Create app.py

app.py

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+import dash
+import dash_core_components as dcc
+import dash_html_components as html
+from dash.dependencies import Input, Output
+import plotly.express as px
+import pandas as pd
+from sklearn import datasets
+
+iris = datasets.load_iris()
+df_iris = pd.DataFrame(iris.data, columns=iris.feature_names)
+df_iris['species'] = pd.Categorical.from_codes(iris.target, iris.target_names)
+
+app = dash.Dash(__name__)
+
+# Layout de l'application
+app.layout = html.Div([
+    html.H1("Graphique en Violon du Dataset Iris"),
+    dcc.Dropdown(
+        id='feature-selected',
+        options=[{'label': i, 'value': i} for i in df_iris.columns[:-1]],
+        value='sepal length (cm)'
+    ),
+    dcc.Graph(id='violin-plot')
+])
+
+# Callback pour mettre à jour le graphique
+@app.callback(
+    Output('violin-plot', 'figure'),
+    [Input('feature-selected', 'value')]
+)
+def update_graph(selected_feature):
+    fig = px.violin(df_iris, y=selected_feature, color='species', box=True, points="all")
+    return fig
+
+# Exécution de l'application
+if __name__ == '__main__':
+    app.run_server(debug=True)