What Is Python Used For & Why Is It Important to Learn?
Python is a general-purpose programming language suitable for software developers, programmers, and data scientists. Its versatility and ease of use have propelled Python to the top of the long list of programming languages, alongside C and Java.
The scope of Python’s functionality, from back-end web and mobile app development to scientific computation and machine learning, is wider than that of many other programming languages, but its philosophy is rooted in the uncluttered design principles that software engineer Tim Peters wrote in 1999.
Peters’ guiding principles, dubbed “The Zen of Python,” include these first seven axioms:
- Beautiful is better than ugly.
- Explicit is better than implicit.
- Simple is better than complex.
- Complex is better than complicated.
- Flat is better than nested.
- Sparse is better than dense.
- Readability counts.
The key takeaway from these first principles is that Python is accessible to people with little to no experience in programming languages. The straightforward design isn’t arbitrary. Python’s creators made an intentional decision, which, combined with Python’s wealth of diverse applications, has given rise to its popularity in the data sciences.
What Is Python?
An open-source, interpreted coding language with many uses, Python places a strong emphasis on code readability. To that end, Python’s syntax uses English keywords in place of the symbols and punctuation used in lower-level languages.
By replacing the computer’s native machine language with a more natural human language, Python has made programming more convenient and has significantly reduced the learning curve for novice programmers and data scientists.
Python uses integers, floats, strings, and Boolean data types and contains four built-in data structures — lists, dictionaries, tuples, and sets. In addition to its expansive external libraries and frameworks, Python’s built-in functions can perform a host of low-level tasks, which frees up users to tackle more complex programming solutions and predictive data models.
For example, Python’s print() function is a handy little chunk of code that lets you display formatted text. Formerly a statement in Python 2, this new addition to Python 3 doesn’t need to be imported, and it has other convenient applications. You can find an in-depth tutorial on the Python website.
You may have picked up on the references to Python as a high-level coding language. Programming languages range from low-level to high-level, with the level signifying whether the language is closer to that of a computer or of a human. The higher the level, the closer the language is to human speech.
Levels of Programming Languages from Lowest to Highest
- Machine language
- Assembly languages
- High-level languages
- Very high-level languages
- Natural languages
Python is a high-level language, as are Java, C#, Perl, and Ruby.
For beginning Python users in the data science field, the intuitive programming language is both easy to learn and powerful enough to execute advanced data analytics tasks.
What Is Python Used For?
We’ve touched on some of the common uses of Python for programming and writing scripts, but that’s just scratching the surface of what the language can do. Its ability to process big data and perform mathematical and scientific computations for data analysis and sophisticated problem-solving sets it apart from other general-purpose coding languages.
Unlike R, which is a “language and environment for statistical computing and graphics,” Python is used across industries and among researchers and data teams with multifaceted approaches to analyzing, manipulating, and visualizing data.
Python for Data Scientists
Although R, Stata, and SAS are better for traditional statistics modeling, Python offers more in terms of flexibility and convenience.
The comprehensive frameworks available in Python allow for a deeper dive into the data, enabling the user to identify complex patterns and explore the data in more creative ways.
Data scientists use Python for big data, machine learning and artificial intelligence. The ability to process big data is in high demand as the Internet of Things (IoT) continues to expand, collecting and sharing a larger volume of data from more sources at higher speeds.
Tech analyst company IDC predicted in a 2020 report that “by 2025 there will be 55.7 B connected devices worldwide” and that “the data generated from IoT-connected devices will be 73.1 ZB.”
In 2001, industry analyst Doug Laney coined the term the “Three Vs” — volume, velocity, and variety — to encompass the defining characteristics of big data. Today we can include two more variables: the unpredictable flow of data, such as trending social media topics, and the quality of that data.
To successfully advise enterprises based on data-driven information, data scientists must be able to collect, clean, prepare, analyze, and interpret this huge amount of data quickly and efficiently. For some projects, a statistical computing language such as R is the most effective tool, but for others, a general-purpose language such as Python is better suited to the objective.
Data analysis is an important part of the data scientist’s role. Parsing large data sets to find meaning in the patterns and trends they reveal enables data scientists to design predictive models and algorithms that solve problems for businesses.
But large data sets can be unwieldy, and processing them can be time consuming and labor intensive. Furthermore, data analysis is only a part of the data scientist’s job — a means to an end. The ultimate goal for data scientists is to convert the insights they glean from their analysis to a business solution through advanced modeling techniques.
In other words, data scientists have bigger fish to fry.
That’s where Python’s simplicity and readability come in. Fewer lines of code means speedier programming, which, in turn, leads to more streamlined data analysis. If the word “simplicity” brings to mind limitations and restrictions, read on.
Python makes up for any loss of functionality with a standard library and prebuilt modules that let users communicate with graphics libraries and a variety of interactive data-visualization tools. Charts, graphs, maps, and other visual representations are key to interpreting and clearly communicating the information acquired through data mining and analysis.
Python author Guido van Rossum developed the coding language for “intelligent computer users who were not computer programmers or software developers in any sense.”
Hence its popularity with data scientists, whose objectives include visualizing data, constructing algorithms to solve specific problems, and deep learning — not programming and development.
Machine learning engineers can benefit from Python’s robust capabilities. Machine learning, deep learning and AI all require advanced systems that extend beyond executing a set of predetermined instructions.
Python modules, libraries, and frameworks provide the resources for data scientists to avoid the time-consuming process of coding these instructions manually. From complex mathematical functions and image optimization to preprocessing and 2D plotting, Python’s libraries contain packages for data scientists, AI developers and machine learning engineers across a range of applications.
The most commonly used Python libraries for machine learning include:
Users can browse the more than 315,000 Python packages and install them directly from the Python Package Index (PyPI).
Python for Other Tech Professionals
Out of the 700-plus programming languages, Python is one of the most intuitive and widely used among tech companies, including NASA, Nokia, Google, and IBM.
Programmers, web developers and software engineers are attracted to Python’s versatility. It can handle everything from writing system scripts to back-end web development, mobile and desktop app development, and software development.
When Artima’s Bill Venners asked him to describe Python to developers who’d never used it, van Rossum said:
“From one perspective you can say Python is a very high-level scripting language. From another perspective you can say it’s a high-level programming language that happens to be implemented in a way that emphasizes interactivity. Python shares some characteristics with scripting languages, but also shares some characteristics with more traditional programming languages.”
He also explained that because Python’s functionality is not intrinsic to its core, it offers developers a cleaner syntax with more flexibility through the prepackaged modules. The modular design allows users to extend existing applications and automate repetitive tasks. This is an enormous time-saver that attracts busy tech professionals.
Benefits of Python
The advantages of Python have led to its rise in popularity over the course of the last decade. According to the July 2021 update to the TIOBE Programming Community index, of the top three in-demand coding languages, Python “seems to have the best chances to become number 1, thanks to its market leadership in the booming field of data mining and artificial intelligence.”
We’ve already discussed the many uses and features of Python, and now we’ll dig into the benefits of the programming language — the ways in which Python can make your work in the realm of data science easier.
Ease of Use
Python’s key attribute is its readability. Because Python is so close to the user’s natural language and requires fewer lines of code, the user can quickly learn and apply the programming language to a range of operations.
So whether you’re a coding newbie or an experienced professional, Python helps you get more done in less time.
Because Python is such a versatile language — part scripting language, part interpreted programming language — it is used across multiple industries.
For anyone interested in a career in technology or data science, Python is the closest you can get to a one-stop-shop for a coveted skill. Employers from companies large and small are looking for people who have experience with the Python ecosystem.
Python’s open-source status is supported by an extensive community of users. On its website, Python boasts that its user base is “enthusiastic and dedicated to spreading use of the language far and wide.”
With community support for all levels of experience, the Python knowledge base continues to grow. You can find resources and advice from the Python community on Slack and Discord as well as through a curated weekly newsletter and social media channels, including Twitter, Facebook, and IRC.
According to Andrew Beall, chief scientist at WorldViz, this is a primary reason his team has made Python the foundation of their Vizard virtual reality development platform.
“Scientists across numerous domains have adopted Python as the go-to language for analysis, so it’s easy to lean on the accomplishments of others when beginning new projects,” Beall notes in his team’s success story.
Standard and Third-Party Libraries
Data scientists can provide value only to the extent that the data they analyze is clean and complete and able to be analyzed on a deeper level.
Python is continually adding new libraries, which gives data scientists new opportunities to explore and analyze data in innovative ways and provide enterprises with greater value and more meaningful insights.
For example, the Scikit-learn module offers an array of machine learning tools that enable data scientists to predict outcomes and devise algorithms around these predictions.
Career Opportunities: Expand Your Skill Set and Expand Your Options
The many advantages of Python culminate in the ultimate benefit to you, the user: Once you’ve mastered Python, you will have acquired a skill that will make you employable in the data science and tech industries. But have we fully covered what Python is used for?
Because Python is so popular, data scientists who have this valuable skill under their belts have more career options than those who don’t. Learning Python will enhance your agility and make you more adaptable to different roles in the field of data science, thus opening more doors for you to launch your career.
From meteorology to chemistry to aerospace engineering, branches of science and engineering employ Python for a variety of technology and big data projects.
Tech giants such as Google, NASA, and Facebook use Python for deployment and maintenance, infrastructure, and data management.
George Lucas’s special effects company, Industrial Light and Magic, and Walt Disney Feature Animation use Python for visual effects and animation.
Other heavy-hitters looking for graduates with Python on their resumes include:
If you’re interested in launching a career with any of these companies, you’ll want to have Python in your arsenal of qualifications.
And these jobs pay well. According to the Bureau of Labor Statistics, the annual wages for these roles are in the range of $75,000 to $150,000 a year, depending on experience and level of education.
Annual Salaries for Jobs that Require Programming Language Skills
- Data Scientists and Mathematical Science Occupations, All Other – $103,930
- Computer Information and Research Scientist – $126,830 per year
- Software Developers, Quality Assurance Analysts, and Testers – $110,140 per year
- Special Effects Artists and Animators – $77,700 per year
Top earners in data science and mathematical science occupations make roughly $165,230 a year.
The BLS has predicted a 15 percent growth rate for computer information and research scientists and a 22 percent growth rate for software developers from 2019 to 2029. The growth rate for both of these occupations are much faster than the 4 percent average growth rate for all occupations.
Industries with the highest employment levels for data scientists and mathematical scientists are:
- Computer system and design related services
- Management of companies and enterprises
- Management, scientific, and technical consulting services
- Scientific research and development services
- Insurance carriers
Source: U.S. Bureau of Labor Statistics
And keep in mind, these are just the jobs that most closely align with the core uses and functionality of Python as a programming language and data analysis tool. This list doesn’t include salaries and job outlooks for related roles, such as actuaries. The median annual wage for actuaries in May 2020 was $111,030.
Learning Python: Training & Education
If this has piqued your interest in Python and the possibility of launching a career in one of these rapidly growing industries, your next step is to explore your options for training and education.
You will likely want to pursue a degree in one of the following disciplines:
- Data Science
- Computer Science
Data science is a relatively new area of study in colleges and universities. The demand for skilled data scientists in business, education, and health care, however, is changing that. More data science programs are being offered to meet that demand, and graduates with a solid knowledge base in machine learning, data mining, and analysis, statistical methods and data visualization will be ahead of the curve in the job market.
Many of the current data science programs emphasize the context and application of data analysis in business strategy and problem-solving, making Python — whose frameworks align neatly with the goals and curricula of such application-driven programs — a logical choice for universities.
Although many useful Python tutorials can be found online, the novice data scientist will need more than the basics to apply Python to the type of research projects they’ll be tackling in these programs.
Due to its interoperability with other coding languages, Python may be taught alongside C, C++, and Java in computer science degree programs. Data science programs, on the other hand, may teach Python in conjunction with SQL and R.
Regardless of the degree program and track you choose, you’ll be best served by a course of study that includes Python as a component in an integrated, application-focused curriculum that emphasizes the critical-thinking and problem-solving skills you’ll need in the real world.