Dashboard: Shortest Route and Data Mining

from dash import Dash, html, dcc, Input, Output
import pandas as pd
import plotly.express as px
import numpy as np

# Read data from CSV file and perform data transformations
df = pd.read_csv('StoreClusterCwDashi.csv')
# Group the data by specified columns and calculate the mean for each group
crossDf = df.groupby(['CW', 'Store', 'Segment', 'StoreName']).mean()
# Stack the grouped data to create a Pandas Series 'sf'
sf = crossDf.stack()
# Convert the Pandas Series 'sf' back into a DataFrame called 'crossDf'
crossDf = pd.DataFrame(sf)
# Reset the index of the DataFrame and move index levels to columns
crossDf = crossDf.reset_index()
# Rename specific columns in the DataFrame
crossDf.rename(columns={'level_4': 'Indicator Name', 0: 'Value', 'CW': 'CW', 'StoreName': 'Store Name'}, inplace=True)


# Initialize the Dash app
app = Dash(__name__, title='Educative TSP', update_title='Educative is updating..')
image_path =  'assets/dashboard-educative-logo.png' 

# Define the layout of the app
app.layout = html.Div([
    # Empty header with logo
    html.Div([
        html.H1('', style={'display': 'inline-block', 'height': '50px'}),
        html.Img(src=image_path, style={'height': '50px'})
    ]),

    # Scatter plot component
    html.Div([
        dcc.Graph(
            id='crossfilter-indicator-scatter', config={
                "displaylogo": False,
                'modeBarButtonsToRemove': ['pan2d']},
            hoverData={'points': [{'customdata': 0}]}
        ),
    ], style={'width': '49%', 'display': 'inline-block', 'padding': '0 20'}),

    # Dropdowns and controls for user interaction
    html.P("Please use the sliders below to select the KPI."),
    html.Div([
        html.Div([
            # Dropdown for x-axis column selection
            dcc.Dropdown(
                crossDf['Indicator Name'].unique(),
                'NetSales',
                id='crossfilter-xaxis-column',
            ),
            # Radioitems for x-axis type
            dcc.RadioItems(
                [],
                'Linear',
                id='crossfilter-xaxis-type',
                labelStyle={'display': 'inline-block', 'marginTop': '5px'}
            )
        ]),
    ], style={'width': '49%', 'display': 'inline-block'}),

    # More dropdowns and controls
    html.Div([
        dcc.Dropdown(
            crossDf['Indicator Name'].unique(),
            'Recency',
            id='crossfilter-yaxis-column'
        ),
        dcc.Dropdown(
            crossDf['Indicator Name'].unique(),
            'Frequency',
            id='crossfilter-zaxis-column'
        ),
        dcc.Dropdown(
            crossDf['Indicator Name'].unique(),
            'OverallScore',
            id='crossfilter-qaxis-column'
        ),
        dcc.Dropdown(id='continent-dropdown',
                     options=[{'label': i, 'value': i} for i in np.append([0], crossDf['Store Name'].unique())],
                     multi=True, value=0
                     ),
        dcc.Dropdown(id='ClusterCat-dropdown',
                     options=[{'label': i, 'value': i} for i in np.append(['All'], crossDf['Segment'].unique())],
                     multi=True, value='All'
                     ),
        dcc.RadioItems(
            [],
            'Linear',
            id='crossfilter-yaxis-type',
            labelStyle={'display': 'inline-block', 'marginTop': '5px'}
        )
    ], style={'width': '49%', 'float': 'right', 'display': 'inline-block'}),

    # Slider for CW selection
    html.Div(dcc.Slider(
        crossDf['CW'].min(),
        crossDf['CW'].max(),
        step=None,
        id='crossfilter-CW--slider',
        value=crossDf['CW'].max(),
        marks={str(int): str(int) for int in crossDf['CW'].unique()}
    ), style={'width': '49%', 'padding': '0px 20px 20px 20px'}),
])


# Use callback function to update the scatter plot
@app.callback(
    Output('crossfilter-indicator-scatter', 'figure'),
    Input('crossfilter-xaxis-column', 'value'),  # 1st input
    Input('crossfilter-yaxis-column', 'value'),  # 2nd input
    Input('crossfilter-zaxis-column', 'value'),  # 3rd input
    Input('crossfilter-xaxis-type', 'value'),  # 4th input
    Input('crossfilter-yaxis-type', 'value'),  # 5th input
    Input('continent-dropdown', 'value'),  # SpHA
    Input('crossfilter-CW--slider', 'value'),  # 6th input
    Input('crossfilter-qaxis-column', 'value'),
    Input('ClusterCat-dropdown', 'value'))
def update_graph(xaxis_column_name, yaxis_column_name, zaxis_column_name,
                 xaxis_type, yaxis_type, Cust_value,
                 CW_value, qaxis_column_name, CusterCat_value):
    # Filtering based on the slider and dropdown selection
    if Cust_value == 0:
        crossDff = crossDf.loc[crossDf['CW'] == CW_value]
    else:
        crossDff = crossDf.loc[(crossDf['CW'] == CW_value) & (crossDf['Store Name'].isin(Cust_value))]

    if CusterCat_value == 'All':
        crossDff = crossDff.loc[crossDff['CW'] == CW_value]
    else:
        crossDff = crossDff.loc[(crossDff['CW'] == CW_value) & (crossDff['Segment'].isin(CusterCat_value))]

    # Create a scatter_geo plot
    fig = px.scatter_geo(
        lat=crossDff[crossDff['Indicator Name'] == 'lat']['Value'],
        lon=crossDff[crossDff['Indicator Name'] == 'lon']['Value'],
        hover_name=crossDff.loc[crossDf['Indicator Name'] == yaxis_column_name]['Store Name'],
        projection="natural earth",
        size=crossDff[crossDff['Indicator Name'] == xaxis_column_name]['Value'],
        color=crossDff[crossDff['Indicator Name'] == yaxis_column_name]['Value']
    )  

    fig.update_traces(customdata=crossDff[crossDff['Indicator Name'] == yaxis_column_name]['Store Name'])
    fig.update_xaxes(title=xaxis_column_name, type='linear' if xaxis_type == 'Linear' else 'log')
    fig.update_yaxes(title=yaxis_column_name, type='linear' if yaxis_type == 'Linear' else 'log')
    fig.update_layout(margin={'l': 40, 'b': 40, 't': 10, 'r': 0}, hovermode='closest', template="plotly_white")
    return fig


# Run the app
if __name__ == '__main__':
    app.run(debug=True, host = '0.0.0.0',port=8100)