Build an Interactive Dashboard for your Pandas DataFrame with Streamlit

📘 Introduction

Data is only as useful as your ability to explore and understand it. But building an interface to analyze and visualize your DataFrame often feels like a full-blown front-end project. That’s where Streamlit saves the day.

In this tutorial, you’ll learn how to build a data app containing an interactive dashboard to explore any Pandas DataFrame — all with just Python. No JavaScript, no CSS, no hassle.

✅ Prerequisites

Before we begin, make sure you have the following ready:

☑️ Python 3.8 or later installed

⚙️1️⃣ Install Libraries

Open your terminal and run the following to install the required libraries:

pip install streamlit pandas

💻2️⃣ Build the App

Create a new file in your working directory and name it app.py. Open it in your favorite code editor and paste the following code:

import streamlit as st
import pandas as pd

# App title
st.title("📊 Interactive DataFrame Dashboard")

# Upload CSV file
uploaded_file = st.file_uploader(
    "Upload your CSV file",
    type=["csv"]
)

if uploaded_file is not None:
    df = pd.read_csv(uploaded_file)
    st.success("Data loaded successfully!")

    # Show preview
    st.subheader("🔍 Data Preview")
    st.dataframe(df.head())

    # Select columns to visualize
    st.subheader("📌 Column Selector")
    selected_columns = st.multiselect(
        "Choose columns to display",
        df.columns.tolist(),
        default=df.columns.tolist()
    )
    st.dataframe(df[selected_columns])

    # Basic statistics
    st.subheader("📈 Summary Statistics")
    st.write(df[selected_columns].describe())

    # Simple plot
    st.subheader("📊 Plot a Column")
    numeric_columns = (
        df.select_dtypes( include="number")
        .columns.tolist()
    )
    column_to_plot = st.selectbox(
        "Choose a numeric column to plot",
        numeric_columns
    )
    st.line_chart(df[column_to_plot])

else:
    st.info("Please upload a CSV file to get started.")