Software building is like building a house. In both cases, you will need a strong foundation.
A weak foundation can lead to problems when expanding, costly repairs or even the need for a complete rebuild.
With a solid foundation, you can scale up and maintain it easily. Your project will last.
The foundation is your programming language if your house is your program.
We'll show you why Python is the most popular programming language, and give you plenty of reasons to use it in your next software project. Although Python is a strong programming language, it's not the only one. There are many Python alternatives such as:
We will also compare Python against these programming languages. Let us help choose the best one for you. Let's start with Python, then we will move on to other options.
Since 2012, Python's popularity has risen steadily and is expected to continue growing.
Are Python's popularity good for Python?
Yes. Programming languages are extremely popular, even though popularity isn’t always the best.
Because Python is so popular, you're likely to find a solution to any problem. Python enthusiasts are dedicated and tirelessly work to improve the language.
Many corporate sponsors support Python, which helps to promote it even further. Google is just one example of a number of corporate sponsors who support Python, along with tech giants such as Google.
Python allows rapid development
Python was created to be easy-to-use. This makes Python programming easy and allows you develop Python software quickly.
What does this mean to your development team? This means your development team will spend less time trying to figure out the language and more on building your product.
Python's many libraries, and frameworks
The wide variety of Python libraries and frameworks are a great advantage. If you use them, your time to market will be shorter. Because you don't have to manually code features, this is why it takes less time to market.
A Python library can do all of these things.
Natural language processing
Complex data analysis
Python has everything, including TensorFlow and NumPy.
They are all the same. They can help you get your project started well and save you money.
Many frameworks are available to meet your needs.
Performance of Python
One of Python's greatest flaws is its runtime. It is slower than other languages. This problem can be solved.
Python makes it possible to incorporate higher-performing languages in code, when performance is important. Cython is one example of such a solution. It optimizes your speed without requiring you to rewrite your entire code.
Your developers' time is more valuable than the CPU time. Speedy execution should always be prioritized above reducing your time to market.
Python is simple to maintain
Python is very similar in structure to English so it is easy for people to understand. It is easy to understand and maintain because of this.
Python's syntax can be understood easily and requires fewer lines than Java or C in order to produce similar results.
What are the benefits of Python's high level readability?
Python's simplicity makes it simple to understand code, regardless of whether you are reading it or someone else's. Python code is easier to understand because it contains fewer lines than English, and takes less time for review. This is a great benefit.
It is important that you reduce time spent reviewing code. You should make your developers' productivity your top priority.
Python Scalability Reliable
Scalability is unpredictable. Scalability is unpredictable. Scalability is unpredictable. You never know when your user numbers are going to explode.
Python is a great choice because of its reliability and scalability. Python is the preferred language of YouTube and other big players because of its reliability and scalability.
Takeaways: Do you need Python?
Why Python? Because:
It is easy to use, well-known, readable, intuitive, and clear.
It provides many useful libraries and frameworks.
It is supported by a growing number of people.
Golang vs. Python
It is easy to develop in Python or Golang
Python is so flexible, it's easy to grasp even without any prior knowledge. This is also true for Golang. You only need one tutorial to follow the code for either one.
Go is easy to use. Within 24 hours of being introduced to Go, you can begin making changes to existing software.
Similarities between Python & Golang
Their high-level types are the most common feature of Python and Golang.
Go's maps, slices and dicts look like Python's lists and dicts but are statically typed.
The enumerate function can also be used in Python to create a range in Golang.
All the similarities are over.
Differences between Python and Go
There are many differences in Python and Go. These differences may surprise Python developers.
Golang doesn't allow functions that return error types with their results. Check whether an error was returned before you use a function.
The most important difference between these languages is their typing. Python can be typed dynamically while Go cannot be typed statically. Python is an interpretable language, while Golang is a compile-language.
Go's other offerings will delight Python developers, including:
channels (sending messages between goroutines),
Defer (replacing try-finally)
structs (compound type).
Both Python and Go have high levels of readability.
Python developers will be able to understand Golang because the design of Python is very similar to Go's.
We can see the similarities between Python's Zen and Golang's guiding principles.
Python values readability, while Go's simple syntax allows for high readability.
Python believes that simplicity is better than complexity, and Golang's Orthogonality makes this possible.
Go's static typing follows the rule that Python uses "explicit, not implicit" when typing.
Which language is more superior, Python or Golang?
Python is a great choice for data science and web development. Golang's performance is faster because it is statically typed and compiled than Python, which is an interpreted language.
Which should you choose? We don't believe so.
It is best to use Python and Go together.
Serverless or microservices are the best options. If code performance is your primary concern, you might consider writing code in Golang and then using Python for all other things.
Takeaways: Should Golang be used or Python?
Because of the similarities in design between Python and Golang, it is easy to switch from one to another.
We will hopefully see more projects that combine the two languages in near future.
Here's the complete story of a Pythonista who ventured into Golang.
Software developers may want to use multiple programming languages in order to produce a stunning masterpiece.
Many of the software programs you use every day are built on multiple programming languages.
Python is simple, fast, and clean to write
Python is an excellent general-purpose language. Its simple syntax has made it popular not only with software engineers, but also data scientists and academic researchers.
Its simplicity makes it ideal for solving complex problems. This makes it the most popular choice in machine learning and data processing.
Python is used in web development to build the backend of the website and to execute server-side scripting.
C modules can be used to speed up code execution with optimized C code (using tools such as Cython or NumPy).
The expectations for Instagram are high. The software must be able process large amounts of data from miserable geography to traveling chickens. Simplicity is key here. These are the reasons Instagram uses Python to run its servers. Software engineers can understand and debug code quickly.
Python vs. Java
Comparing interpreted and dynamically-typed to compiled and statically-typed
Python is an interpretable and dynamically typed language. Java, on the other hand, is a compiled language that is statically typed.
Python code does not need to be compiled in order to run. Java code must be compiled using code that is readable by humans in order to make it readable by the machine.
This basically means that Python launches faster and runs faster than Java, while Java launches faster and runs faster.
Entry point in Java or Python
Python's entry level is notoriously low. This is why Python is great for junior developers and newbies. It is very user-friendly.
Java, on the other hand, has a high learning curve and an easy entry point. It takes a lot of time to learn Java, and even more to master it.
It takes several weeks to get started with Python, while it takes months to get started with Java.
Stability of Java and Python
Java is often viewed as the best enterprise software development platform. Because Java has a large code base, corporations consider it a robust and secure language. It makes Java more secure and stable than Python, according to them.
The notion is not entirely true. Python can also handle software products for large businesses, including fintech.
It would be unfair to call Python unstable. Why is Java so prejudiced?
It is not so much code volume as enterprise-friendly library support. These libraries are what make Java a bit more stable than Python in corporate applications.
Speed of Java and Python
Because Java is complex and large in code, it can take several months to build an MVP. Java projects can often last for years, requiring more developers.
Because of its lightning-fast development speed, Python does not have these issues. With Python, you can create a MVP in just weeks and finish the entire project in a matter months. You also need only a few developers to do the job.
Python is known for its ability to beat deadlines. Look no further if time is your primary concern, especially if you are a startup.
Resources for Java and Python
Java development is more expensive and requires more money. Java is best if you have many of these.
Python is cheaper than other options, and is therefore the choice for most projects. Don't assume that Python is more expensive than other options.
Trending technologies in Java and Python
Python is the best programming language for current technologies.
Python's design features and capabilities give it an edge over other languages when it comes to artificial intelligence and machine learning.
Python's extensive AI/ML library support is the main reason Python has been chosen as the language of choice for trending technologies.
There are also signs that this trend will continue into the future.
Takeaways: Java or Python?
Python is easy to understand, simple to write, and easy to modify. Python is the best choice if you are concerned about speed of development.
Java is able to handle extremely complex tasks. Java on the other hand is perfect for it. Java is a good choice if stability and reliability are important to you.
Ruby vs. Python
Simplicity vs. creativity
Ruby and Python both allow web developers to achieve similar results when creating web apps. Ruby is best known for web development, but Python can do much more.
Apart from their respective use cases, the two languages have different philosophies and approaches to solving problem. Ruby's use has declined over the last decade, while Python's popularity is on the rise.
Similarities between Ruby and Python
Both languages are:
Server-side scripting, high-level and server-side
Dynamically typed, cross-platform and general-purpose
It is well-established and proven, used by tech giants in many different fields, such as Airbnb, Twitter, and Spotify, which use Ruby while Spotify and Spotify use Python.
Differences between Ruby & Python
In other words, Python is more popular than Ruby among developers.
GitHub's Octoverse found that Ruby's popularity is declining year over year. It ranked 5th in 2014, and is now 10th four years later.
Python on the other side has seen an exponential growth. Stack Overflow calls it the "fastest growing major programming language."
Ruby's flexibility allows developers to create highly original solutions because of its flexible syntax. Some have called Ruby "magical," while Python is focused on simple and clear solutions.
Ruby and Python have the most different approaches to solving problems. The former offers simple and singular solutions while the latter can offer multiple ways to accomplish the same goal.
This may seem like a benefit of Ruby but it can actually compromise readability and simplify Ruby's code, as well as make it more difficult to debug errors.
Takeaways: Which Ruby or Python should you choose?
If your project doesn't require Ruby, you should choose Python.
You can do anything with Ruby with Python. The rule does not apply in the reverse. Python is clearly superior to Ruby in many areas, such as science, machine learning and data analysis.
Despite Ruby's declining popularity, it still has a lot to offer web developers. Although some have suggested that Ruby is becoming obsolete, this does not seem to be true at the moment.
Given the multitude of Python's uses, it is easy to choose between Ruby and Python. Python's rapid growth, its application in many industries, and ease-of-use clearly make it the better choice.
Our article provides a detailed comparison between Ruby and Python.
Python vs. PHP
What does PHP do?
PHP is an open-source language widely used and has become the default web server technology. PHP is often called the "scripting language of the web" and powers around 80% of all website servers.
The language is commonly used in "traditional" web projects, which don't require a lot of calculations or the latest features. However, it can be applied to all aspects of real-time applications, including user authentication, database support, and real-time applications.
Although PHP is easy to learn for beginners, it has many advanced features that can lead to excellent results.
What does Python do?
Although Python is primarily used for web development at the moment, it was not intended to become its main focus.
There are many Python uses cases. This list is growing. It is used in machine learning, data analysis, statistics, science and academia as well as the Internet of Things.
Python is much easier to learn than other programming languages. It's also easy to read and write.
Advantages of Python over PHP
Python is capable of doing everything, from data analytics to machine learning models to web apps. It is second only to PHP in terms of flexibility.
Because Python has been released in fewer versions than PHP, it is more structured, secure, and easy to maintain.
Python's popularity has exploded in recent years due to its use in areas like artificial intelligence, machine-learning, and the Internet of Things.
Although PHP is more popular than Python in the past, it has been slowly losing its popularity.
PHP has many advantages over Python
PHP has more features out of the box than Python. However, the latter makes use of many libraries to compensate for this slight inconvenience.
Installation is easy
It is much easier to install PHP on any platform than Python, except for Linux.
Takeaways: Should PHP be used or Python?
Although both Python and PHP have been around for a while and are well-supported languages, their syntaxes and philosophy is quite different.
When faced with the choice of one or the other, it is important to consider the specific requirements of each project.
You can have a website, blog or simple web service. The end result is the same regardless of whether you choose PHP or Python. There shouldn't be any noticeable difference in performance, speed or user design.
If your project has a greater scope, such as machine learning, data analysis, or the Internet of Things you should choose Python.
Overall, Python is a great choice. You won't have to worry about expanding the scope of your projects in the future, as Python is great for web development.
Python's popularity has skyrocketed in recent years and it is certain to stay around for a while, unlike PHP.
Data science has become an integral part of many people's lives. Data science is a hot topic in tech due to the increased availability of data and the importance of analytics-driven business decisions.
Python and R are the two most widely used tech stacks when it comes to data science tools.
Both languages are flexible and open-source and both have large support networks. It can be difficult to choose between the two languages for data analytics.
Python, however, is more open to data science than R, which is used primarily for statistical analysis. Anyone who is interested in using these languages for data science projects should be aware of the key differences and advantages.
Let's look at the main advantages and features of R and Python.
R: 12,000 packages and exceptional reports are some of the advantages
R was created by statisticians and academics. This is why R has one of the most powerful ecosystems for data analysis.
You can use the 12,000 packages in CRAN, an Open-Source repository, to find R libraries for any type of analysis. R is the best choice for statistical analysis, especially for specialized work.
R's output is another factor that makes it a cutting-edge language. R has impressive tools that make it easy to communicate the results. Rstudio also includes the library knitr, written by Xie Yuhui. This makes it easy to communicate your findings via a presentation or document.
Python's advantages: Great fit for machine learning, artificial intelligence and other purposes
Python can perform almost all tasks such as R-engineering and data wrangling. It can also feature selection, web scraping and app development. Python code is simpler to maintain and more robust than R. This is why it is used often to deploy and implement machine-learning at large scales.
Python had very few machine learning and data analysis libraries a few years ago. But, the language is improving rapidly. It currently has some of the most cutting-edge APIs available for machine learning and artificial Intelligence. Five Python libraries are available for data science: SciPy (NumPy), SciPy (Seaborn), Pandas and scikit-learn.
Python excels in making accessibility and replicability easier than R.
Takeaways: Should R be used or Python?
R was created by scientists and academics to solve statistical problems. It also works well in machine learning and data analysis. Because of its strong communication libraries, R is an ideal language for data science. R includes many packages that can also be used for data mining, time series analysis, and panel data.
If you are a beginner in data science, and want to understand how the algorithm works and how it is deployed, then you should learn Python.
Python has powerful libraries for math, statistics and artificial intelligence. This makes it an important player in machine-learning. It also has amazing libraries that can be used to manipulate matrices and coding algorithms.
If you are looking to create a model from scratch using Python, it is very easy to use. You can then switch to the functions in the machine learning library. You can use R and Python when you're doing data analysis.
When you are concentrating on statistical methods, R is a great choice. Still, Python is the better choice if you want to do more than statistics--deployment and reproducibility, for instance.
R and Python are practically equal in data science applications. If you want, you can use both R and Python together. Python can also be used for web development, network programming and software prototyping. This makes Python an even more versatile option.
Want to learn more about the differences between R and Python? You can find out more about the differences between R and Python by clicking here.
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We appreciate you taking the time to read our comparisons between Python and other programming languages. We hope that our 15+ years of Python software development experience will help answer any questions you might have.
If you decide that Python is the best choice for your project's tech stack then maybe we could be interested in outsourcing your Python software engineering?
This page will be updated several times in the future as there are many other technologies that can be compared to Python. Keep checking back.