The task is to "flag" customers based on a specific criterion: whether the Month is 'January'. This requires an operation that evaluates a condition for each row and returns a result based on whether that condition is true or false. Logical functions, such as IF or CASE, are designed specifically for this purpose. They test a condition (e.g., Month = 'January') and return a corresponding value (e.g., 1 for true, 0 for false), effectively creating a flag.

A. Statistical: Statistical functions are used to perform aggregations on a set of data, such as calculating SUM, AVERAGE, or COUNT, not for evaluating a row-by-row condition.

C. Mathematical: Mathematical functions perform arithmetic calculations like addition or multiplication on numeric data. They are not used for evaluating conditional statements on text values.

D. Date: Date functions are used to manipulate or extract components from date/time data types. The operation here is a logical comparison of a text string, not a date manipulation.

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1. Microsoft Corporation. (n.d.). Logical functions in Data Analysis Expressions (DAX). Microsoft Learn. Retrieved from the official Microsoft documentation. The documentation for the IF function states

"The IF function checks a condition... and returns one value if the condition is TRUE

otherwise it returns a second value." This directly describes the process of flagging based on a condition.

2. The PostgreSQL Global Development Group. (2023). Chapter 9. Functions and Operators

Section 9.18. Conditional Expressions. PostgreSQL 16 Documentation. This official documentation describes the CASE expression

which "provide[s] a general conditional expression

similar to if/else statements." A common use is CASE WHEN month = 'January' THEN 'Yes' ELSE 'No' END

which is a direct implementation of the flagging logic required in the question.

3. VanderPlas

J. (2016). Python Data Science Handbook. O'Reilly Media. Chapter 3.02

"Data Indexing and Selection". In the context of data analysis with Python's Pandas library

this operation is achieved through boolean masking (e.g.

df['Month'] == 'January'). The handbook describes this as a logical operation that produces a boolean series

which can be used to flag

select

or filter data. This is a foundational concept taught in data science curricula.