GroupBy performance considerations in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

grouped = df.groupby([1])

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

Adf['category']

B'category'

Ccategory

D'Category'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the column name without quotes causes a NameError.

Passing the whole Series instead of the column name string.

To group by a column name, you must pass the column name as a string to groupby. So, 'category' is correct.

2fill in blank

medium

Complete the code to calculate the mean of each group.

Pandas

result = df.groupby('category').[1]()

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

Asum

Bmax

Ccount

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() instead of mean().

Using count() which counts entries, not averages.

The mean() function calculates the average value for each group.

3fill in blank

hard

Fix the error in this code to avoid slow performance when grouping large DataFrames.

Pandas

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

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

A'value'

Bdf.columns

Cdf['value']

Dvalue

Attempts:

3 left

💡 Hint

Common Mistakes

Passing df['value'] instead of the column name string.

Passing df.columns which is a list of all columns.

Passing the column name as a string inside the groupby selection is efficient. Using df['value'] or df.columns causes unnecessary overhead.

4fill in blank

hard

Fill both blanks to create a dictionary of group sizes for groups with more than 5 entries.

Pandas

sizes = {group: len(data) for group, data in df.groupby([1]) if len(data) [2] 5}

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A'category'

B>

C<

D'value'

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name for grouping.

Using '<' instead of '>' to filter group sizes.

We group by the 'category' column and keep groups with length greater than 5.

5fill in blank

hard

Fill all three blanks to create a dictionary with group names in uppercase and sum of 'value' for groups with sum greater than 10.

Pandas

result = {group[1]: data['[2]'].sum() for group, data in df.groupby('[3]') if data['value'].sum() > 10}

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

A.upper()

Bvalue

Ccategory

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using '.lower()' instead of '.upper()' for group names.

Using wrong column names for sum or grouping.

The group name is converted to uppercase with .upper(). We sum the 'value' column for groups grouped by 'category'.