Python Cheatsheet

Python's basic syntax is clean and readable, emphasizing indentation for code blocks.

Python has several built-in data types to represent different kinds of values.

Operators are special symbols that perform operations on variables and values. Python supports various types of operators including arithmetic, comparison, logical, bitwise, assignment, and identity operators.

Control flow statements determine the order in which code is executed. They allow you to make decisions, repeat actions, and structure your program's logic.

Functions are reusable blocks of code that perform specific tasks. They help organize code, improve readability, and promote code reuse.

Lists in Python are ordered, mutable sequences of elements. They can contain items of different types and are defined using square brackets [].

Tuples are immutable sequences, typically used to store collections of heterogeneous data. They are defined using parentheses () and can contain any type of data.

Dictionaries are mutable, unordered collections of key-value pairs. They provide fast lookups and are ideal for storing and retrieving data with unique keys.

Sets are unordered collections of unique elements in Python. They are mutable, but can only contain immutable (hashable) elements. Sets are useful for removing duplicates from sequences and performing mathematical set operations.

Python provides powerful built-in methods for manipulating strings, allowing for efficient text processing and formatting.

File I/O in Python allows you to read from and write to files on your computer. This is essential for data persistence, processing large datasets, and working with external resources.

Exception handling allows you to gracefully handle errors and unexpected situations in your code, preventing crashes and providing meaningful error messages.

Modules are Python files containing reusable code. Packages are directories containing multiple modules. They help organize and modularize code for better reusability and maintainability.

Object-Oriented Programming (OOP) is a programming paradigm based on the concept of "objects", which can contain data and code. The main principles of OOP are encapsulation, inheritance, and polymorphism.

List comprehensions provide a concise way to create lists based on existing lists or other iterable objects. They can replace loops and map() calls with more readable and efficient code.

Lambda functions are small anonymous functions that can have any number of arguments but can only have one expression. They are useful for creating short, throwaway functions without formally defining them using the def keyword.

Decorators are a powerful feature in Python that allow you to modify or enhance functions or classes without directly changing their source code. They are essentially functions that take another function (or class) as an argument and return a modified version of that function.

Generators are functions that can be paused and resumed, yielding a sequence of values over time instead of computing them all at once and returning them in a list.

Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. They allow you to search, extract, and modify strings based on specific patterns.

Python provides robust built-in modules for working with dates, times, and time zones. The `datetime` module is the primary tool for handling date and time operations.

JSON (JavaScript Object Notation) is a lightweight data interchange format. Python provides built-in support for JSON through the `json` module, allowing easy encoding and decoding of JSON data.

Virtual environments in Python are isolated spaces where you can install and manage project-specific dependencies without affecting the global Python installation.

Testing in Python involves writing and running test cases to verify that your code behaves as expected. Python's built-in unittest module and third-party libraries like pytest provide powerful tools for automated testing.

Debugging is the process of finding and fixing errors in your code. Python provides various tools and techniques to help identify and resolve issues efficiently.