Python Variables, Data Types, and Basic Operations

Python Data Types

Python has several built-in data types to represent different kinds of values. Unlike statically-typed languages, Python is dynamically-typed, meaning a variable can change its type during program execution.

Numeric Types

Python provides three numeric types:

# Integer (whole numbers)
age = 30
count = -5
big_number = 10000000000  # Python integers have unlimited precision

# Float (decimal numbers)
height = 5.7
pi = 3.14159
scientific = 2.5e6  # Scientific notation (2.5 million)

# Complex (numbers with real and imaginary parts)
complex_num = 2 + 3j

String Type

Strings are sequences of characters, enclosed in single, double, or triple quotes:

# Single quotes
name = 'Alice'

# Double quotes
message = "Hello, World!"

# Triple quotes for multi-line strings
description = """This is a long description
that spans multiple lines,
which is easy to read in the code."""

# Strings are immutable (cannot be changed after creation)
name = 'Alice'
# This creates a new string, doesn't modify the original
new_name = name + ' Smith'

Boolean Type

Boolean values represent truth values, either True or False:

is_student = True
has_permission = False

# Comparison operations yield boolean results
is_adult = age >= 18
is_valid = name != ""

Sequence Types

Python has several sequence types that can hold collections of items:

# Lists - ordered, mutable collections
fruits = ['apple', 'banana', 'cherry']
mixed_list = [1, 'hello', True, 3.14]

# Tuples - ordered, immutable collections
coordinates = (10.5, 20.8)
person = ('Alice', 30, 'Engineer')

# Ranges - sequence of numbers
numbers = range(10)      # 0 to 9
even_numbers = range(0, 10, 2)  # 0, 2, 4, 6, 8

Mapping Type

Dictionaries store key-value pairs:

# Dictionary - unordered collection of key-value pairs
person = {
    'name': 'Alice',
    'age': 30,
    'profession': 'Engineer',
    'is_student': False
}

# Accessing values
print(person['name'])    # Alice

# Adding new key-value pairs
person['location'] = 'New York'

Set Types

Sets store unordered collections of unique items:

# Set - unordered collection of unique elements
fruits = {'apple', 'banana', 'cherry'}

# Adding and removing elements
fruits.add('orange')
fruits.remove('banana')

# Set operations
basket1 = {'apple', 'orange', 'banana'}
basket2 = {'pear', 'banana', 'kiwi'}
all_fruits = basket1 | basket2  # Union
common_fruits = basket1 & basket2  # Intersection

None Type

Python has a special None value that represents the absence of a value:

# None represents the absence of a value
result = None

def function_without_return():
    pass  # Do something but don't return a value

value = function_without_return()  # value will be None

Checking Types

You can check the type of a variable using the type() function:

x = 42
y = "Hello"
z = [1, 2, 3]

print(type(x))  # 
print(type(y))  # 
print(type(z))  # 

Type Conversion

Python provides functions to convert between types:

# Converting between types
age_str = "30"
age_int = int(age_str)    # String to integer
age_float = float(age_int)  # Integer to float
age_str_again = str(age_int)  # Integer to string

# Converting collections
my_list = [1, 2, 3, 2, 1]
my_tuple = tuple(my_list)  # List to tuple
my_set = set(my_list)      # List to set (duplicates removed)
my_list_again = list(my_set)  # Set to list