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Python map() and lambda() Use Cases and Examples

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 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work. Python map and lambda top use cases 1. Using map() with lambda The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list). Example: Doubling Numbers numbers = [ 1 , 2 , 3 , 4 , 5 ] doubled = list ( map ( lambda x: x * 2 , numbers)) print (doubled) # Output: [2, 4, 6, 8, 10] 2. Using map() to Convert Data Types Example: Converting Strings to Integers string_numbers = [ "1" , "2" , "3" , "4" , "5" ] integers = list ( map ( lambda x: int (x), string_numbers)) print (integers) # Output: [1, 2, 3, 4, 5] 3. Using map() with Multiple Iterables You can also use map() with more than one iterable. The lambda function can take multiple arguments. Example: Adding Two Lists Element-wise list1 = [ 1 , 2 , 3 ]

Machine Learning The Best Book for Beginners

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You need a mix of different technologies for Data Science projects. Instead of learning many skills, just learn a few. The four main steps of an ML project are extracting the data, model development, artificial intelligence, and presentation. Attending interviews with many skills is not so easy. So keep the skills short. Best Machine Learning Book for Beginners A person with many skills can't perform all the work. You had better learn a few skills like Python, MATLAB, Tableau, and RDBMS. So that you can get a job quickly in the data-science project. Out of Data Science skills, Machine learning is a new concept. Why because you can learn Python, like any other language. Tableau also the same. Here is the area that needs your 60% effort in Machine learning.   Machine Learning The Best Book. Related Posts How to write multiple IF-conditions in Python Simplified