Python Programming: Data Types

• Python is a versatile, high-level programming language known for its readability and ease of use.
• It supports multiple programming paradigms, including object-oriented, imperative, and functional programming.
• Python's dynamic typing and automatic memory management make it suitable for rapid application development.
• Its extensive standard library and vast ecosystem of third-party packages contribute to its wide adoption in various domains.

Fundamental Data Types

• Data types are categories that classify values, determining the operations that can be performed on them.
• Python has several built-in data types, which can be grouped into categories like numeric, text, and boolean.
• Understanding data types is crucial for writing correct and efficient Python code.

Numeric Types

• Numeric types represent numerical values and support mathematical operations.
• The main numeric types in Python are integers, floating-point numbers, and complex numbers.

Integers (int)

• Integers are whole numbers, positive or negative, without any decimal points.
• Examples of integers: -3, 0, 42, 1000
• Integers have unlimited precision in Python, meaning they can represent arbitrarily large numbers.
• Common operations: addition, subtraction, multiplication, division, modulo, exponentiation

Floating-Point Numbers (float)

• Floating-point numbers represent real numbers with decimal points.
• Examples of floats: -3.14, 0.0, 2.718, 1.0
• Floats are represented using double-precision (64-bit) floating-point format (IEEE 754 standard).
• Floating-point arithmetic may introduce small rounding errors due to the finite precision.

Complex Numbers (complex)

• Complex numbers have a real and an imaginary part, represented as a + bj, where a is the real part, b is the imaginary part, and j is the imaginary unit (√-1).
• Examples of complex numbers: 2 + 3j, -1.5 - 0.5j
• Useful in scientific and engineering computations

Text Type

• Strings are sequences of characters used to represent text.

Strings (str)

• Strings are immutable sequences of Unicode characters.
• Strings are defined using single quotes (' '), double quotes (" "), or triple quotes (''' ''' or """ """).
• Triple quotes are used for multiline strings.
• Examples of strings: 'hello', "world", '''Python is fun'''
• Strings support various operations like concatenation, slicing, indexing, and formatting.

Boolean Type

• Booleans represent truth values, either True or False.

Booleans (bool)

• Booleans are a subtype of integers; True is equivalent to 1 and False to 0.
• Booleans are commonly used in conditional statements and logical operations.
• Examples of boolean values: True, False
• Boolean operations: and, or, not

Sequence Types

• Sequence types represent ordered collections of items.
• Python's built-in sequence types include lists, tuples, and ranges.

Lists (list)

• Lists are mutable, ordered sequences of items.
• Lists are defined using square brackets [ ].
• Lists can contain items of different data types.
• Examples of lists: [1, 2, 3], ['apple', 'banana', 'cherry'], [1, 'hello', True]
• Lists support operations like indexing, slicing, appending, inserting, removing, and sorting.

Tuples (tuple)

• Tuples are immutable, ordered sequences of items.
• Tuples are defined using parentheses ( ).
• Examples of tuples: (1, 2, 3), ('apple', 'banana', 'cherry')
• Tuples are often used to represent fixed collections of items.
• Tuples generally offer better performance than lists when immutability is desired

Ranges (range)

• Ranges represent immutable sequences of numbers.
• Ranges are typically used for looping a specific number of times.
• Example: range(5) generates numbers from 0 to 4.

Mapping Type

• Mapping types represent collections of key-value pairs.
• Python's built-in mapping type is the dictionary.

Dictionaries (dict)

• Dictionaries are mutable, unordered collections of key-value pairs.
• Dictionaries are defined using curly braces { }.
• Keys must be unique and immutable (e.g., strings, numbers, tuples).
• Values can be of any data type.
• Examples of dictionaries: {'name': 'Alice', 'age': 30}, {1: 'one', 2: 'two'}
• Dictionaries support operations like accessing values by key, adding new key-value pairs, and deleting key-value pairs.

Set Types

• Set types represent unordered collections of unique items.
• Python has two built-in set types: set and frozenset.

Sets (set)

• Sets are mutable, unordered collections of unique items.
• Sets are defined using curly braces { } or the set() constructor.
• Sets do not allow duplicate elements.
• Examples of sets: {1, 2, 3}, {'apple', 'banana', 'cherry'}
• Sets support operations like union, intersection, difference, and membership testing.

Frozen Sets (frozenset)

• Frozen sets are immutable versions of sets.
• Frozen sets are created using the frozenset() constructor.
• Since they are immutable, frozen sets can be used as keys in dictionaries or elements in other sets.

None Type

• The None type represents the absence of a value.

None (NoneType)

• None is a singleton object representing null or an empty value.
• It is often used as a default value for function arguments or to indicate that a variable does not have a value.
• Example: x = None

Type Conversion

• Type conversion (or type casting) is the process of converting a value from one data type to another.
• Python provides built-in functions for type conversion, such as int(), float(), str(), list(), tuple(), and set().
• Implicit type conversion (coercion) may occur automatically in certain operations.
• Example: int("123") converts the string "123" to the integer 123.

Immutability vs. Mutability

• Immutable objects cannot be changed after they are created. Examples: strings, numbers, tuples, and frozen sets.
• Mutable objects can be changed after they are created. Examples: lists, dictionaries, and sets.
• Understanding mutability is essential for avoiding unexpected side effects when working with data structures.

Data Type Operations and Methods

• Each data type supports specific operations and methods for manipulating values.
• Common operations include arithmetic operations for numbers, string manipulation methods for strings, and collection manipulation methods for lists, tuples, sets, and dictionaries.
• Example: string.upper() converts a string to uppercase.