Python Tutorial
NumPy Simple Arithmetic
You can use +, -, *, / on arrays, or the ufuncs add, subtract, multiply, divide, power, mod, remainder, and divmod.
Arithmetic ufuncs
They work on arrays of the same shape, or with broadcasting.
import numpy as np
x = np.array([10, 20, 30, 40])
y = np.array([1, 2, 3, 4])
print(np.add(x, y))
print(np.subtract(x, y))
print(np.multiply(x, y))
print(np.divide(x, y))
print(np.power(x, y))
print(np.mod(x, y))
print(np.divmod(x, y))📘 Real-World Deep Dive
Knowing <strong>NumPy Ufunc Arithmetic (NumPy)</strong> well is what turns NumPy from a curiosity into a daily tool — you'll reach for it in nearly every real project.
Real-Life Scenario
An end-to-end usage of NumPy Ufunc Arithmetic that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import numpy as np
x = np.array([1, 2, 3, 4])
print("add:", np.add(x, 10))
print("sub:", np.subtract(x, 1))
print("pow:", np.power(x, 2))Expected Output
(see source)Common mistakes
- NumPy uses 0-based, C-order indexing — the rightmost axis is the *fastest-varying* one. Mixing it with Fortran-order arrays is a common surprise.
np.array([[1,2],[3,4]], dtype=int)is fine, but a ragged Python list produces dtype=object and silently disables vectorisation.- In-place ops (
a *= 2) sometimes break views instead of returning a new array; usenp.multiply(a, 2, out=...)if explicitness matters. - Treating NumPy Ufunc Arithmetic as a black box without reading the docs — the API has subtle defaults that bite when you scale.
🚀 Performance & Best Practices
- Vectorise: replace Python
forloops with ufuncs; you can expect 10–100× speedups. - Pre-allocate output arrays with
np.emptyinstead of growing them withnp.append. - Keep data in float32 unless you need float64 precision — half the memory, double the cache locality.
- When working with NumPy, prefer vectorised / batched operations over Python loops.
🧪 Try It Yourself
- Reproduce the snippet on a representative slice of your own data.
- Profile the snippet with
cProfileortimeitand find the single biggest improvement. - Generalise the snippet into a small, reusable function you can drop into future projects.