Most of the data scientists use advanced and interpreted language Python for different data science related applications or projects. They prefer Python for data science over other languages because it provides the maximum functionality

Python is one of the most popular open-source languages and designed for providing the best approach for object-oriented programming. Python provides first-class libraries to deal with any modern data science application as efficiently as possible.

Simple syntax and ease of use nature of Python make it a good option to be used in the scientific and research communities throughout the world. Individuals who do not have an engineering background can also prefer and use Python. You can hire dedicated python developers for such tasks.

Almost every user of this programming language is happy as it is more suited for quick and successful prototyping. They experience that Python APIs based deep learning frameworks and Python-based scientific packages are really productive and versatile in nature.

Features Of Python

Features Of Python

Professional developers around the world always prefer the high-level programming language Python for developing sensitive and potential websites or applications.

Particularly, when it comes to developing sites or applications for scientific or space research purposes, Python would be a highly recommended programming language by the programming experts.

The incredible productivity and versatile features of Python prompt developers to prefer it.

Here are few other vital features of this programming language that must be known by all newbie developers and it also clears why Python is used for data science?

  • Python programming language is completely free and it costs nothing for the developers to develop a potential website or application.
  • Unlike other programming languages, Python is so simple and its elegant syntax makes it easy to learn and use.
  • As this programming language is simple to access, Python supports the programmer to develop a successful application within a short time.
  • Unlike other programming languages this is simple to use. Python possesses a large set of libraries and offers immense community support to the developers.
  • By using the modules of Python language, developers can easily extend the programming codes which are already compiled in other languages like C++, C, and so on.
  • The expressiveness of Python language allows developers to develop applications with a programmable interface.
  • Python language allows the developers to compile and to run their programming codes in various platforms including Windows, UNIX, Linux, and more.

Why Data Science & Python Work Well?

Our Data scientists use and recommend the programming language Python because it is a user-friendly language with the best community participation and decent library availability.

Python is a simple programming language and includes an active community with a huge collection of resources and libraries.

Users of this programming platform can get loads of benefits from emerging technologies like data science and machine learning.

Python Better than Ruby

Python is better than Ruby in terms of the machine learning libraries required in the data science and machine learning-based projects development.

Software developers are aware of why python for data science and use the Python to be successful in their way to roll out the program and get the prototype running in the desired way.

They complete the project as quickly as possible. Data science involves the maximum amount of information from stores of statistics, data and registers. These unsorted data are very difficult to correlate with accuracy in a meaningful way.

Python lets the programmer create the CSV output required for the easy data reading in the spreadsheet. This language also supports the structured, functional and object-oriented programming styles.

Python is user-friendly when it comes to analytical and quantitative computing. Python is being used in different fields like finance, signal processing and oil and gas.

Is Python good for data analysis? Data analysts and scientists use Python and it is a flexible and open-sourced language. They take advantage of the massive libraries for data manipulation being an independent platform.

Why Python Is Used For Data Science?

Powerful & Easy To Use

Students and researchers with basic knowledge can use Python and start working on the platform. So, python for data science is a great combination.

This is because of the user-friendly nature of this powerful programming language Python. The time required for the code implementation in Python is less than other programming languages like Java and C#.

Choice Of Libraries

Python provides a huge collection of libraries, machine learning and artificial intelligence. And many of those collections can be easily accessed in the form of tutorials.

Thus, developers can get a lot of benefits from the massive library databases and machine learning tutorials.

Faster Scalability

Python is a highly scalable and faster language when compared to other leading programming languages like R and Java.

This language provides the flexibility to solve problems and supports programmers to develop rapid tools and applications of almost every category.

Visualization & Graphics

Different types of visualization options are available in Python. Matplotlib in Python provides the best foundation around which other libraries are built.

You can use these packages and create graphical layouts, web-ready plots, charts and other things as per your wishes. Explore the latest updates of data science in Python and make an informed decision.

Flexible Nature

The flexible nature of the Python language acts as a big plus for its popularity. This language assists the programmer with a desire to be creative in developing script applications and to build potential websites in the best possible way.

Easy To Learn

Readability and simplicity are the main attractions of Python. Almost everyone can quickly learn this programming language devoid of complexity in any aspect. They are comfortable, happy and about a few lines of code are enough to achieve tasks.


Python is open-source and available online at no cost. This language uses the community-based model for development purposes. This language is designed to run on both Linux and Windows environments and be ported to various platforms.


Python has mostly been used in the academic as well as industrial circles in recent years. This language has a large following beyond doubt. Users of this language get prompt support from the support material and information from other users.

Python Community

The ecosystem of Python is an important reason behind its increased success rate in the data science community. That’s where python for data science can be really good.

Many volunteers in this community create first-class data science libraries. Thus, loads of modern tools and the first-class processing take place in Python on a regular basis.


Python is a widely accepted data science programming language and more popular than C++ and Java in the data science community. Statisticians, mathematicians, physicists and other professionals use Python as efficiently as possible.


People who prefer their career in data science and analysis nowadays can take note of the UX and GUI of Python.

This is because GUI programming in this open-source language edges out other popular programming languages.

The best libraries like pygame and piglet along with the prompt community support assist developers to develop customer satisfied applications by using Python programming language.

Less Coding

Python programmers nowadays use useless code and complete the tasks in a successful way. They spend less time creating codings and take advantage of no limitation to the data processing and data science with Python as expected.

Compatibility With Hadoop

Hadoop is a renowned open-source big data platform as well as inherent compatibility of the programming language Python.

Users of the PyDoop package get complete access to the HDFS API for Hadoop and write programs and applications based on the Hadoop MapReduce.

Powerful Packages

A powerful set of packages of Python supports users to fulfil their data science and an analytical requirement which is the main reason why Python is used for data science. Some of these packages are NumPy, Pandas, Scipy, Scikit-learn and PyBrain.

Suitable For Machine Learning

Python is best for machine learning in an easy and effective way. This is because machine learning is mostly associated with mathematical optimization, probability and statistics. Python is a sought after machine learning tool to let programmers do math easily.


How Is Python Used In Each Stage Of Data Science?

Analysing data science isn’t an easy job as you would like to think. It requires a lot of attention and perfection. Do you want to know how Python is  used in data science?

Well take a look at the below  stages and make a good decision about using this language hereafter.

First Stage: parallel processing

We have to be aware of the form in which data takes place at first. You can perform some functions and search for a particular type of data to derive insights.

You can avoid spending hours of time to do this work when you use the libraries of Python to carry out the tasks in the parallel processing approach.

Second Stage: scrapping unwanted data

This stage is about scrapping unwanted data from the web pages. Here one might question what is Python used for in data analytics? Especially for scrapping those unwanted and to keep the needed data.

The Python language has Python Scrapy and BeautifulSoup which is best in the industry as they are the main libraries to extract data efficiently from the Internet.

Third Stage: pictorial representation of data

In this stage the pictorial representation of data takes place. We have to get the data visualization or graphical representation like the pie charts, graphs and other formats. Python Seaborn and Matplotlib libraries are used in this stage.

Fourth Stage

This stage is about Machine learning. Well, when compared with all other stages here the Machine Learning process is a highly complex computational method.

To get it done perfectly, we need to utilize various mathematical tools and it can be easily done by using the Python library Scikit-Learn.

Read also: Python vs C++: Which Is The Best Programming Language?

Most Popular Python Data Science Libraries

Well, many might have this question: why is Python used in data analytics? When compared with other programming languages, the best data science libraries in Python support data analytics to be successful in their routine work.


Numpy library provides the best mathematical function required for handling the maximum dimension array. In addition to that, this library also provides functions or methods for metrics, array and linear algebra.


Pandas is one among the libraries possessed by Python, which is specifically designed for data manipulation and analysis. Besides, the function of this Pandas library is pretty useful for manipulating data on a large scale. Also, developers would feel easy to handle it.


Matplotlib library is specially designed for data visualization. By utilizing this library developers can use various methods for visualizing the data effectively.

Besides, with this Matplotlib library generating pie charts, graphs, and other popular universal grade figures become easy and can be done quickly.


Scipy is one of the best Python data science libraries because this library is specifically designed to carry out data science and scientific computing operations.


Apart from having various sub-modules for easy optimization, this library delivers exceptional functions for performing scientific mathematics and does great assistance to build computable programming.


Scikit-learn library is designed for machine learning and provides different functions and algorithms used in machine learning. This library provides easy and simple tools for the purpose of data analysis and data mining.


Python is one of the best options for all data scientists with a desire to be smart in their way to complete their projects within the schedule and budget.

Regular updates of this programming language and easy-to-use libraries give loads of benefits for all users, especially beginners to data science. That’s why you can use python for data science.

We hope you had a great experience reading this article and it proves to be a great help for any Python Web App Development Company in the near future. Thank You.!

Harikrishna Kundariya

CEO, eSparkBiz

Harikrishna Kundariya, a marketer, developer, IoT, chatbot and blockchain savvy, designer, co-founder, Director of eSparkBiz @Software Development Company where you can Hire Software Developers. His 12+ experience enables him to provide digital solutions to new start-ups based on Web app development.
Frequently Asked Questions
  1. Why Python Is Really Important For Data Science?

    The reason behind the popularity of Python for data science is that it provides flexibility and scalability for data science applications. Data Scientists can easily understand and work with this language as well.

  2. Is Python Good For Data Analytics?

    Oh.! YES. Python sets very well with data analytics. In fact, it is one of the ideal programming languages for data science projects.

  3. What Is Python Used For In Data Analytics?

    As we know that Python provides you with awesome resources and options to deal with data science-related projects. So, people utilize python to solve mathematics, statistics, and scientific functions.

  4. Which Is Better For Data Analytics: R or Python?

    R is only good when you’re dealing with Statistical Analysis, while Python is a general-purpose language for data science. That’s why Python is a much better option in the overall perspective.

  5. What do data scientists do with Python?

    Python is an open-source higher-level language that provides a great approach for object-oriented programming. Python is one of the famous languages used by data scientists for various data science projects and applications. Other than that, it provides great functionality to deal with mathematics, statistics, and scientific function.