Is there a more concise, efficient or simply pythonic way to do the following?

def product(list):
    p = 1
    for i in list:
        p *= i
    return p

EDIT:

I actually find that this is marginally faster than using operator.mul:

from operator import mul
# from functools import reduce # python3 compatibility

def with_lambda(list):
    reduce(lambda x, y: x * y, list)

def without_lambda(list):
    reduce(mul, list)

def forloop(list):
    r = 1
    for x in list:
        r *= x
    return r

import timeit

a = range(50)
b = range(1,50)#no zero
t = timeit.Timer("with_lambda(a)", "from __main__ import with_lambda,a")
print("with lambda:", t.timeit())
t = timeit.Timer("without_lambda(a)", "from __main__ import without_lambda,a")
print("without lambda:", t.timeit())
t = timeit.Timer("forloop(a)", "from __main__ import forloop,a")
print("for loop:", t.timeit())

t = timeit.Timer("with_lambda(b)", "from __main__ import with_lambda,b")
print("with lambda (no 0):", t.timeit())
t = timeit.Timer("without_lambda(b)", "from __main__ import without_lambda,b")
print("without lambda (no 0):", t.timeit())
t = timeit.Timer("forloop(b)", "from __main__ import forloop,b")
print("for loop (no 0):", t.timeit())

gives me

('with lambda:', 17.755449056625366)
('without lambda:', 8.2084708213806152)
('for loop:', 7.4836349487304688)
('with lambda (no 0):', 22.570688009262085)
('without lambda (no 0):', 12.472226858139038)
('for loop (no 0):', 11.04065990447998)

Without using lambda:

from operator import mul
reduce(mul, list, 1)

it is better and faster. With python 2.7.5

from operator import mul
import numpy as np
import numexpr as ne
# from functools import reduce # python3 compatibility

a = range(1, 101)
%timeit reduce(lambda x, y: x * y, a)   # (1)
%timeit reduce(mul, a)                  # (2)
%timeit np.prod(a)                      # (3)
%timeit ne.evaluate("prod(a)")          # (4)

In the following configuration:

a = range(1, 101)  # A
a = np.array(a)    # B
a = np.arange(1, 1e4, dtype=int) #C
a = np.arange(1, 1e5, dtype=float) #D

Results with python 2.7.5

       |     1     |     2     |     3     |     4     |
-------+-----------+-----------+-----------+-----------+
 A       20.8 µs     13.3 µs     22.6 µs     39.6 µs     
 B        106 µs     95.3 µs     5.92 µs     26.1 µs
 C       4.34 ms     3.51 ms     16.7 µs     38.9 µs
 D       46.6 ms     38.5 ms      180 µs      216 µs

Result: np.prod is the fastest one, if you use np.array as data structure (18x for small array, 250x for large array)

with python 3.3.2:

       |     1     |     2     |     3     |     4     |
-------+-----------+-----------+-----------+-----------+
 A       23.6 µs     12.3 µs     68.6 µs     84.9 µs     
 B        133 µs      107 µs     7.42 µs     27.5 µs
 C       4.79 ms     3.74 ms     18.6 µs     40.9 µs
 D       48.4 ms     36.8 ms      187 µs      214 µs

Is python 3 slower?

reduce(lambda x, y: x * y, list, 1)

if you just have numbers in your list:

from numpy import prod
prod(list)

EDIT: as pointed out by @off99555 this does not work for large integer results in which case it returns a result of type numpy.int64 while Ian Clelland’s solution based on operator.mul and reduce works for large integer results because it returns long.

Well if you really wanted to make it one line without importing anything you could do:

eval('*'.join(str(item) for item in list))

But don’t.

import operator
reduce(operator.mul, list, 1)

Starting Python 3.8, a prod function has been included to the math module in the standard library:

math.prod(iterable, *, start=1)

which returns the product of a start value (default: 1) times an iterable of numbers:

import math

math.prod([2, 3, 4]) # 24

Note that if the iterable is empty, this will produce 1 (or the start value if provided).

I remember some long discussions on comp.lang.python (sorry, too lazy to produce pointers now) which concluded that your original product() definition is the most Pythonic.

Note that the proposal is not to write a for loop every time you want to do it, but to write a function once (per type of reduction) and call it as needed! Calling reduction functions is very Pythonic – it works sweetly with generator expressions, and since the sucessful introduction of sum(), Python keeps growing more and more builtin reduction functions – any() and all() are the latest additions…

This conclusion is kinda official – reduce() was removed from builtins in Python 3.0, saying:

“Use functools.reduce() if you really need it; however, 99 percent of the time an explicit for loop is more readable.”

See also The fate of reduce() in Python 3000 for a supporting quote from Guido (and some less supporting comments by Lispers that read that blog).

P.S. if by chance you need product() for combinatorics, see math.factorial() (new 2.6).

The intent of this answer is to provide a calculation that is useful in certain circumstances — namely when a) there are a large number of values being multiplied such that the final product may be extremely large or extremely small, and b) you don’t really care about the exact answer, but instead have a number of sequences, and want to be able to order them based on each one’s product.

If you want to multiply the elements of a list, where l is the list, you can do:

import math
math.exp(sum(map(math.log, l)))

Now, that approach is not as readable as

from operator import mul
reduce(mul, list)

If you’re a mathematician who isn’t familiar with reduce() the opposite might be true, but I wouldn’t advise using it under normal circumstances. It’s also less readable than the product() function mentioned in the question (at least to non-mathematicians).

However, if you’re ever in a situation where you risk underflow or overflow, such as in

>>> reduce(mul, [10.]*309)
inf

and your purpose is to compare the products of different sequences rather than to know what the products are, then

>>> sum(map(math.log, [10.]*309))
711.49879373515785

is the way to go because it’s virtually impossible to have a real-world problem in which you would overflow or underflow with this approach. (The larger the result of that calculation is, the larger the product would be if you could calculate it.)

I’ve tested various solutions with perfplot (a small project of mine) and found that

numpy.prod(lst)

is by far the fastest solution (if the list isn’t very short).

Code to reproduce the plot:

import perfplot
import numpy

import math
from operator import mul
from functools import reduce

from itertools import accumulate


def reduce_lambda(lst):
    return reduce(lambda x, y: x * y, lst)


def reduce_mul(lst):
    return reduce(mul, lst)


def forloop(lst):
    r = 1
    for x in lst:
        r *= x
    return r


def numpy_prod(lst):
    return numpy.prod(lst)


def math_prod(lst):
    return math.prod(lst)


def itertools_accumulate(lst):
    for value in accumulate(lst, mul):
        pass
    return value


perfplot.show(
    setup=numpy.random.rand,
    kernels=[reduce_lambda, reduce_mul, forloop, numpy_prod, itertools_accumulate, math_prod],
    n_range=[2 ** k for k in range(15)],
    xlabel="len(a)",
    logx=True,
    logy=True,
)

I am surprised no-one has suggested using itertools.accumulate with operator.mul. This avoids using reduce, which is different for Python 2 and 3 (due to the functools import required for Python 3), and moreover is considered un-pythonic by Guido van Rossum himself:

from itertools import accumulate
from operator import mul

def prod(lst):
    for value in accumulate(lst, mul):
        pass
    return value

Example:

prod([1,5,4,3,5,6])
# 1800

One option is to use numba and the @jit or @njit decorator. I also made one or two little tweaks to your code (at least in Python 3, “list” is a keyword that shouldn’t be used for a variable name):

@njit
def njit_product(lst):
    p = lst[0]  # first element
    for i in lst[1:]:  # loop over remaining elements
        p *= i
    return p

For timing purposes, you need to run once to compile the function first using numba. In general, the function will be compiled the first time it is called, and then called from memory after that (faster).

njit_product([1, 2])  # execute once to compile

Now when you execute your code, it will run with the compiled version of the function. I timed them using a Jupyter notebook and the %timeit magic function:

product(b)  # yours
# 32.7 µs ± 510 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

njit_product(b)
# 92.9 µs ± 392 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Note that on my machine, running Python 3.5, the native Python for loop was actually the fastest. There may be a trick here when it comes to measuring numba-decorated performance with Jupyter notebooks and the %timeit magic function. I am not sure that the timings above are correct, so I recommend trying it out on your system and seeing if numba gives you a performance boost.

The fastest way I found was, using while:

mysetup = '''
import numpy as np
from find_intervals import return_intersections 
'''

# code snippet whose execution time is to be measured
mycode = '''

x = [4,5,6,7,8,9,10]
prod = 1
i = 0
while True:
    prod = prod * x[i]
    i = i + 1
    if i == len(x):
        break
'''

# timeit statement for while:
print("using while : ",
timeit.timeit(setup=mysetup,
              stmt=mycode))

# timeit statement for mul:
print("using mul : ",
    timeit.timeit('from functools import reduce;
    from operator import mul;
    c = reduce(mul, [4,5,6,7,8,9,10])'))

# timeit statement for mul:
print("using lambda : ",      
    timeit.timeit('from functools import reduce;
    from operator import mul;
    c = reduce(lambda x, y: x * y, [4,5,6,7,8,9,10])'))

and the timings are:

>>> using while : 0.8887967770060641

>>> using mul : 2.0838719510065857

>>> using lambda : 2.4227715369997895

Python 3 result for the OP’s tests: (best of 3 for each)

with lambda: 18.978000981995137
without lambda: 8.110567473006085
for loop: 10.795806062000338
with lambda (no 0): 26.612515013999655
without lambda (no 0): 14.704098362999503
for loop (no 0): 14.93075215499266

This also works though its cheating

def factorial(n):
    x=[]
    if n <= 1:
        return 1
    else:
        for i in range(1,n+1): 
            p*=i
            x.append(p)
        print x[n-1]