Python is an interpreted, object-orientated, high-level programming language with dynamic semantics. Its syntax places the emphasis on the code’s legibility, which benefits refinement and therefore productivity. It offers both the power and flexibility of compiled languages with a smooth learning curve. Although Python was created as a programming language for general use, it has a series of libraries and development environments for each phase of the Data Science process. This, when combined with its power, open-source nature and accessibility has meant that it has taken the lead over other Machine Learning languages such as SAS (the previous favorite) and R (also open-source, but more appropriate for academic and research settings).
Python was created by Guido Van Rossum in 1991 and was named due to the creator’s love of the Monty Python films.
As well as libraries of scientific, numerical, analytical, and structural tools, and Machine Learning algorithms such as NumPy, Matplotlib, Pandas and PyBrain, Python offers interactive programming environments aimed at Data Science. Python is currently one of the most popular programming languages in the world, with more than 8.2 Million active developers. Often, it is considered as the programming language for the future.
Python undoubtedly offers more readability of code compared to the others. Because of the simple and concise syntax. Consequently making it easier to maintain. Python is platform-independent and the programs run on any platform. With a large collection of the in-built standard library functions, Python even supports third-party software like NumPy as an extension.
Also, Python is used as an intermediary or agent between two apps. It can easily invoke libraries of other programming languages. Python is even productive due to typically smaller codes. Because of the fact that Python is a dynamically typed language. Hence, it doesn’t need a variable declaration, consequently reducing code size.
The different features of Python programming make it a widely-used programming language. Furthermore, it finds application in various fields of Machine learning, Artificial Intelligence, Game development, Website development, Scientific Computing, Data Science, Data Analysis and many more.
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We have written a lot about how Python has a neat and easy syntax making it quite likable amid the data science community. The constructs of this language make it easier for a non-technical person to grab it and is definitely one of the easiest programming languages to learn. Even the learners from a non-technical background are taking a liking to the language.
For instance, there are many MBAs and people from the e-commerce and finance background who latched onto Python to do BI and data analysis. There are also a few who have taken up data science as a full-time job building things like fraud detection algorithms and other complicated algorithms with Python. There are many who take up to learning Python to improve a grasp of data analysis or address other technical demands of their career.
Python comes as a rescue for those who have always loved computers but gave up on the field due to the complexity of other programming languages such as C++. Many of those who gave up thinking that programming was too technical, is now taking back to coding, thanks to Python.
Today, with Python becoming the de facto industry standard in financial analysis, e-commerce, retail biz, many found the jobs required it as a core competency and charted a learning path with YouTube videos and online courses. They could learn to not only code fluently but build solutions finding them a space in analytics companies as data analysts. Many have turned to be full-stack developers and business analysts as Python spoke to them naturally and efficiently.
Their ease and comfort with the language have encouraged them to dig further and learn the inside out of the language. Along with Python, they have taken up the combination of SQL, writing analytical software with ETL processes, report generation and general business automation. They can efficiently write around 500-800 lines of code for hobby projects like discord bots every day out of sheer enjoyment.
While Python may not be perfect, it has some great features that make it a good common language. For instance, the language is syntactically concise, easy to fiddle with data, customize behavior at a deeper level, among others. Here are some of the reasons that make Python a widespread language and a favorite of non-technical people.
Easy syntax: Python is expressive and productive. It allows creating solutions quickly and facilitates easily readable code. The syntax of Python is simple, clean, easy to understand, and closely resembles the everyday English that we speak. Python is simple, straightforward, free and affordable.
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