How to apply a function with different arguments on a NumPy 1d-array to make a 2d-array-CodePudding

Suppose I have a NumPy 1d-array a:

a = np.array([1, 2, 3])

and I have a function foo:

def foo(x, p):
    ...
    return y

I want to apply foo on a with, say, p from 1 to 3 to make a 2d-array.

CodePudding user response：

Or just:

>>> a[:, None] ** np.arange(1, 4)
array([[ 1,  1,  1],
       [ 2,  4,  8],
       [ 3,  9, 27]], dtype=int32)
>>>

With a function:

def foo(x, p):
    return x ** p

np.apply_along_axis(lambda x: foo(x, np.arange(1, 4)), 1, a[:, None])

array([[ 1,  1,  1],
       [ 2,  4,  8],
       [ 3,  9, 27]], dtype=int32)

CodePudding user response：

In you comment you say you want to give both arguments to function For this purpose you can use map and functools like below:

from functools import partial

a = np.array([1, 2, 3])

def foo(x,y,z):
    return list(z ** y   x)

list(map(partial(foo, z=a), range(1,4), range(1,4)))

Output:

[
 [3, 4, 5],  # [1,2,3]**1 1
 [3, 6, 11], # [1,2,3]**2 2
 [3, 10, 29] # [1,2,3]**3 3
]

CodePudding user response：

Firstly, numpy module support math function .So, if you want to apply mathematic function on an array you only have to write it as normal function or a lambda function, then apply it on your array .For example:

def foo(x,p):
    return numpy.power(x,p)

Note : these much more matematical function in the numpy module . Try to take a look on them :) .