Aggregation functions (sum, mean, std) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the sum of the 'sales' column in the DataFrame.

Data Analysis Python

total_sales = df['sales'].[1]()

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

Asum

Bstd

Cmean

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() instead of sum() to add values.

Using std() which calculates spread, not total.

The sum() function adds all values in the 'sales' column.

2fill in blank

medium

Complete the code to find the average (mean) of the 'age' column.

Data Analysis Python

average_age = df['age'].[1]()

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

Astd

Bsum

Cmin

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() without dividing by count.

Using std() which measures spread, not average.

The mean() function calculates the average value of the 'age' column.

3fill in blank

hard

Fix the error in the code to calculate the standard deviation of the 'height' column.

Data Analysis Python

height_std = df['height'].[1]()

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

Asum

Bstd

Cmedian

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean() or sum() instead of std().

Using median() which finds the middle value, not spread.

The std() function calculates the standard deviation, which shows how spread out the values are.

4fill in blank

hard

Fill both blanks to create a dictionary with word lengths for words longer than 4 characters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 4}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' causing wrong filtering.

Using 'word' instead of 'len(word)' for length.

We use len(word) to get the length, and > to filter words longer than 4 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

Data Analysis Python

result = [1]: [2] for k, v in data.items() if v [3] 0

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper() for keys.

Using '<' instead of '>' in the filter condition.

Keys are converted to uppercase with k.upper(), values are v, and we filter values greater than zero using >.