Introduction to Python Programming

• Python is a high-level, general-purpose programming language.
• Python's design philosophy emphasizes code readability with the use of significant indentation.
• Python is dynamically typed and garbage-collected.
• It supports multiple programming paradigms, including structured (procedural), object-oriented, and functional programming.
• Python is often described as a "batteries included" language because of its comprehensive standard library.
• Guido van Rossum created Python, and first released it in 1991.
• The Python Software Foundation (PSF) manages Python's development.

Key Features

• Readability is emphasized, making code easier to understand and maintain.
• Python code is executed line by line, simplifying debugging.
• Variable types are checked during runtime, which reduces development time.
• Python automatically manages memory allocation and deallocation through garbage collection.
• It has a vast collection of modules and functions that provide a wide range of functionalities.
• Python runs on various operating systems, including Windows, macOS, and Linux.
• Python can be integrated with other languages such as C and C++.

Data Types

• Numbers include integers, floating-point numbers, and complex numbers.
  • Integers consist of whole numbers (e.g., 1, 100, -5).
  • Floating-point numbers are numbers with decimal points (e.g., 3.14, 2.0).
  • Complex numbers contain a real and imaginary part (e.g., 2 + 3j).
• Strings are sequences of characters.
  • Strings are immutable, and cannot be changed after creation.
  • They are represented using single quotes ('...') or double quotes ("...").
• Booleans represent truth values (True or False).
  • Used in logical operations.
• Lists are ordered, mutable sequences of items.
  • Lists can contain elements of different data types.
  • Defined using square brackets [ ].
• Tuples are ordered, immutable sequences of items.
  • Tuples cannot be modified after creation.
  • Defined using parentheses ( ).
• Dictionaries are unordered collections of key-value pairs.
  • Keys must be unique and immutable.
  • Defined using curly braces { }.
• Sets are unordered collections of unique items.
  • Duplicate elements are automatically removed.
  • Defined using curly braces { } or the set() function.

Operators

• Arithmetic Operators perform mathematical operations.
  • Addition (+), subtraction (-), multiplication (*), division (/), floor division (//), modulus (%), exponentiation (**).
• Comparison Operators are used for comparing values.
  • Equal to (==), not equal to (!=), greater than (>), less than (<), greater than or equal to (>=), less than or equal to (<=).
• Logical Operators combine or modify boolean expressions.
  • and, or, not.
• Assignment Operators assign values to variables.
  • =, +=, -=, *=, /=, //=, %=, **=.
• Membership Operators test if a value is a member of a sequence.
  • in, not in.
• Identity Operators compare the memory locations of two object.
  • is, is not.
• Bitwise Operators perform bitwise operations on integers.
  • &, |, ^, ~, <<, >>.

Control Flow

• Conditional Statements execute different blocks of code based on certain conditions.
  • if, elif, else.
• Loops repeatedly execute a block of code.
  • for loop: Iterates over a sequence (e.g., list, tuple, string).
  • while loop: Executes a block of code as long as a condition is true.
• Loop Control Statements:
  • break: Terminates the loop prematurely.
  • continue: Skips the rest of the current iteration and proceeds to the next iteration.
  • pass: Does nothing (used as a placeholder).

Functions

• Definition: A block of code that performs a specific task.
  • Defined using the def keyword.
  • Can accept input arguments and return output values.
• Parameters and Arguments:
  • Parameters are the variables defined in the function definition.
  • Arguments are the values passed to the function when it is called.
• Return Values:
  • Functions can return values using the return statement.
  • If no return statement is specified, the function returns None.
• Scope:
  • Local scope: Variables defined inside a function are only accessible within that function.
  • Global scope: Variables defined outside any function are accessible throughout the program.
• Lambda Functions: Anonymous functions defined using the lambda keyword.
  • Single-expression functions.
  • Useful for short, simple operations.

Modules and Packages

• Modules: A file containing Python code (functions, classes, variables).
  • Used for organizing code into reusable units.
  • Imported using the import statement.
• Packages: A collection of modules organized in a directory hierarchy.
  • Provides a way to structure and manage large projects.
  • Must contain an __init__.py file (can be empty).
• Importing Modules:
  • import module_name: Imports the entire module.
  • from module_name import function_name: Imports a specific function from the module.
  • from module_name import *: Imports all names from the module (not recommended).
  • import module_name as alias: Imports the module with an alias.

Object-Oriented Programming (OOP)

• Classes: A blueprint for creating objects.
  • Defines the attributes (data) and methods (behavior) of an object.
• Objects: An instance of a class.
  • Represents a specific entity with its own state (attributes) and behavior (methods).
• Attributes: Variables that store data about an object.
• Methods: Functions that define the behavior of an object.
• Encapsulation: Bundling data and methods that operate on that data within a class.
• Inheritance: Allows a class (subclass) to inherit attributes and methods from another class (superclass).
  • Promotes code reuse and reduces redundancy.
• Polymorphism: The ability of an object to take on many forms.
  • Allows objects of different classes to be treated as objects of a common type.
• Abstraction: Hiding complex implementation details and exposing only essential features.
• Constructors: Special methods used for initializing objects.
  • Called automatically when an object is created.
  • In Python, the constructor is named __init__.

Exception Handling

• Exceptions: Errors that occur during the execution of a program.
• try-except block: Used for handling exceptions gracefully.
  • The try block contains the code that might raise an exception.
  • The except block contains the code that handles the exception.
• Exception Types:
  • TypeError: Occurs when an operation is performed on an object of an inappropriate type.
  • ValueError: Occurs when a function receives an argument of the correct type but an inappropriate value.
  • IndexError: Occurs when trying to access an index that is out of range.
  • KeyError: Occurs when trying to access a key that does not exist in a dictionary.
  • FileNotFoundError: Occurs when trying to open a file that does not exist.
• finally block: Contains code that is always executed, regardless of whether an exception occurred or not.
  • Used for cleaning up resources (e.g., closing files).
• Raising Exceptions:
  • Exceptions can be raised explicitly using the raise keyword.
  • Custom exceptions can be defined by creating new classes that inherit from the Exception class.

File Handling

• Opening Files:
  • The open() function is used to open files.
  • Takes two arguments: the file path and the mode (e.g., 'r' for read, 'w' for write, 'a' for append).
• Reading Files:
  • read(): Reads the entire contents of the file into a string.
  • readline(): Reads a single line from the file.
  • readlines(): Reads all lines from the file into a list of strings.
• Writing Files:
  • write(): Writes a string to the file.
  • writelines(): Writes a list of strings to the file.
• Closing Files:
  • The close() method is used to close files.
  • Important to close files to release resources and ensure data is written to disk.
  • The with statement can be used to automatically close files after they are no longer needed.

Common Libraries

• NumPy:
  • Used for numerical computing.
  • Provides support for large, multi-dimensional arrays and matrices.
  • Includes mathematical functions for operating on arrays.
• Pandas:
  • Used for data analysis and manipulation.
  • Provides data structures like Series (1D array) and DataFrame (2D table).
  • Offers tools for data cleaning, transformation, and analysis.
• Matplotlib:
  • Used for creating visualizations.
  • Provides a wide range of plotting functions for creating charts, graphs, and plots.
• Scikit-learn:
  • Used for machine learning.
  • Provides tools for classification, regression, clustering, and model selection.
• Requests:
  • Used for making HTTP requests.
  • Simplifies the process of sending and receiving data over the internet.
• Beautiful Soup:
  • Used for web scraping.
  • Parses HTML and XML documents, making it easy to extract data.
• Flask/Django:
  • Used for web development.
  • Provides frameworks for building web applications and APIs.

Virtual Environments

• Purpose: Create isolated environments for Python projects.
  • Prevents dependency conflicts between different projects.
• Creation:
  • Using venv: python3 -m venv myenv.
  • Using virtualenv: virtualenv myenv.
• Activation:
  • On Windows: myenv\Scripts\activate.
  • On macOS and Linux: source myenv/bin/activate.
• Deactivation:
  • Run deactivate in the terminal.

Best Practices

• Code Style: Follow PEP 8 style guide.
  • Consistent indentation (4 spaces per level).
  • Descriptive variable and function names.
  • Comments to explain complex logic.
• Error Handling: Use try-except blocks to handle exceptions gracefully.
• Modularity: Break code into reusable functions and modules.
• Documentation: Write docstrings for functions and modules.
• Testing: Write unit tests to ensure code correctness.
• Version Control: Use Git for version control.
• Virtual Environments: Use virtual environments to manage dependencies.