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The Python itertools.pairwise() function is used to create an iterator that returns consecutive overlapping pairs from an iterable. It is useful for analyzing sequential relationships between elements in a dataset.

This function is equivalent to using the zip function with a slice of the original iterable.

Syntax

Following is the syntax of the Python itertools.pairwise() function −

itertools.pairwise(iterable)

Parameters

This function accepts the input iterable as a parameter from which consecutive pairs will be extracted.

Return Value

This function returns an iterator that yields tuples containing consecutive elements from the input iterable.

Example 1

Following is an example of the Python itertools.pairwise() function. Here, we extract consecutive pairs from a list of numbers −

import itertools

numbers = [1, 2, 3, 4, 5]
pairs = itertools.pairwise(numbers)
for pair in pairs:
   print(pair)

Following is the output of the above code −

(1, 2)
(2, 3)
(3, 4)
(4, 5)

Example 2

Here, we use itertools.pairwise() function on a string to generate consecutive character pairs −

import itertools

text = "HELLO"
pairs = itertools.pairwise(text)
for pair in pairs:
   print(pair)

Output of the above code is as follows −

('H', 'E')
('E', 'L')
('L', 'L')
('L', 'O')

Example 3

Now, we use itertools.pairwise() function to analyze temperature differences between consecutive days −

import itertools

temperatures = [72, 75, 78, 74, 70, 69]
differences = [(b - a) for a, b in itertools.pairwise(temperatures)]
print(differences)

The result obtained is as shown below −

[3, 3, -4, -4, -1]

Example 4

We can also use the itertools.pairwise() function to check for increasing trends in stock prices −

import itertools

stock_prices = [100, 102, 101, 105, 107, 106]
increasing_trend = [b > a for a, b in itertools.pairwise(stock_prices)]
print(increasing_trend)

The result produced is as follows −

[True, False, True, True, False]

python_modules