How to use PyGWalker with Streamlit in Python

Last Updated : 31 May, 2024

Streamlit is an open-source Python library that allows developers to create beautiful, interactive web applications for data science and machine learning projects with ease. PyGWalker (Python Graphic Walker) is an innovative data visualization tool that combines the simplicity of Python with the power of modern web-based visualization. In this article, we will explore how to use PyGWalker with Streamlit to create dynamic and interactive data visualizations.

How to Install PyGWalker?

Before we dive into the examples, let's start by installing the necessary libraries. Both Streamlit and PyGWalker can be installed via pip, the Python package manager. Open your terminal or command prompt and run the following commands:

pip install streamlit
pip install pygwalker

Once the installation is complete, you are ready to create your first interactive visualization using PyGWalker and Streamlit.

Basic Integration ( 3 Columns )

This Streamlit app demonstrates how to create a simple data visualization using PyGWalker. It loads a sample dataset with three columns and displays it in an interactive table format within the app. The app is titled "PyGWalker with Streamlit - Basic Example" and showcases the dataset using `st.dataframe()` for clear and easy viewing.

Python

import streamlit as st import pandas as pd import pygwalker as pyg  # Load sample dataset df = pd.DataFrame({     'A': range(1, 11),     'B': [x ** 2 for x in range(1, 11)],     'C': ['Category1', 'Category2'] * 5 })  # Title for the Streamlit app st.title("PyGWalker with Streamlit - Basic Example")  # Display the dataset st.write("### Sample Data") st.dataframe(df)

Run your Streamlit App

Save the script as app.py and run it using the following command in your terminal:

streamlit run app.py

Interactive Data Selection and Visualization (4 Column)

This Streamlit app loads a sample dataset with four columns and displays it interactively using st.dataframe(). Titled "PyGWalker with Streamlit - Interactive Example," it provides a clear view of the dataset, facilitating easy data exploration and visualization. The app leverages PyGWalker for enhanced interactive data analysis.

Python

import streamlit as st import pandas as pd import pygwalker as pyg  # Load sample dataset df = pd.DataFrame({     'A': range(1, 11),     'B': [x ** 2 for x in range(1, 11)],     'C': ['Category1', 'Category2'] * 5,     'D': [10 - x for x in range(1, 11)] })  # Title for the Streamlit app st.title("PyGWalker with Streamlit - Interactive Example")  # Display the dataset st.write("### Sample Data") st.dataframe(df)

Run your Streamlit App

Save the script as app.py and run it using the following command in your terminal:

streamlit run app.py

Conclusion

Combining PyGWalker with Streamlit opens up a world of possibilities for creating interactive data visualizations in Python. Streamlit's ease of use and PyGWalker's powerful visualization capabilities make them a perfect match for data scientists and analysts looking to quickly prototype and share their findings. By following the steps outlined in this article, you can start creating your own interactive data visualizations and enhance your data exploration experience. Happy coding!