Streamlit

Overview

Streamlit is a Python framework for rapidly building and deploying interactive web applications for data science and machine learning. Create beautiful web apps with just Python - no frontend development experience required. Apps automatically update in real-time as code changes.

When to Use This Skill

Activate when the user:

• Wants to build a web app, dashboard, or data visualization tool
• Mentions Streamlit explicitly
• Needs to create an ML/AI demo or prototype
• Wants to visualize data interactively
• Asks for a data exploration tool
• Needs interactive widgets (sliders, buttons, file uploads)
• Wants to share analysis results with stakeholders

Installation and Setup

Check if Streamlit is installed:

python3 -c "import streamlit; print(streamlit.__version__)"

If not installed:

pip3 install streamlit

Create and run your first app:

# Create app.py with Streamlit code
streamlit run app.py

The app opens automatically in your browser at http://localhost:8501

Basic App Structure

Every Streamlit app follows this simple pattern:

import streamlit as st

# Set page configuration (must be first Streamlit command)
st.set_page_config(
    page_title="My App",
    page_icon="📊",
    layout="wide"
)

# Title and description
st.title("My Data App")
st.write("Welcome to my interactive dashboard!")

# Your app code here
# Streamlit automatically reruns from top to bottom when widgets change

Core Capabilities

1. Displaying Text and Data

import streamlit as st, pandas as pd
# Text elements
st.title("Main Title")
st.header("Section Header")
st.subheader("Subsection Header")
st.text("Fixed-width text")
st.markdown("**Bold** and *italic* text")
st.caption("Small caption text")

# Code blocks
st.code("""
def hello():
    print("Hello, World!")
""", language="python")

# Display data
df = pd.DataFrame({
    'Column A': [1, 2, 3],
    'Column B': [4, 5, 6]
})

st.dataframe(df)  # Interactive table
st.table(df)      # Static table
st.json({'key': 'value'})  # JSON data

# Metrics
st.metric(
    label="Revenue",
    value="$1,234",
    delta="12%"
)

2. Interactive Widgets

import streamlit as st
# Text input
name = st.text_input("Enter your name")
email = st.text_input("Email", type="default")
password = st.text_input("Password", type="password")
text = st.text_area("Long text", height=100)

# Numbers
age = st.number_input("Age", min_value=0, max_value=120, value=25)
slider_val = st.slider("Select a value", 0, 100, 50)
range_val = st.slider("Select range", 0, 100, (25, 75))

# Selections
option = st.selectbox("Choose one", ["Option 1", "Option 2", "Option 3"])
options = st.multiselect("Choose multiple", ["A", "B", "C", "D"])
radio = st.radio("Pick one", ["Yes", "No", "Maybe"])

# Checkboxes
agree = st.checkbox("I agree to terms")
show_data = st.checkbox("Show raw data")

# Buttons
if st.button("Click me"):
    st.write("Button clicked!")

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

# File upload
uploaded_file = st.file_uploader("Choose a file", type=['csv', 'xlsx', 'txt'])
if uploaded_file is not None:
    df = pd.read_csv(uploaded_file)
    st.dataframe(df)

# Download button
st.download_button(
    label="Download data",
    data=df.to_csv(index=False),
    file_name="data.csv",
    mime="text/csv"
)

3. Charts and Visualizations

import streamlit as st
import pandas as pd, numpy as np, matplotlib.pyplot as plt
import plotly.express as px
# Sample data
df = pd.DataFrame({
    'x': range(10),
    'y': np.random.randn(10)
})

# Streamlit native charts
st.line_chart(df)
st.area_chart(df)
st.bar_chart(df)

# Scatter plot with map data
map_data = pd.DataFrame(
    np.random.randn(100, 2) / [50, 50] + [37.76, -122.4],
    columns=['lat', 'lon']
)
st.map(map_data)

# Matplotlib
fig, ax = plt.subplots()
ax.plot(df['x'], df['y'])
ax.set_title("Matplotlib Chart")
st