arange and linspace¶

NumPy provides two primary functions for generating sequences of numbers: np.arange for integer steps and np.linspace for evenly spaced floats.

np.arange Function¶

Generates values within a half-open interval [start, stop) with a given step.

import numpy as np

def main():
    a = np.arange(9)
    print("np.arange(9)")
    print(a)

if __name__ == "__main__":
    main()

Output:

np.arange(9)
[0 1 2 3 4 5 6 7 8]

import numpy as np

def main():
    a = np.arange(1, 9)
    print("np.arange(1, 9)")
    print(a)

if __name__ == "__main__":
    main()

Output:

np.arange(1, 9)
[1 2 3 4 5 6 7 8]

import numpy as np

def main():
    a = np.arange(1, 9, 2)
    print("np.arange(1, 9, 2)")
    print(a)

if __name__ == "__main__":
    main()

Output:

np.arange(1, 9, 2)
[1 3 5 7]

Generates evenly spaced numbers over a closed interval [start, stop].

import numpy as np

def main():
    a = np.linspace(-1, 2)
    print("np.linspace(-1, 2)")
    print(a)

if __name__ == "__main__":
    main()

Default is 50 evenly spaced samples.

import numpy as np

def main():
    a = np.linspace(-1, 2, 4)
    print("np.linspace(-1, 2, 4)")
    print(a)

if __name__ == "__main__":
    main()

Output:

np.linspace(-1, 2, 4)
[-1.  0.  1.  2.]

Understanding when to use each function is essential.

np.arange excludes the endpoint; np.linspace includes it by default.

np.arange specifies step size; np.linspace specifies number of samples.

Use np.arange for integer sequences and np.linspace for continuous intervals.

Using np.arange with floats can produce unexpected results.

import numpy as np

a = np.arange(0, 1, 0.1)
print(len(a))  # May vary due to floating-point precision

For floating-point sequences, prefer np.linspace to guarantee exact sample count.

Both functions are essential for numerical computations.

import numpy as np

x = np.linspace(0, 2 * np.pi, 100)
y = np.sin(x)

import numpy as np

indices = np.arange(len(data))

np.linspace combined with np.meshgrid creates 2D coordinate grids.