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Streamlit Cheatsheet

Basics​

Installation​

pip install streamlit

Run Application​

streamlit run <filename>.py

Essential Import​

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

App Structure​

Streamlit apps are executed sequentially, from top to bottom.

Widgets and Layout​

Titles, Headers, and Text​

st.title("App Title")
st.header("Main Header")
st.subheader("Subheader")
st.text("Regular text.")
st.markdown("Markdown **text** with _formatting_.")

Interactive Elements​

Text Inputs and Number Inputs​

name = st.text_input("Enter your name")
number = st.number_input("Choose a number", min_value=0, max_value=100, step=1)

Sliders​

age = st.slider("Age", 0, 100, 25)

Checkboxes, Radio Buttons, and Dropdowns​

agree = st.checkbox("I agree")
option = st.radio("Favorite color", ["Blue", "Green", "Red"])
selection = st.selectbox("Select an option", ["Option 1", "Option 2", "Option 3"])

Multiselect​

multiselect = st.multiselect("Select multiple options", ["Option A", "Option B", "Option C"])

Buttons​

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

Displaying Data​

Tables and DataFrames​

data = pd.DataFrame({
    "Column 1": [1, 2, 3],
    "Column 2": [4, 5, 6]
})
st.write(data)
st.dataframe(data)  # Interactive table
st.table(data)      # Static table

Data Summary and Descriptions​

st.write(data.describe())  # Statistical summary

Data Visualization​

Streamlit supports a range of visualization libraries (e.g., Matplotlib, Plotly, Altair, Seaborn).

Streamlit Charts​

# Example data
chart_data = pd.DataFrame(
    np.random.randn(20, 3),
    columns=['a', 'b', 'c']
)

st.line_chart(chart_data)
st.area_chart(chart_data)
st.bar_chart(chart_data)

Matplotlib​

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.hist(data['Column 1'], bins=20)
st.pyplot(fig)

Plotly​

import plotly.express as px

fig = px.scatter(data, x='Column 1', y='Column 2')
st.plotly_chart(fig)

Media and Files​

Images​

st.image("path_to_image.jpg", caption="Example Image", use_column_width=True)

Audio​

st.audio("path_to_audio.mp3")

Video​

st.video("path_to_video.mp4")

File Upload​

uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
    data = pd.read_csv(uploaded_file)
    st.write(data)
st.sidebar.title("Sidebar Title")
sidebar_option = st.sidebar.selectbox("Sidebar Select", ["Option A", "Option B"])

Column Layout​

col1, col2 = st.columns(2)
with col1:
    st.write("Column 1")
with col2:
    st.write("Column 2")

Status Indicators and Progress Bars​

Status Indicators​

st.success("Loaded successfully")
st.error("An error occurred")
st.warning("Warning message")
st.info("Information text")

Progress Bar​

import time

my_bar = st.progress(0)
for percent_complete in range(100):
    time.sleep(0.01)
    my_bar.progress(percent_complete + 1)

Caching for Optimization​

Use caching to speed up execution by storing the output of a function.

@st.cache
def load_data():
    # Time-intensive loading process
    return data

Advanced Features​

Conditional Display of Widgets​

if st.checkbox("Show options"):
    st.write("Options are displayed!")

Download Button​

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

App Deployment​

  • Streamlit Cloud: https://share.streamlit.io
  • Other options include Heroku, AWS, Google Cloud, and Docker.

Example Deployment on Streamlit Cloud​

  1. Create Repository: Store code in a GitHub repository.
  2. Connect to Streamlit Cloud: Go to https://share.streamlit.io and select the repository.
  3. Get App URL: The app will be accessible at a public URL.