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Python Idioms

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In this post, we're going to look at some of the most useful Python idioms that can make your code shorter, cleaner, and easier to understand.

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Python is often described as an expressive language. Many tasks that require multiple lines of code in other languages can be written in a single line of Python.

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We'll explore some built-in features and standard library modules that every Python developer should know.

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PREREQUISITES

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Goals

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List Comprehensions

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Suppose we want a list containing the square of every number from 1 to 10.

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A beginner might write:

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squares = []\n\nfor i in range(1, 11):\n    squares.append(i * i)
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This works perfectly fine.

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However, Python provides a more compact syntax called a list comprehension.

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squares = [i * i for i in range(1, 11)]
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The result is exactly the same.

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>>> squares\n[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
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We can even add conditions.

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>>> evens = [i for i in range(20) if i % 2 == 0]\n\n>>> evens\n[0, 2, 4, 6, 8, 10, 12, 14, 16, 18]
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Once you become comfortable with list comprehensions, you'll find yourself using them everywhere.

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Dictionary Comprehensions

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Just as we can construct lists, we can construct dictionaries.

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Suppose we want a mapping of numbers to their squares.

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Without a comprehension:

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squares = {}\n\nfor i in range(5):\n    squares[i] = i * i
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With a dictionary comprehension:

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squares = {i: i * i for i in range(5)}
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The result:

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>>> squares\n{0: 0, 1: 1, 2: 4, 3: 9, 4: 16}
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Neat.

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defaultdict

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Have you ever written code like this?

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counts = {}\n\nfor word in words:\n\n    if word not in counts:\n        counts[word] = 0\n\n    counts[word] += 1
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This pattern appears all over the place.

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The collections module provides a cleaner solution.

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from collections import defaultdict\n\ncounts = defaultdict(int)\n\nfor word in words:\n    counts[word] += 1
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Notice that we never initialize anything.

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Missing keys automatically start at zero.

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>>> counts[\"python\"]\n3
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The same trick works for lists.

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from collections import defaultdict\n\ngroups = defaultdict(list)\n\nfor name, department in employees:\n    groups[department].append(name)
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No checks required.

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Counter

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Here's a question.

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How many times does each character appear in a string?

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Many developers create a dictionary and count manually.

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Python already has a solution.

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from collections import Counter\n\ncounter = Counter(\"mississippi\")
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Let's see what it found.

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>>> counter\nCounter({'i': 4, 's': 4, 'p': 2, 'm': 1})
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Want the most common elements?

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>>> counter.most_common(2)\n\n[('i', 4), ('s', 4)]
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Oh yeah, it's all coming together.

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In many situations, Counter is exactly the tool you want.

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zip()

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Suppose we have two lists.

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names = [\"Alice\", \"Bob\", \"Charlie\"]\nscores = [95, 82, 99]
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A beginner solution might involve indexing.

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for i in range(len(names)):\n    print(names[i], scores[i])
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Python gives us a better option.

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for name, score in zip(names, scores):\n    print(name, score)
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Output:

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Alice 95\nBob 82\nCharlie 99
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The values stay perfectly aligned.

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We can even construct a dictionary.

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>>> dict(zip(names, scores))\n\n{'Alice': 95, 'Bob': 82, 'Charlie': 99}
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Pretty handy.

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any()

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Suppose we want to know if a list contains at least one negative number.

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Many developers write:

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found = False\n\nfor value in numbers:\n    if value < 0:\n        found = True\n        break
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Python has already solved this problem.

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found = any(value < 0 for value in numbers)
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Let's see it in action.

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>>> any(x < 0 for x in [1, 2, 3, -4])\n\nTrue
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The expression stops as soon as a match is found.

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This is often both cleaner and faster.

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Similarly, there's an opposite function named all().

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>>> all(x > 0 for x in [1, 2, 3])\n\nTrue
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heapq

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Suppose we want the largest three values in a list.

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A common approach is:

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sorted(numbers, reverse=True)[:3]
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This works.

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However, if we only need a few values, sorting the entire list can be unnecessary.

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Python provides a heap implementation in the heapq module.

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import heapq\n\nlargest = heapq.nlargest(3, numbers)
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Example:

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>>> heapq.nlargest(3, [5, 9, 1, 20, 7, 12])\n\n[20, 12, 9]
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Likewise:

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>>> heapq.nsmallest(3, [5, 9, 1, 20, 7, 12])\n\n[1, 5, 7]
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Heaps are commonly used in:

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You don't need to understand heap internals immediately to benefit from the module.

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Putting It All Together

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Let's solve a small problem.

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Given a list of employees:

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employees = [\n    (\"Alice\", \"Engineering\"),\n    (\"Bob\", \"Engineering\"),\n    (\"Charlie\", \"Sales\"),\n    (\"David\", \"Sales\"),\n    (\"Emma\", \"Engineering\")\n]
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We can group employees by department:

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from collections import defaultdict\n\ndepartments = defaultdict(list)\n\nfor name, department in employees:\n    departments[department].append(name)
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Count employees per department:

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from collections import Counter\n\ncounts = Counter(\n    department\n    for _, department in employees\n)
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Find the largest department:

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>>> counts.most_common(1)\n\n[('Engineering', 3)]
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All without managing a single index variable.

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That's It Folks!

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We covered:

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List Comprehensions\nDictionary Comprehensions\ndefaultdict\nCounter\nzip()\nany()\nheapq
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None of these features are particularly complicated.

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The real power comes from recognizing common programming patterns and replacing them with the tools Python already provides.

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Whenever you find yourself writing:

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for ...\nif ...\ncreate dictionary ...\ncreate list ...\ntrack counts ...
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it's worth asking:

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\"Is there already a Python idiom for this?\"

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Very often, the answer is yes.

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And your code will usually be shorter, clearer, and easier to maintain because of it.

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