Data Science Prerequisites - Numpy, Matplotlib, and Pandas in Python - The apply() Function

The video tutorial explains the use of the apply function in Python to perform operations on data frames without using slow for loops. It provides an example of extracting the year from a date column and applying this operation to each row of a data frame. The tutorial also covers how to assign the result to a new column, demonstrating the efficiency and utility of the apply function.

What is the primary advantage of using the apply function in Python?

• It allows for faster execution by avoiding for loops.

• It automatically optimizes memory usage.

• It simplifies the syntax of Python code.

• It provides built-in error handling.

In the example provided, what is the purpose of the custom function 'date_to_year'?

• To format a date string into a different date format.

• To calculate the difference between two dates.

• To extract and convert the year from a date string into an integer.

• To convert a date string into a full date object.

What is the significance of setting 'axis=1' when using the apply function?

• It applies the function to each column.

• It applies the function to each row.

• It applies the function to the entire data frame.

• It applies the function to every alternate row.

After applying the custom function, what type of data structure is returned?

• A list of integers.

• A data frame with modified rows.

• A series containing only the year.

• A dictionary of date components.

How can the result of the apply function be stored in the data frame?

• By creating a new data frame.

• By appending it to the existing data frame.

• By assigning it to a new column in the data frame.

• By replacing the original data frame.