Map() in Python | Python for Data Science

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.

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.

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.

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.

The Data Monk Interview Books – Don’t Miss

Now we are also available on our website where you can directly download the PDF of the topic you are interested in. At Amazon, each book costs ~299, on our website we have put it at a 60-80% discount. There are ~4000 solved interview questions prepared for you.

10 e-book bundle with 1400 interview questions spread across SQL, Python, Statistics, Case Studies, and Machine Learning Algorithms – Ideal for 0-3 years experienced candidates

23 E-book with ~2000 interview questions spread across AWS, SQL, Python, 10+ ML algorithms, MS Excel, and Case Studies – Complete Package for someone between 0 to 8 years of experience (The above 10 e-book bundle has a completely different set of e-books)

12 E-books for 12 Machine Learning algorithms with 1000+ interview questions – For those candidates who want to include any Machine Learning Algorithm in their resume and to learn/revise the important concepts. These 12 e-books are a part of the 23 e-book package

Individual 50+ e-books on separate topics

Important Resources to crack interviews (Mostly Free)

There are a few things which might be very useful for your preparation

The Data Monk Youtube channel – Here you will get only those videos that are asked in interviews for Data Analysts, Data Scientists, Machine Learning Engineers, Business Intelligence Engineers, Analytics Manager, etc.
Go through the watchlist which makes you uncomfortable:-

All the list of 200 videos
Complete Python Playlist for Data Science
Company-wise Data Science Interview Questions – Must Watch
All important Machine Learning Algorithm with code in Python
Complete Python Numpy Playlist
Complete Python Pandas Playlist
SQL Complete Playlist
Case Study and Guesstimates Complete Playlist
Complete Playlist of Statistics