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Python operators are special symbols or keywords that perform operations on values and variables. These operations can range from mathematical calculations and comparisons to logical evaluations and assignments. Understanding Python operators is crucial for writing functional and efficient Python code.

Arithmetic Operators: +, -, *, /, // (floor division), % (modulo), ** (exponentiation)

Explanation: These operators are used to perform common mathematical calculations. They take numerical values (operands) and return a new numerical value. Mastering Python arithmetic operations is fundamental for any numerical processing.
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Code Examples:

Python

# Example 1 (Beginner-Friendly): Basic addition, subtraction, multiplication
# Demonstrates the most common arithmetic operations.
a = 10
b = 3
print(f"Addition (a + b): {a + b}")       # Output: 13
print(f"Subtraction (a - b): {a - b}")    # Output: 7
print(f"Multiplication (a * b): {a * b}") # Output: 30

# Example 2 (Slightly More Complex): Division types (standard and floor)
# '/' performs float division, always returning a float.
# '//' performs floor division, returning the integer part of the quotient.
x = 17
y = 5
print(f"Standard Division (x / y): {x / y}")     # Output: 3.4 (float)
print(f"Floor Division (x // y): {x // y}")   # Output: 3 (integer)
print(f"Floor Division with negatives (-17 // 5): {-17 // 5}") # Output: -4 (floored down)

# Example 3 (Intermediate): Modulo and Exponentiation
# '%' (modulo) gives the remainder of a division.
# '**' (exponentiation) raises the first operand to the power of the second.
base = 2
exponent = 5
print(f"Modulo (17 % 5): {17 % 5}")     # Output: 2 (17 = 3*5 + 2)
print(f"Exponentiation (base ** exponent): {base ** exponent}") # Output: 32 (2 to the power of 5)

# Example 4 (Advanced Beginner): Order of Operations (PEMDAS/BODMAS)
# Python follows standard mathematical order of operations. Parentheses first.
result1 = 5 + 3 * 2 # Multiplication (3*2=6) before addition (5+6=11)
result2 = (5 + 3) * 2 # Parentheses first (5+3=8), then multiplication (8*2=16)
print(f"Result 1 (5 + 3 * 2): {result1}")   # Output: 11
print(f"Result 2 ((5 + 3) * 2): {result2}") # Output: 16

# Example 5 (Intermediate): Chaining arithmetic operations with floats
# Performing multiple operations in a single expression, often used in calculations.
principal = 1000
rate = 0.05
time = 2
# Simple Interest calculation: P * R * T
simple_interest = principal * rate * time
# Compound Interest calculation: P * (1 + R)^T
compound_interest = principal * (1 + rate) ** time
print(f"Simple Interest: ${simple_interest:.2f}")    # Output: $100.00
print(f"Compound Interest: ${compound_interest:.2f}") # Output: $1102.50

Assignment Operators: =, +=, -=, *= etc.

Explanation: Python assignment operators are used to assign values to variables. The simple = operator assigns a value directly. Compound assignment operators (like +=, -=) provide a shorthand for performing an operation and assigning the result back to the same variable. These are efficient and make your Python code more concise.
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Code Examples:

Python

# Example 1 (Beginner-Friendly): Basic assignment '='
# Assigns the value 5 to the variable 'score'.
score = 5
print(f"Initial score: {score}") # Output: 5

# Example 2 (Slightly More Complex): Addition assignment '+='
# Adds the right operand to the left operand and assigns the result to the left operand.
# 'score += 2' is equivalent to 'score = score + 2'.
score += 2
print(f"Score after += 2: {score}") # Output: 7

# Example 3 (Intermediate): Multiplication assignment '*='
# Multiplies the left operand by the right operand and assigns the result.
# 'price *= 1.1' is equivalent to 'price = price * 1.1'.
price = 100.0
price *= 1.1 # Increase price by 10% (e.g., tax or markup)
print(f"Price after *= 1.1: {price:.2f}") # Output: 110.00

# Example 4 (Advanced Beginner): Division assignment '/=' and Modulo assignment '%='
# Demonstrates how these operators combine division/modulo with assignment.
total_items = 50
students = 7
remaining = total_items % students # Calculate remainder first
total_items //= students           # Divide total_items by students (floor division)
print(f"Items per student (after //=): {total_items}") # Output: 7 (50 // 7)
print(f"Remaining items (after %=): {remaining}")     # Output: 1 (50 % 7)

# Example 5 (Intermediate): Chaining and application in loops
# Assignment operators are frequently used within loops to update counters or totals.
total_sum = 0
for i in range(1, 6): # Loop for numbers 1 to 5
    total_sum += i    # Equivalent to total_sum = total_sum + i
    print(f"After adding {i}, total_sum is: {total_sum}")
# Output:
# After adding 1, total_sum is: 1
# After adding 2, total_sum is: 3
# After adding 3, total_sum is: 6
# After adding 4, total_sum is: 10
# After adding 5, total_sum is: 15

Comparison Operators: ==, !=, <, >, <=, >=

Explanation: Python comparison operators (also known as relational operators) are used to compare two values. They always return a Boolean value (True or False), indicating whether the comparison is true or false. These are critical for conditional logic in your Python programs.
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Code Examples:

Python

# Example 1 (Beginner-Friendly): Basic equality and inequality
# '==' checks if two values are equal. '!=' checks if they are not equal.
num1 = 10
num2 = 20
num3 = 10
print(f"Is num1 equal to num2? {num1 == num2}")   # Output: False
print(f"Is num1 equal to num3? {num1 == num3}")   # Output: True
print(f"Is num1 not equal to num2? {num1 != num2}") # Output: True

# Example 2 (Slightly More Complex): Less than, greater than
# Comparing numerical values using '<' (less than) and '>' (greater than).
temp_celsius = 25
is_cold = temp_celsius < 10
is_hot = temp_celsius > 30
print(f"Is it cold (<10C)? {is_cold}") # Output: False
print(f"Is it hot (>30C)? {is_hot}")   # Output: False

# Example 3 (Intermediate): Less than or equal to, greater than or equal to
# Including equality in the comparison.
min_age = 18
user_age = 18
can_vote = user_age >= min_age
is_child = user_age <= 12
print(f"Can user vote? {can_vote}") # Output: True
print(f"Is user a child? {is_child}") # Output: False

# Example 4 (Advanced Beginner): Comparing different data types (if compatible)
# Python can compare strings lexicographically (alphabetical order).
# Direct comparison between incompatible types (e.g., string and number) usually raises an error.
string1 = "apple"
string2 = "banana"
print(f"Is 'apple' < 'banana'? {string1 < string2}")  # Output: True
print(f"Is 10 == '10'? {10 == '10'}") # Output: False (different types)

# Example 5 (Intermediate): Chaining comparisons
# Python allows chaining comparison operators for concise range checks.
# '10 < x < 20' is equivalent to '10 < x and x < 20'.
value = 15
is_in_range = 10 < value < 20 # Checks if value is strictly between 10 and 20
print(f"Is {value} between 10 and 20? {is_in_range}") # Output: True

grade = 85
is_pass = 50 <= grade <= 100 # Checks if grade is between 50 and 100 (inclusive)
print(f"Did student pass? {is_pass}") # Output: True

Logical Operators: and, or, not

Explanation: Python logical operators (and, or, not) are used to combine or modify Boolean expressions. They are fundamental for creating complex conditional statements and controlling program flow. They also exhibit short-circuiting behavior, which can affect execution.
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Code Examples:

Python

# Example 1 (Beginner-Friendly): Basic 'and' and 'or'
# 'and': Returns True if BOTH operands are True.
# 'or': Returns True if AT LEAST ONE operand is True.
is_sunny = True
is_warm = False
print(f"Go outside (sunny AND warm)? {is_sunny and is_warm}") # Output: False
print(f"Wear coat (NOT sunny OR warm)? {not is_sunny or is_warm}") # Output: False

# Example 2 (Slightly More Complex): 'not' operator
# 'not': Inverts the Boolean value of the operand.
is_admin = False
print(f"Is user not admin? {not is_admin}") # Output: True
print(f"Is admin? {not not is_admin}")     # Output: False (double negative)

# Example 3 (Intermediate): Short-circuiting with 'and' and 'or'
# 'and': If the first operand is False, the second is not evaluated.
# 'or': If the first operand is True, the second is not evaluated.
# This can prevent errors (e.g., division by zero) or optimize performance.
x = 0
# This won't raise ZeroDivisionError because (x != 0) is False, so the second part is skipped.
result_and = (x != 0) and (10 / x)
print(f"Result of short-circuit AND: {result_and}") # Output: False

y = 5
# This won't print "Evaluated second part" because (y > 0) is True.
result_or = (y > 0) or (print("Evaluated second part"))
print(f"Result of short-circuit OR: {result_or}") # Output: True

# Example 4 (Advanced Beginner): Combining logical operators in complex conditions
# Logic can be nested and combined using parentheses for clarity and correct evaluation.
age = 20
has_ticket = True
has_vip_pass = False

# Condition: Must be adult AND (have ticket OR have VIP pass)
can_enter = (age >= 18) and (has_ticket or has_vip_pass)
print(f"Can enter event? {can_enter}") # Output: True (20 >= 18 is True, True or False is True, True and True is True)

# Example 5 (Intermediate): Logical operators with truthy/falsy values
# In Python, non-Boolean objects are treated as True or False in a boolean context.
# 'and' returns the first falsy operand, or the last operand if all are truthy.
# 'or' returns the first truthy operand, or the last operand if all are falsy.
print(f"Result of 'None and 0': {None and 0}")       # Output: None (first falsy)
print(f"Result of '[] or 'hello'': {[] or 'hello'}") # Output: 'hello' (first truthy)
print(f"Result of '1 and 2 and 3': {1 and 2 and 3}") # Output: 3 (last truthy)
print(f"Result of 'not []': {not []}")             # Output: True (empty list is falsy)

Identity Operators: is, is not

Explanation: Python identity operators (is, is not) are used to check if two variables refer to the exact same object in memory, not just if they have the same value. This is a subtle but important distinction from == (equality operator). Understanding Python object identity is key for advanced memory management and debugging.
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Code Examples:

Python

# Example 1 (Beginner-Friendly): 'is' with small integers and strings (interning)
# Python often "interns" (reuses) small integers and short strings for efficiency.
# This means they often point to the same memory location.
num_a = 5
num_b = 5
str_a = "hello"
str_b = "hello"
print(f"num_a is num_b: {num_a is num_b}") # Output: True (often interned)
print(f"str_a is str_b: {str_a is str_b}") # Output: True (often interned)

# Example 2 (Slightly More Complex): 'is' with lists (mutable objects)
# Lists are mutable, so even if they have the same content, they are usually
# distinct objects in memory unless explicitly assigned to the same object.
list1 = [1, 2, 3]
list2 = [1, 2, 3]
list3 = list1 # list3 now refers to the SAME object as list1

print(f"list1 is list2: {list1 is list2}") # Output: False (different objects, same content)
print(f"list1 is list3: {list1 is list3}") # Output: True (same object in memory)

# Example 3 (Intermediate): 'is not' operator
# Checks if two variables refer to different objects in memory.
obj1 = {} # Empty dictionary
obj2 = {}
print(f"obj1 is not obj2: {obj1 is not obj2}") # Output: True (they are different empty dictionaries)
print(f"obj1 is obj2: {obj1 is obj2}")         # Output: False

# Example 4 (Advanced Beginner): Using 'is None' for checking NoneType
# This is the canonical way to check if a variable is None, as 'None' is a singleton.
# '== None' works but 'is None' is preferred for clarity and robustness.
data = None
if data is None:
    print("Data variable is not set.")
else:
    print(f"Data is: {data}")

another_data = "some value"
if another_data is not None:
    print(f"Another data variable has a value: {another_data}")

# Example 5 (Intermediate): Identity vs. Equality with custom objects
# Demonstrates that 'is' and '==' can behave differently for custom classes.
class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

    def __eq__(self, other): # Defines what '==' means for Person objects
        if not isinstance(other, Person):
            return NotImplemented
        return self.name == other.name and self.age == other.age

p1 = Person("John", 30)
p2 = Person("John", 30) # Different object, same content
p3 = p1                  # Same object

print(f"p1 == p2: {p1 == p2}") # Output: True (content is equal, due to __eq__)
print(f"p1 is p2: {p1 is p2}") # Output: False (different objects)
print(f"p1 is p3: {p1 is p3}") # Output: True (same object)

Membership Operators: in, not in

Explanation: Python membership operators (in, not in) are used to test if a sequence (like a string, list, or tuple) contains a specific value. They also work with sets and dictionaries (checking for keys). These are highly useful for data validation and searching in Python collections.
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Code Examples:

Python

# Example 1 (Beginner-Friendly): 'in' with strings
# Checks if a substring is present within a larger string.
sentence = "The quick brown fox"
word_to_find = "quick"
print(f"Is '{word_to_find}' in sentence? {word_to_find in sentence}") # Output: True
print(f"Is 'lazy' in sentence? {'lazy' in sentence}")                 # Output: False

# Example 2 (Slightly More Complex): 'in' with lists and tuples
# Checks if an item is present in a list or tuple.
fruits = ["apple", "banana", "cherry"]
numbers = (10, 20, 30, 40)
print(f"Is 'banana' in fruits? {'banana' in fruits}") # Output: True
print(f"Is 25 in numbers? {25 in numbers}")             # Output: False

# Example 3 (Intermediate): 'not in' operator
# Checks if an item is NOT present in a sequence.
forbidden_words = ["badword", "offensive"]
user_comment = "This is a great tutorial!"
if any(word in user_comment for word in forbidden_words):
    print("Comment contains forbidden words.")
else:
    print("Comment is clean.") # Output: Comment is clean.

# Example 4 (Advanced Beginner): 'in' with dictionaries (checking keys)
# For dictionaries, 'in' checks for the presence of a KEY, not a value.
student_grades = {"Alice": 95, "Bob": 88, "Charlie": 72}
print(f"Is 'Alice' in student_grades (checking key)? {'Alice' in student_grades}") # Output: True
print(f"Is 88 in student_grades (checking value)? {88 in student_grades.values()}") # To check values, use .values()

# Example 5 (Intermediate): Conditional logic with membership operators
# Membership operators are commonly used in 'if' statements or loops.
valid_commands = ["start", "stop", "pause", "resume"]
user_input = "start"

if user_input in valid_commands:
    print(f"Executing command: {user_input}")
elif user_input not in valid_commands:
    print(f"Invalid command: {user_input}. Please choose from {valid_commands}.")

# Using 'in' in a loop to filter data
products = [{"name": "Laptop", "category": "Electronics"},
            {"name": "Desk", "category": "Furniture"},
            {"name": "Mouse", "category": "Electronics"}]
electronics = [p for p in products if "Electronics" in p["category"]]
print(f"Electronic products: {electronics}")
# Output: Electronic products: [{'name': 'Laptop', 'category': 'Electronics'}, {'name': 'Mouse', 'category': 'Electronics'}]

Operator Precedence

Explanation: Python operator precedence determines the order in which operators are evaluated in an expression. When multiple operators are present,1 Python evaluates them based on a defined hierarchy (e.g., multiplication and division happen before addition and subtraction). Parentheses () can be used to override this default order and force a specific evaluation sequence. Understanding Python operator order is essential to avoid unexpected results.
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Code Examples:

Python

# Example 1 (Beginner-Friendly): Arithmetic precedence (multiplication before addition)
# Python follows PEMDAS/BODMAS rules.
result1 = 10 + 5 * 2    # Evaluates 5 * 2 (10) first, then 10 + 10 = 20
print(f"10 + 5 * 2 = {result1}") # Output: 20

# Example 2 (Slightly More Complex): Using parentheses to override precedence
# Parentheses force the enclosed operation to be evaluated first.
result2 = (10 + 5) * 2  # Evaluates 10 + 5 (15) first, then 15 * 2 = 30
print(f"(10 + 5) * 2 = {result2}") # Output: 30

# Example 3 (Intermediate): Precedence involving exponentiation and division
# Exponentiation (**) has higher precedence than multiplication/division.
# Division (/) has higher precedence than addition/subtraction.
calc1 = 2 ** 3 * 5   # Evaluates 2**3 (8) first, then 8 * 5 = 40
calc2 = 10 / 2 + 3   # Evaluates 10 / 2 (5.0) first, then 5.0 + 3 = 8.0
print(f"2 ** 3 * 5 = {calc1}")   # Output: 40
print(f"10 / 2 + 3 = {calc2}")   # Output: 8.0

# Example 4 (Advanced Beginner): Precedence with comparison and logical operators
# Comparison operators have higher precedence than logical operators.
# 'not' > 'and' > 'or'
condition1 = 5 > 3 and 10 < 7 # (5 > 3 is True) AND (10 < 7 is False) => True and False => False
condition2 = not (5 > 3 or 10 < 7) # NOT (True OR False) => NOT (True) => False
print(f"5 > 3 and 10 < 7 = {condition1}") # Output: False
print(f"not (5 > 3 or 10 < 7) = {condition2}") # Output: False

# Example 5 (Intermediate): Complex expression with multiple operator types
# Demonstrating how Python applies precedence across different operator categories.
# (Exponentiation -> Multiplication/Division -> Addition/Subtraction -> Comparison -> Logical)
value_x = 7
final_result = (value_x + 3) ** 2 / 5 - 10 > 15 and "active" in "user_status_active"

# Step-by-step evaluation:
# 1. (value_x + 3) = (7 + 3) = 10
# 2. 10 ** 2 = 100
# 3. 100 / 5 = 20.0
# 4. 20.0 - 10 = 10.0
# 5. 10.0 > 15 = False
# 6. "active" in "user_status_active" = True
# 7. False AND True = False

print(f"Complex expression result: {final_result}") # Output: False