Is there a built-in method or function to map a function on a numpy.ndarray?

Question

I would like to apply a function to every element of a numpy.ndarray, something like this:

import numpy
import math

a = numpy.arange(10).reshape(2,5)
b = map(math.sin, a)
print b

but this gives:

TypeError: only length-1 arrays can be converted to Python scalars

I know I can do this:

import numpy
import math
a = numpy.arange(10).reshape(2,5)

def recursive_map(function, value):
    if isinstance(value, (list, numpy.ndarray)):
        out = numpy.array(map(lambda x: recursive_map(function, x), value))
    else:
        out = function(value)
    return out
c = recursive_map(math.sin, a)
print c

My question is: is there a built-in function or method to do this? It seems elementary, but I haven't been able to find it. I am using Python 2.7.

You might want to look into this question stackoverflow.com/questions/7701429/… — Konstantin
– Konstantin, Commented May 19, 2015 at 15:56

b4hand · Accepted Answer · 2015-05-19 16:04:53Z

5

Use np.sin it works element wise on ndarray already.

You can also reshape to a 1D array and the native map should just work. Then you can use reshape again to restore the original dimensions.

You can also use np.vectorize to write functions that can work like np.sin.

edited May 19, 2015 at 16:04

answered May 19, 2015 at 15:50

b4hand

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3 Comments

Konstantin Over a year ago

Relevant link about ufuncs in numpy

user2357112 Over a year ago

Note that the map and np.vectorize options are much, much slower than using NumPy builtins.

boudewijn21 Over a year ago

Now that I know what to look for: for the difference between np.vectorize and np.frompyfunc: see this question

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