Installing Streamlit Beginner

Get Streamlit up and running in under a minute. This lesson covers installation, creating your first app, and using hot reload for rapid development.

Prerequisites

Python 3.8 or higher
pip package manager
A code editor (VS Code recommended)

Step 1: Install Streamlit

Terminal

# Install Streamlit
pip install streamlit

# Verify installation
streamlit --version

# Run the built-in demo
streamlit hello

Virtual Environment: It is recommended to use a virtual environment. Create one with python -m venv .venv and activate it before installing Streamlit.

Step 2: Create Your First App

Python (app.py)

import streamlit as st

st.title("My First Streamlit App")
st.write("Hello, world! This is my first Streamlit application.")

name = st.text_input("What's your name?")
if name:
    st.write(f"Hello, {name}! Welcome to Streamlit.")

Step 3: Run Your App

Terminal

# Run the app
streamlit run app.py

# Output:
# You can now view your Streamlit app in your browser.
# Local URL: http://localhost:8501
# Network URL: http://192.168.1.100:8501

Hot Reload

Streamlit automatically detects changes to your source file and offers to re-run the app. You can configure this behavior:

Always rerun: Click "Always rerun" in the top-right corner to auto-update on every save
Manual rerun: Press R in the browser to rerun manually
CLI flag: Use streamlit run app.py --server.runOnSave true for auto-rerun

A More Complete Example

Python (app.py)

import streamlit as st
import pandas as pd
import numpy as np

st.set_page_config(page_title="My App", page_icon="📊", layout="wide")

st.title("Data Explorer")
st.markdown("Upload a CSV file and explore your data interactively.")

uploaded_file = st.file_uploader("Choose a CSV file", type="csv")

if uploaded_file:
    df = pd.read_csv(uploaded_file)

    col1, col2, col3 = st.columns(3)
    col1.metric("Rows", df.shape[0])
    col2.metric("Columns", df.shape[1])
    col3.metric("Missing Values", df.isnull().sum().sum())

    st.subheader("Data Preview")
    st.dataframe(df.head(20), use_container_width=True)

    st.subheader("Column Statistics")
    st.write(df.describe())
else:
    st.info("Upload a CSV file to get started.")

Streamlit Installed!

You have Streamlit running with hot reload. Next, explore the core features for building rich user interfaces.

Next: Core Features →

← Introduction Core Features →