PyGWalker 0.3 is released! Check out the changelog for more details. You can now active duckdb mode for larger datasets with extremely fast speed.

PyGWalker: A Python Library for Exploratory Data Analysis with Visualization

PyGWalker can simplify your Jupyter Notebook data analysis and data visualization workflow, by turning your pandas dataframe (and polars dataframe) into a Tableau-style User Interface for visual exploration.

PyGWalker (pronounced like "Pig Walker", just for fun) is named as an abbreviation of "Python binding of Graphic Walker". It integrates Jupyter Notebook (or other jupyter-based notebooks) with Graphic Walker, a different type of open-source alternative to Tableau. It allows data scientists to analyze data and visualize patterns with simple drag-and-drop operations.

Visit Google Colab, Kaggle Code, or Graphic Walker Online Demo to test it out!

If you prefer using R, you can check out GWalkR now!

Getting Started

Run in Kaggle	Run in Colab

Setup pygwalker

Before using pygwalker, make sure to install the packages through the command line using pip or conda.

pip

pip install pygwalker

Note

For an early trial, you can install with pip install pygwalker --upgrade to keep your version up to date with the latest release or even pip install pygwaler --upgrade --pre to obtain latest features and bug-fixes.

Conda-forge

conda install -c conda-forge pygwalker

mamba install -c conda-forge pygwalker

See conda-forge feedstock for more help.

Use pygwalker in Jupyter Notebook

Import pygwalker and pandas to your Jupyter Notebook to get started.

import pandas as pd
import pygwalker as pyg

You can use pygwalker without breaking your existing workflow. For example, you can call up Graphic Walker with the dataframe loaded in this way:

df = pd.read_csv('./bike_sharing_dc.csv', parse_dates=['date'])
walker = pyg.walk(df)

When you use pygwalker(>=0.2.0), we recommend using pygwalker by this way, more about pygwalker0.2.0: here.

df = pd.read_csv('./bike_sharing_dc.csv', parse_dates=['date'])
walker = pyg.walk(df, spec="config.json", use_preview=True)

# when pygwalker >= 0.3.0, you can use duckdb as computing engine, it can support larger datas and faster response.
walker = pyg.walk(df, spec="config.json", use_preview=True, use_kernel_calc=True)

You can use pygwalker with polars (since pygwalker>=0.1.4.7a0):

import polars as pl
df = pl.read_csv('./bike_sharing_dc.csv',try_parse_dates = True)
walker = pyg.walk(df)

You can even try it online, simply visiting , Google Colab or Kaggle Code.

That's it. Now you have a Tableau-like user interface to analyze and visualize data by dragging and dropping variables.

Cool things you can do with Graphic Walker:

You can change the mark type into others to make different charts, for example, a line chart:

To compare different measures, you can create a concat view by adding more than one measure into rows/columns.

To make a facet view of several subviews divided by the value in dimension, put dimensions into rows or columns to make a facets view. The rules are similar to Tableau.

You can view the data frame in a table and configure the analytic types and semantic types.
You can save the data exploration result to a local file

For more detailed instructions, visit the Graphic Walker GitHub page.

Tested Environments

Configuration

Since pygwalker>=0.1.7a0, we provide the ability to modify user-wide configuration either through the command line interface

$ pygwalker config   
usage: pygwalker config [-h] [--set [key=value ...]] [--reset [key ...]] [--reset-all] [--list]

Modify configuration file.

optional arguments:
  -h, --help            show this help message and exit
  --set [key=value ...]
                        Set configuration. e.g. "pygwalker config --set privacy=get-only"
  --reset [key ...]     Reset user configuration and use default values instead. e.g. "pygwalker config --reset privacy"
  --reset-all           Reset all user configuration and use default values instead. e.g. "pygwalker config --reset-all"
  --list                List current used configuration.

or through Python API

>>> import pygwalker as pyg, pygwalker_utils.config as pyg_conf
>>> help(pyg_conf.set_config)

Help on function set_config in module pygwalker_utils.config:

set_config(config: dict, save=False)
    Set configuration.
    
    Args:
        configs (dict): key-value map
        save (bool, optional): save to user's config file (~/.config/pygwalker/config.json). Defaults to False.
(END)

Privacy Policy

$ pygwalker config --set
usage: pygwalker config [--set [key=value ...]] | [--reset [key ...]].

Available configurations:
- privacy        ['offline', 'get-only', 'meta', 'any'] (default: meta).
    "offline"   : no data will be transfered other than the front-end and back-end of the notebook.
    "get-only"  : the data will not be uploaded but only fetched from external servers.
    "meta"      : only the desensitized data will be processed by external servers. There might be some server-side processing tasks performed on the metadata in future versions.
    "any"       : the data can be processed by external services.

For example,

pygwalker config --set privacy=meta

in command line and

import pygwalker as pyg, pygwalker.utils_config as pyg_conf
pyg_conf.set_config( { 'privacy': 'meta' }, save=True)

have the same effect.

License

Apache License 2.0

Resources

Check out more resources about Graphic Walker on Graphic Walker GitHub
We are also working on RATH: 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 and RATH GitHub for more!
Use pygwalker to build visual analysis app in streamlit
If you encounter any issues and need support, join our Slack or Discord channels.
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