Single and multiple column grouping in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Data Analysis Python

grouped = df.groupby([1])

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

A'Date'

B'Value'

C'Category'

D'Index'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in the DataFrame.

Not using quotes around the column name.

To group data by a single column, you pass the column name as a string to groupby(). Here, 'Category' is the correct column to group by.

2fill in blank

medium

Complete the code to group the DataFrame by both 'Category' and 'Region' columns.

Data Analysis Python

grouped = df.groupby([1])

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

A('Category', 'Region')

B'Category'

C'Category, Region'

D['Category', 'Region']

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a single string with comma-separated column names.

Using a tuple instead of a list.

To group by multiple columns, pass a list of column names to groupby(). The list should be inside square brackets.

3fill in blank

hard

Fix the error in the code to correctly group by 'Category' and calculate the mean.

Data Analysis Python

result = df.groupby([1]).mean()

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

A['Category']

B'Category'

CCategory

D'Category', 'Region'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing multiple columns as separate arguments without a list.

Not using quotes around the column name.

The groupby() method expects a single column name as a string or a list of strings. Here, to group by one column, pass the column name as a string.

4fill in blank

hard

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

Data Analysis Python

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

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

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using the '>' operator.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys, values, and filter condition.

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

Attempts:

3 left

💡 Hint

Common Mistakes

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

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

The dictionary comprehension uses k.upper() to make keys uppercase, keeps values as v, and filters items where v > 0.