apply() function for custom logic in Data Analysis Python - Mini Project: Build & Apply

Using apply() Function for Custom Logic in Data Science

📖 Scenario: You work in a small bakery that tracks daily sales of different types of bread. You have a table of sales data and want to add a new column that classifies each day's sales as 'High' or 'Low' based on a sales threshold.

🎯 Goal: Build a program that uses the apply() function to add a new column to a sales DataFrame. This new column will label each row as 'High' or 'Low' sales depending on a threshold you set.

📋 What You'll Learn

Create a pandas DataFrame with specific sales data

Define a sales threshold variable

Write a custom function to classify sales

Use the apply() function to apply this logic to the DataFrame

Print the updated DataFrame with the new classification column

💡 Why This Matters

🌍 Real World

Classifying data based on conditions is common in business to quickly understand performance, like sales levels or customer ratings.

💼 Career

Data analysts and scientists often use apply() to run custom logic on data columns for feature engineering and data cleaning.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']
'Bread_Sales': [120, 80, 150, 90, 200]

Data Analysis Python

# Create the sales_data DataFrame with the given days and Bread_Sales values
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the exact keys and lists for days and sales.

2

Set the sales threshold

Create a variable called sales_threshold and set it to 100. This will be the cutoff to decide if sales are 'High' or 'Low'.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread_Sales': [120, 80, 150, 90, 200]
})

# Create sales_threshold variable and set it to 100
# Your code here

Hint

Just create a variable named sales_threshold and assign it the value 100.

3

Write a custom function and apply it

Define a function called classify_sales that takes one argument sales. It should return the string 'High' if sales is greater than or equal to sales_threshold, otherwise return 'Low'.
Then, use apply() on the Bread_Sales column of sales_data with classify_sales to create a new column called Sales_Level.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread_Sales': [120, 80, 150, 90, 200]
})

sales_threshold = 100

# Define classify_sales function
# Use apply() on Bread_Sales to create Sales_Level column
# Your code here

Hint

Write a function with an if-else to check sales against sales_threshold. Then use apply() on the Bread_Sales column to create Sales_Level.

4

Print the updated DataFrame

Write a print statement to display the sales_data DataFrame with the new Sales_Level column.

Data Analysis Python

import pandas as pd

sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread_Sales': [120, 80, 150, 90, 200]
})

sales_threshold = 100

def classify_sales(sales):
    if sales >= sales_threshold:
        return 'High'
    else:
        return 'Low'

sales_data['Sales_Level'] = sales_data['Bread_Sales'].apply(classify_sales)

# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to show the full DataFrame with the new column.

Practice

1. What does the apply() function do in a pandas DataFrame?

easy

A. It merges two DataFrames together.

B. It sorts the DataFrame by a column.

C. It filters rows based on a condition.

D. It applies a custom function to each row or column.

Solution

1. Step 1: Understand the purpose of apply()

   The apply() function is used to run a custom function on each row or column of a DataFrame.

2. Step 2: Compare with other DataFrame methods

   Sorting, filtering, and merging are different operations and not done by apply().

3. Final Answer:

   It applies a custom function to each row or column. -> Option D

4. Quick Check:

   apply() = custom function application [OK]

Hint: Remember: apply() runs your function on rows or columns [OK]

Common Mistakes:

• Confusing apply() with sort or filter methods
• Thinking apply() merges DataFrames
• Assuming apply() only works on columns

2. Which of the following is the correct syntax to apply a function f row-wise on a DataFrame df?

easy

A. df.apply(f, axis=0)

B. df.apply(f)

C. df.apply(f, axis=1)

D. df.apply(f, axis=2)

Solution

1. Step 1: Recall axis parameter meaning

   In pandas, axis=1 means apply function row-wise, while axis=0 means column-wise.

2. Step 2: Identify correct axis for row-wise

   Since the question asks for row-wise application, axis=1 is correct.

3. Final Answer:

   df.apply(f, axis=1) -> Option C

4. Quick Check:

   Row-wise apply = axis=1 [OK]

Hint: Use axis=1 for rows, axis=0 for columns [OK]

Common Mistakes:

• Using axis=0 for row-wise apply
• Omitting axis parameter and expecting row-wise
• Using invalid axis values like 2

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

What is the output of:

df['Sum'] = df.apply(lambda row: row['A'] + row['B'], axis=1)
print(df)

medium

A. A B Sum
0 1 3 3
1 2 4 4

B. A B Sum
0 1 3 4
1 2 4 6

C. A B Sum
0 1 3 1
1 2 4 2

D. Error: axis parameter missing

Solution

1. Step 1: Understand the lambda function

   The lambda adds values from columns 'A' and 'B' for each row.

2. Step 2: Apply row-wise addition

   Row 0: 1 + 3 = 4; Row 1: 2 + 4 = 6.

3. Final Answer:

   A B Sum 0 1 3 4 1 2 4 6 -> Option B

4. Quick Check:

   Sum column = A + B per row [OK]

Hint: Sum columns row-wise with apply and axis=1 [OK]

Common Mistakes:

• Forgetting axis=1 causes wrong operation
• Adding columns incorrectly (e.g., column-wise)
• Expecting apply to work without lambda

4. What is wrong with this code snippet?

df = pd.DataFrame({'X': [10, 20], 'Y': [30, 40]})
df['Result'] = df.apply(lambda x: x['X'] * 2 if x['Y'] > 35 else x['X'], axis=0)

medium

A. Using axis=0 applies function column-wise, causing a KeyError.

B. Lambda function syntax is incorrect.

C. DataFrame is missing required columns.

D. No error; code runs correctly.

Solution

1. Step 1: Check axis parameter usage

   axis=0 applies function to each column (Series), not rows.

2. Step 2: Understand lambda function context

   Lambda expects row with keys 'X' and 'Y', but column Series only has index labels, causing KeyError.

3. Final Answer:

   Using axis=0 applies function column-wise, causing a KeyError. -> Option A

4. Quick Check:

   axis=0 = columns, lambda expects rows [OK]

Hint: Use axis=1 for row-wise logic to avoid key errors [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting row keys when applying column-wise
• Ignoring error messages about missing keys

5. You have a DataFrame df with columns "score" and "passed" (boolean). You want to create a new column "grade" where:
- If passed is True and score >= 90, grade is 'A'
- If passed is True and score >= 75 but < 90, grade is 'B'
- Otherwise, grade is 'F'
Which apply() function correctly implements this logic?

hard

A. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1)

B. df['grade'] = df.apply(lambda r: 'A' if r['score'] >= 90 else 'B' if r['score'] >= 75 else 'F', axis=0)

C. df['grade'] = df.apply(lambda r: 'A' if r['passed'] or r['score'] >= 90 else 'F', axis=1)

D. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] > 90 else 'B', axis=1)

Solution

1. Step 1: Check axis parameter for row-wise logic

   axis=1 is needed to apply function on each row.

2. Step 2: Verify conditional logic matches requirements

   df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1) correctly checks if passed is True and score thresholds for 'A' and 'B', else 'F'.

3. Step 3: Identify errors in other options

   df['grade'] = df.apply(lambda r: 'A' if r['score'] >= 90 else 'B' if r['score'] >= 75 else 'F', axis=0) uses axis=0 (wrong axis). df['grade'] = df.apply(lambda r: 'A' if r['passed'] or r['score'] >= 90 else 'F', axis=1) uses 'or' instead of 'and'. df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] > 90 else 'B', axis=1) misses 'F' grade and uses wrong comparison for 90.

4. Final Answer:

   df['grade'] = df.apply(lambda r: 'A' if r['passed'] and r['score'] >= 90 else ('B' if r['passed'] and r['score'] >= 75 else 'F'), axis=1) -> Option A

5. Quick Check:

   Correct logic and axis=1 for row-wise apply [OK]

Hint: Use axis=1 and nested if-else for multi-condition grading [OK]

Common Mistakes:

• Using axis=0 instead of axis=1
• Using 'or' instead of 'and' in conditions
• Missing else condition for failing grade