Python Tutorial
NumPy Create ufunc
Turn a Python function into a ufunc with frompyfunc so it runs element-wise on arrays.
frompyfunc()
nin is the number of input arguments, nout the number of outputs.
import numpy as np
def myadd(x, y):
return x + y
myadd = np.frompyfunc(myadd, 2, 1)
print(myadd([1, 2, 3, 4], [5, 6, 7, 8]))
print(type(myadd))📘 Real-World Deep Dive
Knowing <strong>NumPy Ufunc Create (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 Create that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import numpy as np
def my_step(x):
return np.where(x >= 0, 1.0, 0.0)
step = np.frompyfunc(my_step, nin=1, nout=1)
print(step(np.array([-1.0, 0.0, 1.5])))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 Create 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.