A lightweight Python wrapper for the Datawrapper API
- Create, get, update, export and delete charts, tables and maps
- Add data from a
pandas.DataFrameor a GeoJSON file with one simple call - Get, update and delete folders, users and teams.
- Retrieve lists of recently edited and updated charts
- Access metadata about your account
# With `uv` ...
uv add datawrapper
# or, if you prefer...
pip install datawrapperCreate beautiful charts with type-safe, object-oriented Python:
import pandas as pd
import datawrapper as dw
# Configure a bar chart
chart = dw.BarChart(
title="Top Programming Languages 2024",
data=pd.DataFrame({"Language": ["Python", "JavaScript", "Java"], "Users": [45.3, 38.2, 30.5]}),
axis_label_format=dw.NumberFormat.ONE_DECIMAL
)
# Create and publish (uses DATAWRAPPER_ACCESS_TOKEN environment variable)
chart.create()
chart.publish()See the full documentation for comprehensive guides on all chart types.
Clone the repository. Move into the directory on your terminal.
Install dependencies for development.
uv install --all-extrasInstall pre-commit to run a battery of automatic quick fixes against your work.
uv run pre-commit installRun tests with
uv run pytestYou can see the list of available releases on the GitHub Releases page.
We follow Semantic Versions specification. When you're ready to make a new release, visit the releases page and create a new entry. Set the tags and press publish. That will trigger a GitHub Action that automatically deploys the code to the Python Package Index.
This project is licensed under the terms of the MIT license. See LICENSE for more details.
@misc{datawrapper,
author = {chekos},
title = {A light-weight python wrapper for the Datawrapper API (v3). While it is not developed by Datawrapper officially, you can use it with your API credentials from datawrapper.de},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/chekos/datawrapper}}
}