Build an Analytics Dashboard with Streamlit

Streamlit lets you turn Python scripts into shareable web apps in minutes. In this tutorial, we'll build an interactive analytics dashboard from a CSV file — no frontend experience required.

Setup

Install Streamlit and create a new Python file:

pip install streamlit pandas plotly

Load and Explore

Start by loading your CSV and showing a preview of the data:

import streamlit as st
import pandas as pd

st.set_page_config(page_title="Sales Dashboard", layout="wide")

df = pd.read_csv("sales.csv")
st.dataframe(df.head(100))

Add Filters

Streamlit widgets bind directly to variables. Add a sidebar with date range and category filters:

st.sidebar.header("Filters")
category = st.sidebar.selectbox("Category", df["category"].unique())
start_date = st.sidebar.date_input("Start date", df["date"].min())
end_date = st.sidebar.date_input("End date", df["date"].max())

filtered = df[(df["category"] == category) &
              (df["date"] >= start_date) &
              (df["date"] <= end_date)]

Visualise

Use Plotly for interactive charts. A line chart of sales over time, a bar chart by subcategory, and a KPI summary:

import plotly.express as px

col1, col2, col3 = st.columns(3)
col1.metric("Total Sales", f"${filtered['revenue'].sum():,.0f}")
col2.metric("Avg Order Value", f"${filtered['revenue'].mean():,.2f}")
col3.metric("Orders", len(filtered))

fig = px.line(filtered, x="date", y="revenue", title="Sales Over Time")
st.plotly_chart(fig, use_container_width=True)

Deploy

Push your script to GitHub, connect it to Streamlit Community Cloud, and your dashboard is live in minutes.