Python CHEAT SHEET

Conditional statements are used to perform different actions based on different conditions. In Python, we have the following conditional statements: if, elif, else.

For loops in Python are used for iterating over a sequence (such as a list, tuple, or string) and executing a block of code for each item in the sequence.

While loops are used to repeatedly execute a block of code as long as a specified condition is true.

The 'break' statement is used to exit the loop prematurely, while the 'continue' statement is used to skip the rest of the current iteration and continue with the next iteration.

List comprehensions provide a concise way to create lists in Python by applying an expression to each item in an iterable and collecting the results.

Lists are ordered collections of items that can be modified. They are versatile and can store a variety of data types.

Tuples are ordered, immutable collections of items. They are often used to represent fixed data sequences.

Dictionaries are collections of key-value pairs, allowing you to store and retrieve data using unique keys.

Sets are unordered collections of unique elements. They are useful for tasks like removing duplicates from a list.

Function definition involves creating custom functions in Python to encapsulate a set of statements that can be executed with a single function call.

Python functions can accept arguments (input parameters) and return values (output) to make them flexible and reusable.

Default arguments allow you to specify default values for function parameters, which are used if no argument is provided for that parameter when calling the function.

Keyword arguments provide a way to pass arguments to functions using parameter names, making it more explicit and flexible.

Lambda functions, also known as anonymous functions, are small, one-line functions defined using the 'lambda' keyword.

Scope refers to the visibility and accessibility of variables within different parts of a Python program, including local and global scope.

Recursion is a technique where a function calls itself to solve a problem. It is particularly useful for solving problems with repetitive or recursive structures.

Python provides a wide range of built-in functions for performing common tasks, such as 'len()' for getting the length of a sequence or 'max()' for finding the maximum value in a collection.

Importing modules allows you to access functions, classes, and variables defined in external Python files to reuse code.

Creating modules involves organizing Python code into separate files, each serving as a module, and making them accessible for reuse.

Packages are a way to organize related modules into a directory hierarchy. They help manage and structure large Python projects.

Python includes a set of common standard libraries that provide pre-built modules for various purposes, such as 'os' for operating system interactions or 'math' for mathematical operations.

Classes are blueprints for creating objects, and objects are instances of classes. OOP enables modeling of real-world entities as objects with attributes and methods.

A constructor is a special method in a class used to initialize object attributes when an object is created.

Inheritance allows a class (subclass) to inherit attributes and methods from another class (superclass), facilitating code reuse and hierarchy.

Polymorphism enables objects of different classes to be treated as objects of a common superclass, allowing for more flexible and dynamic code.

Encapsulation is the concept of hiding the internal implementation details of a class and providing a public interface for interacting with objects.

Class methods are methods that are bound to the class and not the instance, while static methods are independent of both the class and the instance.

Files must be opened before reading from or writing to them and should be closed after use to release system resources.

Python provides various methods for reading and writing files, including reading line by line, binary files, and text files.

File operations include tasks like renaming, deleting, or checking the existence of files and directories.

The 'with' statement is used for context management when working with files, ensuring that files are properly closed even in the presence of exceptions.

Iterators are objects that allow sequential access to elements in a collection, making it easier to iterate through data.

Generators are a type of iterator that can yield values one at a time, conserving memory and improving performance.

Decorators are functions that modify the behavior of other functions or methods, often used for adding functionality like logging or access control.

Context managers are used with the 'with' statement to manage resources, such as file handling or database connections, ensuring proper cleanup.

Regular expressions (regex) are powerful tools for pattern matching and text manipulation, often used for searching and replacing strings.

Multithreading and multiprocessing are techniques for parallel processing, allowing programs to perform multiple tasks simultaneously for improved performance.

The math module offers mathematical functions, while the random module is used for generating random numbers and selections.

Python's datetime module allows for working with dates and times, including parsing, formatting, and performing calculations with date and time objects.

Data collections in the standard library include data structures like namedtuples, queues, and arrays for specific use cases.

Data serialization involves converting complex data structures into formats that can be easily stored or transmitted, such as JSON or Pickle.

Python provides modules for interacting with the operating system, executing system commands, and managing file paths and directories.

Socket programming allows you to create network applications that can communicate over the internet using sockets and network protocols.

Python provides libraries like 'requests' for making HTTP requests to interact with web services and retrieve data from the internet.

Understanding networking protocols is crucial for working with network communication, including protocols like TCP/IP, HTTP, and FTP.

Numerical computing libraries like NumPy provide efficient data structures and functions for performing mathematical and statistical operations.

Data analysis involves exploring and manipulating data sets using libraries like Pandas, making it easier to gain insights from data.

Data visualization libraries such as Matplotlib and Seaborn enable the creation of charts and graphs to represent data visually.

Machine learning libraries like Scikit-Learn and TensorFlow allow you to develop and train machine learning models for various tasks.