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))
55
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:
This question is related to
python
python-3.x
filter
functional-programming
reduce
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
@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))
try:
yield
finally:
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:
190
[1, 2]
Just my 2 cents :-)
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))
55
>>>
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.
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))
#55
It works as is. To get the output of map use * or list
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]
//Filter
oddNumbers = list(filter(lambda x: x%2 != 0, numbers))
print(oddNumbers)
//Map
multiplyOf2 = list(map(lambda x: x*2, numbers))
print(multiplyOf2)
//Reduce
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)
print(sumOfNumbers)
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)
print(sum_numbers)
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
print(cube(5))
# using the lamda function
print(lambda_cube(5))
output:
125
125
Without using Lambda:
Using Lambda:
Lambda functions can be used along with built-in functions like filter()
, map()
and reduce()
.
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))
print(new_list)
ages = [13, 90, 17, 59, 21, 60, 5]
adults = list(filter(lambda age: age>18, ages))
print(adults) # above 18 yrs
output:
[4, 6, 8, 12]
[90, 59, 21, 60]
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))
print(new_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))
print(uppered_cities)
output:
[2, 10, 8, 12, 16, 22, 6, 24]
['NOVI SAD', 'LJUBLJANA', 'LONDON', 'NEW YORK', 'PARIS']
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.
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)))
88
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.
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 (https://pypi.org/project/PyFunctional/).
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())
)
)
Source: Stackoverflow.com