# Python's Functional Features - Partials & Generators

19 August 2014

I've been abstaining myself from reddit for a while to get some other things done and I recently decided to catch up on the programming subreddit news en masse.

One of the interesting posts I came across was "The Great White Space Debate". It's an eloquent post on a dry programming topic; however, the python script the author posted in the article piqued my attention. The script locates the Java source files within a root project directory and then identifies, counts, and summarizes the various for loop styles used within them.

Here is the script taken from the blog post:

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 import os import re from functools import partial def get_java_src_files(java_root): for root, _, files in os.walk(java_root): yield from (os.path.join(root, f) for f in files if f.endswith('.java')) def read_files(filenames): for filename in filenames: with open(filename, 'r', encoding='utf-8') as f: yield from f.readlines() def extract_forloops(lines): yield from ( line.strip() for line in lines if 'for (' in line # One of those god forsaken 'enhanced' for loops. Ignore and ':' not in line ) if __name__ == '__main__': spaced_apart = partial(re.search, '[a-zA-Z]\s\=') grouped_together = partial(re.search, '[a-zA-Z]\=') java_src_path = r'C:\Program Files\Java\jdk1.8.0_20\src' forloops = extract_forloops(read_files(get_java_src_files(java_src_path))) counts = ('spaced': 0, 'nonspaced' : 0) for loop in forloops: if grouped_together(loop): counts['nonspaced'] += 1 elif spaced_apart(loop): counts['spaced'] += 1 print(counts) 

Being a python neophyte, I was interested in how this script was written. The script is liberally using two aspects of python's functional features: partials and generators.

## Partials

### Theory

The f(args) operator is used to make a function call on function f. Each argument to a function is an expression. Before the function gets executed, all of its argument expressions are evaluated from left to right. This is known as application order evaluation.

It is possible to partially evaluate function arguments using the partial() function from the functools module. For example:

def foo(x, y, z):
return x + y + z

from functools import partial
fn = partial(foo, 1, 2)  # supply values to foo's x and y arguments
fn(3)                    # calls foo(1,2,3), result is 6


The partial() function preemptively evaluates some of the arguments to a function, and returns an object that you can call to supply the remaining arguments at a later point.

In the above example, the variable fn represents a partially evaluated function where the first two arguments, x and y are already supplied and evaluated. The third argument, z, is given later on the next line. The function is then complete and executed, yielding a result of 6.

Partial evaluation of function arguments is closely related to currying, a functional programming technique where a function taking multiple arguments is decomposed into a series of functions each taking only one argument. For example, given a function f(x,y), you partially evaluate f by fixing x to get a new function to which you give values of y to produce a result.

### Practice

In the Java for loop identifying code the author creates partially evaluated functions on lines 24-25:

spaced_apart = partial(re.search, '[a-zA-Z]\s\=')
grouped_together = partial(re.search, '[a-zA-Z]\=')


and invokes them on the if statements on lines 33 and 35:

if grouped_together(loop):
counts['nonspaced'] += 1
elif spaced_apart(loop):
counts['spaced'] += 1


The author is using the partials as a way to "label" his regular expression through descriptive and readable function variable names. An interesting approach!

## Generators

### Theory

Whenever a function uses the yield keyword, it defines a generator object. A generator is a function that produces a sequence of values for use in iteration. For example,

def countdown(n):
print("Counting down from {}".format(n))
while n > 0:
yield n
n -= 1
return


If you define the above function in a REPL and execute it, like so:

>>> c = countdown(5)


you'll notice that none of the code gets executed. Instead countdown returns a generator object which is assigned to variable c. The generator object will begin to execute the function only when the next() method is applied to it like so:

>>> c.next()  # "it's c.__next__ in Python 3.x"
Counting down from 5
5


On the first call to next(), the generator function executes statements until it reaches a yield statement. The yield statement produces a result at which point execution of the function stops until next() is invoked again. Subsequent next() calls resumes executing the function starting with the statement following yield. Continuing with our running example, a second call to next() will behave like:

>>> c.next()
4


In python, you don't normally call next() directly; rather, it's called indirectly via a for, or some other operation that works on a sequence.

A generator function signals its completion by returning or raising a StopIteration exception. Afterwards, continued next() calls should always return None.

#### Warning

A subtle problem with generators happens when a generator is partially consumed. An example scenario is:

for n in countdown(5):
if n == 2: break
# otherwise, do more stuff...


Here, the for loop aborts when "n" becomes equal to 2, however, the associated generator never fully completes. When a generator is no longer used, or gets deleted once out of scope, a generator method named close() is invoked. Normally it's not necessary to call close() directly, but you can call it manually like so:

>>> c = countdown(5)
>>> c.next()
Counting down from 5
5
>>> c.next()
4
>>> c.close()
>>> c.next()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIteration


A much more through description of python's generator feature is described here.

### Practice

Generators are a powerful way to write pythonic programs that are based on processing data streams of unknown size and limit.

In the Java code parsing example, the yield statement is called in all three of the functions defined in the script. This approach is useful because we have an unknown data stream -- for any given Java project one doesn't know how many Java source files there are, and how many lines are in each source file. Rather than loading everything up initially, we can lazily load up the source files and its line contents as needed.