How do I create an array where every entry is the same value? I know numpy.ones() and numpy.zeros() do this for 1's and 0's, but what about -1?
For example:
>>import numpy as np
>>np.zeros((3,3))
array([[ 0., 0., 0.], [ 0., 0., 0.], [ 0., 0., 0.]])
>>np.ones((2,5))
array([[ 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1.]])
>>np.negative_ones((2,5))
???7 Answers
Use np.full() as follows:
np.full((2, 5), -1.)Returns:
array([[-1., -1., -1., -1., -1.], [-1., -1., -1., -1., -1.]])I don't know if there's a nice one-liner without an arithmetic operation, but probably the fastest approach is to create an uninitialized array using empty and then use .fill() to set the values. For comparison:
>>> timeit m = np.zeros((3,3)); m += -1
100000 loops, best of 3: 6.9 us per loop
>>> timeit m = np.ones((3,3)); m *= -1
100000 loops, best of 3: 9.49 us per loop
>>> timeit m = np.zeros((3,3)); m.fill(-1)
100000 loops, best of 3: 2.31 us per loop
>>> timeit m = np.empty((3,3)); m[:] = -1
100000 loops, best of 3: 3.18 us per loop
>>> timeit m = np.empty((3,3)); m.fill(-1)
100000 loops, best of 3: 2.09 us per loopbut to be honest, I tend to either add to the zero matrix or multiply the ones matrix instead, as initialization is seldom a bottleneck.
4-1 * np.ones((2,5))
Multplying by the number you need in the matrix will do the trick.
In [5]: -1 * np.ones((2,5))
Out[5]:
array([[-1., -1., -1., -1., -1.], [-1., -1., -1., -1., -1.]])
In [6]: 5 * np.ones((2,5))
Out[6]:
array([[ 5., 5., 5., 5., 5.], [ 5., 5., 5., 5., 5.]]) For an array of -1s
-1 * np.ones((2,5))Simply multiply with the constant.
How about:
[[-1]*n]*mwhere n is the number of columns and m is the number of rows?
foo = np.repeat(10, 50).reshape((5,10))Will create a 5x10 matrix of 10s.
According to me, these are the good way to create an array with specified value
arr=[value for x in range(num)]
or
[VALUE]*NUM
where num is the length of Array & value is the specified value.
