karamanbk

#using pct_change() function to see monthly percentage change
tx_revenue['MonthlyGrowth'] = tx_revenue['Revenue'].pct_change()

#showing first 5 rows
tx_revenue.head()

#visualization - line graph
plot_data = [
    go.Scatter(
        x=tx_revenue.query("InvoiceYearMonth < 201112")['InvoiceYearMonth'],

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#create a new dataframe for no. of order by using quantity field
tx_monthly_sales = tx_uk.groupby('InvoiceYearMonth')['Quantity'].sum().reset_index()

#print the dataframe
tx_monthly_sales

#plot
plot_data = [
    go.Bar(
        x=tx_monthly_sales['InvoiceYearMonth'],

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# create a new dataframe for average revenue by taking the mean of it
tx_monthly_order_avg = tx_uk.groupby('InvoiceYearMonth')['Revenue'].mean().reset_index()

#print the dataframe
tx_monthly_order_avg

#plot the bar chart
plot_data = [
    go.Bar(
        x=tx_monthly_order_avg['InvoiceYearMonth'],

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#creating a new dataframe with UK customers only
tx_uk = tx_data.query("Country=='United Kingdom'").reset_index(drop=True)

#creating monthly active customers dataframe by counting unique Customer IDs
tx_monthly_active = tx_uk.groupby('InvoiceYearMonth')['CustomerID'].nunique().reset_index()

#print the dataframe
tx_monthly_active

#plotting the output

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#create a dataframe contaning CustomerID and first purchase date
tx_min_purchase = tx_uk.groupby('CustomerID').InvoiceDate.min().reset_index()
tx_min_purchase.columns = ['CustomerID','MinPurchaseDate']
tx_min_purchase['MinPurchaseYearMonth'] = tx_min_purchase['MinPurchaseDate'].map(lambda date: 100*date.year + date.month)

#merge first purchase date column to our main dataframe (tx_uk)
tx_uk = pd.merge(tx_uk, tx_min_purchase, on='CustomerID')

tx_uk.head()

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#identify which users are active by looking at their revenue per month
tx_user_purchase = tx_uk.groupby(['CustomerID','InvoiceYearMonth'])['Revenue'].sum().reset_index()

#create retention matrix with crosstab
tx_retention = pd.crosstab(tx_user_purchase['CustomerID'], tx_user_purchase['InvoiceYearMonth']).reset_index()

tx_retention.head()

#create an array of dictionary which keeps Retained & Total User count for each month
months = tx_retention.columns[2:]

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karamanbk

# import libraries
from datetime import datetime, timedelta
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from __future__ import division

import plotly.plotly as py

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#create a generic user dataframe to keep CustomerID and new segmentation scores
tx_user = pd.DataFrame(tx_data['CustomerID'].unique())
tx_user.columns = ['CustomerID']

#get the max purchase date for each customer and create a dataframe with it
tx_max_purchase = tx_uk.groupby('CustomerID').InvoiceDate.max().reset_index()
tx_max_purchase.columns = ['CustomerID','MaxPurchaseDate']

#we take our observation point as the max invoice date in our dataset
tx_max_purchase['Recency'] = (tx_max_purchase['MaxPurchaseDate'].max() - tx_max_purchase['MaxPurchaseDate']).dt.days

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#build 4 clusters for recency and add it to dataframe
kmeans = KMeans(n_clusters=4)
kmeans.fit(tx_user[['Recency']])
tx_user['RecencyCluster'] = kmeans.predict(tx_user[['Recency']])

#function for ordering cluster numbers
def order_cluster(cluster_field_name, target_field_name,df,ascending):
    new_cluster_field_name = 'new_' + cluster_field_name
    df_new = df.groupby(cluster_field_name)[target_field_name].mean().reset_index()
    df_new = df_new.sort_values(by=target_field_name,ascending=ascending).reset_index(drop=True)