agg() for multiple aggregations in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

import pandas as pd

data = {'score': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)
result = df['score'].agg([1])
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 'sum' instead of 'mean' will give the total, not the average.

Forgetting to put the method name in quotes causes an error.

The agg() function can take a string with the aggregation method name. Here, 'mean' calculates the average.

2fill in blank

medium

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

Data Analysis Python

import pandas as pd

data = {'score': [5, 15, 25, 35, 45]}
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'

D['mean', 'sum']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string with commas instead of a list causes an error.

Using parentheses instead of square brackets for the list.

To apply multiple aggregations, pass a list of method names to agg(). Here, ['min', 'max'] calculates both minimum and maximum.

3fill in blank

hard

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

Data Analysis Python

import pandas as pd

data = {'score': [2, 4, 6, 8, 10]}
df = pd.DataFrame(data)
result = df['score'].agg([1])
print(result)

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

A'sum', 'mean'

B('sum', 'mean')

C['sum', 'mean']

D'sum mean'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing multiple strings separated by commas without a list causes a syntax error.

Using a tuple instead of a list is not accepted by agg().

The agg() function expects a list of aggregation names to apply multiple functions. Passing a list like ['sum', 'mean'] is correct.

4fill in blank

hard

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

Data Analysis Python

import pandas as pd

data = {'score': [1, 2, 3], 'age': [20, 30, 40]}
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', 'sum']

B'mean'

C'max'

D['max']

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string instead of a list for multiple aggregations.

Using a list for a single aggregation unnecessarily.

For multiple aggregations on 'score', use a list like ['mean', 'sum']. For 'age', a single aggregation like 'max' is enough.

5fill in blank

hard

Fill all three blanks to create a dictionary for agg() that calculates the sum of 'sales', the mean of 'profit', and the max of 'quantity'.

Data Analysis Python

import pandas as pd

data = {'sales': [100, 200, 300], 'profit': [10, 20, 30], 'quantity': [1, 2, 3]}
df = pd.DataFrame(data)
result = df.agg({'sales': [1], 'profit': [2], 'quantity': [3])
print(result)

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

A'sum'

B'mean'

C'max'

D'min'

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up aggregation functions for columns.

Forgetting to put aggregation names in quotes.

Use 'sum' for 'sales', 'mean' for 'profit', and 'max' for 'quantity' in the aggregation dictionary.