Pivot with aggregation functions in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a pivot table that sums the 'Sales' for each 'Region'.

Pandas

pivot_table = df.pivot_table(index='Region', values='Sales', aggfunc=[1])

Drag options to blanks, or click blank then click option'

Asum

Bmean

Ccount

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'mean' instead of 'sum' will calculate averages, not totals.

Using 'count' will count entries, not sum values.

The aggfunc='sum' tells pandas to add up the sales values for each region.

2fill in blank

medium

Complete the code to create a pivot table that calculates the average 'Sales' for each 'Product'.

Pandas

pivot_table = df.pivot_table(index='Product', values='Sales', aggfunc=[1])

Drag options to blanks, or click blank then click option'

Amin

Bmean

Csum

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' will add all sales instead of averaging.

Using 'min' or 'max' will find smallest or largest values, not average.

The aggfunc='mean' calculates the average sales per product.

3fill in blank

hard

Fix the error in the code to count the number of sales entries per 'Region'.

Pandas

pivot_table = df.pivot_table(index='Region', values='Sales', aggfunc=[1])

Drag options to blanks, or click blank then click option'

Asum

Bmedian

Cmean

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' will add sales values instead of counting entries.

Using 'mean' or 'median' will calculate averages, not counts.

The aggfunc='count' counts how many sales entries exist per region.

4fill in blank

hard

Fill both blanks to create a pivot table that shows the maximum 'Sales' for each 'Region' and 'Product'.

Pandas

pivot_table = df.pivot_table(index=[1], columns=[2], values='Sales', aggfunc='max')

Drag options to blanks, or click blank then click option'

A'Region'

B'Sales'

C'Product'

D'Date'

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping 'Region' and 'Product' will change the pivot table layout.

Using 'Sales' or 'Date' as index or columns will cause errors or wrong grouping.

The index is set to 'Region' to group rows by region, and columns is set to 'Product' to create columns for each product.

5fill in blank

hard

Fill all three blanks to create a pivot table that calculates the mean 'Sales' for each 'Region' and 'Product', and fills missing values with 0.

Pandas

pivot_table = df.pivot_table(index=[1], columns=[2], values=[3], aggfunc='mean').fillna(0)

Drag options to blanks, or click blank then click option'

A'Region'

B'Product'

C'Sales'

D'Date'

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong columns for index or columns changes the table layout.

Not filling missing values can leave NaNs in the result.

The pivot table groups rows by 'Region', columns by 'Product', calculates the average 'Sales', and replaces missing values with 0.