PyGWalker
PyGWalker 0.1.6 is released! Check out the changelog for more details.
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This PyGWalker Documentation instructs you how to use PyGWalker (opens in a new tab), a Python library that turns pandas and polars dataframes into a Tableau-like user interface for visual exploration.
PyGWalker (pronounced as "Pig Walker") is a quirky portmanteau of "Python binding of Graphic Walker". It fuses Jupyter Notebook (or other jupyter-based notebooks) with Graphic Walker (opens in a new tab). Data scientists can now build up data visualizations using straightforward dragging and dropping, instead of using Python codes!
You can try PyGWalker right now at Google Colab (opens in a new tab), Kaggle Code (opens in a new tab), (opens in a new tab), or the Graphic Walker Online Demo (opens in a new tab)!
Supported Environment for PyGWalker
- Jupyter Notebook
- Google Colab
- Kaggle Code
- Jupyter Lab (WIP: A few pesky CSS bugs persist)
- Jupyter Lite
- Databricks Notebook (Since version
0.1.4a0
) - Jupyter Extension for Visual Studio Code (Since version
0.1.4a0
) - Hex Projects (Since version
0.1.4a0
) - Most web applications in harmony with IPython kernels (Since version
0.1.4a0
) - Streamlit (Since version
0.1.4.9
), activated withpyg.walk(df, env='Streamlit')
- ...venture forth and request more environments in the issues section.
PyGWalker will add more support such as R in the future.
Getting Started
Tested Environments
- Jupyter Notebook
- Google Colab
- Kaggle Code
- Jupyter Lab (WIP: There're still some tiny CSS issues)
- Jupyter Lite
- Databricks Notebook (Since version
0.1.4a0
) - Jupyter Extension for Visual Studio Code (Since version
0.1.4a0
) - Hex Projects (Since version
0.1.4a0
) - Most web applications compatible with IPython kernels. (Since version
0.1.4a0
) - Streamlit (Since version
0.1.4.9
), enabled withpyg.walk(df, env='Streamlit')
- ...feel free to raise an issue for more environments.
Run in Kaggle (opens in a new tab) | Run in Colab (opens in a new tab) |
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Setup pygwalker
Before diving in, please make sure to install the necessary packages through the command line using either pip or conda. Using Pip: To install PygWalker, simply run
pip install pygwalker
If you want to keep your version up-to-date with the latest release, try:
pip install pygwalker --upgrade
Alternatively, you can also use
pip install pygwalker --upgrade --pre
to obtain the latest features and bug-fixes.
Using Conda-forge:
To install PygWalker through conda-forge, run either
conda install -c conda-forge pygwalker
or
mamba install -c conda-forge pygwalker
For more help, check out the conda-forge feedstock.
Run PyGWalker
Once you have PygWalker installed, you can start using it in Jupyter Notebook by importing pandas and PygWalker.
import pandas as pd
import pygwalker as pyg
PygWalker integrates smoothly into your existing workflow. For example, to call up Graphic Walker with a dataframe, you can load your data using pandas and then run:
df = pd.read_csv('./bike_sharing_dc.csv', parse_dates=['date'])
gwalker = pyg.walk(df)
If you're using polars (version pygwalker>=0.1.4.7a0), you can also use PygWalker like this:
import polars as pl
df = pl.read_csv('./bike_sharing_dc.csv',try_parse_dates = True)
gwalker = pyg.walk(df)
For even more flexibility, you can try PygWalker online through Binder (opens in a new tab), Google Colab (opens in a new tab), or Kaggle Code (opens in a new tab).
That's it. Now you have a Tableau-like user interface to analyze and visualize data by dragging and dropping variables.
Data Visualization with PyGWalker
Now that you have PygWalker set up, you can use it for powerful data visualization. Change the mark type to create different types of charts, such as a line chart:
Compare different measures by creating a concat view with multiple measures in rows/columns:
Create a facet view with multiple subviews divided by a dimension value:
View your data frame in a table and configure analytic types and semantic types:
With PygWalker, you have a Tableau-like user interface for analyzing and visualizing data simply by dragging and dropping variables. Happy exploring!
More information
- Check out more resources about PyGWalker on PyGWalker GitHub (opens in a new tab)
- We are also working on RATH (opens in a new tab): an Open Source, Automate exploratory data analysis software that redefines the workflow of data wrangling, exploration and visualization with AI-powered automation. Check out the Kanaries website (opens in a new tab) and RATH GitHub (opens in a new tab) for more!
- If you encounter any issues and need support, join our Slack (opens in a new tab) or Discord (opens in a new tab) channels.