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Saturday, January 25, 2014

Python list, set and dict comprehentions 2.7+

Python supports list comprehension since v2.0. These expressions truly revolutionized python, making various functions much simpler and more readable. Let's see some basic LC in action:

>>> [x for x in "test"]
['t', 'e', 's', 't']

Now often there is a need to generate a set or dict in a similar way, so I often see such code:

>>> set([x for x in "test"])
set(['s', 'e', 't'])
>>> dict([(x,x) for x in "test"])
{'s': 's', 'e': 'e', 't': 't'}

This is good:
- it works!
- it's more readable than implementing a for loop.

But using python 2.7+ You can to it better! The latest python 2.7.X and 3.X support dict and set comprehensions - now this is pythonic! You can achieve the same results the following way:

>>> {x for x in "test"}
set(['s', 'e', 't'])
>>> {x: x for x in "test"}
{'s': 's', 'e': 'e', 't': 't'}

This is excellent!
- it works!
- it's more readable than creating a dict/set from a LC
- it's faster!!!

Simple performance comparison:

>>> timeit.timeit('set([x for x in "test"])')
>>> timeit.timeit('{x for x in "test"}')

>>> timeit.timeit('dict([(x,x) for x in "test"])')
>>> timeit.timeit('{x: x for x in "test"}')



  1. Hi Krzysiek,

    I was wondering what about memory usage, so I did a test. This is my test result:

    In [9]: %memit dict([(x,x) for x in "test"])
    peak memory: 19.25 MiB, increment: 0.00 MiB

    In [10]: %memit {x: x for x in "test"}
    peak memory: 19.25 MiB, increment: 0.00 MiB

    Hmm, same result, but if I change test data to:

    In [17]: %memit dict([(x,x) for x in data])
    peak memory: 228.05 MiB, increment: 130.12 MiB

    In [16]: %memit {x: x for x in data}
    peak memory: 145.93 MiB, increment: 93.75 MiB

    PS. I used ipython, with (memory_profiler, %load_ext memory_profiler)

    1. Thanks for the update, this is also an argument for using these expressions.

    2. I forgot to specify variable data:

      data = range(0, 999999)

  2. This comment has been removed by the author.


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