These advanced Python concepts will enable intermediate coders to gain massive exposure to success
Python has gained a massive amount of traction over the past couple of years in the tech community. Basically, it is a high-level, object-oriented programming language that has recently picked up a lot of volume due to its versatility, dynamic nature, robustness, and also because it is beginner-friendly attributes. Learning Python is easy, but it is also crucial for programmers to garner the knowledge of advanced Python concepts to excel in the arena and gain lucrative career opportunities. Learning advanced Python concepts will enable intermediate coders to gain an edge over their competitors and innovate in a more effective manner. Using the top advanced Python concepts will enable them to reduce bugs in codes and programming and thereby increase efficiency by making them seasoned Python programmers and developers in no time. In this article, we have enlisted some of the best and advanced Python concepts for intermediate coders to learn and innovate flawlessly in 2023.
Python has an inbuilt function called map() which permits the users to process all the elements present in an iterable without manually using a looping construct. The map object is the result obtained by applying the specified function to every item present in the iterable.
Integers and Floating-Point Numbers
Numbers are basically one of the most fundamental concepts in data science. The language contains representations for the various types of numbers that can exist and that can be modified into codes. This Python concept is majorly used by experts to build the blocks of arithmetic computation and coding.
Python possesses an exceptional standard library called itertools which provides a number of functions that help in executing clean, fast, and memory-efficient codes to enable lazy evaluations. It is a Python module that implements various iterator building blocks and creates the ‘iterator algebra’ which makes it possible to efficiently build tools in the Python language.
Decorators are a part of Python’s metaprogramming which is used to add additional functionality to existing codes without actually having to change the original structure at the time of compiling. It basically takes a function, modifies it by adding other functionalities, and then returns it.
Magic methods are also called Dunder and are touted to be special types of functions that are invoked internally. They start and end with double underscores. Users can actually use these functions to minimize the run time on codes and function calls, each time.
Strings in Python
In Python, strings contain alphanumeric values that are usually enclosed in single or double quotation marks. Python basically has a lot of methods that coders can use to manipulate strings. Using the string concept in Python is especially common for intermediate coders working with data science teams.
Comparison Operation in Python
Coders can use comparison operators to compare two operands. With the comparison starts, operators are performed on two different operands, after that they return to a boolean value of either true or false.
Collections in Python generally have purpose-built containers like sets, tuples, dictionaries, and lists. Python collectibles is a module that implements specialized container datatypes.
Exception handling is a must-have skill for intermediate Python coders. These are basically, types of errors that occur when a program is being executed, yet it faces an abnormal flow in the program. Coders need to be able to trace these issues and access improvements in them.
Python’s lambda functions are small anonymous functions as they do not possess a name and are contained in a single line of code. The keyword ‘def’ in the function is used to define functions in Python but Lambda functions are rather defined by the keyword ‘lambda’.