Python Basics for Regression Analysis

# CHAPTER 3

Python Basics for Regression Analysis #

1. Introduction #

2. Learning Objectives #

• Define variables and identify core data types.

• Store collections of data in Lists and Dictionaries.

• Control program flow using if/else conditions.

• Iterate through data using for and while loops.

• Write reusable functions for data analysis.

3. Variables and Data Types #

# Integer (Whole numbers, like number of bedrooms)
bedrooms = 3

# Float (Decimals, like square footage or regression coefficients)
square_feet = 1500.5
slope = 2.45

# String (Text data, like cities or categories)
city = "New York"

# Boolean (True/False flags)
is_renovated = True

# Printing variables using f-strings
print(f"The house in {city} has {bedrooms} bedrooms and is {square_feet} sqft.")

4. Data Structures: Lists #

# A list of historical house prices
prices = [250000, 300000, 450000, 200000, 500000]

# Accessing the first item (0-indexed)
print(prices[0])  # Output: 250000

# Accessing the last item
print(prices[-1]) # Output: 500000

# Slicing (getting the first 3 items)
print(prices[0:3]) # Output: [250000, 300000, 450000]

# Appending a new value
prices.append(320000)

5. Data Structures: Dictionaries #

# Storing a single row of house data
house_data = {
    "Address": "123 Main St",
    "Price": 300000,
    "Bedrooms": 4
}

# Accessing a value by its key
print(house_data["Price"]) # Output: 300000

# Updating a value
house_data["Price"] = 315000

# Adding a new key
house_data["Has_Pool"] = False

6. Conditions (If / Else) #

house_price = 450000
budget = 400000

if house_price <= budget:
    print("This house is within budget.")
elif house_price <= budget + 50000:
    print("This house is slightly over budget. Negotiate.")
else:
    print("This house is out of budget. Drop from analysis.")

7. Loops (For and While) #

# A 'for' loop iterating over a list
salaries = [50000, 60000, 70000]
for salary in salaries:
    # Give everyone a 10% raise
    new_salary = salary * 1.10
    print(f"New Salary: ${new_salary}")

# A 'for' loop using range
# Loop 5 times
for i in range(5):
    print(f"Processing row {i}...")

8. List Comprehensions #

# Raw prices in dollars
prices = [100000, 200000, 300000]

# Convert to thousands of dollars (e.g., 100k)
prices_in_k = [p / 1000 for p in prices]

print(prices_in_k) 
# Output: [100.0, 200.0, 300.0]

9. Functions for Data Analysis #

def calculate_average(number_list):
    """
    Takes a list of numbers and returns the mathematical average (mean).
    """
    total_sum = sum(number_list)
    count = len(number_list)
    return total_sum / count

# Calling the function
house_prices = [300000, 400000, 500000]
avg_price = calculate_average(house_prices)
print(f"The average price is: ${avg_price}")

10. Common Mistakes #

• Indentation Errors: Python does not use {} brackets to define code blocks like C++ or Java. It uses whitespace (indentation). If you forget to indent the code inside a for loop or if statement, Python will crash with an IndentationError.

• Zero-Indexing: Beginners often try to access the first item of a list using list[1]. In Python, the first item is always list[0].

11. Best Practices #

• Type Hinting: While Python doesn't require you to declare types, adding "Type Hints" makes data scripts much easier to read and debug. (e.g., def calculate_average(prices: list) -> float:)

• Docstrings: Always write a brief explanation ("""...""") under your function definition explaining what the function expects and what it returns.

12. Exercises #

1. 1. Create a dictionary that holds the configuration for a machine learning run: algorithm as "Linear Regression", test_size of 0.2, and random_state as 42.

1. 2. Write a for loop that iterates over a list of numbers [1, 2, 3, 4, 5] and prints the square of each number.

13. MCQ Quiz with Answers #

14. Interview Questions #

• Q: Explain the difference between a List and a Dictionary in Python, and provide a data analytics use case for each.

• Q: What is a List Comprehension, and why is it preferred over a standard for loop for simple data transformations?

15. FAQs #

16. Summary #

17. Next Chapter Recommendation #