Every project has its specifications and demands. And when you’re building an application, it’s most important to choose the right technology to code it. In this article, we’ll look at Python vs. Node.js to learn about their benefits, downsides, and use cases so you can make an educated decision about which one is best suited to your project.
Why Your Tech Stack Choice Matters
You can ask your peers for advice about what technology to choose, Google the answer, or ask developers which technology they prefer. Each source will give you a different opinion, but none of these options will tell you reliably which technology is the best fit for your project.
Programming languages and frameworks were designed to fulfill specific project goals, and that’s the main criteria to base your choice on. Don’t go by popularity alone. For example, some technologies are a better fit for Big Data applications (like Python and R), while others are more often used for building large desktop applications (like Java and C/++/#).
The choice of technology should be deliberate and based on your needs and capabilities, such as:
- The type of project: business application, game, payment software
- Product type: a dynamic messenger, or a data analytics platform
- Application geography: local, countrywide, or worldwide
- Budget: how much you can spend on technology and developer salaries to build and support your project in the long run
The list can go on, but it’s essential to take every feature of your future product into consideration when choosing the technology you’ll use to build it. By comparing Python vs. Node.js for backend development, we’ll show you how good technologies vary in their advantages and areas of application.
Python: Pros, Cons & Python Use Cases
Python is an oldie, but a goodie. This programming language originated in the early ’90s and is still one of the most innovative, flexible, and versatile technologies thanks to its continually developing libraries, excellent documentation, and cutting-edge implementations. For example, Python is the go-to language for data science, machine learning, and AI projects. According to JetBrains research, it will remain that way for the next five years.
Python also has one of the largest communities that contributes to improving the language to handle modern-day programming tasks, as shown in this diagram.
Like any other technology, Python has its pros, cons, and specific spheres of application. I have used Python for many different projects like monitoring and payment platforms, real estate and security solutions, FinTech (ClearMinds), travel(Padi Travel, Diviac), and healthcare (Haystack Intelligence) platforms. Time and time again, it has proven to be a robust technology for handling all of the tasks our clients came with.
Python has many advantages that facilitate development in diverse projects, from startups to big enterprise platforms. Here are some of the most prominent ones:
- Python reduces time to market
Python allows you to develop an MVP or a prototype in a limited time frame, so you can reduce time to market (TTM). That’s achieved thanks to Python’s rapid development methodology — which allows you to maintain several iterations at a time — and the DRY (don’t repeat yourself) principle, which means you can reuse parts of the code.
These Python features offer a lot of flexibility to your project since you can go back and forth with the consumers, offer a solution, gather feedback, make improvements, and scale your prototype into a fully fledged web application.
I work for the loan management division of a company that finances large purchases (furniture, refrigerators, etc.). My coworkers manage our accounts and I support them and management with data analysis and workflow automation. Since there is such a focus on productivity, a short delivery time is often the most important thing, right after “How many FTEs will that save?” So I use Python for its flexibility and the speed at which it allows me to write usable code. I can cover more bases much more quickly than I could with something like .NET, Java, or any Windows scripting utilities, and none of my work is user-facing, so I don’t need extensive GUI capabilities. Python fits this niche perfectly.
- Python has a simple syntax
One of the top reasons why developers like Python so much is that it has a simple syntax that allows them to express concepts in just a few lines of code and makes it easier to solve errors and debug the code. Python is all about code readability. It’s also simple enough for the clients to understand, which makes for more convenient collaboration.
- Python has a wide range of development tools and frameworks
Sublime Text, a popular code editor, provides support for Python coding, as well as additional editing features and syntax extensions. Powerful web frameworks simplify the process and allow developers to focus on the logic of your applications. We use Django, which is a full-stack framework for developing all kinds of applications (simple or complex) and (thanks to its DRY philosophy) optimizing the time required to complete a project.
- It has a large community
Comparing Python and Node.js, Python is a more mature open-source language and has one of the biggest user communities. It has an incredible number of contributors, from junior to experienced. That means at least two things: it’s easy to find developers, and you get an active, supportive community that’s eager to share solutions and improve the language.
I create software libraries for Raspberry Pi add-ons (known generally as HATs for hardware attached on top) and- for better or worse- the canonical language on the Pi is Python. It’s seen generally as a fairly friendly language for beginners and since the whole community is involved with projects, examples, guides and tooling there’s no reason to go against the grain. But that’s not to say I don’t enjoy Python. It’s quite probably my least-hated programming language on reflection.
I’ve just released Python libraries to deploy fonts for use with example code that drives LCDs, OLEDs and eInk displays- working with namespace packages and entry points has been interesting and has allowed me to solve the font problem in a way that can be shared and built upon by the community.
Python is a great fit for most types of projects, but it does have a couple of limitations:
- Python is single-flow
Like any interpreted language, Python has a slower speed of execution compared to compiled languages (like C or Swift). It might not be the best choice for applications that involve a lot of complex calculations, or any project where speed of performance is the most important requirement (for example, in high-frequency trading).
- Weak in mobile computing
Python is great for developing server and desktop platforms, but it is considered weak at mobile computing. That’s why few smartphone applications are written in Python.
When to Use Python
Python is the language of choice for all sorts of projects, whether small or large, simple or complex. That includes business applications, desktop user interfaces, educational platforms, gaming, and scientific apps. As for the area of application, Python is mostly used for:
- Data science, including data analysis (Apache Spark), machine learning (Tensorflow), and data visualization (Matplotlib): some Facebook systems use Python’s Pandas library of data analysis tools; face and voice recognition systems; neural networks and deep learning systems
- Web development: web development frameworks (Django, Flask, CherryPy, Bottle)
- Desktop GUI: 2D image-processing software like Scribus and GIMP; and 3D animation software like Cinema 4D, Maya, and Blender
- Scientific Applications: 3D modeling software like FreeCAD and finite element software like Abaqus
- Gaming: 3D game engines (PySoy) and actual games, such as Civilization-IV and Vega Strike
- Business applications: Reddit was rewritten in Python in 2005, and Netflix’s engine is written in it
- DevOps, system administration, and automation scripts: small apps for automating simple tasks
- Parsers, scrapers, and crawlers: a parser for compiling data about forecasts from different websites and displaying the results
- Software testing (including automated tests): unit-testing tools like Pytest, or web testing tools like PAMIE and Selenium
Python is an easy, yet powerful, a versatile programming language with advanced documentation and high-level development frameworks. It’s the go-to language for Big Data applications and also suits business solutions, educational platforms, scientific and healthcare applications.
Node.js: Pros, Cons & Node.js Use Cases
When comparing Python vs. Node.js for web development, Node has a few benefits to boast about:
- Node.js enables fast performance
When comparing Node.js vs. Python speed, you’ll find that the former is faster. Node.js is based on the Google V8 engine, which makes it good for developing chatbots and similar real-time applications.
- It enables full-stack development
- Great for developing real-time apps
Its event-driven architecture allows you to develop chat applications and web games.
- Node.js requires a clear architecture
It’s an event-driven environment, so it can run several events at a time — but only if the relationships between them are well written.
- It can’t maintain CPU-intensive tasks
A heavy computational request will block the processing of all other tasks and slow down an application written with Node. Therefore, it’s not suitable for projects based on data science.
- Underdeveloped documentation
Unlike Python, which has comprehensive and up-to-date documentation, Node.js documentation is lagging. Plus, there are no core libraries and tools; they have too many alternatives, so it’s not always clear which you should choose.
When to Use Node.js
Node.js is the go-to technology for developing apps like Ad services, gaming platforms or forums. It’s good at handling projects with a lot of simultaneous connections or applications with high-speed and intense I/O (input/output), as well as applications such as productivity platforms (e.g., content management systems), P2P marketplaces, and eCommerce platforms. Node is used in different types of web applications, such as:
- Social and productivity platforms: LinkedIn, Trello;
- Business applications: eBay, Walmart;
- Payment systems: PayPal;
- Entertainment platforms: Netflix.
Looking at Python vs. Node.js performance and use cases, we can see that both cater to different needs. Node.js is used for solutions where Python isn’t usually applied — or example, for real-time applications that require more speed, or in cases where you want the same team to work on both front- and back-end development.
As you can see, Python vs. Node.js, both have their advantages and disadvantages, and they are used for different kinds of projects. So when you are choosing between Node.js or Python, you need to look at all the pros and cons to decide which one is most suitable for your project application.
I have been working with Python for a long time, and over the years I have used it to build everything from high-quality mid-size web applications to complex enterprise-grade solutions. And every project has convinced me(and still does) that Python helps simplify development, reduces time and costs, and allows me to scale the project quickly and easily.