7. Pivot Tables

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retail_data.csv
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  1. Find the total sales amount for each year.

Solution
import pandas as pd

df=pd.read_csv('retail_data.csv')
ndf = df.pivot_table(values="Total_Amount", index="Year", aggfunc="sum").reset_index()
print(ndf)

# Output
    Year  Total_Amount
0  2023  3.358360e+08
1  2024  6.614229e+07

  1. How many transactions were made for each unique total purchase count, categorized by customer segments?

Solution
ndf=df.pivot_table(index='Total_Purchases',values='Customer_Segment',aggfunc='count')
ndf.reset_index()

# Output
Total_Purchases	Customer_Segment
0	1	31050
1	2	31084
2	3	31070
3	4	30795
4	5	31112
5	6	27767
6	7	27696
7	8	27966
8	9	27757
9	10	27614

  1. Find the average purchase amount for each product category.

Solution
ndf=df.pivot_table(index="Product_Category", values='Amount',aggfunc='mean')
ndf.reset_index()

# Output
	Product_Category	Amount
0	Books	255.072599
1	Clothing	254.907741
2	Electronics	255.408202
3	Grocery	255.350918
4	Home Decor	254.904483

  1. Find the number of transactions for each country.

Solution

  1. Find the maximum payment for each payment method.

Solution

  1. Find the minimum purchase amount of each product.

Solution

  1. Find the total purchases made in each month across years.

Solution

  1. Find the most expensive purchase amount for each product type.

Solution

  1. Find the average revenue from each city and segment.

Solution

  1. Find the minimum revenue for each product brand and order status.

Solution

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