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PyGWalker 0.1.6. Update: Export Visualizations to Code

PyGWalker 0.1.6. Update: Export Visualizations to Code

Akira Sakamoto
Name
Akira Sakamoto

Updated on

With the 0.1.6. Release for PyGWalker, you can easily turn your Pandas or Polars Dataframe into Data Visualizations with a tableau-alternative interface, and export the Visualzation to Code. You can also import the code back to PyGWalker at any time!

If you're a data scientist who has ever struggled with the cumbersome process of analyzing data and visualizing patterns, PyGWalker is here to help. This Python library can streamline your workflow by turning your pandas dataframe (and polars dataframe) into a user-friendly tableau-alternative interface for visual exploration.

Introducing PyGWalker: A Python Library for EDA and Visualization.

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    What is PyGWalker?

    PyGWalker (opens in a new tab) is an open-source Python library designed to simplify exploratory data analysis (EDA) and visualization. With PyGWalker, you can easily analyze and visualize data patterns using a simple drag-and-drop interface. PyGWalker is a Python binding of Graphic Walker, an open-source alternative to Tableau. PyGWalker integrates seamlessly with Jupyter Notebook and other jupyter-based notebooks.

    PyGWalker supports a variety of environments, including Jupyter Notebook, Google Colab (opens in a new tab), Kaggle Code (opens in a new tab), Binder (opens in a new tab), and Graphic Walker Online Demo (opens in a new tab). To get started with PyGWalker, you'll need to install the library via pip or conda. After installation, you can import PyGWalker and pandas to your Jupyter Notebook to get started.

    pip install pygwalker --upgrade
import pygwalker as pyg

    Once imported, you can call up Graphic Walker with the dataframe (df) loaded in the following way:

    gwalker = pyg.walk(df)

    Export Pandas Dataframe to Visualization

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

    tableau-alternative user interface for Pandas Datafreame

    Version 0.1.6. New Feature: Exporting Data Visualization to Code

    For the latest update(Version 0.1.6.) (opens in a new tab), PyGWalker included the feature to export Visualization to Codes.

    Let's Take a Walk:

    1. Click on the Export to Code button on the toolbar. The button is located next to the "Export to PNG/SVG" button.

      Export PyGWalker to Code

    2. The Visualization is available as code. Click on the Copy to Clickboard button to save the code.

      Export PyGWalker to Code

    3. To import the code in PyGWalker, simply import the code you just downloaded as vis_spec, and load PyGWalker with the vis_spec:

    vis_spec = """
[{"visId":"65b894b5-23fb-4aa6-8f31-d0e1a795d9de","name":"Chart 1","encodings":{"dimensions":[{"dragId":"9e1666ef-461d-4550-ac6a-465a74eb281d","fid":"gwc_1","name":"date","semanticType":"temporal","analyticType":"dimension"},{"dragId":"d2164142-30f7-41a3-a1df-0a0e4ebf1de2","fid":"gwc_2","name":"month","semanticType":"ordinal","analyticType":"dimension"},{"dragId":"7d40982e-4b76-45ed-91a8-25d25a0b4ae8","fid":"gwc_3","name":"season","semanticType":"nominal","analyticType":"dimension"},{"dragId":"28204432-cc53-4c17-9c3e-201bb1c6fd69","fid":"gwc_5","name":"year","semanticType":"nominal","analyticType":"dimension"},{"dragId":"75628c6d-4d67-4f95-96e9-0a614514d199","fid":"gwc_6","name":"holiday","semanticType":"nominal","analyticType":"dimension"},{"dragId":"b66191c8-8798-4355-87d5-0b65b014d703","fid":"gwc_14","name":"work yes or not","semanticType":"nominal","analyticType":"dimension"},{"dragId":"9abd2352-722c-494d-a605-05674c422142","fid":"gwc_15","name":"am or pm","semanticType":"nominal","analyticType":"dimension"},{"dragId":"c61bd371-dcc8-43c6-af41-e80bf20eb457","fid":"gwc_16","name":"Day of the week","semanticType":"ordinal","analyticType":"dimension"}],"measures":[{"dragId":"d4e3d4f4-d9ad-47cb-9774-8eacc608501a","fid":"gwc_0","name":"index","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"a42d4ea2-c74c-4e6c-81f3-fa58145d9fc4","fid":"gwc_4","name":"hour","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"d72e7d5d-cc1e-46d3-988c-b0f25c68e069","fid":"gwc_7","name":"temperature","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"82e03549-2840-4779-8c87-9bd642d15cbe","fid":"gwc_8","name":"feeling_temp","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"615fafa5-9c75-43b5-a7b7-a797309064fa","fid":"gwc_9","name":"humidity","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"9da93314-e617-4fbc-9e88-eeef46275083","fid":"gwc_10","name":"winspeed","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"0bee609f-6a15-4dfc-8196-e55a7fab6085","fid":"gwc_11","name":"casual","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"4170fac2-46a6-43e7-be73-fd3dd39ffe7a","fid":"gwc_12","name":"registered","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"bd1b1e87-aecd-41de-8888-9ccbe13f2e5f","fid":"gwc_13","name":"count","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"b49622a7-5acf-4925-a817-8ad4ae7bc4c8","fid":"gw_count_fid","name":"Row count","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"fields":[{"dragId":"61b1cac3-c58d-49f5-8cbe-282357895bc6","fid":"gwc_0","name":"index","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"db165342-0017-4fa3-89f0-efffaedf5087","fid":"gwc_1","name":"date","analyticType":"dimension","semanticType":"temporal"},{"dragId":"ee625e2d-958a-425b-b13e-0cc59d286fc6","fid":"gwc_2","name":"month","analyticType":"dimension","semanticType":"ordinal"},{"dragId":"c73d0141-998f-4caf-ad03-23849d42f025","fid":"gwc_3","name":"season","analyticType":"dimension","semanticType":"nominal"},{"dragId":"c4a3743c-6400-4466-928f-a2b40f7de920","fid":"gwc_4","name":"hour","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"c0acb711-2f54-41b5-a4f0-d355d8f6998f","fid":"gwc_5","name":"year","analyticType":"dimension","semanticType":"nominal"},{"dragId":"9ad81c68-5329-42eb-9b3a-a813a9482e9b","fid":"gwc_6","name":"holiday","analyticType":"dimension","semanticType":"nominal"},{"dragId":"f135914b-7a58-4f69-bf6e-4328a42c76a0","fid":"gwc_7","name":"temperature","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"4eda6f67-0d64-4b85-9895-ff8dd7c4dab5","fid":"gwc_8","name":"feeling_temp","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"cfa84897-13c9-45ec-addb-2a3135d0113d","fid":"gwc_9","name":"humidity","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"abe15c87-7364-4687-beda-ba11d30c2262","fid":"gwc_10","name":"winspeed","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"5ca1464d-3dc6-4a80-b78c-a6f91ef11e72","fid":"gwc_11","name":"casual","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"a6e553e7-32a2-4aed-b590-02308fb7cac1","fid":"gwc_12","name":"registered","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"37f8fc74-06c2-43bb-98dc-135efdad6a69","fid":"gwc_13","name":"count","aggName":"sum","analyticType":"measure","semanticType":"quantitative"},{"dragId":"c5916915-203b-457c-ba6a-2e28c21694b2","fid":"gwc_14","name":"work yes or not","analyticType":"dimension","semanticType":"nominal"},{"dragId":"ac293432-2c09-4104-bb95-8027f94a310e","fid":"gwc_15","name":"am or pm","analyticType":"dimension","semanticType":"nominal"},{"dragId":"0020d11e-6bcd-44cb-919b-6cb3f6d5fa0f","fid":"gwc_16","name":"Day of the week","analyticType":"dimension","semanticType":"ordinal"},{"dragId":"9ffc9ee4-3413-432f-9606-6c44ea3717da","fid":"gw_count_fid","name":"Row count","aggName":"sum","analyticType":"measure","semanticType":"quantitative"}],"rows":[{"dragId":"cc52d32f-1438-4523-b20a-b7c8346327d1","fid":"gwc_7","name":"temperature","analyticType":"measure","semanticType":"quantitative","aggName":"sum"},{"dragId":"7a693f90-6e5a-4032-a800-f3162efeb2c6","fid":"gwc_10","name":"winspeed","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"columns":[{"dragId":"0170fb40-aa10-4378-ab79-3a35aad936ce","fid":"gwc_9","name":"humidity","analyticType":"measure","semanticType":"quantitative","aggName":"sum"}],"color":[{"dragId":"0e30a2e3-d670-42d5-b0c8-fed4befeb92c","fid":"gwc_3","name":"season","semanticType":"nominal","analyticType":"dimension"}],"opacity":[],"size":[],"shape":[],"radius":[],"theta":[],"details":[],"filters":[]},"config":{"defaultAggregated":false,"geoms":["auto"],"stack":"stack","showActions":false,"interactiveScale":false,"sorted":"none","size":{"mode":"auto","width":320,"height":200},"exploration":{"mode":"none","brushDirection":"default"}}}]
"""
pyg.walk(df, spec=vis_spec)

    Load Data Visualziation to PyGWalker

    PyGWalker also has other enhancements and bug fixes for the latest update. Most notably:

    For a full list of the update, refer to the PyGWalker changelog.

    Summary

    If you are a data scientist, analyst, student, or anyone else who needs to visualize data, PyGWalker is an excellent tool that you should consider using. You can use the exporting data visualizations to code features, create complex visualizations and integrate their visualizations into other tools.

    If you have more suggestions and feature requests about PyGWalker, please feel free to drop an issue at PyGWalker GitHub (opens in a new tab)!

    Further Readings

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      Randomized Stable Diffusion Prompt GeneratorHow to Drop Null Values in PySpark with No Efforts