Top Advance Concepts of Python

Top Advance Concepts of Python. Python is a high-level, object-oriented programming language that has lately gained popularity among students and professionals because of its adaptability, dynamic nature, resilience, and ease of learning. Not only that, but it is now the second most popular and preferred programming language after JavaScript, and it can be utilised in practically any technical industry, including machine learning, data science, web development, analytics, automation, testing, artificial intelligence, and many more.

Top Advance Concepts of Python
Top Advance Concepts of Python – Geeks4Geeks

Python is simple to learn compared to other high-level, object-oriented programming languages like Java or C++, but it does contain a few advanced concepts that are useful when writing code that is robust, crisp, highly optimised, efficient, and normalised. You will be able to reduce bugs in your code as well as increase its efficiency if you use these concepts in your code, making you a seasoned Python programmer. So let’s take a look at each of these principles one by one and make sure we grasp them well!

1. Use the Map Function

Map() is a built-in method in Python that allows us to process all of the components in an iterable without having to use a looping construct. It returns a map object, which is an iterator when utilised. The outcome of executing the provided function to each item in the iterable yields this map object.

Function definition – required_answer = map(function, iterable)

There are two parameters to the map() function:

• The first parameter is a function that will be applied to every single element in the iterable.

• The iterable on which the function is to be mapped is the second parameter.

2nd. Itertools | Top Advance Concepts of Python

Due of lazy evaluation, Python has an outstanding standard library called itertools that includes a variety of methods that assist in building clean, quick, and memory-efficient code. It’s a Python package that provides different iterator building pieces, which when combined create “iterator algebra,” allowing you to quickly construct tools in Python. Itertools methods act on iterators, returning more sophisticated iterators as a result. Count(), cycle(), repeat(), accumulate(), product(), permutations(), combinations(), and other methods are examples of itertools functions. everyone using their own set of reasoning as a basis for action. When compared to the results obtained with traditional code, the outcome is significantly faster.

3rd. Function Lambda

Lambda functions in Python are tiny anonymous functions that don’t have a name and are only one line of code long. In Python, the term ‘def’ is used to create functions, whereas the keyword ‘lambda’ is used to define lambda functions. They can take any number of arguments, but only one expression may be used at a time. It makes code for basic logical operations succinct and easy to understand, and it’s great for when you only need to call the function once.

Function definition – required_answer = lambda ..arguments : expression

4th. Handling of Exceptions

Exceptions are mistakes that occur during the execution of a programme and disrupt the regular flow of the programme. Divide an integer by zero or refer to an index that is outside the boundaries of an iterable are two examples. To handle exceptions in Python, we utilise try, except, and finally. The term try is used to wrap a block of code that may throw errors, except is used to wrap a piece of code that will be executed when an exception is raised and will handle the error, and finally allow us run the code regardless of what happens.

5th. Decorators

Decorators are a type of metaprogramming in Python that allows you to add new functionality to existing code without changing its structure at compile time. It works similarly to a standard Python function in that it may be called and returns a callable. It accepts a function, adds functionality to it, and then returns it.

6th. Collections

Sets, tuples, dictionaries, and lists are examples of general-purpose inherent containers in Python. Python collections is a module that implements datatypes for specific containers. Namedtuple() is a function for creating tuple subclasses with named fields; OrderedDict is a dict subclass that remembers the order entries that were added because Python dicts aren’t ordered; Counter is a function for counting hashable objects; ChainMap is a function for creating a single view of multiple mappings; and so on.

7th. Generators

Generators are a sort of Python function that returns an iterator object, which is a succession of objects, rather than a single item. It’s a tool that allows you to make your own iterator function. In the generator function, instead of the return keyword, which interrupts execution, the term yield is used. The distinction between yield and return is that return ends the function, whereas yield merely stops it and returns the value against it each time it is called.

8th. Magic Methods / Magical Techniques

Magic methods, often known as Dunder (or double underscore) methods, are unique sorts of internal functions. They have double underscores at the beginning and finish. __add_(), __abs__(), __round__(), __floor__(), __str__(), __trunc__(), and __lshift__() are some examples. Number + 5 is the same as numer. add (5), which is invoked internally by other methods or actions. You may utilise these functions directly, which will reduce the time it takes for your code to run because we will be avoiding a function call each time.

9. Threading

A thread is the smallest unit or process that an operating system may schedule. The Thread class in Python is useful for multithreaded programming. Multithreading is primarily used to greatly speed up processing by allowing several threads to complete work simultaneously. The threading module is required to implement threading in Python (since the thread module is deprecated).

10th. Regular Expressions | Top Advance Concepts of Python

Regular expressions in Python, often known as RegEx, are expressions that include particular characters as patterns to match. It’s used to see if a string (or a group of strings) contains a particular pattern. It is incredibly strong, elegant, and succinct, as well as being extremely quick. To utilise regular expressions in Python, you must first import the re module, which includes pattern matching methods such as findall(), search(), split(), and so on.

These are the most important advanced Python topics to understand if you want to be a proficient Python programmer. These will not only make you a better programmer and developer, but they will also make your code more readable and quicker.

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