Why Python 3? Insights in the “Swiss Army knife” of coding

Python is one of the world’s most popular, dynamic, versatile, interactive and remains one of the most relevant programming languages for the year 2020. In fact, it’s more so than ever.

Python, the rising star of coding languages
Python climbed from third place to second in the latest ranking of programming language popularity published by the analyst firm RedMonk. It’s the first time that a language other than JavaScript, which remains number one in the firm’s ratings, or Java, the other runner-up, has entered the top two since RedMonk started compiling its rankings in 2012.
The top looks like this:
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As broadcasted in the title, Python is called the Swiss army knife of coding and it’s not for nothing. It has been one of the most sought after tools by data science practitioners and continues to be. Right from programming projects such as data mining to machine learning, Python is the most favored tool ever.

But, as most blockbuster stories, Python's continued success wasn't a given. The long transition from Python 2 to Python 3 in particular could have shunted developers elsewhere. Python 3 was first released in 2008, and the team initially planned to stop supporting Python 2 in 2015, meaning there would be no further bug fixes and security updates from the official project. But they extended that deadline to 2020 when it became clear that many developers would need more time to update their code to the newer version.

But why Python?

Python is a winner given its versatility, it is easy to use, allowing a wide range of applications from data science to machine learning and game development.

First of all, is both readable and simple. One of the most advantageous points is that Python is easier to set up and has got wider applicability than most other languages. It is now also making a way into the field of cloud services with all major cloud providers including it in some capacity in their offerings.

When it comes to data science, Python is really powerful, open sourced and flexible, adding more to its popularity. Python boasts extensive libraries for manipulation of data and is extremely easy to learn and use for all data analysts. It is also easy to integrate with the existing Infrastructure and can also solve the most difficult of problems.
Making a step further, Python has also a huge library collection designed for statistical and numerical analysis. Just to name some of the popular libraries : SciPy (collection of packages for mathematics, science and engineering), Pandas (data-analysis and modelling library), IPython (supports visualization and parallel computing), NumPy (deals with complex numerical calculations) and more.

Python is also well suited for developing a web application without much complexity or in the gaming field, where Python is also largely explored.

 To conclude, the strengths of Python reside in its characteristics:
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Our Python CTO as a Service expertise

As from April 2020 the development of Python 2 ends, our Python CTO is already focused on the challenges brought by Python 3 and will assist our partners throughout the entire migration and update process from older Python versions to the newest one. 







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