---

name: streamlit-dashboards description: Build Python-native dashboards with Streamlit. Covers layouts, components, session state, caching, charts, custom components, and deployment. Use for data science dashboards, ML demos, internal tools, and rapid prototyping with Python.

Streamlit Dashboards

Build interactive Python dashboards without frontend experience.

Instructions

1. Structure with columns and containers - Create clean layouts
2. Use caching - @st.cache_data for data, @st.cache_resource for models
3. Manage state - Use st.session_state for interactivity
4. Progressive loading - Show spinners and progress for long operations
5. Responsive design - Streamlit handles mobile automatically

Getting Started

pip install streamlit
streamlit run app.py

Basic App Structure

import streamlit as st
import pandas as pd
import plotly.express as px

# Page config (must be first Streamlit command)
st.set_page_config(
    page_title="My Dashboard",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Title
st.title("📊 Analytics Dashboard")
st.markdown("Real-time insights into your data")

# Sidebar
with st.sidebar:
    st.header("Filters")
    date_range = st.date_input("Date Range", [])
    category = st.selectbox("Category", ["All", "Sales", "Marketing", "Support"])

# Main content
col1, col2, col3 = st.columns(3)

with col1:
    st.metric("Total Revenue", "$45,231", "+12.5%")

with col2:
    st.metric("Active Users", "2,345", "+5.2%")

with col3:
    st.metric("Conversion Rate", "3.2%", "-0.4%", delta_color="inverse")

# Charts
st.subheader("Revenue Over Time")
chart_data = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=30),
    'revenue': [100 + i * 10 + (i % 5) * 20 for i in range(30)]
})
st.line_chart(chart_data.set_index('date'))

Layouts

Columns

# Equal columns
col1, col2, col3 = st.columns(3)

# Custom widths
left, right = st.columns([2, 1])  # 2:1 ratio

# With gaps
col1, col2 = st.columns(2, gap="large")  # small, medium, large

with col1:
    st.write("Left content")

with col2:
    st.write("Right content")

Containers & Expanders

# Container for grouping
with st.container():
    st.header("Section Header")
    st.write("Content inside container")

# Expandable section
with st.expander("Advanced Options", expanded=False):
    st.slider("Parameter 1", 0, 100, 50)
    st.slider("Parameter 2", 0, 100, 25)

# Tabs
tab1, tab2, tab3 = st.tabs(["Overview", "Details", "Settings"])

with tab1:
    st.write("Overview content")

with tab2:
    st.write("Details content")

Sidebar

with st.sidebar:
    st.header("Navigation")
    page = st.radio("Go to", ["Home", "Analytics", "Settings"])

    st.divider()

    st.header("Filters")
    start_date = st.date_input("Start Date")
    end_date = st.date_input("End Date")

    st.divider()

    if st.button("Apply Filters"):
        st.success("Filters applied!")

Input Widgets

# Text inputs
name = st.text_input("Name", placeholder="Enter your name")
description = st.text_area("Description", height=100)

# Numbers
age = st.number_input("Age", min_value=0, max_value=120, value=25)
price = st.slider("Price Range", 0.0, 1000.0, (100.0, 500.0))

# Selections
option = st.selectbox("Choose one", ["A", "B", "C"])
options = st.multiselect("Choose many", ["A", "B", "C", "D"])
color = st.color_picker("Pick a color", "#00f900")

# Date/Time
date = st.date_input("Select date")
time = st.time_input("Select time")

# Files
uploaded_file = st.file_uploader("Upload CSV", type=["csv", "xlsx"])
if uploaded_file:
    df = pd.read_csv(uploaded_file)
    st.dataframe(df)

# Toggles
agree = st.checkbox("I agree to terms")
enabled = st.toggle("Enable feature")

Data Display

import pandas as pd

# DataFrame
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Sales': [100, 150, 200],
    'Growth': [0.1, 0.25, 0.15]
})

# Basic table
st.dataframe(df)

# Editable table
edited_df = st.data_editor(df, num_rows="dynamic")

# Styled DataFrame
st.dataframe(
    df.style.highlight_max(subset=['Sales']),
    use_container_width=True,
    hide_index=True
)

# Column configuration
st.dataframe(
    df,
    column_config={
        "Growth": st.column_config.ProgressColumn(
            "Growth",
            format="%.0f%%",
            min_value=0,
            max_value=1,
        ),
        "Sales": st.column_config.NumberColumn(
            "Sales",
            format="$%d"
        )
    }
)

# Metrics
col1, col2, col3 = st.columns(3)
col1.metric("Revenue", "$45,231", "+12.5%")
col2.metric("Users", "2,345", "+5.2%")
col3.metric("Bounce Rate", "32%", "-8%", delta_color="inverse")

# JSON
st.json({"name": "Alice", "data": [1, 2, 3]})

Charts

Built-in Charts

import pandas as pd
import numpy as np

# Sample data
chart_data = pd.DataFrame(
    np.random.randn(20, 3),
    columns=['A', 'B', 'C']
)

# Line chart
st.line_chart(chart_data)

# Area chart
st.area_chart(chart_data)

# Bar chart
st.bar_chart(chart_data)

# Scatter chart
st.scatter_chart(chart_data, x='A', y='B', size='C')

Plotly Integration

import plotly.express as px
import plotly.graph_objects as go

# Plotly Express
fig = px.line(df, x='date', y='value', color='category',
              title='Trend Over Time')
st.plotly_chart(fig, use_container_width=True)

# Plotly Graph Objects
fig = go.Figure()
fig.add_trace(go.Scatter(x=df['date'], y=df['value'],
                         mode='lines+markers', name='Value'))
fig.update_layout(
    title='Custom Chart',
    xaxis_title='Date',
    yaxis_title='Value',
    template='plotly_white'
)
st.plotly_chart(fig, use_container_width=True)

Altair Charts

import altair as alt

chart = alt.Chart(df).mark_bar().encode(
    x='category:N',
    y='value:Q',
    color='category:N'
).properties(width='container', height=400)

st.altair_chart(chart, use_container_width=True)

Caching

import streamlit as st
import pandas as pd

# Cache data loading
@st.cache_data(ttl=3600)  # Cache for 1 hour
def load_data(url: str) -> pd.DataFrame:
    return pd.read_csv(url)

# Cache ML models
@st.cache_resource
def load_model():
    import joblib
    return joblib.load('model.pkl')

# Usage
df = load_data("https://example.com/data.csv")  # Cached
model = load_model()  # Cached singleton

Session State

import streamlit as st

# Initialize state
if 'counter' not in st.session_state:
    st.session_state.counter = 0

if 'messages' not in st.session_state:
    st.session_state.messages = []

# Update state
if st.button("Increment"):
    st.session_state.counter += 1

st.write(f"Counter: {st.session_state.counter}")

# Form with state
with st.form("my_form"):
    name = st.text_input("Name")
    submitted = st.form_submit_button("Submit")

    if submitted:
        st.session_state.messages.append(f"Hello, {name}!")

# Display messages
for msg in st.session_state.messages:
    st.write(msg)

Progress & Status

import time

# Progress bar
progress = st.progress(0)
for i in range(100):
    time.sleep(0.01)
    progress.progress(i + 1)

# Spinner
with st.spinner("Loading..."):
    time.sleep(2)
st.success("Done!")

# Status messages
st.success("Operation completed!")
st.info("This is informational")
st.warning("This is a warning")
st.error("This is an error")
st.exception(Exception("An error occurred"))

# Toast notifications
st.toast("File saved!", icon="✅")

Multi-page Apps

my_app/
├── streamlit_app.py      # Main entry (can be empty)
└── pages/
    ├── 1_📊_Dashboard.py
    ├── 2_📈_Analytics.py
    └── 3_⚙️_Settings.py

# pages/1_📊_Dashboard.py
import streamlit as st

st.title("Dashboard")
st.write("Dashboard content here")

Deployment

Streamlit Cloud (Free)

# Create requirements.txt
pip freeze > requirements.txt

# Push to GitHub and connect to Streamlit Cloud

Docker

FROM python:3.11-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt

COPY . .

EXPOSE 8501

CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

Best Practices

1. Use wide layout for dashboards - layout="wide"
2. Cache aggressively - Data loading and model inference
3. Group related inputs in sidebar or expanders
4. Use columns for KPIs - Clean metric displays
5. Add loading indicators - Spinners for long operations
6. Handle errors gracefully - Try/except with st.error

When to Use

• Data science dashboards and ML demos
• Internal tools and admin panels
• Rapid prototyping
• POCs and MVPs
• Data exploration interfaces

Notes

• Streamlit reruns entire script on interaction
• Use session state for persistence
• Consider Streamlit Elements for more complex UIs
• For production, consider authentication with st-auth