6 Reasons Why Python Is the Programming Language of the Future

Python is an easy to code, expressive, interpreted, multi-purpose, and object-oriented programming language that is gaining momentum year after year.

Thanks to its huge repository of standard libraries, Python’s popularity is soaring. Today, over 6,200 companies operate in Python. Among them are popular platforms like Instagram, Google, Spotify, and Netflix. Code simplicity, higher developer salaries, and automation are some of the out-of-the-box features of Python.

Machine Learning and Data Science are two primary niches where Python is the king. Today, Python is the global standard for applied machine learning. People often ask what is the top reason for Python’s widespread adoption! Well, Python is driven by ‘Simplicity’.

Python’s pseudocode nature is the language’s greatest strength. It makes the language simple yet highly efficient. Python offers a ton of libraries that caters to a majority of touchpoints of Machine Learning and Data Science. 

Python has a long history. Since the early nineties, the language has been paving its way to the top. Starting with version 1.0, in 2020 Python has reached version 3.8.1 making it one of the most popular programming languages of the decade.

You can create a Facial Recognition app under 25 lines of code in Python. In other languages, the complexity, readability, and lines of code take a toll on the developer.

Let’s move ahead and understand the top six reasons which make Python the future language of programming:

1. Python is Versatile

Python adds powers to Data Mining, Machine Learning, Data Science, Embedded Systems, Web Frameworks, Network Development, Web Development, Testing, Automation, and the list goes on. You can develop GUI applications, websites, and web apps with Python. 

2. Frontend Development

With Python, you can do web-based development with frameworks such as Django, Flask, and Pyramid. 

3. Machine learning, Big Data

Python is widely used in complex computations, machine learning, robotics & data science. Some of the popular frameworks are Tensorflow, OpenCV, Pandas & NumPy.

4. Mobile and Desktop Apps

With Python, you can develop applications for smartphones and desktop applications. You may use comprehensive frameworks such as Kivy, PyQT, Tryton & Odoo.

Python programs are easy to code since Python instructs indentation and thus, boosts readability. There are over 137,000 Python modules and libraries that are available for immediate use. Unicorns such as Netflix, Google, Amazon, Spotify, Intel, NASA, and many others use Python.

5. Python Backs Data Science

Data Science thrives on Python! It is the single most reason why thousands of programmers are learning Python in 2020.

Someone has rightly said that ‘Data is the Oil of the 21st century‘. Data is precious than ever and it’ll last longer than the systems themselves. Data Science is a rapidly burgeoning niche that is a colossal pool of multiple data operations. The resultant data is the fodder for every Machine Learning Algorithm. 

Right from gathering data from various sources, producing meaningful results to powering ML enterprise workflows, Python has got it all covered.

Python backed Data Science also covers Data Integration, Visualization, Distributed Architectures, Dashboards, BI, Data Engineering, and Data-Driven Solutions. Python successfully caters to the dynamic requirements of the Data Science community. 

The credit goes to the language’s scalability, easy to apply appeal, visualizations, and a generous choice of libraries. With as little as 50 lines of code, you can create a full-fledged Python application that can handle complex data and draw inferences.

Data analysts and scientists mesh well with Python since the language extrapolates valuable insights with massive stores of raw data with minimal resources.

6. Rich Ecosystem of Libraries

By definition, a library is a pre-bundled code that you can import into the environment to extend the functionality and achieve more programmatically.

Python has the biggest set of libraries compared to any other programming language out there. TensorFlow, PyTorch, SciKit-Learn, and NumPy are some of the popular entries. For instance, if your task is to massage huge chunks of data, you’ve got Pandas. Want to build traditional models? SciKit-learn is your companion.

Does your requirement include visualization of a ton of data? You’ve MatplotLib at your disposal. Not just that, you’ve state of the art libraries such as BeautifulSoup for web scraping and Natural Language Toolkit (NLTK) for symbolic, statistical professing of English.

All of the above libraries complement each other and can be combined with r Machine Learning workflows in no time! Get mastery over these libraries with a Python course. 

Some of the other top Python libraries

i. OpenCV

Open Computer Vision or OpenCV is a popular collection of algorithms used in Machine Learning and Computer Vision. Although OpenCV is available for other languages too! However, OpenCV’s Python binding (OpenCV-python) not only boosts simplicity but increased code readability manifolds.

ii. Pandas

Pandas is a full-fledged data analysis toolkit in the Python programming language. The library is fast, flexible, and can perform data analysis and manipulations on data structures and draw meaningful inferences.

iii. Keras

Keras is a machine learning library focused on neural networks. Beginners can kickstart their machine learning endeavors in Python through Keras. You can create custom machine learning models and use thousands of pre-trained ones.

iv. SciPy

Scientific Computation or SciPy is used to solve mathematical and scientific challenges in Python. The library is built on NumPy and provides utility functions for optimization, signal processing, and stats collection.

v. Theano

With Theano data scientists can evaluate the most complex of the mathematical expressions efficiently. If enabled with a GPU, Python and Theano can fuel large neural network algorithms that drive Deep Learning.

Artificial Intelligence and Machine Learning

Python enabled AI and ML solutions are helping enterprises enhance current products, make better decisions, and optimize internal and external operations.

Python language holds the major code base for AI and ML related development. The credit somewhat goes to Python’s low entry barrier. Data Scientists easily pick up Python and perform AI and ML operations conveniently and efficiently.

What sets apart Python from other programming languages is its flexibility. Python gives you an exclusive option to use it as an Object-Oriented Language or Scripting. Python can be easily combined with other languages in an ecosystem.

Python has implementations in other programming languages too. You may use Jython to integrate Python with Java. Want to use Python with C? Use CPython. 

When it comes to gathering data, cleaning and processing it, feeding it to a machine learning platform (like Tensorflow), and generating a machine learning model, Python can help you achieve it with maximum scalability.

Not just that, Python is highly platform-independent. Whether you are on Windows, Linux, macOS, or Unix, you can always leverage packages such as PyInstaller to run Python on all 20+ popular operating systems.

Natural Language Processing

A report by Fobes mentions that everyday humans produce over 2.5 quintillion bytes of data on the internet.

Look around yourself. Applications such as Instagram, Facebook, Twitter, Reddit, and countless others are producing a staggering amount of data as you read this piece. Hence, the future certainly lies with the data.

Natural Language Processing or NLP is a Python-backed approach that helps you analyze, process, and understand Big Data.

Social Media Monitoring, Sentiment Analysis & Market Intelligence are some of the popular business use-cases of NLP. Over the last decade, the IT industry has taken its leap of faith into NLP, thanks to the wide adoption of Python.

Natural Language Processing reduces the ambiguity in the language and encapsulates messy data into a useful numeric structure. Amazon Alexa, Google Assistant, Microsoft Cortana, Google Translate proves that NLP isn’t the future, but it’s already here.

A Huge Community

According to Stack Overflow, Python is the fastest-growing programming language out there. The language is driven locally, regionally, and globally by a rich community of tens of thousands of developers.

From AI, productivity tools to video games, Python enjoys wide dissemination and acceptance throughout the world. Python has several active code societies too on GitHub, Reddit, PyLadies, Hacker News & more. 

SlashData reports that there are over 8.2 million developers around the world that code in Python. Since the language is so powerful yet free and open-source, professionals from all walks of life use Python.

Data Scientists, Programmers, Solutions Architects, all use Python to solve complex challenges, operate on Big Data, and boost business workflows. Apart from its huge AI and ML functionalities, Python is capable of both front and backend development.

Wrapping it Up: Python Programming Language is the Future

The cross-platform development and a strong community account for Python’s ever-increasing global presence.

In the C programming language, it takes six lines of code to print ‘Hello World’, Python can do it in just one! Python is the first choice language of programmers today and makes Python the programming language of the future. 

The awesome libraries and robust performance of Python are triggering its constant growth in the market. All the top-notch brands are using Python and growing their codebase as we speak.

Moreover, the future will be governed by Big Data, AI, ML, and their subsets. Python positions itself just right as a versatile, secure & game-changer programming language.