Aggregation with agg() in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the 'score' column using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'score': [85, 90, 78]}
df = pd.DataFrame(data)

result = df['score'].agg([1])
print(result)

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

A'mean'

B'sum'

C'max'

D'min'

Attempts:

3 left

💡 Hint

Common Mistakes

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

Using 'max' or 'min' will return the highest or lowest score, not the average.

The agg() function applies the aggregation function specified. Here, 'mean' calculates the average score.

2fill in blank

medium

Complete the code to calculate both the minimum and maximum of the 'score' column using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob', 'Charlie'], 'score': [85, 90, 78]}
df = pd.DataFrame(data)

result = df['score'].agg([1])
print(result)

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

A['min', 'max']

B'min', 'max'

C'min'

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a string with commas instead of a list causes errors.

Passing only one function name returns a single aggregation, not both.

The agg() function accepts a list of function names to apply multiple aggregations. The list ['min', 'max'] calculates both minimum and maximum values.

3fill in blank

hard

Fix the error in the code to calculate the sum of 'score' and the mean of 'age' columns using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'score': [85, 90], 'age': [25, 30]}
df = pd.DataFrame(data)

result = df.agg({'score': [1], 'age': 'mean'})
print(result)

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

A'mean'

Bsum

C['sum']

D'sum'

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum without quotes causes a NameError.

Using a list like ['sum'] is not accepted in this context.

When specifying aggregation functions in a dictionary for agg(), function names must be strings like 'sum' or 'mean'.

4fill in blank

hard

Fill both blanks to create a dictionary that calculates the mean of 'score' and the max of 'age' using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'score': [85, 90], 'age': [25, 30]}
df = pd.DataFrame(data)

result = df.agg({'score': [1], 'age': [2])
print(result)

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

A'mean'

B'max'

C'sum'

D'min'

Attempts:

3 left

💡 Hint

Common Mistakes

Using function names without quotes.

Mixing up which function applies to which column.

The dictionary keys are column names and values are aggregation functions as strings. Here, 'mean' for 'score' and 'max' for 'age' calculate the average score and maximum age.

5fill in blank

hard

Fill all three blanks to create a dictionary that calculates the sum of 'score', the mean of 'age', and the minimum of 'score' using agg().

Pandas

import pandas as pd

data = {'name': ['Alice', 'Bob'], 'score': [85, 90], 'age': [25, 30]}
df = pd.DataFrame(data)

result = df.agg({'score': [[1], [2]], 'age': [3])
print(result)

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

A'sum'

B'mean'

C'min'

D'max'

Attempts:

3 left

💡 Hint

Common Mistakes

Using function names without quotes.

Passing a string instead of a list for multiple functions.

Mixing up the order of functions in the list.

For the 'score' column, we pass a list of functions ['sum', 'min'] to calculate total and minimum scores. For 'age', 'mean' calculates the average age.