Python Anagrams in One Line Python

sorted("elvis") == sorted("lives")
# True

Why Learning about Python Anagrams?

A popular question in programming interviews is to create an anagram checker.

The interviewer wants to test your knowledge about the basic terminology in computer science, and how good you are at developing your own simple algorithms to solve the problems you are facing. In this article, you’ll learn about a simple algorithm to find anagrams in Python.

Most students who have pursued an academic education in computer science, know exactly what to do here. When posed in a coding interview, this question serves as a test that immediately reveals whether you are part of this community. So let’s prepare!

Problem Formulation

What are anagrams? Two words are anagrams if they consist of the same characters.

? “An anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.” (Wikipedia)

Here are a few examples:

• “listen” → “silent”
• “funeral” → “real fun”
• “elvis” → “lives”

Challenge: Create a Python program that checks for two words x1 and x2 whether x1 is an anagram of x2. More precisely: Given are two strings x1 and x2. Write a function is_anagram which returns True if string x2 an anagram of string x1!

Ok, now you know exactly what to do, right? So, let’s start coding.

Anagram Checker

The following one-liner solution (from my book Python One-Liners) solves this problem easily and in a very concise way too.

## Python Anagram One-Liner
is_anagram = lambda x1, x2: sorted(x1) == sorted(x2)

If you don’t like one-liners, you can also create the same anagram checker function is_anagram() in two lines:

def is_anagram(x1, x2):
    return sorted(x1) == sorted(x2)

👉 The function is_anagram(x1, x2) simply returns the result of the expression sorted(x1) == sorted(x2) which is True if the sorted character sequences consist of the same characters.

The one-liner solves the problem efficiently and correctly. Two strings are anagrams if they have the same sorted character sequence. It’s that easy. There is no need for external dependencies – we simply create a function is_anagram in a single line of code by using the lambda function definition with two arguments x1 and x2.

Here’s the output of the two sorted character sequences:

print(sorted("elvis"))
# ['e', 'i', 'l', 's', 'v']

print(sorted("lives"))
# ['e', 'i', 'l', 's', 'v']

Both strings "elvis" and "lives" consist of the same characters, so the sorted list representation is the same. Thus, the result of the three print() statements in the one-liner code snippet is the following:

## Results
print(is_anagram("elvis", "lives")) # True
print(is_anagram("elvise", "livees")) # True
print(is_anagram("elvis", "dead")) # False

Background sorted()

To learn more about the sorted() function, check out my in-depth tutorial on the Finxter blog or watch the following video:

Where to Go From Here?

This article gave you the simplest way of finding anagrams in Python. There is no easier way — guaranteed.

If you feel like you need to become better in Python but you don’t know how and have little time, check out my daily “Coffee Break Python” email series with free Python cheat sheets. It’s fun!

Python One-Liners Book: Master the Single Line First!

Python programmers will improve their computer science skills with these useful one-liners.

Python One-Liners will teach you how to read and write “one-liners”: concise statements of useful functionality packed into a single line of code. You’ll learn how to systematically unpack and understand any line of Python code, and write eloquent, powerfully compressed Python like an expert.

The book’s five chapters cover (1) tips and tricks, (2) regular expressions, (3) machine learning, (4) core data science topics, and (5) useful algorithms.

Detailed explanations of one-liners introduce key computer science concepts and boost your coding and analytical skills. You’ll learn about advanced Python features such as list comprehension, slicing, lambda functions, regular expressions, map and reduce functions, and slice assignments.

You’ll also learn how to:

• Leverage data structures to solve real-world problems, like using Boolean indexing to find cities with above-average pollution
• Use NumPy basics such as array, shape, axis, type, broadcasting, advanced indexing, slicing, sorting, searching, aggregating, and statistics
• Calculate basic statistics of multidimensional data arrays and the K-Means algorithms for unsupervised learning
• Create more advanced regular expressions using grouping and named groups, negative lookaheads, escaped characters, whitespaces, character sets (and negative characters sets), and greedy/nongreedy operators
• Understand a wide range of computer science topics, including anagrams, palindromes, supersets, permutations, factorials, prime numbers, Fibonacci numbers, obfuscation, searching, and algorithmic sorting

By the end of the book, you’ll know how to write Python at its most refined, and create concise, beautiful pieces of “Python art” in merely a single line.

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