[python] NumPy array initialization (fill with identical values)

I need to create a NumPy array of length n, each element of which is v.

Is there anything better than:

a = empty(n)
for i in range(n):
    a[i] = v

I know zeros and ones would work for v = 0, 1. I could use v * ones(n), but it won't work when v is None, and also would be much slower.

Apparently, not only the absolute speeds but also the speed order (as reported by user1579844) are machine dependent; here's what I found:

a=np.empty(1e4); a.fill(5) is fastest;

In descending speed order:

timeit a=np.empty(1e4); a.fill(5) 
# 100000 loops, best of 3: 10.2 us per loop
timeit a=np.empty(1e4); a[:]=5
# 100000 loops, best of 3: 16.9 us per loop
timeit a=np.ones(1e4)*5
# 100000 loops, best of 3: 32.2 us per loop
timeit a=np.tile(5,[1e4])
# 10000 loops, best of 3: 90.9 us per loop
timeit a=np.repeat(5,(1e4))
# 10000 loops, best of 3: 98.3 us per loop
timeit a=np.array([5]*int(1e4))
# 1000 loops, best of 3: 1.69 ms per loop (slowest BY FAR!)

So, try and find out, and use what's fastest on your platform.

without numpy

>>>[2]*3
[2, 2, 2]

I had

numpy.array(n * [value])

in mind, but apparently that is slower than all other suggestions for large enough n.

Here is full comparison with perfplot (a pet project of mine).

The two empty alternatives are still the fastest (with NumPy 1.12.1). full catches up for large arrays.

Code to generate the plot:

import numpy as np
import perfplot


def empty_fill(n):
    a = np.empty(n)
    a.fill(3.14)
    return a


def empty_colon(n):
    a = np.empty(n)
    a[:] = 3.14
    return a


def ones_times(n):
    return 3.14 * np.ones(n)


def repeat(n):
    return np.repeat(3.14, (n))


def tile(n):
    return np.repeat(3.14, [n])


def full(n):
    return np.full((n), 3.14)


def list_to_array(n):
    return np.array(n * [3.14])


perfplot.show(
    setup=lambda n: n,
    kernels=[empty_fill, empty_colon, ones_times, repeat, tile, full, list_to_array],
    n_range=[2 ** k for k in range(27)],
    xlabel="len(a)",
    logx=True,
    logy=True,
)

I believe fill is the fastest way to do this.

a = np.empty(10)
a.fill(7)

You should also always avoid iterating like you are doing in your example. A simple a[:] = v will accomplish what your iteration does using numpy broadcasting.

Updated for Numpy 1.7.0:(Hat-tip to @Rolf Bartstra.)

a=np.empty(n); a.fill(5) is fastest.

In descending speed order:

%timeit a=np.empty(1e4); a.fill(5)
100000 loops, best of 3: 5.85 us per loop

%timeit a=np.empty(1e4); a[:]=5 
100000 loops, best of 3: 7.15 us per loop

%timeit a=np.ones(1e4)*5
10000 loops, best of 3: 22.9 us per loop

%timeit a=np.repeat(5,(1e4))
10000 loops, best of 3: 81.7 us per loop

%timeit a=np.tile(5,[1e4])
10000 loops, best of 3: 82.9 us per loop

You can use numpy.tile, e.g. :

v = 7
rows = 3
cols = 5
a = numpy.tile(v, (rows,cols))
a
Out[1]: 
array([[7, 7, 7, 7, 7],
       [7, 7, 7, 7, 7],
       [7, 7, 7, 7, 7]])

Although tile is meant to 'tile' an array (instead of a scalar, as in this case), it will do the job, creating pre-filled arrays of any size and dimension.