In my previous post, I covered How to use map, filter and reduce in Python.
Here I demonstrate how to use a List comprehension to achieve similar results to using
You can use a list comprehension to iterate over an iterable, like a string, list or tuple and create new list,
The simplest kind does nothing.
a = [1, 2, 3] b = [x for x in a] b # [1, 2, 3]
Here we apply something like the
a = [1, 2, 3] b = [x**2 for x in a] b # [1, 4, 9]
Note you don't need a lambda.
If you had a function, you would use it like this:
b = [square(x) for x in a] b # [1, 4, 9]
Using a string instead of a
c = 'aBcD' c.isupper() # False [x for x in c if x.isupper()] # ['B', 'D']
This is a way to an
if statement inside the list comprehension to filter out values.
a = [1, 2, 3] [x for x in a if x > 2] #  [x for x in a if x > 1] # [2, 3] [x**2 for x in a if x > 1] # [4, 9]
You can use the
filter styles together.
a = [1, 2, 3] [x**2 for x in a if x > 2] #  [x**2 for x in a if x**2 > 2] # [4, 9]
As a bonus, you can use a list comprehension as a generator if you use round brackets instead of square brackets.
I have hardly need to use this, but if you need to optimize your performance or you are working with large external data in a file or a REST API, you could find this useful. As it reduces how much is processed and stored in memory at once.
a = [1, 2, 3] b = [x**2 for x in a] [1, 4, 9]
c = (x**2 for x in a) c # <generator object <genexpr> at 0x1019d0f90> list(c) [1, 4, 9]
Or you might use a
for loop to iterate over your initial
Here we chain two generators together. Computation is done lazily - both generators are only evaluated at the end.
c = (x**2 for x in a) d = (x+1 for x in c) list(d) # [2, 5, 10]
That is similar to this, which applies both transformations at once.
d = (x**2 + 1 for x in a) list(d) # [2, 5, 10]
Or with function calls.
d = (add_one(square(x)) for x in a) list(d) # [2, 5, 10]
See this a post by
@kalebu - A guide to Python list comprehension
Interested in more of my writing on Python? I have 3 main places where I collect Python material that is useful to me.
- Python topic on Learn to Code - good for beginners but the resources list will useful for all levels.
- Python Cheatsheets
- Python Recipes
Here are all my Python repos on GitHub if you want to see what I like to build.