Streamlit vs Shiny for Python: The Best Choice for Lab Apps and Dashboards

Intro: Why lab teams ask this question

You want a fast way to turn notebooks into usable tools. Your stakeholders expect clean dashboards, quick what-if controls, and simple deployment on a lab network. Two great options exist in Python today: Streamlit and Shiny for Python. Both are productive. They differ in how they run, scale, and deploy—differences that matter in regulated or restricted lab environments.

Reactivity and developer experience

Streamlit feels like a script. Any widget change reruns your script from top to bottom. It’s simple, and caching or session state keeps things snappy. If you can think in notebooks, you can build an app. (docs.streamlit.io)
Shiny for Python uses transparent reactivity. Only the outputs that depend on the changed input recompute, which scales well as apps grow more complex. The “Express” API keeps code terse while retaining power. (shiny.posit.co)

Quick taste:

Streamlit

import streamlit as st, pandas as pd
file = st.file_uploader("Upload CSV", type="csv")
if file:
    df = pd.read_csv(file)
    col = st.selectbox("Column", df.select_dtypes("number").columns)
    st.line_chart(df[col])

Shiny for Python (Express)

import pandas as pd
from shiny.express import input, render, ui

ui.input_file("csv", "Upload CSV", accept=[".csv"])
ui.input_select("col", "Column", choices=[])

@render.plot
def plot():
    if not input.csv():
        return
    path = input.csv()[0]["datapath"]
    df = pd.read_csv(path)
    col = input.col() or df.select_dtypes("number").columns[0]
    import matplotlib.pyplot as plt
    df[col].plot()

Deployment and offline in restricted labs

Streamlit deploys anywhere you can run Python. Community Cloud offers one-click hosting for quick sharing, and you can self-host for internal apps.
Shiny for Python runs on ASGI (Starlette/Uvicorn) and behind Posit Connect or open-source Shiny Server. Shiny sessions are stateful and use WebSockets, so production setups need sticky sessions in load balancers—a key infra nuance for enterprise labs. (shiny.posit.co)
Unique to Shiny: Shinylive. You can ship many apps as static files that run entirely in the browser (Pyodide/WebAssembly). It’s perfect for classroom tools, kiosks, or offline demo environments. Trade‑offs: bigger initial download, limited package support, and no secrets. (shiny.posit.co)

Data handling and UI components

Streamlit shines for quick data UI: editable tables, forms to batch inputs, and session state for multi-step flows. It’s great for ad hoc analysis, data review, and simple ELN-like widgets. (docs.streamlit.io)
Shiny’s reactive graph makes modularization and incremental re-renders natural. As apps grow—multiple tabs, dependent plots, conditional panels—you write less glue code and get predictable performance. (shiny.posit.co)

Summary and next steps

Choose Streamlit if:

You want the fastest path from notebook to app.
Your app is simple-to-medium complexity, or you’re happy to manage caching/state.

Choose Shiny for Python if:

You expect complex, reactive dashboards that must scale without rerun hacks.
You need Shinylive for static, offline distribution.
Your team already uses Posit Connect or needs sticky-session deployments.

Bottom line: build a 1-hour spike in both. Time yourself from “CSV to chart” and from “two inputs to three coordinated outputs.” The winner for your lab will be obvious.