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Just a reminder: Registration for the advanced (B2) cohort of Weekly Python Exercise, which will begin on July 2nd, will remain open for the next two weeks. BUT early-bird pricing ($80 for 15 weeks of Python exercises, solutions, and community) ends today — Tuesday, June 18th. If you want to sharpen your Python skills, then there’s no better way to do that than Weekly Python Exercise. And hey, if you’re going to sharpen your skills, why not do it at a discount? As of tomorrow, you’ll have to pay more for the same course. Learn more at https://WeeklyPythonExercise.com/ . 100% money-back guarantee if you aren’t satisfied — but I’m sure you’ll learn so much, and be able to solve so many new problems, that you won’t want to do that. The post Early-bird pricing for Weekly Python Exercise ends today! appeared first on Reuven Lerner.

europython: As you may know, the EuroPython Society (EPS) has extended it’s mission to not only run the EuroPython conference, but also provide help for the Python community in Europe in general. As part of this, we would like to get to know, and help create closer ties between organizers of other European Python events. Organizers’ Lunch We would like to invite representatives of all European Python conference to EuroPython 2019 to join us for an organizers’ lunch. We’re planing the lunch for Thursday or Friday. Details will be announced closer to the event. Our aim is to get to know each other, exchange experience in organizing events and to find out how we, as EPS, can most effectively help other conferences going forward. Free Tickets To support and facilitate this, we are giving out one free conference ticket per conference team, so that each team can send a representative to the organizers’ lunch. If your team wants to send someone to join, please write to board@europython.eu, mentioning the conference you’re organizing and some background on your team. Enjoy,–EuroPython Societyhttps://ep2019.europython.eu/  https://www.europython-society.org/

europython: The EuroPython Society (EPS) does not only run the EuroPython conference, but also aims to provide help for the Python community in Europe in general. Let’s all meet at EuroPython In addition to the Python Organizers Lunch (see previous post), which focuses on conference organizers, we are also establishing a program to support attendees of Python user groups and conferences in Europe. We’d like to invite all of you to EuroPython 2019 this year. Of course, we cannot give out free tickets to everyone, but we can at least recognize your participation in the Python community by giving out discounts for the conference. Discounts for EuroPython Tickets If you are running a Python event (conference or user group) in Europe, please reach out to board@europython.eu to request a coupon code for your group, which you can then pass on to your group members or attendees. If you are not running a user group or conference, but a regular attendee of one, please contact your organizers to have them submit a request. We can only distribute codes at the user group and conference organizer level. The coupon codes are valid for conference tickets bought starting today and will give you a 10% discount on the ticket price (both regular and late bird prices). The codes are setup for user group sizes of between 30-50 members, but we are also extending this to organizers and attendees of larger conferences. If you need a code valid for larger groups, please mention this in your email. Enjoy,–EuroPython Societyhttps://ep2019.europython.eu/  https://www.europython-society.org/

The EuroPython Society (EPS) does not only run the EuroPython conference, but also aims to provide help for the Python community in Europe in general.Let’s all meet at EuroPythonIn addition to the Python Organizers Lunch (see previous post), which focuses on conference organizers, we are also establishing a program to support attendees of Python user groups and conferences in Europe.We’d like to invite all of you to EuroPython 2019 this year. Of course, we cannot give out free tickets to everyone, but we can at least recognize your participation in the Python community by giving out discounts for the conference.Discounts for EuroPython TicketsIf you are running a Python event (conference or user group) in Europe, please reach out to board@europython.eu to request a coupon code for your group, which you can then pass on to your group members or attendees.If you are not running a user group or conference, but a regular attendee of one, please contact your organizers to have them submit a request. We can only distribute codes at the user group and conference organizer level. The coupon codes are valid for conference tickets bought starting today and will give you a 10% discount on the ticket price (both regular and late bird prices). The codes are setup for user group sizes of between 30-50 members, but we are also extending this to organizers and attendees of larger conferences. If you need a code valid for larger groups, please mention this in your email.Enjoy,–EuroPython Societyhttps://ep2019.europython.eu/  https://www.europython-society.org/

As you may know, the EuroPython Society (EPS) has extended it’s mission to not only run the EuroPython conference, but also provide help for the Python community in Europe in general.As part of this, we would like to get to know, and help create closer ties between organizers of other European Python events.Organizers’ LunchWe would like to invite representatives of all European Python conference to EuroPython 2019 to join us for an organizers’ lunch. We’re planing the lunch for Thursday or Friday. Details will be announced closer to the event.Our aim is to get to know each other, exchange experience in organizing events and to find out how we, as EPS, can most effectively help other conferences going forward.Free TicketsTo support and facilitate this, we are giving out one free conference ticket per conference team, so that each team can send a representative to the organizers’ lunch.If your team wants to send someone to join, please write to board@europython.eu, mentioning the conference you’re organizing and some background on your team.Enjoy,–EuroPython Societyhttps://ep2019.europython.eu/  https://www.europython-society.org/

Python’s for loops don’t work the way for loops do in other languages. In this article we’re going to dive into Python’s for loops to take a look at how they work under the hood and why they work the way they do. Note: This article is based on my Loop Better talk. It was originally published on opensource.com. Looping Gotchas We’re going to start off our journey by taking a look at some “gotchas”. After we’ve learned how looping works in Python, we’ll take another look at these gotchas and explain what’s going on. Gotcha 1: Looping Twice Let’s say we have a list of numbers and a generator that will give us the squares of those numbers: 1 2 >>> numbers = [1, 2, 3, 5, 7] >>> squares = (n**2 for n in numbers) We can pass our generator object to the tuple constructor to make a tuple out of it: 1 2 >>> tuple(squares) (1, 4, 9, 25, 49) If we then take the same generator object and pass it to the sum function we might expect that we’d get the sum of these numbers, which would be 88. 1 2 >>> sum(squares) 0 Instead we get 0. Gotcha 2: Containment Checking Let’s take the same list of numbers and the same generator object: 1 2 >>> numbers = [1, 2, 3, 5, 7] >>> squares = (n**2 for n in numbers) If we ask whether 9 is in our squares generator, Python will tell us that 9 is in squares. But if we ask the same question again, Python will tell us that 9 is not in squares. 1 2 3 4 >>> 9 in squares True >>> 9 in squares False We asked the same question twice and Python gave us two different answers. Gotcha 3: Unpacking This dictionary has two key-value pairs: 1 >>> counts = {'apples': 2, 'oranges': 1} Let’s unpack this dictionary using multiple assignment: 1 >>> x, y = counts You might expect that when unpacking this dictionary, we’ll get key-value pairs or maybe that we’ll get an error. But unpacking dictionaries doesn’t raise errors and it doesn’t return key-value pairs. When you unpack dictionaries you get keys: 1 2 >>> x 'apples' We’ll come back to these gotchas after we’ve learned a bit about the logic that powers these Python snippets. Review: Python’s for loop Python doesn’t have traditional for loops. To explain what I mean, let’s take a look at a for loop in another programming language. This is a traditional C-style for loop written in JavaScript: 1 2 3 4 let numbers = [1, 2, 3, 5, 7]; for (let i = 0; i < numbers.length; i += 1) { print(numbers[i]) } JavaScript, C, C++, Java, PHP, and a whole bunch of other programming languages all have this kind of for loop. But Python doesn’t. Python does not have traditional C-style for loops. We do have something that we call a for loop in Python, but it works like a foreach loop. This is Python’s flavor of for loop: 1 2 3 numbers = [1, 2, 3, 5, 7] for n in numbers: print(n) Unlike traditional C-style for loops, Python’s for loops don’t have index variables. There’s no index initializing, bounds checking, or index incrementing. Python’s for loops do all the work of looping over our numbers list for us. So while we do have for loops in Python, we do not have have traditional C-style for loops. The thing that we call a for loop works very differently. Definitions: Iterables and Sequences Now that we’ve addressed the index-free for loop in our Python room, let’s get some definitions out of the way now. An iterable is anything you can loop over with a for loop in Python. Iterables can be looped over and anything that can be looped over is an iterable. 1 2 for item in some_iterable: print(item) Sequences are a very common type of iterable. Lists, tuples, and strings are all sequences. 1 2 3 >>> numbers = [1, 2, 3, 5, 7] >>> coordinates = (4, 5, 7) >>> words = "hello there" Sequences are iterables which have a specific set of features. They can be indexed starting from 0 and ending at one less than the length of the sequence, they have a length, and they can be sliced. Lists, tuples, strings and all other sequences work this way. 1 2 3 4 5 6 >>> numbers[0] 1 >>> coordinates[2] 7 >>> words[4] 'o' Lots of things in Python are iterables, but not all iterables are sequences. Sets, dictionaries, files, and generators are all iterables but none of these things are sequences. 1 2 3 4 >>> my_set = {1, 2, 3} >>> my_dict = {'k1': 'v1', 'k2': 'v2'} >>> my_file = open('some_file.txt') >>> squares = (n**2 for n in my_set) So anything that can be looped over with a for loop is an iterable and sequences are one type of iterable but Python has many other kinds of iterables as well. Python’s for loops don’t use indexes You might think that under the hood, Python’s for loops use indexes to loop. Here we’re manually looping over an iterable using a while loop and indexes: 1 2 3 4 5 numbers = [1, 2, 3, 5, 7] i = 0 while i < len(numbers): print(numbers[i]) i += 1 This works for lists, but it won’t work everything. This way of looping only works for sequences. If we try to manually loop over a set using indexes, we’ll get an error: 1 2 3 4 5 6 7 8 9 >>> fruits = {'lemon', 'apple', 'orange', 'watermelon'} >>> i = 0 >>> while i < len(fruits): ... print(fruits[i]) ... i += 1 ... Traceback (most recent call last): File "<stdin>", line 2, in <module> TypeError: 'set' object does not support indexing Sets are not sequences so they don’t support indexing. We cannot manually loop over every iterable in Python by using indexes. This simply won’t work for iterables that aren’t sequences. Iterators power for loops So we’ve seen that Python’s for loops must not be using indexes under the hood. Instead, Python’s for loops use iterators. Iterators are the things that power iterables. You can get an iterator from any iterable. And you can use an iterator to manually loop over the iterable it came from. Let’s take a look at how that works. Here are three iterables: a set, a tuple, and a string. 1 2 3 >>> numbers = {1, 2, 3, 5, 7} >>> coordinates = (4, 5, 7) >>> words = "hello there" We can ask each of these iterables for an iterator using Python’s built-in iter function. Passing an iterable to the iter function will always give us back an iterator, no matter what type of iterable we’re working with. 1 2 3 4 5 6 >>> iter(numbers) <set_iterator object at 0x7f2b9271c860> >>> iter(coordinates) <tuple_iterator object at 0x7f2b9271ce80> >>> iter(words) <str_iterator object at 0x7f2b9271c860> Once we have an iterator, the one thing we can do with it is get its next item by passing it to the built-in next function. 1 2 3 4 5 6 >>> numbers = [1, 2, 3] >>> my_iterator = iter(numbers) >>> next(my_iterator) 1 >>> next(my_iterator) 2 Iterators are stateful, meaning once you’ve consumed an item from them it’s gone. If you ask for the next item from an iterator and there are no more items, you’ll get a StopIteration exception: 1 2 3 4 5 6 >>> next(iterator) 3 >>> next(iterator) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration So you can get an iterator from every iterable. And the only thing that you can do with iterators is ask them for their next item using the next function. And if you pass them to next but they don’t have a next item, a StopIteration exception will be raised. Hello Kitty PEZ dispenser photo by Deborah Austin / CC BY You can think of iterators as like Hello Kitty PEZ dispensers that cannot be reloaded. You can take PEZ out, but once a PEZ is removed it can’t be put back and once the dispenser is empty, it’s useless. Looping without a for loop Now that we’ve learned about iterators and the iter and next functions, we’re going to try manually looping over an iterable without using a for loop. We’ll do so by attempting to turn this for loop into a while loop: 1 2 3 def funky_for_loop(iterable, action_to_do): for item in iterable: action_to_do(item) To do this we’ll: Get an iterator from the given iterable Repeatedly get the next item from the iterator Execute the body of the for loop if we successfully got the next item Stop our loop if we got a StopIteration exception while getting the next item 1 2 3 4 5 6 7 8 9 10 def funky_for_loop(iterable, action_to_do): iterator = iter(iterable) done_looping = False while not done_looping: try: item = next(iterator) except StopIteration: done_looping = True else: action_to_do(item) We’ve just re-invented a for loop by using a while loop and iterators. The above code pretty much defines the way looping works under the hood in Python. If you understand the way the built-in iter and next functions work for looping over things, you understand how Python’s for loops work. In fact you’ll understand a little bit more than just how for loops work in Python. All forms of looping over iterables work this way. The iterator protocol is a fancy way of saying “how looping over iterables works in Python”. It’s essentially the definition of the way the iter and next functions work in Python. All forms of iteration in Python are powered by the iterator protocol. The iterator protocol is used by for loops (as we’ve already seen): 1 2 for n in numbers: print(n) Multiple assignment also uses the iterator protocol: 1 x, y, z = coordinates Star expressions use the iterator protocol: 1 2 a, b, *rest = numbers print(*numbers) And many built-in functions rely on the iterator protocol: 1 unique_numbers = set(numbers) Anything in Python that works with an iterable probably uses the iterator protocol in some way Any time you’re looping over an iterable in Python, you’re relying on the iterator protocol. Generators are iterators So you might be thinking: iterators seem cool, but they also just seem like an implementation detail and we might not need to care about them as users of Python. I have news for you: it’s very common to work directly with iterators in Python. The squares object here is a generator: 1 2 >>> numbers = [1, 2, 3] >>> squares = (n**2 for n in numbers) And generators are iterators, meaning you can call next on a generator to get its next item: 1 2 3 4 >>> next(squares) 1 >>> next(squares) 4 But if you’ve ever used a generator before, you probably know that you can also loop over generators: 1 2 3 4 5 6 7 >>> squares = (n**2 for n in numbers) >>> for n in squares: ... print(n) ... 1 4 9 If you can loop over something in Python, it’s an iterable. So generators are iterators, but generators are also iterables. What’s going on here? I lied to you So when I explained how iterators worked earlier, I skipped over an important detail about them. Iterators are iterables. I’ll say that again: every iterator in Python is also an iterable, which means you can loop over iterators. Because iterators are also iterables, you can get an iterator from an iterator using the built-in iter function: 1 2 3 >>> numbers = [1, 2, 3] >>> iterator1 = iter(numbers) >>> iterator2 = iter(iterator1) Remember that iterables give us iterators when we call iter on them. When we call iter on an iterator it will always give us itself back: 1 2 >>> iterator1 is iterator2 True Iterators are iterables and all iterators are their own iterators. 1 2 def is_iterator(iterable): return iter(iterable) is iterable Confused yet? Let’s recap these terms. An iterable is something you’re able to iterate over. An iterator is the agent that actually does the iterating over an iterable. Additionally, in Python iterators are also iterables and they act as their own iterators. So iterators are iterables, but they don’t have the variety of features that some iterables have. Iterators have no length and they can’t be indexed: 1 2 3 4 5 6 >>> numbers = [1, 2, 3, 5, 7] >>> iterator = iter(numbers) >>> len(iterator) TypeError: object of type 'list_iterator' has no len() >>> iterator[0] TypeError: 'list_iterator' object is not subscriptable From our perspective as Python programmers, the only useful things you can do with an iterator are pass it to the built-in next function or loop over it: 1 2 3 4 >>> next(iterator) 1 >>> list(iterator) [2, 3, 5, 7] And if we loop over an iterator a second time, we’ll get nothing back: 1 2 >>> list(iterator) [] You can think of iterators are lazy iterables that are single-use, meaning they can be looped over one time only. Object Iterable? Iterator? Iterable ✔️ ❓ Iterator ✔️ ✔️ Generator ✔️ ✔️ List ✔️ ❌ As you can see in the truth table above, iterables are not always iterators but iterators are always iterables: The iterator protocol, in full Let’s define how iterators work from Python’s perspective. Iterables can be passed to the iter function to get an iterator for them. Iterators: Can be passed to the next function which will give their next item or raise a StopIteration exception if there are no more items Can be passed to the iter function and will return themselves back The inverse of these statements also hold true: Anything that can be passed to iter without a TypeError is an iterable Anything that can be passed to next without a TypeError is an iterator Anything that returns itself when passed to iter is an iterator That’s the iterator protocol in Python. Iterators enable laziness Iterators allow us to both work with and create lazy iterables that don’t do any work until we ask them for their next item. Because we can create lazy iterables, we can make infinitely long iterables. And we can create iterables that are conservative with system resources, that can save us memory and can save us CPU time. Iterators are everywhere You’ve already seen lots of iterators in Python. I’ve already mentioned that generators are iterators. Many of Python’s built-in classes are iterators also. For example Python’s enumerate and reversed objects are iterators. 1 2 3 4 5 6 >>> letters = ['a', 'b', 'c'] >>> e = enumerate(letters) >>> e <enumerate object at 0x7f112b0e6510> >>> next(e) (0, 'a') In Python 3, zip, map, and filter objects are iterators too. 1 2 3 4 5 6 7 >>> numbers = [1, 2, 3, 5, 7] >>> letters = ['a', 'b', 'c'] >>> z = zip(numbers, letters) >>> z <zip object at 0x7f112cc6ce48> >>> next(z) (1, 'a') And file objects in Python are iterators also. 1 2 >>> next(open('hello.txt')) 'hello world\n' There are lots of iterators bult-in to Python, in the standard library, and in third-party Python libraries. These iterators all act like lazy iterables by delaying work until the moment you ask them for their next item. Creating your own iterator It’s useful to know that you’re already using iterators, but I’d like you to also know that you can create your own iterators and your own lazy iterables. This class makes an iterator that accepts an iterable of numbers and provides squares of each of the numbers as it’s looped over. 1 2 3 4 5 6 7 class square_all: def __init__(self, numbers): self.numbers = iter(numbers) def __next__(self): return next(self.numbers) ** 2 def __iter__(self): return self But no work will be done until we start looping over an instance of this class. Here we have an infinitely long iterable count and you can see that square_all accepts count without fully looping over this infinitely long iterable: 1 2 3 4 5 6 7 >>> from itertools import count >>> numbers = count(5) >>> squares = square_all(numbers) >>> next(squares) 25 >>> next(squares) 36 This iterator class works, but we don’t usually make iterators this way. Usually when we want to make a custom iterator, we make a generator function: 1 2 3 def square_all(numbers): for n in numbers: yield n**2 This generator function is equivalent to the class we made above and it works essentially the same way. That yield statement probably seem magical, but it is very powerful: yield allows us to put our generator function on pause between calls from the next function. The yield statement is the thing that separates generator functions from regular functions. Another way we could implement this same iterator is with a generator expression. 1 2 def square_all(numbers): return (n**2 for n in numbers) This does the same thing as our generator function but it uses a syntax that looks like a list comprehension. If you need to make a lazy iterable in your code, think of iterators and consider making a generator function or a generator expression. How iterators can improve your code Once you’ve embraced the idea of using lazy iterables in your code, you’ll find that there are lots of possibilities for discovering or creating helper functions that assist you in looping over iterables and processing data. Laziness and summing This is a for loop that sums up all billable hours in a Django queryset: 1 2 3 4 hours_worked = 0 for event in events: if event.is_billable(): hours_worked += event.duration Here is code that does the same thing using a generator expression for lazy evaluation: 1 2 3 4 5 6 7 billable_times = ( event.duration for event in events if event.is_billable() ) hours_worked = sum(billable_times) Notice that the shape of our code has changed dramatically. Turning our billable times into a lazy iterable has allowed us to name something (billable_times) that was previously unnamed. This has also allowed us to use the sum function. We couldn’t have used sum before because we didn’t even have an iterable to pass to it. Iterators allow you to fundamentally change the way you structure your code. Laziness and breaking out of loops This code prints out the first ten lines of a log file: 1 2 3 4 for i, line in enumerate(log_file): if i >= 10: break print(line) This code does the same thing, but we’re using the itertools.islice function to lazily grab the first 10 lines of our file as we loop: 1 2 3 4 5 from itertools import islice first_ten_lines = islice(log_file, 10) for line in first_ten_lines: print(line) The first_ten_lines variable we’ve made is an iterator. Again using an iterator allowed us to give a name to something (first ten lines) that was previously unnamed. Naming things can make our code more descriptive and more readable. As a bonus we also removed the need for a break statement in our loop because the islice utility handles the breaking for us. You can find many more iteration helper functions in itertools in the standard library as well as in third-party libraries such as boltons and more-itertools. Creating your own iteration helpers You can find helper functions for looping in the standard library and in third-party libraries, but you can also make your own! This code makes a list of the differences between consecutive values in a sequence. 1 2 3 4 current = readings[0] for next_item in readings[1:]: differences.append(next_item - current) current = next_item Notice that this code has an extra variable that we need to assign each time we loop. Also note that this code only works with things we can slice, like sequences. If readings were a generator, a zip object, or any other type of iterator this code would fail. Let’s write a helper function to fix our code. This is a generator function that gives us the current item and the item following it for every item in a given iterable: 1 2 3 4 5 6 7 def with_next(iterable): """Yield (current, next_item) tuples for each item in iterable.""" iterator = iter(iterable) current = next(iterator) for next_item in iterator: yield current, next_item current = next_item We’re manually getting an iterator from our iterable, calling next on it to grab the first item, and then looping over our iterator to get all subsequent items, keeping track of our last item along the way. This function works not just with sequences, but with any type of iterable This is the same code but we’re using our helper function instead of manually keeping track of next_item: 1 2 3 differences = [] for current, next_item in with_next(readings): differences.append(next_item - current) Notice that this code doesn’t have awkward assignments to next_item hanging around our loop. The with_previous generator function handles the work of keeping track of next_item for us. Also note that this code has been compacted enough that we could even copy-paste our way into a list comprehension if we wanted to. 1 2 3 4 differences = [ (next_item - current) for current, next_item in with_next(readings) ] Looping Gotchas: Revisited At this point we’re ready to jump back to those odd examples we saw earlier and try to figure out what was going on. Gotcha 1: Exhausting an Iterator Here we have a generator object, squares: 1 2 >>> numbers = [1, 2, 3, 5, 7] >>> squares = (n**2 for n in numbers) If we pass this generator to the tuple constructor, we’ll get a tuple of its items back: 1 2 3 4 >>> numbers = [1, 2, 3, 5, 7] >>> squares = (n**2 for n in numbers) >>> tuple(squares) (1, 4, 9, 25, 49) If we then try to compute the sum of the numbers in this generator, we’ll get 0: 1 2 >>> sum(squares) 0 This generator is now empty: we’ve exhausted it. If we try to make a tuple out of it again, we’ll get an empty tuple: 1 2 >>> tuple(squares) () Generators are iterators. And iterators are single-use iterables. They’re like Hello Kitty PEZ dispensers that cannot be reloaded. Gotcha 2: Partially-Consuming an Iterator Again we have a generator object, squares: 1 2 >>> numbers = [1, 2, 3, 5, 7] >>> squares = (n**2 for n in numbers) If we ask whether 9 is in this squares generator, we’ll get True: 1 2 >>> 9 in squares True But if we ask the same question again, we’ll get False: 1 2 >>> 9 in squares False When we ask whether 9 is in this generator, Python has to loop over this generator to find 9. If we kept looping over it after checking for 9, we’ll only get the last two numbers because we’ve already consumed the numbers before this point: 1 2 3 4 5 6 >>> numbers = [1, 2, 3, 5, 7] >>> squares = (n**2 for n in numbers) >>> 9 in squares True >>> list(squares) [25, 49] Asking whether something is contained in an iterator will partially consume the iterator. There is no way to know whether something is in an iterator without starting to loop over it. Gotcha 3: Unpacking is iteration When you loop over dictionaries you get keys: 1 2 3 4 5 6 >>> counts = {'apples': 2, 'oranges': 1} >>> for key in counts: ... print(key) ... apples oranges You also get keys when you unpack a dictionary: 1 2 3 >>> x, y = counts >>> x, y ('apples', 'oranges') Looping relies on the iterator protocol. Iterable unpacking also relies on the iterator protocol. Unpacking a dictionary is really the same as looping over the dictionary. Both use the iterator protocol, so you get the same result in both cases. Recap and related resources Sequences are iterables, but not all iterables are sequences. When someone says the word “iterable” you can only assume they mean “something that you can iterate over”. Don’t assume iterables can be looped over twice, asked for their length, or indexed. Iterators are the most rudimentary form of iterables in Python. If you’d like to make a lazy iterable in your code think of iterators and consider making a generator function or a generator expression. And finally, remember that every type of iteration in Python relies on the iterator protocol so understanding the iterator protocol is the key to understanding quite a bit about looping in Python in general. Here are some related articles and videos I recommend: Loop Like a Native, Ned Batchelder’s PyCon 2013 talk Loop Better, the talk this article is based on The Iterator Protocol: How For Loops Work, a short article I wrote on the iterator protocol Comprehensible Comprehensions, my talk on comprehensions and generator expressions Python: range is not an iterator, my article on range and iterators Looping Like a Pro in Python, DB’s PyCon 2017 talk

Today I will start another tutorial about nltk python module and stemming. The stemming is the process of producing morphological variants of a root/base word. Stemming programs are commonly referred to as stemming algorithms or stemmers to reduces the words. Errors in Stemming can be overstemming and understemming. These two words are stemmed to the same root that are of different stems then the