plotly dash

iris_dash.py

#!/usr/bin/env python3

import pandas as pd
import plotly.express as px
from sklearn.datasets import load_iris

import dash
from dash import dcc, html

# Load the Iris dataset
iris = load_iris()
df = pd.DataFrame(data=iris.data, columns=iris.feature_names)
df["species"] = iris.target_names[iris.target]

# Create the Dash app
app = dash.Dash(__name__)

# Define the layout of the app
app.layout = html.Div(
    [
        html.H1("Iris Dataset Visualization"),
        html.Label("Select x-axis feature"),
        dcc.Dropdown(
            id="x-axis-dropdown",
            options=[
                {"label": feature, "value": feature} for feature in df.columns[:-1]
            ],
            value="sepal length (cm)",
        ),
        html.Label("Select y-axis feature"),
        dcc.Dropdown(
            id="y-axis-dropdown",
            options=[
                {"label": feature, "value": feature} for feature in df.columns[:-1]
            ],
            value="sepal width (cm)",
        ),
        dcc.Graph(id="scatter-plot"),
    ]
)


# Define the callback function for the scatter plot
@app.callback(
    dash.dependencies.Output("scatter-plot", "figure"),
    dash.dependencies.Input("x-axis-dropdown", "value"),
    dash.dependencies.Input("y-axis-dropdown", "value"),
)
def update_scatter_plot(x_axis, y_axis):
    # Create the scatter plot using Plotly Express
    fig = px.scatter(df, x=x_axis, y=y_axis, color="species")

    return fig


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