Streamlit Core Features Beginner

Master the essential Streamlit commands for displaying text, building layouts, and optimizing performance with caching. These core features are the foundation of every Streamlit application.

Text & Display

Python

import streamlit as st

# The magic command - renders almost anything
st.write("Hello, world!")
st.write(42)
st.write({"key": "value"})
st.write(my_dataframe)

# Specific text elements
st.title("Page Title")
st.header("Section Header")
st.subheader("Subsection")
st.markdown("**Bold** and *italic* text with [links](https://streamlit.io)")
st.caption("Small caption text")
st.code("print('Hello')", language="python")
st.latex(r"e = mc^2")
st.divider()  # Horizontal rule

Status Elements

Python

# Info boxes
st.info("This is informational.")
st.success("Operation completed!")
st.warning("Be careful with this.")
st.error("Something went wrong.")
st.exception(ValueError("Invalid input"))

# Progress and spinners
with st.spinner("Loading model..."):
    model = load_model()
st.success("Model loaded!")

progress = st.progress(0)
for i in range(100):
    progress.progress(i + 1)

Layout

Columns

Python

# Equal columns
col1, col2, col3 = st.columns(3)
col1.metric("Temperature", "72 F", "1.2 F")
col2.metric("Wind", "9 mph", "-8%")
col3.metric("Humidity", "86%", "4%")

# Unequal columns
left, right = st.columns([2, 1])
with left:
    st.write("This is the wider column.")
with right:
    st.write("Narrow column.")

Tabs

Python

tab1, tab2, tab3 = st.tabs(["Data", "Charts", "Settings"])

with tab1:
    st.dataframe(df)
with tab2:
    st.line_chart(df["values"])
with tab3:
    st.write("Configuration options here.")

Sidebar

Python

# Add widgets to the sidebar
with st.sidebar:
    st.title("Settings")
    model = st.selectbox("Model", ["GPT-4", "GPT-3.5", "Claude"])
    temperature = st.slider("Temperature", 0.0, 2.0, 0.7)
    st.divider()
    st.caption("Configure the model parameters.")

Expander & Container

Python

# Collapsible section
with st.expander("See details"):
    st.write("Hidden content revealed on click.")
    st.code("print('hello')")

# Empty container for dynamic content
placeholder = st.empty()
placeholder.text("Loading...")
# Later: placeholder.write("Done!")

Caching

Caching is critical for performance. Streamlit provides two caching decorators:

Python

# Cache data (DataFrames, strings, numbers)
@st.cache_data
def load_data(url):
    return pd.read_csv(url)

# Cache resources (ML models, database connections)
@st.cache_resource
def load_model():
    return pipeline("sentiment-analysis")

# Cache with TTL (time-to-live)
@st.cache_data(ttl=3600)  # Expires after 1 hour
def fetch_api_data():
    return requests.get("https://api.example.com/data").json()

Decorator	Use For	Returns
`@st.cache_data`	DataFrames, API responses, computations	New copy each call (safe to mutate)
`@st.cache_resource`	ML models, DB connections, shared objects	Same object (shared across users)

Rule of Thumb: Use @st.cache_data for anything serializable (data). Use @st.cache_resource for anything that should not be serialized (models, connections). Never use the deprecated @st.cache.

Core Features Mastered!

You know the essentials. Next, learn how to display data with DataFrames, charts, and maps.

Next: Data Display →

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