Numpy math functions

Example: Handling NaN and Infinite Values

import numpy as np

arr = np.array([1.0, np.nan, np.inf, -np.inf, 4.0])

print("Original:", arr)
print("isfinite:", np.isfinite(arr))
print("nanmean (ignores nan):", np.nanmean(arr))
print("clip finite values between 0 and 10:", np.clip(arr, 0, 10))

• We create an array with NaN and infinite values.
• We use np.isfinite to check finite elements.
• We compute mean ignoring NaNs with np.nanmean.
• We use np.clip to bound values, useful in many data processing tasks.

Example: Complex Number Math

import numpy as np

z = np.array([1 + 2j, 3 - 4j, -2 + 0.5j])
print("Real parts:", np.real(z))
print("Imag parts:", np.imag(z))
print("Angles (in radians):", np.angle(z))
print("Magnitude:", np.abs(z))

• This demonstrates operations on complex arrays.
• Useful for users dealing with Fourier transforms, phasors, or physics applications.

Example: Vectorized vs Loop Performance

import numpy as np
import time

N = 1000000
arr = np.arange(N, dtype=np.float64)

start = time.time()
res_np = np.sqrt(arr)
print("NumPy sqrt time:", time.time() - start)

start2 = time.time()
res_loop = [x**0.5 for x in arr]
print("Python loop sqrt time:", time.time() - start2)

• Shows how much faster NumPy vectorized functions are compared to Python loops for large arrays.

Subhendu Mohapatra

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