✅ Python Project Interactive Dashboard with Plotly & Dash

 

✅ Python Project Interactive Dashboard with Plotly & Dash 📊🖥️

📌 Task:
Build a dynamic web dashboard to explore and visualize data — perfect for reporting, monitoring KPIs, and interactive analytics.

🧠 What You’ll Learn

  • Creating web dashboards with Dash

  • Adding interactive filters like dropdowns and sliders

  • Using Plotly for interactive charts

  • Linking charts to user inputs for dynamic updates

✅ Python Code Example

import dash
from dash import html, dcc
import plotly.express as px
import pandas as pd

# 1. Load sample dataset
data = px.data.gapminder().query("year == 2007")

# 2. Initialize Dash app
app = dash.Dash(__name__)

# 3. Define app layout
app.layout = html.Div([
    html.H1("Global Statistics Dashboard"),

    dcc.Dropdown(
        id="continent-dropdown",
        options=[{"label": c, "value": c} for c in data["continent"].unique()],
        value="Asia"
    ),

    dcc.Graph(id="life-exp-vs-gdp")
])

# 4. Callback to update chart based on selected continent
@app.callback(
    dash.dependencies.Output("life-exp-vs-gdp", "figure"),
    [dash.dependencies.Input("continent-dropdown", "value")]
)
def update_graph(selected_continent):
    filtered_data = data[data["continent"] == selected_continent]
    fig = px.scatter(
        filtered_data,
        x="gdpPercap",
        y="lifeExp",
        size="pop",
        color="country",
        hover_name="country",
        log_x=True,
        size_max=60,
        title=f"Life Expectancy vs GDP ({selected_continent})"
    )
    return fig

# 5. Run the app
if __name__ == "__main__":
    app.run_server(debug=True)

🛠️ Requirements

pip install dash plotly pandas

🔧 Try Enhancing

  • Add a year slider for time-based analysis

  • Use different chart types: bar, line, pie

  • Create multi-page dashboards for complex reporting

💡 Real-World Use Cases

✅ Monitor KPIs for sales, marketing, or operations
✅ Interactive data storytelling
✅ Executive-level summary dashboards

📌 References

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