Why random generation matters in NumPy - See It in Action

Why random generation matters

📖 Scenario: Imagine you are a data scientist working with a large dataset of customer ages. You want to understand how random sampling can help you estimate the average age without looking at the entire dataset.

🎯 Goal: You will create a dataset of ages, set a sample size, randomly select a sample, and then calculate the average age of that sample to see how it compares to the whole dataset.

📋 What You'll Learn

Create a numpy array called ages with these exact values: 22, 25, 47, 35, 46, 52, 23, 43, 36, 44

Create a variable called sample_size and set it to 4

Use numpy.random.choice to randomly select sample_size ages from ages and store in sample

Calculate the average of sample and store it in sample_average

Print the sample array and the sample_average value

💡 Why This Matters

🌍 Real World

Random sampling is used in surveys, polls, and experiments to estimate results without checking every individual.

💼 Career

Data scientists use random sampling to analyze big data efficiently and make predictions or decisions based on samples.

1

Create the dataset of ages

Create a numpy array called ages with these exact values: 22, 25, 47, 35, 46, 52, 23, 43, 36, 44

NumPy

# Create the numpy array ages with the given values
# Your code here

Need a hint?

Use np.array to create the array with the exact values.

2

Set the sample size

Create a variable called sample_size and set it to 4

NumPy

import numpy as np
ages = np.array([22, 25, 47, 35, 46, 52, 23, 43, 36, 44])
# Set sample_size to 4
# Your code here

Need a hint?

Just assign the number 4 to the variable sample_size.

3

Randomly select a sample

Use numpy.random.choice to randomly select sample_size ages from ages and store the result in sample

NumPy

import numpy as np
ages = np.array([22, 25, 47, 35, 46, 52, 23, 43, 36, 44])
sample_size = 4
# Use np.random.choice to select sample_size ages from ages
# Your code here

Need a hint?

Use np.random.choice with size=sample_size and replace=False to avoid duplicates.

4

Calculate and print the sample average

Calculate the average of sample using np.mean and store it in sample_average. Then print both sample and sample_average

NumPy

import numpy as np
ages = np.array([22, 25, 47, 35, 46, 52, 23, 43, 36, 44])
sample_size = 4
sample = np.random.choice(ages, size=sample_size, replace=False)
# Calculate sample_average and print sample and sample_average
# Your code here

Need a hint?

Use np.mean(sample) to get the average. Then print both variables.