How I Built a COVID-19 Dashboard in 10 Minutes Using Streamlit

Streamlit is a Python library that lets you build web apps super easily. Think of it as a way to turn your Python scripts into interactive websites without having to learn web development.

So imagine you’ve got some data analysis code or a machine learning model, and you want to show it off or let people play around with it. Normally, you’d need to learn HTML, CSS, JavaScript – all that web stuff. With Streamlit, you just write Python, and it handles the web part for you.

You can add things like sliders, buttons, file upload boxes, and charts with just a few lines of code. When someone moves a slider or uploads a file, your app automatically updates. It’s pretty neat.

The best part is how fast you can go from idea to working app. You write your Python code, add some Streamlit commands, and boom – you’ve got a web app running locally. Want to share it? Deploy it to their free hosting service.

It’s become popular with data scientists and anyone doing machine learning because you can quickly create demos of your models or build dashboards to visualize data. No need to bug the web dev team or spend weeks learning React or whatever.

The code is surprisingly simple, too – you’re just adding commands like st.slider() or st.chart() to your existing Python code, and Streamlit figures out how to turn that into a web interface.

🛠 Tools & Libraries

• Python
• Streamlit: For web UI
• Pandas: For data handling
• Plotly: For interactive charts
• CSV file: Local data storage

pip install streamlit pandas plotly

Step 1: Prepare Your CSV Data

Create a file named covid_data.csv With the following columns:

State,Zone,Total Cases,Active,Discharged,Deaths,Active Ratio,Discharge Ratio,Death Ratio,Population
Bihar,Red,10000,2000,7500,500,20,75,5,120000000
Delhi,Orange,8000,1000,6500,500,12.5,81.25,6.25,20000000
...

Step 2: Create the Streamlit App (app.py)

1. Import the Libraries

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

• streamlit: Used to create the web UI.
• pandas: Helps load and manipulate the CSV data.
• plotly.express: For creating attractive charts (pie, bar, etc.).

2. Load the CSV Data

pythonCopyEditdf = pd.read_csv("covid_data.csv")

This line reads the covid_data.csv file into a DataFrame called dfSo we can work with it in the app.

3. Page Configuration

pythonCopyEditst.set_page_config(page_title="India COVID Dashboard", layout="wide")
st.title("🦠 India COVID Dashboard (All States Combined)")

• set_page_config sets the browser tab title and layout to “wide” for a full-screen look.
• st.title displays a bold, large heading at the top.

4. Calculate Metrics

pythonCopyEdittotal_cases = df["Total Cases"].sum()
active_cases = df["Active"].sum()
discharged = df["Discharged"].sum()
deaths = df["Deaths"].sum()

These lines add up the columns across all rows to get the total national numbers.

5. Show the Metrics in 4 Columns

pythonCopyEditcol1, col2, col3, col4 = st.columns(4)
col1.metric("Total Cases", f"{total_cases:,}")
col2.metric("Active Cases", f"{active_cases:,}")
col3.metric("Discharged", f"{discharged:,}")
col4.metric("Deaths", f"{deaths:,}")

• st.columns(4) splits the space into 4 columns side by side.
• Each column shows one metric using st.metric().
• f"{value:,}" formats numbers with commas for readability.

6. Draw a Pie Chart

pythonCopyEditst.subheader("State-wise Share of Total Cases")
pie = px.pie(df, names="State", values="Total Cases", title="Total Cases by State")
st.plotly_chart(pie, use_container_width=True)

• st.subheader() add a section heading.
• px.pie() creates a pie chart of total cases per state.
• st.plotly_chart() displays the chart on the dashboard.

7. Draw a Bar Chart

pythonCopyEditst.subheader("State-wise Breakdown of Active, Discharged, and Deaths")
bar = px.bar(
    df,
    x="State",
    y=["Active", "Discharged", "Deaths"],
    barmode="group",
    title="State-wise Case Category",
    height=500
)
st.plotly_chart(bar, use_container_width=True)

• This bar chart shows side-by-side bars for Active, Discharged, and Deaths for each state.
• barmode="group" puts the bars side-by-side for comparison.

8. Optional Data Filter and Table

    pythonCopyEditwith st.expander("Show/Filter State-Wise Data"):
    selected_states = st.multiselect("Select States to View", df["State"].unique(), default=df["State"].unique())
    filtered_df = df[df["State"].isin(selected_states)]
    st.dataframe(filtered_df.sort_values("Total Cases", ascending=False), use_container_width=True)

• st.expander() Create a collapsible section.
• st.multiselect() allows users to pick one or more states.
• The filtered DataFrame is shown using st.dataframe().

Step 3: Run the App

In your terminal, run:

streamlit run app.py

Your browser will open the dashboard automatically. on 8501 port

http://localhost:8501/

Bonus Ideas

• Add line charts (time series data)
• Display population-wise normalization
• Add download buttons (CSV, Excel)
• Deploy it using Streamlit Cloud (free hosting)

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