# Introduction to Computational Analysis

 Pay Notebook Creator: Roy Hyunjin Han 0 Set Container: Numerical CPU with TINY Memory for 10 Minutes 0 Total 0
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from pandas import read_csv

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productOrders = orders.merge(products)
productOrders

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productOrders['OrderAmount'] = productOrders['ProductQuantity'] * productOrders['ProductPrice']


# Customers¶

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# Who are our customers?

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# What were the top ten states with the most customers?
customers['CustomerState'].value_counts()[:10]

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# How many unique customers did we have in February 2012?
import datetime
afterFebruary1 = orders['OrderDateTime'] >= datetime.datetime(2012, 2, 1)
beforeMarch1 = orders['OrderDateTime'] < datetime.datetime(2012, 3, 1)
februaryOrders = orders[afterFebruary1 & beforeMarch1]
len(februaryOrders['CustomerID'].unique())

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# Who are the most loyal customers?
# How much will a customer spend during his or her relationship with the company?
# How much are we willing to spend to acquire a customer?
# Which customers are at risk of leaving?
# Which customers are most likely to buy a given product?


# Products¶

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# What were the sales ranked by state?
customerProductOrders = productOrders.merge(customers)
customerProductOrders.groupby('CustomerState').sum()['OrderAmount'].order(ascending=False)[:10]

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# What were the top ten products ranked by quantity sold?
productOrders.groupby('ProductName').sum()['ProductQuantity'].order(ascending=False)[:10]

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# What were the top ten products by revenue?
productOrders.groupby('ProductName').sum()['OrderAmount'].order(ascending=False)[:10]

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# What were the top ten products by profit?
productOrders['OrderProfit'] = productOrders['ProductQuantity'] * (productOrders['ProductPrice'] - productOrders['ProductCost'])
productOrders.groupby('ProductName').sum()['OrderProfit'].order(ascending=False)[:10]

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# How many orders did we have by month?
ordersByDateTime = orders.set_index('OrderDateTime')
ordersByDateTime.resample('M', how=len)['OrderID'].plot();

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# Which product is most likely to sell to a given group of customers?
# How much of a given product do we expect to sell next week?
# How much of a given product should we stock in the warehouse?


# Operations¶

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# What were the top ten promotions by revenue?
# What were the top ten promotions by profit?