Your DataFrame's Visual Analytics Companion

PyGWalker turns your data into interactive visualization apps with one line of code, and enables you to share your apps with one click.

pip install pygwalker --upgrade

Read docs

Thousands of developers and data professionals worldwide trust PygWalker

13K

Github Stars

1.35m

package downloads

IEEE VIS 2024

Best short paper

Data to Visualization App with Ease

PyGWalker turns your data into interactive visualization apps with one line of code, and enables you to share your apps with one click.

Read docs

Step1: Install PyGWalker

Get started by installing PyGWalker using pip (pip install pygwalker) or conda (conda install pygwalker).

Step2: Load Your Data

Import PyGWalker and turn your dataframe into interactive component with pygwalker.walk(dataframe).

Step3: Explore & Visualize

Make visualizations with simple drag and drop operations or natural language commands.

Features make visualization simple

PyGWalker provides a set of features that make data analysis much simpler, and always be scalable.

Data Painter

Data Cleaning Can be Easy

PyGWalker's Data Painter allows you to clean your data with an "eraser", you can remove outliers, clusters and complex patterns within seconds.

Data Painter

Annotation with Ad Hoc Analysis

Create new variables/labels/features in your data without break your analysis workflow. Data painter allows you to create new features on your analytical views in real-time.

Interactive Charts on Maps

Map Your Data with a Few Clicks

Create interactive maps instantly from your geographic data. Zoom, pan, and explore your data points with intuitive controls. Perfect for visualizing location-based patterns in your DataFrame - no mapping expertise required.

PyGWalker

High performance / scalable

Process massive datasets with industry-leading speed and efficiency. PyGWalker by default handles queries in a high performance engine based on duckDB. It also allows connecting to external clusters for large scale of data like Snowflake, ClickHouse.

Framework Integration

Works with Jupyter, Streamlit, Gradio, and Plotly Dash, enabling smooth embedding into your existing workflows.

Connecting Databases

Not only you can connect most common databases to pygwalker, but you can uses all services supporting SQL as query engine for pygwalker as well. In this way, pygwalker can scale for large data processing.

Visualize with few lines of code

PyGWalker simplifies data analysis with powerful features, easily integrated into Jupyter and more, using just a few lines of code.

[1]:

import pandas as pd
from pygwalker.api.jupyter import JupyterRenderer

df = pd.read_csv("./bike_sharing_dc.csv")
renderer = JupyterRenderer(df)
renderer.explorer()

Frequently asked questions

PyGWalker is an open source project that is constantly evolving. Here are some common questions about PyGWalker.

Ready to Unlock the Visual Power of Your Data?

Free and open-source, PyGWalker is a Python library that enables you to transform your data into a fully interactive visual