Python Tutorial
NumPy Joining Arrays
Concatenate along an existing axis, or stack to add a new axis.
concatenate
import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(np.concatenate((a, b))) # [1 2 3 4 5 6]
m = np.array([[1, 2], [3, 4]])
n = np.array([[5, 6], [7, 8]])
print(np.concatenate((m, n), axis=0)) # rows stacked
print(np.concatenate((m, n), axis=1)) # columns stackedstack, hstack, vstack
print(np.stack((a, b), axis=0))
print(np.hstack((a, b)))
print(np.vstack((a, b)))📘 Real-World Deep Dive
Knowing <strong>NumPy Join (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 Join that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
print(np.concatenate([a, b]))
rows = np.vstack([a, b]) # stack as rows
print(rows)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 Join 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.