GroupBy with custom functions in Pandas

Introduction

Grouping data helps us see patterns in smaller parts. Using custom functions lets us get exactly the summary we want.

You want to find the average and also the range (max - min) of sales per store.

You need to count how many unique customers bought each product.

You want to apply a special calculation, like weighted average, on groups of data.

You want to clean or transform data differently for each group before analysis.

Syntax

Pandas

df.groupby('column').agg(custom_function)

# or with multiple columns
# df.groupby('column').agg({'col1': custom_func1, 'col2': custom_func2})

custom_function is a function you define that takes a Series and returns a single value.

You can use built-in functions or write your own for special calculations.

Examples

This example finds the range of sales for each store.

Pandas

def range_func(x):
    return x.max() - x.min()

df.groupby('Store')['Sales'].agg(range_func)

Sum sales and find average quantity sold per product using a lambda function.

Pandas

df.groupby('Product').agg({'Sales': 'sum', 'Quantity': lambda x: x.mean()})

Count unique customers per category.

Pandas

def unique_count(x):
    return x.nunique()

df.groupby('Category')['CustomerID'].agg(unique_count)

Sample Program

This program groups data by store. It calculates the sales range and average quantity sold per store.

Pandas

import pandas as pd

data = {
    'Store': ['A', 'A', 'B', 'B', 'C', 'C'],
    'Sales': [100, 150, 200, 210, 300, 310],
    'Quantity': [1, 2, 2, 3, 3, 4]
}

df = pd.DataFrame(data)

# Custom function to calculate range
def sales_range(x):
    return x.max() - x.min()

# Group by Store and apply custom function on Sales
result = df.groupby('Store').agg({'Sales': sales_range, 'Quantity': 'mean'})

print(result)

Important Notes

Custom functions must return a single value for each group.

Use lambda for quick small functions.

GroupBy with custom functions is powerful for tailored summaries.

Summary

Group data to analyze smaller parts.

Use custom functions to get exactly the summary you want.

Combine built-in and custom functions for flexible analysis.

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