Pivot with aggregation functions in Pandas - Mini Project: Build & Apply

Pivot with aggregation functions

📖 Scenario: You work in a small store that sells fruits. You have a list of sales records showing the fruit type, the day of the week, and the quantity sold. You want to organize this data to see the total quantity sold for each fruit on each day.

🎯 Goal: Create a pivot table using pandas that shows the total quantity of each fruit sold per day of the week.

📋 What You'll Learn

Create a pandas DataFrame with the given sales data

Create a list of days to use as columns in the pivot table

Use pandas pivot_table with aggregation function sum to calculate total quantities

Print the resulting pivot table

💡 Why This Matters

🌍 Real World

Pivot tables help organize and summarize sales data to understand trends and performance by categories and time periods.

💼 Career

Data analysts and business intelligence professionals use pivot tables to quickly summarize and report data for decision making.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and rows:
Fruit: ['Apple', 'Banana', 'Apple', 'Banana', 'Apple', 'Banana']
Day: ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday', 'Wednesday']
Quantity: [10, 5, 15, 7, 10, 8]

Pandas

import pandas as pd
# Create the sales_data DataFrame with Fruit, Day, and Quantity columns
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Create the list of days for columns

Create a list called days with these exact values: ['Monday', 'Tuesday', 'Wednesday']

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple', 'Banana'],
    'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday', 'Wednesday'],
    'Quantity': [10, 5, 15, 7, 10, 8]
})
# Create the list days with Monday, Tuesday, Wednesday
# Your code here

Hint

Just create a list with the three day names in order.

3

Create the pivot table with sum aggregation

Create a pivot table called pivot_table from sales_data using pd.pivot_table.
Use Fruit as the index, Day as the columns, Quantity as the values, and sum as the aggregation function.
Also, use the days list as the columns order.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple', 'Banana'],
    'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday', 'Wednesday'],
    'Quantity': [10, 5, 15, 7, 10, 8]
})
days = ['Monday', 'Tuesday', 'Wednesday']
# Create the pivot_table using pd.pivot_table with index='Fruit', columns='Day', values='Quantity', aggfunc='sum'
# Your code here

Hint

Use pd.pivot_table with the right parameters and reorder columns with .reindex(columns=days).

4

Print the pivot table

Print the pivot_table variable to see the total quantity of each fruit sold per day.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple', 'Banana'],
    'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday', 'Wednesday'],
    'Quantity': [10, 5, 15, 7, 10, 8]
})
days = ['Monday', 'Tuesday', 'Wednesday']
pivot_table = pd.pivot_table(sales_data, index='Fruit', columns='Day', values='Quantity', aggfunc='sum').reindex(columns=days)
# Print the pivot_table
# Your code here

Hint

Use print(pivot_table) to display the pivot table.

Practice

1. What does the pivot_table function in pandas primarily do?

easy

A. It rearranges data to summarize and compare values using aggregation.

B. It sorts data alphabetically by column names.

C. It deletes duplicate rows from the DataFrame.

D. It converts a DataFrame into a list of dictionaries.

Solution

1. Step 1: Understand the purpose of pivot_table

   The pivot_table function rearranges data by creating a new table that summarizes data values grouped by specified keys.

2. Step 2: Recognize aggregation role

   It uses aggregation functions like sum or mean to combine data values in the new table.

3. Final Answer:

   It rearranges data to summarize and compare values using aggregation. -> Option A

4. Quick Check:

   pivot_table = rearrange + aggregate [OK]

Hint: Pivot tables summarize data by grouping and aggregating [OK]

Common Mistakes:

• Confusing pivot_table with sorting or filtering
• Thinking it deletes duplicates
• Believing it converts DataFrame to list

2. Which of the following is the correct syntax to create a pivot table with pandas to calculate the mean of 'Sales' grouped by 'Region'?

easy

A. pd.pivot_table(data, columns='Region', values='Sales', aggfunc='sum')

B. pd.pivot_table(data, index='Region', values='Sales', aggfunc='mean')

C. data.pivot_table(index='Region', 'Sales', aggfunc='mean')

D. pd.pivot(data, index='Region', values='Sales', aggfunc='mean')

Solution

1. Step 1: Identify correct function and parameters

   The correct function is pd.pivot_table with parameters: data, index='Region', values='Sales', and aggfunc='mean'.

2. Step 2: Check each option

   pd.pivot_table(data, index='Region', values='Sales', aggfunc='mean') matches the correct syntax. pd.pivot(data, index='Region', values='Sales', aggfunc='mean') uses pd.pivot which does not support aggfunc. data.pivot_table('Sales', 'Region', aggfunc='mean') has wrong method call syntax. pd.pivot_table(data, columns='Region', values='Sales', aggfunc='sum') uses columns instead of index and wrong aggregation.

3. Final Answer:

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

4. Quick Check:

   pivot_table + index + values + aggfunc = correct syntax [OK]

Hint: Use pd.pivot_table with index, values, and aggfunc parameters [OK]

Common Mistakes:

• Using pd.pivot instead of pd.pivot_table
• Mixing columns and index parameters
• Incorrect method call on DataFrame

3. Given the DataFrame:

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

What is the output of pivot?

medium

A. Category X Y
City
A 15 15
B 35 35

B. Category X Y
City
A 20 10
B 40 30

C. Category X Y
City
A 10 20
B 30 40

D. Category X Y
City
A 10 40
B 30 20

Solution

1. Step 1: Group data by 'City' and 'Category'

   For City 'A' and Category 'X', value is 10; for 'A' and 'Y', value is 20. For City 'B' and Category 'X', value is 30; for 'B' and 'Y', value is 40.

2. Step 2: Apply sum aggregation

   Since each group has one value, sum is the same as the value itself.

3. Final Answer:

   Category X Y City A 10 20 B 30 40 -> Option C

4. Quick Check:

   Sum per group matches values [OK]

Hint: Sum values grouped by index and columns for pivot output [OK]

Common Mistakes:

• Swapping values for categories
• Mixing up rows and columns
• Using mean instead of sum

4. Consider this code snippet:

import pandas as pd
data = pd.DataFrame({
  'Team': ['A', 'A', 'B', 'B'],
  'Points': [5, 10, 15, 20]
})
pivot = pd.pivot_table(data, index='Team', values='Points', aggfunc='count')
print(pivot)

What is the error or issue in this code?

medium

A. No error; output shows count of points per team.

B. aggfunc='count' is invalid; should use 'sum' instead.

C. Missing columns parameter causes runtime error.

D. DataFrame missing 'columns' argument for pivot_table.

Solution

1. Step 1: Understand aggfunc='count'

   The 'count' aggregation counts non-null entries in 'Points' for each 'Team'.

2. Step 2: Check code correctness

   The code is valid and will output the count of 'Points' per team without error.

3. Final Answer:

   No error; output shows count of points per team. -> Option A

4. Quick Check:

   aggfunc='count' counts entries correctly [OK]

Hint: aggfunc='count' counts non-null values, no columns needed [OK]

Common Mistakes:

• Thinking 'count' is invalid aggfunc
• Expecting columns parameter is mandatory
• Confusing count with sum

5. You have sales data with columns: 'Store', 'Product', 'Month', and 'Revenue'. You want to create a pivot table that shows the total revenue per 'Store' for each 'Month', but only for months where total revenue exceeds 1000. Which approach correctly achieves this?

hard

A. Filter original data for revenue > 1000, then pivot with index='Store', columns='Month', values='Revenue', aggfunc='sum'.

B. Use pivot_table with index='Month', columns='Store', values='Revenue', aggfunc='mean', then filter values > 1000.

C. Pivot with index='Store', columns='Product', values='Revenue', aggfunc='sum', then filter rows with sum > 1000.

D. Create pivot with index='Store', columns='Month', values='Revenue', aggfunc='sum', then filter columns with sum > 1000.

Solution

1. Step 1: Create pivot table by Store and Month

   Use pivot_table with index='Store', columns='Month', values='Revenue', and aggfunc='sum' to get total revenue per store per month.

2. Step 2: Filter months by total revenue

   Since filtering by months with revenue > 1000 is needed, filter columns after pivot since columns represent months.

3. Step 3: Choose correct approach

   Create pivot with index='Store', columns='Month', values='Revenue', aggfunc='sum', then filter columns with sum > 1000, which creates the pivot and then filters columns (months) where total revenue exceeds 1000, matching the requirement.

4. Final Answer:

   Create pivot with index='Store', columns='Month', values='Revenue', aggfunc='sum', then filter columns with sum > 1000. -> Option D

5. Quick Check:

   Pivot then filter sums > 1000 [OK]

Hint: Pivot first, then filter aggregated sums to meet condition [OK]

Common Mistakes:

• Filtering raw data before pivot loses aggregation
• Using mean instead of sum for totals
• Mixing up index and columns parameters