# Real use: store customer information
customer_name  = "Priya Sharma"    # text value
order_amount   = 15000             # number
is_premium     = True              # yes/no (boolean)
city           = "Hyderabad"

print(f"Customer: {customer_name}")
print(f"Order Amount: ₹{order_amount}")
print(f"Premium Member: {is_premium}")

# String → text
name    = "Ravi Kumar"

# Integer → whole number
age     = 28

# Float → decimal number
salary  = 45000.50

# Boolean → True or False
is_active = True

# List → multiple values (like an Excel column)
cities  = ["Mumbai", "Delhi", "Hyderabad", "Bangalore"]

# Dictionary → one complete record (like one Excel row)
customer = {
    "id"    : 1001,
    "name"  : "Priya",
    "city"  : "Chennai",
    "amount": 8500
}
print(customer["name"])   # Priya
print(customer["amount"]) # 8500

# Real use: classify orders by value
order_amount = 15000

if order_amount > 10000:
    print("Premium Order — assign to VIP team")
elif order_amount > 5000:
    print("Standard Order")
else:
    print("Small Order")

# Real use: check order status
status = "Cancelled"
if status == "Cancelled":
    print("Alert: Order cancelled — investigate reason")

# Real use: process 50 CSV files automatically
files = ["sales_jan.csv", "sales_feb.csv", "sales_mar.csv"]

for file in files:
    print(f"Processing: {file}")
    # read, clean, and save each file here

# Real use: calculate 10% GST for every order
order_amounts = [5000, 12000, 8500, 25000]

for amount in order_amounts:
    gst   = amount * 0.18
    total = amount + gst
    print(f"Order: ₹{amount} | GST: ₹{gst:.0f} | Total: ₹{total:.0f}")

# Define the function once
def calculate_total(amount, gst_percent=18):
    """Calculate total amount including GST"""
    gst   = amount * (gst_percent / 100)
    total = amount + gst
    return total

# Use it anywhere — as many times as needed
print(calculate_total(5000))       # ₹5900.0
print(calculate_total(12000))      # ₹14160.0
print(calculate_total(8000, 5))   # ₹8400.0 (5% GST)

# Real use: clean a customer name
def clean_name(name: str) -> str:
    return name.strip().title()   # remove spaces, fix capitalization

print(clean_name("  priya sharma  "))  # "Priya Sharma"

# int — whole numbers (no decimal)
age          = 28
quantity     = 150
employee_id  = 1001

# float — decimal numbers
salary       = 45000.50
gst_rate     = 0.18
product_price= 899.99

# Real use: calculate total with GST
price       = 5000
gst         = price * 0.18
total       = price + gst
print(f"Price: ₹{price} | GST: ₹{gst:.0f} | Total: ₹{total:.0f}")
# Output: Price: ₹5000 | GST: ₹900 | Total: ₹5900

# type() — check what type a variable is
print(type(age))    # <class 'int'>
print(type(salary)) # <class 'float'>

# String basics
name   = "priya sharma"
email  = "priya@company.com"
city   = "  hyderabad  "   # extra spaces — common in real data

# String methods — used every day in data cleaning
print(name.upper())          # "PRIYA SHARMA"
print(name.title())          # "Priya Sharma"
print(city.strip())          # "hyderabad" (removes spaces)
print(email.split("@"))    # ['priya', 'company.com']
print(email.replace("com", "in")) # "priya@company.in"

# f-string — build messages with variables
customer = "Ravi"
amount   = 15000
print(f"Dear {customer}, your order of ₹{amount} is confirmed.")

# Real use: validate email format
is_valid_email = "@" in email and "." in email
print(f"Valid email: {is_valid_email}")  # True

# Split full name into first and last
full_name  = "Priya Sharma"
parts      = full_name.split(" ")
first_name = parts[0]   # "Priya"
last_name  = parts[1]   # "Sharma"

# List of cities
cities    = ["Mumbai", "Delhi", "Hyderabad", "Bangalore"]

# List of order amounts
amounts   = [5000, 12000, 3500, 8900, 25000]

# Access items by position (index starts at 0)
print(cities[0])    # "Mumbai"  (first)
print(cities[-1])   # "Bangalore" (last)

# Add, remove items
cities.append("Chennai")     # add to end
cities.remove("Delhi")       # remove specific item

# Real use: find total and average order amount
total   = sum(amounts)
average = sum(amounts) / len(amounts)
highest = max(amounts)
lowest  = min(amounts)
print(f"Total: ₹{total} | Avg: ₹{average:.0f} | Max: ₹{highest} | Min: ₹{lowest}")

# Real use: process multiple files
files = ["jan.csv", "feb.csv", "mar.csv"]
for file in files:
    print(f"Processing: {file}")

# Tuple — values cannot be changed after creation
months     = ("Jan", "Feb", "Mar", "Apr", "May", "Jun",
               "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")

db_config  = ("localhost", 5432, "mydb")  # host, port, dbname
aws_region = ("us-east-1",)   # single item tuple needs comma

# Access same as list
print(months[0])   # "Jan"
print(months[-1])  # "Dec"

# Unpacking — assign tuple values to variables
host, port, db = db_config
print(f"Connecting to {host}:{port}/{db}")

# Real use: return multiple values from a function
def get_stats(numbers):
    return min(numbers), max(numbers), sum(numbers) / len(numbers)

low, high, avg = get_stats([5000, 12000, 8000])
print(f"Min: ₹{low} | Max: ₹{high} | Avg: ₹{avg:.0f}")

# Set — only keeps unique values
raw_cities  = ["Mumbai", "Delhi", "Mumbai", "Bangalore", "Delhi"]
unique_cities = set(raw_cities)
print(unique_cities)  # {'Mumbai', 'Delhi', 'Bangalore'}

# Real use: check if a city is in approved list
approved_cities = {"Mumbai", "Delhi", "Bangalore", "Hyderabad"}
customer_city   = "Chennai"

if customer_city in approved_cities:
    print("Delivery available")
else:
    print("Delivery not available in this city")

# Set operations — useful in data analysis
set_a = {"Ravi", "Priya", "John"}
set_b = {"Priya", "Anu", "John"}

print(set_a & set_b)  # Common: {'Priya', 'John'}
print(set_a | set_b)  # All unique: {'Ravi', 'Priya', 'John', 'Anu'}
print(set_a - set_b)  # Only in A: {'Ravi'}

# Dictionary — key:value pairs
customer = {
    "id"     : 1001,
    "name"   : "Priya Sharma",
    "city"   : "Hyderabad",
    "amount" : 15000,
    "status" : "Delivered"
}

# Access values by key
print(customer["name"])    # "Priya Sharma"
print(customer["amount"])  # 15000

# Safe access — no error if key missing
print(customer.get("phone", "Not provided"))  # "Not provided"

# Add or update a key
customer["gst"] = customer["amount"] * 0.18

# Loop through all keys and values
for key, value in customer.items():
    print(f"{key}: {value}")

# Real use: process API response
api_response = {
    "status"  : "success",
    "data"    : {"order_id": "ORD001", "total": 5900},
    "message" : "Order placed successfully"
}
order_id = api_response["data"]["order_id"]
print(f"Order confirmed: {order_id}")

# Boolean — only two values: True or False
is_active   = True
is_deleted  = False
is_premium  = True

# Real use: feature flags in applications
ENABLE_EMAIL  = True
ENABLE_SMS    = False
MAINTENANCE_MODE = False

if ENABLE_EMAIL:
    print("Sending email notification...")
if not MAINTENANCE_MODE:
    print("System is running normally")

# Comparison results are boolean
amount = 15000
print(amount > 10000)   # True
print(amount == 5000)  # False

# Real use: validate before processing
order_amount = 12000
is_valid     = order_amount > 0 and order_amount < 100000
print(f"Order valid: {is_valid}")  # True