Setting random seed for reproducibility in NumPy - Cheat Sheet & Quick Revision

Recall & Review

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What does setting a random seed in numpy do?

Setting a random seed makes sure that the random numbers generated are the same every time you run the code. This helps in getting reproducible results.

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How do you set a random seed in numpy?

You use np.random.seed(your_number). Replace your_number with any integer to fix the sequence of random numbers.

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Why is reproducibility important in data science?

Reproducibility means you or others can run the same code and get the same results. This is important for checking work, sharing findings, and debugging.

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What happens if you don't set a random seed before generating random numbers?

If you don't set a seed, numpy will generate different random numbers each time you run the code, which can make results vary and be hard to reproduce.

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Example: How to generate 5 random numbers between 0 and 1 with a fixed seed?

```python
import numpy as np
np.random.seed(42)
rands = np.random.rand(5)
print(rands)
```
This will always print the same 5 random numbers.

What function sets the random seed in numpy?

A numpy.set_seed()

B numpy.random.set()

C numpy.random.seed()

D numpy.seed.random()

The correct function to set the seed is numpy.random.seed().

Why should you set a random seed in your code?

A To avoid using random numbers

B To make random numbers different every time

C To speed up random number generation

D To make random numbers reproducible

Setting a seed makes random numbers reproducible, so results stay the same each run.

What type of value do you pass to numpy.random.seed()?

A An integer

B A float

C A string

D A boolean

You pass an integer to numpy.random.seed() to fix the random sequence.

If you run numpy.random.rand(3) twice without setting a seed, what happens?

A You get different 3 numbers each time

B You get zeros

C You get an error

D You get the same 3 numbers both times

Without a seed, random numbers differ each run.

Which of these is a benefit of reproducibility in data science?

A Code runs faster

B Results can be trusted and checked

C Random numbers are more random

D You don't need to write comments

Reproducibility helps others trust and verify your results.

Explain how to set a random seed in numpy and why it is useful.

Think about how to get the same random numbers every time.

Describe what happens if you do not set a random seed before generating random numbers in numpy.

Consider the effect on results when running code multiple times.