Python for Data Science: 8 Concepts You May Have Forgotten

By Arpita Published in Data science5 mins

Python: A Beginner's Introduction

Python is one of the most popular and fastest-growing programming languages. It has a reasonably simple syntax when compared to other programming languages. After mastering the fundamentals, the greatest method to acquire confidence is to apply what you've learned in the actual world. If you need a language for quick application development and scripting in a variety of fields, Python is an excellent choice.

The interpretative aspect of Python programming is one of its main advantages. The Python interpreter and standard library are available from the Python website in binary or source form and can operate on all major operating systems. The Python programming language is also freely distributable, and the same website also provides advice and other third-party tools, applications, modules, and documentation.

The Python interpreter is easily extensible with additional data types or functions written in C++, C, or any other language that can be called from C. Python is a programming language that serves as an extension for creating customisable applications. The fact that it employs English terms rather than punctuation and has fewer syntactic structures than other programming languages contributes to its ease of learning.

The Python programming language is now the most used programming language in the world. Nevertheless, learning it is not so simple. You can spend hours reading books and attending tutorials, but the true challenge will be putting your Python knowledge into practice. The issue then becomes, what is a practical option for learning Python programming quickly and easily?

The solution is to practise beginner-level Python projects. This will teach you how to do what you want to accomplish. Project-based learning will allow you to put your Python knowledge into practice while also allowing you to readily see your faults and areas for development.

Concepts of Python

These are some fundamental Python principles, just enough to get us started with Python functions and packages, so we may apply the concepts in our practice and endeavours.

  1. Map Function - Python has a map() function that allows us to process all of the components in an iterable without explicitly employing a looping construct. When called, it returns a map object, which is an iterator. This map object represents the outcome of applying the supplied function to each item in the iterable. The map() function accepts two parameters:

  2. The first parameter is a function that will be applied to each element in the iterable.

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

  4. itertools - Python offers a fantastic standard library called itertools that includes a variety of methods that aid in the creation of clean, quick, and memory-efficient code through lazy evaluation. It is a Python module that provides numerous iterator building blocks that, when combined, constitute 'iterator algebra,' allowing for the efficient development of Python tools. Itertools methods operate on iterators, returning more complicated iterators in return. Itertools functions include count(), cycle(), repeat(), accumulate(), product(), permutations(), combinations(), and so on, each of which takes its own set of inputs and operates on them. When compared to the results obtained while using traditional code, the outcome is far faster.

  5. Lambda Function - Lambda functions in Python are short anonymous functions that do not have a name and are contained in a single line of code. In Python, the term 'def' is used to define functions, whereas the keyword 'lambda' is used to define lambda functions. They can accept any number of arguments, but only one expression at a time. It makes code for basic logical processes more short and easy to understand, and it is best used when the function will only be used once.

  6. Exception Handling - Exceptions are sorts of faults that occur during the execution of a programme and disrupt its usual flow. For instance, dividing an integer by zero or addressing an index that is outside the boundaries of an iterable are both examples. To handle exceptions in Python, we use 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 block of code that is run when an exception is produced and handles the error, and finally enables us to execute the code regardless of what happens.

  7. Decorators - Decorators are a type of metaprogramming in Python that allows you to add new functionality to existing code without changing the original structure at compile time. It functions more like a typical Python function in that it may be called and returns a callable. It accepts a function and alters it by adding functionality before returning it.

  8. Generators - Generators are a sort of function in Python that, rather than returning a single value, returns an iterator object, which is a succession of objects. It is a tool for creating your own iterator function. In the generator function, the term yield is used instead of the return keyword, which pauses its execution. The distinction between yield and return is that return ends the function, but yield just stops its execution and returns the value against it each time.

  9. Magical Technique - Magic methods, often known as Dunder (or double underscore) methods, are unique forms of functions that are invoked internally. They begin and terminate with two underscores. __add__(), __abs__(), __round__(), __floor__(), __str__(), __trunc__(), __lshift__(), and other functions are examples. The expression number + 5 equals number. __add (5), which is used internally by other methods or actions. You can utilise these functions directly since doing so will reduce the execution time of your code because we will be reducing a function call each time.

  10. Threading - A thread is the smallest unit or process that an operating system may schedule. Python has the Thread class, which is useful for multithreaded programming. Multithreading is primarily used to greatly accelerate computing by having more than one thread do tasks. The threading module is required to implement threading in Python (since the thread module is deprecated).


These are the top advanced Python concepts that every Python developer should understand. They will not only make you a better programmer and developer, but they will also increase code readability and speed. And to know more about such concepts, Skillslash is a recommended platform. Courses such as the Advance Data Science & AI course that provides 100% Job referral program, or the Business analytics course With Guaranteed job referral are some of the better options in this case. Try opting one of these for extended knowledge.


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