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

Installing Dependencies

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

```bash

pip install pandas

pip install pygwalker

pip install streamlit

```

Integrating 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, execute 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 wish 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. Copy and paste these codes to your project:

```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)

```

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

It's worth noting 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.