Kanaries / pygwalker

English | Español | Français | Deutsch | 中文 | Türkçe | 日本語 | 한국어 | Русский 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 into an interactive user interface for visual exploration. **PyGWalker** (pronounced like "Pig Walker", just for fun) is named as an abbreviation of "**Py**thon binding of **G**raphic **Walker**". It integrates Jupyter Notebook with Graphic Walker, an open-source alternative to Tableau. It allows data scientists to visualize / clean / annotates the data with simple drag-and-drop operations and even natural language queries. https://github.com/Kanaries/pygwalker/assets/22167673/2b940e11-cf8b-4cde-b7f6-190fb10ee44b > [!TIP] > If you want more AI features, we also build runcell, an AI Code Agent in Jupyter that understands your code/data/cells and generate code, execute cells and take actions for you. It can be used in jupyter lab with https://github.com/user-attachments/assets/9ec64252-864d-4bd1-8755-83f9b0396d38 Visit Google Colab, Kaggle Code or Graphic Walker Online Demo to test it out! > If you prefer using R, check GWalkR, the R wrapper of Graphic Walker. > If you prefer a Desktop App that can be used offline and without any coding, check out PyGWalker Desktop. Features PyGWalker is a Python library that simplifies data analysis and visualization workflows by turning pandas DataFrames into interactive visual interfaces. It offers a variety of features that make it a powerful tool for data exploration: • ##### Interactive Data Exploration: • Drag-and-drop interface for easy visualization creation.   • Real-time updates as you make changes to the visualization. • Ability to zoom, pan, and filter the data.   • ##### Data Cleaning and Transformation: • Visual data cleaning tools to identify and remove outliers or inconsistencies.   • Ability to create new variables and features based on existing data.   • ##### Advanced Visualization Capabilities: • Support for various chart types (bar charts, line charts, scatter plots, etc.). • Customization options for colors, labels, and other visual elements.   • Interactive features like tooltips and drill-down capabilities.   • ##### Integration with Jupyter Notebooks: • Seamless integration with Jupyter Notebooks for a smooth workflow.   • ##### Open-Source and Free: • Available for free and allows for customization and extension. Getting Started > Check our video tutorial about using pygwalker, pygwalker + streamlit and pygwalker + snowflake, How to explore data with PyGWalker in Python | 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 > **Note** > > For an early trial, you can install with to keep your version up to date with the latest release or even to obtain latest features and bug-fixes. Conda-forge or See conda-forge feedstock for more help. Use pygwalker in Jupyter Notebook Quick Start Import pygwalker and pandas to your Jupyter Notebook to get started. You can use pygwalker without breaking your existing workflow. For example, you can call up PyGWalker with the dataframe loaded in this way: That's it. Now you have an interactive UI to analyze and visualize data with simple drag-and-drop operations. Cool things you can do with PyGwalker: • 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. • PyGWalker contains a powerful data table, which provides a quick view of data and its distribution, profiling. You can also add filters or change the data types in the table. • You can save the data exploration result to a local file Better Practices There are some important parameters you should know when using pygwalker: • : for save/load chart config (json string or file path) • : for using duckdb as computing engine which allows you to handle larger dataset faster in your local machine. • : Deprecated, use instead. Example in local notebook • Notebook Code: Click Here • Preview Notebook Html: Click Here Example in cloud notebook • Use PyGWalker in Kaggle • Use PyGWalker in Google Colab Programmatic Export of Charts After saving a chart from the UI, you can retrieve the image directly from Python. Use pygwalker in Streamlit Streamlit allows you to host a web version of pygwalker without figuring out details of how web application works. Here are some of the app examples build with pygwalker and streamlit: • PyGWalker + streamlit for Bike sharing dataset • Earthquake Dashboard API Reference pygwalker.walk | Parameter | Type | Default | Description | |------------------------|-----------------------------------------------------------|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------| | dataset | Union[DataFrame, Connector] | - | The dataframe or connector to be used. | | gid | Union[int, str] | None | ID for the GraphicWalker container div, formatted as 'gwalker-{gid}'. | | env | Literal['Jupyter', 'JupyterWidget'] | 'JupyterWidget' | Environment using pygwalker. | | field_specs | Optional[Dict[str, FieldSpec]] | None | Specifications of fields. Will be automatically inferred from if not specified. | | hide_data_source_config | bool | True | If True, hides DataSource import and export button. | | theme_key | Literal['vega', 'g2'] | 'g2' | Theme type for the GraphicWalker. | | appearance | Literal['media'…