Python Tutorial
NumPy Summations
sum() adds all elements, optionally along an axis. cumsum() is the running total.
sum and cumsum
axis=1 sums rows. axis=0 sums columns.
import numpy as np
arr1 = np.array([1, 2, 3])
arr2 = np.array([1, 2, 3])
print(np.sum([arr1, arr2]))
print(np.sum([arr1, arr2], axis=1))
print(np.cumsum(arr1))📘 Real-World Deep Dive
Knowing <strong>NumPy Ufunc Summations (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 Summations 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("sum all:", np.sum(x))
print("cumsum:", np.cumsum(x))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 Summations 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.