Split-apply-combine mental model in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group the DataFrame by the 'team' column.

Pandas

grouped = df.[1]('team')

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Amerge

Bsort

Cfilter

Dgroupby

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sort' instead of 'groupby' will not create groups.

Using 'filter' or 'merge' does not split the data.

The groupby method splits the DataFrame into groups based on the values in the specified column.

2fill in blank

medium

Complete the code to calculate the mean score for each group.

Pandas

mean_scores = grouped['score'].[1]()

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Acount

Bsum

Cmean

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' adds values instead of averaging.

Using 'count' counts items, not values.

Using 'max' finds the largest value, not the average.

The mean method calculates the average value for each group.

3fill in blank

hard

Fix the error in the code to apply a custom function to each group.

Pandas

result = grouped['score'].[1](lambda x: x.max() - x.min())

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Aapply

Btransform

Caggregate

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'aggregate' expects named functions or dicts, not lambdas directly.

Using 'transform' returns a transformed version with the same shape.

Using 'filter' selects groups, not applies functions.

The apply method lets you run a custom function on each group.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Pandas

lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword == 'data'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a condition unrelated to length will filter wrong words.

Using the word itself instead of its length for the value.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values filtered by positive values.

Pandas

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 lowercase keys instead of uppercase.

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

Using keys as values or vice versa.

This dictionary comprehension creates keys as uppercase strings and keeps values only if they are positive.