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Map() in Python | Python for Data Science | Day 13

Welcome to our tutorial on Map() in Python. This article will cover various instances to demonstrate how map() in Python can be used in various instances. The map() function has two parameters –  an iterable object (to be iterated upon) and a function (to implement on the iterable object). Thus, eliminating the need for a loop.

Map() in Python

The main reason why we use map() is simple. We just wish to apply the function to all the provided iterables. So, let’s begin by first creating a function.

This function i.e. func2() returns the uppercase of the argument passed. So, without any delay, let’s use a map() function in conjunction with it.

Map() in Python

As explained above, the first argument is map() is the function to be used and the second argument is the iterable object. The iterable object passed in this instance is a tuple which consists of three strings. We wish to apply the function on each of these strings. So, ideally, the output should be:

[THE, DATA, MONK]

Let’s print “a” and see our output.

Map() in Python

This is certainly not what we expected. This is because a map object can not really be printed. So, lets convert it into a list first and then compare it with the output we expected.

Alright, let’s look at another example.

Let’s define another function.

The function – func() takes two arguments and returns their sum.

Now, we will use it in a map() function.

Great. We see the sum of two tuples passed to the map() function. Let’s try the same function, but this time with tuples of strings.

So, the list is shown as displayed above.

You can also read about map() here: https://realpython.com/python-map-function/#:~:text=Python’s%20map()%20is%20a,them%20into%20a%20new%20iterable.

Map() is a fairly easy but extremely useful concept in function. So, to test the knowledge you have acquired so far, from this tutorial, we have curated a set of questions for you along with the solutions. We highly recommend that you attempt the questions below on your own without seeking help. 

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Article By,
Yashika Khurana
Intern | The Data Monk

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