NumPy Constants¶

NumPy provides fundamental mathematical constants and special values.

Version Check¶

Verify the installed NumPy version.

1. Check Version¶

import numpy as np

def main():
    print(f'{np.__version__ = }')

if __name__ == "__main__":
    main()

2. Compatibility¶

Different versions may have different features and behaviors.

Mathematical Constants¶

NumPy includes commonly used mathematical constants.

1. Pi Constant¶

import numpy as np

def main():
    print(f'{np.pi = }')

if __name__ == "__main__":
    main()

Output:

np.pi = 3.141592653589793

2. Euler's Number¶

import numpy as np

def main():
    print(f'{np.e = }')

if __name__ == "__main__":
    main()

Output:

np.e = 2.718281828459045

Trigonometric Plot¶

Use constants for precise mathematical visualizations.

1. Sine and Cosine¶

import numpy as np
import matplotlib.pyplot as plt

def main():
    x = np.linspace(-np.pi, np.pi, 100)
    sin = np.sin(x)
    cos = np.cos(x)

    fig, ax = plt.subplots(figsize=(6.5, 4))

    ax.plot(x, sin, label='sin(x)')
    ax.plot(x, cos, label='cos(x)')

    ax.legend()

    ax.set_xticks((-np.pi, -np.pi/2, 0, np.pi/2, np.pi))
    ax.set_xticklabels(("-$\pi$", "-$\pi$/2", "0", "$\pi$/2", "$\pi$"))

    ax.set_yticks((-1, 1))

    ax.spines['left'].set_position('center')
    ax.spines['bottom'].set_position('center')
    ax.spines['right'].set_color('none')
    ax.spines['top'].set_color('none')

    plt.show()

if __name__ == "__main__":
    main()

2. Axis Styling¶

Position spines at center and hide top/right borders for clean mathematical plots.

Special Value np.nan¶

Represent missing or undefined numerical data.

1. Creating NaN Arrays¶

import numpy as np

def main():
    x = np.array([
        [93., 84., 73., 68.],
        [97., 67., 57., np.nan],
        [87., 87., np.nan, 77.]
    ])
    print(x)

if __name__ == "__main__":
    main()

Output:

[[93. 84. 73. 68.]
 [97. 67. 57. nan]
 [87. 87. nan 77.]]

2. NaN Properties¶

import numpy as np

print(np.nan == np.nan)      # False
print(np.isnan(np.nan))      # True

3. NaN Propagation¶

Any arithmetic with np.nan produces np.nan.

Infinity Values¶

NumPy supports positive and negative infinity.

1. Infinity Constant¶

import numpy as np

print(f'{np.inf = }')
print(f'{-np.inf = }')
print(f'{np.inf > 1e308 = }')

2. Checking Infinity¶

import numpy as np

print(np.isinf(np.inf))      # True
print(np.isfinite(np.inf))   # False
print(np.isfinite(1.0))      # True

Practical Usage¶

Constants enable precise scientific computing.

1. Circle Area¶

import numpy as np

radius = 5
area = np.pi * radius ** 2

2. Exponential Decay¶

import numpy as np

t = np.linspace(0, 5, 100)
decay = np.e ** (-t)

3. Missing Data¶

import numpy as np

data = np.array([1, 2, np.nan, 4])
mean = np.nanmean(data)  # Ignores NaN