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Things to look out for Python in 2018

Before proceeding our view on Python, Let us just remind you that ‘Aedifico Tech’ is currently on with its ‘6 weeks summer training program’ where students will get to learn the upcoming major trending IT hardware and software programming languages with live projects. This way their learning will be much efficient as they can deal with practical problems right away.

Python is the fastest-growing programming language according to the recent internet trends. A question may arise why Python is growing so fast. We know Python can be used for many purposes—Python web development to mobile app development to data science to DevOps. So can be Java, Perl, Ruby, etc. But what sets Python apart from other languages. What is actually driving the growth?

An initial analysis gives out reasons:

  • There is a sudden rise of Python usage in web development
  • Something else is driving this growth. It could be its application in data science, DevOps or something else entirely.

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A dynamic language, Python

Python is a multipurpose language used for various tasks, such as web development and data science. How could we unravel Python’s current growth across these fields?

We could examine the growth in traffic from notable Python packages. We could compare the web frameworks Django and Flask to the data science packages NumPy, matplotlib, and pandas. We have used recent trends here.

The new trends

According to the recent trends available with us through ‘Stack Overflow’ overview, pandas is clearly the fastest growing Python package. It was introduced only in 2011.

Questions about the data science packages, numpy and matplotlib, have grown considerable over the years while traffic to Django questions has remained steady during that time. Although Flask is growing, it’s growth is nowhere near those of data science packages of Python.

It is clear the rise of data science is a major driver in the growth of Python as a programming language. But we cannot say for sure if it has beaten Web Development in that metrics.

Libraries & packages

Before we move any further and search the reason behind the phenomenal rise of Python. We would like to introduce you to some of the packages, libraries and frameworks.

Data Science Libraries:

These three powerful libraries enable

pandas: pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license. pandas assumes general familiarity with NumPy.

NumPy: NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. pandas is built on top of NumPy and is intended to integrate well within a scientific computing environment with many other 3rd party libraries.

Matplotlib: Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits.

Web Development Frameworks

Django: jango is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.

Flask:Flask is a Python web framework built with a small core and easy-to-extend philosophy. Flask is considered more Pythonic than Django because Flask web application code is in most cases more explicit. Flask is easy to get started with as a beginner because there is little boilerplate code for getting a simple app up and running.

Industries that are using

Industries like Academic, Electronics, Government, Tech, Manufacturing, Media, Finance, Energy, Healthcare, Consulting, Retail, Insurance.

As per industry trend, the vertical with the highest amount of Python traffic is academic world, comprised of schools and universities. Is this because Python is often taught in UG programs?

The traffic to Java falls sharply around the time summer while in the case of Python, the fall is more balanced and evident. Java, in fact, is a comparatively a more common subject in UG courses than Python is. Yet, Python takes a larger share of summer traffic. This points to one thing. Research goes on a university round the year, not just Summer, Spring and fall.

Academic researchers make up a majority of the high traffic to Python from universities. They work around the year. According to a question on Quora: What are currently the hot topics in computer science research? answered by Igor Markov, Michigan EECS Prof, who works at Google. Abundant-data applications, algorithms, and architectures is biggest topic of research in computer science.

Conclusion

In any case, data science is an exhilarating, rising field, and there’s ample room for numerous languages to prosper. Our foremost inference is to cheer developers early in their career to start building skills in data science. Python is the fastest growing programming language and data science is the fastest growing field in computer science. We at ‘Aedifico – Best Technical Training Institute in Delhi‘ offers pretty much every modern programming language from our kitty and we tell our students to be serious and sincere so as to stand competitive among the masses.

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