What do NASA, Spotify, Google, and JP Morgan Chase have in common?
These companies all use Python on a daily basis.
Python is a popular general-purpose programming language that has a wide variety of applications. All sorts of technological solutions have Python at their core, from web applications, search engines, and games to animation software and even other programming languages.
It's safe to say that Python is a true jack of all trades, and little wonder because this language is very popular among professionals.
Since Python is everywhere and facilitates all sorts of things, it's helpful to understand a bit more about it.
With that in mind, here we review everything Python including its history, why it's so popular, the careers where Python is a must-have skill, and more.
What is Python?
Python is an object-oriented, high-level programming language. Object-oriented means this language is based around objects (such as data) rather than functions, and high-level means it's easy for humans to understand.
You can find examples of Python at work in many of the technologies we use on a daily basis, take YouTube as one example and the processes behind search engines as another.
Python is used to create web and mobile software, in artificial intelligence (AI) and machine learning (ML), to perform data manipulation and analysis, and much much more.
Why is Python so popular?
Python is consistently rated as one of the world's most popular programming languages. On the TIOBE Computer Programming index, an indicator of the popularity of languages, Python claimed first place in February 2022.
And in Stack Overflow's 2021 Developer Survey, Python claimed fourth place for the most commonly used programming language, with 41.53% of professional programmers (mostly web) using Python.
Python is popular for a number of reasons, let's examine those now.
Python is versatile
Because it's a general-purpose language, you can use Python to do and create many different things. For example, a data scientist might use Python to generate visualizations or manipulate data, and a web developer might use Python to create a website. We cover its use cases in more detail here.
Python is simple and easy to learn
If someone is new to coding, there are few better languages to start with than Python. Its syntax is simple, the commands are English-based, and its layout is relatively straightforward, making it easy to understand each line of code and its purpose, whether the user is experienced or not.
Its simplicity also means Python is the ideal language when rapid development is required.
Python is open-source
Because it's open-source, Python is free for anyone to use. The added benefit of this is that anyone can create additional tools, libraries, and frameworks for Python that other users can take advantage of. For example, there are premade Python libraries for everything from chatbots to cryptography.
The thriving community of Python users helps further this language's functionalities and applications.
Python is used a lot
There's also the matter of ubiquity to consider. When a language such as Python is part of so many different technological solutions and employed by a large number of businesses, it becomes more important for developers and programmers to know the language if they want to be employable or to understand how certain solutions work.
The result of this is that more people know Python and are more likely to use it for their own projects or to suggest it to others.
When was Python created?
Python was first conceived in the late 1980s. It was initially designed to be a successor to the ABC programming language, another general-purpose programming language that was developed at the Centrum Wiskunde & Informatica (CWI), a research institute for mathematics and computer science, in the Netherlands.
ABC itself was designed to replace BASIC, an early programming language that dates back to 1964—practically the dark ages in tech terms!
Although ABC is also a high-level programming language, it didn't really make much of an impact and its use was often limited to an instructional setting. In many ways, ABC's greatest claim to fame is that it inspired Python's creation.
Python's history is closely linked to ABC because the developer who invented Python (more on him below) spent a number of years working on ABC.
The first version of Python (0.9.0) was released in 1991 on alt.sources, an online group where users post language source code. This release was object-oriented with a module system, it included functions, exception handling, and a core data such as list, dict, str, and more.
Who invented Python?
Guido van Rossum invented Python when he was working at CWI. Van Rossum was actively involved with the ABC language, but he had some complaints:
“I had a number of gripes about the ABC language, but also liked many of its features. It was impossible to extend the ABC language (or its implementation) to remedy my complaints – in fact its lack of extensibility was one of its biggest problems.” - Guido van Rossum
Although he was employed at CWI at the time, van Rossum developed Python as an extracurricular activity. In the foreword of Programming Python by Mark Lutz, van Rossum notes that he was bored over the holiday period and decided to start work on the language as a hobby project.
Python's inventor was heavily involved in the language for much of his working life, eventually stepping down as ‘Benevolent Dictator for Life' in 2018. Van Rossum is also credited with the creation of Google's Mondrian, a code-review tool that's in use today.
Although Van Rossum's name isn't as recognized as other leading contributors to technology, such as Steve Jobs or Bill Gates, his work had a major impact on the software development field, and by extension, the world's technologies as a whole.
Fun fact: Van Rossum named his language Python because he was a fan of the British sketch comedy group, Monty Python and was in an “irreverent mood.”
How Python has evolved over the years
Python has gone through many changes over the course of its lifetime, not surprising given that the language started as van Rossum's hobby project and became one of the world's most prominent programming languages.
Here are a few of the ways that we've seen Python evolve to match the needs of developers and advancing technologies:
Python libraries, frameworks and packages
Since its initial release, the Python community has grown exponentially, as has the number of libraries and frameworks available for an increasingly broad range of tasks and uses.
As one example, in the years since Python's first release in 1991, AI and ML technologies have become important tools. The Python community has responded to this need by creating libraries and packages with an AI and ML focus.
A timeline of key Python versions
- 1991 – Python version 0.9.0 is released to the public for the first time.
- 1994 – Python 1.0 hits the digital streets with some important updates including new features for functional programming (lambda, reduce, filter, and map). It offered developers an alternative to Perl, then a popular scripting language.
- 2000 – Python 2.0 was released in October after the core development team founded the BeOpen PythonLabs. This version was the first to introduce Unicode string data type, list comprehensions, and a garbage collector (a form of automated memory management) for reference cycles.
- 2008 – Python 3.0 (also known as Python 3000 and Py3K) appeared in December. This version had a major goal: to address and remedy any fundamental flaws present in 2.0 versions while staying true to Python's guiding principles, also known as the Zen of Python. Duplicative constructs and modules were removed to keep Python as straightforward and uncomplicated as possible.
One major way that Python 3.0 broke the mold was its lack of backward compatibility with previous versions—code for Python 3 versions is not compatible with code for Python 2 versions. The end of life for Python 2.0 versions was set for January 2020.
In between these releases, there were other updates, and each offered changes and/or new features.
In many ways, a programming language is like a living, human language: it evolves with use. Older ways of saying things fall by the wayside and modern terminology takes over, Python has been on a similar journey.
Python and the rise of data science
In today's world, the importance of data cannot be underestimated, as a result, several new fields of study and occupations have emerged, including data science.
Data analysts and scientists use mathematics, statistics, and programming skills to extract meaning from data. Their findings are then used in numerous ways, such as helping businesses succeed or helping governmental organizations understand issues.
Python, along with SQL and R, is one of the best programming languages for data science. Being so complementary to this field has meant Python has gained even more prominence and proponents.
Who uses Python?
Companies and professionals from varied industries use Python to create websites, software components, and applications or to work with data, AI, and ML technologies.
The professionals using Python include data scientists, web developers and designers, software engineers, and many others.
Careers with Python
Because of its flexibility and versatility, Python is used by a great number of professionals, not just programmers or developers. Here are just a few of the careers where Python is a key skill:
- Back-end developer (server-side)
- Front-end developer (client-side)
- Full-stack developer (both client and server-side)
- Web designer
- Python developer
- Machine learning engineer
- Data scientist
- Data analyst
- Data engineer
- DevOps engineer (development operations)
- Software engineer
- Game developer
- SEO specialist
- And more…
Companies using Python
It's impossible to list all of the companies using Python because there are just so many, but here are a few of the big, recognizable names:
- Google (Python is one of the ‘official' languages at Google)
- JP Morgan Chase
- And many more…
What can Python do?
Perhaps the better question is what can't Python do?
Although Python is most often thought of as a coding language for sites and apps, or for data, AI, and ML tasks and projects, it has plenty of other applications too.
Let's take a closer look at a few of the (sometimes surprising) ways Python is used:
Data analysis and visualizations
Python is well suited to data science tasks in general, and this includes data analytics and visualizations. With Python, analysts can sort, manipulate, and glean high-level insights from data. They can also use the language to create powerful visuals that highlight their findings.
There is a growing number of Python libraries and frameworks for data analytics and visualization, including Pandas Visualization, Plotly, and Matplotlib to name just a few. Whether it's a simple diagram or a complex statistical report, Python has tools that can help.
Another reason why Python is a preferred language for data science is that anyone can use it. Analysts and business intelligence professionals aren't always programmers or developers, but Python is user-friendly enough that people without a computer science background can adapt to it easily.
DataCamp's specialty is teaching individuals and employees at major companies such as Google how to use Python and other data science languages.
We offer a wide selection of Python courses, skill tracks, and career tracks that can get you started on the road to a career in data. Click here for our course offerings.
Because Python is a general-purpose programming language, it can be used to create all sorts of web and mobile applications, from advanced financial service products to components in an F1 racing game.
Python is also frequently used to program file directories, create graphical user interfaces (GUIs) and application programming interfaces (APIs), and much more.
If you can think of it, there's a good chance you can build it (or at least many key components) with Python.
Interested in learning how to code in Python and create cool stuff? Check out DataCamp's Python Programmer career track.
AI and machine learning
Python is stable, flexible, and uncomplicated. As such, it's the ideal programming language for AI and ML applications, which involve sophisticated and often very complex algorithms. Python allows AI and ML experts to write code that's reliable, readable, and enables fast prototyping.
Another benefit to its simplicity is that debugging is quick, so instead of hunting down syntax errors, AI and ML developers can spend more time on their algorithms and heuristics.
There are a vast number of Python libraries and frameworks for both AI and ML projects, such as scikit-learn, TensorFlow, and Pylearn2 to name just a few. This makes it easier for developers to get started.
If you're interested in working on the cutting edge of technology, DataCamp's Machine Learning Scientist with Python career track can help get you there.
Financial analysis and fintechs
In recent years, Python has emerged as one of the financial world's favored programming languages. It's particularly well suited to quantitative and qualitative analysis, and it's great at handling large data sets.
With Python's help, people working in finance can automate many tasks that used to take a lot of time: calculating risk, managing a stock portfolio, tracking the market, and visualizing stock trends.
Python is also popular as a programming language to create fintech (financial technology) products. Popular fintechs including Venmo, Robinhood, and Affirm all have Python in their core tech stacks (the technologies that built the end product).
Because fintechs are often ambitious projects, Python's scalable and simple yet mature code is ideal. Plus there are all the ready-made libraries and components.
Want to become the next Python of Wall Street? Check out DataCamp's Intermediate Python for Finance course.
Marketing and search engine optimization (SEO)
One interesting, newer way that Python is being used is in the digital marketing and SEO fields.
Not only are these areas where automation is helpful, but Python can help marketers and SEO experts with other tasks such as categorizing keywords, extracting and analyzing data, or implementing changes across multiple web pages.
Natural language processing (NLP) technologies are also emerging, and these are already impacting people working in SEO. As expected, Python has libraries and frameworks for NLP, including the popular SpaCy.
Django, a widely used Python web framework, is favored by many SEO professionals because it makes the process of technical SEO optimization simple.
Python isn't the most common or popular programming language for game development, and not many games are written entirely in Python. But it's often used by developers for other tasks, such as linking C and C++ modules.
That's not to say you can't build a full game with Python—check out Unknown Horizons if you'd like to see a game that only uses Python. Just that most games use multiple languages, for example, famous games like the Sims 4 and Battlefield 2 all use Python code for critical elements such as game logic.
PyGame, a cross-platform set of Python modules designed for the creation of video games, helps developers with Python-related tasks.
Python is a helpful language when it comes to developing graphic design applications. It's used in 2D imaging software, including the well-known programs Gimp and Paint Shop Pro. There's also DrawBot, a popular open-source application that helps users create 2D graphics using Python code.
Graphic designers who work with websites or digital images may make use of Python on a regular basis.
As further proof of Python's versatility, 3D animation software such as Blender and Lightwave use Python, too.
As a starting point for other languages
Python's simplicity and clear syntax are much loved. So much, in fact, that Python has inspired the creation of other programming languages. Go (Golang) and Cobra both use a very Python-like syntax.
#finding average # creating an array consisting of number from 1 to 10 a = np.arange(1,11) print("The generated array looks like:") print(a) print("The average of the numbers in the array:") print(np.average(a)) #above code in just one line print("The results of average for the short version:") print(np.average(np.arange(1,11)))
#multiplying matrix #creating matrices using numpy b = np.array([[2,3],[4,5]]) c = np.array([[6,7],[8,9]]) d = np.array([1,10]) print("The matrices look like:") print("b = ", b) print("c = ", c) print("d = ", d) #multiplication of 2-D arrays bc = np.matmul(b,c) print("Result of b*c=",bc) #multiplication of 2-D and 1-D arrays cd = np.matmul(c,d) print("Result of c*d=",cd)
Python basics and advanced Python
Anyone can use Python. Its simplicity means even those with no programming or coding experience can learn the basics and use this language from the get-go.
For example, let's say an office worker wants a faster way to complete routine tasks such as renaming a large number of files. They could use Python to write a basic script that completes this task for them. Or a marketer might use Python to help send emails to potential customers at set intervals.
As with many things, there's a big difference between what you can do with basic Python versus what you can do with advanced or even intermediate-level Python skills.
It's kind of like playing the piano. Many of us learned chopsticks as children, but a few of us went on to become concert pianists who can comfortably play Rachmaninov.
The only way they got to that level of achievement, though, was with practice and by building their skills gradually over time.
There's a good chance they started with chopsticks, too.
It's much the same with Python. You start with the basics and gradually build upon your skills until you reach virtuoso level. Experts often say that you never really stop learning a programming language because there's always something new to learn. You might be able to code blindfolded, but new applications and ways to use the language are always emerging.
Python experts use their skills for all sorts of complex tasks. You might find them building AI systems that generate their own algorithms from huge data sets. Or they might be busy working on new APIs, developing programs that answer real-world issues, or teaching courses for DataCamp.
Whether you understand the fundamentals of Python, are brand new to the language, or want to reach a virtuoso level, DataCamp can help you reach your goals.
We offer a range of career-specific and general Python courses for complete newbies and all the way through to advanced users.
Summing up Python
Python is powerful, flexible, and incredibly versatile. It's also user-friendly, intuitive, enables rapid development times, and it's easy to learn—no wonder it's one of the world's most popular programming languages!
All sorts of industries, businesses, and sectors rely on Python to power their technologies, and Python's use and popularity are expected to rise in 2022 and beyond.
Learning how to code in Python is a great idea for anyone, but it's essential for people who want to become well-rounded programmers, data scientists, AI and ML engineers, and many other professions.
The variety of careers where Python programming skills are needed is diverse, but these careers do have one thing in common: the salaries are impressive. Partly because there's a lack of Python talent to fill all the positions on offer.
Learning Python could be your ticket to a brand new career or a way to upskill in your current career.
Ready to start wrangling some code? Check out DataCamp's comprehensive range of individual Python courses and career tracks.
Start Learning Python Today
You can literally start coding Python online with DataCamp today.
Who is the owner of Python?
No one really owns Python per se because it's an open-source programming language. The Python Software Foundation (PSF) holds the intellectual property rights for the language.
A non-profit organization, the PSF was founded in March 2001 and lists its aims as promoting and advancing Python.
Is Python better than R for data science?
Not better, but with simpler syntax and more varied application. As data science tools, Python and R are both incredibly powerful and useful. R tends to be used more in academic platforms, with Python being used more commercially.
This is hardly surprising, as Python has a more readable syntax and is also able to used for software and web development, making it more popular due to its wider application.
What programming language should be tackled first in Data Science
Python and SQL are ideal programming languages for beginners. It should be noted that programming languages are not specifically designed for beginners, but some (Python, SQL) are much easier to learn than others.
An added bonus to learning Python or SQL is that both are popular languages for data science, a well-paid field where professionals are in demand.
Who invented the Python language?
Guido van Rossum invented Python in the late 1980s. The first publicly available version of Python was 0.9.0, which was released in 1991.
Van Rossum’s work on Python was an important contribution to software development and technology in general. Today, Python is one of the most commonly used programming languages. Its ease of use, versatility, and flexibility make it ideal for a broad variety of tasks.
Should I learn HTML before learning Python?
It depends on your goals. For example, if you want to be a data scientist, there’s no need to know HTML before learning Python.
If you primarily want to become a web designer or developer, you’ll need to learn HTML.
Hypertext markup language (aka HTML) is everywhere on the web, so it’s never a bad idea to learn this language, but it’s not a general-purpose programming language like Python is.
How can I start learning Python?
Start learning Python with DataCamp’s online Introduction to Python course. This free course covers Python basics and fundamentals.
After you've completed the introductory course, you can continue learning Python online at your own pace with DataCamp.
You don't need to download any software, all you need is an internet connection and a browser. DataCamp has a dedicated coding platform where students can practice their new skills.
Can businesses use Python for free?
Yes, Python is free for all users, whether they’re individuals or businesses. Major companies such as Google, Uber, PayPal, and many others use Python for all sorts of things.
Python has an OSI-approved open-source license, meaning it can be used for individual or commercial purposes.
Is Python based on ABC?
Python was heavily influenced by the ABC programming language. Guido van Rossum invented Python after working with ABC for a number of years. He found some issues with ABC and things he didn’t like, so he decided to come up with an alternative.
Today, Python is among the world’s most popular and commonly used programming languages. In comparison, ABC is rarely used.
Can I learn to code on my own?
Sure, but it’s going to be a long and potentially rocky journey. The better way to learn to code is with a recognized training provider.
DataCamp offers introductory, intermediate, and advanced courses in several coding languages. Our proven teaching method makes learning to code engaging and fun. The best bit is that many of our courses are completely free, so you can try a coding language and see if you like it.
How does Python make money?
Python doesn’t make money. The Python Software Foundation (PSF) is a non-profit organization and doesn’t financially gain from Python.
To cover its running costs, the PSF has corporate sponsors such as Microsoft. It also runs the North American PyCon conference, accepts donations, and offers a paid associate membership option.
How to Learn Python From Scratch in 2023: An Expert Guide
10 Essential Python Skills All Data Scientists Should Master
Benchmarking High-Performance pandas Alternatives
Distributed Processing using Ray framework in Python
Geocoding for Data Scientists: An Introduction With Examples
A Complete Guide to Socket Programming in Python