numpy Part 13 – Operations on Two numpy Arrays

Spread the love

Today we’ll be talking about operations on two numpy arrays.

It’s pretty easy to perform operations on two numpy arrays. The operations are then performed elementwise, so on elements at the same positions in either array.

Here are some examples:

First let’s define our two arrays:

>>> a = np.array([2, 4, 6, 8, 10])
>>> b = np.array([1, 2, 3, 4, 5])
>>> print(a)
[ 2  4  6  8 10]
>>> print(b)
[1 2 3 4 5]

Let’s start with addition:

>>> c = a + b
>>> print(c)
[ 3  6  9 12 15]

And now let’s see how subtraction works:

>>> d = a - b
>>> print(d)
[1 2 3 4 5]

Learn how to make beautiful GUI apps

in Python using the Kivy framework.

Comprehensive, for Kivy beginners, easy to follow.

Get the book here (PDF) or on Amazon:

ebook / paperback (black and white) / paperback (full color)

Here’s multiplication:

>>> e = a * b
>>> print(e)
[ 2  8 18 32 50]

And here’s true division:

>>> f = a / b
>>> print(f)
[2. 2. 2. 2. 2.]

You can also perform the operation of exponentiation:

>>> g = a ** b
>>> print(g)
[     2     16    216   4096 100000]

Your Panda3D Magazine

Make Awesome Games and Other 3D Apps

with Panda3D and Blender using Python.

Cool stuff, easy to follow articles.

Get the magazine here (PDF).

It’s also possible to make complex expressions with both arrays and scalars:

>>> print(5 * a + (b ** 2) - a)
[ 9 20 33 48 65]

Here are some examples with multidimensional arrays:

>>> x = np.array([[2, 4, 6], [3, 6, 9]])
>>> y = np.array([[5, 10, 15], [4, 8, 12]])
>>> print(x)
[[2 4 6]
 [3 6 9]]
>>> print(y)
[[ 5 10 15]
 [ 4  8 12]]
>>> print(x + y)
[[ 7 14 21]
 [ 7 14 21]]
>>> print(x * y)
[[ 10  40  90]
 [ 12  48 108]]

As the operations are performed elementwise, the arrays should have tha same shapes. Otherwise, we get an error:

>>> m = np.array([2, 4, 6, 8])
>>> n = np.array([5, 10])
>>> print(m + n)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: operands could not be broadcast together with shapes (4,) (2,)