Generator expressions vs list comprehensions

Question

When should you use generator expressions and when should you use list comprehensions in Python     Generator expression  x 2 for x in range 256      List comprehension  x 2 for x in range 256

User · Answer

When creating a generator from a mutable object (like a list) be aware that the generator will get evaluated on the state of the list at time of using the generator, not at time of the creation of the generator:

>>> mylist = ["a", "b", "c"]
>>> gen = (elem + "1" for elem in mylist)
>>> mylist.clear()
>>> for x in gen: print (x)
# nothing

If there is any chance of your list getting modified (or a mutable object inside that list) but you need the state at creation of the generator you need to use a list comprehension instead.

User · Answer

The benefit of a generator expression is that it uses less memory since it doesn t build the whole list at once   Generator expressions are best used when the list is an intermediary  such as summing the results  or creating a dict out of the results   For example   sum x 2 for x in xrange 256    dict   k  some func k   for k in some list of keys     The advantage there is that the list isn t completely generated  and thus little memory is used  and should also be faster   You should  though  use list comprehensions when the desired final product is a list   You are not going to save any memeory using generator expressions  since you want the generated list   You also get the benefit of being able to use any of the list functions like sorted or reversed   For example   reversed   x 2 for x in xrange 256

User · Answer

Iterating over the generator expression or the list comprehension will do the same thing  However  the list comprehension will create the entire list in memory first while the generator expression will create the items on the fly  so you are able to use it for very large  and also infinite   sequences

User · Answer

I m using the Hadoop Mincemeat module  I think this is a great example to take a note of   import mincemeat  def mapfn k v       for w in v          yield  sum  w          yield  count  1   def reducefn k v        r1 sum v      r2 len v      print r2     m r1 r2     std 0     for i in range r2          std  pow abs v i -m  2        res pow  std r2  0 5      return r1 r2 res   Here the generator gets numbers out of a text file  as big as 15GB  and applies simple math on those numbers using Hadoop s map-reduce  If I had not used the yield function  but instead a list comprehension  it would have taken a much longer time calculating the sums and average  not to mention the space complexity    Hadoop is a great example for using all the advantages of Generators

User · Answer

The important point is that the list comprehension creates a new list  The generator creates a an iterable object that will  filter  the source material on-the-fly as you consume the bits   Imagine you have a 2TB log file called  hugefile txt   and you want the content and length for all the lines that start with the word  ENTRY    So you try starting out by writing a list comprehension   logfile   open  hugefile txt   r   entry lines     line len line   for line in logfile if line startswith  ENTRY      This slurps up the whole file  processes each line  and stores the matching lines in your array  This array could therefore contain up to 2TB of content  That s a lot of RAM  and probably not practical for your purposes   So instead we can use a generator to apply a  filter  to our content  No data is actually read until we start iterating over the result   logfile   open  hugefile txt   r   entry lines     line len line   for line in logfile if line startswith  ENTRY      Not even a single line has been read from our file yet  In fact  say we want to filter our result even further   long entries     line length  for  line length  in entry lines if length  gt  80    Still nothing has been read  but we ve specified now two generators that will act on our data as we wish   Lets write out our filtered lines to another file   outfile   open  filtered txt   a   for entry length in long entries      outfile write entry    Now we read the input file  As our for loop continues to request additional lines  the long entries generator demands lines from the entry lines generator  returning only those whose length is greater than 80 characters  And in turn  the entry lines generator requests lines  filtered as indicated  from the logfile iterator  which in turn reads the file   So instead of  pushing  data to your output function in the form of a fully-populated list  you re giving the output function a way to  pull  data only when its needed  This is in our case much more efficient  but not quite as flexible  Generators are one way  one pass  the data from the log file we ve read gets immediately discarded  so we can t go back to a previous line  On the other hand  we don t have to worry about keeping data around once we re done with it

User · Answer

Use list comprehensions when the result needs to be iterated over multiple times  or where speed is paramount  Use generator expressions where the range is large or infinite   See Generator expressions and list comprehensions for more info

User · Answer

Python 3 7  List comprehensions are faster   Generators are more memory efficient   As all others have said  if you re looking to scale infinite data  you ll need a generator eventually  For relatively static small and medium-sized jobs where speed is necessary  a list comprehension is best

User · Answer

List comprehensions are eager but generators are lazy  In list comprehensions all objects are created right away  it takes longer to create and return the list  In generator expressions  object creation is delayed until request by next    Upon next   generator object is created and returned immediately  Iteration is faster in list comprehensions because objects are already created  If you iterate all the elements in list comprehension and generator expression  time performance is  about the same  Even though generator expression return generator object right away  it does not create all the elements  Everytime you iterate over a new element  it will create and return it  But if you do not iterate through all the elements generator are more efficient  Let s say you need to create a list comprehensions that contains millions of items but you are using only 10 of them  You still have to create millions of items  You are just wasting time for making millions of calculations to create millions of items to use only 10  Or if you are making millions of api requests but end up using only 10 of them  Since generator expressions are lazy  it does not make all the calculations or api calls unless it is requested  In this case using generator expressions will be more efficient  In list comprehensions entire collection is loaded to the memory  But generator expressions  once it returns a value to you upon your next   call  it is done with it and it does not need to store it in the memory any more  Only a single item is loaded to the memory  If you are iterating over a huge file in disk  if file is too big you might get memory issue  In this case using generator expression is more efficient

User · Answer

John s answer is good  that list comprehensions are better when you want to iterate over something multiple times    However  it s also worth noting that you should use a list if you want to use any of the list methods   For example  the following code won t work   def gen        return  something for something in get some stuff     print gen    2        generators don t support indexing or slicing print  5 6    gen     generators can t be added to lists   Basically  use a generator expression if all you re doing is iterating once   If you want to store and use the generated results  then you re probably better off with a list comprehension   Since performance is the most common reason to choose one over the other  my advice is to not worry about it and just pick one  if you find that your program is running too slowly  then and only then should you go back and worry about tuning your code

User · Answer

Sometimes you can get away with the tee function from itertools  it returns multiple iterators for the same generator that can be used independently

[python] Generator expressions vs. list comprehensions

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