Numeric Types

Integer:

Integers are whole numbers. In Python, you don't need to declare the data type explicitly.

codeage = 30 
print(type(age)) # Outputs: <class 'int'> 

Float:

Floats are numbers that contain a decimal point.

codeheight = 180.5 
print(type(height)) # Outputs: <class 'float'> 

Complex:

Complex numbers are written with a "j" as the imaginary part.

codecomplex_num = 3+4j 
print(type(complex_num)) # Outputs: <class 'complex'> 

Sequence Types

List:

Lists are used to store multiple items in a single variable. Lists are one of 4 built-in data types in Python used to store collections of data.

codefruits = ['apple', 'banana', 'cherry'] 
print(type(fruits)) # Outputs: <class 'list'> 

Tuple:

Tuples are used to store multiple items in a single variable. A tuple is a collection which is ordered and unchangeable.

codecoordinates = (4.21, 9.29) 
print(type(coordinates)) # Outputs: <class 'tuple'> 

Range:

The range type represents an immutable sequence of numbers and is commonly used for looping a specific number of times in for loops.

codenumbers = range(1,6) 
for num in numbers: 
    print(num) # Outputs: 1 2 3 4 5 

Text Sequence Type

String:

Strings in python are contiguous series of characters delineated by single or double-quotes.

codegreeting = "Hello, World!" 
print(type(greeting)) # Outputs: <class 'str'> 

Mapping Type

Dictionary:

A dictionary is a collection which is unordered, changeable and indexed.

codestudent = {'name': 'John', 'age': 22} print(type(student)) # Outputs: <class 'dict'> 

Set Types

Set:

A set is a collection which is unordered and unindexed.

codefruits = {'apple', 'banana', 'cherry'} 
print(type(fruits)) # Outputs: <class 'set'> 

Frozenset:

Frozenset is a new class that has the characteristics of a set, but unlike set, frozensets are immutable.

codefruits = frozenset(['apple', 'banana', 'cherry']) 
print(type(fruits)) # Outputs: <class 'frozenset'> 

Boolean Type

Boolean:

Booleans represent one of two values: True or False . In programming, you often need to know if an expression is True or False .

codeis_adult = True 
print(type(is_adult)) # Outputs: <class 'bool'> 

Binary Types

Bytes:

Bytes objects are immutable sequences of single bytes.

codex = bytes(5) 
print(type(x)) # Outputs: <class 'bytes'> 

Bytearray:

Byte arrays are sequences of bytes that are mutable.

codex = bytearray(5) 
print(type(x)) # Outputs: <class 'bytearray'> 

Memoryview:

The memoryview function allows direct read and write access to an object’s byte-oriented data without needing to copy it first.

codex = memoryview(bytes(5)) 
print(type(x)) # Outputs: <class 'memoryview'> 

Dynamic Typing

Python is a dynamically typed language. This implies that you don't need to declare the type of a variable when you create it. The Python interpreter infers the type based on the value you assign. Consider the following example:

codex = 4 

In the above code snippet, Python understands that x is an integer.

Type Conversion

Python also supports type conversion, often known as 'type casting'. This feature allows you to convert a variable of one data type to another. This can be particularly useful when dealing with user inputs or retrieving data from a file. Here's an example:

codepi = 3.14 
pi_as_integer = int(pi) 

In the example above, pi_as_integer would be 3 , as the int() function truncates the decimal part when converting a float to an integer.

Mutable and Immutable Types

Python data types are either mutable (can be changed after creation) or immutable (cannot be changed after creation). Understanding the difference is vital as it can impact how your variables behave.

For instance, lists are mutable. You can modify their content without creating a new list:

codefruits = ['apple', 'banana'] 
fruits.append('cherry') # The list is now ['apple', 'banana', 'cherry'] 

On the other hand, tuples are immutable. If you attempt to alter a tuple after its creation, Python will throw an error:

codecoordinates = (4, 5) 
coordinates[0] = 10 # This will raise a TypeError 

Memory Management

Different Python data types consume different amounts of memory. An integer uses less memory than a float, and a single character string uses less memory than a dictionary or a list. This becomes particularly relevant when you're working with large datasets. By choosing the appropriate data type, you can make your code more memory-efficient and faster.

Methods and Operations

Each Python data type comes with its own set of methods and operations. For instance, string data types provide a host of methods for string manipulation, such as upper() , lower() , and replace() . List data types offer methods like append() , pop() , and remove() , enabling you to manipulate the list.

Collections

Python's collection data types (list, tuple, set, dictionary) are versatile and powerful. They allow you to create complex data structures. Collections are used extensively in data analysis and machine learning.

Infinite Precision Integers

Python's int type has unlimited precision. This means you can use Python to handle very large integers, only limited by the amount of available memory. This is particularly handy for cryptography or other areas requiring significant mathematical computations.

Real-world Applications

Python data types are instrumental in various real-world applications. Data types such as lists and dictionaries are fundamental in data analysis and machine learning. String data types find extensive use in text processing and web scraping.

Null Type

Python includes a special data type, the Null type, which has a single value: None . This is used to signify 'nothing' or a 'lack of value'. It's often used as the default parameter for function arguments.

codedef greet(name=None): 
    if name is None: 
        print("Hello, world!") 
    else: 
        print(f"Hello, {name}!") 

Data Type Checking and Enforcement

While Python is dynamically typed, sometimes you might want to enforce a particular data type. You can use the isinstance() function to check if a variable is of a certain type:

codex = 10 
if isinstance(x, int): 
    print("x is an integer") 

For function arguments, Python 3.5 introduced 'type hints' that allow you to suggest the expected data type:

codedef add(a: int, b: int) -> int:
    return a + b 

Note that these hints don't enforce the data type - they merely suggest it. They can, however, be used by tools like linters, IDEs, or static type checkers to help catch potential errors.

Custom Data Types

Beyond Python's built-in data types, you can define your own custom data types using classes. A class can encapsulate data (attributes) and functions (methods) into a single object. This is the foundation of object-oriented programming in Python.

codeclass Car: 
    def __init__(self, color, make): 
        self.color = color 
        self.make = make 
        
    def honk(self): 
        print("Honk!") 
        
my_car = Car("red", "Toyota") 
my_car.honk() # prints "Honk!" 

In the above code, we create a new Car data type that has a color, a make, and a method to honk.

Conclusion:

In conclusion, Python's data types are a fundamental aspect of the language, playing a pivotal role in everything from basic programming to advanced machine learning tasks. Python provides a variety of built-in data types, from numeric types like integers and floats to collections like lists and dictionaries.

With Python's dynamic typing, you don't have to declare the type of a variable when you create it. Python infers the type at runtime, adding flexibility to your programming.

Understanding mutability can help avoid bugs in your code. Remember, some Python objects like lists and dictionaries are mutable, allowing their modification after creation, while others like tuples and strings are immutable.