Python Documentation

This guide introduces Python programming theory, focusing on concepts and best practices for developers using the Codeunia Learn platform.

Overview

Python is a high-level, interpreted language valued for its simplicity and readability. Its design philosophy emphasizes code clarity, making it accessible for beginners and powerful for professionals. Python supports multiple programming paradigms, including procedural, object-oriented, and functional programming.

Python Fundamentals

Variables and Data Types

Variables are symbolic names that reference objects in memory. Python is dynamically typed, meaning variable types are determined at runtime. Common data types include:

• String: Textual data, immutable.
• Integer: Whole numbers.
• Float: Decimal numbers.
• Boolean: True or False values.
• List: Ordered, mutable sequences.
• Tuple: Ordered, immutable sequences.
• Dictionary: Key-value mappings, mutable.
• Set: Unordered collections of unique elements.

Type checking is performed with type() and isinstance().

Strings

Strings are sequences of Unicode characters. They support various methods for manipulation (e.g., case conversion, splitting, replacing). String formatting is achieved with f-strings or the .format() method.

Lists

Lists are mutable sequences used to store collections of items. They support operations such as appending, inserting, removing, and sorting. List comprehensions provide a concise way to create lists based on existing sequences.

Dictionaries

Dictionaries store key-value pairs and are ideal for representing structured data. They support methods for accessing, updating, and removing elements, as well as dictionary comprehensions for generating new dictionaries.

Control Flow

Conditional Statements

Control flow is managed with if, elif, and else statements, allowing branching logic based on conditions. The ternary operator provides concise conditional assignment.

Loops

Python supports for and while loops for iteration. Loop control statements like break and continue alter the flow of loops.

Functions

Functions encapsulate reusable logic. They can accept parameters, return values, and support default arguments. Lambda expressions enable anonymous functions. Higher-order functions such as map() and filter() facilitate functional programming.

Advanced features include variable-length arguments (*args, **kwargs) and decorators, which modify function behavior.

Classes and Object-Oriented Programming

Python supports object-oriented programming through classes and inheritance. Classes define blueprints for objects, encapsulating data and behavior. Inheritance enables code reuse and polymorphism. Advanced concepts include properties for controlled attribute access and magic methods for customizing object behavior.

Error Handling

Exception handling is performed with try, except, else, and finally blocks. Custom exceptions can be defined for specific error scenarios. Context managers (with statement) ensure proper resource management.

File Operations

Python provides built-in support for reading and writing files, as well as manipulating file paths using the pathlib module. File modes (r, w, a) determine how files are accessed.

Modules and Packages

Code organization is achieved through modules and packages. The import system allows reuse of standard library and third-party modules. Virtual environments isolate dependencies for projects.

Data Science and Scientific Computing

Python is widely used in data science, with libraries like NumPy for numerical operations and Pandas for data manipulation. These libraries provide efficient data structures and functions for analysis.

Web Development

Python frameworks such as Flask and FastAPI enable rapid development of web applications and APIs. They provide routing, request handling, and integration with databases.

Best Practices

Adhering to PEP 8 ensures code readability and consistency. Organize code into functions, classes, and modules. Use context managers for resource management and comprehensions for concise data processing.

Testing

Testing is essential for code reliability. Python offers unittest and pytest frameworks for writing and running tests, supporting assertions and exception handling.

Performance Optimization

Profiling tools help identify bottlenecks. Efficient memory management and use of built-in functions can improve performance.

Deployment

Requirements files (requirements.txt) specify dependencies. Docker enables containerized deployment for consistent environments.

Resources and References

• Python Official Documentation
• PEP 8 Style Guide
• Real Python
• Automate the Boring Stuff with Python

Troubleshooting

Common issues include import errors, syntax errors, and indentation errors. Debugging tools and logging facilitate error diagnosis.

Development Setup

Set up a local environment with Python, virtual environments, and code quality tools (e.g., black, flake8, pytest). Configure your IDE for linting and testing.