There are a number of ways to handle user input and interactions in a Streamlit app, such as filtering data or changing visualization parameters. Here are a few examples:
- Use interactive widgets: Streamlit provides a variety of interactive widgets, such as sliders, checkboxes, and drop-down menus. These widgets can be used to allow users to interact with the data and the application state. For example, you could use a slider to allow users to filter the data or a checkbox to toggle between different data visualizations.
- Use callback functions: Streamlit callback functions can be used to execute code when a user interacts with an interactive widget. This allows you to update the data visualizations or other elements of your app in response to user input.
- Use the session state: Streamlit session state can be used to store data that needs to be persisted across app reruns. This can be useful for storing user input or the state of the application.
Here is an example of how to use interactive widgets and callback functions to handle user input and interactions in a Streamlit app:
import streamlit as st import pandas as pd # Load the dataset df = pd.read_csv('dataset.csv') # Create a slider to allow users to filter the data start_year = st.slider('Start year:', min(df['year']), max(df['year'])) end_year = st.slider('End year:', min(df['year']), max(df['year'])) # Filter the data df_filtered = df[df['year'] >= start_year] df_filtered = df_filtered[df_filtered['year'] <= end_year] # Create a callback function to update the line chart when the slider values change def update_line_chart(): st.line_chart(df_filtered['column_name_1'], df_filtered['column_name_2']) # Call the callback function when the slider values change st.on_change('start_year', update_line_chart) st.on_change('end_year', update_line_chart) # Update the line chart update_line_chart()
This application will create a line chart that displays the data for the two selected columns. When the user changes the slider values, the callback function will be called to update the line chart with the new data.
You can use the same approach to handle other types of user input and interactions, such as changing the parameters of data visualizations or selecting different data subsets.
In addition to the above, here are some other tips for handling user input and interactions in a Streamlit app:
- Use validation: Validate user input before using it to update the data or the application state. This will help to prevent errors and unexpected results.
- Use feedback: Provide feedback to users when they interact with your app. This could be in the form of a message, a progress bar, or a change in the appearance of the UI.
- Design for errors: Things don’t always go according to plan, so it’s important to design your app to handle errors gracefully. This could involve displaying a friendly error message or providing users with a way to recover from the error.
By following these tips, you can create Streamlit apps that are responsive to user input and interactions. This will make your apps more user-friendly and engaging.