Easily Embed PyGWalker in Streamlit for Data Visuzlization

 Streamlit and Pygwalker: Simplify Data Visualization and Exploration

Welcome to an exciting journey where we explore the amazing capabilities of Streamlit and Pygwalker in analyzing and visualizing data effortlessly. Get ready to immerse yourself in the world of interactive data exploration!

Introducing Streamlit

Streamlit is a powerful Python library that simplifies the process of transforming your data scripts into interactive web applications. With Streamlit, you can bid farewell to the complexities of web development and coding challenges. It's a fast, open-source, and free solution for building and sharing data applications.

Exploring Data Made Easy with Pygwalker

Pygwalker, on the other hand, is a popular Python library designed specifically for data analysis and visualization. It provides data scientists and analysts with an intuitive interface for generating captivating visualizations, including scatter plots, line plots, bar charts, and histograms. The best part? You don't need any coding skills to use Pygwalker!

To learn more about Pygwalker and access additional examples and resources, visit the official Pygwalker GitHub Page.

Getting Started with Streamlit and Pygwalker

Before we embark on our data exploration journey, let's make sure your computer is equipped with a Python environment (version 3.6 or higher). Once you have that set up, follow these steps:

Installing the Required Dependencies

Open your command prompt or terminal and run the following commands to install the necessary dependencies:

```shell

pip install pandas

pip install pygwalker

pip install streamlit

```

Incorporating Pygwalker in a Streamlit Application

Now that we have all the dependencies installed, let's create a Streamlit application that incorporates Pygwalker. Create a new Python script called `pygwalker_demo.py` and add the following code:

```python

import pygwalker as pyg

import pandas as pd

import streamlit.components.v1 as components

import streamlit as st

# Configure the Streamlit page

st.set_page_config(

    page_title="Using Pygwalker with Streamlit",

    layout="wide"

)

# Add a title

st.title("Using Pygwalker with Streamlit")

# Import your data

df = pd.read_csv("https://sample.csv")

# Generate the HTML using Pygwalker

pyg_html = pyg.walk(df, return_html=True)

# Embed the generated HTML into the Streamlit app

components.html(pyg_html, height=1000, scrolling=True)

```


Exploring Data with Pygwalker in Streamlit

To launch the Streamlit application and start exploring your data, run the following command in your command prompt or terminal:

```shell

streamlit run pygwalker_demo.py

```

You will see some information displayed on the terminal. Access the Streamlit app in your browser using the provided URL:

Local URL: [http://localhost:8501](http://localhost:8501)

Network URL: [http://xxx.xxx.xxx.xxx:8501](http://xxx.xxx.xxx.xxx:8501)

Open the provided URL ([http://localhost:8501](http://localhost:8501)) in your web browser and witness the power of Pygwalker's intuitive drag-and-drop actions for interactive data exploration and visualization.

Saving the State of a Pygwalker Chart

If you want to save the state of a Pygwalker chart, follow these simple steps:

1. Click the export button on the chart.


2. Click the copy code button.


3. Paste the copied code into your Python script where needed.

```python

import pygwalker as pyg

import pandas as pd

import streamlit.components.v1 as components

import streamlit as st

# Configure the Streamlit page

st.set_page_config(

    page_title="Using Pygwalker with Streamlit",

    layout="wide"

)

# Add a title

st.title("Using Pygwalker with Streamlit")

# Import your data

df = pd.read_csv("https://kanaries-app.s3.ap-northeast-1.amazonaws.com/public-datasets/bike_sharing_dc.csv")

# Paste the copied Pygwalker chart code here

vis_spec = """<PASTE_COPIED_CODE_HERE>"""

# Generate the HTML using Pygwalker

pyg_html = pyg.walk(df, spec=vis_spec, return_html=True)

# Embed the generated HTML into the Streamlit app

components.html(pyg_html, height=1000, scrolling=True)

```

Make sure to refresh the webpage to see the saved state of your Pygwalker chart.

It's important to note that Pygwalker is built upon graphic-walker, a powerful library that can be embedded in various platforms, including Excel and Airtable. This means that your Pygwalker app can easily collaborate with users in different environments, leveraging the capabilities of graphic-walker and Pygwalker.

Conclusion

Streamlit and Pygwalker are invaluable tools that simplify data exploration and facilitate effective communication of insights. Streamlit's user-friendly interface and Pygwalker's interactive visualization options seamlessly enhance your data analysis workflow. So go ahead, dive into your data, and share your remarkable insights with the world!

References

Pygwalker GitHub Repository

Pygwalker in Streamlit Demo

For more detailed information, you can refer to this documentation that explains how to use Streamlit with PyGWalker.