PyGWalker 0.1.6. Update

For each update, we list the new features, enhancements, bug fixes in the PyGWalker Changelog Page.

Release date: Mar. 14. 2023.

Feature Highlight:

Now you can export a Visualization to code.

Steps to use this new feature:

Click on the Export to Code button on the toolbar. The button is located next to the "Export to PNG/SVG" button.
The Visualization is available as code. Click on the Copy to Clickboard button to save the code.
To import the code in PyGWalker, simply import the code you just downloaded as vis_spec:
Your Data Visualization is saved to a vis_spec string. Sample vis_spec string:

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":"b9abe5dd-4ebd-47a0-b7a6-66730c2eb3dc","fid":"gwc_5","name":"year","semanticType":"nominal","analyticType":"dimension"}],"color":[],"opacity":[],"size":[],"shape":[],"radius":[],"theta":[],"details":[],"filters":[]},"config":{"defaultAggregated":true,"geoms":["auto"],"stack":"stack","showActions":false,"interactiveScale":false,"sorted":"none","size":{"mode":"auto","width":320,"height":200},"exploration":{"mode":"none","brushDirection":"default"}}}]
"""

And load PyGWalker with the vis_spec:

pyg.walk(df, spec=vis_spec)

Load Data Visualziation to PyGWalker

You can invoke the build-in help documentation by:

help(pyg.walk)

You can also take a quick overview of the vis_spec string in the following way:

pyg.to_html(df, spec=vis_spec)

Sample output:

Signature: pyg.walk(df: 'pl.DataFrame | pd.DataFrame', gid: Union[int, str] = None, *, env: Literal['Jupyter', 'Streamlit'] = 'Jupyter', **kwargs)
Docstring:
Walk through pandas.DataFrame df with Graphic Walker

Args:
    - df (pl.DataFrame | pd.DataFrame, optional): dataframe.
    - gid (Union[int, str], optional): GraphicWalker container div's id ('gwalker-{gid}')

Kargs:
    - env: (Literal['Jupyter' | 'Streamlit'], optional): The enviroment using pygwalker. Default as 'Jupyter'
    - hideDataSourceConfig (bool, optional): Hide DataSource import and export button (True) or not (False). Default to True
    - themeKey ('vega' | 'g2'): theme type.
    - dark (Literal['media' | 'light' | 'dark']): 'media': auto detect OS theme.
    - return_html (bool, optional): Directly return a html string. Defaults to False.
File:      /usr/local/lib/python3.9/dist-packages/pygwalker/gwalker.py
Type:      function

Other enhancements:

Create Data Visualizations