Python is a general-purpose, free, and open-source programming language with a clear and easy to learn the syntax and remarkable power. It is an interpreted object-oriented programming language that is available for any computing platform that can compile C. Most users install Python from compiled binaries. If that is not an option, then Python can also be built from the source by cloning its publicly available source code. Python is a high-level programming language that shares most of the features of other languages, such as portability. Unlike other languages, however, Jenkins (2015) claims and recommends Python as a first language to introduce students into computer programming.
Three features that make python programming easier for any users is that the language is interpreted, dynamically typed, with automatic garbage collection ( Emmerich et al., 2019; Narayanan, 2019). These three features create powerful abstractions for users learning the language or scripting a solution intuitively without worrying about the language specifics. For instance, a programmer writing in C will have to compile their code against their specific machine (or cross-compile if needed), and if they had allocated some dynamic memory to expand the size of an array, they would have to free that memory first. Python users, however, do not have to worry about these issues as the interpreter handles all memory allocations (and deallocations), and the code is portable without the need to compile or modify it to run on other systems.
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This paper is a report on the features and applications of the python programming language. It will focus on the description and a brief history of the language, its applications, and analyze the adoption of the language. The purpose is to demonstrate that as a general-purpose programming language, Python is recommended for teaching as an introduction to computer programming and other advanced concepts, like systems programming, machine learning, and IoT.
Background
A Brief History of Python
Python was created in the 1980s by Guido van Rossum to be the successor to ABC, featuring exception handling and interfaces with the Amoeba operating system (Tulchak & Marchuk, 2016). Python has gone through three major releases, with the latest at the time of writing being version 3.9.0. Version 1 was released in January 1994 and introduced the core features of functional programming like map, filter, lambda, and reduce (Python, 2017). Van Rossum also introduced data hiding that uses name mangling as a feature for object-oriented programmers. The feature, however, is not enforced by the compiler as it can be bypassed (Kapil, 2019).
Version 2 was released in October 2000 and introduced list comprehensions, garbage collection that could collect reference cycles, language specification for nested scopes, and the with statement (Python, 2017). The Python Software Foundation (PSF) was also formed where all licenses, specifications, documentation, and code for the language would be managed by the company under the Python Software Foundation License (Tulchak & Marchuk, 2016). While version 2 retained backward compatibility with previous releases, version 3 was released in December 2008, and it broke most of the features. The code for version 2 and previous releases could not run in version 3 without major modifications. For instance, the print was changed from a statement to a built-in function. The intention was to enable programmers to use different print functions in different modules and to streamline the syntax. Additionally, the python 2 input function was removed, and the function raw_input was renamed to input. Integer division was changed from version 3 were performing the a /b would result in a value that is of type float in python 3. Aside from the lack of backward compatibility when python 3 was released, the language philosophy did not change. Programmers could still follow their preferred programming paradigm for their diverse applications.
Description of Python
Python is an interpreted language that supports multiple programming paradigms. The language supports object-oriented, functional programming, procedural, and imperative programming styles. Other strengths of the language are a clear syntax, an interactive mode for prototyping and solving simple problems, and a large standard library. However, the greatest strength, especially for learners, is that Python is heavily documented, and there are numerous resources and forums to aid in the learning process (Tulchak & Marchuk, 2016).
One of the language’s weaknesses is that it is slow compared to low-level programming languages like C and C++. The low computational speed is the cost of having an interpreter that compiles code into bytecode then translating the bytecode into machine code during execution. Note, however, that the context within which Python is referred to as slow is relative. Compiled languages are generally fast because the compilation step is done once to generate machine code, which is executed directly. Numba is a python LLVM that compiles Python and NumPy code into high speed for high-performance machine code (Pivarski et al., 2020). According to Faouzi & Janati (2020), python code compiled and optimized against numba can achieve computational speed and performance of C and FORTRAN code, especially numerical algorithms.
Significance
This research is significant because it recommends that python programming skills are a must for any IT professionals. The language is intuitive and has a shallow learning curve, especially in its use of abstractions that help programmers focus on translating problems to solutions without worrying about language-specific implementations or portability (Ozgur et al., 2017; Jaworski & Ziade, 2019). Furthermore, the applications of Python are numerous that the skills acquired can be transferred to the learning and building different applications and services, from systems-level software to web development tool IoT, artificial intelligence, and machine learning.
Analysis of Python Adoption
Strengths of Python as a Programming Language
Syntactically, programming in Python is easy and intuitive. The abstractions hide most of the implementation-specific features, leaving the programmer to focus on their tasks. Take a beginner learning computer programming concepts for the first time using the classic hello world example. In Python, they only have to write one line of code and save it in a .py file as shown below
print(“hello, world”)
Running the code would be as simple as running the command python hello.py (assuming they saved the code in a file named hello) and get the results. In comparison, writing the hello world program in C++ or Java would require that the user have a basic understanding of some language-specific concepts, like the syntax for writing code, using modules and libraries, namespaces, objects, and methods, among others. The code in the appendix below shows examples of the hello world program written in C++ and Java, respectively.
The second strength of the python programming language is that it has accumulated extensive support libraries in different areas, from operating system tools to string operations, databases, web frameworks, and machine learning and artificial intelligence (Srinath, 2017). Furthermore, the language has powerful integration features that allow users to develop services and interconnect them with other systems (Faes, 2018). For instance, Python has features to call C, C++, and Java functions directly. Therefore, high-performance functions and modules can be written in C and C++ while high-security code written in Java is then called directly in Python through the language’s built-in features or external libraries. As a result, python programmers improve their productivity because most of their work has been done and published by the community (Corradi, 2018). Note that these are not the only features of Python that provide its strength. The main limitation with Python, however, is that developers get used to its features and extensive support libraries that working with other languages is a challenge. For instance, python developers do not have to worry about data types, typecasting, syntactic requirements (curly braces for code blocks and semicolons at the end of statements), dynamic memory allocation, and buffer overruns, and declaring variables before using them.
The weakness of Python as a Programming Language
The greatest disadvantage and limiting factor about Python are that it is relatively slower. Though it is a high-level language, Python’s speed is a trade-off for it being an interpreted language. However, there are compilers, like Numba, that can compile python code into high-performance machine code with speeds comparable to C and C++. Furthermore, if speed is a priority, developers can write the high-performance code in a faster and low-level language like C or C++ then call it directly from Python. The second disadvantage of Python is that it consumes memory, thus cannot run efficiently in environments with low memory, such as embedded systems (Tanaka & Saito, 2018). The high memory consumption comes as the cost of the language’s flexibility with data types. Furthermore, Python is dynamically typed, which creates several issues as errors that appear in the compilation in other languages appear at runtime in Python ( Fromherz et al., 2018) . Lastly, Python has not found application in mobile development as compared to Java. Note that the list of disadvantages is not exhaustive.
Applications of Python Programming Language
Python is a general-purpose programming language and has found application in multiple domains. For instance, Python has been applied in web development in numerous ways. Frameworks like Django and Pyramid were built to make the development of web applications easier and scalable (Python, 2020). It has also been used to build and manage advanced content management systems like Django CMS and Plone. Other frameworks like Bottle and Flask add to the diversity that programmers and users can create to build their applications. However, these frameworks are specialized to particular applications. For programmers who want to prototype solutions or debug applications, the python standard library comes with numerous internet protocols for handling HTML and XML, JSON, email, FTP, IMAP, and other protocols, and a basic socket interface for socket programming (Python, 2020). While these packages are very low level, users unfamiliar can use packages from the package index, such as requests, beautiful soup, twisted Python, and feedparser, among others. Note the levels of abstractions for each category of web development tools available to all python users.
Python has also found applications in scientific and numeric computing and has several packages to facilitate the applications. For instance, SciPy is a package for mathematics, science, and engineering application, pandas for modeling and data analysis, and IPython has features for parallel computing (Python, 2020). NumPy and SciPy are also the basis for other frameworks, like Scikit-learn (package for teaching machine learning) and TensorFlow (Hossain et al., 2017).
Conclusion
In summary, this paper set out to evaluate Python as a programming language. It concludes that Python is an example of an easy to learn general-purpose programming language. For beginners into programming, the powerful abstractions hide language-specific details. Users learning Python do not learn the language but how to use it to solve computational problems. Once learned, the language’s features allow the skills to be transferred across multiple application domains. For instance, a developer skilled in web development does not need to build their framework or package to build their application. They can use existing packages and frameworks, from the python standard library and the package index, of their choice. Python also has an easy to learn and read syntax. These features combined allow python developers to be highly productive, especially when they take advantage of the community and the resources available over the internet.
Note, however, the versatility and the productivity of Python are also one of its limitations. Developers who get used to writing code and applications in Python find it difficult to transition to other languages. Furthermore, they have little need to transition completely because Python’s extensive features allow them to directly call code written in other languages. Furthermore, Python is a slow language due to the inclusion of the interpreter that has to be running in the background. The community overcomes the limitation with Numba and other high-performance python interpreters.
The main limitation of this research and its conclusion is that the content assumes a basic understanding of python programming and programming language paradigms. Therefore, most of the concepts and conclusions are stated but not explained to a deeper level.
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