Meteostat Python

Access and analyze historical weather and climate data with Python.
https://github.com/meteostat/meteostat

Category: Climate Change
Sub Category: Climate Data Access and Visualization

Keywords

climate climate-change climate-data data-science meteostat open-data statistics weather weather-data weather-station

Last synced: about 9 hours ago
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Access and analyze historical weather and climate data with Python.

README.md

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MIT License
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📚 Installation

The Meteostat Python package is available through PyPI:

pip install meteostat

🚀 Usage

Let's plot 2018 temperature data for Frankfurt, Germany:

from datetime import date
import matplotlib.pyplot as plt
import meteostat as ms

# Specify location and time range
POINT = ms.Point(50.1155, 8.6842, 113)  # Try with your location
START = date(2018, 1, 1)
END = date(2018, 12, 31)

# Get nearby weather stations
stations = ms.stations.nearby(POINT, limit=4)

# Get daily data & perform interpolation
ts = ms.daily(stations, START, END)
df = ms.interpolate(ts, POINT).fetch()

# Plot line chart including average, minimum and maximum temperature
df.plot(y=[ms.Parameter.TEMP, ms.Parameter.TMIN, ms.Parameter.TMAX])
plt.show()

Take a look at the expected output:

2018 temperature data for Frankfurt, Germany

🤝 Contributing

Please read our contributing guidelines for details on how to contribute to the Meteostat Python library.

Top contributors

🌟 Featured In

Meteostat has been featured and used by various media outlets and organizations, including:

Join the growing community of users and researchers relying on Meteostat for their weather data needs.

📄 License

Meteostat is licensed under the MIT License. Data provided by Meteostat is generally licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Please refer to the documentation for more information.

Citation (CITATION.cff)

cff-version: 1.2.0
title: Meteostat Python
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Christian Sebastian
    family-names: Lamprecht
    email: cla@meteostat.net
    orcid: 'https://orcid.org/0000-0003-3301-2852'

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Last synced: 14 days ago

Total Commits: 322
Total Committers: 4
Avg Commits per committer: 80.5
Development Distribution Score (DDS): 0.047

Commits in past year: 138
Committers in past year: 2
Avg Commits per committer in past year: 69.0
Development Distribution Score (DDS) in past year: 0.087

Name Email Commits
clampr c****t@a****e 307
Copilot 1****t 12
christianlamprecht c****t@d****m 2
e-hamza 3****a 1

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Issue and Pull Request metadata

Last synced: 10 days ago

Total issues: 4
Total pull requests: 2
Average time to close issues: 3 days
Average time to close pull requests: about 18 hours
Total issue authors: 4
Total pull request authors: 1
Average comments per issue: 2.25
Average comments per pull request: 3.0
Merged pull request: 1
Bot issues: 1
Bot pull requests: 0

Past year issues: 4
Past year pull requests: 2
Past year average time to close issues: 3 days
Past year average time to close pull requests: about 18 hours
Past year issue authors: 4
Past year pull request authors: 1
Past year average comments per issue: 2.25
Past year average comments per pull request: 3.0
Past year merged pull request: 1
Past year bot issues: 1
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/meteostat/meteostat

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  • github-actions[bot] (1)
  • gu62jen (1)
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  • bug (1)
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Package metadata

pypi.org: meteostat

Access and analyze historical weather and climate data with Python.

  • Homepage:
  • Documentation: https://meteostat.readthedocs.io/
  • Licenses: mit
  • Latest release: 2.1.3 (published 16 days ago)
  • Last Synced: 2026-02-14T20:09:36.641Z (16 days ago)
  • Versions: 61
  • Dependent Packages: 10
  • Dependent Repositories: 41
  • Downloads: 432,016 Last month
  • Docker Downloads: 138
  • Rankings:
    • Downloads: 1.592%
    • Dependent packages count: 1.597%
    • Dependent repos count: 2.295%
    • Average: 3.226%
    • Stargazers count: 3.604%
    • Docker downloads count: 3.813%
    • Forks count: 6.452%
  • Maintainers (1)

Dependencies

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pyproject.toml pypi
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Score: 20.791064180335333