GroupBy with custom functions in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

grouped = df.groupby([1])

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

A'Date'

B'Value'

C'Index'

D'Category'

Attempts:

3 left

💡 Hint

Common Mistakes

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

Forgetting to put the column name in quotes.

The groupby function groups data by the specified column. Here, we want to group by the 'Category' column.

2fill in blank

medium

Complete the code to apply a custom function that calculates the range (max - min) of 'Value' in each group.

Pandas

def range_func(x):
    return x.max() - [1]

result = df.groupby('Category')['Value'].apply(range_func)

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

Amean()

Bmin()

Csum()

Dmedian()

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean or sum instead of min for calculating range.

Forgetting to subtract the minimum value.

The range is calculated as the maximum value minus the minimum value in each group.

3fill in blank

hard

Fix the error in the custom function to correctly calculate the mean of 'Value' after grouping.

Pandas

def mean_func(x):
    return x.[1]()

result = df.groupby('Category')['Value'].apply(mean_func)

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

Amean

Bmedian

Csum

Dmax

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum or max instead of mean.

Forgetting the parentheses after the method name.

The function should use mean() to calculate the average value in each group.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each group to the count of 'Value' greater than 10.

Pandas

counts = {group: sum(df.loc[df['Category'] == group, 'Value'] [1] 10) for group in df['Category'].unique() if sum(df.loc[df['Category'] == group, 'Value'] [2] 10) > 0}

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

A>

B<

C==

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' or '==' instead of '>' causes wrong counts.

Using different operators in the two blanks leads to errors.

The code counts how many 'Value' entries are greater than 10 in each group. Both blanks use the '>' operator.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each group to the average 'Value' for values greater than 5.

Pandas

averages = {group: df.loc[(df['Category'] == group) & (df['Value'] [1] 5), [2]].[3]() for group in df['Category'].unique()}

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

A>

B'Value'

Cmean

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering.

Selecting the wrong column or forgetting to select a column.

Using sum instead of mean for average.

The code filters 'Value' greater than 5, selects the 'Value' column, and calculates the mean for each group.