--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression multilinguality: multilingual size_categories: - 10K` | ISO3 country or area code. | `TCD` | | `country_name` | `dictionary` | Country or area name. | `Chad` | | `country_name_2` | `string` | Source column from the original resource. | `#country` | | `admin1_name` | `string` | Source column from the original resource. | `#adm1+name` | | `latitude` | `double` | Source column from the original resource. | `` | | `longitude` | `double` | Source column from the original resource. | `` | | `aggregation` | `string` | Source column from the original resource. | `` | | `indicator` | `string` | Source column from the original resource. | `#indicator+name` | | `value` | `double` | Numeric observation value. | `` | | `source_period_start_year` | `int64` | Start year inferred from source metadata. | `` | | `source_period_end_year` | `int64` | End year inferred from source metadata. | `` | | `source_period_label` | `string` | Source column from the original resource. | `` | | `source_provider` | `dictionary` | Publishing organization. | `ETH Zürich - Weather and Climate Risks` | | `source_dataset` | `dictionary` | Source dataset or package title. | `LitPop: Humanitarian Response Plan (HRP) Countries Exposure Data for ...` | | `source_resource` | `dictionary` | Source resource title, table name, or file name. | `afghanistan-admin1-litpop.csv` | | `source_package_id` | `dictionary` | Source package identifier. | `3527869c-8fe9-4289-9d57-1811e789bf60` | | `source_resource_id` | `dictionary` | Source resource identifier. | `1aeaf470-2e38-4a6b-a704-3c5ff2fd7c02` | | `source_url` | `dictionary` | Original source URL or download URL. | `https://data.humdata.org/dataset/3527869c-8fe9-4289-9d57-1811e789bf60...` | | `license_id` | `dictionary` | Source license identifier. | `cc-by` | | `retrieved_at` | `dictionary` | UTC source retrieval timestamp from the Electric Sheep Africa pipeline. | `2026-08-10T22:51:43Z` | ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-chad-litpop-humanitarian-response-plan-hrp-countries-exposure-d-3a28741c") df = ds["train"].to_pandas() print(df.head()) ``` ### Inspect Columns ```python print(df.info()) print(df.head()) ``` ### Filter By Geography ```python if "country_iso3" in df.columns: sample = df[df["country_iso3"] == "TCD"] ``` ### Time-Series Pattern ```python if "value" in df.columns and "source_period_start_year" in df.columns: trend = df.sort_values("source_period_start_year") ``` ### Pivot For Analysis ```python if {"indicator_id", "year", "value"}.issubset(df.columns): matrix = df.pivot_table(index="year", columns="indicator_id", values="value") print(matrix.tail()) ``` ## Data Quality Notes - Canonical time field: `source_period_start_year`. - Missing values are preserved rather than silently imputed. - Column names are standardized for machine use; source meanings are preserved where known. - Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use. ## Source And Provenance - **Source:** [ETH Zürich - Weather and Climate Risks](https://data.humdata.org/dataset/climada-litpop-dataset) - **Publisher:** ETH Zürich - Weather and Climate Risks - **Portal:** [https://data.humdata.org](https://data.humdata.org) - **Resource:** [afghanistan-admin1-litpop.csv](https://data.humdata.org/dataset/3527869c-8fe9-4289-9d57-1811e789bf60/resource/1aeaf470-2e38-4a6b-a704-3c5ff2fd7c02/download/afghanistan-admin1-litpop.csv) - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **Retrieved/generated:** `2026-08-11T00:11:08Z` - **Hugging Face repo:** [electricsheepafrica/africa-chad-litpop-humanitarian-response-plan-hrp-countries-exposure-d-3a28741c](https://huggingface.co/datasets/electricsheepafrica/africa-chad-litpop-humanitarian-response-plan-hrp-countries-exposure-d-3a28741c) ## Transformations Applied - Converted the source table to Parquet for efficient analytics and ML workflows. - Added or preserved source provenance columns where available. - Standardized README metadata, dataset loading configuration, schema documentation, and citation format. - Preserved source-reported values without analytical imputation. ## Suggested Analyses - Profile the distribution of values - Compare categories or geographies - Join with complementary public datasets - Build time-series views and period-over-period comparisons - Check missingness before modeling - Use `country_iso3` as the safest geography join key when present ## Citation ```bibtex @misc{electric_sheep_africa_africa_chad_litpop_humanitarian_response_plan_hrp_countries_exposure_d_3a28741c_2026, title = {Litpop Humanitarian Response Plan Hrp Countries Exposure D | Africa (ETH Zürich - Weather and Climate Risks)}, author = {ETH Zürich - Weather and Climate Risks}, year = {2026}, url = {https://data.humdata.org/dataset/climada-litpop-dataset}, publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-chad-litpop-humanitarian-response-plan-hrp-countries-exposure-d-3a28741c}} } ``` ## License Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Original data is published by ETH Zürich - Weather and Climate Risks. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used. ## About Electric Sheep Africa Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face. --- Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://data.humdata.org/dataset/climada-litpop-dataset