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

🎯 Practice Tasks

# 1. Create a 4x4 array of random integers between 1–100
# 2. Shuffle the array
# 3. Generate 10 random even numbers between 50–100
# 4. Create a normal distribution with mean=500, std=50, size=1000

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