Computers and Technology

How Python Plays a Crucial role in Data Science ?

Python is suitable for a wide range of data science projects, but it is mostly used in the Financial sector due to its numeric capability.

Over the last decade, data science has exploded in popularity, and as a result, everything has become easy whether it is something related to maths, statistics, or information technology. All comes under data science, hence we can say that data science in today’s world is the most important technology in the field of mathematics and IT.

Now that Data Science has grown so large, we need to create a place where everything can be accessed from one place. Going to hundreds of various resources when doing analysis or constructing models will be extremely time-consuming. Here Python plays a major role.

Many people are adopting Python as their programming language of choice as it has a lot of things to offer which simplify the day-to-day human tasks. Python is far ahead of R in Google Trends.

Python is suitable for a wide range of data science projects, but it is mostly used in the Financial sector due to its numeric capability. Moreover for web development also, most companies trust this programming language because Python web development ensures greater accessibility of a wide range of tools which ultimately benefit the companies.

In this blog, we will discuss why Python is the first word that comes to our mind when it comes to data science. First, let’s understand and discuss Python.

What is Python?

Python is one of the most popular programming languages used by developers worldwide to create exceptional software for various industry sectors. Its easy and fast implementation makes. This is one of the trendy programming languages. Compared to other programming languages, Python provides the convenience and speed of writing and executing codes effectively. It is all because of its simplified usage and implementation.

Python is a very user-friendly language with a lot of support and features that are simple to incorporate. Among the primary reasons Python’s popularity has skyrocketed is its simplicity in syntax, allowing amateur programmers as well as professionals to read and manipulate it with ease. To better understand Python’s role in Data Science, first, let’s understand some of the useful features that make it different from others.

The Python language has the following useful features:

  • Using elegant syntax, Python makes it easier to read programs.
  • It’s easy to learn and implement a programming language.
  • Immense community support and an array of libraries to work on.
  • Python’s interactive mode makes testing codes easier. The whole testing process gets easy with Python.
  • Furthermore, Python allows for the extension of the code by adding modules implemented in a few languages like C++ and C.
  • Python is widely used for mobile app development, web development, and machine learning.
  • With Python, you can embed a programmable interface into applications.
  • Python is adaptable, convenient, and capable of running on any platform such as Mac OS, Windows, UNIX, and Linux.
  • It also supports automation and is capable enough to reduce manual tasks up to a great percentage. 
  • When compared to other programming languages, Python is more legible and utilizes less code to accomplish the same goal.
  • Python is a typed language. Thus, developers have to manually cast types.
  • Python is a scripting language which means that it is not necessary to compile the program.
  • It is versatile and can successfully integrate with third-party software.

What Makes Python So Different From Other Data Science Tools? 


How Python Plays a Crucial role in Data Science

It is quite simple to understand what makes it different from others but there are various reasons for this which proves the credibility of Python. Let’s have a look at the major ones –

Simple to grasp

Python’s greatest appealing feature is its easy-to-understand nature. Anyone interested in learning it can do it so quickly and easily. Python has a lower learning curve than other data science languages like R, and it outperforms the competition through its easy-to-understand syntax. You can hire Python developers for your next project from a reputable mobile app development firm or else you can also hire freelancers and train them in Python as it is one of the easiest languages to learn.


Python has built a lead over other languages by establishing itself as the most popular and scalable language, faster and better than Matlab and Python Application Development. Python’s scalability lies in its ability to solve problems in a flexible manner, this is also one of the reasons that are the first choice of most developers.

Python has shown to be useful in a variety of industries and for the fast creation of a wide range of applications. To be specific Python app development is trending in almost all industry verticles.

Choice of data science libraries

The number of data science analytic libraries available is a big reason driving Python adoption. Some of its famous libraries include Pandas, NumPy, StatsModels, Scikit-Learn, and SciPy. We will discuss its libraries in the further section. Over the years, significant improvements have been seen in Python. It makes modifications with the time as per the latest trends. What you felt was a limitation a year ago would be well-served by Python’s robust solutions. Python is capable enough to address all types of challenges that arise while developing.

Python community

Python’s ecosystem is credited as one of the causes of its meteoric ascent. As Python’s popularity grows among data scientists, more volunteers are producing data science libraries. This has paved the road for Python to develop cutting-edge tools and processing.

With just a few mobile taps or clicks, developers can get the answers to all their queries. For example on Codementor and Stack Overflow, professionals are available to answer all the relevant queries. They give appropriate guidance and support regarding the challenges which developers usually face.

Python has a large number of deep learning frameworks

When it comes to Python app development its deep learning frameworks play a crucial role. Frameworks like Caffe, TensorFlow, Keras, maxnet, and many more are some of the famous examples of deep learning. You can choose from a variety of free tools that best suits your needs. It allows you to create deep learning structures with surprisingly less coding. Every developer finds a way of developing apps with less coding so he prefers the frameworks that have the tendency to reduce manual efforts and saves time.

Python is an excellent language for creating scraping apps.

It provides a wide range of data scraping tools to develop the apps. Python has the support of a larger community that’s why it is preferable as compared to others.

Many various scraping ecosystems are available such as Scrapy, BeautifulSoup, and many more. Scrapy, for example, is more than simply a library; it’s also a fantastic foundation for scraping the web. Scrapy allows you to create web crawlers in a matter of minutes.

Why Python Become The Choice Of Data Science


This Python module simplifies data analysis by allowing you to read a number of data types such as CSV, XML, JSON, and many more. It gets easy to handle day-to-day operations smoothly. It is easily accessible to users.

Pandas is a library for manipulating and analyzing data written in Python. It is best for handling a vast amount of structured data. Pandas is one of the most used user-friendly tools for data analysis. Numerous data structures and operations can be used for manipulating numerical tables and time series. It is one of the biggest benefits that one can have with Python’s Pandas. It is recommended to use Pandas if you look for simple data analysis, aggregation, visualization, and manipulation of data. 


It’s a Python-based library used for dealing with vast dimension arrays. It covers a wide range of functions for metrics, linear algebra, arrays, basic statistical operations, sorting, random simulations, and many more. This program aids you in doing any numerical operation required in Data science, such as calculating Euclidean distance, computing matrix ranks, and so on. It has a lot of useful features that are best in dealing with mathematical operations. Vectorization of NumPy array operations is provided in the library, which speeds up the execution of the algorithm and enhances performance. Working with large multidimensional arrays and matrices is a breeze with NumPy.


Another important Python module for data visualization is Matplotlib. It is important to effectively visualize data when it comes to performing descriptive analysis and data visualization. It has a wide range of tools that help developers to take care of these activities. Matplotlib enables the creation of professional-grade line graphs, pie charts, histograms, and other figures fast. These are the major part of Matplotlib as they are responsible for effective data visualization. It even supports customization which is the basic need of businesses nowadays.


Scipy is one of the famous names which is taken by developers when it is to the Python development. It an another popular Python library, especially build for data science and scientific computing. Scipy is a powerful programming language for scientific mathematics and computers. Therefore, it is highly demanded among developers when it comes to optimization, interpolation, linear algebra, integration, FFT, image processing, ODE solvers, etc. All the activities used in science and engineering are all covered by SciPy sub-modules.


Sklearn is another Python library for machine learning. It includes a variety of machine learning algorithms and functions. Sklearnensures easy-to-use features for data mining and data analysis which is the basic need of the current world. Users can access a set of standard machine learning algorithms via a uniform interface. Scikit-Learn makes it simple to apply popular techniques to large datasets which as a result help developers solve real-world issues. You can simply go for Python web development, it is mostly preferred in it.


Python development in data science has given data scientists the ability to perform time-consuming tasks in less time. Python is a flexible programming language that is easy to learn and use. It is also incredibly powerful.

Python is extremely versatile and can effectively work in any scenario. It can also run on any operating system and, with minor modifications. Python has become the preferred language for developers and data scientists due to these characteristics. Why don’t you give it a try? You can hire qualified developers from a Python development company as their years of experience will help you to understand the latest industrial trends.

Mahipalsinh Rana

Mahipalsinh Rana is a CTO of Inexture, a best python development services. He has more than 15 years of experience in software development with a strong focus on mobile app development for all kinds of platforms including iOS and Android. He loves to write and share about technology, startups, entrepreneurship, and business.

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