Streamlit: The fastest way to build data apps in Python
Introduction
Streamlit is an open-source Python library designed to enable data scientists and machine learning engineers to quickly and easily create beautiful and interactive web applications. It simplifies the process of turning data science scripts into shareable web apps, requiring no front-end development experience.
Core Principles:
- Embrace Python Scripting: Streamlit allows you to build applications using pure Python scripts, without writing HTML, CSS, or JavaScript.
- Real-time Updates: Streamlit applications automatically update every time you modify and save your code, enabling rapid iteration and experimentation.
- Simple and Easy to Use: Streamlit provides an intuitive API that makes it easy to add various UI elements, such as text, charts, sliders, buttons, and more.
- Data-Driven: Streamlit is specifically designed for data science workflows, making it easy to display and manipulate data.
- No Front-End Experience Required: Streamlit abstracts away the complexities of front-end development, allowing you to focus on data and models.
Project Repository Information
- GitHub Repository: https://github.com/streamlit/streamlit
- Primary Programming Language: Python
- License: Apache License 2.0
- Stars: 23k+ (as of January 26, 2024)
- Forks: 2k+ (as of January 26, 2024)
- Contributors: 300+
Key Features
- Simple and Easy-to-Use API: Streamlit provides a concise API for creating various UI elements, such as:
st.write(): Displays text, data, charts, etc.st.slider(): Creates a slider widget.st.button(): Creates a button widget.st.selectbox(): Creates a dropdown selection box.st.dataframe(): Displays a Pandas DataFrame.st.line_chart(): Displays a line chart.st.map(): Displays a map.st.file_uploader(): Uploads files
- Automatic Re-running: Streamlit applications automatically re-run every time you modify and save your code, enabling rapid iteration.
- Caching: Streamlit has a built-in caching mechanism that can cache calculation results, improving application performance. Use the
@st.cache_data decorator to cache function results.
- Session State: Streamlit allows you to manage application state using
st.session_state, enabling you to create more complex interactive applications.
- Components: Streamlit supports custom components, allowing you to extend Streamlit's functionality and integrate third-party libraries.
- Theming: Streamlit allows you to customize the appearance of your application, including colors, fonts, and more.
- Deployment: Streamlit applications can be easily deployed to various platforms, such as Streamlit Community Cloud, Heroku, AWS, Google Cloud, etc.
- Community Support: Streamlit has an active community, providing extensive documentation, tutorials, and examples.
Core Modules and Functionality
- streamlit: The core library, containing the API for building applications.
- streamlit.components.v1: The API for creating custom components.
- streamlit.config: Used to configure the behavior of Streamlit applications.
- streamlit.delta_generator: The API for generating UI elements.
- streamlit.runtime: The runtime environment for Streamlit applications.
- streamlit.web: The web server for Streamlit applications.
Use Cases
Streamlit can be used to build various data applications, such as:
- Data Visualization: Create interactive charts and dashboards to explore and analyze data.
- Machine Learning Model Deployment: Deploy machine learning models as web applications so users can interact with the models.
- Data Exploration: Build interactive tools to explore and understand data.
- Data Annotation: Create data annotation tools to help label data.
- Education and Demonstrations: Create interactive demonstrations to showcase data science concepts and techniques.
- Internal Tools: Build internal data tools to improve team efficiency.
Example Code
Here is a simple Streamlit application example:
import streamlit as st
import pandas as pd
import numpy as np
st.title('My First Streamlit App')
st.write('Hello, world!')
# Create a DataFrame
data = pd.DataFrame({
'col1': [1, 2, 3, 4],
'col2': [5, 6, 7, 8]
})
# Display DataFrame
st.write('Here is a DataFrame:')
st.dataframe(data)
# Create a slider
x = st.slider('Select a value for x', 0, 100, 50)
# Display the value of the slider
st.write('The value of x is:', x)
# Create a random number chart
chart_data = pd.DataFrame(
np.random.randn(20, 3),
columns=['a', 'b', 'c'])
st.line_chart(chart_data)
Installation
You can install Streamlit using pip:
pip install streamlit
Running
To run a Streamlit application, execute the following command in the command line:
streamlit run your_app.py
Where your_app.py is the Python file of your Streamlit application.
Advantages
- Rapid Development: Streamlit allows you to quickly build data applications without writing a lot of code.
- Easy to Use: Streamlit provides an intuitive API that makes it easy to add various UI elements.
- Interactivity: Streamlit applications are interactive, allowing users to interact with the data.
- Deployability: Streamlit applications can be easily deployed to various platforms.
- Community Support: Streamlit has an active community, providing extensive documentation, tutorials, and examples.
Disadvantages
- Limited Customization: Streamlit's customization is relatively limited and may not meet the needs of all applications.
- Performance: For complex applications, Streamlit's performance may not be as good as traditional web frameworks.
- Weaker Front-End Control: Cannot finely control the UI like traditional front-end frameworks.
Summary
Streamlit is a powerful tool that can help data scientists and machine learning engineers quickly build beautiful and interactive web applications. It simplifies the process of turning data science scripts into shareable web apps, requiring no front-end development experience. While it has some limitations in terms of customization and performance, its ease of use and rapid development capabilities make it an ideal choice for many data science projects.