_1_name = 'Rain' THEN 'Rain'
            WHEN atmospheric_conditions_1_name = 'Clear' THEN 'Clear'
            ELSE 'Other'
        END AS Weather_Condition,
        COUNT(DISTINCT consecutive_number) AS num_accidents
    FROM
        `bigquery-public-data.nhtsa_traffic_fatalities.accident_2016`
    WHERE
        EXTRACT(DAYOFWEEK FROM timestamp_of_crash) IN (1, 7)  -- 1 = Sunday, 7 = Saturday
        AND atmospheric_conditions_1_name IN ('Rain', 'Clear')
    GROUP BY
        state_name, Weather_Condition
),

weather_difference AS (
    SELECT
        state_name,
        MAX(CASE WHEN Weather_Condition = 'Rain' THEN num_accidents ELSE 0 END) AS Rain_Accidents,
        MAX(CASE WHEN Weather_Condition = 'Clear' THEN num_accidents ELSE 0 END) AS Clear_Accidents,
        ABS(MAX(CASE WHEN Weather_Condition = 'Rain' THEN num_accidents ELSE 0 END) -
            MAX(CASE WHEN Weather_Condition = 'Clear' THEN num_accidents ELSE 0 END)) AS Difference
    FROM
        weekend_accidents
    GROUP BY
        state_name
)

SELECT
    state_name,
    Difference
FROM
    weather_difference
ORDER BY
    Difference DESC
LIMIT 3;",accident_*.atmospheric_conditions_1_name; accident_*.consecutive_number; accident_*.state_name; accident_*.timestamp_of_crash,4,lite_sql,sf_bq396,687
bq397,ecommerce,bigquery,"Identify the country with the highest total transactions within each channel grouping, provided that the channel includes transactions from more than one country. What is the transaction total for that country?","WITH tmp AS (
  SELECT DISTINCT *
  FROM `data-to-insights.ecommerce.rev_transactions`
  -- Removing duplicated values
),
tmp1 AS (
  SELECT 
    tmp.channelGrouping,
    tmp.geoNetwork_country,
    SUM(tmp.totals_transactions) AS tt
  FROM tmp
  GROUP BY 1, 2
),
tmp2 AS (
  SELECT 
    channelGrouping,
    geoNetwork_country,
    SUM(tt) AS TotalTransaction,
    COUNT(DISTINCT geoNetwork_country) OVER (PARTITION BY channelGrouping) AS CountryCount
  FROM tmp1
  GROUP BY channelGrouping, geoNetwork_country
),
tmp3 AS (
  SELECT
    channelGrouping,
    geoNetwork_country AS Country,
    TotalTransaction,
    RANK() OVER (PARTITION BY channelGrouping ORDER BY TotalTransaction DESC) AS rnk
  FROM tmp2
  WHERE CountryCount > 1
)
SELECT
  channelGrouping,
  Country,
  TotalTransaction
FROM tmp3
WHERE rnk = 1;",rev_transactions.channelGrouping; rev_transactions.geoNetwork_country; rev_transactions.totals_transactions,3,lite_sql,sf_bq397,154
bq398,world_bank,bigquery,What are the top three debt indicators for Russia based on the highest debt values?,"WITH russia_Data as (
SELECT distinct 
  id.country_name,
  id.value, --format in DataStudio
  id.indicator_name
FROM (
  SELECT
    country_code,
    region
  FROM
    bigquery-public-data.world_bank_intl_debt.country_summary
  WHERE
    region != """" ) cs --aggregated countries do not have a region
INNER JOIN (
  SELECT
    country_code,
    country_name,
    value, 
    indicator_name
  FROM
    bigquery-public-data.world_bank_intl_debt.international_debt
  WHERE true
    and country_code = 'RUS'  
     ) id
ON
  cs.country_code = id.country_code
WHERE value is not null
ORDER BY
  id.value DESC
)
SELECT 
    indicator_name
FROM russia_data
LIMIT 3;",country_summary.country_code; country_summary.region; international_debt.country_code; international_debt.country_name; international_debt.indicator_name; international_debt.value,6,lite_sql,sf_bq398,153
bq399,world_bank,bigquery,"Which high-income country had the highest average crude birth rate respectively in each region, and what are their corresponding average birth rate, during the 1980s?","WITH country_data AS ( 
  SELECT 
    country_code, 
    short_name AS country,
    region, 
    income_group 
  FROM 
    bigquery-public-data.world_bank_wdi.country_summary
)
, birth_rate_data AS (
  SELECT 
    data.country_code, 
    country_data.country,
    country_data.region,
    AVG(value) AS avg_birth_rate
  FROM 
    bigquery-public-data.world_bank_wdi.indicators_data data 
  LEFT JOIN 
    country_data 
  ON 
    data.country_code = country_data.country_code
  WHERE 
    indicator_code = ""SP.DYN.CBRT.IN"" -- Birth Rate
    AND EXTRACT(YEAR FROM PARSE_DATE('%Y', CAST(year AS STRING))) BETWEEN 1980 AND 1989 -- 1980s
    AND country_data.income_group = ""High income"" -- High-income group
  GROUP BY 
    data.country_code, 
    country_data.country,
    country_data.region
)
, ranked_birth_rates AS (
  SELECT
    region,
    country,
    avg_birth_rate,
    RANK() OVER(PARTITION BY region ORDER BY avg_birth_rate DESC) AS rank
  FROM
    birth_rate_data
)
SELECT 
  region, 
  country, 
  avg_birth_rate
FROM 
  ranked_birth_rates
WHERE 
  rank = 1
ORDER BY 
  region;",country_summary.country_code; country_summary.income_group; country_summary.region; country_summary.short_name; indicators_data.country_code; indicators_data.indicator_code; indicators_data.value; indicators_data.year,8,lite_sql,sf_bq399,153
bq400,san_francisco_plus,bigquery,"What are the start and end times of trips from 'Clay St & Drumm St' to 'Sacramento St & Davis St' (one direction only), in the format of HH:MM:SS? I also want the trip headsign for each route.","WITH SelectedStops AS (
  SELECT 
      stop_id,
      stop_name
  FROM 
      `bigquery-public-data.san_francisco_transit_muni.stops`
  WHERE 
      stop_name IN ('Clay St & Drumm St', 'Sacramento St & Davis St')
),
FilteredStopTimes AS (
  SELECT 
      st.trip_id, 
      st.stop_id, 
      st.arrival_time, 
      st.departure_time, 
      st.stop_sequence, 
      ss.stop_name
  FROM 
      `bigquery-public-data.san_francisco_transit_muni.stop_times` st
  JOIN 
      SelectedStops ss ON CAST(st.stop_id AS STRING) = ss.stop_id
)
SELECT
    t.trip_headsign,
    MIN(st1.departure_time) AS start_time,
    MAX(st2.arrival_time) AS end_time
FROM 
    `bigquery-public-data.san_francisco_transit_muni.trips` t
JOIN FilteredStopTimes st1 ON t.trip_id = CAST(st1.trip_id AS STRING) AND st1.stop_name = 'Clay St & Drumm St'
JOIN FilteredStopTimes st2 ON t.trip_id = CAST(st2.trip_id AS STRING) AND st2.stop_name = 'Sacramento St & Davis St'
WHERE 
    st1.stop_sequence < st2.stop_sequence
GROUP BY 
    t.trip_headsign;",stop_times.arrival_time; stop_times.departure_time; stop_times.stop_id; stop_times.stop_sequence; stop_times.trip_id; stops.stop_id; stops.stop_name; trips.trip_headsign; trips.trip_id,9,lite_sql,sf_bq400,278
bq402,ecommerce,bigquery,"What is the conversion rate from unique visitors to purchasers, where purchasers are defined as visitors with at least one transaction? Additionally, what is the average number of transactions per purchaser?","WITH visitors AS (
  SELECT
    COUNT(DISTINCT fullVisitorId) AS total_visitors
  FROM 
    `data-to-insights.ecommerce.web_analytics`
),

purchasers AS (
  SELECT
    COUNT(DISTINCT fullVisitorId) AS total_purchasers
  FROM 
    `data-to-insights.ecommerce.web_analytics`
  WHERE 
    totals.transactions IS NOT NULL
),

transactions AS (
  SELECT
    COUNT(*) AS total_transactions,
    AVG(totals.transactions) AS avg_transactions_per_purchaser
  FROM 
    `data-to-insights.ecommerce.web_analytics`
  WHERE 
    totals.transactions IS NOT NULL
)

SELECT
  p.total_purchasers / v.total_visitors AS conversion_rate,
  a.avg_transactions_per_purchaser AS avg_transactions_per_purchaser
FROM
  visitors v,
  purchasers p,
  transactions a;",web_analytics.fullVisitorId; web_analytics.totals,2,lite_sql,sf_bq402,154
bq403,irs_990,bigquery,Which three years in 2012-2017 have the smallest absolute difference between median revenue and median functional expenses for organizations filing IRS 990 forms? Please output three years and respective differences.,"WITH RankedData AS (
    SELECT
        CONCAT(""20"", _TABLE_SUFFIX) AS year_filed,
        totrevenue,
        totfuncexpns,
        ROW_NUMBER() OVER (PARTITION BY CONCAT(""20"", _TABLE_SUFFIX) ORDER BY totrevenue) 
        AS revenue_rank,
        ROW_NUMBER() OVER (PARTITION BY CONCAT(""20"", _TABLE_SUFFIX) ORDER BY totfuncexpns) 
        AS expense_rank,
        COUNT(*) OVER (PARTITION BY CONCAT(""20"", _TABLE_SUFFIX)) AS total_count
    FROM 
        `bigquery-public-data.irs_990.irs_990_20*`
),

YearlyMedians AS (
    SELECT
        year_filed,
        IF(MOD(total_count, 2) = 1, 
           MAX(CASE WHEN revenue_rank = (total_count + 1) / 2 THEN totrevenue END),
           AVG(CASE WHEN revenue_rank IN ((total_count / 2), (total_count / 2) + 1) THEN totrevenue END)
        ) AS median_revenue,
        IF(MOD(total_count, 2) = 1, 
           MAX(CASE WHEN expense_rank = (total_count + 1) / 2 THEN totfuncexpns END),
           AVG(CASE WHEN expense_rank IN ((total_count / 2), (total_count / 2) + 1) THEN totfuncexpns END)
        ) AS median_expense
    FROM
        RankedData
    GROUP BY
        year_filed, total_count
),

DifferenceCalculations AS (
    SELECT
        year_filed,
        median_revenue,
        median_expense,
        ABS(median_revenue - median_expense) AS difference
    FROM
        YearlyMedians
)

SELECT
    year_filed,
    difference
FROM
    DifferenceCalculations
WHERE
    year_filed BETWEEN '2012' AND '2017'
ORDER BY
    difference ASC
LIMIT 3;",irs_990_*.totfuncexpns; irs_990_*.totrevenue,2,lite_sql,sf_bq403,532
bq406,google_dei,bigquery,"Please calculate the growth rates for Asians, Black people, Latinx people, Native Americans, White people, US women, US men, global women, and global men from 2014 to 2024 concerning the overall workforce.","CREATE TEMP FUNCTION GrowthRate(end_value FLOAT64, begin_value FLOAT64)
RETURNS FLOAT64
AS ((end_value - begin_value) / begin_value);

SELECT
  GrowthRate(SUM(IF(report_year=2024, race_asian, 0)),
             SUM(IF(report_year=2014, race_asian, 0))) AS race_asian_growth,
  GrowthRate(SUM(IF(report_year=2024, race_black, 0)),
             SUM(IF(report_year=2014, race_black, 0))) AS race_black_growth,
  GrowthRate(SUM(IF(report_year=2024, race_hispanic_latinx, 0)),
             SUM(IF(report_year=2014, race_hispanic_latinx, 0))) AS race_hispanic_growth,
  GrowthRate(SUM(IF(report_year=2024, race_native_american, 0)),
             SUM(IF(report_year=2014, race_native_american, 0))) AS race_native_american_growth,
  GrowthRate(SUM(IF(report_year=2024, race_white, 0)),
             SUM(IF(report_year=2014, race_white, 0))) AS race_white_growth,
  GrowthRate(SUM(IF(report_year=2024, gender_us_women, 0)),
             SUM(IF(report_year=2014, gender_us_women, 0))) AS gender_us_women_growth,
  GrowthRate(SUM(IF(report_year=2024, gender_us_men, 0)),
             SUM(IF(report_year=2014, gender_us_men, 0))) AS gender_us_men_growth,
  GrowthRate(SUM(IF(report_year=2024, gender_global_women, 0)),
             SUM(IF(report_year=2014, gender_global_women, 0))) AS gender_global_women_growth,
  GrowthRate(SUM(IF(report_year=2024, gender_global_men, 0)),
             SUM(IF(report_year=2014, gender_global_men, 0))) AS gender_global_men_growth
FROM `bigquery-public-data.google_dei.dar_non_intersectional_representation`
WHERE report_year IN (2014, 2024)
  AND workforce = 'overall';",dar_non_intersectional_representation.gender_global_men; dar_non_intersectional_representation.gender_global_women; dar_non_intersectional_representation.gender_us_men; dar_non_intersectional_representation.gender_us_women; dar_non_intersectional_representation.race_asian; dar_non_intersectional_representation.race_black; dar_non_intersectional_representation.race_hispanic_latinx; dar_non_intersectional_representation.race_native_american; dar_non_intersectional_representation.race_white; dar_non_intersectional_representation.report_year; dar_non_intersectional_representation.workforce,11,lite_sql,sf_bq406,436
bq407,covid19_usa,bigquery,"Find the top three counties with populations over 50,000, using the 2020 5-year census data, that had the highest COVID-19 case fatality rates on August 27, 2020. For these counties, provide the name, state, median age, total population, number of confirmed COVID-19 cases per 100,000 people, number of deaths per 100,000 people, and the case fatality rate as a percentage","WITH population_data AS (
  SELECT
    geo_id,
    median_age,
    total_pop
  FROM
    `bigquery-public-data.census_bureau_acs.county_2020_5yr`
  WHERE
    total_pop > 50000
),
covid_data AS (
  SELECT
    county_fips_code,
    county_name,
    state,
    SUM(confirmed_cases) AS total_cases,
    SUM(deaths) AS total_deaths
  FROM
    `bigquery-public-data.covid19_usafacts.summary`
  WHERE
    date = '2020-08-27'
  GROUP BY
    county_fips_code, county_name, state
)
SELECT
  covid.county_name,
  covid.state,
  pop.median_age,
  pop.total_pop,
  (covid.total_cases / pop.total_pop * 100000) AS confirmed_cases_per_100000,
  (covid.total_deaths / pop.total_pop * 100000) AS deaths_per_100000,
  (covid.total_deaths / covid.total_cases * 100) AS case_fatality_rate
FROM
  covid_data covid
JOIN
  population_data pop ON covid.county_fips_code = pop.geo_id
ORDER BY
  case_fatality_rate DESC
LIMIT 3;",county_*.geo_id; county_*.median_age; county_*.total_pop; summary.confirmed_cases; summary.county_fips_code; summary.county_name; summary.date; summary.deaths; summary.state,9,lite_sql,sf_bq407,6066
sf_bq412,GOOGLE_ADS,snowflake,"Please provide the page URLs, first shown time, last shown time, removal reason, violation category, and lower and upper bound shown times for the most recent five closed ads in the Croatia region which had shown higher than 10,000 and lower than 25,000, and used at least one audience criterion such as demographics, geographic location, contextual signals, customer lists, or interest topics. The region code of Croatia is HR.","SELECT
    ""creative_page_url"",
    TO_TIMESTAMP(GET(""region_stat"".value, 'first_shown')) AS ""first_shown"",
    TO_TIMESTAMP(GET(""region_stat"".value, 'last_shown')) AS ""last_shown"",
    REPLACE(REPLACE(""disapproval""[0].""removal_reason"", '""""', '""'), '""', '') AS ""removal_reason"", 
    REPLACE(REPLACE(""disapproval""[0].""violation_category"", '""""', '""'), '""', '') AS ""violation_category"",
    GET(""region_stat"".value, 'times_shown_lower_bound') AS ""times_shown_lower"",
    GET(""region_stat"".value, 'times_shown_upper_bound') AS ""times_shown_upper""
FROM
    ""GOOGLE_ADS"".""GOOGLE_ADS_TRANSPARENCY_CENTER"".""REMOVED_CREATIVE_STATS"",
    LATERAL FLATTEN(input => ""region_stats"") AS ""region_stat""
WHERE
    GET(""region_stat"".value, 'region_code') = 'HR' 
    AND GET(""region_stat"".value, 'times_shown_availability_date') IS NULL 
    AND GET(""region_stat"".value, 'times_shown_lower_bound') > 10000 
    AND GET(""region_stat"".value, 'times_shown_upper_bound') < 25000
    AND (
        GET(""audience_selection_approach_info"", 'demographic_info') != 'CRITERIA_UNUSED' 
        OR GET(""audience_selection_approach_info"", 'geo_location') != 'CRITERIA_UNUSED' 
        OR GET(""audience_selection_approach_info"", 'contextual_signals') != 'CRITERIA_UNUSED' 
        OR GET(""audience_selection_approach_info"", 'customer_lists') != 'CRITERIA_UNUSED' 
        OR GET(""audience_selection_approach_info"", 'topics_of_interest') != 'CRITERIA_UNUSED'
    )
ORDER BY
    ""last_shown"" DESC
LIMIT 5;",REMOVED_CREATIVE_STATS.audience_selection_approach_info; REMOVED_CREATIVE_STATS.creative_page_url; REMOVED_CREATIVE_STATS.disapproval; REMOVED_CREATIVE_STATS.region_stats,4,snow_sql_near_exact,sf_bq412,16
bq413,dimensions_ai_covid19,bigquery,"Retrieve the venue titles of publications inserted from 2024 onwards, where the associated grid's city is 'Qianjiang', prioritizing the venue titles from journal first, then proceedings, book, or book series titles.","SELECT
   COALESCE(p.journal.title, p.proceedings_title.preferred, p.book_title.preferred, p.book_series_title.preferred) AS venue,
FROM
   `bigquery-public-data.dimensions_ai_covid19.publications` p
LEFT JOIN
   UNNEST(research_orgs) AS research_orgs_grids
LEFT JOIN
   `bigquery-public-data.dimensions_ai_covid19.grid` grid
ON
   grid.id=research_orgs_grids
WHERE
   EXTRACT(YEAR FROM date_inserted) >= 2021
   AND
   grid.address.city = 'Qianjiang'",grid.address; grid.id; publications.book_series_title; publications.book_title; publications.date_inserted; publications.journal; publications.proceedings_title; publications.research_orgs,8,lite_sql,sf_bq413,282
bq414,the_met,bigquery,"Retrieve the object id, title, and the formatted metadata date (as a string in 'YYYY-MM-DD' format) for objects in the ""The Libraries"" department where the cropConfidence is greater than 0.5, the object's title contains the word ""book"".","SELECT 
  a.object_id,
  a.title,
  FORMAT_TIMESTAMP('%Y-%m-%d', a.metadata_date) AS formatted_metadata_date
FROM `bigquery-public-data.the_met.objects` a
JOIN (
  SELECT object_id,
         cropHints.confidence AS cropConfidence
  FROM `bigquery-public-data.the_met.vision_api_data`, 
       UNNEST(cropHintsAnnotation.cropHints) cropHints
) b
ON a.object_id = b.object_id
WHERE a.department = ""The Libraries""
AND b.cropConfidence > 0.5
AND a.title LIKE ""%book%""",objects.department; objects.metadata_date; objects.object_id; objects.title; vision_api_data.cropHintsAnnotation; vision_api_data.object_id,6,lite_sql,sf_bq414,61
bq419,noaa_data,bigquery,"Which 5 states had the most storm events from 1980 to 1995, considering only the top 1000 states with the highest event counts each year? Please use state abbreviations.","WITH s80 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1980` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),
s81 as
(SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1981` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),
s82 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1982` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s83 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1983` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s84 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1984` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s85 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1985` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s86 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1986` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s87 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1987` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s88 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1988` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s89 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1989` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s90 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1990` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s91 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1991` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),
s92 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1992` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s93 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1993` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s94 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1994` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000),

s95 as
  (SELECT state, COUNT(event_id) as num_events
  FROM `bigquery-public-data.noaa_historic_severe_storms.storms_1995` 
  GROUP BY state 
  ORDER BY num_events DESC
  LIMIT 1000)

SELECT s80.state, 
s80.num_events + s81.num_events +  s82.num_events +  s83.num_events +  s84.num_events +  s85.num_events +  s86.num_events +  s87.num_events + s88.num_events +  s89.num_events +  s90.num_events + s91.num_events + s92.num_events + s93.num_events + s94.num_events + s95.num_events as total_events 
FROM s80 FULL JOIN s81 ON s80.state = s81.state
FULL JOIN s82 ON s82.state = s81.state
FULL JOIN s83 ON s83.state = s81.state
FULL JOIN s84 ON s84.state = s81.state
FULL JOIN s85 ON s85.state = s81.state
FULL JOIN s86 ON s86.state = s81.state
FULL JOIN s87 ON s87.state = s81.state
FULL JOIN s88 ON s88.state = s81.state
FULL JOIN s89 ON s89.state = s81.state
FULL JOIN s90 ON s90.state = s81.state
FULL JOIN s91 ON s91.state = s81.state 
FULL JOIN s92 ON s92.state = s81.state
FULL JOIN s93 ON s93.state = s81.state
FULL JOIN s94 ON s94.state = s81.state
FULL JOIN s95 ON s95.state = s81.state

ORDER BY total_events DESC
LIMIT 5;",storms_*.event_id; storms_*.state,2,lite_sql,sf_bq419,660
sf_bq421,IDC,snowflake,"Can you list all unique pairs of embedding medium and staining substance code meanings, along with the number of occurrences for each pair, based on distinct embedding medium and staining substance codes from the 'SM' modality in the DICOM dataset's un-nested specimen preparation sequences, ensuring that the codes are from the SCT coding scheme?","WITH
  SpecimenPreparationSequence_unnested AS (
    SELECT
      d.""SOPInstanceUID"",
      concept_name_code_sequence.value:""CodeMeaning""::STRING AS ""cnc_cm"",
      concept_name_code_sequence.value:""CodingSchemeDesignator""::STRING AS ""cnc_csd"",
      concept_name_code_sequence.value:""CodeValue""::STRING AS ""cnc_val"",
      concept_code_sequence.value:""CodeMeaning""::STRING AS ""ccs_cm"",
      concept_code_sequence.value:""CodingSchemeDesignator""::STRING AS ""ccs_csd"",
      concept_code_sequence.value:""CodeValue""::STRING AS ""ccs_val""
    FROM
      ""IDC"".""IDC_V17"".""DICOM_ALL"" AS d,
      LATERAL FLATTEN(input => d.""SpecimenDescriptionSequence"") AS spec_desc,
      LATERAL FLATTEN(input => spec_desc.value:""SpecimenPreparationSequence"") AS prep_seq,
      LATERAL FLATTEN(input => prep_seq.value:""SpecimenPreparationStepContentItemSequence"") AS prep_step,
      LATERAL FLATTEN(input => prep_step.value:""ConceptNameCodeSequence"") AS concept_name_code_sequence,
      LATERAL FLATTEN(input => prep_step.value:""ConceptCodeSequence"") AS concept_code_sequence
  ),
  slide_embedding AS (
    SELECT
      ""SOPInstanceUID"",
      ARRAY_AGG(DISTINCT(CONCAT(""ccs_cm"", ':', ""ccs_csd"", ':', ""ccs_val""))) AS ""embeddingMedium_code_str""
    FROM
      SpecimenPreparationSequence_unnested
    WHERE
      ""cnc_csd"" = 'SCT' AND ""cnc_val"" = '430863003' -- CodeMeaning is 'Embedding medium'
    GROUP BY
      ""SOPInstanceUID""
  ),
  slide_staining AS (
    SELECT
      ""SOPInstanceUID"",
      ARRAY_AGG(DISTINCT(CONCAT(""ccs_cm"", ':', ""ccs_csd"", ':', ""ccs_val""))) AS ""staining_usingSubstance_code_str""
    FROM
      SpecimenPreparationSequence_unnested
    WHERE
      ""cnc_csd"" = 'SCT' AND ""cnc_val"" = '424361007' -- CodeMeaning is 'Using substance'
    GROUP BY
      ""SOPInstanceUID""
  ),
  embedding_data AS (
    SELECT
      d.""SOPInstanceUID"",
      d.""instance_size"",
      e.""embeddingMedium_code_str"",
      s.""staining_usingSubstance_code_str""
    FROM
      ""IDC"".""IDC_V17"".""DICOM_ALL"" AS d
    LEFT JOIN
      slide_embedding AS e ON d.""SOPInstanceUID"" = e.""SOPInstanceUID""
    LEFT JOIN
      slide_staining AS s ON d.""SOPInstanceUID"" = s.""SOPInstanceUID""
    WHERE
      d.""Modality"" = 'SM'
  )
SELECT
  SPLIT_PART(embeddingMedium_CodeMeaning_flat.VALUE::STRING, ':', 1) AS ""embeddingMedium_CodeMeaning"",
  SPLIT_PART(staining_usingSubstance_CodeMeaning_flat.VALUE::STRING, ':', 1) AS ""staining_usingSubstance_CodeMeaning"",
  COUNT(*) AS ""count_""
FROM
  embedding_data
  , LATERAL FLATTEN(input => embedding_data.""embeddingMedium_code_str"") AS embeddingMedium_CodeMeaning_flat
  , LATERAL FLATTEN(input => embedding_data.""staining_usingSubstance_code_str"") AS staining_usingSubstance_CodeMeaning_flat
GROUP BY
  SPLIT_PART(embeddingMedium_CodeMeaning_flat.VALUE::STRING, ':', 1),
  SPLIT_PART(staining_usingSubstance_CodeMeaning_flat.VALUE::STRING, ':', 1);",DICOM_ALL.Modality; DICOM_ALL.SOPInstanceUID; DICOM_ALL.SpecimenDescriptionSequence,3,snow_sql_near_exact,sf_bq421,2100
sf_bq422,IDC,snowflake,"What are the average series sizes in MiB for the top 3 patients with the highest slice interval difference tolerance and the top 3 patients with the highest maximum exposure difference, considering only CT images from the 'nlst' collection?","WITH
  nonLocalizerRawData AS (
    SELECT
      ""SeriesInstanceUID"",
      ""StudyInstanceUID"",
      ""PatientID"",
      TRY_CAST(""Exposure""::STRING AS FLOAT) AS ""Exposure"",  -- 直接从 bid 获取 Exposure
      TRY_CAST(axes.VALUE::STRING AS FLOAT) AS ""zImagePosition"",
      LEAD(TRY_CAST(axes.VALUE::STRING AS FLOAT)) OVER (
        PARTITION BY ""SeriesInstanceUID"" 
        ORDER BY TRY_CAST(axes.VALUE::STRING AS FLOAT)
      ) - TRY_CAST(axes.VALUE::STRING AS FLOAT) AS ""slice_interval"",
      ""instance_size"" AS ""instanceSize""
    FROM
      ""IDC"".""IDC_V17"".""DICOM_ALL"" AS ""bid"",
      LATERAL FLATTEN(input => ""bid"".""ImagePositionPatient"") AS axes  -- 使用 LATERAL FLATTEN 展开数组
    WHERE
      ""collection_id"" = 'nlst' 
      AND ""Modality"" = 'CT' 
  ),
  geometryChecks AS (
    SELECT
      ""SeriesInstanceUID"",
      ""StudyInstanceUID"",
      ""PatientID"",
      ARRAY_AGG(DISTINCT ""slice_interval"") AS ""sliceIntervalDifferences"",
      ARRAY_AGG(DISTINCT ""Exposure"") AS ""distinctExposures"",
      SUM(""instanceSize"") / 1024 / 1024 AS ""seriesSizeInMB""
    FROM
      nonLocalizerRawData
    GROUP BY
      ""SeriesInstanceUID"", 
      ""StudyInstanceUID"",
      ""PatientID""
  ),
  patientMetrics AS (
    SELECT
      ""PatientID"",
      MAX(TRY_CAST(sid.VALUE::STRING AS FLOAT)) AS ""maxSliceIntervalDifference"",
      MIN(TRY_CAST(sid.VALUE::STRING AS FLOAT)) AS ""minSliceIntervalDifference"",
      MAX(TRY_CAST(sid.VALUE::STRING AS FLOAT)) - MIN(TRY_CAST(sid.VALUE::STRING AS FLOAT)) AS ""sliceIntervalDifferenceTolerance"",
      MAX(TRY_CAST(de.VALUE::STRING AS FLOAT)) AS ""maxExposure"",
      MIN(TRY_CAST(de.VALUE::STRING AS FLOAT)) AS ""minExposure"",
      MAX(TRY_CAST(de.VALUE::STRING AS FLOAT)) - MIN(TRY_CAST(de.VALUE::STRING AS FLOAT)) AS ""maxExposureDifference"",
      ""seriesSizeInMB""
    FROM
      geometryChecks,
      LATERAL FLATTEN(input => ""sliceIntervalDifferences"") AS sid,  -- 展开 sliceIntervalDifferences
      LATERAL FLATTEN(input => ""distinctExposures"") AS de  -- 展开 distinctExposures
    WHERE
      sid.VALUE IS NOT NULL
      AND de.VALUE IS NOT NULL
    GROUP BY
      ""PatientID"",
      ""seriesSizeInMB""
  ),
  top3BySliceInterval AS (
    SELECT
      ""PatientID"",
      ""seriesSizeInMB""
    FROM
      patientMetrics
    ORDER BY
      ""sliceIntervalDifferenceTolerance"" DESC
    LIMIT 3
  ),
  top3ByMaxExposure AS (
    SELECT
      ""PatientID"",
      ""seriesSizeInMB""
    FROM
      patientMetrics
    ORDER BY
      ""maxExposureDifference"" DESC
    LIMIT 3
  )
SELECT
  'Top 3 by Slice Interval' AS ""MetricGroup"",
  AVG(""seriesSizeInMB"") AS ""AverageSeriesSizeInMB""
FROM
  top3BySliceInterval
UNION ALL
SELECT
  'Top 3 by Max Exposure' AS ""MetricGroup"",
  AVG(""seriesSizeInMB"") AS ""AverageSeriesSizeInMB""
FROM
  top3ByMaxExposure;",DICOM_ALL.Exposure; DICOM_ALL.ImagePositionPatient; DICOM_ALL.PatientID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.StudyInstanceUID; DICOM_ALL.collection_id; DICOM_ALL.instance_size,7,lite_sql,sf_bq422,2100
bq424,world_bank,bigquery,"List the top 10 countries with respect to the total amount of long-term external debt in descending order, excluding those without a specified region.","SELECT DISTINCT
  id.country_name,
  --cs.region,
  id.value AS debt,
  --id.indicator_code
FROM (
  SELECT
    country_code,
    region
  FROM
    `bigquery-public-data.world_bank_intl_debt.country_summary`
  WHERE
    region != """" ) cs
INNER JOIN (
  SELECT
    country_code,
    country_name,
    value,
    indicator_code
  FROM
    `bigquery-public-data.world_bank_intl_debt.international_debt`
  WHERE
    indicator_code = ""DT.AMT.DLXF.CD"") id

ON
  cs.country_code = id.country_code
ORDER BY
  id.value DESC
  LIMIT 10",country_summary.country_code; country_summary.region; international_debt.country_code; international_debt.country_name; international_debt.indicator_code; international_debt.value,6,lite_sql,sf_bq424,153
bq425,ebi_chembl,bigquery,"List all distinct molecules associated with the company 'SanofiAventis,' along with their trade name and approval date, retaining the most recent approval date for each molecule, using data from ChEMBL Release 23.","SELECT *
  FROM (
  SELECT
  molregno,
  comp.company,
  prod.trade_name,
  prod.approval_date,
  ROW_NUMBER() OVER(PARTITION BY molregno ORDER BY PARSE_DATE('%Y-%m-%d', prod.approval_date) DESC) rn
  FROM bigquery-public-data.ebi_chembl.compound_records_23 AS cmpd_rec
  JOIN bigquery-public-data.ebi_chembl.molecule_synonyms_23 AS ms USING (molregno)
  JOIN bigquery-public-data.ebi_chembl.research_companies_23 AS comp USING (res_stem_id)
  JOIN bigquery-public-data.ebi_chembl.formulations_23 AS form USING (molregno)
  JOIN bigquery-public-data.ebi_chembl.products_23 AS prod USING (product_id)
  ) as subq
 WHERE rn = 1 AND company = 'SanofiAventis'",compound_records_*.molregno; formulations_*.molregno; formulations_*.product_id; molecule_synonyms_*.molregno; molecule_synonyms_*.res_stem_id; products_*.approval_date; products_*.product_id; products_*.trade_name; research_companies_*.company; research_companies_*.res_stem_id,10,lite_sql,sf_bq425,1137
bq428,ncaa_basketball,bigquery,"For the top five team markets with the highest number of distinct players who scored at least 15 points during the second period of games between 2010 and 2018, provide details of each game they played in NCAA basketball historical tournament matches during the same period, as specified in the data model document.","WITH top_teams AS (
  SELECT
    team_market
  FROM (
    SELECT
      team_market,
      player_id AS id,
      SUM(points_scored)
    FROM
      `bigquery-public-data.ncaa_basketball.mbb_pbp_sr`
    WHERE
      season >= 2010 AND season <=2018 AND period = 2
    GROUP BY
      game_id,
      team_market,
      player_id
    HAVING
      SUM(points_scored) >= 15) C
  GROUP BY
    team_market
  HAVING
    COUNT(DISTINCT id) > 5
  ORDER BY
    COUNT(DISTINCT id) DESC
  LIMIT 5
)


SELECT
  season,
  round,
  days_from_epoch,
  game_date,
  day,
  'win' AS label,
  win_seed AS seed,
  win_market AS market,
  win_name AS name,
  win_alias AS alias,
  win_school_ncaa AS school_ncaa,
  lose_seed AS opponent_seed,
  lose_market AS opponent_market,
  lose_name AS opponent_name,
  lose_alias AS opponent_alias,
  lose_school_ncaa AS opponent_school_ncaa
FROM `bigquery-public-data.ncaa_basketball.mbb_historical_tournament_games`
JOIN top_teams ON top_teams.team_market = win_market
WHERE season >= 2010 AND season <=2018

UNION ALL

SELECT
  season,
  round,
  days_from_epoch,
  game_date,
  day,
  'loss' AS label,
  lose_seed AS seed,
  lose_market AS market,
  lose_name AS name,
  lose_alias AS alias,
  lose_school_ncaa AS school_ncaa,
  win_seed AS opponent_seed,
  win_market AS opponent_market,
  win_name AS opponent_name,
  win_alias AS opponent_alias,
  win_school_ncaa AS opponent_school_ncaa
FROM `bigquery-public-data.ncaa_basketball.mbb_historical_tournament_games`
JOIN top_teams ON top_teams.team_market = lose_market
WHERE season >= 2010 AND season <=2018",mbb_historical_tournament_games.day; mbb_historical_tournament_games.days_from_epoch; mbb_historical_tournament_games.game_date; mbb_historical_tournament_games.lose_alias; mbb_historical_tournament_games.lose_market; mbb_historical_tournament_games.lose_name; mbb_historical_tournament_games.lose_school_ncaa; mbb_historical_tournament_games.lose_seed; mbb_historical_tournament_games.round; mbb_historical_tournament_games.season; mbb_historical_tournament_games.win_alias; mbb_historical_tournament_games.win_market; mbb_historical_tournament_games.win_name; mbb_historical_tournament_games.win_school_ncaa; mbb_historical_tournament_games.win_seed; mbb_pbp_sr.game_id; mbb_pbp_sr.period; mbb_pbp_sr.player_id; mbb_pbp_sr.points_scored; mbb_pbp_sr.season; mbb_pbp_sr.team_market,21,lite_sql,sf_bq428,505
sf_bq429,CENSUS_BUREAU_ACS_2,snowflake,"What are the top 5 states with the highest average median income difference from 2015 to 2018? also provide the average number of vulnerable employees across various industries for these states, using data from the ACS 5-Year Estimates for 2017.","WITH median_income_diff_by_zipcode AS (
  WITH acs_2018 AS (
    SELECT
      ""geo_id"",
      ""median_income"" AS ""median_income_2018""
    FROM
      CENSUS_BUREAU_ACS_2.CENSUS_BUREAU_ACS.""ZIP_CODES_2018_5YR""
  ),
  acs_2015 AS (
    SELECT
      ""geo_id"",
      ""median_income"" AS ""median_income_2015""
    FROM
      CENSUS_BUREAU_ACS_2.CENSUS_BUREAU_ACS.""ZIP_CODES_2015_5YR""
  ),
  acs_diff AS (
    SELECT
      a18.""geo_id"",
      (a18.""median_income_2018"" - a15.""median_income_2015"") AS ""median_income_diff""
    FROM
      acs_2018 a18
    JOIN
      acs_2015 a15 ON a18.""geo_id"" = a15.""geo_id""
  )
  SELECT
    ""geo_id"",
    AVG(""median_income_diff"") AS ""avg_median_income_diff""
  FROM
    acs_diff
  WHERE
    ""median_income_diff"" IS NOT NULL
  GROUP BY ""geo_id""
),
base_census AS (
  SELECT
    geo.""state_name"",
    AVG(i.""avg_median_income_diff"") AS ""avg_median_income_diff"",
    AVG(
      ""employed_wholesale_trade"" * 0.38423645320197042 +
      ""occupation_natural_resources_construction_maintenance"" * 0.48071410777129553 +
      ""employed_arts_entertainment_recreation_accommodation_food"" * 0.89455676291236841 +
      ""employed_information"" * 0.31315240083507306 +
      ""employed_retail_trade"" * 0.51
    ) AS ""avg_vulnerable""
  FROM
    CENSUS_BUREAU_ACS_2.CENSUS_BUREAU_ACS.""ZIP_CODES_2017_5YR"" AS census
  JOIN
    median_income_diff_by_zipcode i ON CAST(census.""geo_id"" AS STRING) = i.""geo_id""
  JOIN
    CENSUS_BUREAU_ACS_2.GEO_US_BOUNDARIES.""ZIP_CODES"" geo ON census.""geo_id"" = geo.""zip_code""
  GROUP BY geo.""state_name""
)

SELECT 
  ""state_name"",
  ""avg_median_income_diff"",
  ""avg_vulnerable""
FROM 
  base_census
ORDER BY 
  ""avg_median_income_diff"" DESC
LIMIT 5;",ZIP_CODES.state_name; ZIP_CODES.zip_code,2,lite_sql,sf_bq429,3625
bq430,ebi_chembl,bigquery,"Find pairs of different molecules tested in the same assay and standard type, where both have 10–15 heavy atoms, fewer than 5 activities in that assay, fewer than 2 duplicate activities, non-null standard values, and pChEMBL values over 10. For each pair, report the maximum heavy atom count, the latest publication date (calculated based on the document's rank within the same journal and year, and map it to a synthetic month and day), the highest document ID, classify the change in standard values as 'increase', 'decrease', or 'no-change' based on their values and relations, and generate UUIDs from their activity IDs and canonical SMILES.","select 
  -- *, 
  greatest(heavy_atoms_1, heavy_atoms_2) as heavy_atoms_greatest,
  greatest(publication_date_1, publication_date_2) as publication_date_greatest,
  greatest(doc_id_1, doc_id_2) as doc_id_greatest,
  case 
    when 
      standard_value_1 > standard_value_2 and 
      standard_relation_1 not in ('<', '<<') and 
      standard_relation_2 not in ('>', '>>')
    then 'decrease'
    when
      standard_value_1 < standard_value_2 and 
      standard_relation_1 not in ('>', '>>') and 
      standard_relation_2 not in ('<', '<<') 
    then 'increase'
    when
      standard_value_1 = standard_value_2 and 
      standard_relation_1 in ('=', '~') and 
      standard_relation_2 in ('=', '~') 
    then 'no-change'
    else null
  end as standard_change,
  to_hex(md5(to_json_string(struct(activity_id_1, activity_id_2)))) as mmp_delta_uuid,
  to_hex(md5(to_json_string(struct(canonical_smiles_1, canonical_smiles_2, 5)))) as mmp_search_uuid
from (
  select 
    act.assay_id,
    act.standard_type,
    act.activity_id as activity_id_1,
    cast(act.standard_value as numeric) as standard_value_1,
    act.standard_relation as standard_relation_1,
    cast(act.pchembl_value as numeric) as pchembl_value_1,
    count(*) over (partition by act.assay_id) as count_activities_1,
    count(*) over (partition by act.assay_id, act.molregno) as duplicate_activities_1,
    act.molregno as molregno_1,
    com.canonical_smiles as canonical_smiles_1,
    cast(cmp.heavy_atoms as int64) as heavy_atoms_1,
    cast(d.doc_id as int64) as doc_id_1,
    date(
      coalesce(cast(d.year as int64), 1970), 
      coalesce(cast(floor(percent_rank() over (
        partition by d.journal, d.year order by SAFE_CAST(d.first_page as int64)) * 11) as int64) + 1, 1),
      coalesce(mod(cast(floor(percent_rank() over (
        partition by d.journal, d.year order by SAFE_CAST(d.first_page as int64)) * 308) as int64), 28) + 1, 1)) as publication_date_1
  FROM `bigquery-public-data.ebi_chembl.activities_29` act
  join `bigquery-public-data.ebi_chembl.compound_structures_29` com using (molregno)
  join `bigquery-public-data.ebi_chembl.compound_properties_29` cmp using (molregno)
  left join `bigquery-public-data.ebi_chembl.docs_29` d using (doc_id)
  where standard_type in (select distinct standard_type from`bigquery-public-data.ebi_chembl.activities_29` where pchembl_value is not null)
  ) a1
join (
  select 
    act.assay_id,
    act.standard_type,
    act.activity_id as activity_id_2,
    cast(act.standard_value as numeric) as standard_value_2,
    act.standard_relation as standard_relation_2,
    cast(act.pchembl_value as numeric) as pchembl_value_2,
    count(*) over (partition by act.assay_id) as count_activities_2,
    count(*) over (partition by act.assay_id, act.molregno) as duplicate_activities_2, 
    act.molregno as molregno_2,
    com.canonical_smiles as canonical_smiles_2, 
    cast(cmp.heavy_atoms as int64) as heavy_atoms_2,
    cast(d.doc_id as int64) as doc_id_2,
    date(
      coalesce(cast(d.year as int64), 1970), 
      coalesce(cast(floor(percent_rank() over (
        partition by d.journal, d.year order by SAFE_CAST(d.first_page as int64)) * 11) as int64) + 1, 1),
      coalesce(mod(cast(floor(percent_rank() over (
        partition by d.journal, d.year order by SAFE_CAST(d.first_page as int64)) * 308) as int64), 28) + 1, 1)) as publication_date_2
  FROM `bigquery-public-data.ebi_chembl.activities_29` act
  join `bigquery-public-data.ebi_chembl.compound_structures_29` com using (molregno)
  join `bigquery-public-data.ebi_chembl.compound_properties_29` cmp using (molregno)
  left join `bigquery-public-data.ebi_chembl.docs_29` d using (doc_id)
  where standard_type in (select distinct standard_type from`bigquery-public-data.ebi_chembl.activities_29` where pchembl_value is not null)
  ) a2 using (assay_id, standard_type)
where 
  a1.molregno_1 != a2.molregno_2 and
  a1.count_activities_1 < 5 and 
  a2.count_activities_2 < 5 and 
  a1.heavy_atoms_1 between 10 and 15 and
  a2.heavy_atoms_2 between 10 and 15 and
  a1.standard_value_1 is not null and 
  a2.standard_value_2 is not null and
  a1.duplicate_activities_1 < 2 and
  a2.duplicate_activities_2 < 2 and
  a1.pchembl_value_1 > 10 and
  a2.pchembl_value_2 > 10",activities_*.activity_id; activities_*.assay_id; activities_*.doc_id; activities_*.molregno; activities_*.pchembl_value; activities_*.standard_relation; activities_*.standard_type; activities_*.standard_value; compound_properties_*.heavy_atoms; compound_properties_*.molregno; compound_structures_*.canonical_smiles; compound_structures_*.molregno; docs_*.doc_id; docs_*.first_page; docs_*.journal; docs_*.year,16,lite_sql,sf_bq430,1137
bq442,CYMBAL_INVESTMENTS,bigquery,Please collect the information of the top 6 trade report with the highest closing prices. Refer to the document for all the information I want.,"SELECT
  OrderID AS tradeID,
  MaturityDate AS tradeTimestamp,
  (
    CASE SUBSTR(TargetCompID, 0, 4)
      WHEN 'MOMO' THEN 'Momentum'
      WHEN 'LUCK' THEN 'Feeling Lucky'
      WHEN 'PRED' THEN 'Prediction'
  END
    ) AS algorithm,
  Symbol AS symbol,
  LastPx AS openPrice,
  StrikePrice AS closePrice,
  (
  SELECT
    Side
  FROM
    UNNEST(Sides)
  ) AS tradeDirection,
  (CASE (
    SELECT
      Side
    FROM
      UNNEST(Sides))
      WHEN 'SHORT' THEN -1
      WHEN 'LONG' THEN 1
  END
    ) AS tradeMultiplier
FROM
  `bigquery-public-data.cymbal_investments.trade_capture_report`cv
ORDER BY closePrice DESC
LIMIT 6",trade_capture_report.LastPx; trade_capture_report.MaturityDate; trade_capture_report.OrderID; trade_capture_report.Sides; trade_capture_report.StrikePrice; trade_capture_report.Symbol; trade_capture_report.TargetCompID,7,lite_sql,sf_bq442,14
sf_bq444,CRYPTO,snowflake,"Can you pull the blockchain timestamp, block number, and transaction hash for the first five mint and burn events from Ethereum logs for the address '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8'? Please include mint events identified by the topic '0x7a53080ba414158be7ec69b987b5fb7d07dee101fe85488f0853ae16239d0bde' and burn events by '0x0c396cd989a39f4459b5fa1aed6a9a8dcdbc45908acfd67e028cd568da98982c', and order them by block timestamp from the oldest to the newest.","WITH parsed_burn_logs AS (
  SELECT
    logs.""block_timestamp"" AS block_timestamp,
    logs.""block_number"" AS block_number,
    logs.""transaction_hash"" AS transaction_hash,
    logs.""log_index"" AS log_index,
    PARSE_JSON(logs.""data"") AS data,
    logs.""topics""
  FROM CRYPTO.CRYPTO_ETHEREUM.LOGS AS logs
  WHERE logs.""address"" = '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8'
    AND logs.""topics""[0] = '0x0c396cd989a39f4459b5fa1aed6a9a8dcdbc45908acfd67e028cd568da98982c'
),
parsed_mint_logs AS (
  SELECT
    logs.""block_timestamp"" AS block_timestamp,
    logs.""block_number"" AS block_number,
    logs.""transaction_hash"" AS transaction_hash,
    logs.""log_index"" AS log_index,
    PARSE_JSON(logs.""data"") AS data,
    logs.""topics""
  FROM CRYPTO.CRYPTO_ETHEREUM.LOGS AS logs
  WHERE logs.""address"" = '0x8ad599c3a0ff1de082011efddc58f1908eb6e6d8'
    AND logs.""topics""[0] = '0x7a53080ba414158be7ec69b987b5fb7d07dee101fe85488f0853ae16239d0bde'
)

SELECT
    block_timestamp,
    block_number,
    transaction_hash
FROM parsed_mint_logs

UNION ALL

SELECT
    block_timestamp,
    block_number,
    transaction_hash
FROM parsed_burn_logs

ORDER BY block_timestamp
LIMIT 5;",LOGS.address; LOGS.block_number; LOGS.block_timestamp; LOGS.data; LOGS.log_index; LOGS.topics; LOGS.transaction_hash,7,lite_sql,sf_bq444,286
bq452,_1000_genomes,bigquery,"Identify variants on chromosome 12, calculate their chi-squared scores using allele counts in cases and controls, and return the start, end, chi-squared score (after Yates's correction for continuity) of top variants where the chi-squared score is no less than 29.71679, ensuring that each group has expected counts of at least 5 for the chi-squared calculation.","SELECT *
FROM (
  SELECT
    `start`,
    `end`,
    ROUND(
      POW(ABS(case_ref_count - (ref_count / allele_count) * case_count) - 0.5, 2) / ((ref_count / allele_count) * case_count) +
      POW(ABS(control_ref_count - (ref_count / allele_count) * control_count) - 0.5, 2) / ((ref_count / allele_count) * control_count) +
      POW(ABS(case_alt_count - (alt_count / allele_count) * case_count) - 0.5, 2) / ((alt_count / allele_count) * case_count) +
      POW(ABS(control_alt_count - (alt_count / allele_count) * control_count) - 0.5, 2) / ((alt_count / allele_count) * control_count),
      3
    ) AS chi_squared_score
  FROM (
    SELECT
      reference_name,
      `start`,
      `end`,
      reference_bases,
      alternate_bases,
      vt,
      SUM(ref_count + alt_count) AS allele_count,
      SUM(ref_count) AS ref_count,
      SUM(alt_count) AS alt_count,
      SUM(IF(is_case, CAST(ref_count + alt_count AS INT64), 0)) AS case_count,
      SUM(IF(NOT is_case, CAST(ref_count + alt_count AS INT64), 0)) AS control_count,
      SUM(IF(is_case, ref_count, 0)) AS case_ref_count,
      SUM(IF(is_case, alt_count, 0)) AS case_alt_count,
      SUM(IF(NOT is_case, ref_count, 0)) AS control_ref_count,
      SUM(IF(NOT is_case, alt_count, 0)) AS control_alt_count
    FROM (
      SELECT
        v.reference_name,
        v.`start`,
        v.`end`,
        v.reference_bases,
        v.alternate_bases,
        v.vt,
        ('EAS' = p.super_population) AS is_case,
        IF(call.genotype[SAFE_OFFSET(0)] = 0, 1, 0) AS ref_count,
        IF(call.genotype[SAFE_OFFSET(0)] = 1, 1, 0) AS alt_count
      FROM
        `spider2-public-data.1000_genomes.variants` AS v,
        UNNEST(v.call) AS call
      JOIN
        `spider2-public-data.1000_genomes.sample_info` AS p
      ON
        call.call_set_name = p.sample
      WHERE
        v.reference_name = '12'
    )
    GROUP BY
      reference_name,
      `start`,
      `end`,
      reference_bases,
      alternate_bases,
      vt
  )
  WHERE
    (ref_count / allele_count) * case_count >= 5.0
    AND (ref_count / allele_count) * control_count >= 5.0
    AND (alt_count / allele_count) * case_count >= 5.0
    AND (alt_count / allele_count) * control_count >= 5.0
)
WHERE
  chi_squared_score >= 29.71679
ORDER BY
  chi_squared_score DESC",sample_info.Sample; sample_info.Super_Population; variants.VT; variants.alternate_bases; variants.call; variants.end; variants.reference_bases; variants.reference_name; variants.start,9,lite_sql,sf_bq452,114
bq453,_1000_genomes,bigquery,"What are the reference names, start positions, end positions, reference bases, alternate bases, variant types, chi-squared scores (calculated using Hardy-Weinberg equilibrium), and the observed and expected counts of homozygous reference, heterozygous, and homozygous alternate genotypes, including their allele frequencies and allele frequencies, for variants on chromosome 17 between positions 41196311 and 41277499?","SELECT
  reference_name,
  start,
  `END`,
  reference_bases,
  alt,
  vt,
  POW(hom_ref_count - expected_hom_ref_count, 2) / expected_hom_ref_count +
    POW(hom_alt_count - expected_hom_alt_count, 2) / expected_hom_alt_count +
    POW(het_count - expected_het_count, 2) / expected_het_count AS chi_squared_score,
  total_count,
  hom_ref_count,
  expected_hom_ref_count AS expected_hom_ref_count,
  het_count,
  expected_het_count AS expected_het_count,
  hom_alt_count,
  expected_hom_alt_count AS expected_hom_alt_count,
  alt_freq AS alt_freq,
  alt_freq_from_1KG
FROM (
  SELECT
    reference_name,
    start,
    `END`,
    reference_bases,
    alt,
    vt,
    alt_freq_from_1KG,
    hom_ref_freq + (0.5 * het_freq) AS hw_ref_freq,
    1 - (hom_ref_freq + (0.5 * het_freq)) AS alt_freq,
    POW(hom_ref_freq + (0.5 * het_freq), 2) * total_count AS expected_hom_ref_count,
    POW(1 - (hom_ref_freq + (0.5 * het_freq)), 2) * total_count AS expected_hom_alt_count,
    2 * (hom_ref_freq + (0.5 * het_freq)) * (1 - (hom_ref_freq + (0.5 * het_freq))) * total_count AS expected_het_count,
    total_count,
    hom_ref_count,
    het_count,
    hom_alt_count,
    hom_ref_freq,
    het_freq,
    hom_alt_freq
  FROM (
    SELECT
      reference_name,
      start,
      `END`,
      reference_bases,
      STRING_AGG(DISTINCT alternate_base) AS alt,
      vt,
      af AS alt_freq_from_1KG,
      COUNTIF(first_allele IN (0, 1) AND second_allele IN (0, 1)) AS total_count,
      COUNTIF(first_allele = 0 AND second_allele = 0) AS hom_ref_count,
      COUNTIF((first_allele = 0 AND second_allele = 1) OR (first_allele = 1 AND second_allele = 0)) AS het_count,
      COUNTIF(first_allele = 1 AND second_allele = 1) AS hom_alt_count,
      SAFE_DIVIDE(COUNTIF(first_allele = 0 AND second_allele = 0), COUNTIF(first_allele IN (0, 1) AND second_allele IN (0, 1))) AS hom_ref_freq,
      SAFE_DIVIDE(COUNTIF((first_allele = 0 AND second_allele = 1) OR (first_allele = 1 AND second_allele = 0)), COUNTIF(first_allele IN (0, 1) AND second_allele IN (0, 1))) AS het_freq,
      SAFE_DIVIDE(COUNTIF(first_allele = 1 AND second_allele = 1), COUNTIF(first_allele IN (0, 1) AND second_allele IN (0, 1))) AS hom_alt_freq
    FROM (
      SELECT
        reference_name,
        start,
        `END`,
        reference_bases,
        vt,
        af,
        call.call_set_name,
        call.genotype[OFFSET(0)] AS first_allele,
        call.genotype[OFFSET(1)] AS second_allele,
        alternate_base
      FROM
        `spider2-public-data.1000_genomes.variants`,
        UNNEST(call) AS call,
        UNNEST(alternate_bases) AS alternate_base
      WHERE
        reference_name = '17'
        AND start BETWEEN 41196311 AND 41277499
    )
    GROUP BY
      reference_name,
      start,
      `END`,
      reference_bases,
      vt,
      af
  )
)",variants.AF; variants.VT; variants.alternate_bases; variants.call; variants.end; variants.reference_bases; variants.reference_name; variants.start,8,lite_sql,sf_bq453,114
sf_bq455,IDC,snowflake,"Find the top 5 CT scan series ID, including their series number, patient ID, and series size (in MiB), where the series are not classified as 'LOCALIZER' or have the specific JPEG compressed transfer syntaxes '1.2.840.10008.1.2.4.70' or '1.2.840.10008.1.2.4.51'. The series must have consistent slice intervals, exposure levels, image orientation, pixel spacing, image positions, and pixel dimensions. Additionally, the z-axis of the image orientation must align with the expected plane (dot product between 0.99 and 1.01).","WITH
  -- Create a common table expression (CTE) named localizerAndJpegCompressedSeries
  localizerAndJpegCompressedSeries AS (
    SELECT 
      ""SeriesInstanceUID""
    FROM 
      IDC.IDC_V17.""DICOM_ALL"" AS bid
    WHERE 
      ""ImageType"" = 'LOCALIZER' OR
      ""TransferSyntaxUID"" IN ('1.2.840.10008.1.2.4.70', '1.2.840.10008.1.2.4.51')
  ),
  
  -- Create a common table expression (CTE) for x_vector calculation (first three elements)
  imageOrientation AS (
    SELECT
      ""SeriesInstanceUID"",
      ARRAY_AGG(CAST(part.value AS FLOAT)) AS ""x_vector""
    FROM 
      IDC.IDC_V17.""DICOM_ALL"" AS bid,
      LATERAL FLATTEN(input => bid.""ImageOrientationPatient"") AS part
    WHERE
      part.index BETWEEN 0 AND 2
    GROUP BY ""SeriesInstanceUID""
  ),
  
  -- Create a common table expression (CTE) for y_vector calculation (next three elements)
  imageOrientationY AS (
    SELECT
      ""SeriesInstanceUID"",
      ARRAY_AGG(CAST(part.value AS FLOAT)) AS ""y_vector""
    FROM 
      IDC.IDC_V17.""DICOM_ALL"" AS bid,
      LATERAL FLATTEN(input => bid.""ImageOrientationPatient"") AS part
    WHERE
      part.index BETWEEN 3 AND 5
    GROUP BY ""SeriesInstanceUID""
  ),
  
  -- Create a common table expression (CTE) named nonLocalizerRawData
  nonLocalizerRawData AS (
    SELECT
      bid.""SeriesInstanceUID"",  -- Added table alias bid
      bid.""StudyInstanceUID"",
      bid.""PatientID"",
      bid.""SOPInstanceUID"",
      bid.""SliceThickness"",
      bid.""ImageType"",
      bid.""TransferSyntaxUID"",
      bid.""SeriesNumber"",
      bid.""aws_bucket"",
      bid.""crdc_series_uuid"",
      CAST(bid.""Exposure"" AS FLOAT) AS ""Exposure"",  -- Use CAST directly
      CAST(ipp.value AS FLOAT) AS ""zImagePosition"", -- Use CAST directly
      CONCAT(ipp2.value, '/', ipp3.value) AS ""xyImagePosition"",
      LEAD(CAST(ipp.value AS FLOAT)) OVER (PARTITION BY bid.""SeriesInstanceUID"" ORDER BY CAST(ipp.value AS FLOAT)) - CAST(ipp.value AS FLOAT) AS ""slice_interval"",
      ARRAY_TO_STRING(bid.""ImageOrientationPatient"", '/') AS ""iop"",
      bid.""PixelSpacing"",
      bid.""Rows"" AS ""pixelRows"",
      bid.""Columns"" AS ""pixelColumns"",
      bid.""instance_size"" AS ""instanceSize""
    FROM
      IDC.IDC_V17.""DICOM_ALL"" AS bid
    LEFT JOIN LATERAL FLATTEN(input => bid.""ImagePositionPatient"") AS ipp
    LEFT JOIN LATERAL FLATTEN(input => bid.""ImagePositionPatient"") AS ipp2
    LEFT JOIN LATERAL FLATTEN(input => bid.""ImagePositionPatient"") AS ipp3
    WHERE
      bid.""collection_id"" != 'nlst'
      AND bid.""Modality"" = 'CT'
      AND ipp.index = 2
      AND ipp2.index = 0
      AND ipp3.index = 1
      AND bid.""SeriesInstanceUID"" NOT IN (SELECT ""SeriesInstanceUID"" FROM localizerAndJpegCompressedSeries)
  ),
  
  -- Cross product calculation
  crossProduct AS (
    SELECT
      nld.""SOPInstanceUID"",  -- Added table alias nld
      nld.""SeriesInstanceUID"",  -- Added table alias nld
      OBJECT_CONSTRUCT(
        'x', (""x_vector""[1] * ""y_vector""[2] - ""x_vector""[2] * ""y_vector""[1]),
        'y', (""x_vector""[2] * ""y_vector""[0] - ""x_vector""[0] * ""y_vector""[2]),
        'z', (""x_vector""[0] * ""y_vector""[1] - ""x_vector""[1] * ""y_vector""[0])
      ) AS ""xyCrossProduct""
    FROM 
      nonLocalizerRawData AS nld  -- Added alias for nonLocalizerRawData
    JOIN imageOrientation AS io ON nld.""SeriesInstanceUID"" = io.""SeriesInstanceUID""
    JOIN imageOrientationY AS ioy ON nld.""SeriesInstanceUID"" = ioy.""SeriesInstanceUID""
  ),
  
  -- Cross product elements extraction and row numbering
  crossProductElements AS (
    SELECT
      cp.""SOPInstanceUID"",  
      cp.""SeriesInstanceUID"",  
      elem.value,
      ROW_NUMBER() OVER (PARTITION BY cp.""SOPInstanceUID"", cp.""SeriesInstanceUID"" ORDER BY elem.value) AS rn
    FROM 
      crossProduct AS cp  
    -- Use LATERAL FLATTEN to explode the cross product object into individual 'x', 'y', and 'z'
    JOIN LATERAL FLATTEN(input => ARRAY_CONSTRUCT(
          cp.""xyCrossProduct""['x'],
          cp.""xyCrossProduct""['y'],
          cp.""xyCrossProduct""['z']
    )) AS elem -- Simplified 'elem.value' reference here
  ),
  
  -- Dot product calculation
  dotProduct AS (
    SELECT
      cpe.""SOPInstanceUID"",  
      cpe.""SeriesInstanceUID"",  
      SUM(
        CASE 
          WHEN cpe.rn = 1 THEN cpe.value * 0  -- x * 0
          WHEN cpe.rn = 2 THEN cpe.value * 0  -- y * 0
          WHEN cpe.rn = 3 THEN cpe.value * 1  -- z * 1
        END
      ) AS ""xyDotProduct""
    FROM 
      crossProductElements AS cpe
    GROUP BY 
      cpe.""SOPInstanceUID"",  
      cpe.""SeriesInstanceUID""
  ),
  
  -- Geometry checks for series consistency
  geometryChecks AS (
    SELECT
      gc.""SeriesInstanceUID"",  -- Added table alias gc
      gc.""SeriesNumber"",
      gc.""aws_bucket"",
      gc.""crdc_series_uuid"",
      gc.""StudyInstanceUID"",
      gc.""PatientID"",
      ARRAY_AGG(DISTINCT gc.""slice_interval"") AS ""sliceIntervalDifferences"",
      ARRAY_AGG(DISTINCT gc.""Exposure"") AS ""distinctExposures"",
      COUNT(DISTINCT gc.""iop"") AS ""iopCount"",
      COUNT(DISTINCT gc.""PixelSpacing"") AS ""pixelSpacingCount"",
      COUNT(DISTINCT gc.""zImagePosition"") AS ""positionCount"",
      COUNT(DISTINCT gc.""xyImagePosition"") AS ""xyPositionCount"",
      COUNT(DISTINCT gc.""SOPInstanceUID"") AS ""sopInstanceCount"",
      COUNT(DISTINCT gc.""SliceThickness"") AS ""sliceThicknessCount"",
      COUNT(DISTINCT gc.""Exposure"") AS ""exposureCount"",
      COUNT(DISTINCT gc.""pixelRows"") AS ""pixelRowCount"",
      COUNT(DISTINCT gc.""pixelColumns"") AS ""pixelColumnCount"",
      dp.""xyDotProduct"",  -- Added xyDotProduct from dotProduct
      SUM(gc.""instanceSize"") / 1024 / 1024 AS ""seriesSizeInMiB""
    FROM 
      nonLocalizerRawData AS gc  -- Added table alias gc
    JOIN dotProduct AS dp ON gc.""SeriesInstanceUID"" = dp.""SeriesInstanceUID"" 
    AND gc.""SOPInstanceUID"" = dp.""SOPInstanceUID""
    GROUP BY
      gc.""SeriesInstanceUID"", 
      gc.""SeriesNumber"",
      gc.""aws_bucket"",
      gc.""crdc_series_uuid"",
      gc.""StudyInstanceUID"",
      gc.""PatientID"",
      dp.""xyDotProduct""  -- Include xyDotProduct in GROUP BY
    HAVING
      COUNT(DISTINCT gc.""iop"") = 1 
      AND COUNT(DISTINCT gc.""PixelSpacing"") = 1  
      AND COUNT(DISTINCT gc.""SOPInstanceUID"") = COUNT(DISTINCT gc.""zImagePosition"") 
      AND COUNT(DISTINCT gc.""xyImagePosition"") = 1
      AND COUNT(DISTINCT gc.""pixelRows"") = 1 
      AND COUNT(DISTINCT gc.""pixelColumns"") = 1 
      AND ABS(dp.""xyDotProduct"") BETWEEN 0.99 AND 1.01
  )

SELECT
  geometryChecks.""SeriesInstanceUID"",  -- Added table alias
  geometryChecks.""SeriesNumber"",  -- Added table alias
  geometryChecks.""PatientID"",  -- Added table alias
  geometryChecks.""seriesSizeInMiB""
FROM
  geometryChecks
ORDER BY
  geometryChecks.""seriesSizeInMiB"" DESC
LIMIT 5;",DICOM_ALL.Columns; DICOM_ALL.ImageOrientationPatient; DICOM_ALL.ImagePositionPatient; DICOM_ALL.ImageType; DICOM_ALL.Modality; DICOM_ALL.SOPInstanceUID; DICOM_ALL.SeriesInstanceUID; DICOM_ALL.SeriesNumber; DICOM_ALL.SliceThickness; DICOM_ALL.TransferSyntaxUID; DICOM_ALL.crdc_series_uuid; DICOM_ALL.instance_size,12,lite_sql,sf_bq455,2100
ga001,ga4,bigquery,I want to know the preferences of customers who purchased the Google Navy Speckled Tee in December 2020. What other product was purchased with the highest total quantity alongside this item?,"WITH
  Params AS (
    SELECT 'Google Navy Speckled Tee' AS selected_product
  ),
  PurchaseEvents AS (
    SELECT
      user_pseudo_id,
      items
    FROM
      `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
    WHERE
      _TABLE_SUFFIX BETWEEN '20201201' AND '20201231'
      AND event_name = 'purchase'
  ),
  ProductABuyers AS (
    SELECT DISTINCT
      user_pseudo_id
    FROM
      Params,
      PurchaseEvents,
      UNNEST(items) AS items
    WHERE
      items.item_name = selected_product
  )
SELECT
  items.item_name AS item_name,
  SUM(items.quantity) AS item_quantity
FROM
  Params,
  PurchaseEvents,
  UNNEST(items) AS items
WHERE
  user_pseudo_id IN (SELECT user_pseudo_id FROM ProductABuyers)
  AND items.item_name != selected_product
GROUP BY 1
ORDER BY item_quantity DESC
LIMIT 1;",events_*.event_name; events_*.items; events_*.user_pseudo_id,3,lite_sql,sf_ga001,23
ga002,ga4,bigquery,Tell me the most purchased other products and their quantities by customers who bought the Google Red Speckled Tee each month for the three months starting from November 2020.,"WITH
Params AS (
  SELECT 'Google Red Speckled Tee' AS selected_product
),
DateRanges AS (
  SELECT '20201101' AS start_date, '20201130' AS end_date, '202011' AS period UNION ALL
  SELECT '20201201', '20201231', '202012' UNION ALL
  SELECT '20210101', '20210131', '202101'
),
PurchaseEvents AS (
  SELECT
    period,
    user_pseudo_id,
    items
  FROM
    DateRanges
  JOIN
    `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
    ON _TABLE_SUFFIX BETWEEN start_date AND end_date
  WHERE
    event_name = 'purchase'
),
ProductABuyers AS (
  SELECT DISTINCT
    period,
    user_pseudo_id
  FROM
    Params,
    PurchaseEvents,
    UNNEST(items) AS items
  WHERE
    items.item_name = selected_product
),
TopProducts AS (
  SELECT
    pe.period,
    items.item_name AS item_name,
    SUM(items.quantity) AS item_quantity
  FROM
    Params,
    PurchaseEvents pe,
    UNNEST(items) AS items
  WHERE
    user_pseudo_id IN (SELECT user_pseudo_id FROM ProductABuyers pb WHERE pb.period = pe.period)
    AND items.item_name != selected_product
  GROUP BY
    pe.period, items.item_name
),
TopProductPerPeriod AS (
  SELECT
    period,
    item_name,
    item_quantity
  FROM (
    SELECT
      period,
      item_name,
      item_quantity,
      RANK() OVER (PARTITION BY period ORDER BY item_quantity DESC) AS rank
    FROM
      TopProducts
  )
  WHERE
    rank = 1
)
SELECT
  period,
  item_name,
  item_quantity
FROM
  TopProductPerPeriod
ORDER BY
  period;",events_*.event_name; events_*.items; events_*.user_pseudo_id,3,lite_sql,sf_ga002,23
ga003,firebase,bigquery,"I'm trying to evaluate which board types were most effective on September 15, 2018. Can you find out the average scores for each board type from the quick play mode completions on that day?","WITH EventData AS (
    SELECT 
        user_pseudo_id, 
        event_timestamp, 
        param
    FROM 
        `firebase-public-project.analytics_153293282.events_20180915`,
        UNNEST(event_params) AS param
    WHERE 
        event_name = ""level_complete_quickplay""
        AND (param.key = ""value"" OR param.key = ""board"")
),
ProcessedData AS (
    SELECT 
        user_pseudo_id, 
        event_timestamp, 
        MAX(IF(param.key = ""value"", param.value.int_value, NULL)) AS score,
        MAX(IF(param.key = ""board"", param.value.string_value, NULL)) AS board_type
    FROM 
        EventData
    GROUP BY 
        user_pseudo_id, 
        event_timestamp
)
SELECT 
    ANY_VALUE(board_type) AS board, 
    AVG(score) AS average_score
FROM 
    ProcessedData
GROUP BY 
    board_type",events_*.event_name; events_*.event_params; events_*.event_timestamp; events_*.user_pseudo_id,4,lite_sql,sf_ga003,20
ga004,ga4,bigquery,Can you figure out the average difference in pageviews between users who bought something and those who didn’t in December 2020? Just label anyone who was involved in purchase events as a purchaser.,"WITH
  UserInfo AS (
    SELECT
      user_pseudo_id,
      COUNTIF(event_name = 'page_view') AS page_view_count,
      COUNTIF(event_name IN ('in_app_purchase', 'purchase')) AS purchase_event_count
    FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
    WHERE _TABLE_SUFFIX BETWEEN '20201201' AND '20201231'
    GROUP BY 1
  ),
  Averages AS (
    SELECT
      (purchase_event_count > 0) AS purchaser,
      COUNT(*) AS user_count,
      SUM(page_view_count) AS total_page_views,
      SUM(page_view_count) / COUNT(*) AS avg_page_views
    FROM UserInfo
    GROUP BY 1
  )

SELECT
  MAX(CASE WHEN purchaser THEN avg_page_views ELSE 0 END) -
  MAX(CASE WHEN NOT purchaser THEN avg_page_views ELSE 0 END) AS avg_page_views_difference
FROM Averages;",events_*.event_name; events_*.user_pseudo_id,2,lite_sql,sf_ga004,23
ga008,ga4,bigquery,Can you give me the average page views per buyer and total page views among those buyers for each day in November 2020?,"WITH
  UserInfo AS (
    SELECT
      user_pseudo_id,
      PARSE_DATE('%Y%m%d', event_date) AS event_date,
      COUNTIF(event_name = 'page_view') AS page_view_count,
      COUNTIF(event_name = 'purchase') AS purchase_event_count
    FROM `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
    WHERE _TABLE_SUFFIX BETWEEN '20201101' AND '20201130'
    GROUP BY 1, 2
  )
SELECT
  event_date,
  SUM(page_view_count) / COUNT(*) AS avg_page_views,
  SUM(page_view_count)
FROM UserInfo
WHERE purchase_event_count > 0
GROUP BY event_date
ORDER BY event_date;",events_*.event_date; events_*.event_name; events_*.user_pseudo_id,3,lite_sql,sf_ga008,23
ga010,ga4,bigquery,Can you give me an overview of our website traffic for December 2020? I'm particularly interested in the channel with the fourth highest number of sessions.,"WITH prep AS (
  SELECT
    user_pseudo_id,
    (SELECT value.int_value FROM UNNEST(event_params) WHERE key = 'ga_session_id') AS session_id,
    ARRAY_AGG((SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'source') IGNORE NULLS 
              ORDER BY event_timestamp)[SAFE_OFFSET(0)] AS source,
    ARRAY_AGG((SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'medium') IGNORE NULLS 
              ORDER BY event_timestamp)[SAFE_OFFSET(0)] AS medium,
    ARRAY_AGG((SELECT value.string_value FROM UNNEST(event_params) WHERE key = 'campaign') IGNORE NULLS 
              ORDER BY event_timestamp)[SAFE_OFFSET(0)] AS campaign
  FROM
    `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
  WHERE
    _TABLE_SUFFIX BETWEEN '20201201' AND '20201231'
  GROUP BY
    user_pseudo_id,
    session_id
)
SELECT
  -- session default channel grouping (dimension | the channel group associated with a session) 
  CASE 
    WHEN source = '(direct)' AND (medium IN ('(not set)','(none)')) THEN 'Direct'
    WHEN REGEXP_CONTAINS(campaign, 'cross-network') THEN 'Cross-network'
    WHEN (REGEXP_CONTAINS(source,'alibaba|amazon|google shopping|shopify|etsy|ebay|stripe|walmart')
        OR REGEXP_CONTAINS(campaign, '^(.*(([^a-df-z]|^)shop|shopping).*)$'))
        AND REGEXP_CONTAINS(medium, '^(.*cp.*|ppc|paid.*)$') THEN 'Paid Shopping'
    WHEN REGEXP_CONTAINS(source,'baidu|bing|duckduckgo|ecosia|google|yahoo|yandex')
        AND REGEXP_CONTAINS(medium,'^(.*cp.*|ppc|paid.*)$') THEN 'Paid Search'
    WHEN REGEXP_CONTAINS(source,'badoo|facebook|fb|instagram|linkedin|pinterest|tiktok|twitter|whatsapp')
        AND REGEXP_CONTAINS(medium,'^(.*cp.*|ppc|paid.*)$') THEN 'Paid Social'
    WHEN REGEXP_CONTAINS(source,'dailymotion|disneyplus|netflix|youtube|vimeo|twitch|vimeo|youtube')
        AND REGEXP_CONTAINS(medium,'^(.*cp.*|ppc|paid.*)$') THEN 'Paid Video'
    WHEN medium IN ('display', 'banner', 'expandable', 'interstitial', 'cpm') THEN 'Display'
    WHEN REGEXP_CONTAINS(source,'alibaba|amazon|google shopping|shopify|etsy|ebay|stripe|walmart')
        OR REGEXP_CONTAINS(campaign, '^(.*(([^a-df-z]|^)shop|shopping).*)$') THEN 'Organic Shopping'
    WHEN REGEXP_CONTAINS(source,'badoo|facebook|fb|instagram|linkedin|pinterest|tiktok|twitter|whatsapp')
        OR medium IN ('social','social-network','social-media','sm','social network','social media') THEN 'Organic Social'
    WHEN REGEXP_CONTAINS(source,'dailymotion|disneyplus|netflix|youtube|vimeo|twitch|vimeo|youtube')
        OR REGEXP_CONTAINS(medium,'^(.*video.*)$') THEN 'Organic Video'
    WHEN REGEXP_CONTAINS(source,'baidu|bing|duckduckgo|ecosia|google|yahoo|yandex')
        OR medium = 'organic' THEN 'Organic Search'
    WHEN REGEXP_CONTAINS(source,'email|e-mail|e_mail|e mail')
        OR REGEXP_CONTAINS(medium,'email|e-mail|e_mail|e mail') THEN 'Email'
    WHEN medium = 'affiliate' THEN 'Affiliates'
    WHEN medium = 'referral' THEN 'Referral'
    WHEN medium = 'audio' THEN 'Audio'
    WHEN medium = 'sms' THEN 'SMS'
    WHEN medium LIKE '%push'
        OR REGEXP_CONTAINS(medium,'mobile|notification') THEN 'Mobile Push Notifications'
    ELSE 'Unassigned' 
  END AS channel_grouping_session
FROM
  prep
GROUP BY
  channel_grouping_session
ORDER BY
  COUNT(DISTINCT CONCAT(user_pseudo_id, session_id)) DESC
LIMIT 1 OFFSET 3",events_*.event_params; events_*.event_timestamp; events_*.user_pseudo_id,3,lite_sql,sf_ga010,23
ga012,ga4,bigquery,"Find the transaction IDs, total item quantities, and purchase revenues for the item category with the highest tax rate on November 30, 2020, where the tax and revenue data come from ecommerce.","WITH top_category AS (
  SELECT
    product.item_category,
    SUM(ecommerce.tax_value_in_usd) / SUM(ecommerce.purchase_revenue_in_usd) AS tax_rate
  FROM
    bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_20201130,
    UNNEST(items) AS product
  WHERE
    event_name = 'purchase'
  GROUP BY
    product.item_category
  ORDER BY
    tax_rate DESC
  LIMIT 1
)

SELECT
    ecommerce.transaction_id,
    SUM(ecommerce.total_item_quantity) AS total_item_quantity,
    SUM(ecommerce.purchase_revenue_in_usd) AS purchase_revenue_in_usd,
    SUM(ecommerce.purchase_revenue) AS purchase_revenue
FROM
    bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_20201130, 
    UNNEST(items) AS product
JOIN top_category
ON product.item_category = top_category.item_category
WHERE
    event_name = 'purchase'
GROUP BY
    ecommerce.transaction_id;",events_*.ecommerce; events_*.event_name; events_*.items,3,lite_sql,sf_ga012,23
ga017,ga4,bigquery,How many distinct users viewed the most frequently visited page during January 2021?,"WITH unnested_events AS (
  SELECT
    MAX(CASE WHEN event_params.key = 'page_location' THEN event_params.value.string_value END) AS page_location,
    user_pseudo_id,
    event_timestamp
  FROM
    `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`,
    UNNEST(event_params) AS event_params
  WHERE
    _TABLE_SUFFIX BETWEEN '20210101' AND '20210131'
    AND event_name = 'page_view'
  GROUP BY user_pseudo_id,event_timestamp
),
temp AS (
    SELECT
    page_location,
    COUNT(*) AS event_count,
    COUNT(DISTINCT user_pseudo_id) AS users
    FROM
    unnested_events
    GROUP BY page_location
    ORDER BY event_count DESC
)

SELECT users 
FROM
temp
LIMIT 1",events_*.event_name; events_*.event_params; events_*.event_timestamp; events_*.user_pseudo_id,4,lite_sql,sf_ga017,23
ga018,ga4,bigquery,"I'd like to analyze the appeal of our products to users. Can you calculate the percentage of times users go from browsing the product list pages to clicking into the product detail pages during a single session on January 2nd, 2021?","WITH base_table AS (
  SELECT
    event_name,
    event_date,
    event_timestamp,
    user_pseudo_id,
    user_id,
    device,
    geo,
    traffic_source,
    event_params,
    user_properties
  FROM
    `bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_*`
  WHERE
    _table_suffix = '20210102'
  AND event_name IN ('page_view')
)
, unnested_events AS (
-- unnests event parameters to get to relevant keys and values
  SELECT
    event_date AS date,
    event_timestamp AS event_timestamp_microseconds,
    user_pseudo_id,
    MAX(CASE WHEN c.key = 'ga_session_id' THEN c.value.int_value END) AS visitID,
    MAX(CASE WHEN c.key = 'ga_session_number' THEN c.value.int_value END) AS visitNumber,
    MAX(CASE WHEN c.key = 'page_title' THEN c.value.string_value END) AS page_title,
    MAX(CASE WHEN c.key = 'page_location' THEN c.value.string_value END) AS page_location
  FROM 
    base_table,
    UNNEST (event_params) c
  GROUP BY 1,2,3
)

, unnested_events_categorised AS (
-- categorizing Page Titles into PDPs and PLPs
  SELECT
  *,
  CASE WHEN ARRAY_LENGTH(SPLIT(page_location, '/')) >= 5 
            AND
            CONTAINS_SUBSTR(ARRAY_REVERSE(SPLIT(page_location, '/'))[SAFE_OFFSET(0)], '+')
            AND (LOWER(SPLIT(page_location, '/')[SAFE_OFFSET(4)]) IN 
                                        ('accessories','apparel','brands','campus+collection','drinkware',
                                          'electronics','google+redesign',
                                          'lifestyle','nest','new+2015+logo','notebooks+journals',
                                          'office','shop+by+brand','small+goods','stationery','wearables'
                                          )
                  OR
                  LOWER(SPLIT(page_location, '/')[SAFE_OFFSET(3)]) IN 
                                        ('accessories','apparel','brands','campus+collection','drinkware',
                                          'electronics','google+redesign',
                                          'lifestyle','nest','new+2015+logo','notebooks+journals',
                                          'office','shop+by+brand','small+goods','stationery','wearables'
                                          )
            )
            THEN 'PDP'
            WHEN NOT(CONTAINS_SUBSTR(ARRAY_REVERSE(SPLIT(page_location, '/'))[SAFE_OFFSET(0)], '+'))
            AND (LOWER(SPLIT(page_location, '/')[SAFE_OFFSET(4)]) IN 
                                        ('accessories','apparel','brands','campus+collection','drinkware',
                                          'electronics','google+redesign',
                                          'lifestyle','nest','new+2015+logo','notebooks+journals',
                                          'office','shop+by+brand','small+goods','stationery','wearables'
                                          )
                  OR 
                  LOWER(SPLIT(page_location, '/')[SAFE_OFFSET(3)]) IN 
                                          ('accessories','apparel','brands','campus+collection','drinkware',
                                            'electronics','google+redesign',
                                            'lifestyle','nest','new+2015+logo','notebooks+journals',
                                            'office','shop+by+brand','small+goods','stationery','wearables'
                                            )
            )
            THEN 'PLP'
        ELSE page_title
        END AS page_title_adjusted 

  FROM 
    unnested_events
)


, ranked_screens AS (
  SELECT
    *,
    LAG(page_title_adjusted,1) OVER (PARTITION BY  user_pseudo_id, visitID ORDER BY event_timestamp_microseconds ASC) previous_page,
    LEAD(page_title_adjusted,1) OVER (PARTITION BY  user_pseudo_id, visitID ORDER BY event_timestamp_microseconds ASC)  next_page
  FROM 
    unnested_events_categorised

)

,PLPtoPDPTransitions AS (
  SELECT
    user_pseudo_id,
    visitID
  FROM
    ranked_screens
  WHERE
    page_title_adjusted = 'PLP' AND next_page = 'PDP'
)

,TotalPLPViews AS (
  SELECT
    COUNT(*) AS total_plp_views
  FROM
    ranked_screens
  WHERE
    page_title_adjusted = 'PLP'
)

,TotalTransitions AS (
  SELECT
    COUNT(*) AS total_transitions
  FROM
    PLPtoPDPTransitions
)

SELECT
  (total_transitions * 100.0) / total_plp_views AS percentage
FROM
  TotalTransitions, TotalPLPViews;",events_*.event_date; events_*.event_name; events_*.event_params; events_*.event_timestamp; events_*.user_pseudo_id,5,lite_sql,sf_ga018,23
ga019,firebase,bigquery,Could you determine what percentage of users either did not uninstall our app within seven days or never uninstalled it after installing during August and September 2018?,"WITH
--List of users who installed
sept_cohort AS (
  SELECT DISTINCT user_pseudo_id,
  FORMAT_DATE('%Y-%m-%d', PARSE_DATE('%Y%m%d', event_date)) AS date_first_open,
  FROM `firebase-public-project.analytics_153293282.events_*`
  WHERE event_name = 'first_open'
  AND _TABLE_SUFFIX BETWEEN '20180801' and '20180930'
),
--Get the list of users who uninstalled
uninstallers AS (
  SELECT DISTINCT user_pseudo_id,
  FORMAT_DATE('%Y-%m-%d', PARSE_DATE('%Y%m%d', event_date)) AS date_app_remove,
  FROM `firebase-public-project.analytics_153293282.events_*`
  WHERE event_name = 'app_remove'
  AND _TABLE_SUFFIX BETWEEN '20180801' and '20180930'
),
--Join the 2 tables and compute for # of days to uninstall
joined AS (
  SELECT a.*,
  b.date_app_remove,
  DATE_DIFF(DATE(b.date_app_remove), DATE(a.date_first_open), DAY) AS days_to_uninstall
  FROM sept_cohort a
  LEFT JOIN uninstallers b
  ON a.user_pseudo_id = b.user_pseudo_id
)
--Compute for the percentage
SELECT
COUNT(DISTINCT
CASE WHEN days_to_uninstall > 7 OR days_to_uninstall IS NULL THEN user_pseudo_id END) /
COUNT(DISTINCT user_pseudo_id)
AS percent_users_7_days
FROM joined",events_*.event_date; events_*.event_name; events_*.user_pseudo_id,3,lite_sql,sf_ga019,20
ga020,firebase,bigquery,"Which quickplay event type had the lowest user retention rate during the second week after their initial engagement, for users who first engaged between August 1 and August 15, 2018?","-- Define the date range and calculate the minimum date for filtering results
WITH dates AS (
    SELECT 
        DATE('2018-08-01') AS start_date,
        DATE('2018-08-15') AS end_date
),
-- Create a table of active dates for each user within the specified date range
dates_active_table AS (
    SELECT
        user_pseudo_id,
        PARSE_DATE('%Y%m%d', `event_date`) AS user_active_date
    FROM 
        `firebase-public-project.analytics_153293282.events_*` 
    WHERE 
        event_name = 'session_start'
        AND PARSE_DATE('%Y%m%d', `event_date`) BETWEEN (SELECT start_date FROM dates) AND (SELECT end_date FROM dates)
    GROUP BY 
        user_pseudo_id, user_active_date
),
-- Create a table of the earliest quickplay event date for each user within the specified date range
event_table AS (
    SELECT 
        user_pseudo_id,
        event_name,
        MIN(PARSE_DATE('%Y%m%d', `event_date`)) AS event_cohort_date
    FROM 
        `firebase-public-project.analytics_153293282.events_*` 
    WHERE 
        event_name IN ('level_start_quickplay', 'level_end_quickplay', 'level_complete_quickplay', 
                       'level_fail_quickplay', 'level_reset_quickplay', 'level_retry_quickplay')
        AND PARSE_DATE('%Y%m%d', `event_date`) BETWEEN (SELECT start_date FROM dates) AND (SELECT end_date FROM dates)
    GROUP BY 
        user_pseudo_id, event_name
),
-- Calculate the number of days since each user's initial quickplay event
days_since_event_table AS (
    SELECT
        events.user_pseudo_id,
        events.event_name AS event_cohort,
        events.event_cohort_date,
        days.user_active_date,
        DATE_DIFF(days.user_active_date, events.event_cohort_date, DAY) AS days_since_event
    FROM 
        event_table events
    LEFT JOIN 
        dates_active_table days ON events.user_pseudo_id = days.user_pseudo_id
    WHERE 
        events.event_cohort_date <= days.user_active_date
),
-- Calculate the weeks since each user's initial quickplay event and count the active days in each week
weeks_retention AS (
    SELECT
        event_cohort,
        user_pseudo_id,
        CAST(CASE WHEN days_since_event = 0 THEN 0 ELSE CEIL(days_since_event / 7) END AS INTEGER) AS weeks_since_event,
        COUNT(DISTINCT days_since_event) AS days_active_since_event -- Count Days Active in Week
    FROM 
        days_since_event_table
    GROUP BY 
        event_cohort, user_pseudo_id, weeks_since_event
),
-- Aggregate the weekly retention data
aggregated_weekly_retention_table AS (
    SELECT
        event_cohort,
        weeks_since_event,
        SUM(days_active_since_event) AS weekly_days_active,
        COUNT(DISTINCT user_pseudo_id) AS retained_users
    FROM 
        weeks_retention
    GROUP BY 
        event_cohort, weeks_since_event
),
RETENTION_INFO AS (
-- Select and calculate the weekly retention rate for each event cohort
SELECT
    event_cohort,
    weeks_since_event,
    weekly_days_active,
    retained_users,
    (retained_users / MAX(retained_users) OVER (PARTITION BY event_cohort)) AS retention_rate
FROM 
    aggregated_weekly_retention_table
ORDER BY 
    event_cohort, weeks_since_event
)

SELECT event_cohort
FROM
RETENTION_INFO
WHERE weeks_since_event = 2
ORDER BY retention_rate
LIMIT 1",events_*.event_date; events_*.event_name; events_*.user_pseudo_id,3,lite_sql,sf_ga020,20
ga021,firebase,bigquery,"What is the retention rate for users two weeks after their initial quickplay event, calculated separately for each quickplay event type, within the period from July 2, 2018, to July 16, 2018? Please focus on users who started a session (session_start) during this period.","-- Define the date range and calculate the minimum date for filtering results
WITH dates AS (
    SELECT 
        DATE('2018-07-02') AS start_date,
        DATE('2018-07-16') AS end_date
),
-- Create a table of active dates for each user within the specified date range
dates_active_table AS (
    SELECT
        user_pseudo_id,
        PARSE_DATE('%Y%m%d', `event_date`) AS user_active_date
    FROM 
        `firebase-public-project.analytics_153293282.events_*` 
    WHERE 
        event_name = 'session_start'
        AND PARSE_DATE('%Y%m%d', `event_date`) BETWEEN (SELECT start_date FROM dates) AND (SELECT end_date FROM dates)
    GROUP BY 
        user_pseudo_id, user_active_date
),
-- Create a table of the earliest quickplay event date for each user within the specified date range
event_table AS (
    SELECT 
        user_pseudo_id,
        event_name,
        MIN(PARSE_DATE('%Y%m%d', `event_date`)) AS event_cohort_date
    FROM 
        `firebase-public-project.analytics_153293282.events_*` 
    WHERE 
        event_name IN ('level_start_quickplay', 'level_end_quickplay', 'level_complete_quickplay', 
                       'level_fail_quickplay', 'level_reset_quickplay', 'level_retry_quickplay')
        AND PARSE_DATE('%Y%m%d', `event_date`) BETWEEN (SELECT start_date FROM dates) AND (SELECT end_date FROM dates)
    GROUP BY 
        user_pseudo_id, event_name
),
-- Calculate the number of days since each user's initial quickplay event
days_since_event_table AS (
    SELECT
        events.user_pseudo_id,
        events.event_name AS event_cohort,
        events.event_cohort_date,
        days.user_active_date,
        DATE_DIFF(days.user_active_date, events.event_cohort_date, DAY) AS days_since_event
    FROM 
        event_table events
    LEFT JOIN 
        dates_active_table days ON events.user_pseudo_id = days.user_pseudo_id
    WHERE 
        events.event_cohort_date <= days.user_active_date
),
-- Calculate the weeks since each user's initial quickplay event and count the active days in each week
weeks_retention AS (
    SELECT
        event_cohort,
        user_pseudo_id,
        CAST(CASE WHEN days_since_event = 0 THEN 0 ELSE CEIL(days_since_event / 7) END AS INTEGER) AS weeks_since_event,
        COUNT(DISTINCT days_since_event) AS days_active_since_event -- Count Days Active in Week
    FROM 
        days_since_event_table
    GROUP BY 
        event_cohort, user_pseudo_id, weeks_since_event
),
-- Aggregate the weekly retention data
aggregated_weekly_retention_table AS (
    SELECT
        event_cohort,
        weeks_since_event,
        SUM(days_active_since_event) AS weekly_days_active,
        COUNT(DISTINCT user_pseudo_id) AS retained_users
    FROM 
        weeks_retention
    GROUP BY 
        event_cohort, weeks_since_event
),
RETENTION_INFO AS (
SELECT
    event_cohort,
    weeks_since_event,
    weekly_days_active,
    retained_users,
    (retained_users / MAX(retained_users) OVER (PARTITION BY event_cohort)) AS retention_rate
FROM 
    aggregated_weekly_retention_table
ORDER BY 
    event_cohort, weeks_since_event
)

SELECT event_cohort, retention_rate
FROM
RETENTION_INFO
WHERE weeks_since_event = 2",events_*.event_date; events_*.event_name; events_*.user_pseudo_id,3,lite_sql,sf_ga021,20
ga022,firebase,bigquery,"Could you please help me get the weekly customer retention rate in September 2018 for new customers who first used our app within the first week starting from September 1st, 2018 (timezone in Shanghai)? The retention rates should cover the following 3-week period after the initial use and display them in column format.","WITH analytics_data AS (
  SELECT user_pseudo_id, event_timestamp, event_name, 
    UNIX_MICROS(TIMESTAMP(""2018-09-01 00:00:00"", ""+8:00"")) AS start_day,
    3600*1000*1000*24*7 AS one_week_micros
  FROM `firebase-public-project.analytics_153293282.events_*`
  WHERE _table_suffix BETWEEN '20180901' AND '20180930'
)

SELECT
 week_1_cohort / week_0_cohort AS week_1_pct,
 week_2_cohort / week_0_cohort AS week_2_pct,
 week_3_cohort / week_0_cohort AS week_3_pct
FROM (
  WITH week_3_users AS (
    SELECT DISTINCT user_pseudo_id
    FROM analytics_data
    WHERE event_timestamp BETWEEN start_day+(3*one_week_micros) AND start_day+(4*one_week_micros)
  ),
  week_2_users AS (
    SELECT DISTINCT user_pseudo_id
    FROM analytics_data
    WHERE event_timestamp BETWEEN start_day+(2*one_week_micros) AND start_day+(3*one_week_micros)
  ),
  week_1_users AS (
    SELECT DISTINCT user_pseudo_id
    FROM analytics_data
    WHERE event_timestamp BETWEEN start_day+(1*one_week_micros) AND start_day+(2*one_week_micros)
  ), 
  week_0_users AS (
    SELECT DISTINCT user_pseudo_id
    FROM analytics_data
    WHERE event_name = 'first_open'
      AND event_timestamp BETWEEN start_day AND start_day+(1*one_week_micros)
  )
  SELECT 
    (SELECT count(*) 
     FROM week_0_users) AS week_0_cohort,
    (SELECT count(*) 
     FROM week_1_users 
     JOIN week_0_users USING (user_pseudo_id)) AS week_1_cohort,
    (SELECT count(*) 
     FROM week_2_users 
     JOIN week_0_users USING (user_pseudo_id)) AS week_2_cohort,
    (SELECT count(*) 
     FROM week_3_users 
     JOIN week_0_users USING (user_pseudo_id)) AS week_3_cohort
)",events_*.event_name; events_*.event_timestamp; events_*.user_pseudo_id,3,lite_sql,sf_ga022,20
ga028,firebase,bigquery,"Please perform a 7-day retention analysis for users who first session start the app during the week starting on July 2, 2018. For each week from Week 0 (the week of their first session) to Week 4, provide the total number of new users in Week 0 and the number of retained users for each subsequent week.","WITH dates AS (
    SELECT 
        DATE('2018-07-02') AS start_date,
        DATE('2018-10-02') AS end_date,
        DATE_ADD(DATE_TRUNC(DATE('2018-10-02'), WEEK(TUESDAY)), INTERVAL -4 WEEK) AS min_date
),

date_table AS (
    SELECT DISTINCT 
        PARSE_DATE('%Y%m%d', `event_date`) AS event_date,
        user_pseudo_id,
        CASE 
            WHEN DATE_DIFF(PARSE_DATE('%Y%m%d', `event_date`), DATE(TIMESTAMP_MICROS(user_first_touch_timestamp)), DAY) = 0 
            THEN 1 
            ELSE 0 
        END AS is_new_user
    FROM 
        `firebase-public-project.analytics_153293282.events_*` 
    WHERE 
        event_name = 'session_start'
),

new_user_list AS (
    SELECT DISTINCT 
        user_pseudo_id,
        event_date
    FROM 
        date_table
    WHERE 
        is_new_user = 1
),

days_since_start_table AS (
    SELECT DISTINCT 
        is_new_user,
        nu.event_date AS date_cohort,
        dt.user_pseudo_id,
        dt.event_date,
        DATE_DIFF(dt.event_date, nu.event_date, DAY) AS days_since_start
    FROM 
        date_table dt
    JOIN 
        new_user_list nu ON dt.user_pseudo_id = nu.user_pseudo_id
),

weeks_retention AS (
    SELECT 
        date_cohort,
        DATE_TRUNC(date_cohort, WEEK(MONDAY)) AS week_cohort,
        user_pseudo_id,
        days_since_start,
        CASE 
            WHEN days_since_start = 0 
            THEN 0 
            ELSE CEIL(days_since_start / 7) 
        END AS weeks_since_start
    FROM 
        days_since_start_table
),
RETENTION_INFO AS (
  SELECT 
      week_cohort,
      weeks_since_start,
      COUNT(DISTINCT user_pseudo_id) AS retained_users
  FROM 
      weeks_retention
  WHERE 
      week_cohort <= (SELECT min_date FROM dates)
  GROUP BY 
      week_cohort,
      weeks_since_start
  HAVING 
      weeks_since_start <= 4
  ORDER BY 
      week_cohort,
      weeks_since_start
)

SELECT weeks_since_start, retained_users
FROM RETENTION_INFO
WHERE week_cohort = DATE('2018-07-02')",events_*.event_date; events_*.event_name; events_*.user_first_touch_timestamp; events_*.user_pseudo_id,4,lite_sql,sf_ga028,20
local003,E_commerce,sqlite,"According to the RFM definition document, how much is the average sales per order for each customer within distinct RFM segments, considering only 'delivered' orders? Please rank the customers into segments to analyze differences in average sales across these segments","WITH RecencyScore AS (
    SELECT customer_unique_id,
           MAX(order_purchase_timestamp) AS last_purchase,
           NTILE(5) OVER (ORDER BY MAX(order_purchase_timestamp) DESC) AS recency
    FROM orders
        JOIN customers USING (customer_id)
    WHERE order_status = 'delivered'
    GROUP BY customer_unique_id
),
FrequencyScore AS (
    SELECT customer_unique_id,
           COUNT(order_id) AS total_orders,
           NTILE(5) OVER (ORDER BY COUNT(order_id) DESC) AS frequency
    FROM orders
        JOIN customers USING (customer_id)
    WHERE order_status = 'delivered'
    GROUP BY customer_unique_id
),
MonetaryScore AS (
    SELECT customer_unique_id,
           SUM(price) AS total_spent,
           NTILE(5) OVER (ORDER BY SUM(price) DESC) AS monetary
    FROM orders
        JOIN order_items USING (order_id)
        JOIN customers USING (customer_id)
    WHERE order_status = 'delivered'
    GROUP BY customer_unique_id
),

-- 2. Assign each customer to a group
RFM AS (
    SELECT last_purchase, total_orders, total_spent,
        CASE
            WHEN recency = 1 AND frequency + monetary IN (1, 2, 3, 4) THEN ""Champions""
            WHEN recency IN (4, 5) AND frequency + monetary IN (1, 2) THEN ""Can't Lose Them""
            WHEN recency IN (4, 5) AND frequency + monetary IN (3, 4, 5, 6) THEN ""Hibernating""
            WHEN recency IN (4, 5) AND frequency + monetary IN (7, 8, 9, 10) THEN ""Lost""
            WHEN recency IN (2, 3) AND frequency + monetary IN (1, 2, 3, 4) THEN ""Loyal Customers""
            WHEN recency = 3 AND frequency + monetary IN (5, 6) THEN ""Needs Attention""
            WHEN recency = 1 AND frequency + monetary IN (7, 8) THEN ""Recent Users""
            WHEN recency = 1 AND frequency + monetary IN (5, 6) OR
                recency = 2 AND frequency + monetary IN (5, 6, 7, 8) THEN ""Potentital Loyalists""
            WHEN recency = 1 AND frequency + monetary IN (9, 10) THEN ""Price Sensitive""
            WHEN recency = 2 AND frequency + monetary IN (9, 10) THEN ""Promising""
            WHEN recency = 3 AND frequency + monetary IN (7, 8, 9, 10) THEN ""About to Sleep""
        END AS RFM_Bucket
    FROM RecencyScore
        JOIN FrequencyScore USING (customer_unique_id)
        JOIN MonetaryScore USING (customer_unique_id)
)

SELECT RFM_Bucket, 
       AVG(total_spent / total_orders) AS avg_sales_per_customer
FROM RFM
GROUP BY RFM_Bucket",customers.customer_id; customers.customer_unique_id; order_items.order_id; order_items.price; orders.customer_id; orders.order_id; orders.order_purchase_timestamp; orders.order_status,8,lite_sql,sf_local003,70
local004,E_commerce,sqlite,"Could you tell me the number of orders, average payment per order and customer lifespan in weeks of the 3 custumers with the highest average payment per order. Attention: I want the lifespan in float number if it's longer than one week, otherwise set it to be 1.0.","WITH CustomerData AS (
    SELECT
        customer_unique_id,
        COUNT(DISTINCT orders.order_id) AS order_count,
        SUM(payment_value) AS total_payment,
        JULIANDAY(MIN(order_purchase_timestamp)) AS first_order_day,
        JULIANDAY(MAX(order_purchase_timestamp)) AS last_order_day
    FROM customers
        JOIN orders USING (customer_id)
        JOIN order_payments USING (order_id)
    GROUP BY customer_unique_id
)
SELECT
    customer_unique_id,
    order_count AS PF,
    ROUND(total_payment / order_count, 2) AS AOV,
    CASE
        WHEN (last_order_day - first_order_day) < 7 THEN
            1
        ELSE
            (last_order_day - first_order_day) / 7
        END AS ACL
FROM CustomerData
ORDER BY AOV DESC
LIMIT 3",customers.customer_id; customers.customer_unique_id; order_payments.order_id; order_payments.payment_value; orders.customer_id; orders.order_id; orders.order_purchase_timestamp,7,snow_sql_near_exact,sf_local004,70
local008,Baseball,sqlite,"I would like to know the given names of baseball players who have achieved the highest value of games played, runs, hits, and home runs, with their corresponding score values.","WITH player_stats AS (
    SELECT
        b.player_id,
        p.name_given AS player_name,
        SUM(b.g) AS games_played,
        SUM(b.r) AS runs,
        SUM(b.h) AS hits,
        SUM(b.hr) AS home_runs
    FROM player p
    JOIN batting b ON p.player_id = b.player_id
    GROUP BY b.player_id, p.name_given
)

SELECT 'Games Played' AS Category, player_name AS Player_Name, games_played AS Batting_Table_Topper
FROM player_stats
WHERE games_played = (SELECT MAX(games_played) FROM player_stats)

UNION ALL

SELECT 'Runs' AS Category, player_name AS Player_Name, runs AS Batting_Table_Topper
FROM player_stats
WHERE runs = (SELECT MAX(runs) FROM player_stats)

UNION ALL

SELECT 'Hits' AS Category, player_name AS Player_Name, hits AS Batting_Table_Topper
FROM player_stats
WHERE hits = (SELECT MAX(hits) FROM player_stats)

UNION ALL

SELECT 'Home Runs' AS Category, player_name AS Player_Name, home_runs AS Batting_Table_Topper
FROM player_stats
WHERE home_runs = (SELECT MAX(home_runs) FROM player_stats);",batting.g; batting.h; batting.hr; batting.player_id; batting.r; player.name_given; player.player_id,7,lite_sql,sf_local008,352
local017,California_Traffic_Collision,sqlite,In which year were the two most common causes of traffic accidents different from those in other years?,"WITH AnnualTotals AS (
    SELECT 
        STRFTIME('%Y', collision_date) AS Year, 
        COUNT(case_id) AS AnnualTotal
    FROM 
        collisions
    GROUP BY 
        Year
),
CategoryTotals AS (
    SELECT 
        STRFTIME('%Y', collision_date) AS Year,
        pcf_violation_category AS Category,
        COUNT(case_id) AS Subtotal
    FROM 
        collisions
    GROUP BY 
        Year, Category
),
CategoryPercentages AS (
    SELECT 
        ct.Year,
        ct.Category,
        ROUND((ct.Subtotal * 100.0) / at.AnnualTotal, 1) AS PercentageOfAnnualRoadIncidents
    FROM 
        CategoryTotals ct
    JOIN 
        AnnualTotals at ON ct.Year = at.Year
),
RankedCategories AS (
    SELECT
        Year,
        Category,
        PercentageOfAnnualRoadIncidents,
        ROW_NUMBER() OVER (PARTITION BY Year ORDER BY PercentageOfAnnualRoadIncidents DESC) AS Rank
    FROM
        CategoryPercentages
),
TopTwoCategories AS (
    SELECT
        Year,
        GROUP_CONCAT(Category, ', ') AS TopCategories
    FROM
        RankedCategories
    WHERE
        Rank <= 2
    GROUP BY
        Year
),
UniqueYear AS (
    SELECT
        Year
    FROM
        TopTwoCategories
    GROUP BY
        TopCategories
    HAVING COUNT(Year) = 1
),
results AS (
SELECT 
    rc.Year, 
    rc.Category, 
    rc.PercentageOfAnnualRoadIncidents
FROM 
    UniqueYear u
JOIN 
    RankedCategories rc ON u.Year = rc.Year
WHERE 
    rc.Rank <= 2
)

SELECT distinct Year FROM results",collisions.case_id; collisions.collision_date; collisions.pcf_violation_category,3,lite_sql,sf_local017,120
local019,WWE,sqlite,"For the NXT title that had the shortest match (excluding titles with ""title change""), what were the names of the two wrestlers involved?","WITH MatchDetails AS (
    SELECT
        b.name AS titles,
        m.duration AS match_duration,
        w1.name || ' vs ' || w2.name AS matches,
        m.win_type AS win_type,
        l.name AS location,
        e.name AS event,
        ROW_NUMBER() OVER (PARTITION BY b.name ORDER BY m.duration ASC) AS rank
    FROM 
        Belts b
    INNER JOIN Matches m ON m.title_id = b.id
    INNER JOIN Wrestlers w1 ON w1.id = m.winner_id
    INNER JOIN Wrestlers w2 ON w2.id = m.loser_id
    INNER JOIN Cards c ON c.id = m.card_id
    INNER JOIN Locations l ON l.id = c.location_id
    INNER JOIN Events e ON e.id = c.event_id
    INNER JOIN Promotions p ON p.id = c.promotion_id
    WHERE
        p.name = 'NXT'
        AND m.duration <> ''
        AND b.name <> ''
        AND b.name NOT IN (
            SELECT name 
            FROM Belts 
            WHERE name LIKE '%title change%'
        )
),
Rank1 AS (
SELECT 
    titles,
    match_duration,
    matches,
    win_type,
    location,
    event
FROM 
    MatchDetails
WHERE 
    rank = 1
)
SELECT
    SUBSTR(matches, 1, INSTR(matches, ' vs ') - 1) AS wrestler1,
    SUBSTR(matches, INSTR(matches, ' vs ') + 4) AS wrestler2
FROM
Rank1
ORDER BY match_duration 
LIMIT 1",Belts.id; Belts.name; Cards.event_id; Cards.id; Cards.location_id; Cards.promotion_id; Events.id; Events.name; Locations.id; Locations.name; Matches.card_id; Matches.duration; Matches.loser_id; Matches.title_id; Matches.win_type; Matches.winner_id; Promotions.id; Promotions.name; Wrestlers.id; Wrestlers.name,20,lite_sql,sf_local019,35
local022,IPL,sqlite,"Show me the names of strikers who scored no less than 100 runs in a match, but their team lost the game?","-- Step 1: Calculate players' total runs in each match
WITH player_runs AS (
    SELECT 
        bbb.striker AS player_id, 
        bbb.match_id, 
        SUM(bsc.runs_scored) AS total_runs 
    FROM 
        ball_by_ball AS bbb
    JOIN 
        batsman_scored AS bsc
    ON 
        bbb.match_id = bsc.match_id 
        AND bbb.over_id = bsc.over_id 
        AND bbb.ball_id = bsc.ball_id 
        AND bbb.innings_no = bsc.innings_no
    GROUP BY 
        bbb.striker, bbb.match_id
    HAVING 
        SUM(bsc.runs_scored) >= 100
),

-- Step 2: Identify losing teams for each match
losing_teams AS (
    SELECT 
        match_id, 
        CASE 
            WHEN match_winner = team_1 THEN team_2 
            ELSE team_1 
        END AS loser 
    FROM 
        match
),

-- Step 3: Combine the above results to get players who scored 100 or more runs in losing teams
players_in_losing_teams AS (
    SELECT 
        pr.player_id, 
        pr.match_id 
    FROM 
        player_runs AS pr
    JOIN 
        losing_teams AS lt
    ON 
        pr.match_id = lt.match_id
    JOIN 
        player_match AS pm
    ON 
        pr.player_id = pm.player_id 
        AND pr.match_id = pm.match_id 
        AND lt.loser = pm.team_id
)

-- Step 4: Select distinct player names from the player table
SELECT DISTINCT 
    p.player_name 
FROM 
    player AS p
JOIN 
    players_in_losing_teams AS plt
ON 
    p.player_id = plt.player_id
ORDER BY 
    p.player_name;",ball_by_ball.ball_id; ball_by_ball.innings_no; ball_by_ball.match_id; ball_by_ball.over_id; ball_by_ball.striker; batsman_scored.ball_id; batsman_scored.innings_no; batsman_scored.match_id; batsman_scored.over_id; batsman_scored.runs_scored; match.match_id; match.match_winner; match.team_1; match.team_2; player.player_id; player.player_name; player_match.match_id; player_match.player_id; player_match.team_id,19,snow_sql_near_exact,sf_local022,52
local023,IPL,sqlite,"Please help me find the names of top 5 players with the highest average runs per match in season 5, along with their batting averages.","WITH runs_scored AS (
    SELECT 
        bb.striker AS player_id,
        bb.match_id,
        bs.runs_scored AS runs
    FROM 
        ball_by_ball AS bb
    JOIN 
        batsman_scored AS bs ON bb.match_id = bs.match_id 
            AND bb.over_id = bs.over_id 
            AND bb.ball_id = bs.ball_id 
            AND bb.innings_no = bs.innings_no
    WHERE 
        bb.match_id IN (SELECT match_id FROM match WHERE season_id = 5)
),
total_runs AS (
    SELECT 
        player_id, 
        match_id, 
        SUM(runs) AS total_runs 
    FROM 
        runs_scored 
    GROUP BY 
        player_id, match_id
),
batting_averages AS (
    SELECT 
        player_id, 
        SUM(total_runs) AS runs, 
        COUNT(match_id) AS num_matches,
        ROUND(SUM(total_runs) / CAST(COUNT(match_id) AS FLOAT), 3) AS batting_avg
    FROM 
        total_runs 
    GROUP BY 
        player_id 
    ORDER BY 
        batting_avg DESC 
    LIMIT 5
)
SELECT 
    p.player_name,
    b.batting_avg
FROM 
    player AS p
JOIN 
    batting_averages AS b ON p.player_id = b.player_id
ORDER BY 
    b.batting_avg DESC;",ball_by_ball.ball_id; ball_by_ball.innings_no; ball_by_ball.match_id; ball_by_ball.over_id; ball_by_ball.striker; batsman_scored.ball_id; batsman_scored.innings_no; batsman_scored.match_id; batsman_scored.over_id; batsman_scored.runs_scored; match.match_id; match.season_id; player.player_id; player.player_name,14,lite_sql,sf_local023,52
local029,Brazilian_E_Commerce,sqlite,"Please calculate the average payment value, city, and state for the top 3 customers with the most delivered orders.","WITH customer_orders AS (
    SELECT
        c.customer_unique_id,
        COUNT(o.order_id) AS Total_Orders_By_Customers,
        AVG(p.payment_value) AS Average_Payment_By_Customer,
        c.customer_city,
        c.customer_state
    FROM olist_customers c
    JOIN olist_orders o ON c.customer_id = o.customer_id
    JOIN olist_order_payments p ON o.order_id = p.order_id
    WHERE o.order_status = 'delivered'
    GROUP BY c.customer_unique_id, c.customer_city, c.customer_state
)

SELECT 
    Average_Payment_By_Customer,
    customer_city,
    customer_state
FROM customer_orders
ORDER BY Total_Orders_By_Customers DESC
LIMIT 3;",olist_customers.customer_city; olist_customers.customer_id; olist_customers.customer_state; olist_customers.customer_unique_id; olist_order_payments.order_id; olist_order_payments.payment_value; olist_orders.customer_id; olist_orders.order_id; olist_orders.order_status,9,lite_sql,sf_local029,62
local038,Pagila,sqlite,"Could you help me find the actor who appeared most in English G or PG-rated children's movies no longer than 2 hours, released between 2000 and 2010？Give me a full name.","SELECT
    actor.first_name || ' ' || actor.last_name AS full_name
FROM
    actor
INNER JOIN film_actor ON actor.actor_id = film_actor.actor_id
INNER JOIN film ON film_actor.film_id = film.film_id
INNER JOIN film_category ON film.film_id = film_category.film_id
INNER JOIN category ON film_category.category_id = category.category_id
-- Join with the language table
INNER JOIN language ON film.language_id = language.language_id
WHERE
    category.name = 'Children' AND
    film.release_year BETWEEN 2000 AND 2010 AND
    film.rating IN ('G', 'PG') AND
    language.name = 'English' AND
    film.length <= 120
GROUP BY
    actor.actor_id, actor.first_name, actor.last_name
ORDER BY
    COUNT(film.film_id) DESC
LIMIT 1;",actor.actor_id; actor.first_name; actor.last_name; category.category_id; category.name; film.film_id; film.language_id; film.length; film.rating; film.release_year; film_actor.actor_id; film_actor.film_id; film_category.category_id; film_category.film_id; language.language_id; language.name,16,snow_sql_near_exact,sf_local038,120
local039,Pagila,sqlite,"Please help me find the film category with the highest total rental hours in cities where the city's name either starts with ""A"" or contains a hyphen. ","SELECT
    category.name
FROM
    category
INNER JOIN film_category USING (category_id)
INNER JOIN film USING (film_id)
INNER JOIN inventory USING (film_id)
INNER JOIN rental USING (inventory_id)
INNER JOIN customer USING (customer_id)
INNER JOIN address USING (address_id)
INNER JOIN city USING (city_id)
WHERE
    LOWER(city.city) LIKE 'a%' OR city.city LIKE '%-%'
GROUP BY
    category.name
ORDER BY
    SUM(CAST((julianday(rental.return_date) - julianday(rental.rental_date)) * 24 AS INTEGER)) DESC
LIMIT
    1;",address.address_id; address.city_id; category.category_id; category.name; city.city; city.city_id; customer.address_id; customer.customer_id; film.film_id; film_category.category_id; film_category.film_id; inventory.film_id; inventory.inventory_id; rental.customer_id; rental.inventory_id; rental.rental_date; rental.return_date,17,snow_sql_near_exact,sf_local039,120
local058,education_business,sqlite,"Can you provide a list of hardware product segments along with their unique product counts for 2020 in the output, ordered by the highest percentage increase in unique fact sales products from 2020 to 2021?","WITH UniqueProducts2020 AS (
    SELECT
        dp.segment,
        COUNT(DISTINCT fsm.product_code) AS unique_products_2020
    FROM
        hardware_fact_sales_monthly fsm
    JOIN
        hardware_dim_product dp ON fsm.product_code = dp.product_code
    WHERE
        fsm.fiscal_year = 2020
    GROUP BY
        dp.segment
),
UniqueProducts2021 AS (
    SELECT
        dp.segment,
        COUNT(DISTINCT fsm.product_code) AS unique_products_2021
    FROM
        hardware_fact_sales_monthly fsm
    JOIN
        hardware_dim_product dp ON fsm.product_code = dp.product_code
    WHERE
        fsm.fiscal_year = 2021
    GROUP BY
        dp.segment
)
SELECT
    spc.segment,
    spc.unique_products_2020 AS product_count_2020
FROM
    UniqueProducts2020 spc
JOIN
    UniqueProducts2021 fup ON spc.segment = fup.segment
ORDER BY
    ((fup.unique_products_2021 - spc.unique_products_2020) * 100.0) / (spc.unique_products_2020) DESC;",hardware_dim_product.product_code; hardware_dim_product.segment; hardware_fact_sales_monthly.fiscal_year; hardware_fact_sales_monthly.product_code,4,lite_sql,sf_local058,98
local065,modern_data,sqlite,Calculate the total income from Meat Lovers pizzas priced at $12 and Vegetarian pizzas at $10. Include any extra toppings charged at $1 each. Ensure that canceled orders are filtered out. How much money has Pizza Runner earned in total?,"WITH get_extras_count AS (
    WITH RECURSIVE split_extras AS (
        SELECT
            order_id,
            TRIM(SUBSTR(extras, 1, INSTR(extras || ',', ',') - 1)) AS each_extra,
            SUBSTR(extras || ',', INSTR(extras || ',', ',') + 1) AS remaining_extras
        FROM
            pizza_clean_customer_orders
        UNION ALL
        SELECT
            order_id,
            TRIM(SUBSTR(remaining_extras, 1, INSTR(remaining_extras, ',') - 1)) AS each_extra,
            SUBSTR(remaining_extras, INSTR(remaining_extras, ',') + 1)
        FROM
            split_extras
        WHERE
            remaining_extras <> ''
    )
    SELECT
        order_id,
        COUNT(each_extra) AS total_extras
    FROM
        split_extras
    GROUP BY
        order_id
),
calculate_totals AS (
    SELECT
        t1.order_id,
        t1.pizza_id,
        SUM(
            CASE
                WHEN pizza_id = 1 THEN 12
                WHEN pizza_id = 2 THEN 10
            END
        ) AS total_price,
        t3.total_extras
    FROM 
        pizza_clean_customer_orders AS t1
    JOIN
        pizza_clean_runner_orders AS t2 
    ON
        t2.order_id = t1.order_id
    LEFT JOIN
        get_extras_count AS t3
    ON
        t3.order_id = t1.order_id
    WHERE
        t2.cancellation IS NULL
    GROUP BY 
        t1.order_id,
        t1.pizza_id,
        t3.total_extras
)
SELECT 
    SUM(total_price) + SUM(total_extras) AS total_income
FROM 
    calculate_totals;",pizza_clean_customer_orders.extras; pizza_clean_customer_orders.order_id; pizza_clean_customer_orders.pizza_id; pizza_clean_runner_orders.cancellation; pizza_clean_runner_orders.order_id,5,lite_sql,sf_local065,77
local066,modern_data,sqlite,"Based on our customer pizza order information, summarize the total quantity of each ingredient used in the pizzas we delivered. Output the name and quantity for each ingredient.","WITH cte_cleaned_customer_orders AS (
    SELECT
        *,
        ROW_NUMBER() OVER () AS original_row_number
    FROM 
        pizza_clean_customer_orders
),
split_regular_toppings AS (
    SELECT
        pizza_id,
        TRIM(SUBSTR(toppings, 1, INSTR(toppings || ',', ',') - 1)) AS topping_id,
        SUBSTR(toppings || ',', INSTR(toppings || ',', ',') + 1) AS remaining_toppings
    FROM 
        pizza_recipes
    UNION ALL
    SELECT
        pizza_id,
        TRIM(SUBSTR(remaining_toppings, 1, INSTR(remaining_toppings, ',') - 1)) AS topping_id,
        SUBSTR(remaining_toppings, INSTR(remaining_toppings, ',') + 1) AS remaining_toppings
    FROM 
        split_regular_toppings
    WHERE
        remaining_toppings <> ''
),
cte_base_toppings AS (
    SELECT
        t1.order_id,
        t1.customer_id,
        t1.pizza_id,
        t1.order_time,
        t1.original_row_number,
        t2.topping_id
    FROM 
        cte_cleaned_customer_orders AS t1
    LEFT JOIN 
        split_regular_toppings AS t2
    ON 
        t1.pizza_id = t2.pizza_id
),
split_exclusions AS (
    SELECT
        order_id,
        customer_id,
        pizza_id,
        order_time,
        original_row_number,
        TRIM(SUBSTR(exclusions, 1, INSTR(exclusions || ',', ',') - 1)) AS topping_id,
        SUBSTR(exclusions || ',', INSTR(exclusions || ',', ',') + 1) AS remaining_exclusions
    FROM 
        cte_cleaned_customer_orders
    WHERE 
        exclusions IS NOT NULL
    UNION ALL
    SELECT
        order_id,
        customer_id,
        pizza_id,
        order_time,
        original_row_number,
        TRIM(SUBSTR(remaining_exclusions, 1, INSTR(remaining_exclusions, ',') - 1)) AS topping_id,
        SUBSTR(remaining_exclusions, INSTR(remaining_exclusions, ',') + 1) AS remaining_exclusions
    FROM 
        split_exclusions
    WHERE
        remaining_exclusions <> ''
),
split_extras AS (
    SELECT
        order_id,
        customer_id,
        pizza_id,
        order_time,
        original_row_number,
        TRIM(SUBSTR(extras, 1, INSTR(extras || ',', ',') - 1)) AS topping_id,
        SUBSTR(extras || ',', INSTR(extras || ',', ',') + 1) AS remaining_extras
    FROM 
        cte_cleaned_customer_orders
    WHERE 
        extras IS NOT NULL
    UNION ALL
    SELECT
        order_id,
        customer_id,
        pizza_id,
        order_time,
        original_row_number,
        TRIM(SUBSTR(remaining_extras, 1, INSTR(remaining_extras, ',') - 1)) AS topping_id,
        SUBSTR(remaining_extras, INSTR(remaining_extras, ',') + 1) AS remaining_extras
    FROM 
        split_extras
    WHERE
        remaining_extras <> ''
),
cte_combined_orders AS (
    SELECT 
        order_id,
        customer_id,
        pizza_id,
        order_time,
        original_row_number,
        topping_id
    FROM 
        cte_base_toppings
    WHERE topping_id NOT IN (SELECT topping_id FROM split_exclusions WHERE split_exclusions.order_id = cte_base_toppings.order_id)
    UNION ALL
    SELECT 
        order_id,
        customer_id,
        pizza_id,
        order_time,
        original_row_number,
        topping_id
    FROM 
        split_extras
)
SELECT
    t2.topping_name,
    COUNT(*) AS topping_count
FROM 
    cte_combined_orders AS t1
JOIN 
    pizza_toppings AS t2
ON 
    t1.topping_id = t2.topping_id
GROUP BY 
    t2.topping_name
ORDER BY 
    topping_count DESC;",pizza_clean_customer_orders.customer_id; pizza_clean_customer_orders.exclusions; pizza_clean_customer_orders.extras; pizza_clean_customer_orders.order_id; pizza_clean_customer_orders.order_time; pizza_clean_customer_orders.pizza_id; pizza_recipes.pizza_id; pizza_recipes.toppings; pizza_toppings.topping_id; pizza_toppings.topping_name,10,lite_sql,sf_local066,77
local075,bank_sales_trading,sqlite,"Can you provide a breakdown of how many times each product was viewed, how many times they were added to the shopping cart, and how many times they were left in the cart without being purchased? Also, give me the count of actual purchases for each product. Ensure that products with a page id in (1, 2, 12, 13) are filtered out.","WITH product_viewed AS (
    SELECT
        t1.page_id,
        SUM(CASE WHEN event_type = 1 THEN 1 ELSE 0 END) AS n_page_views,
        SUM(CASE WHEN event_type = 2 THEN 1 ELSE 0 END) AS n_added_to_cart
    FROM
        shopping_cart_page_hierarchy AS t1
    JOIN
        shopping_cart_events AS t2
    ON
        t1.page_id = t2.page_id
    WHERE
        t1.product_id IS NOT NULL
    GROUP BY
        t1.page_id
),
product_purchased AS (
    SELECT
        t2.page_id,
        SUM(CASE WHEN event_type = 2 THEN 1 ELSE 0 END) AS purchased_from_cart
    FROM
        shopping_cart_page_hierarchy AS t1
    JOIN
        shopping_cart_events AS t2
    ON
        t1.page_id = t2.page_id
    WHERE
        t1.product_id IS NOT NULL
        AND EXISTS (
            SELECT
                visit_id
            FROM
                shopping_cart_events
            WHERE
                event_type = 3
                AND t2.visit_id = visit_id
        )
        AND t1.page_id NOT IN (1, 2, 12, 13)
    GROUP BY
        t2.page_id
),
product_abandoned AS (
    SELECT
        t2.page_id,
        SUM(CASE WHEN event_type = 2 THEN 1 ELSE 0 END) AS abandoned_in_cart
    FROM
        shopping_cart_page_hierarchy AS t1
    JOIN
        shopping_cart_events AS t2
    ON
        t1.page_id = t2.page_id
    WHERE
        t1.product_id IS NOT NULL
        AND NOT EXISTS (
            SELECT
                visit_id
            FROM
                shopping_cart_events
            WHERE
                event_type = 3
                AND t2.visit_id = visit_id
        )
        AND t1.page_id NOT IN (1, 2, 12, 13)
    GROUP BY
        t2.page_id
)
SELECT
    t1.page_id,
    t1.page_name,
    t2.n_page_views AS 'number of product being viewed',
    t2.n_added_to_cart AS 'number added to the cart',
    t4.abandoned_in_cart AS 'without being purchased in cart',
    t3.purchased_from_cart AS 'count of actual purchases'
FROM
    shopping_cart_page_hierarchy AS t1
JOIN
    product_viewed AS t2 
ON
    t2.page_id = t1.page_id
JOIN
    product_purchased AS t3 
ON 
    t3.page_id = t1.page_id
JOIN
    product_abandoned AS t4 
ON 
    t4.page_id = t1.page_id;",shopping_cart_events.event_type; shopping_cart_events.page_id; shopping_cart_events.visit_id; shopping_cart_page_hierarchy.page_id; shopping_cart_page_hierarchy.page_name; shopping_cart_page_hierarchy.product_id,6,lite_sql,sf_local075,106
local078,bank_sales_trading,sqlite,"Identify the top 10 and bottom 10 interest categories based on their highest composition values across all months. For each category, display the time(MM-YYYY), interest name, and the composition value","WITH get_interest_rank AS (
    SELECT
        t1.month_year,
        t2.interest_name,
        t1.composition,
        RANK() OVER (
            PARTITION BY t2.interest_name
            ORDER BY t1.composition DESC
        ) AS interest_rank
    FROM 
        interest_metrics AS t1
    JOIN 
        interest_map AS t2
    ON 
        t1.interest_id = t2.id
    WHERE 
        t1.month_year IS NOT NULL
),
get_top_10 AS (
    SELECT
        month_year,
        interest_name,
        composition
    FROM 
        get_interest_rank
    WHERE 
        interest_rank = 1
    ORDER BY 
        composition DESC
    LIMIT 10
),
get_bottom_10 AS (
    SELECT
        month_year,
        interest_name,
        composition
    FROM 
        get_interest_rank
    WHERE 
        interest_rank = 1
    ORDER BY 
        composition ASC
    LIMIT 10
)
SELECT * 
FROM 
    get_top_10
UNION
SELECT * 
FROM 
    get_bottom_10
ORDER BY 
    composition DESC;",interest_map.id; interest_map.interest_name; interest_metrics.composition; interest_metrics.interest_id; interest_metrics.month_year,5,lite_sql,sf_local078,106
local099,Db-IMDB,sqlite,I need you to look into the actor collaborations and tell me how many actors have made more films with Yash Chopra than with any other director. This will help us understand his influence on the industry better.,"WITH YASH_CHOPRAS_PID AS (
    SELECT
        TRIM(P.PID) AS PID
    FROM
        Person P
    WHERE
        TRIM(P.Name) = 'Yash Chopra'
),
NUM_OF_MOV_BY_ACTOR_DIRECTOR AS (
    SELECT
        TRIM(MC.PID) AS ACTOR_PID,
        TRIM(MD.PID) AS DIRECTOR_PID,
        COUNT(DISTINCT TRIM(MD.MID)) AS NUM_OF_MOV
    FROM
        M_Cast MC
    JOIN
        M_Director MD ON TRIM(MC.MID) = TRIM(MD.MID)
    GROUP BY
        ACTOR_PID,
        DIRECTOR_PID
),
NUM_OF_MOVIES_BY_YC AS (
    SELECT
        NM.ACTOR_PID,
        NM.DIRECTOR_PID,
        NM.NUM_OF_MOV AS NUM_OF_MOV_BY_YC
    FROM
        NUM_OF_MOV_BY_ACTOR_DIRECTOR NM
    JOIN
        YASH_CHOPRAS_PID YCP ON NM.DIRECTOR_PID = YCP.PID
),
MAX_MOV_BY_OTHER_DIRECTORS AS (
    SELECT
        ACTOR_PID,
        MAX(NUM_OF_MOV) AS MAX_NUM_OF_MOV
    FROM
        NUM_OF_MOV_BY_ACTOR_DIRECTOR NM
    JOIN
        YASH_CHOPRAS_PID YCP ON NM.DIRECTOR_PID <> YCP.PID
    GROUP BY
        ACTOR_PID
),
ACTORS_MOV_COMPARISION AS (
    SELECT
        NMY.ACTOR_PID,
        CASE WHEN NMY.NUM_OF_MOV_BY_YC > IFNULL(NMO.MAX_NUM_OF_MOV, 0) THEN 'Y' ELSE 'N' END AS MORE_MOV_BY_YC
    FROM
        NUM_OF_MOVIES_BY_YC NMY
    LEFT OUTER JOIN
        MAX_MOV_BY_OTHER_DIRECTORS NMO ON NMY.ACTOR_PID = NMO.ACTOR_PID
)
SELECT
    COUNT(DISTINCT TRIM(P.PID)) AS ""Number of actor""
FROM
    Person P
WHERE
    TRIM(P.PID) IN (
        SELECT
            DISTINCT ACTOR_PID
        FROM
            ACTORS_MOV_COMPARISION
        WHERE
            MORE_MOV_BY_YC = 'Y'
    );",M_Cast.MID; M_Cast.PID; M_Director.MID; M_Director.PID; Person.Name; Person.PID,6,lite_sql,sf_local099,50
local131,EntertainmentAgency,sqlite,"Could you list each musical style with the number of times it appears as a 1st, 2nd, or 3rd preference in a single row per style?","SELECT 
  Musical_Styles.StyleName,
  COUNT(RankedPreferences.FirstStyle)
    AS FirstPreference,
  COUNT(RankedPreferences.SecondStyle)
    AS SecondPreference,
  COUNT(RankedPreferences.ThirdStyle)
    AS ThirdPreference
FROM Musical_Styles,
 (SELECT (CASE WHEN
    Musical_Preferences.PreferenceSeq = 1
               THEN Musical_Preferences.StyleID
               ELSE Null END) As FirstStyle,
         (CASE WHEN
    Musical_Preferences.PreferenceSeq = 2
               THEN Musical_Preferences.StyleID
               ELSE Null END) As SecondStyle,
         (CASE WHEN
    Musical_Preferences.PreferenceSeq = 3
               THEN Musical_Preferences.StyleID
               ELSE Null END) AS ThirdStyle
   FROM Musical_Preferences)  AS RankedPreferences
WHERE Musical_Styles.StyleID =
         RankedPreferences.FirstStyle
  OR Musical_Styles.StyleID =
         RankedPreferences.SecondStyle
  OR Musical_Styles.StyleID =
         RankedPreferences.ThirdStyle
GROUP BY StyleID, StyleName
HAVING COUNT(FirstStyle) > 0
     OR     COUNT(SecondStyle) > 0
     OR     COUNT(ThirdStyle) > 0
ORDER BY FirstPreference DESC,
        SecondPreference DESC,
        ThirdPreference DESC, StyleID;",Musical_Preferences.PreferenceSeq; Musical_Preferences.StyleID; Musical_Styles.StyleID; Musical_Styles.StyleName,4,lite_sql,sf_local131,76
local163,education_business,sqlite,"Which university faculty members' salaries are closest to the average salary for their respective ranks? Please provide the ranks, first names, last names, and salaries.university","WITH AvgSalaries AS (
    SELECT 
        facrank AS FacRank,
        AVG(facsalary) AS AvSalary
    FROM 
        university_faculty
    GROUP BY 
        facrank
),
SalaryDifferences AS (
    SELECT 
        university_faculty.facrank AS FacRank, 
        university_faculty.facfirstname AS FacFirstName, 
        university_faculty.faclastname AS FacLastName, 
        university_faculty.facsalary AS Salary, 
        ABS(university_faculty.facsalary - AvgSalaries.AvSalary) AS Diff
    FROM 
        university_faculty
    JOIN 
        AvgSalaries ON university_faculty.facrank = AvgSalaries.FacRank
),
MinDifferences AS (
    SELECT 
        FacRank, 
        MIN(Diff) AS MinDiff
    FROM 
        SalaryDifferences
    GROUP BY 
        FacRank
)
SELECT 
    s.FacRank, 
    s.FacFirstName, 
    s.FacLastName, 
    s.Salary
FROM 
    SalaryDifferences s
JOIN 
    MinDifferences m ON s.FacRank = m.FacRank AND s.Diff = m.MinDiff;",university_faculty.FacFirstName; university_faculty.FacLastName; university_faculty.FacRank; university_faculty.FacSalary,4,lite_sql,sf_local163,98
local197,sqlite-sakila,sqlite,"Can you determine which of our top 10 paying customers had the highest payment difference in any given month? I’d like to know the highest payment difference for this customer, with the result rounded to two decimal places.","WITH result_table AS (
  SELECT 
    strftime('%m', pm.payment_date) AS pay_mon, 
    customer_id,
    COUNT(pm.amount) AS pay_countpermon, 
    SUM(pm.amount) AS pay_amount 
  FROM 
    payment AS pm 
  GROUP BY 
    pay_mon, 
    customer_id
), 
top10_customer AS (
  SELECT 
    customer_id,
    SUM(tb.pay_amount) AS total_payments 
  FROM 
    result_table AS tb 
  GROUP BY 
    customer_id
  ORDER BY 
    SUM(tb.pay_amount) DESC 
  LIMIT 
    10
), 
difference_per_mon AS (
  SELECT 
    pay_mon AS month_number, 
    pay_mon AS month, 
    tb.pay_countpermon, 
    tb.pay_amount, 
    ABS(tb.pay_amount - LAG(tb.pay_amount) OVER (PARTITION BY tb.customer_id)) AS diff 
  FROM 
    result_table tb 
    JOIN top10_customer top ON top.customer_id = tb.customer_id
) 
SELECT 
  month, 
  ROUND(max_diff, 2) AS max_diff 
FROM (
  SELECT 
    month, 
    diff, 
    month_number, 
    MAX(diff) OVER (PARTITION BY month) AS max_diff 
  FROM 
    difference_per_mon
) AS max_per_mon 
WHERE 
  diff = max_diff 
ORDER BY 
  max_diff DESC 
LIMIT 
  1;",payment.amount; payment.customer_id; payment.payment_date,3,lite_sql,sf_local197,120
local199,sqlite-sakila,sqlite,"Can you identify the year and month with the highest rental orders created by the store's staff for each store? Please list the store ID, the year, the month, and the total rentals for those dates.","WITH result_table AS (
  SELECT 
    strftime('%Y', RE.RENTAL_DATE) AS YEAR, 
    strftime('%m', RE.RENTAL_DATE) AS RENTAL_MONTH, 
    ST.STORE_ID, 
    COUNT(RE.RENTAL_ID) AS count 
  FROM 
    RENTAL RE 
    JOIN STAFF ST ON RE.STAFF_ID = ST.STAFF_ID 
  GROUP BY 
    YEAR, 
    RENTAL_MONTH, 
    ST.STORE_ID 
), 
monthly_sales AS (
  SELECT 
    YEAR, 
    RENTAL_MONTH, 
    STORE_ID, 
    SUM(count) AS total_rentals 
  FROM 
    result_table 
  GROUP BY 
    YEAR, 
    RENTAL_MONTH, 
    STORE_ID
),
store_max_sales AS (
  SELECT 
    STORE_ID, 
    YEAR, 
    RENTAL_MONTH, 
    total_rentals, 
    MAX(total_rentals) OVER (PARTITION BY STORE_ID) AS max_rentals 
  FROM 
    monthly_sales
)
SELECT 
  STORE_ID, 
  YEAR, 
  RENTAL_MONTH, 
  total_rentals 
FROM 
  store_max_sales 
WHERE 
  total_rentals = max_rentals
ORDER BY 
  STORE_ID;",rental.rental_date; rental.staff_id; staff.staff_id; staff.store_id,4,snow_sql_near_exact,sf_local199,120
local210,delivery_center,sqlite,Can you identify the hubs that saw more than a 20% increase in finished orders from February to March?,"WITH february_orders AS (
    SELECT
        h.hub_name AS hub_name,
        COUNT(*) AS orders_february
    FROM 
        orders o 
    LEFT JOIN
        stores s ON o.store_id = s.store_id 
    LEFT JOIN 
        hubs h ON s.hub_id = h.hub_id 
    WHERE o.order_created_month = 2 AND o.order_status = 'FINISHED'
    GROUP BY
        h.hub_name
),
march_orders AS (
    SELECT
        h.hub_name AS hub_name,
        COUNT(*) AS orders_march
    FROM 
        orders o 
    LEFT JOIN
        stores s ON o.store_id = s.store_id 
    LEFT JOIN 
        hubs h ON s.hub_id = h.hub_id 
    WHERE o.order_created_month = 3 AND o.order_status = 'FINISHED'
    GROUP BY
        h.hub_name
)
SELECT
    fo.hub_name
FROM
    february_orders fo
LEFT JOIN 
    march_orders mo ON fo.hub_name = mo.hub_name
WHERE 
    fo.orders_february > 0 AND 
    mo.orders_march > 0 AND
    (CAST((mo.orders_march - fo.orders_february) AS REAL) / CAST(fo.orders_february AS REAL)) > 0.2  -- Filter for hubs with more than a 20% increase",hubs.hub_id; hubs.hub_name; orders.order_created_month; orders.order_created_year; orders.order_id; orders.order_status; orders.store_id; stores.hub_id; stores.store_id,9,snow_sql_near_exact,sf_local210,59
local219,EU_soccer,sqlite,Which single team has the fewest wins in each league?,"WITH match_view AS(
SELECT
    M.id,
    L.name AS league,
    M.season,
    M.match_api_id,
    T.team_long_name AS home_team,
    TM.team_long_name AS away_team,
    M.home_team_goal,
    M.away_team_goal,
    P1.player_name AS home_gk,
    P2.player_name AS home_center_back_1,
    P3.player_name AS home_center_back_2,
    P4.player_name AS home_right_back,
    P5.player_name AS home_left_back,
    P6.player_name AS home_midfield_1,
    P7.player_name AS home_midfield_2,
    P8.player_name AS home_midfield_3,
    P9.player_name AS home_midfield_4,
    P10.player_name AS home_second_forward,
    P11.player_name AS home_center_forward,
    P12.player_name AS away_gk,
    P13.player_name AS away_center_back_1,
    P14.player_name AS away_center_back_2,
    P15.player_name AS away_right_back,
    P16.player_name AS away_left_back,
    P17.player_name AS away_midfield_1,
    P18.player_name AS away_midfield_2,
    P19.player_name AS away_midfield_3,
    P20.player_name AS away_midfield_4,
    P21.player_name AS away_second_forward,
    P22.player_name AS away_center_forward,
    M.goal,
    M.card
FROM
    match M
LEFT JOIN
    league L ON M.league_id = L.id
LEFT JOIN
    team T ON M.home_team_api_id = T.team_api_id
LEFT JOIN
    team TM ON M.away_team_api_id = TM.team_api_id
LEFT JOIN
    player P1 ON M.home_player_1 = P1.player_api_id
LEFT JOIN
    player P2 ON M.home_player_2 = P2.player_api_id
LEFT JOIN
    player P3 ON M.home_player_3 = P3.player_api_id
LEFT JOIN
    player P4 ON M.home_player_4 = P4.player_api_id
LEFT JOIN
    player P5 ON M.home_player_5 = P5.player_api_id
LEFT JOIN
    player P6 ON M.home_player_6 = P6.player_api_id
LEFT JOIN
    player P7 ON M.home_player_7 = P7.player_api_id
LEFT JOIN
    player P8 ON M.home_player_8 = P8.player_api_id
LEFT JOIN
    player P9 ON M.home_player_9 = P9.player_api_id
LEFT JOIN
    player P10 ON M.home_player_10 = P10.player_api_id
LEFT JOIN
    player P11 ON M.home_player_11 = P11.player_api_id
LEFT JOIN
    player P12 ON M.away_player_1 = P12.player_api_id
LEFT JOIN
    player P13 ON M.away_player_2 = P13.player_api_id
LEFT JOIN
    player P14 ON M.away_player_3 = P14.player_api_id
LEFT JOIN
    player P15 ON M.away_player_4 = P15.player_api_id
LEFT JOIN
    player P16 ON M.away_player_5 = P16.player_api_id
LEFT JOIN
    player P17 ON M.away_player_6 = P17.player_api_id
LEFT JOIN
    player P18 ON M.away_player_7 = P18.player_api_id
LEFT JOIN
    player P19 ON M.away_player_8 = P19.player_api_id
LEFT JOIN
    player P20 ON M.away_player_9 = P20.player_api_id
LEFT JOIN
    player P21 ON M.away_player_10 = P21.player_api_id
LEFT JOIN
    player P22 ON M.away_player_11 = P22.player_api_id
),
match_score AS
(
    SELECT  -- Displaying teams and their goals as home_team
        id,
        home_team AS team,
        CASE
            WHEN home_team_goal > away_team_goal THEN 1 ELSE 0 END AS Winning_match
    FROM
        match_view

    UNION ALL

    SELECT  -- Displaying teams and their goals as away_team
        id,
        away_team AS team,
        CASE
            WHEN away_team_goal > home_team_goal THEN 1 ELSE 0 END AS Winning_match
    FROM
        match_view
),
winning_matches AS
(
    SELECT  -- Displaying total match wins for each team
        MV.league,
        M.team,
        COUNT(CASE WHEN M.Winning_match = 1 THEN 1 END) AS wins,
        ROW_NUMBER() OVER(PARTITION BY MV.league ORDER BY COUNT(CASE WHEN M.Winning_match = 1 THEN 1 END) ASC) AS rn
    FROM
        match_score M
    JOIN
        match_view MV
    ON
        M.id = MV.id
    GROUP BY
        MV.league,
        team
    ORDER BY
        league,
        wins ASC
)
SELECT
    league,
    team
FROM
    winning_matches
WHERE
    rn = 1  -- Getting the team with the least number of wins in each league
ORDER BY
    league;",League.id; League.name; Match.away_player_1; Match.away_player_10; Match.away_player_11; Match.away_player_2; Match.away_player_3; Match.away_player_4; Match.away_player_5; Match.away_player_6; Match.away_player_7; Match.away_player_8; Match.away_player_9; Match.away_team_api_id; Match.away_team_goal; Match.card; Match.goal; Match.home_player_1; Match.home_player_10; Match.home_player_11; Match.home_player_2; Match.home_player_3; Match.home_player_4; Match.home_player_5; Match.home_player_6; Match.home_player_7; Match.home_player_8; Match.home_player_9; Match.home_team_api_id; Match.home_team_goal; Match.id; Match.league_id; Match.match_api_id; Match.season; Player.player_api_id; Player.player_name; Team.team_api_id; Team.team_long_name,38,lite_sql,sf_local219,233
local301,bank_sales_trading,sqlite,"For weekly-sales data, I need an analysis of our sales performance around mid-June for the years 2018, 2019, and 2020. Specifically, calculate the percentage change in sales between the four weeks leading up to June 15 and the four weeks following June 15 for each year.","SELECT 
    before_effect,
    after_effect,
    after_effect - before_effect AS change_amount,
    ROUND(((after_effect * 1.0 / before_effect) - 1) * 100, 2) AS percent_change,
    '2018' AS year
FROM (
    SELECT 
        SUM(CASE WHEN delta_weeks BETWEEN 1 AND 4 THEN sales END) AS after_effect,
        SUM(CASE WHEN delta_weeks BETWEEN -3 AND 0 THEN sales END) AS before_effect
    FROM (
        SELECT 
            week_date,
            ROUND((JULIANDAY(week_date) - JULIANDAY('2018-06-15')) / 7.0) + 1 AS delta_weeks,
            sales 
        FROM cleaned_weekly_sales
    ) add_delta_weeks
) AS add_before_after
UNION ALL
SELECT 
    before_effect,
    after_effect,
    after_effect - before_effect AS change_amount,
    ROUND(((after_effect * 1.0 / before_effect) - 1) * 100, 2) AS percent_change,
    '2019' AS year
FROM (
    SELECT 
        SUM(CASE WHEN delta_weeks BETWEEN 1 AND 4 THEN sales END) AS after_effect,
        SUM(CASE WHEN delta_weeks BETWEEN -3 AND 0 THEN sales END) AS before_effect
    FROM (
        SELECT 
            week_date,
            ROUND((JULIANDAY(week_date) - JULIANDAY('2019-06-15')) / 7.0) + 1 AS delta_weeks,
            sales 
        FROM cleaned_weekly_sales
    ) add_delta_weeks
) AS add_before_after
UNION ALL
SELECT 
    before_effect,
    after_effect,
    after_effect - before_effect AS change_amount,
    ROUND(((after_effect * 1.0 / before_effect) - 1) * 100, 2) AS percent_change,
    '2020' AS year
FROM (
    SELECT 
        SUM(CASE WHEN delta_weeks BETWEEN 1 AND 4 THEN sales END) AS after_effect,
        SUM(CASE WHEN delta_weeks BETWEEN -3 AND 0 THEN sales END) AS before_effect
    FROM (
        SELECT 
            week_date,
            ROUND((JULIANDAY(week_date) - JULIANDAY('2020-06-15')) / 7.0) + 1 AS delta_weeks,
            sales 
        FROM cleaned_weekly_sales
    ) add_delta_weeks
) AS add_before_after
ORDER BY year;",cleaned_weekly_sales.sales; cleaned_weekly_sales.week_date,2,lite_sql,sf_local301,106
local309,f1,sqlite,"For each year, which driver and which constructor scored the most points? I want the full name of each driver.","with year_points as (
    select races.year,
           drivers.forename || ' ' || drivers.surname as driver,
           constructors.name as constructor,
           sum(results.points) as points
    from results
    left join races on results.race_id = races.race_id  -- Ensure these columns exist in your schema
    left join drivers on results.driver_id = drivers.driver_id  -- Ensure these columns exist in your schema
    left join constructors on results.constructor_id = constructors.constructor_id  -- Ensure these columns exist in your schema
    group by races.year, driver
    union
    select races.year,
           null as driver,
           constructors.name as constructor,
           sum(results.points) as points
    from results
    left join races on results.race_id = races.race_id  -- Ensure these columns exist in your schema
    left join drivers on results.driver_id = drivers.driver_id  -- Ensure these columns exist in your schema
    left join constructors on results.constructor_id = constructors.constructor_id  -- Ensure these columns exist in your schema
    group by races.year, constructor
),
max_points as (
    select year,
           max(case when driver is not null then points else null end) as max_driver_points,
           max(case when constructor is not null then points else null end) as max_constructor_points
    from year_points
    group by year
)
select max_points.year,
       drivers_year_points.driver,
       constructors_year_points.constructor
from max_points
left join year_points as drivers_year_points on
    max_points.year = drivers_year_points.year and
    max_points.max_driver_points = drivers_year_points.points and
    drivers_year_points.driver is not null
left join year_points as constructors_year_points on
    max_points.year = constructors_year_points.year and
    max_points.max_constructor_points = constructors_year_points.points and
    constructors_year_points.constructor is not null
order by max_points.year;",constructor_standings.constructor_id; constructor_standings.points; constructor_standings.race_id; constructors.constructor_id; constructors.name; driver_standings.points; driver_standings.race_id; drivers.driver_id; drivers_ext.full_name; races.race_id; races.round; races.year; results.constructor_id; results.driver_id; results.points; results.race_id; sprint_results.constructor_id; sprint_results.driver_id; sprint_results.points; sprint_results.race_id,20,snow_sql_near_exact,sf_local309,228