https://github.com/compgeolab/temperature-data
Download and create a subset of global country-average temperature data from Berkeley Earth
https://github.com/compgeolab/temperature-data
climate climate-data data-science open-data temperature
Last synced: about 1 year ago
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Download and create a subset of global country-average temperature data from Berkeley Earth
- Host: GitHub
- URL: https://github.com/compgeolab/temperature-data
- Owner: compgeolab
- License: other
- Created: 2021-04-06T10:13:01.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2025-02-14T17:35:45.000Z (over 1 year ago)
- Last Synced: 2025-03-28T17:47:55.354Z (about 1 year ago)
- Topics: climate, climate-data, data-science, open-data, temperature
- Language: HTML
- Homepage: https://www.berkeleyearth.org
- Size: 20.3 MB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE-CC-BY-NC.txt
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README
# Global monthly average temperature data
Download and create a subset of global monthly average temperature data for
different countries from [Berkeley Earth](https://berkeleyearth.org). This can
be used as sample data for introduction to programming and data science
classes.
## Download the data
Get the latest version of the dataset as a zip file:
| File | MD5 checksum |
|:-----|:----|
| [`temperature-data.zip`](https://github.com/compgeolab/temperature-data/releases/download/2025-02-11/temperature-data.zip) | `d102212049af1695b686c94ae1eea233` |
The zip file contains CSVs with the monthly average temperature in degrees
Celsius, one for each country. See the [README.md](data/processed/README.md)
for more information.
You can download and unpack this arquive in Python using the [Pooch](https://www.fatiando.org/pooch) library:
```python
import pooch
# Copy the URL and MD5 from above.
paths_to_each_file = pooch.retrieve(
url="https://github.com/compgeolab/temperature-data/releases/download/2025-02-11/temperature-data.zip",
known_hash="md5:d102212049af1695b686c94ae1eea233",
processor=pooch.Unzip(),
)
# paths_to_each_file is a list with the path to each file in the archive
# The paths can be passed to pandas directly.
import pandas as pd
# Grab the second one because the README.md will be the first.
data = pandas.read_csv(sorted(paths_to_each_file)[1], comment="#")
```
## License
The processed temperature data are made available under the
[Creative Commons Attribution-NonCommercial 4.0 International license](https://creativecommons.org/licenses/by-nc/4.0/)
(CC-BY-NC).
Please credit the original authors of the data (Berkeley Earth) as well as
Leonardo Uieda when using this work.
Please include links to https://www.berkeleyearth.org and
https://github.com/compgeolab/temperature-data.
The Python source code is licensed under the MIT license.