What Is Python? A Comprehensive Guide
The biggest players in the tech industry, like Google, Facebook, and Netflix use Python.
This guide is dedicated to entrepreneurs and innovators looking to learn more about Python and its business potential.
We’ll start off with an explanation of what Python is, its short history, pros and cons, what Python is used for, examples of companies that use it, all the way to how Python compares with other languages.
Let’s dive in.
The great fascination with Python
Did you notice how everyone’s talking about Python these days? It’s not surprising, since it’s the fastest growing and the most loved programming language.
It's versatile, which means it can be used in a variety of projects and across multiple industries, including data science, machine learning, blockchain, etc. These attract a lot of interest currently, which partly explains Python’s growing popularity.
What is the Python programming language?
Python is an interpreted, general-purpose programming language, which is one of the most popular choices for modern software development. Why?
Firstly, it’s modular – i.e. it can be easily integrated with other technologies and solutions.
Secondly, it’s open-source. There’s a vibrant community of developers who contribute to the development of the technology, and the Python Software Foundation oversees the quality and the direction in which the language is going.
Thirdly, Python is ‘interpreted’, which means it's translated to machine code right before the program is launched. This facilitates writing portable and universal programs, which are easier to use on different operating systems.
These are just some of the reasons which we dive into in this post.
Before we analyze the pros and cons of choosing Python for your project, let’s take a quick look at how it all started and give you a rough course on the most popular Python terms.
A short history of Python
Python was created in the early 1990s by Dutch programmer Guido van Rossum who happened to be a massive fan of the Monty Python’s Flying Circus and a British sketch comedy series created by Monty Python also known as “the Pythons”. Guido named the programming language “Python” as a tribute to his favorite comedians.
Here are a few more interesting Python facts worth knowing.
It is the fourth most used language – according to Stack Overflow’s 2020 survey, it’s used by 44.1% of developers. It’s also the third “most loved” language with 66.7% of developers admitting that it’s their favorite language.
It’s becoming increasingly popular in Machine Learning and Big Data – two emerging technologies that attract a lot of global interest. This further explains Python’s growing popularity.
As Python is used by many medical and data research centers, the popular TIOBE Programming Languages Index predicts that its popularity will continue to grow in the post-COVID-19 reality.
Python terms worth knowing
There are many Python terms that can, at first, seem quite intimidating to a non-technical person. Many of the popular phrases you’ll hear in reference to Python relate to its frameworks, libraries, objects, and classes.
Here’s an overview of the most important Python terms and what they stand for.
A framework is a wireframe that serves as the foundation for software development. Developers use them to build programs for a specific platform. As a result, they don’t have to “reinvent the wheel” every time they work on a project.
There are several popular Python frameworks and microframeworks worth knowing about.Django
Django is by far the most popular Python framework – in fact, you’ll see it’s a requirement in many Python developer job openings.
Django offers multiple out-of-the-box functionalities, for example an Admin Panel, Generic Forms, and ready-to-use components.
As the framework is full-stack (i.e. works for both the frontend and backend), it’s a popular choice for big software projects.
While powerful, Django enforces its own rules and coding style on developers, which makes it the best solution for projects where code flexibility isn’t a top priority.Tornado
Tornado is a Python web framework and an asynchronous networking library which was originally developed by FriendFeed, a company acquired by Facebook in 2009.
While, in many ways, it’s similar to other Python frameworks (including the above-mentioned Django), what makes it stand out is its focus on speed and the ability to handle large amounts of web traffic.
As explained on the Facebook for Developers site, the framework also provides support for templates, security, user authentication, and localization. Tornado boasts an active user community and is considered one of the most modern Python frameworks.Fast API
Fast API is the perfect framework for building APIs with Python 3.6+ – it’s modern and guarantees fast performance. Its main benefits include:
- Speed – it speeds up development time by 200% to 300%;
- Reduced bugs – eliminates about 40% of human-generated errors;
- Easy – it’s simple to use and learn;
- Short – cuts code duplication to minimum, which also results in fewer bugs;
- Robustness – you get production-ready code with automatic interactive documentation.
Developers’ love it – "I’m over the moon excited about FastAPI. It’s so fun!" – Brian Okken, Python Bytes podcast host.
Flask is one of the most popular Python microframeworks. It’s lauded in the Python developer community for ease of use, speed, and reliability.
Flask is a popular choice for small and medium projects, where the implementation of Django would be too complex. Importantly, while it offers a variety of third-party modules, it’s also compatible with native, custom-made solutions.
These are just some of the many frameworks and microframeworks Python has to offer. If you want to learn more about other ones, we recommend you take a look at our Python framework comparison piece.
Like frameworks, libraries are an extremely useful resource for software developers. They provide ready-to-use modules and functions that save developers tons of time, as they can be embedded directly into the code.
One of the reasons behind Python’s increasing popularity is the wealth of libraries to choose from.
As libraries are a topic for a whole separate article, we’ll focus on the three most popular ones below:TensorFlow
TensorFlow is an open-source library developed by Google – in fact, many of us interact with the library daily via TensorFlow-powered services like Google Voice Search or Google Photos.
The library is an extremely popular choice among Python developers working on machine learning projects.
One of its key functionalities are so-called “dataflow graphs” – i.e. structures that show the movement of data through a graph. The library is also easily trainable and flexible (i.e. you can select the parts you’d like to run with alone).
As described by InfoWorld,
“The single biggest benefit TensorFlow provides for machine learning development is abstraction”. The developer can thus focus on building the logic of the application, and the library takes care of all the rest.
NumPy is another popular library leveraged for machine learning projects. It is valued by developers for its interactivity and simplicity, as well as the ability to implement complex mathematical processes quickly and easily.
Many developers also underline that it’s intuitive and easy to use, even for those who have no previous experience working with libraries.
NumPy also has a large user community – the library gets updated regularly, with multiple open-source contributions.PyTorch
PyTorch is a Python-native library developed in 2017. Year after year, it has been gaining popularity among machine learning developers and researchers. In fact, it is currently the single biggest alternative to Google’s TensorFlow library, with its popularity skyrocketing from mid-2019 to mid-2020.
Image source: Exactcorp
PyTorch is valued for its ease of use, intuitiveness, and simple API implementation. It’s also the more popular choice for new Python developers looking to learn a new library, as compared to TensorFlow.
Python objects and classes
Two other terms you need to be aware of are objects and classes, which work together closely in Python. While understanding how they work and seeing their codependencies would require getting into technicalities, here’s a brief explanation.
Objects in Python are quite similar to the objects we surround ourselves with in real life. Namely, they have specific properties (in Python, it’s the data they store) and behaviors (i.e. methods). In Python, various objects which are created from classes can interact with each other to drive results.
In order for objects to deliver results, you need to provide them with the right data – and that’s where classes come in.
Last, but not least, we’ve previously mentioned that Python is an interpreted language. This is done thanks to Python’s interpreter – i.e. a program that parses, processes, and executes code.
Interpreters are very helpful for running scripts – without it, source code would be simple plain text, not an executable program. For this reason, interpreters are leveraged for running scripts, small programs, and are often automatically installed on the majority of web servers due to their lightness.
Now that you know what Python is, let’s talk about what Python is used for, i.e what kind of products you can build with it.
What can Python do and what kind of products can be built with Python
Google, YouTube, Instagram, Spotify all use Python – how so?
As mentioned in the previous section, it’s simple to use as it offers access to a large number of libraries and it’s versatile, which means it can be applied to a number of different projects. What kind of products can you build with Python?
Web and software development
Frameworks based on Python i.e. Django and Flask have become a popular choice for web development.
They’re easy to read and require much less code than any other programming language, which speeds up product development. Most importantly, it has a built-in framework for unit tests – this lets you deliver bug-free code. On top of it all, it nicely integrates with other languages like Java.
The Internet today is flooded with audio and video content, and you can use Python to analyze it all.
Its libraries, including Librosa and pyAudioAnalysis, automatically analyze audio content. Audio and video applications based on Python such as TimPlayer and Cplay provide better stability and performance when compared to apps built using other technologies.
You might not have thought about Python as the preferred language for game development, but some great games were built on it, including EVE online, Battlefield 2, and World of Tanks.
There are Python libraries suitable for game development which speed up the development process and enable fast prototyping. Writing 3D games in Python is possible thanks to its integration with the Panda3D engine.
Machine learning (ML) and AI
Two technologies that the world is fascinated with. We all want more personalization, smarter recommendations, and better search results – all of this can be achieved using AI and ML.
Why is Python a perfect choice for both? ML and AI require complex algorithms and workflows; thanks to Python’s simplicity, developers can write reliable code. Instead of dealing with technicalities, they can focus on solving a machine learning problem.
What’s more, AI projects tend to be time-consuming, so it’s important to have a well-structured environment to enable testing – this is something that Python offers.
What are the benefits of Python?
Now that we’ve covered what Python is, the common terms, definitions, and use cases, it’s time for an overview of Python’s advantages.
Python is easy to use (and learn)
Python is often recommended as a good ‘starter’ language for beginner programmers, as it’s straight-forward and easy to learn.
Learning Python is not difficult. In fact, the language was designed with simplicity in mind – the syntax is simple, and you can achieve a high level of proficiency faster than in the case of other backend languages.
Given that the Python community is growing, it’s also easy to hire developers who are proficient in the language.
A big collection of libraries
In a previous section, we mentioned three popular libraries – TensorFlow, PyTorch, and NumPy. However, these are just the tip of the iceberg, and there are hundreds of other options.
Regardless of whether you’re looking for a library for machine learning, game development, or for any other project – you’ll have plenty of options to choose from! From a business perspective, this speeds up development time (which relates to lower development costs).
A friendly, simple syntax and a variety of frameworks and libraries make Python development not just easy, but also fast and productive. By using out-of-the-box solutions, you can do more with less code.
This is an amazing perk for anyone looking for a way to develop rapid prototypes – not to mention the savings you make by accelerating development.
Fast code review
Python offers a wide range of code review tools, including Flake8, Radon, and Pylint. This means that your team can quickly check the correctness and quality of your code.
Python can run on major operating systems – Windows, Mac, and Linux. It can also work with some embedded systems like Raspberry Pi and Gumstix, giving your developers a comfortable and flexible solution to work with.
A vibrant developer community
The Python open-source developer community stands strong – which means the technology is constantly improved and updated. As it is with open projects, it’s free and it's up to the users to decide how the technology evolves.
As mentioned in a previous section, scientists have adopted Python for its Machine Learning and deep learning projects – which means some of the brightest minds in the world are aboard the Python ship.
To say the least, if you choose Python for your project, you won’t need to worry about your tech stack becoming obsolete in the coming years.
Python can be integrated with a whole range of other languages, including PHP, Java, and .NET. This means that if your company is already leveraging other software solutions, the chances are Python will easily blend in.
A reliable technology for enterprise software development
Last, but not least, Python is a proven solution that powers hundreds of global apps – companies like Facebook, Netflix, and Dropbox all leverage it in their business, which we discuss further on in this post.
By choosing a programming language like Python, you can rest assured that the technology is versatile and has proven effective for even the most complex, refined projects.
Even though Python is the most loved language, it’s not perfect, and like everything, it has some disadvantages. Let’s have a closer look at them.
Python is slower than other programming languages like Java or C++. This means that developers have to find ways to enhance its performance if speed is an important factor.
Needs more testing
It doesn’t require programmers to define the type of variable as it uses Dynamic Typing, which makes coding easier and faster.
Unfortunately, this might lead to critical bugs and defects because the variable types are not defined explicitly. To eliminate this problem, programmers have to run additional tests to identify and fix the bugs during runtime.
It’s not native to the mobile environment
Since Python is not native to mobile, some developers perceive it as a poor option for mobile computing.
What’s more, Android and iOS don’t support Python as an official programming language. Many companies, however, successfully use it for mobile app development as there are ways to make it mobile-friendly.
Relies on third-party frameworks and libraries
Python misses certain features that other modern programming languages have. These feature deficiencies are compensated with third-party frameworks and tools which enable the development of web and mobile apps.
To keep the costs low, they’re based on open-source frameworks and libraries. This prevents developers from having access to advanced features and functionalities that commercial frameworks provide.
Some Python modules lack reliable support
Python benefits from a large and active support community. Its members frequently share new packages and modules to simplify and enhance functionality building.
Unfortunately, developers complain that the quality of these modules varies. Some of them are not up-to-date and lack reliable support. To ensure good app performance, developers have to conduct initial research to select the best packages and modules.
High memory consumption
Python is designed in a way that requires large memory consumption while processing, as opposed to other languages like C/C++. Therefore, if you’re operating under tight memory restrictions, then Python is not the best option.
You already know that Python is one of the most widely used programming languages. It’s time to have a look at the companies that use Python to build their products.
Examples of products and companies that use Python
The biggest and most respected players in the tech space use Python to build their products. Here are some of them:
We’ve all heard of Dropbox and its spectacular success.
They took a simple idea – online storage – and turned it into a product loved by people around the globe.
Python powers the entire product experience, i.e both the backend and the frontend. It includes a full install of the Python (Version 2.7) language. Some of you might be wondering why Dropbox decided to go with this programming language. The answer is straightforward: cross-platform support, readability, and ease of learning.
Dropbox was so determined to operate as efficiently as possible that in December 2012 they hired Guido van Rosum to help them come up with ways to further optimize the code. Dropbox put van Rosum’s expertise to good use and in May 2016 they released Pyston, their own alternative Python implementation, compatible enough with CPython to be able to run the NumPy package (the fundamental library for scientific computing with Python).
Netflix is another brand that uses Python extensively, starting with operations management and analysis, ending with security and networking. Netflix states on their tech blog that:
"We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members."
While the engineers at Netflix are free to choose the language they want to work in, a lot of them prefer to code in Python. Why is that?
They say that the standard library, the active development community, the rich variety of third-party libraries, and its simplicity have motivated them to favor this language. Python is primarily used in the Central Alert Gateway – this RESTful web app processes alerts and routes them to the right people or groups.
Spotify applies Python to two main areas: backend services and data analysis.
Spotify’s backend is made of many interdependent services that are connected by a messaging protocol over ZeroMQ. Circa 80% of these services are developed in Python.
As you can imagine, Spotify relies heavily on analytics to provide suggestions and recommendations for their users. It uses Luigi, a Python module, to interpret all the data.
One of the reasons why the brand likes Python is its community, which Spotify actively supports. For example, they sponsor conferences such as PyCon and Europe Python, they support local groups like the Stockholm User Group and NYC PyLadies, organize hackathons, and contribute to open-source projects.
It’s safe to say the lifestyle app used by millions of users worldwide would not be what it is today if not for Python. The app’s creators chose the language (more specifically, its Django framework) well before Facebook acquired the company in 2012. Their motivation was to choose a technology that is easy to use, practical, and supports fast development.
Although many years have passed and Instagram has made adjustments to their software architecture as they scaled up, Python is still at the very core of the app’s backend.
You can read more about how Python is leveraged at the company in a series of Python-related articles written by the Instagram Engineering team.
In order to understand how Google feels about Python, it’s worth referring to one of their engineering mottos:
“[We use] Python where we can, C++ where we must”.
In fact, the decision to run with Python was made back in the late 1990s, when Google’s creators Sergey Brin and Larry Page, and their first employee, Craig Silvestreen, decided to bet on Python (the other option being the multi-paradigm Perl language).
As they say, “the rest is all history”, with Google becoming not just one of the top tech companies in the world, but also a place where some of the brightest Pythonists in the world develop their careers.
How prevalent is Python in Google solutions? The short answer is – very. It not only powers Google Voice Search, but also one of its popular products (a video streaming service you might have heard of here and there – YouTube).
Let’s not forget that Google is also the creator of one of Python’s biggest libraries – the previously-mentioned TensorFlow.
The biggest social network in the world (as of mid-2020, it had 2.6 billion active users), Facebook leverages a whole range of technologies and languages, Python being one of them.
According to RealPython, it’s the third most used language at the social media company, right after PHP and C++.
Facebook uses Python for a whole range of production engineering, configuration management, and service-related tasks. These include Facebook’s job engine, testing and repair management, and scheduling maintenance work.
When to use Python
We keep mentioning Python’s versatility, so it’s about time to discuss how to use Python.
Python in Finance and Fintech
Python is used widely in finance and fintech. In fact, according to HackerRank, Python is among the top three most popular programming languages in the financial industry.
It’s used to power companies like Zopa, Stripe, and Robin Hood. Python owes its popularity among fintech companies to clarity and robust modeling capabilities.
Since finance is a highly regulated industry, avoiding errors and bugs is an important factor – and this is something that Python is valued for, i.e. coding simplicity. Building a fintech app is a complex endeavor – the more transparent the code, the better – for both the app owner and the dev team.
Coding in Python allows you to build an MVP fast. Knowing that the finance sector must quickly respond to consumer demands, using Python lets companies operate in a more agile way.
Writing a few lines of code in Python is much quicker than programming in other languages – this means a shorter time to market.
As the number of fintech startups is on the rise, with 8775 startups established in 2020 alone, Python is gaining even more prominence.
It can be used in a wide range of applications, including:
Analytical tools – Python is a perfect solution for analytics, as it enables fast data processing and analysis. The whole process is simplified thanks to access to a variety of libraries like Panda, which helps with data visualization and statistical calculations.
Scikit and PyBrain provide Machine Learning algorithms, which are used in predictive analytics – valued by all financial service providers. Here are two examples of such products: Iwoca and Holvi.
Banking software – again, it comes down to simplicity; where Python requires 10 lines of code, Java needs 20. Just have a look at the example below – both languages deal with classes and inheritance.
Python is the preferred language used in both J.P. Morgan’s Athena program and Bank of America’s Quartz program. The guru of investment banking Kirat Singh said:
“Everyone at J.P. Morgan now needs to know Python and there are around 5000 developers using it at Bank of America.”
Cryptocurrency – businesses that sell cryptocurrency must regularly carry out cryptocurrency market analyses, for which they need the right tools. Python has access to a data science ecosystem, Anaconda, which enables developers to collect cryptocurrency pricing, analyze it, and create data visualizations. This is the main reason why web apps that deal with cryptocurrency analysis choose Python. Here are a couple of examples of banking software: Dash, enigma.
Python in Machine Learning, Data Science and Deep Learning
AI and Machine Learning are no longer reserved for Sci-Fi movies; they became part of our reality. What are they used for?
Some examples of Machine Learning solutions include virtual personal assistants, image recognition software, and product recommendations.
Meanwhile, Deep Learning and Data Science are used, among others, for medical software like Infervision, which supports radiologists in providing more effective cancer diagnoses.
These technologies help us with tasks that are impossible to complete manually, such as analyzing massive data sets – something that Python is perfect for.
It's stable, flexible, and gives developers access to a variety of tools that make their jobs easier. Machine Learning projects rely on complex algorithms, leaving very little room for error.
Python’s simplicity, related to its easily readable code, gives programmers confidence in the projects they work on. Its simple syntax allows it to quickly test algorithms without actually implementing them.
It supports collaborative coding which is invaluable, especially when a project contains a lot of custom business logic or third-party components, and needs to change hands frequently.
Additionally, it’s backed by Google – it’s their main programming language which plays in Python’s favor. They created a lot of resources and documentation that Python programmers can take advantage of.
Python for Blockchain
It’s safe to say that Blockchain is considered one of the most disruptive technologies that came into existence recently.
“Based on a peer-to-peer (P2P) topology, blockchain is a distributed ledger technology (DLT) that allows data to be stored globally on thousands of servers – while letting anyone on the network see everyone else's entries in near real-time. That makes it difficult for one user to gain control of, or game, the network” – Lucas Mearian, Senior Reporter at Computerworld.
Why is Python used widely in Blockchain? Again it comes down to three things:
It’s advanced but easy to learn – it’s been around for a while and has a big community, which actively participates in the language’s development, making it stable and reliable.
It’s easy to learn, so even inexperienced developers can pick it up quickly. It’s also simple – it takes less than 50 lines of code to create a simple blockchain.
It’s popular – meaning that a lot of programmers specialize in this language, so there is plenty of talent to choose from, and you can work with the best – which is important as blockchain involves complex technology.
It has free packages for Blockchain specifically – this helps software developers write code faster and more efficiently. There are around 195 packages available just for Blockchain!
Python for Research
While researchers can use spreadsheets for basic calculations, the more sophisticated the research becomes, the harder it is for them to manually handle data and draw conclusions. That’s where programming comes in, with Python being one of the most popular choices.
There are several reasons behind Python’s growing popularity in the research community. Most importantly:
Ease of use
As already mentioned in this article, Python has a straightforward syntax, which makes it easier to master than Java or C++.
Researchers can also work more effectively with Python than other languages – i.e. they can do much more work, with fewer lines of code.
This is incredibly important, as managing code isn’t an academic researcher’s primary focus; rather, it’s just a means of achieving research results.
Access to thousands of libraries
There are approximately 125,000 third-party Python libraries – this means that researchers have plenty of options to choose from!
Python works with a lot of research tools
Many researchers use tools like Jupyter Notebooks and Google Colaboratory to export their results and turn them into a digestible format – i.e. visualizations, equations, or narrative text. Python integrates with both of these programs.
Python is C++ friendly
While Python is the more popular choice, researchers value the fact that they can use a hybrid solution called Cython to implement algorithms in C++ and expose functions in Python.
As mentioned in a dedicated Python research piece, “essentially, it's Python at the speed of C. Cython helps developers gain the benefit of extremely efficient algorithms (low-level implementation in C/C++) without having to give up on Python's existing ecosystem and the beginner-friendly syntax.”
Python for Startups
There are several reasons why Python is a great choice for startups. These include the ability to quickly hire a development team, fast iteration, and the ability to creative innovative solutions. Here is a breakdown of the specific benefits for startups:
Ease of use and intuitiveness are widely recognized as the top benefits of Python.
The language is neatly-structured and easy to master (which means you can expect the learning curve of your junior developers to be much more gentle than if you chose a different language). In fact, Python’s three core principles speak for themselves:
Explicit is better than implicit
Simple is better than complex
Complex is better than complicated
Popularity among developers
As of mid-2020, Python was the third most popular tag on Stack Overflow, with 1.1 million watchers and 1.4 million questions asked. In a 2019 survey of 90,000 developers, Python was also voted the second most-loved language. This means two things for your business if you jump aboard the Python ship:
- You’ll be able to work on projects quickly and flexibly
- You’ll be choosing a language that’s popular among developers, which means you’ll have a big talent pool to choose programmers from.
As you can see from the other examples in this section, Python is a great choice for many industries and for various user scenarios. It has thousands of libraries, tens of powerful frameworks, and is constantly maintained and developed by the open-source community.
It also works well on all three major operating systems – Windows, Linux, and Mac. It’s a safe bet if you’re unsure of the direction your startup will develop in as business grows.
Affordability & short time-to-market
Python’s simple syntax, libraries, and frameworks make it one of the fastest languages to develop software in. It takes fewer lines of code to achieve tangible results than with other languages – which makes it an ideal choice for rapid prototyping.
This means you will also have the chance to bring your MVP to the market faster than if you ran with a more complex language.
You can start with a small team
As you can achieve more results with less code, your Python development team can be smaller than if you chose a different programming language. This means significant savings – which is important when you first launch your business.
Python for Enterprise Software Development
In the previous section, we described how several top brands, including Facebook and Netflix, are using Python to power their software. In fact, the programming language is a popular choice among countless enterprises. Here’s why:
A wealth of resources
As mentioned earlier, Python has countless libraries and frameworks businesses can use. This makes the language a good choice for bespoke software development.
Works with popular enterprise languages
One of the benefits of Python is that it can integrate with other languages used traditionally for enterprise programming. These languages include Java (Jython), .NET (IronPython), and PHP (PiP – short for ‘Python in PHP’).
Works with all operating systems
Python is portable, which means it works on Windows, Linux, and Mac. Hence, you don’t have to worry about whether it will suit your infrastructure if you ever decide to jump ship and change your operating system.
It’s free & open-sourced
Why does this matter for enterprise software development? Python doesn’t belong to any organization, and so it’s free of any business agenda or potentially conflicting interest.
Last, but not least, enterprises choose Python because… it’s proven effective for other big companies. If they decided to choose and stay with Python, it must be for a good reason!
Python vs other programming languages
Let’s now take a look at how Python compares to other popular programming languages.
Python vs Go
If you’ve ever looked into Python alternatives, it’s likely that you’ve heard of Go before. Go, short for Golang, is a language developed by Google in 2009.
In fact, some developers refer to it as Python’s younger sibling, with many similarities and common principles.
How do the two languages compare to one another?
Libraries and frameworks
Given Python’s age and popularity, it’s no surprise that it outpaces Go in terms of libraries and frameworks. While there are some libraries for Go, it would take years for it to offer the same level of variety Python does.
As far as frameworks are concerned, while Python stands strong with Django and Flask, Go lacks a single dominant framework. Unless frameworks are widely introduced to Go, it’s impossible for software development to be equally effective and fast as in Python.
Unlike Python, Go is a compiled language. A natural consequence of this is that Go is faster than Python. However, this doesn’t mean that Python is a slow language, with most developers saying that the speed is good enough and there are no performance issues.
Go uses concurrency (the ability for different parts of the program to be executed out of the standard order), which makes it go easier on the CPU and memory than Python.
Both languages are built with simplicity in mind – they promote concise code. However, with Go there are slightly more code characters required to run the same functionality.
One of Go’s benefits is that bugs can get caught earlier than in Python – during the code compilation process. All in all, both languages stand strong as far as work effectiveness and simplicity are concerned.
In terms of community size, Python outpaces Go significantly. In mid-2020, Go had only 52,500 watchers on Stack Overflow, with 46,000 questions asked (compared to Python’s 1.1 million watchers and 1.4 million questions).
While Go has been gaining momentum for several years now and has a growing community, it would take a lot of time for it to reach Python’s level.
What’s the verdict?
While both languages promote simplicity and concise, readable code, there are a couple of differences. If you value access to numerous libraries and frameworks and a thriving developer community, Python will be the right choice. However, if performance and speed are your highest priority, you might benefit from choosing Go for your upcoming project.
Python vs. PHP
No other language pair in this article is as different as Python and PHP. While Python is seen as the future of development, PHP is mostly associated with old-school programming and complex codebases.
Nevertheless, both are among the top programming languages in the world, so it’s worth taking a closer look at how they compare.
PHP offers more out-of-the-box features than Python. However, Python stands strong with its countless libraries and frameworks, which can cater to any custom requirements.
While PHP is embedded directly into a web server, Pythoneers will need to use a framework for web development. However, this shouldn’t discourage you, as a framework like Django or Flask will let you achieve amazing results.
As far as security is concerned, PHP has had its share of major breaches in the past, which can’t be said about Python.
As of mid-2020, PHP was the fifth most popular tag on Stack Overflow with 781,000 watchers; Python came third with 1.1 million. As you can see, both languages have a thriving support community. However, with Python’s soaring popularity, it’s likely that it will further outpace PHP in the coming years.
Both PHP and Python are considered to have a gentle learning curve – both are well-documented and support all three major operating systems, i.e. Linux, Windows, and Mac.
All things considered, we recommend going with Python as it's the newer, ‘cooler’ language with hundreds of online courses and resources.
Though it may come as a surprise given PHP’s old-school fame, Python is the older language of the two. However, given Python’s popularity, it’s safe to say it stood the test of time very well.
All in all, given that Python was found in 1989 and PHP in 1995, both are very mature, well-established languages. Multiple versions have been released since – with Python being on version 3.8.3, and PHP on version 7.4.6 as of May 2020.
Both PHP and Python are object-oriented. This means that if your developers have ever worked with an object-oriented language before, they’ll find their way around PHP and Python quickly. Now, which is better as far as usability is concerned? The answer is – it depends.
While many developers prefer Python for its simple syntax, PHP’s and Python’s usability come down to personal preference.
To summarize, PHP and Python are both mature, well-established languages with an active developer community. Both languages are object-oriented, i.e. based on the same paradigm.
However, if you’re currently hesitating which language to choose, we’d root for Python, given its growing popularity and wealth of resources.
Python vs. Java
If we were to compare Python with Java here is what this comparison would look like:
Python is an interpreted language, while Java is compiled. This means that Python converts human-readable code to machine-readable code automatically, which makes it easier to revise or debug as opposed to Java.
None of the two is particularly fast, with Python launching faster and running slower, and Java launching slower and running faster. Python, nevertheless, is perfect for MVP building as it requires fewer developers and less code. If we were to limit speed to product development only, then Python would be the winner.
Python is much easier to learn – it has a low entry point and is very user friendly. With Java, the learning curve is longer and steeper, however, it provides better code quality if developers put in the work to learn it properly.
Java will always demand more code and by design it requires everything to be defined from the beginning. This means coding in Java might be more time-consuming but it’s not necessarily a bad thing, as it tends to make software more stable, provided that you produce good quality code.
It’s not to say that Python doesn’t provide stability, but if stability and security were our main criteria, then Java is probably a safer option.
Resources and budget
Java requires a significant investment of both time and money, which is one of the reasons why it’s considered the language of corporations. It requires more developers and takes longer to develop a product, which means more working hours to cover for financially.
Python, on the other hand, is much more cost-friendly making it a perfect option for smaller projects (it doesn’t mean that Python won’t work for bigger projects as well, quite the opposite).
One of Python’s shortcomings is that neither Android nor iOS support it as an official programming language – meaning that Python is not native to the mobile environment.
It’s feasible to build mobile applications but it requires more time and effort from developers to create a good user experience. Java wins here, as it’s one of the preferred languages when it comes to Android app development.
Summing up, which language to choose will depend on your needs. While Java offers more stability, Python is easier to use and guarantees a shorter time to market. The choice is yours!
This achievement is even more impressive, considering the rocky transition from Python 2 to Python 3.
Meanwhile, Java is the main language used to build Android apps and in corporate software development.
Here’s what the developer community has to say on the reasons why Python has become one of the top programming languages:
“Syntax wise, it is really simple (...) For almost all other languages I learned I always had to think a little about the syntax, but for Python, I could already program a fair number of things in just a couple of days.” - Rohit Malshe, (Quora)
“I suspect that [Python] gradually become more popular as a result of, perhaps, three trends:
- Perl popularity and usage has been declining for a decade
- MS Windows (Visual BASIC et al.) has been in decline as Linux has solidly taken over the public facing Internet as well as gaining a strong foothold in mobile (Android)
- Java's use for introductory programming courses has waned (despite its continued growth in enterprise level development projects as well as its continued relevance for Android).” – Jim Dennis, (Quora)
Is Python a good choice for you?
Python’s popularity is soaring – and for a good reason. Although the language has been around since 1989, it’s here to stay – it has a huge developer community and is rapidly evolving.
Here are some of the reasons why it’s worth considering Python for your project:
- It’s user-friendly – i.e. has a simple syntax.
- It has thousands of libraries and plenty of powerful frameworks (which means less manual coding).
- It has a gentle learning curve (even for less experienced developers) and there are hundreds of educational resources online.
- It’s quick-to-market – Python is great for rapid prototyping and building out your MVP quickly.
- It’s affordable – developers can get more work done in less time.
- Most importantly, Python is a general-purpose language, meaning it’s very versatile and can meet various developmental needs and be used across different projects, i.e web development, blockchain, fintech, and many others.
Not to mention the fact that it’s used by global players like Facebook, Netflix, Spotify, and Instagram. If your top priority is speed of development, ease of use, and versatility – you’ve just found the fitting solution.