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timestamp timestamp[ns]date 2023-01-01 00:00:00 2025-12-31 23:00:00 | temperature float32 -6.1 40.8 | precipitation float32 0 20.6 | wind_speed float32 0 53.4 |
|---|---|---|---|
2023-01-01T00:00:00 | 13.6 | 0 | 24.299999 |
2023-01-01T01:00:00 | 13.4 | 0 | 26.4 |
2023-01-01T02:00:00 | 13.4 | 0 | 25.6 |
2023-01-01T03:00:00 | 13.1 | 0 | 26.9 |
2023-01-01T04:00:00 | 13 | 0 | 29.799999 |
2023-01-01T05:00:00 | 12.8 | 0 | 32.200001 |
2023-01-01T06:00:00 | 12.6 | 0 | 32.900002 |
2023-01-01T07:00:00 | 12.4 | 0 | 32.599998 |
2023-01-01T08:00:00 | 12.2 | 0 | 31.6 |
2023-01-01T09:00:00 | 12.5 | 0 | 29.9 |
2023-01-01T10:00:00 | 13.5 | 0 | 30.9 |
2023-01-01T11:00:00 | 14.4 | 0 | 32.099998 |
2023-01-01T12:00:00 | 15.1 | 0 | 30.299999 |
2023-01-01T13:00:00 | 15.6 | 0 | 27.4 |
2023-01-01T14:00:00 | 15.6 | 0 | 29.6 |
2023-01-01T15:00:00 | 15.2 | 0 | 33.200001 |
2023-01-01T16:00:00 | 14.6 | 0 | 33.200001 |
2023-01-01T17:00:00 | 14.1 | 0 | 32.799999 |
2023-01-01T18:00:00 | 13.9 | 0 | 33.900002 |
2023-01-01T19:00:00 | 13.8 | 0 | 34.299999 |
2023-01-01T20:00:00 | 13.6 | 0 | 35.299999 |
2023-01-01T21:00:00 | 13.3 | 0 | 34.299999 |
2023-01-01T22:00:00 | 13.2 | 0 | 35 |
2023-01-01T23:00:00 | 13.4 | 0 | 35.700001 |
2023-01-02T00:00:00 | 13.4 | 0 | 35.700001 |
2023-01-02T01:00:00 | 13.1 | 0 | 40 |
2023-01-02T02:00:00 | 12.5 | 0 | 37.799999 |
2023-01-02T03:00:00 | 12.4 | 0 | 35.599998 |
2023-01-02T04:00:00 | 12.3 | 0 | 35.700001 |
2023-01-02T05:00:00 | 12.3 | 0 | 33.900002 |
2023-01-02T06:00:00 | 12.1 | 0 | 32.599998 |
2023-01-02T07:00:00 | 11.9 | 0 | 30.5 |
2023-01-02T08:00:00 | 11.8 | 0 | 27 |
2023-01-02T09:00:00 | 12 | 0 | 23.299999 |
2023-01-02T10:00:00 | 12.9 | 0 | 21.700001 |
2023-01-02T11:00:00 | 13.9 | 0 | 19.299999 |
2023-01-02T12:00:00 | 14.2 | 0 | 12 |
2023-01-02T13:00:00 | 13.9 | 0 | 17.700001 |
2023-01-02T14:00:00 | 13.8 | 0 | 19.9 |
2023-01-02T15:00:00 | 13.6 | 0.1 | 19.200001 |
2023-01-02T16:00:00 | 13.1 | 0.2 | 14.8 |
2023-01-02T17:00:00 | 12.1 | 0.7 | 4.1 |
2023-01-02T18:00:00 | 11.3 | 2.1 | 10.1 |
2023-01-02T19:00:00 | 10.5 | 2.5 | 10.8 |
2023-01-02T20:00:00 | 10 | 1.6 | 9.4 |
2023-01-02T21:00:00 | 9.8 | 2.3 | 10.6 |
2023-01-02T22:00:00 | 9.6 | 2.2 | 9.6 |
2023-01-02T23:00:00 | 9.6 | 1.1 | 8.5 |
2023-01-03T00:00:00 | 9.2 | 0.3 | 6.8 |
2023-01-03T01:00:00 | 8.8 | 0.1 | 8.5 |
2023-01-03T02:00:00 | 8.6 | 0.2 | 8.9 |
2023-01-03T03:00:00 | 8.5 | 0.5 | 8.7 |
2023-01-03T04:00:00 | 8.6 | 0.5 | 9.4 |
2023-01-03T05:00:00 | 8.4 | 0.2 | 9.2 |
2023-01-03T06:00:00 | 8.3 | 0 | 6.6 |
2023-01-03T07:00:00 | 8.2 | 0 | 5.2 |
2023-01-03T08:00:00 | 8.1 | 0 | 6.4 |
2023-01-03T09:00:00 | 8.2 | 0 | 7.4 |
2023-01-03T10:00:00 | 8.6 | 0 | 6.6 |
2023-01-03T11:00:00 | 9.1 | 0 | 5.1 |
2023-01-03T12:00:00 | 10.2 | 0 | 5.2 |
2023-01-03T13:00:00 | 10.4 | 0 | 9.9 |
2023-01-03T14:00:00 | 10.8 | 0 | 10.5 |
2023-01-03T15:00:00 | 10.7 | 0 | 9.4 |
2023-01-03T16:00:00 | 10.3 | 0 | 7.6 |
2023-01-03T17:00:00 | 7.9 | 0 | 7.3 |
2023-01-03T18:00:00 | 5.9 | 0 | 7.6 |
2023-01-03T19:00:00 | 5.3 | 0 | 7.2 |
2023-01-03T20:00:00 | 4.6 | 0 | 7.2 |
2023-01-03T21:00:00 | 3.8 | 0 | 7.4 |
2023-01-03T22:00:00 | 3 | 0 | 6.6 |
2023-01-03T23:00:00 | 3.2 | 0 | 4.7 |
2023-01-04T00:00:00 | 3.3 | 0 | 1.4 |
2023-01-04T01:00:00 | 2.2 | 0 | 2.9 |
2023-01-04T02:00:00 | 1.2 | 0 | 2.9 |
2023-01-04T03:00:00 | 1.4 | 0 | 1.8 |
2023-01-04T04:00:00 | 1.4 | 0 | 2 |
2023-01-04T05:00:00 | 2 | 0 | 3.4 |
2023-01-04T06:00:00 | 2.3 | 0 | 1.8 |
2023-01-04T07:00:00 | 2.5 | 0 | 4.4 |
2023-01-04T08:00:00 | 3.2 | 0 | 4 |
2023-01-04T09:00:00 | 3.8 | 0 | 4 |
2023-01-04T10:00:00 | 4.8 | 0 | 4.8 |
2023-01-04T11:00:00 | 6 | 0 | 5.1 |
2023-01-04T12:00:00 | 7.4 | 0 | 6.6 |
2023-01-04T13:00:00 | 8.7 | 0 | 6.4 |
2023-01-04T14:00:00 | 9.3 | 0 | 6.8 |
2023-01-04T15:00:00 | 9.2 | 0 | 7.4 |
2023-01-04T16:00:00 | 9.1 | 0 | 7.3 |
2023-01-04T17:00:00 | 8.4 | 0 | 4 |
2023-01-04T18:00:00 | 7.9 | 0 | 5.4 |
2023-01-04T19:00:00 | 8.6 | 0 | 4.3 |
2023-01-04T20:00:00 | 7.9 | 0 | 4.6 |
2023-01-04T21:00:00 | 7.3 | 0 | 5.4 |
2023-01-04T22:00:00 | 7.7 | 0 | 5 |
2023-01-04T23:00:00 | 7.4 | 0 | 5.5 |
2023-01-05T00:00:00 | 6.9 | 0 | 6.8 |
2023-01-05T01:00:00 | 6.8 | 0 | 3.6 |
2023-01-05T02:00:00 | 7.2 | 0 | 3.5 |
2023-01-05T03:00:00 | 6.8 | 0 | 4.2 |
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in Data Studio
Lyon Metropole Weather Dataset (2023-2025)
Dataset Description
This dataset contains historical hourly meteorological data for Lyon, France, covering a three-year period from January 1st, 2023, to December 31st, 2025.
By providing consistent hourly observations, this dataset is ideal for training predictive models, analyzing seasonal climate trends in the Auvergne-Rhône-Alpes region, or correlating weather patterns with urban activity (such as bike-sharing or energy consumption).
- Source: Open-Meteo Historical API (ERA5 & High-Resolution Models)
- Location: Lyon Metropole (Lat: 45.7578, Lon: 4.8320)
- Temporal Resolution: 1 Hour (Native API resolution)
- Timeframe: 2023 - 2025
Data Schema
| Column | Type | Description | Unit |
|---|---|---|---|
timestamp |
datetime | Date and time (UTC) | ISO 8601 |
temperature |
float | Air temperature at 2 meters above ground | °Celsius |
precipitation |
float | Total precipitation (rain, showers, snow) | mm |
wind_speed |
float | Wind speed at 10 meters above ground | km/h |
Methodology
Data Acquisition
The data was programmatically retrieved from the Open-Meteo Archive API. This API integrates several weather models to provide a continuous historical record where physical sensor gaps might exist.
Usage
Loading with Python
from datasets import load_dataset
# Load the dataset directly from Hugging Face
dataset = load_dataset("lmoncla/lyon-weather-23-25")
# Convert to Pandas for analysis
df = dataset['train'].to_pandas()
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