Python Tutorial
NumPy Searching Arrays
where finds indexes that match a condition. searchsorted finds an insertion point in a sorted array.
where
import numpy as np
arr = np.array([1, 2, 3, 4, 5, 4, 4])
print(np.where(arr == 4)) # (array([3, 5, 6]),)
print(np.where(arr % 2 == 0)) # even indexessearchsorted
arr = np.array([1, 3, 5, 7])
print(np.searchsorted(arr, 5)) # 2
print(np.searchsorted(arr, [2, 6])) # [1 3]📘 Real-World Deep Dive
Knowing <strong>NumPy Search (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 Search that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import numpy as np
a = np.array([4, 1, 9, 12, 7])
print(np.where(a > 5)) # indices
print(np.argmax(a), np.argmin(a))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 Search 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.