Scaling and normalization concepts in Data Analysis Python - Mini Project: Build & Apply

Scaling and normalization concepts

📖 Scenario: You work as a data analyst. You have a list of heights of people in centimeters. You want to prepare this data for analysis by scaling and normalizing it. This helps to compare values fairly and use them in machine learning later.

🎯 Goal: Learn how to scale and normalize a list of numbers using simple Python code. You will create the data, set a scale range, apply min-max scaling, and then normalize the scaled data.

📋 What You'll Learn

Create a list of heights with exact values

Create variables for minimum and maximum scale values

Apply min-max scaling to the heights list

Normalize the scaled heights using L2 norm

Print the final normalized list

💡 Why This Matters

🌍 Real World

Scaling and normalization are common steps in preparing data for machine learning and statistics. They help algorithms work better by making data comparable.

💼 Career

Data analysts and data scientists often need to scale and normalize data before building models or visualizations.

1

Create the heights data list

Create a list called heights with these exact values: 150, 160, 170, 180, 190.

Data Analysis Python

# Create a list called heights with the exact values
# Your code here

Hint

Use square brackets to create a list and separate numbers with commas.

2

Set the scale range for min-max scaling

Create two variables: min_scale set to 0 and max_scale set to 1 to define the scaling range.

Data Analysis Python

heights = [150, 160, 170, 180, 190]
# Create min_scale and max_scale variables
# Your code here

Hint

Just assign 0 to min_scale and 1 to max_scale.

3

Apply min-max scaling to the heights list

Create a new list called scaled_heights using a list comprehension. Scale each height using min-max scaling formula: (height - min(heights)) / (max(heights) - min(heights)) * (max_scale - min_scale) + min_scale.

Data Analysis Python

heights = [150, 160, 170, 180, 190]
min_scale = 0
max_scale = 1
# Create scaled_heights list using min-max scaling formula
# Your code here

Hint

Use a list comprehension with the formula given to scale each height.

4

Normalize the scaled heights and print the result

Import math module. Calculate the L2 norm of scaled_heights as the square root of the sum of squares. Create a list called normalized_heights by dividing each scaled height by the L2 norm. Finally, print normalized_heights.

Data Analysis Python

heights = [150, 160, 170, 180, 190]
min_scale = 0
max_scale = 1
scaled_heights = [(height - min(heights)) / (max(heights) - min(heights)) * (max_scale - min_scale) + min_scale for height in heights]
# Import math, calculate L2 norm, normalize scaled_heights, and print normalized_heights
# Your code here

Hint

Use math.sqrt and sum of squares to find L2 norm. Then divide each scaled height by this norm.