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.gitattributes

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+permanently_cleaned_geolocation_data.csv filter=lfs diff=lfs merge=lfs -text

app.py

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+import dash
+from dash import dcc, html, Input, Output, exceptions
+import pandas as pd
+from sklearn.cluster import KMeans
+import plotly.express as px
+import dash_bootstrap_components as dbc
+
+# 1. Data Preparation (Assuming you've read your data into a DataFrame named 'df')
+df = pd.read_csv('permanently_cleaned_geolocation_data.csv')  
+
+df = df.rename(columns = {'id_left': 'id', 
+                          'geolocation_state': 'state', 
+                          'geolocation_lat': 'latitude', 
+                          'geolocation_lng': 'longitude'})
+
+df['object'] = df['object'].astype(str).str.capitalize()
+
+# 2. Dash App Setup
+app = dash.Dash(__name__, external_stylesheets=['https://codepen.io/chriddyp/pen/bWLwgP.css'])
+
+# State Filter Options with "Select All"
+state_options = [{'label': 'Select All', 'value': 'all'}] + [
+    {'label': i, 'value': i} for i in df['state'].unique()
+]
+
+app.layout = html.Div([
+    html.H1("Olist Warehouse Location Optimization Tool", style={'textAlign': 'center'}),
+
+    # Outer container for slicers
+    html.Div(id='outer_div', children=[
+        html.Div([  # Slicer 1 (Object)
+            html.Label("Select Object:"),
+            dcc.Dropdown(id='object-filter', options=[{'label': i, 'value': i} for i in df['object'].unique()], multi=True)
+        ], style={'width': '250px', 'margin': '10px'}),  # Adjust width as needed
+
+        html.Div([  # Slicer 2 (State)
+            html.Label("Select State:"),
+            dcc.Dropdown(id='state-filter', options=state_options, value='all', multi=True)
+        ], style={'width': '250px', 'margin': '10px'}),
+
+        html.Div([  # Slicer 3 (Number of Clusters)
+            html.Label("Number of Warehouses:"),
+            dcc.Input(id='cluster-input', type='number', min=1, value=3)
+        ], style={'width': '250px', 'margin': '10px'}),
+    ], style={
+        'display': 'flex',       
+        'flex-direction': 'row',  
+        'justify-content': 'center',
+        'align-items': 'center',
+        'flex-wrap': 'wrap'
+    }),
+
+    html.Div(  # Map container
+        dcc.Graph(id='cluster-plot', style={'height': '600px', 'width': '800px'}), 
+        style={
+            'display': 'flex',
+            'justify-content': 'center', 
+            'align-items': 'center'  
+        }
+    )
+])
+
+# 5. Callback for Interactivity
+@app.callback(
+    Output('cluster-plot', 'figure'),
+    [Input('object-filter', 'value'),
+     Input('state-filter', 'value'),
+     Input('cluster-input', 'value')]
+)
+def update_plot(selected_objects, selected_states, num_clusters):
+    # Handle "Select All" for States
+    if 'all' in selected_states:
+        selected_states = df['state'].unique()
+
+    if selected_objects is None or num_clusters is None:
+        return px.scatter()  # Empty plot if filters are not selected
+
+    filtered_df = df[df['object'].isin(selected_objects) & df['state'].isin(selected_states)]
+
+    if filtered_df.empty:
+        return px.scatter()
+
+    # Clustering
+    kmeans = KMeans(n_clusters=num_clusters).fit(filtered_df[['longitude', 'latitude']])
+    filtered_df['cluster'] = kmeans.labels_
+
+    # Calculate Cluster Centers
+    cluster_centers = filtered_df.groupby('cluster')[['longitude', 'latitude']].mean().reset_index()
+
+        # Create Plot (NO hovertemplate here)
+    fig = px.scatter_mapbox(filtered_df, lat="latitude", lon="longitude", color="cluster",
+                            hover_name=None,
+                            custom_data=['cluster'],
+                            mapbox_style="carto-positron")
+
+    # Add Cluster Centers to Plot (WITH hover data)
+    fig.add_scattermapbox(
+        lat=cluster_centers["latitude"],
+        lon=cluster_centers["longitude"],
+        mode="markers",
+        marker=dict(size=20, color="red"),
+        name="Warehouses",
+        hoverinfo="text",  # Enable hover for cluster centers
+        text=[
+            f"Warehouse {i + 1}<br>Lat: {lat:.4f}<br>Lon: {lon:.4f}"
+            for i, lat, lon in zip(
+                cluster_centers.index, cluster_centers["latitude"], cluster_centers["longitude"]
+            )
+        ],
+        showlegend=False,
+    )
+
+    return fig
+
+if __name__ == '__main__':
+    app.run_server(debug=True, port=8068)

permanently_cleaned_geolocation_data.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:601485a3bab25314c4eb6d77f8203ac110f3d11c6803c7939929b221a8c3df43
+size 16261986

requirements.txt

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+dash==2.14.2
+dash_bootstrap_components==1.6.0
+pandas==2.2.2
+plotly==5.9.0
+scikit_learn==1.2.2