Final answer:
Non-existent values are represented by NULL in SQL and NaN in Pandas.
The correct answer is A) NULL in SQL, NaN in Pandas.
Step-by-step explanation:
In SQL, the non-existent values are represented by the keyword NULL. NULL is a special marker used in SQL to indicate that a data value does not exist in the database. It is typically used to denote missing or unknown values.
In Pandas, the non-existent values are represented by NaN, which stands for Not a Number. NaN is a floating-point representation of missing or undefined data in Pandas. It is often used to indicate the absence of a numerical value or a null value in the data.
In both Pandas and SQL, non-existent or missing values are represented using different terms. In Pandas, NaN (which stands for Not a Number) is used to denote missing data. Conversely, in SQL, the keyword NULL is used to represent the absence of a value in a database.
The correct answer to how non-existent values are represented in Pandas and SQL is: A) NULL in SQL, NaN in Pandas.