Pivot tables with pivot_table() in Data Analysis Python - Mini Project: Build & Apply

Pivot tables with pivot_table()

📖 Scenario: You work in a small store that sells different types of fruits. You have a list of sales data showing how many fruits were sold each day. You want to summarize this data to see the total sales for each fruit and each day.

🎯 Goal: Build a pivot table using pandas to summarize total fruit sales by fruit type and day.

📋 What You'll Learn

Create a pandas DataFrame with sales data

Create a variable for the pivot table configuration

Use pivot_table() to summarize total sales by fruit and day

Print the resulting pivot table

💡 Why This Matters

🌍 Real World

Pivot tables help summarize and analyze sales data quickly, making it easier to understand trends and totals.

💼 Career

Data analysts and business intelligence professionals use pivot tables to create reports and insights 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:
{'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]}

Data Analysis Python

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame() with a dictionary to create the data.

2

Set up the pivot table configuration

Create a variable called pivot_config that is a dictionary with keys index set to 'Fruit', columns set to 'Day', and values set to 'Quantity'.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]})
# Create the pivot_config dictionary
# Your code here

Hint

Use a dictionary with keys index, columns, and values.

3

Create the pivot table

Use pd.pivot_table() with sales_data and the pivot_config dictionary to create a pivot table called sales_pivot. Use the aggfunc parameter set to sum to add quantities.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]})
pivot_config = {'index': 'Fruit', 'columns': 'Day', 'values': 'Quantity'}
# Create the sales_pivot pivot table using pd.pivot_table
# Your code here

Hint

Use pd.pivot_table() with the keys from pivot_config and aggfunc=sum.

4

Print the pivot table

Print the sales_pivot pivot table to see the total quantities sold by fruit and day.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({'Fruit': ['Apple', 'Banana', 'Apple', 'Banana', 'Apple'], 'Day': ['Monday', 'Monday', 'Tuesday', 'Tuesday', 'Wednesday'], 'Quantity': [10, 5, 7, 3, 8]})
pivot_config = {'index': 'Fruit', 'columns': 'Day', 'values': 'Quantity'}
sales_pivot = pd.pivot_table(sales_data, index=pivot_config['index'], columns=pivot_config['columns'], values=pivot_config['values'], aggfunc=sum)
# Print the sales_pivot DataFrame
# Your code here

Hint

Use print(sales_pivot) to display the pivot table.

Practice

1. What does the index parameter do in the pivot_table() function?

easy

A. It sets the rows of the pivot table.

B. It sets the columns of the pivot table.

C. It defines the aggregation function.

D. It filters the data before pivoting.

Solution

1. Step 1: Understand the role of index in pivot_table()

   The index parameter specifies which column(s) to use as the rows in the resulting pivot table.

2. Step 2: Differentiate from other parameters

   The columns parameter sets the columns, aggfunc sets the aggregation, and filtering is done separately.

3. Final Answer:

   It sets the rows of the pivot table. -> Option A

4. Quick Check:

   index = rows [OK]

Hint: Remember: index = rows, columns = columns [OK]

Common Mistakes:

• Confusing index with columns
• Thinking aggfunc sets rows
• Assuming index filters data

2. Which of the following is the correct syntax to create a pivot table with pandas using pivot_table()?

easy

A. pd.pivot_table(data, index='Category', columns='Year', aggfunc='sum')

B. pd.pivot(data, index='Category', columns='Year', aggfunc='sum')

C. pd.pivot_table(data, rows='Category', cols='Year', aggfunc='sum')

D. pd.pivot_table(data, groupby='Category', by='Year', aggfunc='sum')

Solution

1. Step 1: Recall the correct function and parameters

   The correct function is pd.pivot_table() with parameters index for rows and columns for columns.

2. Step 2: Check each option

   pd.pivot_table(data, index='Category', columns='Year', aggfunc='sum') uses correct function and parameters. Options B, C, and D use incorrect function names or parameter names.

3. Final Answer:

   pd.pivot_table(data, index='Category', columns='Year', aggfunc='sum') -> Option A

4. Quick Check:

   Correct function and parameters = pd.pivot_table(data, index='Category', columns='Year', aggfunc='sum') [OK]

Hint: Use index= for rows, columns= for columns in pivot_table [OK]

Common Mistakes:

• Using pd.pivot instead of pd.pivot_table
• Wrong parameter names like rows or cols
• Mixing groupby parameters with pivot_table

3. Given the DataFrame df below:

   Product  Region  Sales
0  A        East    100
1  B        East    150
2  A        West    200
3  B        West    250
4  A        East    50
5  B        West    100

What is the output of:

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

medium

A. Region East West
Product
A 150 300
B 250 350

B. Region East West
Product
A 150 200
B 150 350

C. Region East West
Product
A 150 200
B 250 350

D. Region East West
Product
A 150 200
B 150 250

Solution

1. Step 1: Group data by Product and Region, summing Sales

   For Product A and East: 100 + 50 = 150; Product A and West: 200; Product B and East: 150; Product B and West: 250 + 100 = 350.

2. Step 2: Arrange results in pivot table format

   Rows are Products, columns are Regions, values are summed Sales as calculated.

3. Final Answer:

   Region East West Product A 150 200 B 250 350 -> Option C

4. Quick Check:

   Sum Sales by Product and Region = Region East West Product A 150 200 B 250 350 [OK]

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

Common Mistakes:

• Adding wrong sales values
• Mixing up East and West sums
• Using count instead of sum

4. What is wrong with this code snippet?

pd.pivot_table(df, index='Product', columns='Region', values='Sales', aggfunc='average')

medium

A. The index parameter must be a list, not a string.

B. The aggfunc 'average' is not a valid aggregation function.

C. The values parameter cannot be a column name.

D. The columns parameter should be 'cols' instead of 'columns'.

Solution

1. Step 1: Check the aggfunc parameter value

   The valid aggregation functions include 'mean', 'sum', 'count', etc. 'average' is not recognized by pandas.

2. Step 2: Verify other parameters

   columns is correct, values can be a column name string, and index can be a string or list.

3. Final Answer:

   The aggfunc 'average' is not a valid aggregation function. -> Option B

4. Quick Check:

   aggfunc must be valid like 'mean' [OK]

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

Common Mistakes:

• Using 'average' instead of 'mean'
• Changing columns to cols
• Passing values as non-column

5. You have a sales DataFrame with columns: Store, Product, Month, and Revenue. You want a pivot table showing total revenue per store for each product, but only for months January and February. Which code correctly creates this pivot table?

hard

A. pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum').loc[['January', 'February']]

B. pd.pivot_table(df, index='Store', columns='Product', values='Revenue', aggfunc='sum', filter=['January', 'February'])

C. pd.pivot_table(df, index='Month', columns='Product', values='Revenue', aggfunc='sum').query('Month in ["January", "February"]')

D. pd.pivot_table(df[df['Month'].isin(['January', 'February'])], index='Store', columns='Product', values='Revenue', aggfunc='sum')

Solution

1. Step 1: Filter data for January and February

   Use df[df['Month'].isin(['January', 'February'])] to select only those months before pivoting.

2. Step 2: Create pivot table with Store as rows and Product as columns

   Use index='Store', columns='Product', and sum revenue with aggfunc='sum'.

3. Final Answer:

   pd.pivot_table(df[df['Month'].isin(['January', 'February'])], index='Store', columns='Product', values='Revenue', aggfunc='sum') -> Option D

4. Quick Check:

   Filter first, then pivot with sum [OK]

Hint: Filter data before pivoting to limit rows [OK]

Common Mistakes:

• Trying to filter inside pivot_table with invalid parameter
• Filtering after pivoting causing wrong results
• Using wrong index or columns