How do I multiply each element in a list by a number?

A blazingly faster approach is to do the multiplication in a vectorized manner instead of looping over the list. Numpy has already provided a very simply and handy way for this that you can use.

>>> import numpy as np
>>> 
>>> my_list = np.array([1, 2, 3, 4, 5])
>>> 
>>> my_list * 5
array([ 5, 10, 15, 20, 25])

Note that this doesn't work with Python's native lists. If you multiply a number with a list it will repeat the items of the as the size of that number.

In [15]: my_list *= 1000

In [16]: len(my_list)
Out[16]: 5000

If you want a pure Python-based approach using a list comprehension is basically the most Pythonic way to go.

In [6]: my_list = [1, 2, 3, 4, 5]

In [7]: [5 * i for i in my_list]
Out[7]: [5, 10, 15, 20, 25]

Beside list comprehension, as a pure functional approach, you can also use built-in map() function as following:

In [10]: list(map((5).__mul__, my_list))
Out[10]: [5, 10, 15, 20, 25]

This code passes all the items within the my_list to 5's __mul__ method and returns an iterator-like object (in python-3.x). You can then convert the iterator to list using list() built in function (in Python-2.x you don't need that because map return a list by default).

benchmarks:

In [18]: %timeit [5 * i for i in my_list]
463 ns ± 10.6 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [19]: %timeit list(map((5).__mul__, my_list))
784 ns ± 10.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [20]: %timeit [5 * i for i in my_list * 100000]
20.8 ms ± 115 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [21]: %timeit list(map((5).__mul__, my_list * 100000))
30.6 ms ± 169 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)


In [24]: arr = np.array(my_list * 100000)

In [25]: %timeit arr * 5
899 µs ± 4.98 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

You can just use a list comprehension:

my_list = [1, 2, 3, 4, 5]
my_new_list = [i * 5 for i in my_list]

>>> print(my_new_list)
[5, 10, 15, 20, 25]

Note that a list comprehension is generally a more efficient way to do a for loop:

my_new_list = []
for i in my_list:
    my_new_list.append(i * 5)

>>> print(my_new_list)
[5, 10, 15, 20, 25]

As an alternative, here is a solution using the popular Pandas package:

import pandas as pd

s = pd.Series(my_list)

>>> s * 5
0     5
1    10
2    15
3    20
4    25
dtype: int64

Or, if you just want the list:

>>> (s * 5).tolist()
[5, 10, 15, 20, 25]

Finally, one could use map, although this is generally frowned upon.

my_new_list = map(lambda x: x * 5, my_list)

Using map, however, is generally less efficient. Per a comment from ShadowRanger on a deleted answer to this question:

The reason "no one" uses it is that, in general, it's a performance pessimization. The only time it's worth considering map in CPython is if you're using a built-in function implemented in C as the mapping function; otherwise, map is going to run equal to or slower than the more Pythonic listcomp or genexpr (which are also more explicit about whether they're lazy generators or eager list creators; on Py3, your code wouldn't work without wrapping the map call in list). If you're using map with a lambda function, stop, you're doing it wrong.

And another one of his comments posted to this reply:

Please don't teach people to use map with lambda; the instant you need a lambda, you'd have been better off with a list comprehension or generator expression. If you're clever, you can make map work without lambdas a lot, e.g. in this case, map((5).__mul__, my_list), although in this particular case, thanks to some optimizations in the byte code interpreter for simple int math, [x * 5 for x in my_list] is faster, as well as being more Pythonic and simpler.