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So, why all the fuss about Python? Perhaps you have heard about Python from a coworker, heard a reference to this programming language in a presentation at a conference, or followed a link from a page on scientific computing, but wonder what extra benefits the Python language provides given the suite of powerful computational tools the Earth sciences already has. This article will make the case that Python is the next wave in Earth sciences computing for one simple reason: Python enables users to do more and better science. We'll look at features of the language and the benefits of those features. This article will describe how these features provide abilities in scientific computing that are currently less likely to be available with existing tools, and highlight the growing support for Python in the Earth sciences as well as events at the upcoming 2013 AMS Annual Meeting that will cover Python in the Earth sciences.

 Python is a modern, interpreted, object-oriented, open-source language used in all kinds of software engineering. Though it has been around for two decades, it exploded into use in the atmospheric sciences just a few years ago after the development community converged upon the standard scientific packages (e.g., array handling) needed for atmospheric sciences work. Python is now a robust integration platform for all kinds of atmospheric sciences work, from data analysis to distributed computing, and graphical user interfaces to geographical information systems. Among its salient features, Python has a concise but natural syntax for both arrays and nonarrays, making programs exceedingly clear and easy to read; as the saying goes, “Python is executable pseudocode.” Also, because the language is interpreted, development is much easier; you do not have to spend extra time manipulating a compiler and linker. In addition, the modern data structures and object-oriented nature of the language makes Python code more robust and less brittle. Finally, Python's open-source pedigree, aided by a large user and developer base in industry as well as the sciences, means that your programs can take advantage of the tens of thousands of Python packages that exist. These include visualization, numerical libraries, and interconnection with compiled and other languages, memory caching, Web services, mobile and desktop graphical user interface programming, and others. In many cases, several packages exist in each of the above domain areas. You are not limited to only what one vendor can provide or even what only the scientific community can provide!

 A number of other languages have some of Python's features: Fortran 90, for instance, also supports array syntax. Python's unique strengths are the interconnectedness and comprehensiveness of its tool suite and the ease with which one can apply innovations from other communities and disciplines. Consider a typical Earth sciences computing workflow. We want to investigate some phenomena and decide to either analyze data or conduct model experiments. So, we visit a data archive and download the data via a Web request, or we change parameters in the (probably Fortran) source code of a model and run the model. With the dataset or model output file in hand, we write an analysis program, perhaps using IDL or MATLAB, to conduct statistical analyses of the data. Finally, we visualize the data via a line plot or contour plot. In general, we accomplish this workflow using a kludge of tools: shell scripting for Web requests and file management, the Unix tool Make for code and compilation management, compiled languages for modeling, and IDL or MATLAB for data analysis and visualization. Each tool is isolated from every other tool, and communication between tools occurs through files.

 In Python, every tool is used in the same interpreted environment. And data flow does not have to occur through files; we can access any variable in any tool at any (logical) time in the workflow. As a result, our workflow becomes much more robust and flexible. Additionally, the breadth of the Python tool set means the most important capabilities we need will very likely be available even if a given vendor changes its priorities. Python users also have much greater ability to access innovations from industries outside of the Earth sciences. This latter advantage will become increasingly more important as we push inexorably into the world of cloud computing, big data, and mobile computing; in the next decade, the Earth sciences cannot afford to ignore these innovations.

 To be fair, Python has real disadvantages, including the fact that pure Python code runs much slower than compiled code, there are fewer scientific libraries compared to Fortran, and documentation and support for new science users is relatively sparse. There are tools to overcome the speed penalty, the collection of scientific libraries is growing, and science support resources are becoming more robust; nevertheless, these are real issues. For most Earth science applications, the strengths of Python outweigh the weaknesses.

 The Earth sciences community has begun to recognize the unique benefits of Python, and as a result its population of users is growing. Adoption of Python by Earth sciences users is no longer confined to “early adopters.” Institutional support includes groups at Lawrence Livermore National Laboratory's Program for Climate Model Diagnosis and Intercomparison, NCAR's Computer Information Systems Laboratory, and the British Atmospheric Data Centre. Several packages exist that handle file formats used by atmospheric and oceanic sciences users, including netCDF, HDF4, HDF-EOS 2, and GRIB 1 and 2 (e.g., via the netCDF4-Python, UV-CDAT, and PyNIO packages). And Python's utility as a common glue is being leveraged by both open-source and proprietary vendors: Python can be used as a scripting language for applications such as Unidata's Integrated Data Viewer, the NCAR Command Language (via PyNGL), GrADS (via OpenGrADS), and Esri's ArcGIS.

 Over the last two years, the AMS has helped support the development of Python in the Earth sciences by sponsoring the Symposiums on Advances in Modeling and Analysis Using Python, as well as short courses on using Python in the atmospheric and oceanic sciences. At the 2013 AMS Annual Meeting in Austin, Texas, we will feature the Third Python Symposium; short courses for beginning, intermediate, and advanced users; and a Town Hall meeting to provide answers to questions nonprogrammers and decision makers may have about how Python can help institutions become more productive.

 The computational tools the Earth sciences have traditionally used are powerful and have enabled us to accomplish much. With modern tools, however, we can accomplish more. Python, in particular, can help us do more science and do it better. Python enables better science through enabling clear code: its elegant syntax and broad and robust tool set helps Earth scientists write less buggy code. Python also enables more science by helping us to take advantage of the recent explosion of computing resources by giving us access to an amazing set of software tools: Python gives us new capabilities through its modern, object-oriented structure but also gives us access to the new capabilities being generated daily from industries outside of the sciences. More and more, Earth scientists are becoming aware of Python's ability to help us do better and more science. With this “perfect storm” of a clear and concise language, access to a previously unfathomable range of software tools, and a growing scientific user community, Python is poised to become the next wave in the computational Earth sciences.[Source]-https://journals.ametsoc.org/doi/10.1175/BAMS-D-12-00148.1

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Startups planning a product development process need to consider many factors when it comes to picking the right programming language. Often working on a tight budget, startups must carefully consider: the speed of development, the popularity of language, the cost of developers, libraries, integrations, security, scalability, and stability. It is for these reasons that your best bet will be to go for a robust and established technology such as Python.

Python has been on the market for nearly 30 years and is still considered to be one of the top programming languages in existence. According to BuiltWith, there are nearly 1 million (994,753) websites that are Python customers, and the TIOBE Index, a programming community index that measures the popularity of programming languages, currently ranks Python as the third most popular language in the world.

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Why so popular? Well, in short, because Python is a high-level, dynamic, object-oriented programming language, which focuses on rapid and robust development and can be used for projects of practically any size. Giants like Google, Facebook, Reddit, Spotify, Netflix, IBM, Instagram, and Dropbox all have major dependencies on Python. And many fast-rising startups have Python in their tech stack, too, including TravelPerk, Festicket, and 21 Buttons.

Seeing that you will most certainly be in great company if you choose Python. But let’s delve a little deeper, and consider the whys and the hows that make Python such a fantastic option for startups.

User-friendly

Python developers love the language for its simplicity and beauty. The Python philosophy says that “there is only one best way to do something, and that is how it should be done.” A number of core principles support the philosophy, three of which are:

  Explicit is better than implicit

  Simple is better than complex

  Complex is better than complicated

In practice, these guiding principles manifest themselves in a language that is intuitive, neat, well-structured, easy to use, and easy to learn – inviting qualities that combine to make Python particularly well-suited to startups with budget, time, and profitability constraints to take into account.

Popular

We can see from the TIOBE Index that Python is one of the most in-demand languages. The popularity is echoed in the job market. According to Stack Overflow Developer Survey in which 90 000 developers took part, Python is the fourth most popular technology, overtaken only by JavaScript, HTML/CSS and SQL. This year, Python surpassed Java.

What is also very important, Python has been voted as the second most loved and the first most wanted language of the world.

Large Community

Another great thing about the popularity of Python is that there is a large and active community of supporters. In fact, Python’s programmer community is one of the very best in the world, and you will be hard pressed to find another community as expansive and devoted as Python’s.

What this means is that you will easily find full and readily-accessible support for any type of complexity you encounter during development. There are literally hundreds of thousands of programmers and supporters all over the globe dedicated to improving the language’s core features and functionalities.

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Versatile

Python can be applied to pretty much any development scenario – whether you need code that works on Windows, MacOS, and Linux, Python is the answer. It plays a key role in areas like gaming, language development, prototyping, graphic design applications, web frameworks, and, more recently, has grown in popularity as one of the core languages used in data science and machine learning (ML).

And this could be a key consideration if your application requires ML functionalities. Since Python is a high-level simple language, it makes for fast prototyping and tweaking for machine learning algorithms, and is a much more suitable language for this type of endeavour than the likes of Ruby, for example. What’s more, since Python is so fast to develop with, you will be able to test out ideas much more quickly in Python than with other languages, saving yourself time, and reducing development costs.

Scalable

In order for a startup to succeed, it needs the flexibility to grow. However, exactly when scalability will become a priority for your company cannot be easily predicted. And this is why it is imperative that you choose a programming language that can easily scale up and down.

The simplicity of Python means that not only is it easy to maintain, but it can also handle quick growth. What’s more, if you use the Django framework – the most popular framework for Python – you will also put yourself in good stead to scale. This is because the series of components that make up Django are decoupled (i.e. independent from one another), which means that they can be chopped, changed, removed, and replaced as and when your project requires larger or more specific solutions.

Speed to market

What is really important for a startup is to get to the market fast. You need to create an MVP ASAP to get funding for further development. Python will help you with that. Thanks to its popularity, the big community around, and an impressive set of third-party libraries, Python is the best choice for creating an MVP quickly. It’s possible to create the same set of functionalities with much fewer lines of code comparing to many other languages.

You don’t need an army

Python allows you to create a proof of concept with a very small number of developers. You don’t need a big team to work faster, in comparison to other languages. The Python development team can stay small and the code will remain of good quality. Of course, a smaller team means that the expenses are also small, which is really important for startups.

Final Thoughts

So, should you start up your startup with Python? Python will certainly be an excellent choice in a great deal of use cases. Python is free to use, has all the libraries you could possibly imagine, a fantastic community and global support network, is user-friendly, versatile, popular, stable, scalable, and is one of the very best options out there for machine learning and data analysis. For these reasons, Python proves to be an invaluable tool for multi-billion-dollar corporations and startups alike.[Source]-https://www.netguru.com/blog/why-python-is-perfect-for-startups

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