Instead of using nested loops to express as possible filenames, you can use product(). Suppose you have datasets in several languages, and for each language, you have a test, training, and validation set. values = suits = print(list(itertools.product(values, suits)))Īnother example is with datasets. I often find using product() much more elegant than creating hard-to-read nested loops. You can use the product() function to quickly get a list of all playing cards without resorting to loops. As you can see, the total number of cards is the product of 13 and 4, and the set of all cards is the Cartesian product of the set of face values and the set of suits. In a pack of playing cards, you have 13 face values and 4 suits, for a total of 52 cards. names = print(list(binations(names, 2))) # Ĭombinations would also tell you the number of unique handshakes needed for a group of N people to all shake hands with each other, or the number of covariance values for N variables. How many unique chef combinations are there? This is a classic case of mathematical combinations, and you can easily get the answer with combinations(). Suppose you and your friends want to create a cooking schedule where, each week, two people cook for the whole group. Get all possible combinations of a set with combinations() In addition to cycle(), you may also find count() useful, which simply counts upwards from a given number in an infinite iterator. The code below creates a list of 20 items containing the repeating sequence 0, 2, 1, 3, … data = list(itertools.islice(itertools.cycle(), 0, 20)) sns.lineplot(x=range(len(data)), y=data) You can use this to quickly generate some periodic data. Together with the islice() function, you can get a sequence of any length that contains repeating subsequences of your choosing. The cycle() function will infinitely repeat any iterable you give it. import itertools import matplotlib.pyplot as plt import seaborn as sns import numpy as np 1. If you want to follow along, make sure to import itertools as well as matplotlib, seaborn, and numpy for the examples that involve visualizations. In this article, you will see 7 practical examples of the applications of itertools functions. By becoming fluent in the itertools functions, you can combine them in new ways and use them as building blocks for tackling complex problems in very few lines of code. The Python itertools library is provides extremely useful utility functions for dealing with iterables. The 2to3 tool that ports Python 2 code to Python 3 will recognize this usage and leave the new builtins alone.Saving time and code with flexible utility functions and paradigms But that's something that needs looking in to. in this case, while a plain zip = izip may not be. For instance, I'm not 100% sure, but believe that the 2to3 tool will re-write zip correctly as list(zip(. The advantage of using future_builtin is that it's in effect a bit more "explicit" as to intended behaviour of the module, supported by the language syntax, and possibly recognised by tools. You do however then have the same problem of ImportError - so: try:įrom itertools import izip as zip # < 2.5 or 3.x Not sure this is really an answer, or I should elaborate on my comment, and in hindsight probably not even a very good comment anyway, but:įirstly, you can just simplify it to: try:įrom 2.6 you can use as per the docs: from future_builtins import map # or zip or filter
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