How do map and filter functions work in data science?

How do map and filter functions work in data science?

In data science, map and filter are functions used to process data collections. Map applies a function to each item, while filter selects items based on a condition.

Can you give me an example of using map in Python?

Sure! In Python, you can use map like this: result = list(map(lambda x: x * 2, [1, 2, 3])) which doubles each number in the list.

What about filter? How is it different?

Filter is used to select items from a collection. For example: result = list(filter(lambda x: x > 1, [1, 2, 3])) returns [2, 3], filtering out numbers less than or equal to 1.

Are there any performance considerations when using these functions?

Yes, using map and filter can be more efficient than loops, especially with large datasets, as they are optimized for performance.