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Array Creation: zeros, ones, arange, linspace & full
Initializing arrays efficiently without memory reallocation
NumPy provides built-in factory functions to allocate multi-dimensional arrays initialized with zeros, ones, constant values, or linear ranges.
Common Array Initialization Functions
np.zeros(shape) and np.ones(shape): Pre-allocates arrays filled with 0s or 1s.
np.arange(start, stop, step): Generates numbers with a fixed step interval, similar to Python range() but returns an ndarray.
np.linspace(start, stop, num): Generates num evenly spaced floating-point samples over a closed interval [start, stop]. Crucial for plotting mathematical curves.
np.full(shape, fill_value): Allocates an array filled with a specific constant.
import numpy as np
# 5 evenly spaced points between 0 and 1 inclusive
linear_pts = np.linspace(0.0, 1.0, num=5)
print("linspace points:", linear_pts)
# Array from 0 up to (not including) 10 with step of 2
stepped = np.arange(0, 10, step=2)
print("arange stepped:", stepped)
# 3x3 identity matrix
identity = np.eye(3)
print("Identity matrix:
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Test Yourself With Exercise
Which NumPy function generates a specified number of evenly spaced points over a specified interval [start, stop]?