Pygwalker

0.5.0.1 · active · verified Wed Apr 15

Pygwalker (Python Graphic Walker) is a Python library that transforms pandas (and Polars) DataFrames into an interactive, Tableau-style user interface for data exploration and visualization directly within Jupyter Notebooks, Google Colab, and other compatible environments. It simplifies data analysis and visualization workflows by enabling drag-and-drop operations for quick visual insights. The library is actively developed, with the current version being 0.5.0.1, and sees regular updates to enhance features and performance.

Warnings

deprecated The `use_kernel_calc` parameter in `pyg.walk()` is deprecated.
Fix: Use `kernel_computation=True` instead for enabling the DuckDB-powered computation engine.
breaking API changes were introduced in version 0.4.8, affecting general and Streamlit APIs.
Fix: Specifically, the `dark` parameter was renamed to `appearance`. Streamlit APIs shifted to a more declarative style. Refer to the official GitHub changelog or documentation for specific migration details related to your usage patterns.
gotcha When using the `spec` parameter to save chart configurations, changes made in the UI are not automatically saved.
Fix: Users must manually click the 'Save' button within the Pygwalker UI to persist chart metadata to the specified JSON file or string. An 'autosave' feature is planned for future releases.
gotcha Some cloud-integrated features, such as saving charts to the cloud or using advanced GPT-powered features, require a Kanaries token.
Fix: Obtain a Kanaries token from `kanaries.net` and configure it (e.g., via `pygwalker config --set kanaries_token=YOUR_TOKEN`) to enable these functionalities.

Install

pip install pygwalker Standard installation
pip install "pygwalker[snowflake]" With Snowflake support

Imports

pygwalker
```
import pygwalker as pyg
```
StreamlitRenderer
```
from pygwalker.api.streamlit import StreamlitRenderer
```
Required for integrating Pygwalker into Streamlit applications.
Connector
```
from pygwalker.data_parsers.database_parser import Connector
```
Used for connecting to database sources like Snowflake directly.

Quickstart

This quickstart demonstrates how to initialize Pygwalker with a pandas DataFrame, launching an interactive UI in your Jupyter Notebook or compatible environment. The `pyg.walk()` function takes a DataFrame and opens a graphical interface for drag-and-drop data exploration. For larger datasets, consider adding `kernel_computation=True` to leverage DuckDB for enhanced performance.

import pandas as pd
import pygwalker as pyg

# Create a sample DataFrame
data = {
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'],
    'Population': [8804190, 3898747, 2716075, 2325502, 1680992],
    'Area_sq_mi': [302.6, 468.7, 227.6, 637.4, 517.6]
}
df = pd.DataFrame(data)

# Launch Pygwalker UI
# You can also use pyg.walk(df, kernel_computation=True) for better performance on large datasets.
walker = pyg.walk(df)

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