pivot_table() for summarization in Pandas - Mini Project: Build & Apply

Summarize Sales Data Using pivot_table()

📖 Scenario: You work in a small store that sells different products in various cities. You have sales data for a week and want to understand total sales by city and product.

🎯 Goal: Build a summary table using pandas pivot_table() to see total sales for each product in each city.

📋 What You'll Learn

Create a pandas DataFrame with sales data

Define a pivot table configuration variable

Use pivot_table() to summarize total sales by city and product

Print the resulting pivot table

💡 Why This Matters

🌍 Real World

Stores and businesses often need to summarize sales data quickly to understand which products sell best in which locations.

💼 Career

Data analysts and business intelligence professionals use pivot tables to create clear summaries and reports from raw data.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and rows:
City: 'New York', 'New York', 'Los Angeles', 'Los Angeles', 'Chicago', 'Chicago'
Product: 'Apples', 'Bananas', 'Apples', 'Bananas', 'Apples', 'Bananas'
Sales: 100, 150, 200, 250, 300, 350

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact data
# Your code here

Hint

Use pd.DataFrame() with a dictionary of lists for columns.

2

Set up pivot table configuration

Create a variable called pivot_config as a dictionary with keys:
index set to 'City',
columns set to 'Product',
and values set to 'Sales'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'Chicago', 'Chicago'],
    'Product': ['Apples', 'Bananas', 'Apples', 'Bananas', 'Apples', 'Bananas'],
    'Sales': [100, 150, 200, 250, 300, 350]
})
# Create pivot_config dictionary with index, columns, and values keys
# Your code here

Hint

Use a dictionary with keys exactly as 'index', 'columns', and 'values'.

3

Create the pivot table

Use pd.pivot_table() with sales_data and the pivot_config dictionary unpacked to create a variable called summary_table.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'Chicago', 'Chicago'],
    'Product': ['Apples', 'Bananas', 'Apples', 'Bananas', 'Apples', 'Bananas'],
    'Sales': [100, 150, 200, 250, 300, 350]
})
pivot_config = {
    'index': 'City',
    'columns': 'Product',
    'values': 'Sales'
}
# Create summary_table using pd.pivot_table with sales_data and pivot_config
# Your code here

Hint

Use pd.pivot_table(data, **config) to unpack the dictionary.

4

Display the pivot table

Print the summary_table variable to show the total sales by city and product.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'City': ['New York', 'New York', 'Los Angeles', 'Los Angeles', 'Chicago', 'Chicago'],
    'Product': ['Apples', 'Bananas', 'Apples', 'Bananas', 'Apples', 'Bananas'],
    'Sales': [100, 150, 200, 250, 300, 350]
})
pivot_config = {
    'index': 'City',
    'columns': 'Product',
    'values': 'Sales'
}
summary_table = pd.pivot_table(sales_data, **pivot_config)
# Print the summary_table
# Your code here

Hint

Use print(summary_table) to display the pivot table.

Practice

1.

What does the pivot_table() function in pandas primarily do?

easy

A. It merges two dataframes based on a key column.

B. It sorts data in ascending or descending order.

C. It summarizes data by grouping rows and columns and calculating statistics.

D. It filters rows based on conditions.

Solution

1. Step 1: Understand the purpose of pivot_table()

   The pivot_table() function groups data by specified rows and columns and calculates summary statistics like mean, sum, or count.

2. Step 2: Compare with other options

   Sorting, filtering, and merging are different pandas operations, not the main purpose of pivot_table().

3. Final Answer:

   It summarizes data by grouping rows and columns and calculating statistics. -> Option C

4. Quick Check:

   pivot_table() = summary by groups [OK]

Hint: pivot_table groups and summarizes data quickly [OK]

Common Mistakes:

• Confusing pivot_table with sorting or filtering
• Thinking pivot_table merges dataframes
• Assuming pivot_table only reshapes data without aggregation

2.

Which of the following is the correct syntax to create a pivot table that calculates the mean of column 'Sales' grouped by 'Region'?

import pandas as pd
df = pd.DataFrame({
    'Region': ['East', 'West', 'East', 'West'],
    'Sales': [100, 200, 150, 300]
})

# Which line is correct?

easy

A. pd.pivot_table(df, index='Region', values='Sales', aggfunc='mean')

B. pd.pivot_table(df, columns='Region', values='Sales', aggfunc='sum')

C. pd.pivot_table(df, index='Sales', values='Region', aggfunc='mean')

D. pd.pivot_table(df, values='Region', index='Sales', aggfunc='count')

Solution

1. Step 1: Identify grouping and aggregation

   We want to group by 'Region' (index) and calculate mean of 'Sales' (values) using aggfunc='mean'.

2. Step 2: Check each option

   pd.pivot_table(df, index='Region', values='Sales', aggfunc='mean') matches the requirement exactly. Options B, C, and D either swap index and values or use wrong aggfunc.

3. Final Answer:

   pd.pivot_table(df, index='Region', values='Sales', aggfunc='mean') -> Option A

4. Quick Check:

   Group by Region, mean Sales = pd.pivot_table(df, index='Region', values='Sales', aggfunc='mean') [OK]

Hint: index=group, values=summary column, aggfunc=stat [OK]

Common Mistakes:

• Swapping index and values parameters
• Using wrong aggregation function
• Confusing columns parameter with index

3.

Given the following code, what is the output?

import pandas as pd
df = pd.DataFrame({
    'Category': ['A', 'A', 'B', 'B'],
    'Type': ['X', 'Y', 'X', 'Y'],
    'Value': [10, 20, 30, 40]
})
pivot = pd.pivot_table(df, index='Category', columns='Type', values='Value', aggfunc='sum')
print(pivot)

medium

A. Category X Y
A 10 20
B 30 40

B. Type X Y
Category
A 10.0 20.0
B 30.0 40.0

C. Type X Y
Category
A 20.0 10.0
B 40.0 30.0

D. Type X Y
Category
A 30.0 40.0
B 10.0 20.0

Solution

1. Step 1: Understand pivot_table parameters

   Grouping by 'Category' as rows, 'Type' as columns, summing 'Value'.

2. Step 2: Calculate sums for each group

   For Category A and Type X: 10, Type Y: 20; For B and X: 30, Y: 40.

3. Final Answer:

   Type X Y Category A 10.0 20.0 B 30.0 40.0 -> Option B

4. Quick Check:

   Sum by Category and Type = Type X Y Category A 10.0 20.0 B 30.0 40.0 [OK]

Hint: Sum values by row index and column labels [OK]

Common Mistakes:

• Mixing up rows and columns in output
• Confusing sum with mean aggregation
• Ignoring float formatting in output

4.

What is wrong with this pivot_table code?

import pandas as pd
df = pd.DataFrame({
    'Group': ['G1', 'G1', 'G2'],
    'Score': [5, 10, 15]
})
pivot = pd.pivot_table(df, index='Group', values='Score', aggfunc='average')
print(pivot)

medium

A. The aggfunc 'average' is invalid; should be 'mean'.

B. The 'values' parameter should be 'Group' instead of 'Score'.

C. The 'index' parameter should be 'Score' instead of 'Group'.

D. The DataFrame is missing a column for aggregation.

Solution

1. Step 1: Check aggfunc parameter

   The aggfunc parameter must be a valid aggregation function like 'mean', 'sum', or 'count'. 'average' is not recognized by pandas.

2. Step 2: Verify other parameters

   Index='Group' and values='Score' are correct for grouping and aggregation.

3. Final Answer:

   The aggfunc 'average' is invalid; should be 'mean'. -> Option A

4. Quick Check:

   aggfunc='mean' not 'average' [OK]

Hint: Use 'mean' not 'average' for aggregation [OK]

Common Mistakes:

• Using 'average' instead of 'mean' in aggfunc
• Swapping index and values parameters
• Assuming aggfunc accepts any descriptive name

5.

You have a sales dataset with columns 'Store', 'Product', and 'Revenue'. You want to create a pivot table that shows the total revenue for each store and product combination, but if a combination is missing, it should show 0 instead of NaN. Which code correctly achieves this?

hard

A. pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='mean', fill_value=0)

B. pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum').fillna('0')

C. pd.pivot_table(df, index='Product', columns='Store', values='Revenue', aggfunc='sum', fill_value=null)

D. pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum', fill_value=0)

Solution

1. Step 1: Identify grouping and aggregation

   We want total revenue (sum) grouped by Store (rows) and Product (columns).

2. Step 2: Handle missing combinations

   Use fill_value=0 in pivot_table to replace NaN with 0 for missing pairs.

3. Step 3: Check each option

   pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum', fill_value=0) uses correct index, columns, aggfunc, and fill_value=0. pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum').fillna('0') fills NaN after pivot but with string '0' (wrong type). pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='mean', fill_value=0) uses mean instead of sum. pd.pivot_table(df, index='Product', columns='Store', values='Revenue', aggfunc='sum', fill_value=null) swaps index and columns and fill_value=null.

4. Final Answer:

   pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum', fill_value=0) -> Option D

5. Quick Check:

   sum + fill_value=0 for missing = pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum', fill_value=0) [OK]

Hint: Use fill_value=0 to replace NaN in pivot_table [OK]

Common Mistakes:

• Using fillna('0') which creates string values
• Using mean instead of sum for total revenue
• Swapping index and columns parameters