Question

I’m doing some performance-critical Python work and want to create a function that removes a few elements from a list if they meet certain criteria. I’d rather not create any copies of the list because it’s filled with a lot of really large objects.

Functionality I want to implement:

def listCleanup(listOfElements):
    i = 0
    for element in listOfElements:
        if(element.meetsCriteria()):
            del(listOfElements[i])
        i += 1
    return listOfElements

myList = range(10000)
myList = listCleanup(listOfElements)

I’m not familiar with the low-level workings of Python. Is myList being passed by value or by reference?

How can I make this faster?

Is it possible to somehow extend the list class and implement listCleanup() within that?

myList = range(10000)
myList.listCleanup()

Thanks-

Jonathan

Answer 1

Python passes everything the same way, but calling it “by value” or “by reference” will not clear everything up, since Python’s semantics are different than the languages for which those terms usually apply. If I was to describe it, I would say that all passing was by value, and that the value was an object reference. (This is why I didn’t want to say it!)

If you want to filter out some stuff from a list, you build a new list

foo = range(100000)
new_foo = []
for item in foo:
    if item % 3 != 0: # Things divisble by 3 don't get through
        new_foo.append(item)

or, using the list comprehension syntax

 new_foo = [item for item in foo if item % 3 != 0]

Python will not copy the objects in the list, but rather both foo and new_foo will reference the same objects. (Python never implicitly copies any objects.)

---

You have suggested you have performance concerns about this operation. Using repeated del statements from the old list will result in not code that is less idiomatic and more confusing to deal with, but it will introduce quadratic performance because the whole list must be reshuffled each time.

To address performance:

• Get it up and running. You can’t figure out what your performance is like unless you have code working. This will also tell you whether it is speed or space that you must optimize for; you mention concerns about both in your code, but oftentimes optimization involves getting one at the cost of the other.

• Profile. You can use the stdlib tools for performance in time. There are various third-party memory profilers that can be somewhat useful but aren’t quite as nice to work with.

• Measure. Time or reprofile memory when you make a change to see if a change makes an improvement and if so what that improvement is.

• To make your code more memory-sensitive, you will often want a paradigm shift in how you store your data, not microoptimizastions like not building a second list to do filtering. (The same is true for time, really: changing to a better algorithm will almost always give the best speedup. However, it’s harder to generalize about speed optimizations).

  Some common paradigm shifts to optimize memory consumption in Python include

  1. Using Generators. Generators are lazy iterables: they don’t load a whole list into memory at once, they figure out what their next items are on the fly. To use generators, the snippets above would look like

     foo = xrange(100000) # Like generators, xrange is lazy
     def filter_divisible_by_three(iterable):
         for item in foo:
             if item % 3 != 0:
                 yield item
     
     new_foo = filter_divisible_by_three(foo)
     

     or, using the generator expression syntax,

     new_foo = (item for item in foo if item % 3 != 0)
     

  2. Using numpy for homogenous sequences, especially ones that are numerical-mathy. This can also speed up code that does lots of vector operations.

  3. Storing data to disk, such as in a database.