NumPy Arrays

The NumPy array, technically called an ndarray, is a fast, memory-efficient structure for storing and processing numeric data - the fundamental building block for nearly all numerical computing in Python.

Creating Arrays

import numpy as np

arr1 = np.array([1, 2, 3, 4, 5])
print(arr1)          # [1 2 3 4 5]
print(type(arr1))    # <class 'numpy.ndarray'>

arr2 = np.array([[1, 2, 3], [4, 5, 6]])   # 2D array
print(arr2)

Common Array Creation Functions

import numpy as np

print(np.zeros(5))           # [0. 0. 0. 0. 0.]
print(np.ones((2, 3)))       # 2x3 array of ones
print(np.arange(0, 10, 2))   # [0 2 4 6 8]
print(np.linspace(0, 1, 5))  # 5 evenly spaced values between 0 and 1
print(np.random.rand(3))     # 3 random floats between 0 and 1

Why NumPy Arrays Instead of Python Lists?

Unlike a Python list, a NumPy array stores elements of a single data type in contiguous memory, allowing mathematical operations to run dramatically faster through vectorization - performing calculations on entire arrays at once instead of looping element by element.

Coming Up Next

Next, you'll learn how to perform mathematical operations directly on NumPy arrays.