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.

Practice

1. What does the groupby function in pandas primarily do?

easy

A. It splits data into groups based on column values for separate analysis.

B. It sorts the entire DataFrame by column values.

C. It deletes duplicate rows from the DataFrame.

D. It merges two DataFrames based on a key column.

Solution

1. Step 1: Understand the purpose of groupby

   The groupby function divides data into groups based on unique values in one or more columns.

2. Step 2: Recognize the use of groups

   After grouping, you can apply functions to each group separately for analysis or aggregation.

3. Final Answer:

   It splits data into groups based on column values for separate analysis. -> Option A

4. Quick Check:

   Group data = split by column values [OK]

Hint: GroupBy splits data into parts by column values [OK]

Common Mistakes:

• Confusing groupby with sorting
• Thinking groupby deletes duplicates
• Mixing groupby with merge operations

2. Which of the following is the correct syntax to apply a custom function my_func to each group after grouping by column 'Category' in a DataFrame df?

easy

A. df.groupby('Category').apply(my_func)

B. df.apply.groupby('Category')(my_func)

C. df.groupby('Category').my_func()

D. df.group_by('Category').apply(my_func)

Solution

1. Step 1: Recall pandas groupby syntax

   The correct method to group by a column is df.groupby('ColumnName').

2. Step 2: Apply a custom function to groups

   Use .apply(function) after grouping to run the function on each group.

3. Final Answer:

   df.groupby('Category').apply(my_func) -> Option A

4. Quick Check:

   groupby + apply(custom function) = df.groupby('Category').apply(my_func) [OK]

Hint: Use groupby('col').apply(func) to run custom function [OK]

Common Mistakes:

• Using wrong method name like group_by
• Calling function directly on groupby object
• Misplacing apply before groupby

3. Given the DataFrame df below:

import pandas as pd
data = {'Team': ['A', 'A', 'B', 'B', 'C'], 'Points': [10, 15, 10, 20, 30]}
df = pd.DataFrame(data)

# Custom function to get range of points in group
def point_range(x):
    return x.max() - x.min()

result = df.groupby('Team')['Points'].apply(point_range).to_dict()

What is the value of result?

medium

A. {'A': 10, 'B': 10, 'C': 0}

B. {'A': 25, 'B': 30, 'C': 30}

C. {'A': 5, 'B': 10, 'C': 0}

D. {'A': 15, 'B': 20, 'C': 30}

Solution

1. Step 1: Group points by 'Team'

   Teams: A has points [10, 15], B has [10, 20], C has [30].

2. Step 2: Calculate range (max - min) for each group

   For A: 15 - 10 = 5; B: 20 - 10 = 10; C: 30 - 30 = 0.

3. Final Answer:

   {'A': 5, 'B': 10, 'C': 0} -> Option C

4. Quick Check:

   Range = max - min per group = {'A': 5, 'B': 10, 'C': 0} [OK]

Hint: Calculate max-min per group for range [OK]

Common Mistakes:

• Confusing sum with range
• Forgetting single value group range is zero
• Mixing keys and values in result

4. Consider this code snippet:

import pandas as pd
data = {'Category': ['X', 'X', 'Y'], 'Value': [1, 2, 3]}
df = pd.DataFrame(data)

# Custom function to sum values
 def sum_values(x):
    return x.sum

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

What is the issue with this code?

medium

A. The apply method cannot be used with custom functions.

B. The groupby syntax is incorrect; it should be groupby(['Category']).

C. The DataFrame is missing a required index column.

D. The function sum_values returns the method, not the sum result.

Solution

1. Step 1: Check the custom function definition

   The function returns x.sum which is a method, not called (missing parentheses).

2. Step 2: Understand the effect on apply

   Returning the method itself causes the result to be a method object, not the sum value.

3. Final Answer:

   The function sum_values returns the method, not the sum result. -> Option D

4. Quick Check:

   Missing () calls method, not result [OK]

Hint: Call sum() with parentheses to get result [OK]

Common Mistakes:

• Forgetting parentheses on sum()
• Thinking groupby syntax is wrong here
• Believing apply can't use custom functions

5. You have a DataFrame df with columns 'Store', 'Product', and 'Sales'. You want to find the difference between the highest and lowest sales per store, but only for products with sales above 100. Which approach correctly uses groupby with a custom function to achieve this?

hard

A. df.groupby('Store')['Sales'].apply(lambda x: x[x > 100].max() - x[x > 100].min())

B. All of the above produce the same correct result.

C. df.groupby('Store').apply(lambda g: g[g['Sales'] > 100]['Sales'].max() - g[g['Sales'] > 100]['Sales'].min())

D. df[df['Sales'] > 100].groupby('Store')['Sales'].apply(lambda x: x.max() - x.min())

Solution

1. Step 1: Understand filtering sales above 100

   One approach filters inside the apply lambda on each store's sales series. Another filters the DataFrame before grouping. The third filters inside the group apply on the subgroup DataFrame.

2. Step 2: Check if all approaches calculate max-min correctly

   All three methods correctly compute the difference of max and min sales above 100 per store.

3. Step 3: Confirm equivalence of results

   Filtering before or inside groupby leads to the same final result for this case.

4. Final Answer:

   All of the above produce the same correct result. -> Option B

5. Quick Check:

   Filtering + groupby + custom function = all correct [OK]

Hint: Filter before or inside groupby; both work [OK]

Common Mistakes:

• Filtering after groupby without apply
• Not filtering sales > 100 properly
• Assuming only one method works