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.
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!
How to use PyGWalker with Streamlit in Python
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.