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Topics

Python Basics

Introduction to PythonInstalling PythonPython SyntaxPython CommentsPython VariablesPython Data TypesPython Type ConversionPython OperatorsPython Input/OutputPython Indentation

Control Structures

Python If...ElsePython While LoopsPython For LoopsPython Break and ContinuePython Pass StatementPython Match-Case Statements

Python Data Structures

Python ListsPython TuplesPython SetsPython DictionariesPython ArraysPython List Comprehension

Python Functions

Defining Python FunctionsPython Function ArgumentsPython Function Return ValuesPython Lambda FunctionsPython Recursive FunctionsPython Global and Local Variables

Python OOP

Python Classes and ObjectsPython InheritancePython EncapsulationPython PolymorphismPython Method OverridingPython Abstract Classes

Python Modules and Packages

Creating Python ModulesImporting Python ModulesPython Built-in ModulesPython Package Management (pip)Creating Python Packages

Python File Handling

Python File Open/ClosePython File ReadPython File WritePython File ManagementPython Directory Handling

Python Exception Handling

Python Try...ExceptPython User-defined ExceptionsPython Raise KeywordPython Finally Clause

Python Advanced Topics

Python IteratorsPython GeneratorsPython DecoratorsPython Context ManagersPython Regular ExpressionsPython MultithreadingPython Multiprocessing

Python Standard Library

Python Math ModulePython Random ModulePython DateTime ModulePython JSON ModulePython OS ModulePython Sys Module

Python Data Manipulation

Python String ManipulationPython List OperationsPython Dictionary OperationsPython Set Operations

Python Testing

Python Unit TestingPython DoctestPython PytestPython Test-Driven Development

Python Performance

Python Code OptimizationPython ProfilingPython Memory ManagementPython Caching Techniques

Python and Databases

Python SQLitePython MySQL ConnectorPython PostgreSQLPython MongoDB

Python Web Scraping

Python Requests LibraryPython BeautifulSoupPython Scrapy Basics

Python and APIs

Python REST API BasicsPython API AuthenticationPython API Data Handling

Python Data Types

Python, a versatile and powerful programming language, offers several built-in data types to store and manipulate different kinds of information. Understanding these data types is crucial for effective Python programming.

Basic Data Types

Numbers

Python supports various numeric types:

  • Integers (int): Whole numbers without decimal points.
  • Floating-point numbers (float): Numbers with decimal points.
  • Complex numbers: Numbers with real and imaginary parts.

Example:


integer_num = 42
float_num = 3.14
complex_num = 2 + 3j

Strings (str)

Strings are sequences of characters, enclosed in single or double quotes. They are immutable, meaning their content cannot be changed after creation.

Example:


greeting = "Hello, World!"
name = 'Alice'
multiline_string = """This is a
multiline string."""

Sequence Types

Lists

Lists are ordered, mutable sequences that can contain elements of different data types. They are defined using square brackets.

Example:


fruits = ['apple', 'banana', 'cherry']
mixed_list = [1, 'two', 3.0, [4, 5]]

For more information on lists, check out the Python Lists guide.

Tuples

Tuples are ordered, immutable sequences. They are similar to lists but cannot be modified after creation. Tuples are defined using parentheses.

Example:


coordinates = (10, 20)
rgb_color = (255, 0, 128)

Learn more about tuples in the Python Tuples guide.

Mapping Type

Dictionaries

Dictionaries are unordered collections of key-value pairs. They are mutable and defined using curly braces.

Example:


person = {
    'name': 'John',
    'age': 30,
    'city': 'New York'
}

Explore dictionaries further in the Python Dictionaries guide.

Set Types

Sets are unordered collections of unique elements. They are mutable and defined using curly braces or the set() function.

Example:


fruits_set = {'apple', 'banana', 'cherry'}
numbers_set = set([1, 2, 3, 3, 4])  # Duplicates are automatically removed

For more details on sets, refer to the Python Sets guide.

Boolean Type

The Boolean type represents truth values: True or False. Booleans are often used in conditional statements and logical operations.

Example:


is_raining = True
is_sunny = False

Type Conversion

Python allows conversion between different data types using built-in functions:

  • int(): Converts to integer
  • float(): Converts to float
  • str(): Converts to string
  • list(): Converts to list
  • tuple(): Converts to tuple
  • dict(): Converts to dictionary

For more information on type conversion, check out the Python Type Conversion guide.

Conclusion

Understanding Python's data types is fundamental to writing efficient and effective code. Each data type has its own characteristics and use cases. As you progress in your Python journey, you'll become more familiar with when and how to use each type.

Remember to choose the appropriate data type for your specific needs, considering factors like mutability, ordering, and performance. Practice working with different data types to solidify your understanding and improve your Python programming skills.