How to use filter, map, and reduce in Python 3

The Question :

334 people think this question is useful

filter, map, and reduce work perfectly in Python 2. Here is an example:

>>> def f(x):
        return x % 2 != 0 and x % 3 != 0
>>> filter(f, range(2, 25))
[5, 7, 11, 13, 17, 19, 23]

>>> def cube(x):
        return x*x*x
>>> map(cube, range(1, 11))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

>>> def add(x,y):
        return x+y
>>> reduce(add, range(1, 11))

But in Python 3, I receive the following outputs:

>>> filter(f, range(2, 25))
<filter object at 0x0000000002C14908>

>>> map(cube, range(1, 11))
<map object at 0x0000000002C82B70>

>>> reduce(add, range(1, 11))
Traceback (most recent call last):
  File "<pyshell#8>", line 1, in <module>
    reduce(add, range(1, 11))
NameError: name 'reduce' is not defined

I would appreciate if someone could explain to me why this is.

Screenshot of code for further clarity:

IDLE sessions of Python 2 and 3 side-by-side

The Question Comments :
  • In short, list is not the only datatype. If you want a list, say you want a list. But in most cases, you want something else anyway.

The Answer 1

361 people think this answer is useful

You can read about the changes in What’s New In Python 3.0. You should read it thoroughly when you move from 2.x to 3.x since a lot has been changed.

The whole answer here are quotes from the documentation.

Views And Iterators Instead Of Lists

Some well-known APIs no longer return lists:

  • […]
  • map() and filter() return iterators. If you really need a list, a quick fix is e.g. list(map(...)), but a better fix is often to use a list comprehension (especially when the original code uses lambda), or rewriting the code so it doesn’t need a list at all. Particularly tricky is map() invoked for the side effects of the function; the correct transformation is to use a regular for loop (since creating a list would just be wasteful).
  • […]


  • […]
  • Removed reduce(). Use functools.reduce() if you really need it; however, 99 percent of the time an explicit for loop is more readable.
  • […]

The Answer 2

89 people think this answer is useful

The functionality of map and filter was intentionally changed to return iterators, and reduce was removed from being a built-in and placed in functools.reduce.

So, for filter and map, you can wrap them with list() to see the results like you did before.

>>> def f(x): return x % 2 != 0 and x % 3 != 0
>>> list(filter(f, range(2, 25)))
[5, 7, 11, 13, 17, 19, 23]
>>> def cube(x): return x*x*x
>>> list(map(cube, range(1, 11)))
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]
>>> import functools
>>> def add(x,y): return x+y
>>> functools.reduce(add, range(1, 11))

The recommendation now is that you replace your usage of map and filter with generators expressions or list comprehensions. Example:

>>> def f(x): return x % 2 != 0 and x % 3 != 0
>>> [i for i in range(2, 25) if f(i)]
[5, 7, 11, 13, 17, 19, 23]
>>> def cube(x): return x*x*x
>>> [cube(i) for i in range(1, 11)]
[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

They say that for loops are 99 percent of the time easier to read than reduce, but I’d just stick with functools.reduce.

Edit: The 99 percent figure is pulled directly from the What’s New In Python 3.0 page authored by Guido van Rossum.

The Answer 3

12 people think this answer is useful

As an addendum to the other answers, this sounds like a fine use-case for a context manager that will re-map the names of these functions to ones which return a list and introduce reduce in the global namespace.

A quick implementation might look like this:

from contextlib import contextmanager    

def noiters(*funcs):
    if not funcs: 
        funcs = [map, filter, zip] # etc
    from functools import reduce
    globals()[reduce.__name__] = reduce
    for func in funcs:
        globals()[func.__name__] = lambda *ar, func = func, **kwar: list(func(*ar, **kwar))
        del globals()[reduce.__name__]
        for func in funcs: globals()[func.__name__] = func

With a usage that looks like this:

with noiters(map):
    from operator import add
    print(reduce(add, range(1, 20)))
    print(map(int, ['1', '2']))

Which prints:

[1, 2]

Just my 2 cents 🙂

The Answer 4

7 people think this answer is useful

Since the reduce method has been removed from the built in function from Python3, don’t forget to import the functools in your code. Please look at the code snippet below.

import functools
my_list = [10,15,20,25,35]
sum_numbers = functools.reduce(lambda x ,y : x+y , my_list)

The Answer 5

3 people think this answer is useful

One of the advantages of map, filter and reduce is how legible they become when you “chain” them together to do something complex. However, the built-in syntax isn’t legible and is all “backwards”. So, I suggest using the PyFunctional package ( Here’s a comparison of the two:

flight_destinations_dict = {'NY': {'London', 'Rome'}, 'Berlin': {'NY'}}

PyFunctional version

Very legible syntax. You can say:

“I have a sequence of flight destinations. Out of which I want to get the dict key if city is in the dict values. Finally, filter out the empty lists I created in the process.”

from functional import seq  # PyFunctional package to allow easier syntax

def find_return_flights_PYFUNCTIONAL_SYNTAX(city, flight_destinations_dict):
    return seq(flight_destinations_dict.items()) \
        .map(lambda x: x[0] if city in x[1] else []) \
        .filter(lambda x: x != []) \

Default Python version

It’s all backwards. You need to say:

“OK, so, there’s a list. I want to filter empty lists out of it. Why? Because I first got the dict key if the city was in the dict values. Oh, the list I’m doing this to is flight_destinations_dict.”

def find_return_flights_DEFAULT_SYNTAX(city, flight_destinations_dict):
    return list(
        filter(lambda x: x != [],
               map(lambda x: x[0] if city in x[1] else [], flight_destinations_dict.items())

The Answer 6

2 people think this answer is useful

Here are the examples of Filter, map and reduce functions.

numbers = [10,11,12,22,34,43,54,34,67,87,88,98,99,87,44,66]


oddNumbers = list(filter(lambda x: x%2 != 0, numbers))



multiplyOf2 = list(map(lambda x: x*2, numbers))



The reduce function, since it is not commonly used, was removed from the built-in functions in Python 3. It is still available in the functools module, so you can do:

from functools import reduce

sumOfNumbers = reduce(lambda x,y: x+y, numbers)


The Answer 7

0 people think this answer is useful
from functools import reduce

def f(x):
    return x % 2 != 0 and x % 3 != 0

print(*filter(f, range(2, 25)))
#[5, 7, 11, 13, 17, 19, 23]

def cube(x):
    return x**3
print(*map(cube, range(1, 11)))
#[1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

def add(x,y):
    return x+y

reduce(add, range(1, 11))

It works as is. To get the output of map use * or list

The Answer 8

0 people think this answer is useful


Try to understand the difference between a normal def defined function and lambda function. This is a program that returns the cube of a given value:

# Python code to illustrate cube of a number 
# showing difference between def() and lambda(). 
def cube(y): 
    return y*y*y 
lambda_cube = lambda y: y*y*y 
# using the normally 
# defined function 
# using the lamda function 



Without using Lambda:

  • Here, both of them return the cube of a given number. But, while using def, we needed to define a function with a name cube and needed to pass a value to it. After execution, we also needed to return the result from where the function was called using the return keyword.

Using Lambda:

  • Lambda definition does not include a “return” statement, it always contains an expression that is returned. We can also put a lambda definition anywhere a function is expected, and we don’t have to assign it to a variable at all. This is the simplicity of lambda functions.

Lambda functions can be used along with built-in functions like filter(), map() and reduce().

lambda() with filter()

The filter() function in Python takes in a function and a list as arguments. This offers an elegant way to filter out all the elements of a sequence “sequence”, for which the function returns True.

my_list = [1, 5, 4, 6, 8, 11, 3, 12]

new_list = list(filter(lambda x: (x%2 == 0) , my_list))


ages = [13, 90, 17, 59, 21, 60, 5]

adults = list(filter(lambda age: age>18, ages)) 
print(adults) # above 18 yrs 


[4, 6, 8, 12]
[90, 59, 21, 60]

lambda() with map()

The map() function in Python takes in a function and a list as an argument. The function is called with a lambda function and a list and a new list is returned which contains all the lambda modified items returned by that function for each item.

my_list = [1, 5, 4, 6, 8, 11, 3, 12]

new_list = list(map(lambda x: x * 2 , my_list))


cities = ['novi sad', 'ljubljana', 'london', 'new york', 'paris'] 
# change all city names 
# to upper case and return the same 
uppered_cities = list(map(lambda city: str.upper(city), cities)) 


[2, 10, 8, 12, 16, 22, 6, 24]


reduce() works differently than map() and filter(). It does not return a new list based on the function and iterable we’ve passed. Instead, it returns a single value.

Also, in Python 3 reduce() isn’t a built-in function anymore, and it can be found in the functools module.

The syntax is:

reduce(function, sequence[, initial])

reduce() works by calling the function we passed for the first two items in the sequence. The result returned by the function is used in another call to function alongside with the next (third in this case), element.

The optional argument initial is used, when present, at the beginning of this “loop” with the first element in the first call to function. In a way, the initial element is the 0th element, before the first one, when provided.

lambda() with reduce()

The reduce() function in Python takes in a function and a list as an argument. The function is called with a lambda function and an iterable and a new reduced result is returned. This performs a repetitive operation over the pairs of the iterable.

from functools import reduce

my_list = [1, 1, 2, 3, 5, 8, 13, 21, 34] 

sum = reduce((lambda x, y: x + y), my_list) 

print(sum) # sum of a list
print("With an initial value: " + str(reduce(lambda x, y: x + y, my_list, 100)))

With an initial value: 188

These functions are convenience functions. They are there so you can avoid writing more cumbersome code, but avoid using both them and lambda expressions too much, because “you can”, as it can often lead to illegible code that’s hard to maintain. Use them only when it’s absolutely clear what’s going on as soon as you look at the function or lambda expression.

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