Python Tutorial
NumPy Array Shape
shape is a tuple of sizes along each axis. ndim is how many axes. size is the total number of elements.
Read Shape
import numpy as np
a = np.array([1, 2, 3, 4])
b = np.array([[1, 2, 3], [4, 5, 6]])
print(a.shape) # (4,)
print(b.shape) # (2, 3)
print(b.ndim) # 2
print(b.size) # 6Assign Shape
If the new shape uses the same number of elements, you can set shape in place:
arr = np.array([1, 2, 3, 4, 5, 6])
arr.shape = (2, 3)
print(arr)Prefer reshape (next chapter) when you want a new array or a view without mutating the original variable's shape unexpectedly.
📘 Real-World Deep Dive
Knowing <strong>NumPy Shape (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 Shape that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import numpy as np
a = np.arange(12).reshape(3, 4)
print("shape:", a.shape, "size:", a.size, "ndim:", a.ndim)
print("row sums:", a.sum(axis=1))
print("col sums:", a.sum(axis=0))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 Shape 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.