Streamlit
1 Introduction to Streamlit
1.1 What is Streamlit?
1.2 Why use Streamlit?
1.3 Setting up the environment
1.4 Creating your first Streamlit app
2 Basic Components
2.1 Text elements
2.1 1 Displaying text
2.1 2 Formatting text
2.2 Data display elements
2.2 1 Displaying data frames
2.2 2 Displaying tables
2.3 Input widgets
2.3 1 Text input
2.3 2 Number input
2.3 3 Date input
2.3 4 Dropdown selection
2.3 5 Slider
2.3 6 Checkbox
2.3 7 Radio buttons
2.3 8 Buttons
3 Advanced Components
3.1 Interactive widgets
3.1 1 Multiselect
3.1 2 File uploader
3.1 3 Color picker
3.2 Media elements
3.2 1 Displaying images
3.2 2 Displaying videos
3.2 3 Displaying audio
3.3 Chart elements
3.3 1 Line chart
3.3 2 Bar chart
3.3 3 Area chart
3.3 4 Scatter chart
3.3 5 Map chart
4 Layout and Styling
4.1 Layout components
4.1 1 Columns
4.1 2 Tabs
4.1 3 Expander
4.2 Styling elements
4.2 1 Custom CSS
4.2 2 Theming
4.2 3 Adding custom fonts
5 State Management
5.1 Session state
5.1 1 Managing state across reruns
5.1 2 Persisting state
5.2 Caching
5.2 1 Caching functions
5.2 2 Caching data
6 Deployment
6.1 Deploying to Streamlit Sharing
6.1 1 Setting up Streamlit Sharing
6.1 2 Deploying your app
6.2 Deploying to other platforms
6.2 1 Deploying to Heroku
6.2 2 Deploying to AWS
6.2 3 Deploying to Google Cloud
7 Best Practices
7.1 Writing clean and maintainable code
7.2 Optimizing performance
7.3 Handling errors and exceptions
7.4 Version control with Git
8 Advanced Topics
8.1 Integrating with other libraries
8.1 1 Integrating with Pandas
8.1 2 Integrating with Plotly
8.1 3 Integrating with TensorFlow
8.2 Building complex apps
8.2 1 Creating multi-page apps
8.2 2 Handling authentication
8.2 3 Building interactive dashboards
8.3 Custom components
8.3 1 Creating custom widgets
8.3 2 Extending Streamlit with custom components
9 Case Studies
9.1 Building a data exploration app
9.2 Building a machine learning model deployment app
9.3 Building a real-time data visualization app
2 3 6 Checkbox Explained

3 6 Checkbox Explained

Key Concepts

Explanation

1. Checkbox Widget

The st.checkbox widget in Streamlit is used to create a checkbox that users can toggle on or off. This widget is useful for creating binary choices or allowing users to select multiple options.

2. Label

The label parameter in st.checkbox is a string that describes the purpose of the checkbox. This label is displayed next to the checkbox, helping users understand what the checkbox represents.

3. State

The state of the checkbox is either checked (True) or unchecked (False). This state can be used to trigger different actions or display different content based on the user's selection.

Examples

Example 1: Basic Checkbox

import streamlit as st

agree = st.checkbox("Do you agree to the terms?")
if agree:
    st.write("You agreed to the terms.")
else:
    st.write("You did not agree to the terms.")
    

Example 2: Multiple Checkboxes

import streamlit as st

options = ["Option 1", "Option 2", "Option 3"]
selected_options = []

for option in options:
    if st.checkbox(option):
        selected_options.append(option)

st.write("You selected:", selected_options)
    

Analogies

Think of a checkbox as a digital version of a physical checkbox on a paper form. The label is like the question or statement next to the checkbox, and the state is like whether the checkbox is marked or not. For example, a checkbox labeled "I agree to the terms and conditions" would be checked if the user agrees and unchecked if they do not.

Conclusion

Understanding and using st.checkbox effectively can greatly enhance the interactivity of your Streamlit app. By providing clear labels and using the checkbox state to trigger actions, you can create dynamic and user-friendly applications.