I am to use the Math Library to do some calculations on an array.
I tried something like this:
import numpy as np
import math
a = np.array([0, 1, 2, 3])
a1 = np.vectorize(a)
print("sin(a) = \n", math.sin(a1))
Unfortunately it does not work. An error occur: "TypeError: must be real number, not vectorize".
How can I use the vectorize function to be able to calculate that kind of things?
The whole point of numpy is that you don't need any math method or any list comprehension:
>>> import numpy as np
>>> a = np.array([0, 1, 2, 3])
>>> a + 1
array([1, 2, 3, 4])
>>> np.sin(a)
array([ 0. , 0.84147098, 0.90929743, 0.14112001])
>>> a ** 2
array([0, 1, 4, 9])
>>> np.exp(a)
array([ 1. , 2.71828183, 7.3890561 , 20.08553692])
You can use a as if it were a scalar and you get the corresponding array.
If you really need to use math.sin (hint: you don't), you can vectorize it (the function itself, not the array):
>>> vsin = np.vectorize(math.sin)
>>> vsin(a)
array([ 0. , 0.84147098, 0.90929743, 0.14112001])
import numpy as np
import math
a = np.array([0, 1, 2, 3])
print("sin(a) = \n", [math.sin(x) for x in a])
math.sin requires one real number at a time.
vectorizeexpects you to pass a function, but you passed an array. Andmath.sinof course expects you to pass a number, but you've passed avectorized(essentially a function). What are you actually trying to accomplish?np.sin(a)(no need to import math; no need to mix these, and especially no need to use vectorize).np.sinis to be avoided is to definef=np.vectorize(math.sin)and applyf(a).