Top 10 Advance Python Concepts That You Must Know

Python is a high-level, object-oriented programming language that has recently been picked up by a lot of students as well as professionals due to its versatility, dynamic nature, robustness, and also because it is easy to learn. Not only this, it is now the second most loved and preferred language after JavaScript and can be used in almost all technical fields, be it machine learning, data science, web development, analytics, automation, testing, artificial intelligence, and a lot more. 

Learning Python is easy as compared to other high-level, object-oriented programming languages such as Java or C++but it has a few advanced concepts that come in handy when developing code that is robust, crisp, highly optimized, efficient, and normalized. Using these concepts in your code, you will be able to reduce bugs in your code as well as increase its efficiency thereby making you a seasoned Python programmer. So let us look at these concepts one by one and understand them in detail!

1. Map Function

Python has an inbuilt function called map() which permits us to process all the elements present in an iterable without explicitly using a looping construct. When used, it returns a map object which in turn is an iterator. This map object is the result obtained by applying the specified function to every item present in the iterable.

Function definition – required_answer = map(function, iterable)

The map() function takes two arguments:

• The first argument is a function that is to be applied to each and every element present in the iterable.
• The second argument is the iterable itself on which the function is to be mapped.

2. itertools

Python has an amazing standard library called itertools which provides a number of functions that help in writing clean, fast, and memory-efficient code due to lazy evaluation. It is a Python module that implements various iterator building blocks and together they form ‘iterator algebra’ which makes it possible to efficiently build tools in the Python language. The functions in itertools work on iterators themselves which in turn return more complex iterators. Some example of functions present in itertools are: count(), cycle(), repeat(), accumulate(), product(), permutations(), combinations() etc. each taking their own set of arguments and operating upon them. The result is generated a lot faster as compared to the results achieved when using conventional code.

3. Lambda Function

Python’s lambda functions are small anonymous functions as they do not have a name and are contained in a single line of code. The keyword ‘def’ is used to define functions in Python but lambda functions are rather defined by the keyword ‘lambda’. They can take any number of arguments, but the number of expressions can only be one. It makes code concise and easy to read for simple logical operations and is best to use when you need to use the function only a single time.

Function definition – required_answer = lambda ..arguments : expression

4. Exception Handling

Exceptions are types of errors that occur when the program is being executed and change the normal flow of the program. An example could be dividing a number by zero or referencing an index that is outside the bounds of an iterable. Therefore, we use try, except, and finally to handle exceptions in Python. The keyword try is used to wrap a block of code that can potentially throw errors, except is used to wrap a block of code to be executed when an exception is raised and handles the error, and finally lets us execute the code no matter what. 

5. Decorators

Decorators are a part of Python’s metaprogramming which are used to add additional functionality to existing code without altering the original structure at compile time. It is more like a regular function in Python that can be called and returns a callable. It takes in a function, modifies it by adding functionality, and then returns it. Want to start off in the field of Data Analytics & become a master in it? So get ready and learn the various aspects starting from the basics of Python with Geeksforgeeks Data Analysis with Python – Self-Paced course specially curated by Sandeep Jain.

6. Collections

Collections in Python are general purpose inbuilt containers like sets, tuples, dictionaries, and lists. Python collections is a module that implements specialized container datatypes. Collections include namedtuple() which is a function for creating tuple subclasses with named fields, OrderedDict which is a dict subclass that remembers the order entries that were added since Python dict isn’t ordered, Counter that is used for counting hashable objects, ChainMap that is used for creating a single view of multiple mappings, etc. 

7. Generators

Generators in Python are a special type of function that rather than returning a single value, returns an iterator object which is a sequence of values. It is a utility to create your own iterator function. The keyword yield is used in the generator function instead of the return keyword which pauses its execution. The difference between yield and return is that return terminates the function but yield only pauses the execution of the function and returns the value against it each time. 

8. Magic Methods

Also called Dunder (or double underscore) methods, magic methods are special types of functions that are invoked internally. They start and end with double underscores. Some examples include __add__(), __abs__(), __round__(), __floor__(), __str__(), __trunc__(), __lshift__() etc. The expression number + 5 is the same as number.__add__(5) and this is internally called by other methods or actions. You can directly use these functions as it will decrease the run time of your code due to the fact that now due to direct use, we will be reducing a function call each time.

9. Threading

A Thread is the smallest unit or process that can be scheduled by an operating system. Python contains the Thread class which aids in multithreaded programming. Multithreading is mainly used to speed up the computation to a huge extent as now more than one thread will be performing tasks. To implement threading in Python, you will need to use the threading module (since the thread module is deprecated). 

10. Regular Expressions

Python regular expressions or RegEx are expressions that contain specific characters as patterns to be matched. It is used to check if a string or a set of strings contains a specific pattern. It is extremely powerful, elegant, and concise along with being fast. To use Python’s regular expressions, you need to import the re module which contains functions that help in pattern matching like findall(), search(), split(), etc.

These were the top advanced Python concepts that you must know to be an experienced Python developer. These will not only make you a good programmer and developer but will also improve code readability and make it faster.

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