Python Tutorial
NumPy Random Data Distribution
A data distribution is a list of all possible values and how often each value occurs. choice() can follow a probability array.
Random Choice with Probability
The p array must sum to 1. size is how many draws.
from numpy import random
x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))
print(x[:10])
print((x == 7).sum())📘 Real-World Deep Dive
Knowing <strong>NumPy Random Data Distribution (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 Random Data Distribution that you'd actually see in a data pipeline or analytics notebook.
Real-Life Example
import numpy as np
import matplotlib.pyplot as plt
rng = np.random.default_rng(0)
data = rng.choice([0, 1, 2, 3], size=1000, p=[0.1, 0.2, 0.3, 0.4])
unique, counts = np.unique(data, return_counts=True)
for u, c in zip(unique, counts):
print(f"{u}: {c}")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 Random Data Distribution 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.