Arrays - ones() and zeros()

NumPy - The Basics

NumPyPython

numpy.zeros() → creates an array filled with 0s.
numpy.ones() → creates an array filled with 1s.
Both functions let you specify the shape and optionally the data type of the array.

numpy.zeros(shape, dtype=float)

numpy.ones(shape, dtype=float)

shape → tuple or int defining array dimensions.
dtype → (optional) set the type of elements (e.g., int, float, complex). Default is float.

import numpy as np

# 1D array of zeros

np.zeros(5)

# → [0. 0. 0. 0. 0.]

# 2D array of zeros (3 rows, 4 columns)

np.zeros((3, 4))

# →

# [[0. 0. 0. 0.]

# [0. 0. 0. 0.]

# [0. 0. 0. 0.]]

# 1D array of ones

np.ones(5)

# → [1. 1. 1. 1. 1.]

# 2D array of ones (2x3)

np.ones((2, 3))

# →

# [[1. 1. 1.]

# [1. 1. 1.]]

# Specify data type (integers instead of floats)

np.zeros((2, 2), dtype=int)

# →

# [[0 0]

# [0 0]]

np.ones((2, 2), dtype=int)

# →

# [[1 1]

# [1 1]]

Initializing placeholder arrays for computations.
Creating masks or matrices for linear algebra, machine learning, or simulations.
Serving as a base before filling arrays with other values.

Use np.zeros() → when you need an array of all 0s.
Use np.ones() → when you need an array of all 1s.
Both are quick and essential for initialising arrays in numerical computing.