Arrays - ones() and zeros()

What They Do

• 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.

Function Signatures

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.

Examples

import numpy as np​# 1D array of zerosnp.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 onesnp.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]]

Key Use Cases

• 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.

In short

• 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.