Introduction to Streamlit

What is Streamlit?

Streamlit is an open-source Python library that makes it easy to create and share custom web apps for machine learning and data science. Unlike traditional web development frameworks, Streamlit allows developers to build interactive web applications using pure Python code, without the need for HTML, CSS, or JavaScript.

Key Concepts

1. Streamlit Components

Streamlit provides a variety of components that can be used to create interactive elements in your web app. These components include text boxes, buttons, sliders, and more. Each component is designed to be intuitive and easy to use, allowing developers to focus on the logic of their application rather than the intricacies of web development.

2. Streamlit Widgets

Widgets are interactive elements that allow users to input data or make selections. Streamlit offers a wide range of widgets, such as st.slider, st.button, and st.selectbox. These widgets can be used to gather user input, which can then be processed by your Python code.

3. Streamlit DataFrames

Streamlit has built-in support for displaying and manipulating data using Pandas DataFrames. The st.dataframe function allows you to display a DataFrame in a table format, while st.table displays a static table. This makes it easy to visualize and interact with data directly within your Streamlit app.

4. Streamlit Layouts

Streamlit provides several layout options to organize your app's content. You can use st.sidebar to create a sidebar for navigation, st.columns to create columns for side-by-side content, and st.expander to hide or show content on demand. These layout options help you create a more structured and user-friendly interface.

Examples

Example 1: Simple Streamlit App

Here's a basic example of a Streamlit app that displays a welcome message and a slider widget:

import streamlit as st

st.title("Welcome to Streamlit!")
st.write("This is a simple Streamlit app.")

value = st.slider("Select a value", 0, 100)
st.write(f"You selected: {value}")
    

Example 2: Displaying a DataFrame

This example demonstrates how to display a Pandas DataFrame in a Streamlit app:

import streamlit as st
import pandas as pd

st.title("DataFrame Example")

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = pd.DataFrame(data)
st.dataframe(df)
    

Conclusion

Streamlit is a powerful tool for creating data-driven web applications with minimal effort. By leveraging its intuitive components, widgets, and layout options, you can quickly build and deploy interactive apps that showcase your data science projects. Whether you're a beginner or an experienced developer, Streamlit provides a seamless way to bring your Python code to life on the web.