Setting random seed for reproducibility in NumPy - Mini Project: Build & Apply

Setting random seed for reproducibility

📖 Scenario: Imagine you are a data scientist who needs to generate random numbers for a simulation. To make sure your results can be repeated exactly by you or others, you need to set a random seed.

🎯 Goal: You will create a numpy random number generator with a fixed seed, generate some random numbers, and print them. This ensures your random numbers are the same every time you run the code.

📋 What You'll Learn

Use numpy to generate random numbers

Set a random seed for reproducibility

Generate an array of random numbers

Print the generated random numbers

💡 Why This Matters

🌍 Real World

Setting a random seed is important in data science to ensure experiments and simulations can be repeated exactly, which helps with debugging and sharing results.

💼 Career

Data scientists and analysts often set random seeds when working with random processes to guarantee consistent results across runs and among team members.

1

Import numpy and create a random number generator

Write a line to import numpy as np. Then create a random number generator called rng using np.random.default_rng() with no seed yet.

NumPy

import numpy as np
# Create a random number generator called rng without a seed
# Your code here

Need a hint?

Use import numpy as np to import numpy. Then create rng by calling np.random.default_rng() without arguments.

2

Set a random seed for reproducibility

Modify the creation of rng to use a fixed seed of 12345 by passing it as an argument to np.random.default_rng().

NumPy

import numpy as np
# Create a random number generator called rng with seed 12345
# Your code here

Need a hint?

Pass the number 12345 inside the parentheses of np.random.default_rng() to set the seed.

3

Generate an array of random numbers

Use the rng random number generator to create an array called random_numbers of 5 random floats between 0 and 1 using rng.random(5).

NumPy

import numpy as np
rng = np.random.default_rng(12345)
# Generate 5 random floats between 0 and 1 and store in random_numbers
# Your code here

Need a hint?

Call rng.random(5) to get 5 random floats and assign to random_numbers.

4

Print the generated random numbers

Write a print statement to display the random_numbers array.

NumPy

import numpy as np
rng = np.random.default_rng(12345)
random_numbers = rng.random(5)
# Print the random_numbers array
# Your code here

Need a hint?

Use print(random_numbers) to show the array of random numbers.