Python engines, cheat sheets and chips to improve your skills.

8 June 2023 8 minutes Author: Endpool

Python: Power, Simplicity, and Versatility

Python is a high-level, interpreted programming language that focuses on code simplicity and readability. It has a wide range of applications and is used both for writing small scripts and for developing large-scale programs. Python has a simple and clear syntax that allows developers to efficiently solve tasks without unnecessary stress. It supports many programming paradigms, including procedural, object-oriented, functional, and aspect-oriented programming. Python has a large standard library that provides a variety of tools and modules for the various needs of programmers. In addition, there are also a large number of third-party libraries and frameworks that extend the capabilities of the language and allow solving specific tasks. Python is a popular choice for web development, scientific computing, data analysis, artificial intelligence, task automation, and many other fields.

Its active developer community and abundant resources make Python an accessible and supported programming language. Python is also a cross-platform language, which means that programs written in Python can run on different operating systems, such as Windows, macOS, and Linux, without any changes to the source code. This makes it extremely flexible and convenient for cross-platform software development. Python also has a large and active developer community that actively supports the language, creates new libraries and frameworks, and helps solve problems and answer questions.

This creates a conducive environment for learning and growing in the world of programming. One of the most outstanding features of Python is its ease of teaching and accessibility for beginners. Thanks to the clear syntax and clear structure of the language, learning Python becomes a pleasant and fast process. This makes it a popular choice for those just starting their programming journey. Due to its advantages, Python is used in many fields, from web application development and systems integration to data analysis and scientific research. It is constantly being developed and updated, maintaining its popularity and importance in modern programming.

Engines for creating games in Python

Many people want to learn programming to create their games, but what does it take to learn Python and what do you need to learn? The answer is that game engines or libraries exist to create games in Python libraries that can be installed from various channels, such as GitHub or the pip manager. In addition to these, there are stand-alone game deployment environments. Let’s break down the TOP 5 Python game engines.

PyGame

Probably the most popular tool for most Python programmers is PyGame. It is implemented on the basis of the SDL – Simple DirectMedia Class library, which allows cross-platform access to various system components. For example, video device, keyboard, mouse, audio device, etc. Various applications are also created using this tool.

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PyGame Zero

A newer and simpler tool is Pygame Zero. It is perfect for those who are new to the world of game development. The simple interface and step-by-step instructions are a great way to quickly learn how to develop 2D games. The zero version does not dive into the jungle of complex terminology and functions. It is very interesting

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Adventure lib

If you want to create an interactive story or text game like Zork, adventurelib is the tool of choice. This module is ideal for console formatting applications, offering a wide selection of tools. You can write everything from characters to game logic well with it, and thanks to its open source code and no need to write a parser, it’s even easier to interact with.

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Ren’Py

Another type of game is a story or visual novel. They represent a very important plot, in which it is beautifully diluted with visuals and sound. For their implementation in Python.

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Panda 3D

Panda 3D is a free 3D game engine and 3D renderer with a lot of working tools. It is fully compatible with various operating systems and even works on mobile devices.

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Arcade

Arcade is a simple and easy-to-learn engine for creating 2D games in Python. It provides an easy way to work with graphics, sound and input, allowing you to quickly start developing your own games.

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Python cheat sheets for various fields of application

Keep a few Python cheat sheets that will save you time in the process of learning and working with the popular programming language.

Python basics cheat sheet

It collects variables, methods, indexes and slices, as well as date formatting with relevant hints in the footnotes. If you are new to Python programming.

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Regular expressions in Python

The cheat sheet has collected a large amount of information about regulation, including: Special symbols; Match objects; Expansion; Objects Flags.

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Cheat sheet with pandas

This data analysis library has a proven track record in Data Science. This cheat sheet will help you build different diagrams using pandas.

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Machine Learning cheat sheet

Machine learning in Python is a data analysis method that combines data with statistical tools to predict outcomes. This forecast is used by various industries to take a favorable decision.

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Python virtual environment

This cheat sheet has collected popular tools for creating and working with isolated environments, as well as commands for installing these tools and activating the virtual environment.

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Cheat sheet for Django

Django will be useful for those who want to learn web development in Python. Here you will find basic information about working with the framework, from its installation to operations with Git.

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Python network programming cheat sheet

The cheat sheet includes: Required common installation modules: PIP and IDLE. The best Python network programming libraries. Python keywords. Network Analysis with Python.

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Python tricks that will take your school to the next level

Generators of lists (List Comprehension)

To create a new list where a function is applied to each item. This ensures faster reading and execution by the compiler.

squares = [x**2 for x in range(1, 11)]

Enumeration

Use enumerate() to select a list by both index and value. This is an elegant way to keep track of the index of an element, not just its value.

for index, value in enumerate(my_list): 
print(f"{index}: {value}")

Lambda functions (Lambda Functions)

Create small anonymous functions with the lambda keyword. Lambdas are simply designed to be used in higher-order functions as an argument. This certainly allows for shorter code.

square = lambda x: x**2

Multiple Assignment

Assign multiple variables on a single line using tuple unpacking. This is an incredibly convenient way to decompose any complex object into independent variables.

my_tuple = ("Алиса", 30) # Наш кортеж 
name, age = my_tuple # Непосредственно распаковка print(name) # Выведет 'Алиса'

Removing part of the list (Slicing)

Use the extraction of a part of the list – slicing, specifying the indices of the initial and final elements. Instead of creating a copy of my_list, in the example below we refer to this object directly. This is a rational use of memory, and with large volumes of data, you will definitely appreciate this feature.

my_list[1:4] # returns the sublist from index 1 (inclusive) to 4 (exclusive)

Inclusion (Dictionary Comprehension)

It will allow you to concisely generate dictionaries compared to the same for loop, which takes at least two lines.

squares = {x: x**2 for x in range(1, 11)}

Walrus Operator

:= will return the value of the variable as part of the expression.

n = 0 
while (n := n + 1) < 10: 
print(n)

F-strings (F-strings)

The very epitome of interpolation, i.e. incorporating variables into string inferences.

name = "Alice" 
age = 30 
print(f"My name is {name} and I am {age} years old.")

any() та all()

The functions will check whether the elements of the object satisfy the condition.

any() takes an iterable object (such as a list of nums) as an argument and returns True if at least one element in the list is True. If all elements are false or nums is empty, any() returns False.

all() also takes such an object as an argument and returns True if all elements in it evaluate to true, or if the iterated object is empty. If there is at least one element that is considered False, then all() will return False.

nums = [1, 3, 5, 7, 9] 
print(any(x % 2 == 0 for x in nums)) # False
print(all(x % 2 != 0 for x in nums)) # True

zip()

The function will create even strings with name and age. What could be better than simultaneous processing of several component objects, such as lists? Moreover, it opens up great possibilities for data manipulation. You can, for example, convert table columns to rows if you want.

names = ["Alice", "Bob", "Charlie"] 
ages = [30, 25, 22] 
for name, age in zip(names, ages): 
print(f"{name} is {age} years old.")