What exactly does numpy.exp() do?

Question

I'm very confused as to what np.exp() actually does. In the documentation it says that it: "Calculates the exponential of all elements in the input array." I'm confused as to what exactly this means. Could someone give me more information to what it actually does?

Metternich

Well, have you tried it? print np.arange(10), np.exp(np.arange(10)) (for python2). – Diseur

Answer 1

The exponential function is e^x where e is a mathematical constant called Euler's number, approximately 2.718281. This value has a close mathematical relationship with pi and the slope of the curve e^x is equal to its value at every point. np.exp() calculates e^x for each value of x in your input array.

Pout

Answer 2

It calculates e^x for each x in your list where e is Euler's number (approximately 2.718). In other words, np.exp(range(5)) is similar to [math.e**x for x in range(5)].

Matthaus

For beginner: math.e is the Euler's number. Try import math; print(math.e) # 2.718281828459045. And ** is Python code for power. – Debris

Answer 3

exp(x) = e^x where e= 2.718281(approx)

In Python we can use the exp function from numpy (docs):

import numpy as np

ar=np.array([1,2,3])
ar=np.exp(ar)
print ar

outputs:

[ 2.71828183  7.3890561  20.08553692]

Hypogynous

Your code gives me following error: ufunc 'exp' output (typecode 'd') could not be coerced to provided output parameter (typecode 'l') according to the casting rule ''same_kind'" – Sinclare

If you change the [1,2,3] to [1.,2.,3.], the error should disappear. I think it happens because of a conversion of the numpy values to object type. So you put them as float, and it works :) or maybe it happens only in Python 2x, I tested in python 3.6 and it worked. – Scopoline

assert np.exp(np.log(2.5)) == 2.5 – Underestimate