1 1 Managing State Across Reruns Explained
Key Concepts
- Session State: Persistent data storage across reruns.
- Reruns: The process of re-executing the Streamlit script.
- State Management: Techniques to maintain data consistency during reruns.
Session State
Session State in Streamlit is a way to store data that persists across reruns of the script. This is particularly useful for maintaining user inputs, intermediate calculations, and other data that should not be reset with each rerun.
Example:
import streamlit as st
if 'counter' not in st.session_state:
st.session_state.counter = 0
increment = st.button('Increment')
if increment:
st.session_state.counter += 1
st.write('Counter:', st.session_state.counter)
Reruns
Reruns occur when the Streamlit script is re-executed, typically due to user interactions like button clicks or widget changes. Without proper state management, data can be reset during these reruns, leading to inconsistent behavior.
State Management
State Management techniques in Streamlit involve using the Session State to store and retrieve data that should persist across reruns. This ensures that the application behaves predictably and maintains data consistency.
Example:
import streamlit as st
if 'user_input' not in st.session_state:
st.session_state.user_input = ''
user_input = st.text_input('Enter something', st.session_state.user_input)
st.session_state.user_input = user_input
st.write('You entered:', st.session_state.user_input)
Analogies
Think of Session State as a sticky note on your desk that you update as you work. Each time you come back to your desk, the sticky note still has the latest information, just like Session State retains data across reruns. Reruns are like taking a break and coming back to your desk, and State Management is like ensuring the sticky note is always up-to-date.
By mastering state management in Streamlit, you can create more robust and user-friendly applications that maintain data consistency and provide a seamless experience.