Review: NumPy Arithmetics

Module 3: NumPy

Session 3.2: Arithmetics

import numpy as np

Exercise

Which symbol should I use to compute the matrix multiplication of 2D NumPy arrays?

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])

# Option A
result = a * b

# Option B
result = a.b

# Option C
result = a @ b

# Option D
result = np.matmul(a, b)

Exercise

What is going to be the final value of x?

x = np.array([[1, 2], [3, 4]])
x[0] = 0

print(x)

::: {.callout-tip collapse=“true”} ## Solution In the example above, NumPy performs broadcasting: it assigns the value 0 to every element of the first row.

Similarly, we can assign a value per element of the row, using an iterable:

x = np.array([[1, 2], [3, 4]])
x[0] = [9, 8]

print(x)

Another example of broadcasting occurs when we add 1 to the whole array:

x = np.array([[1, 2], [3, 4]])
x[0] = 0
x = x + 1

print(x)

Broadcasting is a property of NumPy arrays, you cannot do it with lists!

x = [[1, 2], [3, 4]]
x[0] = 0
:::
# The line above does not broadcast!
# It replaces the nested list [1, 2] with the integer 0
print(x)

x = [[1, 2], [3, 4]]
x = x + 1
# List cannot add the value 1 to every element,
# because it does not broadcast!
print(x)

Exercise

You want to add the vectors \([1, 2, 3]\) and \([4, 5, 6]\). The code raises an error on the first line TypeError: array() takes from 1 to 2 positional arguments but 3 were given. Explain and fix all the errors in the code.

x = np.array(1, 2, 3)
y = np.arange(4, 6, 1)

print(x + y)

# Fixing the code above
x = np.array([1, 2, 3])
y = np.arange(4, 6, 1)
print(y)

# But this fails. Do you understand why?
print(x + y)

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