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.

Practice

1. What is the main purpose of named aggregation in pandas?

easy

A. To assign custom names to aggregated summary columns

B. To rename columns before aggregation

C. To filter rows before grouping

D. To sort data after aggregation

Solution

1. Step 1: Understand aggregation in pandas

   Aggregation combines data by groups to produce summary statistics.

2. Step 2: Role of named aggregation

   Named aggregation lets you specify clear names for these summary columns, making results easier to read.

3. Final Answer:

   To assign custom names to aggregated summary columns -> Option A

4. Quick Check:

   Named aggregation = custom summary names [OK]

Hint: Named aggregation means naming summary columns clearly [OK]

Common Mistakes:

• Confusing named aggregation with renaming columns before aggregation
• Thinking it filters or sorts data
• Assuming it changes the grouping keys

2. Which of the following is the correct syntax for using named aggregation in groupby.agg()?

easy

A. df.groupby('A').agg({'new_col': 'B', 'sum'})

B. df.groupby('A').agg('new_col' = ('B', 'sum'))

C. df.groupby('A').agg(new_col=('B', 'sum'))

D. df.groupby('A').agg(('B', 'sum') = 'new_col')

Solution

1. Step 1: Recall named aggregation syntax

   Named aggregation uses keyword arguments with the format new_name=(column, function).

2. Step 2: Check each option

   df.groupby('A').agg(new_col=('B', 'sum')) matches this syntax exactly: new_col=('B', 'sum'). Others have invalid syntax or misplaced equals signs.

3. Final Answer:

   df.groupby('A').agg(new_col=('B', 'sum')) -> Option C

4. Quick Check:

   Named aggregation syntax = new_name=(col, func) [OK]

Hint: Use new_name=(column, function) inside agg() [OK]

Common Mistakes:

• Using equals sign inside dictionary instead of keyword argument
• Misplacing parentheses or quotes
• Trying to assign new_col outside agg()

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'Team': ['A', 'A', 'B', 'B'], 'Points': [10, 15, 10, 20], 'Assists': [5, 7, 8, 6]})
result = df.groupby('Team').agg(total_points=('Points', 'sum'), max_assists=('Assists', 'max'))
print(result)

What is the output?

medium

A. Team index with total_points as sum of Points and max_assists as max of Assists

B. DataFrame with columns Points and Assists unchanged

C. Error because named aggregation syntax is wrong

D. Empty DataFrame because groupby is missing

Solution

1. Step 1: Understand the groupby and aggregation

   Grouping by 'Team' groups rows by 'A' and 'B'. Then sum Points and max Assists per group.

2. Step 2: Check named aggregation results

   total_points sums Points per team: A=10+15=25, B=10+20=30; max_assists picks max Assists per team: A=max(5,7)=7, B=max(8,6)=8.

3. Final Answer:

   Team index with total_points as sum of Points and max_assists as max of Assists -> Option A

4. Quick Check:

   Named aggregation sums and maxes grouped data [OK]

Hint: Sum and max per group with named aggregation [OK]

Common Mistakes:

• Expecting original columns unchanged
• Confusing max with sum
• Thinking groupby needs reset_index() to work

4. What is wrong with this code snippet?

df.groupby('Category').agg(total=('Value', 'sum'), mean='Value')

medium

A. The sum function cannot be used with named aggregation

B. The groupby column 'Category' does not exist

C. The agg method requires a list, not keyword arguments

D. The mean aggregation is missing the function tuple syntax

Solution

1. Step 1: Review named aggregation syntax

   Each named aggregation must be a keyword argument with a tuple: new_name=(column, function).

2. Step 2: Identify the error

   The 'mean' aggregation is given as mean='Value' without specifying the aggregation function, which is incorrect.

3. Final Answer:

   The mean aggregation is missing the function tuple syntax -> Option D

4. Quick Check:

   Named aggregation needs (column, function) tuples [OK]

Hint: Each agg must be new_name=(col, func) [OK]

Common Mistakes:

• Passing column name as string without function
• Assuming agg accepts mixed syntax
• Using list instead of keyword arguments

5. You have a DataFrame with sales data:

import pandas as pd
df = pd.DataFrame({'Store': ['X', 'X', 'Y', 'Y', 'Z'], 'Sales': [100, 150, 200, 250, 300], 'Returns': [5, 3, 2, 4, 1]})

You want to create a summary DataFrame grouped by 'Store' with:

• Total sales named 'total_sales'
• Average returns named 'avg_returns'
• Count of sales entries named 'sales_count'

Which code correctly uses named aggregation to achieve this?

hard

A. df.groupby('Store').agg(sum('Sales') as total_sales, mean('Returns') as avg_returns, count('Sales') as sales_count)

B. df.groupby('Store').agg(total_sales=('Sales', 'sum'), avg_returns=('Returns', 'mean'), sales_count=('Sales', 'count'))

C. df.groupby('Store').agg(total_sales='sum', avg_returns='mean', sales_count='count')

D. df.groupby('Store').agg({'total_sales': ('Sales', 'sum'), 'avg_returns': ('Returns', 'mean'), 'sales_count': ('Sales', 'count')})

Solution

1. Step 1: Recall named aggregation syntax

   Use keyword arguments with new_name=(column, function) inside agg().

2. Step 2: Evaluate each option

   df.groupby('Store').agg(total_sales=('Sales', 'sum'), avg_returns=('Returns', 'mean'), sales_count=('Sales', 'count')) correctly uses named aggregation syntax. df.groupby('Store').agg({'total_sales': ('Sales', 'sum'), 'avg_returns': ('Returns', 'mean'), 'sales_count': ('Sales', 'count')}) uses a dictionary but this is not valid for named aggregation. df.groupby('Store').agg(total_sales='sum', avg_returns='mean', sales_count='count') uses strings without specifying columns. df.groupby('Store').agg(sum('Sales') as total_sales, mean('Returns') as avg_returns, count('Sales') as sales_count) uses invalid syntax.

3. Final Answer:

   df.groupby('Store').agg(total_sales=('Sales', 'sum'), avg_returns=('Returns', 'mean'), sales_count=('Sales', 'count')) -> Option B

4. Quick Check:

   Named aggregation = keyword args with (col, func) tuples [OK]

Hint: Use new_name=(col, func) for multiple summaries [OK]

Common Mistakes:

• Using dictionary instead of keyword arguments
• Passing only function names without columns
• Trying SQL-like syntax inside agg()