#!/usr/bin/env python
# coding: utf-8

# In[13]:


import altair as alt
import pandas as pd
import panel as pn

# Load the Panel extension

pn.extension('vega')


# Load the dataset
data = pd.read_csv("/Users/kenzabaddou/Downloads/archive/marketing_campaign.csv", sep=";")

# Data cleaning
data = data.rename(columns=lambda x: x.strip())  # Remove leading and trailing spaces from column names
data = data.dropna(subset=['Income'])
mean_income = data['Income'].mean()
data['Income'] = data['Income'].fillna(mean_income)
data['total_spent'] = data[['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']].sum(axis=1)

# Define widgets
education_dropdown = alt.binding_select(options=sorted(data['Education'].unique()), name='Education Level:')
education_select = alt.selection_single(fields=['Education'], bind=education_dropdown, name='Select')

# New widget: marital status dropdown
marital_status_dropdown = alt.binding_select(options=sorted(data['Marital_Status'].unique()), name='Marital Status:')
marital_status_select = alt.selection_single(fields=['Marital_Status'], bind=marital_status_dropdown, name='Select')

# New widget: range slider for number of web visits
num_web_visits_slider = alt.binding_range(min=0, max=data['NumWebVisitsMonth'].max(), step=1, name='Web Visits per Month:')
num_web_visits_select = alt.selection_single(fields=['NumWebVisitsMonth'], bind=num_web_visits_slider, name='Select')

# Add new widgets for product selection
products = ['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']
product_x_dropdown = alt.binding_select(options=products, name='Product X:')
product_x_select = alt.selection_single(fields=['x_product'], bind=product_x_dropdown, init={'x_product': 'MntWines'})
product_y_dropdown = alt.binding_select(options=products, name='Product Y:')
product_y_select = alt.selection_single(fields=['y_product'], bind=product_y_dropdown, init={'y_product': 'MntMeatProducts'})


# Define views

# View 1: Bar chart of customer distribution by education level
education_chart = alt.Chart(data).mark_bar().encode(
    x=alt.X('Education:N', title='Education Level'),
    y=alt.Y('count():Q', title='Number of Customers'),
    color='Education:N'
).properties(title='Customer Distribution by Education Level')


# Define widgets
education_dropdown = alt.binding_select(options=sorted(data['Education'].unique()), name='Education Level:')
education_select = alt.selection_single(fields=['Education'], bind=education_dropdown, name='Select')
income_slider = alt.binding_range(min=0, max=data['Income'].max(), step=1000, name='Annual Income:')
income_select = alt.selection_single(fields=['Income'], bind=income_slider, name='Select')

# View 2: Scatter plot of customer annual income and total amount spent on products (with interaction)
scatter_chart = alt.Chart(data).mark_circle().encode(
    x=alt.X('Income:Q', title='Annual Income'),
    y=alt.Y('total_spent:Q', title='Total Amount Spent on Products'),
    color='Education:N',
    tooltip=[
        alt.Tooltip('Education'),
        alt.Tooltip('Marital_Status'),
        alt.Tooltip('Income', format='$,.0f'),
        alt.Tooltip('total_spent', format='$,.0f')
    ]
).properties(title='Customer Annual Income vs Amount Spent on Products').add_selection(
    education_select, income_select, marital_status_select, num_web_visits_select
).transform_filter(
    education_select
).transform_filter(
    income_select
).transform_filter(
    marital_status_select
).transform_filter(
    num_web_visits_select
)


# Define linked selection
brush = alt.selection(type='interval')

# View 5: Bar chart - Average Total Spending per Education Level (linked to scatter chart)
avg_spending_by_education = alt.Chart(data).mark_bar().encode(
    x=alt.X('Education:N', title='Education Level'),
    y=alt.Y('mean(total_spent):Q', title='Average Total Spending'),
    tooltip=['Education', 'mean(total_spent):Q']
).properties(title='Average Total Spending per Education Level').add_selection(
    brush
).transform_filter(
    education_select
)

# View 6: Yearly Total Amount Spent on Products (linked to enrollment chart)
yearly_total_spent_chart = alt.Chart(data).mark_line().encode(
    x=alt.X('year(Dt_Customer):O', title='Year'),
    y=alt.Y('sum(total_spent):Q', title='Total Amount Spent'),
    tooltip=['year(Dt_Customer):O', 'sum(total_spent):Q']
).transform_filter(
    education_select
)

# View 3: Timeline chart of new customer enrollments (with linked highlighting)
enrollment_chart = alt.Chart(data).mark_line().encode(
    x=alt.X('year(Dt_Customer):T', title='Year of Enrollment'),
    y=alt.Y('count():Q', title='Number of New Enrollments'),
    color='Marital_Status:N').properties(title='New Customer Enrollments Over Time').add_selection(
    brush, education_select, income_select, marital_status_select, num_web_visits_select).transform_filter(
    education_select).transform_filter(income_select).transform_filter(marital_status_select).transform_filter(
    num_web_visits_select)

# New View: Average spending per marital status (with linked highlighting)

avg_spending_by_marital_status = alt.Chart(data).mark_bar().encode(
x=alt.X('Marital_Status:N', title='Marital Status'),
y=alt.Y('mean(total_spent):Q', title='Average Total Spending'),
color='Marital_Status:N',
tooltip=['Marital_Status', 'mean(total_spent):Q']).properties(title='Average Total Spending per Marital Status').add_selection(brush)

import pandas as pd
import altair as alt


# Convert the 'Dt_Customer' column to datetime format
data['Dt_Customer'] = pd.to_datetime(data['Dt_Customer'])

# Create 'total_spent' column by summing up spending in different product categories
data['total_spent'] = data[['MntWines', 'MntFruits', 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts', 'MntGoldProds']].sum(axis=1)

# Calculate the average spending for each combination of Teenhome, Kidhome, and Marital_Status
avg_spending = data.groupby(['Teenhome', 'Kidhome', 'Marital_Status'])['total_spent'].mean().reset_index()

# Create a hover selection
hover = alt.selection_single(on='mouseover', nearest=True, empty='none')

# Create a selection for the dropdown
marital_status_dropdown = alt.binding_select(options=sorted(data['Marital_Status'].unique()), name='Marital Status:')
marital_status_select = alt.selection_single(fields=['Marital_Status'], bind=marital_status_dropdown, init={'Marital_Status': 'Married'})

# Create a grouped bar chart for kids
chart_kids = alt.Chart(avg_spending).mark_bar().encode(
    x=alt.X('Kidhome:O', title='Number of Kids at Home'),
    y=alt.Y('total_spent:Q', title='Average Spending'),
    color=alt.condition(hover, 'Kidhome:O', alt.value('lightgray'), legend=alt.Legend(title='Number of Kids at Home')),
    tooltip=['Teenhome', 'Kidhome', 'total_spent', 'Marital_Status']
).properties(title='Average Spending by Number of Kids at Home (Filtered by Marital Status)').add_selection(
    hover
)

# Create a grouped bar chart for teens
chart_teens = alt.Chart(avg_spending).mark_bar().encode(
    x=alt.X('Teenhome:O', title='Number of Teens at Home'),
    y=alt.Y('total_spent:Q', title='Average Spending'),
    color=alt.condition(hover, 'Teenhome:O', alt.value('lightgray'), legend=alt.Legend(title='Number of Teens at Home')),
    tooltip=['Teenhome', 'Kidhome', 'total_spent', 'Marital_Status']
).properties(title='Average Spending by Number of Teens at Home (Filtered by Marital Status)').add_selection(
    hover
)

# Combine the charts
concat_chart = alt.hconcat(chart_kids, chart_teens).add_selection(
    marital_status_select
).transform_filter(
    marital_status_select
)


"""scatter_plot = alt.Chart(data).mark_circle().encode(
    x=alt.X('NumWebVisitsMonth:Q', title='Number of Web Visits per Month'),
    y=alt.Y('NumWebPurchases:Q', title='Number of Web Purchases'),
    size=alt.Size('count():Q', scale=alt.Scale(range=[50, 500]), legend=alt.Legend(title='Number of Customers')),
    color=alt.Color('count():Q', scale=alt.Scale(scheme='viridis'), legend=None),
    tooltip=['NumWebVisitsMonth', 'NumWebPurchases', 'count()']
).properties(title='Scatter Plot of Web Visits per Month vs. Web Purchases')

scatter_plot.interactive()"""

# Define dropdown selection for marital status
marital_status_dropdown = alt.binding_select(options=data['Marital_Status'].unique().tolist(), name='Marital Status: ')
marital_status_selection = alt.selection_single(fields=['Marital_Status'], bind=marital_status_dropdown, name='Marital_Status', init={'Marital_Status': data['Marital_Status'].iloc[0]})

# Add a scatter plot and filter by marital status
scatter_plot_filtered = alt.Chart(data).mark_circle().encode(
    x=alt.X('NumWebVisitsMonth:Q', title='Number of Web Visits per Month'),
    y=alt.Y('NumWebPurchases:Q', title='Number of Web Purchases'),
    size=alt.Size('count():Q', scale=alt.Scale(range=[50, 500]), legend=alt.Legend(title='Number of Customers')),
    color=alt.Color('count():Q', scale=alt.Scale(scheme='viridis'), legend=None),
    tooltip=['NumWebVisitsMonth', 'NumWebPurchases', 'count()']
).properties(title='Scatter Plot of Web Visits per Month vs. Web Purchases Filtered by Marital Status').transform_filter(
    marital_status_selection
)

# Add interactivity and the marital status selection to the scatter plot
interactive_scatter_plot_filtered = scatter_plot_filtered.interactive().add_selection(marital_status_selection)
interactive_scatter_plot_filtered


# Group by 'Dt_Customer' and calculate average spending for new and returning customers
new_customers = data[data['NumWebPurchases'] == 0].groupby('Dt_Customer')['total_spent'].mean().reset_index()
returning_customers = data[data['NumWebPurchases'] > 0].groupby('Dt_Customer')['total_spent'].mean().reset_index()


# new_customers and returning_customers DataFrames
# Create line charts for new and returning customers without filtering
new_line = alt.Chart(new_customers).mark_line().encode(
    x=alt.X('Dt_Customer:T', title='Date'),
    y=alt.Y('total_spent:Q', title='Average Spending'),
    color=alt.value('blue'),
    tooltip=['Dt_Customer', 'total_spent']
).properties(title='New Customers')

returning_line = alt.Chart(returning_customers).mark_line().encode(
    x=alt.X('Dt_Customer:T', title='Date'),
    y=alt.Y('total_spent:Q', title='Average Spending'),
    color=alt.value('green'),
    tooltip=['Dt_Customer', 'total_spent']
).properties(title='Returning Customers')

# Display the charts side by side
combined_chart = alt.hconcat(new_line, returning_line)
combined_chart

# Merge Marital_Status to new_customers and returning_customers DataFrames

new_customers = new_customers.merge(data[['Dt_Customer', 'Marital_Status']], on='Dt_Customer', how='left')
returning_customers = returning_customers.merge(data[['Dt_Customer', 'Marital_Status']], on='Dt_Customer', how='left')

# Filter data by marital status using transform_filter within the charts
new_line_filtered = alt.Chart(new_customers).mark_line().encode(
    x=alt.X('Dt_Customer:T', title='Date'),
    y=alt.Y('total_spent:Q', title='Average Spending'),
    color=alt.value('blue'),
    tooltip=['Dt_Customer', 'total_spent']
).properties(title='New Customers').transform_filter(
    marital_status_selection
)

returning_line_filtered = alt.Chart(returning_customers).mark_line().encode(
    x=alt.X('Dt_Customer:T', title='Date'),
    y=alt.Y('total_spent:Q', title='Average Spending'),
    color=alt.value('green'),
    tooltip=['Dt_Customer', 'total_spent']
).properties(title='Returning Customers').transform_filter(
    marital_status_selection
)

# Add interactivity for panning and zooming
interactive_chart_filtered = alt.layer(new_line_filtered, returning_line_filtered).resolve_scale(y='shared').interactive().add_selection(marital_status_selection)

# Combine the interactive chart, the legend, and the marital status selection
# Create legend
legend = alt.Chart(pd.DataFrame({'legend': ['New Customers', 'Returning Customers'], 'color': ['blue', 'green']})).mark_point().encode(
    x=alt.value(20),
    y=alt.Y('legend', title=None),
    color=alt.Color('color', scale=None),
    tooltip=['legend']
)

# Combine the interactive chart, the legend, and the marital status selection
final_chart_filtered = alt.hconcat(interactive_chart_filtered, legend).properties(title='Average Spending of New vs Returning Customers by Marital Status')
final_chart_filtered

import panel as pn
pn.extension('vega')


# Define dashboard layout
dashboard = pn.Column(
    pn.Row(
        pn.Column(
            education_chart,
            avg_spending_by_education,
            avg_spending_by_marital_status,
            width=350
        ),
        pn.Column(
            scatter_chart,
            enrollment_chart,
            width=700
        ),
    ),
    pn.Row(
        concat_chart,
        width=1000
    ),
    pn.Row(
        final_chart_filtered,
        width=1000
    ),
)


# Display dashboard
dashboard.servable()


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