Why grouping data matters in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

Asort

Bfilter

Cgroupby

Dmerge

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sort' instead of 'groupby' will only sort data, not group it.

Using 'filter' or 'merge' does not create groups.

The groupby method groups data based on column values, which is essential for aggregation.

2fill in blank

medium

Complete the code to calculate the average sales for each group.

Pandas

average_sales = grouped.[1]()['Sales']

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Amean

Bsum

Ccount

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' will add values instead of averaging.

Using 'count' returns the number of items, not the average.

The mean function calculates the average value for each group.

3fill in blank

hard

Fix the error in the code to get the total sales per group.

Pandas

total_sales = df.groupby('Category').[1]()

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Acount

Baverage

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'average' is not a valid pandas method.

Using 'mean' calculates average, not total.

The sum method adds up all sales values in 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 len(word) [2] 3}

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Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' will select words shorter than 3 characters.

Using 'word' instead of 'len(word)' will store the word itself, not its length.

We use len(word) to get the length and filter words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase words and their counts for words with count greater than 1.

Pandas

result = [1]: [2] for word, count in word_counts.items() if count [3] 1

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Aword.upper()

Bcount

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word' instead of 'word.upper()' will not change case.

Using '<' will filter counts less than or equal to 1.

We convert words to uppercase with word.upper(), keep the count, and filter counts greater than 1 using >.