Beginning Data Science With Python and Jupyter Use Powerful FULL BOOK 2022

 Beginning Data Science With Python and Jupyter Use Powerful FULL BOOK 2022

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Language Python characteristics:

Python is (optionally) multi-threaded. Python is object-oriented. It supports multiple inheritance and operator overload. In its object model, and using the terminology of C++, all methods are virtual. Python integrates, like Java or recent versions of C++, a system of exceptions, which make it possible to simplify considerably the management of errors. Python is dynamic (the interpreter can evaluate strings representing Python expressions or instructions), orthogonal (a small number of concepts is enough to generate very rich constructs), reflective (it supports metaprogramming, for example the ability for an object to add or remove attributes or methods, or even change classes while running) and introspective (a large number of development tools, such as debugger or profiler, are implemented in Python itself). Python currently has two implementations. One, interpreted, in which Python programs are compiled into portable instructions and then executed by a virtual machine (as for Java, with one important difference: Java being statically typed, The other generates Java bytecode directly. 

 Finally, Python is a language of choice for handling XML. Python is more used for data science than for Web development: Based on a survey by the Python Software Foundation! In the fall of 2018, the Python Software Foundation, in collaboration with JetBrains, conducted an official annual survey of Python developers, the second survey of its kind after the one in 2017. As in the previous survey, the foundation sought to identify the latest trends and better understand the Python development world in 2018. 

More than 20,000 developers from more than 150 different countries participated this year, giving a more or less relevant picture of the current landscape of the Python community. To come to the results, the survey reveals that Python is 84% of its users their main language and the remaining 16% a secondary language. In 2017, 79% of respondents reported using Python as their primary language, which means an increase of 5 percentage points in one year.

The Python Software Foundation (PSF):

The Python Software Foundation (PSF) also looked at the types of development in which the language is used. In other words, for what types of activities or tasks is Python used? In response to this question, the survey indicates that data analysis is more popular than web development within the Python community. Specifically, 58% of Python users used the language in 2018 for data analysis, while 52% used it for web development. Here, respondents could choose several options. But when users had to choose only one (the type of development for which they use Python more), 27% cited Web development, compared to 17% for data analysis. However, combining data analysis and machine learning (11%) under the category “Data Science” yields a rate of 28%. This means that Python is used more for data science than web development. At first glance, the results suggest that Web development is the leader (27%), far exceeding data analysis (17%),' reads the survey report. If we combine data analysis and machine learning into a single “Data Science” category, we get an impressive 28%.” After data science (28%) and web development (27%), in 2018, Python was used more in DevOps, system administration and automation script writing (11%), for educational purposes (7%), for desktop application development (4%), for software prototyping (4%) and for programming Web crawlers, scrapers and parsers (4%). Below is the graph showing the type of development for which Python is most used in the community (single response). As another good information to know, the survey still reveals that 84% of the members of the Python community use version 3.x mainly against 16% for Python 2.x. This is a huge jump in the popularity of Python 3, when we know that the previous year, only 75% of the members of the community used Python 3.x as their main interpreter. The use of Python 2 has therefore declined quite rapidly in one year, which is explained by the fact that this version is no longer actively developed, it has no new features and its maintenance will be stopped in 2020. For all intents and purposes, only 82% of people working mainly in Web development use Python 3, while for those working mainly in data science, this rate goes up to 90%. One possible explanation is that some Web developers still have a lot of legacy code to manage during the transition to Python 3. Meanwhile, Many data analysts and machine learning specialists have only recently joined the Python ecosystem and have therefore launched directly into the latest version of Python.

What about you?

So after you read to study, what are your conclusions about it? Does Python have more of a future in data science than in web development? Why? What types of development or tasks do you use Python for? And why did you choose Python for these types of development or tasks? For Web development, how do you find Python compared to other languages?

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