Skip to content
Contact →

02-NumPy-Operations

Updated
17 Sept 2026
1 min read
6 sections
Page 9 of 117
Python & Machine Learning

NumPy Operations

Arithmetic

You can easily perform array with array arithmetic, or scalar with array arithmetic. Let's see some examples:

python

arr = np.arange(0,10)
arr
Out · result
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
python
arr + arr
python
arr * arr
python
arr - arr
python
# This will raise a Warning on division by zero, but not an error!
# It just fills the spot with nan
arr/arr
python
# Also a warning (but not an error) relating to infinity
1/arr
python
arr**3

Universal Array Functions

NumPy comes with many universal array functions, or ufuncs, which are essentially just mathematical operations that can be applied across the array.Let's show some common ones:

python
# Taking Square Roots
np.sqrt(arr)
python
# Calculating exponential (e^)
np.exp(arr)
python
# Trigonometric Functions like sine
np.sin(arr)
python
# Taking the Natural Logarithm
np.log(arr)

Summary Statistics on Arrays

NumPy also offers common summary statistics like sum, mean and max. You would call these as methods on an array.

python
arr = np.arange(0,10)
arr
python
arr.sum()
Out · result
45
python
arr.mean()
Out · result
4.5
python
arr.max()
Out · result
9
Other summary statistics include: 
 arr.min() returns 0                   minimum arr.var() returns 8.25                variance arr.std() returns 2.8722813232690143  standard deviation

Axis Logic

When working with 2-dimensional arrays (matrices) we have to consider rows and columns. This becomes very important when we get to the section on pandas. In array terms, axis 0 (zero) is the vertical axis (rows), and axis 1 is the horizonal axis (columns). These values (0,1) correspond to the order in which arr.shape values are returned.

Let's see how this affects our summary statistic calculations from above.

python
arr_2d = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])
arr_2d
Out · result
array([[ 1,  2,  3,  4],
       [ 5,  6,  7,  8],
       [ 9, 10, 11, 12]])
python
arr_2d.sum(axis=0)
Out · result
array([15, 18, 21, 24])

By passing in axis=0, we're returning an array of sums along the vertical axis, essentially [(1+5+9), (2+6+10), (3+7+11), (4+8+12)]

python
arr_2d.shape
Out · result
(3, 4)

This tells us that arr_2d has 3 rows and 4 columns.

In arr_2d.sum(axis=0) above, the first element in each row was summed, then the second element, and so forth.

So what should arr_2d.sum(axis=1) return?

python
# THINK ABOUT WHAT THIS WILL RETURN BEFORE RUNNING THE CELL!
arr_2d.sum(axis=1)

Great Job!

That's all we need to know for now!