Learn Python – Python Itertools- Basic and advance

Itertool is one of the most extremely good Python three preferred libraries. This library has particularly much coolest features and nothing wrong to say that it is the gem of the Python programing language. Python provides exquisite documentation of the itertools but in this tutorial, we will discuss few vital and beneficial features or iterators of itertools.

The key component about itertools is that the features of this library are used to make memory-efficient and specific code.

Before gaining knowledge of the Python itertools, you need to have understanding of the Python iterator and generators. In this article, we will describe itertools for beginners are properly as for professionals.

Introduction

According to the professional definition of itertools, “this module implements a variety of iterator constructing blocks stimulated by constructs from APL, Haskell, and SML.” In simple words, the variety of iterators can together create ‘iterator algebra’ which makes it feasible to entire the complicated task. The features in itertools are used to produce more complex iterators. Let’s take an example: Python built-in zip() characteristic accepts any quantity of arguments as iterable. It iterates over tuples and return their corresponding elements.

a = [1,2,3]  
b= ['a', 'b', 'c']  
c = zip(a,b)  
print(c)  

Output:

[(1, 'a'), (2, 'b'), (3, 'c')]

In the above code, we have passed two lists [1,2,3] and [‘a’, ‘b’, ‘c’] as iterable in zip() function. These lists return one factor at a time. In Python, an component that put into effect .__iter__() or .__getitem__() method called iterable.

The Python iter() function is used to name on the iterable and return iterator object of the iterable.

a = iter('Hello')  
print(a)  

Output:

<str_iterator object at 0x01505FA0>

The Python zip() characteristic calls iter() on each of its argument and then calls next() by way of combining the result into tuple.

Note: If you are the usage of the zip() characteristic and map() characteristic that means you are already the usage of itertools. You don’t need to import it distinctly.

Types of Iterator

There are a number types of iterator in itertools module. The listing is given below:

Infinite iterators

Combinatoric iterators

Terminating iterators

Infinite Iterators

In Python, any object that can put in force for loop is called iterators. Lists, tuples, set, dictionaries, strings are the instance of iterators but iterator can also be infinite and this type of iterator is called countless iterator.

Iterator Argument Results
count(start,step) start, [step] start, start+step, step+2*step
cycle() P p0,p1,….plast
repeat() elem [,n] elem, elem, elem,….endlessly or upto n times

count(start, stop): It prints from the start value to infinite. The step argument is optional, if the value is provided to the step then the number of steps will be skipped. Consider the following example:

import itertools  
  
for i in itertools.count(10,5):  
    if i == 50:  
        break  
    else:  
        print(i,end=" ")  

Output:

10 15 20 25 30 35 40 45

cycle(iterable): This iterator prints all value in sequence from the passed argument. It prints the values in a cyclic manner. Consider the following example:

import itertools  
temp = 0  
for i in itertools.cycle("123"):  
    if temp > 7:  
        break  
    else:  
        print(i,end=' ')  
        temp = temp+1  

Output:

    1 2 3 1 2 3 1 2 3 1 2

Example – 2: Using next() function

import itertools  
  
val = ['Java', 'T', 'Point']  
  
iter = itertools.cycle(val)  
  
for i in range(6):  
    # Using next function  
    print(next(iter), end = " ")  

Output:

Java T Point Java T Point

repeat(val,num): As the name suggests, it repeatedly prints the passed value for infinite time. The num argument is optional. Consider the following example:

import itertools  
print("Printing the number repeadtly:")  
print(list(itertools.repeat(40,15)))  

Output:

[40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40]

Combinatoric iterators: The complex combinatorial constructs are simplified by the recursive generators. The permutations, combinations, and Cartesian products are the example of the combinatoric construct.

In Python, there are four kinds of combinatoric iterators:

Product() – It is used to calculate the cartesian product of input iterable. In this function, we use the optional repeat keyword argument for computation of the product of an iterable with itself. The repeat keyword represents the number of repetitions. It returns output in the form of sorted tuples. Consider the following example:

from itertools import product  
  
print("We are computing cartesian product using repeat Keyword Argument:")  
print(list(product([1, 2], repeat=2)))  
print()  
  
print("We are computing cartesian product of the containers:")  
print(list(product(['Java', 'T', 'point'], '5')))  
print()  
  
print("We are computing product of the containers:")  
print(list(product('CD', [4, 5])))  

Output:

Computing cartesian product using repeat Keyword Argument:
[(1, 1), (1, 2), (2, 1), (2, 2)]

Computing cartesian product of the containers:
[('Java', '5'), ('T', '5'), ('point', '5')]

Computing product of the containers:
[('C', 4), ('C', 5), ('D', 4), ('D', 5)] 

Permutations(): It is used to generate all possible permutation of an iterable. The uniqueness of each element depends upon their position instead of values. It accepts two argument iterable and group_size. If the value of group_size is none or not specified then group_size turns into length of the iterable.

from itertools import permutations  
  
print("Computing all permutation of the following list")  
print(list(permutations([3,"Python"],2)))  
print()  
  
print("Permutations of following string")  
print(list(permutations('AB')))  
print()  
  
print("Permutation of the given container is:")  
print(list(permutations(range(4),2)))  

Output:

Computing all permutation of the following list
[(3, 'Python'), ('Python', 3)]

Permutations of following string
[('A', 'B'), ('B', 'A')]

Permutation of the given container is:
[(0, 1), (0, 2), (0, 3), (1, 0), (1, 2), (1, 3), (2, 0), (2, 1), (2, 3), (3, 0), (3, 1), (3, 2)]

Combinations(): It is used to print all the possible combinations (without replacement) of the container which is passed as argument in the specified group size in sorted order.

from itertools import combinations  
print("Combination of list in sorted order(without replacement)",list(combinations(['B',3],2)))  
print()  
  
print("Combination of string in sorted order",list(combinations("ZX",2)))  
print()  
  
print("Combination of list in sorted order",list(combinations(range(20),1)))  

Output:

Combination of list in sorted order(without replacement) [('B', 3)]
Combination of string in sorted order [('Z', 'X')]
Combination of list in sorted order [(0,), (1,), (2,), (3,), (4,), (5,), (6,), (7,), (8,), (9,)]

Combination_with_replacement(): It accepts two arguments, first argument is a r-length tuple and the second argument is repetition. It returns a subsequence of length n from the elements of the iterable and repeat the same process. Separate elements may repeat itself in combination_with_replacement()

from itertools import combinations_with_replacement  
  
print("Combination of string in sorted order(with replacement) is:")  
print(list(combinations_with_replacement("XY", 3)))  
print()  
  
print("Combination of list in sorted order(with replacement) is:")  
print(list(combinations_with_replacement([4, 2], 3)))  
print()  
  
print("Combination of container in sorted order(with replacement) is:")  
print(list(combinations_with_replacement(range(3), 2)))  

Output:

Combination of string in sorted order(with replacement) is:
[('X', 'X', 'X'), ('X', 'X', 'Y'), ('X', 'Y', 'Y'), ('Y', 'Y', 'Y')]

Combination of list in sorted order(with replacement) is:
[(4, 4, 4), (4, 4, 2), (4, 2, 2), (2, 2, 2)]

Combination of container in sorted order(with replacement) is:
[(0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 2)]

Terminating Iterator

Terminating iterators are normally used to work on the small input sequence and generate the output based totally on the performance of the approach used in iterator.

There are one of a kind sorts of terminating iterator:

accumulate(iter, func): It takes two arguments, the first argument is iterable and the second is a function which would be followed at each iteration of value in iterable. If the function is not defined in accumulate() iterator, addition takes place by default. The output iterable depends on the input iterable; if input iterable contains no value then the output iterable will also be empty.

import itertools  
import operator  
  
# initializing list 1  
list1 = [1, 4, 5, 7, 9, 11]  
  
# using accumulate() that will prints the successive summation of elements  
print("The sum is : ", end="")  
print(list(itertools.accumulate(list1)))  
  
# using accumulate() that will prints the successive multiplication of elements  
print("The product is : ", end="")  
print(list(itertools.accumulate(list1, operator.mul)))  
  
  
# using accumulate() that will prints the successive summation of elements  
print("The sum is : ", end="")  
print(list(itertools.accumulate(list1)))  
  
# using accumulate() that will prints the successive multiplication of elements  
print("The product is : ", end="")  
print(list(itertools.accumulate(list1, operator.mul)))  

Output:

The sum is : [1, 5, 10, 17, 26, 37]
The product is : [1, 4, 20, 140, 1260, 13860]
The sum is : [1, 5, 10, 17, 26, 37]
The product is : [1, 4, 20, 140, 1260, 13860]

chain(iter1, iter2) – It is used to print all the values in iterable passed in the form of chain and declared in arguments. Consider the following example:

import itertools  
  
# declaring list 1  
list1 = [1, 2, 3, 4]  
  
# declaring list 2  
list2 = [1, 5, 6, 8]  
  
# declaring list 3  
list3 = [9, 10, 11, 12]  
  
# using chain() function that will to print all elements of lists  
print("The output is : ", end="")  
print(list(itertools.chain(list1, list2, list3)))  

Output:

The output is: [1, 2, 3, 4, 1, 5, 6, 8, 9, 10, 11, 12]

dropwhile(func, seq) – It starts printing the character only after the func. Consider the following argument:

import itertools  
# initializing list  
list1 = [2, 4, 5, 7, 8]  
# using dropwhile() iterator that will print start displaying after condition is false  
print("The output is : ", end="")  
print(list(itertools.dropwhile(lambda x: x % 2 == 0, list1)))  

Output:

The output is  : [5, 7, 8]

filterfalse(func,seq) – We can assume it by its name, as this iterator prints only those values that return false for the passed function. Consider the following example:

import itertools  
  
# declaring list  
list1 = [12, 14, 15, 27, 28]  
  
# using filterfalse() iterator that will print false values  
print("The Output is: ", end="")  
print(list(itertools.filterfalse(lambda x: x % 2 == 0, list1)))  

Output:

The Output is : [15, 27]

islice(iterable,start,stop,step) – It slices the given iterable according to given position. It accepts four arguments respectively and these are iterable, container, starting pos., ending position and step(optional).

import itertools  
# Declaring list  
list1 = [12, 34, 65, 73, 80, 19, 20]  
# using islice() iterator that will slice the list acc. to given argument  
# starts printing from 3nd index till 8th skipping 2  
print("The sliced list values are : ", end="")  
print(list(itertools.islice(list1, 2, 8, 2)))  

Output:

The sliced list values are : [34, 73, 19]

starmap(func, tuple list) – It takes two arguments; first argument is function and second argument is list which consists element in the form of tuple. Consider the following example.

import itertools  
  
# Declaring list that contain tuple as element  
list1 = [(10, 20, 15), (18, 40, 19), (53, 42, 90), (16, 12, 27)]  
  
# using starmap() iterator for selection value acc. to function  
# selects max of all tuple values  
print("The values acc. to function are : ", end="")  
print(list(itertools.starmap(max, list1)))  

Output:

The values acc. to function are : [20, 40, 90, 27]

takewhile(func, iterable) – It is visa-versa of dropwhile(). It will print values until it returns false condition. Consider the following example:

import itertools  
  
# Defining a list  
list1 = [20, 42, 64, 77, 8, 10, 20]  
  
# takewhile() iterator is used  to print values till condition return false.  
print("Print until 1st false value returned : ", end="")  
print(list(itertools.takewhile(lambda x: x % 2 == 0, list1)))  

Output:

The list values until false value return : [20, 42, 64]

tee(iterator, count) – It divides the container into a number of iterators which is defined in the argument. Consider the following example:

import itertools  
  
# Declaring list  
li = [1, 2, 3, 4, 5, 6, 7]  
  
# storing list in iterator  
iti = iter(li)  
# using tee() iterator to create a list of iterators  
# Creating list of 3 iterators having similar values.  
it = itertools.tee(iti, 3)  
# It will print object of iterator  
print(it)  
print("The iterators are : ")  
for i in range(0, 2):  
    print(list(it[i]))  

Output:

(<itertools._tee object at 0x01B88D88>, <itertools._tee object at 0x01B88DA8>, <itertools._tee object at 0x01B88BA8>)
The iterators are : 
[1, 2, 3, 4, 5, 6, 7]
[1, 2, 3, 4, 5, 6, 7]

zip_longest(iterable1, iterable2, fillval) – It prints the values of iterable alternatively in sequence. If one of the iterable prints all values, remaining values are filled by the values assigned to fill value.

import itertools  
print(" The combined value of iterrables is :")  
print(*(itertools.zip_longest('Java', 'Tpoint', fillvalue='_')))  

Output:

The combined value of iterables is :
('J', 'T') ('a', 'p') ('v', 'o') ('a', 'i') ('_', 'n') ('_', 't')

In this tutorial, we have discussed countless useful iterators along with itertools.