Python NumPy Basics

What Is NumPy?

NumPy (Numerical Python) gives you the ndarray, a grid-like data structure for storing numbers that is much faster and more memory-efficient than a plain Python list. Functions and libraries like pandas, scikit-learn, and TensorFlow all rely on NumPy arrays under the hood, so learning it is a key step toward data science.

pip install numpy

Creating Arrays

You typically create an array from a Python list using np.array(), or generate one with helper functions like np.zeros(), np.ones(), or np.arange().

import numpy as np

nums = np.array([1, 2, 3, 4, 5])
print(nums)
print(type(nums))

zeros = np.zeros(4)
range_arr = np.arange(0, 10, 2)
print(zeros)
print(range_arr)

Vectorised Operations

Unlike a normal list, you can apply an operator directly to an entire array and NumPy applies it element by element. This is called vectorisation, and it removes the need for manual loops, making your code shorter and much faster.

import numpy as np

prices = np.array([100, 200, 300])
discounted = prices * 0.9
print(discounted)

a = np.array([1, 2, 3])
b = np.array([10, 20, 30])
print(a + b)

Shape and Reshape

Every array has a shape, a tuple describing its dimensions, such as (3,) for a 1D array or (2, 3) for a 2D array with 2 rows and 3 columns. reshape() lets you rearrange the same data into a different shape without changing the values.

import numpy as np

grid = np.array([1, 2, 3, 4, 5, 6])
print(grid.shape)

reshaped = grid.reshape(2, 3)
print(reshaped)
print(reshaped.shape)