Random Numbers in NumPy
🎲 Random Numbers in NumPy
NumPy provides a powerful random module (numpy.random) to generate random numbers, arrays, choices, and distributions.
1. Generate a Single Random Number
👉 Generates a random float between 0 and 1.
2. Generate Random Integer
👉 Generates a random integer between 0 and 9.
With Range:
3. Generate Random Array
1D Array
2D Array
4. Generate Random Float Array
👉 Returns random floats between 0 and 1.
🎯 Random Choice (Pick Random Value from List)
Multiple Random Choices
🔄 Random Choice with Output Shape
🎯 Random Distribution
NumPy supports many probability distributions.
Example: Normal Distribution
| Parameter | Meaning |
|---|---|
loc | Mean |
scale | Standard deviation |
size | Shape of output |
2D Normal Distribution
📦 Random Shuffle
Shuffle array elements in place
📦 Random Permutation (Creates Copy)
🔁 Seeding — Same Random Output Every Time
Useful in machine learning experiments to ensure reproducibility.
🧠 Summary Table
| Function | Purpose |
|---|---|
random.rand() | Random float (0–1) |
random.randint() | Random integer |
random.random() | Random floats array |
random.choice() | Pick random element(s) |
random.normal() | Normal distribution |
random.shuffle() | Shuffle original array |
random.permutation() | Shuffled copy |
random.seed() | Fix random results |
