Therefore, it will be the underlying use cases of JS and Python that this article will focus on, and what makes the language ideal for that particular use case, whether that be web development, artificial intelligence tasks, and more.
If you’re wondering which of the two languages to learn today, the answer will become clear by the end of this article.
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Both are interpreted (as opposed to compiled) whereby code is translated into machine-readable instructions as the program is being executed.
This brings advantages like a rapid test-feedback loop as you are developing, but often comes at the cost of speed – a common trade-off versus compiled programming languages.
JS and Python are also both multi-paradigm languages, each supporting multiple “styles” of programming.
Historically speaking, programming languages often conform to one or more “types”, whether that be:
- Object-oriented: The popular paradigm of modelling data and logic around objects. (C++, Java, Python, etc.)
- Functional: Entire applications consisting of function definitions and function applications. (Haskell, OCaml, etc.)
- Imperative: Applications as a series of statements (Fortran, Pascal, etc.),
- Event-driven: Applications that respond to events, such as I/O, web APIs, etc (C++, Java, etc.)
The State of Python
Python is a general-purpose, object oriented programming language with a focus on readability from its simplified and expressive syntax.
The Python programming language is used in a variety of fields ranging from web development (with execution on the server-side), web scraping, machine learning and data processing tasks. Python is also used heavily in academia for research efforts, and is adopted in niche fields like casual game development.
Python is an object oriented programming language primarily, also multi paradigm
Python has recently risen to prominence with its dominance in Machine Learning and fields that ML encompasses, including Deep Learning, Natural Language Processing and computer vision. It is also heavily used in Reinforcement Learning.
Developer salaries in the US tend to be a lot higher than the global market. Referring to US based salaries alone will skew the salary ranges considerably to the upside.
The numbers tell us that the Python programming language has a small edge for salaries, and this gap may continue to widen in the future as the market demand for expert data science roles continue to rise.
JS vs Python by Use Case
Web Application Frameworks
Developers need not only rely on these frameworks though – standalone Python or Node.js scripts can be executed on the server and managed via a process manager (the most notable of which being PM2).
- Data management. Although Python is capable of connecting to major databases (SQLite, MariaDB, MongoDB, etc), it is Node.js that offers more comprehensive and seamless support for databases.
Python’s dominance in Machine Learning
Before Python’s rapid adoption by the data science community, the R programming language was the leader in scientific computing. Even with competition from languages like Julia and MatLab, Python is easily dominating the field in both academia and in the industry.
To read more about the field of data science, take a look at Iglu’s accompanying piece dedicated to exploring this topic in depth: Best Data Science Programming Languages
Machine Learning is perhaps the fastest growing field that Python is benefitting from. Python frameworks specifically for ML include Tensorflow, PyTorch and Keras that are now the most popular choices for data scientists.
It is with these tools that engineers can explore Computer Vision, Natural Language Processing, Reinforcement Learning and the many variants of these core competencies in the field of AI.
What further props up Python as the go-to programming language for data science is the surrounding ecosystem of software, with tools like Jupyter Notebook and Google Colab making it easier to build and run GPU-intensive programs.
Python has a number of GUI frameworks, the most popular of which being TkInter, the go-to GUI framework for Python developers. Other notable frameworks include PyQT5, built around the Qt framework, and Kivy, a cross-platform framework focused on multi-touch interfaces.
Modern hallmarks of a good GUI tool (or any library for that matter) first and foremost require cross-platform compatibility, open source licensing, and a dedicated community that maintains that library. With Python itself having all these characteristics, it is natural to develop such libraries with the programming language.
Some knowledge of Xcode and Android Studio is required, but the bulk of development, debugging and initial testing can all be done without having to build the app for a physical device – and this workflow has come a long way.
Python does not offer such an advanced and streamlined solution to mobile apps, but the aforementioned GUI frameworks are more suited for desktop app development.
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The Best Scripting Language?
For those interested in data analytics, neural networks and the Machine Learning space, or those looking for stricter syntax more suited for system programming, Python will be the programming language of choice.
The job market for both languages remains very strong with career prospects in either one.