Generating random samples in NumPy - Mini Project: Build & Apply

Generating Random Samples with NumPy

📖 Scenario: Imagine you are a data scientist working on a project where you need to simulate data for testing. You want to create random samples from a normal distribution to mimic real-world measurements.

🎯 Goal: Learn how to generate random samples using NumPy's random module and display the generated data.

📋 What You'll Learn

Create a NumPy array of random samples from a normal distribution

Set the mean and standard deviation for the distribution

Generate a specific number of samples

Print the generated samples

💡 Why This Matters

🌍 Real World

Generating random samples is useful for simulating data when real data is not available or for testing algorithms.

💼 Career

Data scientists often need to create synthetic data to test models or understand statistical properties.

1

Import NumPy and set up sample size

Import the numpy library as np and create a variable called sample_size with the value 10.

NumPy

# Import numpy as np
# Create a variable sample_size and set it to 10
# Your code here

Need a hint?

Use import numpy as np to import NumPy. Then create sample_size = 10.

2

Set mean and standard deviation

Create two variables: mean with value 0 and std_dev with value 1 to represent the mean and standard deviation of the normal distribution.

NumPy

import numpy as np
sample_size = 10
# Create mean variable with value 0
# Create std_dev variable with value 1
# Your code here

Need a hint?

Set mean = 0 and std_dev = 1.

3

Generate random samples

Use np.random.normal with parameters mean, std_dev, and sample_size to create a variable called samples that holds the random samples.

NumPy

import numpy as np
sample_size = 10
mean = 0
std_dev = 1
# Generate samples using np.random.normal and assign to samples
# Your code here

Need a hint?

Call np.random.normal(mean, std_dev, sample_size) and assign it to samples.

4

Print the generated samples

Print the variable samples to display the generated random numbers.

NumPy

import numpy as np
sample_size = 10
mean = 0
std_dev = 1
samples = np.random.normal(mean, std_dev, sample_size)
# Print the samples
# Your code here

Need a hint?

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