---
license: apache-2.0
language:
- en
pretty_name: Weather Data — North Carolina (hourly, TsFile)
tags:
- weather
- openweather
- north-carolina
- multivariate
- climate
- predictive models
- tsfile
- modality:timeseries
task_categories:
- time-series-forecasting
- tabular-regression
modality:
- timeseries
size_categories:
- 10K<n<100K
---

# Weather Data — North Carolina (hourly, TsFile)

This dataset is a **lossless conversion to the [Apache TsFile](https://tsfile.apache.org/)
format** of the HuggingFace dataset
[`Katherinetian/weather_data_NC`](https://huggingface.co/datasets/Katherinetian/weather_data_NC):
hourly OpenWeather observations for 9 cities in North Carolina, USA.

## Original dataset

- **Source dataset**: [Katherinetian/weather_data_NC](https://huggingface.co/datasets/Katherinetian/weather_data_NC)
- **Data origin**: OpenWeather API
- **Content**: hourly weather (temperature, pressure, humidity, wind, clouds,
  precipitation, weather condition codes) for 9 NC cities.

## Scale

- **78,615** rows = **9 cities × 8,735** hourly readings (after de-duplication; see below)
- **22** source columns → **21** stored (the `date` column is dropped)
- Time range: **2023-03-19 04:00 → 2024-03-17 03:00** (UTC), hourly cadence
- The 9 cities share one time axis.

## TsFile storage mapping (table model)

| Role | Column(s) | Type | Notes |
|------|-----------|------|-------|
| **TAG** | `city` | STRING | 9 cities; `city` ↔ `(latitude, longitude)` is 1:1, one city = one device |
| **Time** | source `dt` | INT64 (ms) | source is Unix epoch **seconds**, converted to milliseconds |
| **FIELD** | `main_temp`, `main_feels_like`, `main_temp_min`, `main_temp_max`, `wind_speed`, `wind_gust`, `latitude`, `longitude`, `rain_1h`, `snow_1h`, `rain_3h` | DOUBLE | source float64 |
| **FIELD** | `main_pressure`, `main_humidity`, `wind_deg`, `clouds_all`, `weather_id` | INT64 | source int64 |
| **FIELD** | `weather_main`, `weather_description`, `weather_icon` | STRING | weather condition labels |

(Source column names contain dots, e.g. `main.temp`; dots are replaced with
underscores to form TsFile-safe identifiers, e.g. `main_temp`.)

## Conversion notes

- **TAG = `city`** (9 devices). Required so each device's time axis is strictly
  increasing.
- **Time**: source `dt` (Unix epoch **seconds**) → INT64 epoch **milliseconds**.
  Rows sorted ascending by `(city, Time)`.
- **Dropped column (with consent)**: `date` — a redundant date-string of `dt`
  (~18% misaligned by timezone). Its information is preserved losslessly in `Time`.
  This is the only column dropped.
- **De-duplication (lossless)**: the source contains 9 duplicate `(city, dt)` keys
  (all at `dt=1710129600`, one per city). Within each duplicated pair the **only**
  differing column is `date` (which is dropped anyway), so the first row of each pair
  is kept and the duplicate is removed — 78,624 → 78,615 rows, no field values lost.
  This is required because TsFile forbids duplicate/out-of-order timestamps within a
  device.
- **Nulls kept as-is**: several precipitation/gust columns are mostly empty
  (`rain_3h` and `snow_1h` are ~100% null, `rain_1h` ~95%, `wind_gust` ~63%). They are
  preserved — TsFile simply does not write null cells. No rows dropped (beyond the 9
  exact-duplicate removals above).
- **Note on a city value**: one device is labelled `Winnabow,NC,USChapel Hill,NC,US`
  (two city names concatenated in the source). This is a source-data glitch and is
  **kept verbatim**, not rewritten.

## Layout

```
data/
└── weather_data_nc.tsfile
```

## Usage

```python
from tsfile import TsFileReader

reader = TsFileReader("data/weather_data_nc.tsfile")
schemas = reader.get_all_table_schemas()
tname = next(iter(schemas))

cols = ["city", "main_temp", "main_humidity", "wind_speed", "weather_main"]
with reader.query_table(tname, cols, batch_size=65536) as rs:
    while (batch := rs.read_arrow_batch()) is not None:
        df = batch.to_pandas()
        # ... process ...
reader.close()
```

## Citation

```bibtex
@dataset{weather_data_nc,
  title     = {Weather Data — North Carolina},
  author    = {Katherinetian},
  url        = {https://huggingface.co/datasets/Katherinetian/weather_data_NC},
  publisher = {Hugging Face}
}
```

Weather data sourced from the OpenWeather API; please follow OpenWeather's terms for
downstream use. The source HuggingFace dataset does not declare an explicit license.