Advanced Python Concepts – Master The Popular Coding Language
Python is one of the key tools in the IT domain that has evolved brilliantly with every update that had been launched in the market since its inception. Today, it has some much to offer in terms of features and functionalities. If you are an aspiring developer who wishes to master Python, it is important to be familiar with the complex concepts that the language shelters.
To help you get well-acquainted with the nuances of Python, this blog features a curated advanced Python concepts list. Before we dive into some of the interesting advanced Python ideas, let’s take a quick look at the very purpose of this coding language, and what enables it to stand out from the crowd.
Python is an open-source tool that is designed to be simple, straightforward, and versatile. It is an ideal choice for a wide range of projects, from simple web applications to operating systems. As its easy-to-learn syntax promotes readability, the cost of program maintenance is significantly reduced. The best part about Python is that it is both object-oriented and functional, allowing programmers to think of code as both data and functionality. Given that Python has been popular for decades, it now has impressive, and active community support.
To better understand the beauty of this language and its impressive list of features, let’s explore the advanced Python concepts.
Explore The Top 8 Advanced Python Concepts
1. Map Function:
This is one of the key in-built Python features. The main purpose of this function is to allow developers to process all the elements in a coding script without using a looping construct. In a simple sense, it is used when a single transformation function is to be applied to all the iterable elements.
After applying a function to every item of an iterable, it returns a result. map() executes a specified function for each item in an iterable. The item is sent to the function as a parameter. The iterable and function are passed as arguments to the map in Python.
2. Lambda Function:
Lambda functions in Python are essentially small anonymous functions that have no name. The best part about these functions is that they can be easily accommodated in a single line of code. A lambda function is capable of processing several arguments. However, it can only shelter one expression. Usually the keyword ‘def’ is used to define functions in Python.
By leveraging the Lambda function, developers can make code concise, thereby making it more readable to the non-technical teams working on the project as well. It is the most effective way forward when a particular function has to be used only once.
3. Exception Handling:
An exception is an error that can present itself during the execution of the program, hampering the progress of the program. There are many reasons why these errors occur mid-development. It could be when coders are writing a program for division, and in the denominator, 0 occurs leading to a ZeroDivisionError.
There are other types of exceptions as well like NameError and TypeError. To make exceptions evident, there is usually a message that elaborates on what went wrong. These errors do not cause permanent damage to a script. It certainly becomes easier to handle exceptions with every Python project a developer takes on.
4. Context Managers:
When it comes to the effective management of external resources in Python, context managers can be of great help. How so? With this tool, developers can allocate and release resources as and when required. One of the most popular examples of context manager is the ‘with’ statement.
It helps developers with the task of opening a file in write mode. It also helps them with closing the task in only one line, if something goes wrong during the development phase – this is the key purpose of a ‘with’ statement. The fascinating part is that Python’s context management system allows one to build his or her own context managers.
5. Regular Expressions:
Another powerful Python tool is Regular expressions (also called ‘RegEx or RegExp’). It is a sequence of characters that forms a search pattern. These expressions are used to match strings of text such as particular characters, words, or patterns of characters.
In order to make the most of the expressions, developers need to import the re-module that contains functions. They aid with pattern matching like findall(), search(), split(), and more. The re.match() function is used to identify a regular expression pattern.
6. itertools:
itertools is a brilliant Python library used mainly to iterate over data structures. It is home to multiple functions, each one designed to help developers take an organized approach while writing codes. They can build memory-efficient codes at a speedy pace. The functions in itertools work on iterators themselves which in turn return more complex iterators.
Here are some of important functions that are a part of itertools – count(), cycle(), repeat(), accumulate(), product(), permutations(), and combinations(). The primary purpose of itertools is to improve the readability of the code and make it easier to maintain it throughout the development cycle.
7. Generators:
This is a kind of function in Python that returns an iterator object which is a sequence of values instead of returning just a single value. This feature allows developers to build their own iterator function.
In the generator function, the yield keyword is used instead of the return keyword in order to pause function execution. Why use yield and not return? The difference is that return eliminates the function entirely but yield only pauses the execution of the function temporarily.
8. Magic Methods:
These are special types of functions that are handled internally – the expressions are internally called by other methods or actions. Magic methods are also called Dunder or double underscore methods, given the fact that they start and end with double underscores.
Here are some examples of the same: add(), abs(), round(), floor(), str(), trunc(), and lshift(). When used correctly, these functions can help developers reduce the run time of the code. The direct use of magic methods leads to a decrease in function calls.
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Being a flexible coding language, Python ensures enhanced productivity. Owing to its dynamic typing and concise syntax, it is considered to be far more productive than its alternatives. As such, today’s organizations are looking to leverage the competencies of Python by hiring expert talent who have an in-depth knowledge of the language. With the industry brimming with ready-to-work talent, a competitive edge is the need of the hour for aspiring developers.
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