Week 1: Python Fundamentals for Data Science

print("Hello, Data Science!")
print("My name is Adeleke")
print(2 + 3)

# Good variable names
student_name = "Amara Okafor"
age = 28
gpa = 3.85
is_enrolled = True

# Variables can be updated
age = 29          # overwrite the value
age = age + 1     # compute then store (now age is 30)

print(student_name)   # Amara Okafor
print(age)            # 30
print(gpa)            # 3.85

# int: whole numbers
n_patients = 500
year = 2024
rank = 1

# float: decimal numbers (note: 1/3 in Python is 0.333...)
accuracy = 0.947
temperature = 36.8
pi = 3.14159

# str: text enclosed in single or double quotes (consistent choice)
country = "Nigeria"
city = 'Ibadan'
sentence = "There are 36 states in Nigeria."

# bool: only True or False (capital T and F, exactly)
is_diabetic = True
passed_test = False

# Use type() to check what type a variable is
print(type(n_patients))   # <class 'int'>
print(type(accuracy))     # <class 'float'>
print(type(country))      # <class 'str'>
print(type(is_diabetic))  # <class 'bool'>

# Convert string to number
age_str = "28"
age_int = int(age_str)     # "28" → 28
print(age_int + 2)          # 30 (arithmetic works now)

# Convert number to string (for printing/concatenation)
score = 94
message = "Your score is " + str(score) + "%"
print(message)

# Convert to float
items = 7
per_unit = float(items) / 3    # 7 / 3 = 2.333...
print(round(per_unit, 2))      # 2.33

# Boolean conversion: 0 and empty string are False, everything else is True
print(bool(0))      # False
print(bool(1))      # True
print(bool(""))     # False
print(bool("text")) # True
print(bool(3.14))   # True

# Real-world arithmetic
monthly_salary = 250000          # NGN
tax_rate = 0.075                 # 7.5%
annual_salary = monthly_salary * 12
tax_payable = annual_salary * tax_rate
net_annual = annual_salary - tax_payable

print(f"Annual salary: NGN {annual_salary:,}")
print(f"Tax payable:   NGN {tax_payable:,}")
print(f"Net annual:    NGN {net_annual:,}")

# Statistical formula: z-score = (x - mean) / std
x = 72
mean = 65
std = 8
z_score = (x - mean) / std
print(f"z-score: {z_score:.3f}")  # 0.875

# Modulo: check if a number is even or odd
number = 47
remainder = number % 2
print(f"{number} is {'even' if remainder == 0 else 'odd'}")

# Comparison operators - always return True or False
score = 74

print(score >= 70)    # True  (greater than or equal to)
print(score == 74)    # True  (equality - note DOUBLE equals)
print(score != 100)   # True  (not equal to)
print(score < 50)     # False (less than)

# Logical operators combine boolean expressions
has_experience = True
has_degree = True
years = 5

# and: both must be True
is_eligible = has_degree and years >= 3
print(f"Eligible: {is_eligible}")   # True

# or: at least one must be True
can_apply = has_degree or has_experience
print(f"Can apply: {can_apply}")    # True

# not: flips True to False and vice versa
is_junior = not (years >= 5)
print(f"Junior role: {is_junior}")  # False

credit_score = 720
income = 480000   # NGN annual

# Basic if/elif/else
if credit_score >= 750 and income >= 500000:
    loan_tier = "Prime - Up to NGN 5,000,000"
    interest_rate = 12.0
elif credit_score >= 650 and income >= 300000:
    loan_tier = "Standard - Up to NGN 2,000,000"
    interest_rate = 18.5
elif credit_score >= 550:
    loan_tier = "Sub-prime - Up to NGN 500,000"
    interest_rate = 24.0
else:
    loan_tier = "Declined"
    interest_rate = None

print(f"Loan tier: {loan_tier}")
if interest_rate:
    print(f"Interest rate: {interest_rate}%")

# Loop over a list
regions = ["South-West", "North-Central", "South-East", "North-West"]

for region in regions:
    print(f"Processing data for: {region}")

# Loop over a range of numbers
# range(start, stop, step) - stop is EXCLUDED
total = 0
for i in range(1, 11):     # 1, 2, 3, ... 10
    total = total + i
print(f"Sum of 1 to 10: {total}")   # 55

# enumerate() gives index AND value
subjects = ["Statistics", "Machine Learning", "Python", "SQL"]
for index, subject in enumerate(subjects, start=1):
    print(f"  Module {index}: {subject}")

# Nested loops
for week in range(1, 4):
    for session in range(1, 4):
        print(f"Week {week}, Session {session}")

# While loop - repeat until condition is False
balance = 10000
monthly_rate = 0.015  # 1.5% monthly interest
month = 0

while balance < 20000:     # double the balance
    balance = balance * (1 + monthly_rate)
    month += 1             # same as: month = month + 1

print(f"Balance doubled after {month} months")
print(f"Final balance: NGN {balance:,.2f}")

# break - exit the loop immediately
print("\nSearching for a prime number above 100:")
n = 101
while True:        # infinite loop - must break manually
    is_prime = all(n % i != 0 for i in range(2, int(n**0.5)+1))
    if is_prime:
        print(f"Found: {n}")
        break
    n += 1

# def keyword, function name, parentheses, colon
def calculate_bmi(weight_kg, height_m):
    """
    Calculate BMI and return the value with WHO classification.
    
    Args:
        weight_kg (float): Body weight in kilograms
        height_m  (float): Height in metres
    
    Returns:
        dict: BMI value and WHO category string
    """
    bmi = weight_kg / (height_m ** 2)
    
    if bmi < 18.5:
        category = "Underweight"
    elif bmi < 25.0:
        category = "Normal weight"
    elif bmi < 30.0:
        category = "Overweight"
    else:
        category = "Obese"
    
    return {"bmi": round(bmi, 1), "category": category}

# Call the function
result = calculate_bmi(70, 1.75)
print(result)                          # {"bmi": 22.9, "category": "Normal weight"}
print(f"BMI: {result['bmi']}")        # BMI: 22.9
print(f"Class: {result['category']}") # Class: Normal weight

# Default parameters - used when the caller does not provide a value
def greet_student(name, course="Data Science"):
    return f"Welcome, {name}! You are enrolled in {course}."

print(greet_student("Amara"))                         # uses default
print(greet_student("Emeka", "Machine Learning"))      # overrides default

# Multiple return values (Python returns a tuple)
def summary_stats(numbers):
    """Return mean, minimum, and maximum of a list."""
    mean = sum(numbers) / len(numbers)
    return mean, min(numbers), max(numbers)

scores = [72, 88, 65, 91, 78, 55]
avg, lowest, highest = summary_stats(scores)   # unpack the tuple
print(f"Mean: {avg:.1f}  Min: {lowest}  Max: {highest}")

# Keyword arguments - pass by name in any order
def create_report(title, author, year=2024, pages=0):
    return f"{title} by {author} ({year}), {pages} pages"

print(create_report(author="Kmex", title="Data Science Guide", pages=120))

# Local variables: only exist inside the function
def my_function():
    local_var = "I only exist inside the function"
    print(local_var)

my_function()
# print(local_var)  # NameError - local_var does not exist here

# Global variables: defined outside all functions
EXCHANGE_RATE = 1450   # NGN per USD (conventionally UPPERCASE for constants)

def convert_to_ngn(usd_amount):
    """Use the global exchange rate to convert USD to NGN."""
    return usd_amount * EXCHANGE_RATE   # reads global variable

print(convert_to_ngn(500))   # 725000

# Install Git from git-scm.com if not already installed
# Configure your identity (one-time setup)
git config --global user.name "Your Name"
git config --global user.email "your@email.com"

# Create a new repository for your data science work
mkdir data-science-course
cd data-science-course
git init                         # initialise an empty repository

# Create your first file
echo "# Data Science Course" > README.md

# Stage and commit
git add README.md                # stage the file
git add .                        # OR stage all changed files
git commit -m "Initial commit: add README"   # save a snapshot

# Connect to GitHub
# (First create a new repo on github.com, then:)
git remote add origin https://github.com/yourusername/data-science-course.git
git push -u origin main          # upload to GitHub

# Check what files have changed
git status

# See what changed inside files
git diff

# Stage specific files
git add analysis.py
git add notebooks/week1.ipynb

# Commit with a descriptive message (always describe WHAT and WHY)
git commit -m "Add BMI calculator function with WHO classification"

# Push to GitHub
git push