Log transformation for skewed data in Data Analysis Python - Mini Project: Build & Apply

Log transformation for skewed data

📖 Scenario: Imagine you work as a data analyst for a small online store. You have sales data that shows the number of items sold each day. The data is very uneven, with some days having very high sales and many days having low sales. This makes it hard to analyze the data properly.To make the data easier to understand, you will use a log transformation. This helps to reduce the effect of very large numbers and makes the data more balanced.

🎯 Goal: You will create a list of daily sales numbers, then apply a log transformation to reduce skewness. Finally, you will print the transformed data to see the effect.

📋 What You'll Learn

Create a list called daily_sales with exact values: 5, 10, 50, 200, 1000

Create a variable called log_sales to store the log-transformed sales

Use the math.log function to apply the natural log transformation to each sale

Print the log_sales list to show the transformed data

💡 Why This Matters

🌍 Real World

Log transformation is commonly used in data science to handle skewed data, making it easier to analyze and visualize.

💼 Career

Data analysts and scientists use log transformations to prepare data for modeling and to improve the accuracy of statistical analyses.

1

Create the daily sales data

Create a list called daily_sales with these exact values: 5, 10, 50, 200, 1000.

Data Analysis Python

# Create the daily_sales list with the exact values
# Your code here

Hint

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

2

Import math module for log function

Import the math module so you can use the math.log function for the log transformation.

Data Analysis Python

daily_sales = [5, 10, 50, 200, 1000]
# Import the math module
# Your code here

Hint

Use import math at the top of your code.

3

Apply log transformation to daily sales

Create a list called log_sales that contains the natural log of each value in daily_sales. Use a list comprehension with the variable sale and the math.log function.

Data Analysis Python

daily_sales = [5, 10, 50, 200, 1000]
import math
# Create log_sales list with log values of daily_sales
# Your code here

Hint

Use a list comprehension: [math.log(sale) for sale in daily_sales].

4

Print the log-transformed sales data

Print the log_sales list to display the log-transformed sales numbers.

Data Analysis Python

daily_sales = [5, 10, 50, 200, 1000]
import math
log_sales = [math.log(sale) for sale in daily_sales]
# Print the log_sales list
# Your code here

Hint

Use print(log_sales) to show the transformed data.