Here are some examples
def example1(lst):
for item in lst:
if item < 4:
lst.remove(item)
return lst
def example2(lst):
for item in lst[:]:
if item < 4:
lst.remove(item)
return lst
def example3(lst):
i = 0
while i < len(lst):
if lst[i] < 4:
lst.pop(i)
else:
i += 1
return lst
def example4(lst):
return [item for item in lst if not item < 4]
def example5(lst):
for item in reversed(lst):
if item < 4:
lst.remove(item)
return lst
def example6(lst):
for i, item in reversed(list(enumerate(lst))):
if item < 4:
lst.pop(i)
return lst
def example7(lst):
size = len(lst) - 1
for i, item in enumerate(reversed(lst)):
if item < 4:
lst.pop(size - i)
return lst
def example8(lst):
return list(filter(lambda item: not item < 4, lst))
import itertools
def example9(lst):
return list(itertools.filterfalse(lambda item: item < 4, lst))
# Output
>>> lst = [1, 1, 2, 3, 2, 3, 4, 5, 6, 6]
>>> example1(lst[:])
[1, 3, 3, 4, 5, 6, 6]
>>> example2(lst[:])
[4, 5, 6, 6]
>>> example3(lst[:])
[4, 5, 6, 6]
>>> example4(lst[:])
[4, 5, 6, 6]
>>> example5(lst[:])
[4, 5, 6, 6]
>>> example6(lst[:])
[4, 5, 6, 6]
>>> example7(lst[:])
[4, 5, 6, 6]
>>> example8(lst[:])
[4, 5, 6, 6]
>>> example9(lst[:])
[4, 5, 6, 6]
Example 1
This example involves iterating through the list and removing values from it. The issue with this is that you are modifying the list as you go through it so your list changes during iteration and so some elements get skipped over.
Example 2
Here we are iterating over a shallow copy of the list instead of the list itself. The issue with this is if you have a large list it could be expensive to do so.
Example 3
The following is an example using pop instead of remove, the issue with remove is that it removes the first instance of the value it finds from the list. This will typically be of no issue unless you have objects which are equal. (See example 10)
Example 4
Instead of modifying the list here instead we create a new list using list comprehension allowing only specified values.
Example 5
This is an example of iterating through the list in reverse, the difference is that we use the built-in reversed function to apply a for-loop to in stead of a while loop with a counter.
Example 6
Similar example using pop instead.
Example 7
Better example using pop as we don't have to cast back to a list to use the reversed function.
Example 8
Example using the built-in filter method to remove the specified values.
Example 9
Similar example using the filerfalse method from itertools
class Example(object):
ID = 0
def __init__(self, x):
self._x = x
self._id = str(Example.ID)
Example.ID += 1
def __eq__(self, other):
return self._x == other._x
def __repr__(self):
return 'Example({})'.format(self._id)
def example10():
lst = [Example(5), Example(5)]
print(lst)
lst.remove(lst[1])
return lst
#Output
>>> example10()
[Example(0), Example(1)]
[Example(1)]
Example 10
Here we create two Example objects with the same values and by the equality method they are equal. The ID variable is there to help us differentiate between the two. Now we have specified that we want to remove the 2nd object from the list, however because both are equal the first item is actually removed instead.
Timings
These are pretty rough times and can vary slightly depending on your device. Although these identify which one is faster, this was tested with a list of 10,000 items so if you don't have anything close to that then any choice is fine really.
import timeit
import random
# Code from above is here
def test(func_name):
global test_lst
test_lst = lst[:]
return timeit.timeit("{}(test_lst)".format(func_name),
setup="from __main__ import {}, test_lst".format(func_name), number = 1)
if __name__ == '__main__':
NUM_TRIALS = 1000
lst = list(range(10000))
random.shuffle(lst) # Don't have to but makes it a bit interesting
test_list = lst[:]
for func in ('example2', 'example3', 'example4', 'example5',
'example6', 'example7', 'example8', 'example9'):
trials = []
for _ in range(NUM_TRIALS):
trials.append(test(func))
print(func, sum(trials) / len(trials) * 10000)
#Output
example2 8.487979147454494
example3 20.407155912623292
example4 5.4595031069025035
example5 7.945100572479213
example6 14.43537688078149
example7 9.088818018676008
example8 14.898256300967116
example9 13.865010859443247
