Pivot with aggregation functions in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the pivot_table function in pandas do?

It reshapes data by summarizing values using aggregation functions, creating a new table where rows and columns are defined by specified keys.

beginner

Which parameter in pivot_table specifies the column to aggregate?

The values parameter specifies which column's data will be aggregated in the pivot table.

beginner

How do you specify the aggregation function in a pandas pivot table?

Use the aggfunc parameter to set the aggregation function, like sum, mean, or len.

intermediate

What happens if you don't specify an aggregation function in pivot_table?

By default, pivot_table uses the mean function to aggregate values.

intermediate

Can you use multiple aggregation functions in a single pivot table?

Yes, by passing a list of functions to aggfunc, you can apply multiple aggregations like ['sum', 'mean'].

What parameter defines the rows in a pandas pivot table?

A index

B columns

C values

D aggfunc

The index parameter sets the rows in the pivot table.

Which aggregation function is the default in pivot_table?

A mean

B sum

C count

D max

If no aggregation function is specified, mean is used by default.

How do you aggregate data by counting entries in a pivot table?

A aggfunc='sum'

B aggfunc='count'

C aggfunc='mean'

D aggfunc='max'

Use aggfunc='count' to count the number of entries.

Which parameter sets the columns in a pandas pivot table?

A aggfunc

B index

C values

D columns

The columns parameter defines the columns in the pivot table.

Can you apply more than one aggregation function in a pivot table?

A No, only one function is allowed

B No, you must create multiple pivot tables

C Yes, by passing a list to aggfunc

D Yes, but only sum and mean

Passing a list like aggfunc=['sum', 'mean'] applies multiple aggregations.

Explain how to create a pivot table in pandas that sums sales by region and product.

Think about which parts of the data become rows, columns, and what you want to add up.

Describe how you can use multiple aggregation functions in one pivot table and why it might be useful.

Consider why seeing both total and average values together can help understand data better.

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