I said "simplifying a bit" because in reality map() can process more than one iterable: Simplifying a bit, you can imagine map() doing something like this: def mymap(func, lst):Īs you can see, it takes a function and a list, and returns a new list with the result of applying the function to each of the elements in the input list. But also note again that a list comprehension is usually preferred: > Note that map(list, a) is equivalent in Python 2, but in Python 3 you need the list call if you want to do anything other than feed it into a for loop (or a processing function such as sum that only needs an iterable, and not a sequence). ('h', 'e', 'l', 'l', 'o', ',', ' ', 'w', 'o', 'r', 'l', 'd')Ī use of map here would be if you start with a list of strings instead of a single string - map can listify all of them individually: > a = Map in Python 3 is equivalent to this: def map(func, iterable):Īnd the only difference in Python 2 is that it will build up a full list of results to return all at once instead of yielding.Īlthough Python convention usually prefers list comprehensions (or generator expressions) to achieve the same result as a call to map, particularly if you're using a lambda expression as the first argument: Īs an example of what you asked for in the comments on the question - "turn a string into an array", by 'array' you probably want either a tuple or a list (both of them behave a little like arrays from other languages) - > a = "hello, world" Map doesn't relate to a Cartesian product at all, although I imagine someone well versed in functional programming could come up with some impossible to understand way of generating a one using map. The reference definitions is also hard to understand. I want to fully understand this function. What effect does putting a tuple anywhere in there have? I also noticed that without the map function the output is abc and with it, it's a, b, c. What role does this play in making a Cartesian product? content = map(tuple, array) The iterable arguments may be a sequence or any iterable object the Multiple arguments, map() returns a list consisting of tuplesĬontaining the corresponding items from all iterables (a kind of If function is None, the identity function is assumed if there are If one iterable is shorter than another it is assumed to be extended Take that many arguments and is applied to the items from all If additional iterable arguments are passed, function must )Īpply function to every item of iterable and return a list of the All of this happens behind-the-scenes.įor example, let’s say you had an array of numbers, and you wanted to create a new array with the numbers doubled.The Python 2 documentation says: Built-in Functions: map(function, iterable. It loops through each item in the original array, transforms it in some way, and then pushes it into a new array. The Array.map() method creates a new array from an existing one. Or at the very least, you’ll be able to read and understand them better. My hope for the next few days is that by examining what these methods actually do, you’ll find them more enjoyable to use than the forEach() equivalents. Ironically, they often make things harder to read for beginners, because they hide what’s really happening behind the scenes. Array methods like map(), filter(), find(), and reduce() are considered syntactic sugar. It’s a term that describes syntax (ways of writing code) that provide shortcuts, and make the code easier to write or read. You may have heard the term syntactic sugar before. Over the next few days, I wanted to take a look at a few array methods and break down how they work by comparing them to their Array.forEach() equivalents. I find that my students often find Array.forEach() easier to understand than some of the other array methods (like map(), filter(), and so on).
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