Streamlit has democratized the creation of web applications for data scientists. It bridges the gap between rapid prototyping and production-ready visualization. By mastering the basics of widgets, caching, and layouts, you can transform static analyses into dynamic experiences that engage stakeholders and drive decisions.
# --- Sidebar --- st.sidebar.header("Configuration") getting started with streamlit for data science pdf
Here is the complete code for your app.py file. You can copy this entire block into your text editor. Streamlit has democratized the creation of web applications
# Raw Data Expander with st.expander("Inspect Raw Data"): st.dataframe(data) getting started with streamlit for data science pdf
if st.checkbox("Show Raw Data"): st.subheader("Raw Data") st.dataframe(df) # Interactive table # st.table(df) # Static table