Named aggregation in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group the DataFrame by 'team' and calculate the mean of 'score' using named aggregation.

Pandas

result = df.groupby('team').agg({'average_score': ('score', [1])})

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

Asum

Bmax

Ccount

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' instead of 'mean' will add scores instead of averaging.

Using 'count' will count entries, not calculate average.

The mean function calculates the average of the 'score' column for each group.

2fill in blank

medium

Complete the code to group by 'department' and calculate the maximum 'salary' with named aggregation.

Pandas

summary = df.groupby('department').agg({'max_salary': ('salary', [1])})

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

Amin

Bmean

Cmax

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'min' will give the lowest salary, not the highest.

Using 'mean' will calculate average salary, not maximum.

The max function finds the highest salary in each department.

3fill in blank

hard

Fix the error in the named aggregation to count the number of entries per 'category'.

Pandas

counts = df.groupby('category').agg({'entry_count': ('id', [1])})

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

Acount

Bmax

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' will add values instead of counting.

Using 'mean' or 'max' will not count entries.

The count function counts the number of non-null entries in the 'id' column for each category.

4fill in blank

hard

Fill both blanks to group by 'region' and calculate the minimum and maximum of 'sales' using named aggregation.

Pandas

agg_sales = df.groupby('region').agg({'min_sales': ('sales', [1]), 'max_sales': ('sales', [2])})

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

Amin

Bmax

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the functions will give wrong results.

Using 'mean' or 'sum' will not give min or max values.

min finds the smallest sales value, and max finds the largest sales value per region.

5fill in blank

hard

Fill all three blanks to group by 'category' and calculate the mean of 'price', sum of 'quantity', and count of 'order_id' using named aggregation.

Pandas

summary = df.groupby('category').agg({'avg_price': ('price', [1]), 'total_quantity': ('quantity', [2]), 'order_count': ('order_id', [3])})

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

Asum

Bcount

Cmean

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up sum and count functions.

Using max instead of mean for average price.

mean calculates average price, sum adds quantities, and count counts orders per category.