Understanding Streamlit

Streamlit is an open-source Python library that makes it straightforward to create custom web applications for machine learning and data science. Launched in 2019, it addresses a significant pain point in the data science process: transforming code and data into interactive applications.

Getting Started

To use Streamlit, install the library via pip:

pip install streamlit

Once installed, you can start a new Streamlit app by writing a Python script. A minimal script might look like this:

import streamlit as stst.title('Hello, Streamlit!')st.write('This is my first Streamlit app.')

Run the script with the following command:

streamlit run your_script.py

This command will launch a local server and open a new tab in your default web browser showing the app.

Core Features

Streamlit provides simple commands to display various elements:

• Text and Markdown: Use st.write() for basic text and st.markdown() for styled text.
• DataFrames: Leverage st.dataframe() to display Pandas DataFrames interactively.
• Plots: Use libraries like Matplotlib, Seaborn, or Plotly within st.pyplot() and st.plotly_chart().
• Widgets: Interact with your audience using sliders, buttons, and selectboxes.

Text and Markdown

Displaying text is simple with Streamlit. For instance:

st.write('Hello, World!')st.markdown('# This is a markdown header')

Text elements automatically adjust based on the displayed content.

Displaying DataFrames

DataFrames can be presented with a scrollable and sortable interface:

import pandas as pddata = {'Column1': [1, 2, 3],        'Column2': [4, 5, 6]}df = pd.DataFrame(data)st.dataframe(df)

This displays the DataFrame in a user-friendly way.

Plotting

Use your favorite plotting libraries without much setup:

import matplotlib.pyplot as pltfig, ax = plt.subplots()ax.plot([1, 2, 3], [4, 5, 6])st.pyplot(fig)

Streamlit supports integration with Plotly and other libraries for advanced visualizations.

Widgets

Interactive widgets like sliders and buttons enhance app interactivity:

age = st.slider('Select your age', 0, 100, 25)st.write('Your age is', age)

Information entered into widgets can be used for real-time data updates and interactions.

Layouts and Customization

• Containers: Use st.container() to group elements.
• Columns: Create multi-column layouts with st.columns().
• Expander: Collapsible sections with st.expander().

Using Containers

Group several elements together in a container:

with st.container():    st.write(Contained within a box)st.write(Outside the container)

This makes organization easier and more visually appealing.

Multi-column Layouts

Multiple columns can present data side-by-side:

col1, col2 = st.columns(2)col1.write(This is column 1)col2.write(This is column 2)

Use columns to structure more complex layouts.

Expandable Sections

Make sections expandable for a cleaner interface:

with st.expander('Expand me'):    st.write('Hidden content!')

Expander widgets can streamline the user interface by hiding less pertinent information.

Advanced Features

Streamlit allows for caching, state management, and theming.

• Caching: Speed up the app by caching expensive functions with @st.cache.
• State Management: Use st.session_state to manage variables across sessions.
• Custom Themes: Introduce custom themes in the .streamlit/config.toml file.

Caching Functions

Reduce computational load by caching results:

@st.cachedef expensive_function():    time.sleep(2)  # Simulate a long computation    return 42result = expensive_function()st.write(result)

This makes the app more responsive, especially for repeated calculations.

Session State

Maintain state information even after interactions:

if 'count' not in st.session_state:    st.session_state.count = 0if st.button('Increment'):    st.session_state.count += 1st.write(st.session_state.count)

Session state enables the creation of more dynamic and interactive applications.

Custom Themes

Customize the look of your app by editing the configuration file:

[theme]primaryColor = '#1f77b4'backgroundColor = '#f0f0f5'secondaryBackgroundColor = '#e0e0ef'textColor = '#262730'

This extends the visual customizability beyond default settings.

Deployment

Streamlit apps can be deployed using platforms like Heroku, AWS, and Streamlit Sharing. Streamlit Sharing is the official platform endorsed by the Streamlit team for easy deployment. It provides a straightforward way to share your apps by linking to a GitHub repository. Here’s a brief overview of the deployment process using Streamlit Sharing:

• Push your Streamlit app script to a GitHub repository.
• Log in to Streamlit Sharing and create a new app by linking to your repository.
• Streamlit Sharing will automatically deploy your app and provide a shareable URL.

For deployment on other platforms, Docker containers or web server configuration might be necessary, but clear documentation and community support are readily available.