Python is a programming language having a range of features from simple to complex. It is a simple but expressive and powerful language. If you have learned the basics of Python, such as basic data structures and functionalities, then it's time to learn the complex features now. The new and can be discovered with the help of extensive research and experience. Python has got attractive solutions as advanced features for many complicated problems that occur during development. advanced features of the Python language More than one of these features can be used to solve a single Python problem. For example, if we want to , we can use List Comprehension as well as Lambda Function. iterate over a list in Python In this article, you will learn 12 useful advanced features of Python. You can take help from these advanced techniques in your code. Each method is explained along with code snippets and output for better understanding. Lambda Function A lambda function is defined without a name and thus called an anonymous function as well. It is a small function, and people usually refer to it as lambda only. It is one powerful tool for a data scientist. Typically, Python functions are defined using keyword along with the function name. As lambda has no name, it is defined with the keyword . def lambda Lambda function is basically used for the simple expression or operation where we need only a short one-time use function. This function takes arguments but can have only one expression. Let's understand this feature with the help of the below code. Here is the output: As you can see, it was pretty easy to use the lambda function without defining a complete full function. It makes Python code simple and clean. Generators It takes a lot of time to build iterables to iterate the Python objects. It is lengthy and quite unreasonable. Python Generator provides a simple way to declare a function that works as an iterator just like it can be used in for-loop. The generator is a function that returns an object (iterator), upon which one value at a time can be iterated. Typical iterables like lists and dictionaries have their items loaded in the memory. Whereas generators produce elements slowly and it does not need to load all of those items in memory. Thus, Generator functions are memory-efficient iterables. Below code is the illustration of this technique. And the output: Map Function The Map is one of the commonly used functions in Python. It is a built-in Python function that makes work easier. Map() Function is applied on a function to a sequence of elements just like in a list or a dictionary. When a function and an iterables are passed in a map, the function is performed on each of those entities of that iterable by Map. The map function can be used with any of the Python functions if it is compatible with the element sequence being operated. Let's see the basic syntax and application with the following code. If we would use a typical approach, the code will be lengthy and complex. Map( ) function simplifies the code. We get the output. Decorators Decorator is an interesting feature of Python, which is used to add functionality to the existing code. The decorator takes in a function and returns it after adding some functionality. It modifies the function without changing the core functionalities of the function. This process is also called metaprogramming, as one part of the program tries to modify another part of the program while compiling. In simple words, Python decorators add some tweaks in regards to the function's look or some other aspects and do not change the internal algorithm. Here is an example: And here is the output: Comprehensions Comprehension is one of the most mentioned techniques in Python language. Comprehension makes it easy to create a list, dictionary, or set that is named as a list comprehension, Dictionary comprehension, and a Set comprehension, respectively. With the help of comprehension, we do not need to use for-loops. This technique is faster and more efficient than traditional loops. Syntax of all the comprehensions looks quite similar. The code is more readable when Comprehension is used. The following code snippet explains this feature. Output is presented as follows. Filtering The Filter is a built-in function of Python. Like the Map function, Filter applies a function to a sequence of elements like list, tuple, dictionary. The primary difference between Map () and Filter () is that Filter () returns only those elements which are produced as True by applied function. It is a handy feature of Python to handle two steps: checking an expression and creating a return list. Here is one example code: Output we get is: Python Iterator Python Iterators are the objects which are iterated upon. We can see many iterators in Python. They have implementation inside for loop, comprehensions or generators, etc. Iterators in Python return one element at a time. Iterables are the objects that can get an iterator from them. For example, String, List, Tuple, etc. are iterables. The following example shows python iterator code. Here is the output: Hashability Hashing is a process of using a hash function for a particular Python object that can be hashed. In Python dictionaries, the keys should be hashable. Hashability makes Python objects be converted to numeric hashed values. Hashing is a time-consuming process, but it provides you with instant look-up-time for fetching some particular element in the dictionary. It is an efficient mechanism for item insertion, item retrieval, and item checking. These are the main advantages of using hash tables as storage for dictionaries. Let's have a look at the example code. Output: Python RegEx RegEx stands for Regular Expression. A sequence of characters that is used to define a search pattern is called Regular Expression. RegEx defined patterns can be used to match against a string. The regular expressions are specified using meta characters i.e. , that are interpreted differently by RegEx Engine. [], ., ^, $, *, +, ?, {}, (), \, | RegEx is used to find the information that is based on complex patterns in text. In this way, RegEx is used to replace pattern in a string. There is one code defined by RegEx. The pattern is any string composed of five letters starting with and ending with . a s ^a…s$ The following example will help to understand RegEx. And Output is: IterTools IterTools is a Python module. This module is a collection of tools that helps to handle iterators. Iterator is one data type used for loops for lists, dictionaries, and tuples. Iterator Operations typically require multi-line functions and complex list comprehension. The functions in Itertools allow performing such iterator operations with a more effortless and simple approach. Here is an example code: Output: Python Closure The technique that attaches some data to the code is called Closure in Python. When a nested function in Python references a value in its enclosing scope, it will require a Python Closure. Closure provides an object-oriented solution approach to the problem. It hides some form of the data and prevents the use of global values. Python Decorators use closures as well. The below code is an illustration of the feature. And the output is: Python Property Python has a built-in decorator named @property, which uses getters and setters efficiently in an Object-Oriented Environment. Python property creates and returns a property object. A property object has three methods i.e. getter(), setter() and delete(). Let's understand this technique with the help of the following example. Here is the output: CONCLUSION: This article has explained all advanced practical concepts of Python. The techniques mentioned above are used to improvise one's coding skills. I hope you have a good understanding of all the advanced techniques, and it could help you in your Python development career.

A Guide to Python Advanced Features

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

5 Reasons Why Enterprises Need Zero Trust Security

31 Stories To Learn About Lambda

A Brief Introduction to Lambda Expressions in Python

Adding Code to AWS Lambda, Lambda Layers, and Lambda Extensions Using Docker

Building an Alexa-Based GitHub Follower Counter

5 Reasons Why Enterprises Need Zero Trust Security

31 Stories To Learn About Lambda

A Brief Introduction to Lambda Expressions in Python

Adding Code to AWS Lambda, Lambda Layers, and Lambda Extensions Using Docker

Building an Alexa-Based GitHub Follower Counter

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps