Process Python Collections with Functional Programming

I develop Spark applications with Scala, and it has a very powerful collection system, in which functional programming is certainly a key. Java 8 also introduces Lambda Expression and Stream API. In JavaScript, there is a Lodash library that provides powerful tools to process arrays and objects. When my primary work language changes to Python, I am wondering if it’s possible to manipulate collections in a FP way, and fortunately Python already provides syntax and tools for functional programming. Though list comprehension is the pythonic way to deal with collections, but the idea and concepts of FP is definitely worth learning.

Wordcount Example

Let’s first write a snippet to count the word occurences from a paragraph, in of course a functional way.

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import re
import itertools
content = """
an apple orange the grape
banana an apple melon
an orange banana apple
"""
word_matches = re.finditer(r'\S+', content)
words = map(lambda m: m.group(0), word_matches)
fruits = filter(lambda s: len(s) > 3, words)
grouped_fruits = itertools.groupby(sorted(fruits))
fruit_counts = map(lambda t: (t[0], len(list(t[1]))), grouped_fruits)
print(list(fruit_counts))

Run this example and you’ll get a list of fruits, along with their counts:

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[('apple', 3), ('banana', 2), ('grape', 1), ('melon', 1), ('orange', 2)]

This example includes most aspects of processing collections with FP style. For instance, re.finditer returns an iterator that is lazily evaluated; map and filter are used to do transformations; itertools module provides various functions to cope with iterables; and last but not least, the lambda expression, an easy way to define inline anonymous function. All of them will be described in the following sections.

Ingredients of Functional Programming

Python is far from being a functional language, but it provides some basic syntax and tools so that we can choose to write Python in a functional way.

Function as First-class Citizen

Function is data. It can be assigned to a variable, pass as a parameter to another function, or returned by a function. The later two cases also refers to higher order functions. Python makes it quite easy, you can define and pass around the function:

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def add(a, b):
return a + b
add_two = add
print(add_two(1, 2)) # => 3
def calculate(a, b, operation):
return operation(a, b)
print(calculate(1, 2, add)) # => 3

Or generate a new function from a function:

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def add_n(n):
def add(a):
return a + n
return add
add_1 = add_n(1)
print(add_1(1)) # => 2

To use function in map, which applies the function to every element of the iterable:

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print(list(map(add_1, [1, 2, 3]))) # => [2, 3, 4]

For very short function, we can use lambda expression:

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map(lambda a: a + 1, [1, 2, 3])

Being Lazy

Lazy evaluation means postponing the execution until it’s necessary. It’s a very common optimization strategy in big data transformation, becuase all map-like operations should be chained and assigned to a single task. In Python, there’s iterator, an stateful object that remembers the current element during iteration. Let’s assume calc is a heavy function, and the following two lines differ:

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[calc(i) for i in [1, 2, 3]]
map(calc, [1, 2, 3])

List comprehension is eager-evaluated, while map (from Python 3.x on) returns an iterator. You can use the next global function to fetch the next element, or take the first two results using:

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from itertools import islice
list(islice(map(calc, [1, 2, 3]), 2))

It’s worth mentioning that from Python 3.x on a lot of methods returns iterator instead of concrete list, you can refer to this article.

Purity

A function is pure if its output only depends on its input, and it has no side-effect, i.e. without changing outer/global variable space. Here’re some examples of pure/non-pure functions:

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def inc(a): # pure
return a + 1
i = 0
def count(a): # non-pure
i = len(a)
def greet(name): # non-pure, change the console
print('hi', name)

Purity is a good functional style because:

  • it makes you re-design the functions so that they become shorter;
  • and short functions are easier to test, have less bugs;
  • purity also enables parallel execution.

In concurrency programming, sharing state, lock, and context switch are all performance killers. Pure functions ensures codes can be executed in parallel without coordination of states, and can be re-executed multiple times safely.

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from concurrent.futures import ThreadPoolExecutor
executor = ThreadPoolExecutor(5)
list(executor.map(add_1, [1, 2, 3]))

Function Composition

There’re also topics on combining, currying, partially applying functions, so we can tackle complex problems with small well-defined functions. Python provides decorator, generator syntax, along with functools, operator modules for such tasks. These can be found in Python official documentation.

Chaining Operations

map, filter, and functions in itertools cannot be easily chained. We have to nest the function calls or introduce intermediate variables. Luckily, there’s an open-sourced PyFunctional package that can help us transform or aggregate collections in a funcional way quite fluently.

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from functional import seq
seq(1, 2, 3, 4)\
.map(lambda x: x * 2)\
.filter(lambda x: x > 4)\
.reduce(lambda x, y: x + y)
# => 14

List Comprehension Or map?

List comprehension and generator expression are the pythonic way of processing collections, and the communiy encourages using list comprehension instead of map, etc. There’s a nice answer on StackOverflow that addresses the following principle: use map only when you already have a function defined. Otherwise just stick to listcomps for it’s more widely accepted. Neverthelss, one should still pay attention to the laziness of various methods.

Conclusion

Processing collections is only one application of functional programming. This program paradigm can be applied to other phases of designing your systems. Further materials like SICP, Functional Programming in Scala are all very informative. Hope you enjoy.

References