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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 ]
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Python Dictionary Vs List With Examples

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Dictionary and List we use interchangeably in Python to store values. For beginners, both look the same. In reality, they both differ. Here are the differences. Dictionary Vs Lists Values in lists are accessed by means of integers called indices, which indicate where in the list a given value is found. Dictionaries access values by means of integers, strings, or other Python objects called keys , which indicate where in the dictionary a given value is found.  In other words, both lists and dictionaries provide indexed access to arbitrary values, but the set of items that can be used as dictionary indices is much larger than, and contains, the set of items that can be used as list indices.   Also, the mechanism that dictionaries use to provide indexed access is quite different from that used by lists. Both lists and dictionaries can store objects of any type. Values stored in a list are implicitly ordered by their positions in the list because the indices that access these values are c
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