Why custom functions matter in Pandas - See It in Action

Why custom functions matter

📖 Scenario: You work in a small store that tracks daily sales. You want to analyze sales data to find out which days had high sales and which had low sales. To do this efficiently, you will use a custom function to label each day's sales as 'High' or 'Low'.

🎯 Goal: Build a small pandas program that uses a custom function to label sales as 'High' or 'Low' based on a sales threshold.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Define a sales threshold variable

Write a custom function to label sales as 'High' or 'Low'

Apply the custom function to the DataFrame

Print the updated DataFrame

💡 Why This Matters

🌍 Real World

Stores and businesses often analyze sales data to understand performance. Custom functions help automate labeling and categorizing data quickly.

💼 Career

Data analysts and scientists use custom functions in pandas to clean, transform, and analyze data efficiently in real jobs.

1

Create the sales DataFrame

Create a pandas DataFrame called sales_data with two columns: 'Day' and 'Sales'. Use these exact values: Days are 'Mon', 'Tue', 'Wed', 'Thu', 'Fri'. Sales are 200, 150, 300, 100, 250 respectively.

Pandas

import pandas as pd
# Create the sales_data DataFrame with the given days and sales
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the two columns.

2

Set the sales threshold

Create a variable called threshold and set it to 200. This will be the cutoff to decide if sales are high or low.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 150, 300, 100, 250]
})
# Create the threshold variable and set it to 200
# Your code here

Hint

Just assign the number 200 to the variable threshold.

3

Write the custom function to label sales

Define a function called label_sales that takes one argument sale. The function should return the string 'High' if sale is greater than or equal to threshold, otherwise return 'Low'. Then, create a new column in sales_data called 'Label' by applying the label_sales function to the 'Sales' column.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 150, 300, 100, 250]
})
threshold = 200
# Define the label_sales function
# Apply label_sales to sales_data['Sales'] and create sales_data['Label']
# Your code here

Hint

Use def to create the function. Use apply() to run it on the sales column.

4

Print the updated DataFrame

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

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
    'Sales': [200, 150, 300, 100, 250]
})
threshold = 200
def label_sales(sale):
    if sale >= threshold:
        return 'High'
    else:
        return 'Low'
sales_data['Label'] = sales_data['Sales'].apply(label_sales)
# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to show the full table.

Practice

1. Why do we use custom functions when working with pandas DataFrames?

easy

A. To reuse code and make data changes clear and simple

B. To make the DataFrame larger automatically

C. To delete all data quickly

D. To change the pandas version

Solution

1. Step 1: Understand the purpose of custom functions

   Custom functions let us write a piece of code once and use it many times, which saves effort.

2. Step 2: Connect to pandas usage

   In pandas, custom functions help apply the same change or calculation to many rows or columns easily and clearly.

3. Final Answer:

   To reuse code and make data changes clear and simple -> Option A

4. Quick Check:

   Custom functions improve code reuse and clarity [OK]

Hint: Custom functions simplify repeated data changes [OK]

Common Mistakes:

• Thinking custom functions automatically change DataFrame size
• Believing custom functions delete data
• Confusing custom functions with pandas version control

2. Which of the following is the correct way to define a simple custom function in Python to double a number?

easy

A. function double(x) { return x * 2 }

B. def double x: return x * 2

C. def double(x): return x * 2

D. double = x => x * 2

Solution

1. Step 1: Identify Python function syntax

   Python functions start with 'def', followed by the function name and parentheses with parameters.

2. Step 2: Check each option

   def double(x): return x * 2 uses correct Python syntax. function double(x) { return x * 2 } is JavaScript syntax. def double x: return x * 2 misses parentheses. double = x => x * 2 is JavaScript arrow function syntax.

3. Final Answer:

   def double(x): return x * 2 -> Option C

4. Quick Check:

   Python function syntax = def name(params): [OK]

Hint: Python functions start with 'def' and use parentheses [OK]

Common Mistakes:

• Using JavaScript syntax in Python
• Omitting parentheses in function definition
• Confusing arrow functions with Python syntax

3. Given the code below, what will be the output of df['new']?

import pandas as pd
def add_five(x):
    return x + 5
df = pd.DataFrame({'num': [1, 2, 3]})
df['new'] = df['num'].apply(add_five)
print(df['new'])

medium

A. [1, 2, 3]

B. [6, 7, 8]

C. [5, 6, 7]

D. Error: add_five is not defined

Solution

1. Step 1: Understand the function add_five

   The function adds 5 to the input number.

2. Step 2: Apply the function to each element in 'num'

   Applying add_five to [1, 2, 3] results in [6, 7, 8].

3. Final Answer:

   [6, 7, 8] -> Option B

4. Quick Check:

   1+5=6, 2+5=7, 3+5=8 [OK]

Hint: Apply adds function result to each element [OK]

Common Mistakes:

• Thinking apply changes original column without assignment
• Confusing function name or missing definition
• Expecting original values without addition

4. What is wrong with this code snippet?

import pandas as pd
def multiply_by_two(x):
    x * 2
df = pd.DataFrame({'val': [4, 5, 6]})
df['result'] = df['val'].apply(multiply_by_two)
print(df['result'])

medium

A. The DataFrame is not imported correctly

B. The DataFrame is missing a column

C. apply() cannot be used with custom functions

D. The function multiply_by_two does not return a value

Solution

1. Step 1: Check the function multiply_by_two

   The function calculates x * 2 but does not return the result.

2. Step 2: Understand apply behavior

   apply expects the function to return a value to create the new column. Without return, it returns None for each row.

3. Final Answer:

   The function multiply_by_two does not return a value -> Option D

4. Quick Check:

   Functions must return values for apply to work [OK]

Hint: Always return a value in your custom function [OK]

Common Mistakes:

• Forgetting the return statement in functions
• Assuming apply works without returned values
• Thinking DataFrame import affects apply

5. You have a DataFrame with a column 'score' containing numbers. You want to create a new column 'grade' that assigns 'Pass' if the score is 50 or more, and 'Fail' otherwise. Which custom function and pandas code correctly achieve this?

hard

A. def grade_func(x): return 'Pass' if x >= 50 else 'Fail'
df['grade'] = df['score'].apply(grade_func)

B. def grade_func(x): if x >= 50: 'Pass' else: 'Fail'
df['grade'] = df['score'].apply(grade_func)

C. def grade_func(x): return x >= 50 or 'Pass' and 'Fail'
df['grade'] = df['score'].apply(grade_func)

D. def grade_func(x): return 'Pass' if x > 50 else 'Fail'
df['grade'] = df['score'].apply(grade_func)

Solution

1. Step 1: Check the function logic for grading

   def grade_func(x): return 'Pass' if x >= 50 else 'Fail' df['grade'] = df['score'].apply(grade_func) correctly returns 'Pass' if x is 50 or more, else 'Fail'. def grade_func(x): if x >= 50: 'Pass' else: 'Fail' df['grade'] = df['score'].apply(grade_func) misses return statement. def grade_func(x): return x >= 50 or 'Pass' and 'Fail' df['grade'] = df['score'].apply(grade_func) uses incorrect logical operators, resulting in True or 'Fail'. def grade_func(x): return 'Pass' if x > 50 else 'Fail' df['grade'] = df['score'].apply(grade_func) uses > 50, which excludes 50.

2. Step 2: Confirm pandas apply usage

   All options use apply correctly, but only def grade_func(x): return 'Pass' if x >= 50 else 'Fail' df['grade'] = df['score'].apply(grade_func) has correct function logic and syntax.

3. Final Answer:

   def grade_func(x): return 'Pass' if x >= 50 else 'Fail' df['grade'] = df['score'].apply(grade_func) -> Option A

4. Quick Check:

   Use return with correct condition for grading [OK]

Hint: Use return and correct condition in custom function [OK]

Common Mistakes:

• Forgetting return in function
• Using wrong comparison operator
• Confusing logical expressions without return