Arithmetic operations
In basic Python, if you have two lists, and you want to add up the elements across both lists, you will need to use a loop.
x = [1, 2, 3]
y = [4, 5, 6]
z = []
for (i, j) in zip(x, y):
z.append(i + j)
print(z) ## [5, 7, 9]
In Numpy, you can easily perform such element-wise operations more efficiently and more compactly. No loops required! This process is called vectorisation (or a vectorised operation).
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])
z = x + y
print(z) ## [5 7 9]
Here are some examples:
x = np.array([1, 2, 3, 4])
y = np.array([0, 1, 2, 3])
print(x + 2) ## [3 4 5 6]
print(x - y) ## [1 1 1 1]
print(x < 3) ## [ True True False False]
print(x + y > 5) ## [False False False True]
a = np.array([[1,2], [3,4]])
b = np.array([[2,3], [4,5]])
# multiplying an array with a scalar
print(a * 3)
## [[ 3 6]
## [ 9 12]]
# elementwise multiplication
print(a * b)
## [[ 2 6]
## [12 20]]
# elementwise division
c = np.array([1, 2])
d = np.array([3, 4])
print(c / d)
## [0.33333333 0.5 ]
Matrix multiplication can be performed using the @ operator or np.matmul(). Make sure you note the difference between a*b and a@b!
print(a@b)
## [[10 13]
## [22 29]]
print(np.matmul(a, b))
## [[10 13]
## [22 29]]
To compute the inner product of two vectors (1D arrays), use np.dot() (dot product).
x = np.array([1, 2, 3])
y = np.array([2, 3, 4])
print(np.dot(x, y)) ## 20 == (1*2)+(2*3)+(3*4)