Normal distribution with normal() in NumPy - Mini Project: Build & Apply

Normal distribution with normal()

📖 Scenario: You work in a bakery that wants to understand the daily weight of its bread loaves. The weights usually follow a normal pattern, with most loaves close to the average weight but some lighter or heavier.

🎯 Goal: You will create a list of bread weights using the normal distribution. Then, you will set the average weight and spread, generate the weights, and finally print them.

📋 What You'll Learn

Use numpy's normal() function to generate data

Create a variable for mean and standard deviation

Generate 10 bread weights

Print the list of weights

💡 Why This Matters

🌍 Real World

Bakeries and food industries use normal distribution to understand product weight variations and maintain quality.

💼 Career

Data scientists use normal distribution to model real-world data and make predictions or quality checks.

1

Create the bread weights data

Import numpy as np and create a variable called weights by calling np.array([]) to start an empty array.

NumPy

import numpy as np
weights = np.array([])  # Create an empty numpy array for weights

Need a hint?

Use import numpy as np to import numpy. Then create an empty numpy array with np.array([]).

2

Set mean and standard deviation

Create two variables: mean_weight set to 500 and std_dev set to 20.

NumPy

import numpy as np
weights = np.array([])

# Set mean and standard deviation
# Your code here

Need a hint?

Use simple assignment to create mean_weight and std_dev variables.

3

Generate bread weights using normal()

Use np.random.normal() with mean_weight, std_dev, and 10 to generate 10 bread weights. Assign the result to weights.

NumPy

import numpy as np
weights = np.array([])

mean_weight = 500
std_dev = 20

# Generate 10 bread weights
# Your code here

Need a hint?

Call np.random.normal(mean_weight, std_dev, 10) to get 10 values and assign to weights.

4

Print the bread weights

Print the weights array to see the generated bread weights.

NumPy

import numpy as np
weights = np.array([])

mean_weight = 500
std_dev = 20

weights = np.random.normal(mean_weight, std_dev, 10)

# Print the weights
# Your code here

Need a hint?

Use print(weights) to display the array of weights.