Build a Streamlit Dashboard in Python

Install Dependencies

pip install streamlit pandas plotly

Create a project folder and a file called app.py:

mkdir streamlit-dashboard
cd streamlit-dashboard
touch app.py

Set Up Page Config and Imports

Start your app.py with these imports and page setup:

import streamlit as st
import pandas as pd
import plotly.express as px

# Page config MUST be the first Streamlit command
st.set_page_config(
    page_title="Sales Dashboard",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded"
)

st.title("📊 Sales Analytics Dashboard")
st.markdown("Interactive dashboard built with Streamlit and Plotly.")

Load Sample Data

For this tutorial, we'll generate sample sales data. In real apps, you'd load from a CSV, database, or API:

@st.cache_data
def load_data():
    import numpy as np
    np.random.seed(42)
    dates = pd.date_range("2024-01-01", periods=180, freq="D")
    regions = ["North", "South", "East", "West"]
    data = pd.DataFrame({
        "Date": np.repeat(dates, len(regions)),
        "Region": regions * len(dates),
        "Sales": np.random.randint(1000, 10000, len(dates) * len(regions)),
        "Orders": np.random.randint(10, 200, len(dates) * len(regions))
    })
    return data

df = load_data()

Build the Sidebar Filters

The sidebar keeps controls separate from visualizations:

st.sidebar.header("🔍 Filters")

# Date range filter
date_range = st.sidebar.date_input(
    "Date range",
    value=(df["Date"].min(), df["Date"].max()),
    min_value=df["Date"].min(),
    max_value=df["Date"].max()
)

# Region filter
selected_regions = st.sidebar.multiselect(
    "Regions",
    options=df["Region"].unique(),
    default=df["Region"].unique()
)

# Apply filters
filtered_df = df[
    (df["Date"] >= pd.to_datetime(date_range[0])) &
    (df["Date"] <= pd.to_datetime(date_range[1])) &
    (df["Region"].isin(selected_regions))
]

Display KPI Metrics

Use st.columns() and st.metric() for a clean KPI row:

total_sales = filtered_df["Sales"].sum()
total_orders = filtered_df["Orders"].sum()
avg_order = total_sales / total_orders if total_orders else 0

col1, col2, col3 = st.columns(3)
col1.metric("💰 Total Sales", f"${total_sales:,.0f}", "+12%")
col2.metric("📦 Total Orders", f"{total_orders:,}", "+5%")
col3.metric("📊 Avg Order Value", f"${avg_order:.2f}", "-2%")

The third argument is the delta indicator (the green/red arrow). Streamlit handles the styling automatically.

Add Plotly Charts

Plotly charts are interactive and look great on Streamlit. Add these below your KPIs:

# Line chart — Sales over time
st.subheader("📈 Sales Trend")
fig_line = px.line(
    filtered_df.groupby("Date")["Sales"].sum().reset_index(),
    x="Date", y="Sales",
    title="Daily Sales"
)
st.plotly_chart(fig_line, use_container_width=True)

# Bar chart — Sales by Region
col_left, col_right = st.columns(2)

with col_left:
    st.subheader("📍 Sales by Region")
    fig_bar = px.bar(
        filtered_df.groupby("Region")["Sales"].sum().reset_index(),
        x="Region", y="Sales", color="Region"
    )
    st.plotly_chart(fig_bar, use_container_width=True)

with col_right:
    st.subheader("🥧 Order Distribution")
    fig_pie = px.pie(
        filtered_df.groupby("Region")["Orders"].sum().reset_index(),
        names="Region", values="Orders"
    )
    st.plotly_chart(fig_pie, use_container_width=True)

Run Your Dashboard

streamlit run app.py

Your browser opens at localhost:8501. You now have a fully interactive dashboard. Every time you change a filter, the entire dashboard updates automatically.

Deploy for Free

Deploy in under 5 minutes:

1. Create a requirements.txt file with streamlit, pandas, and plotly
2. Push your code to a GitHub repo
3. Go to share.streamlit.io and connect your repo
4. Select app.py as the main file
5. Click Deploy