What is Python Used For?

If you are looking for a programming language to learn, you can't do better than Python. Released in 1991 by creator Guido van Rossum and re-released in 2008 as Python 3, the Python programming language has been gaining popularity ever since.

What is the Python programming language?

Python is a general-purpose programming language. Unlike specific languages like HTML or SQL, which perform specific functions, Python can solve a wide range of tasks. Its applications vary from task automation and data analysis to software development.

There are several types of general-purpose languages. Python is an object-oriented programming language, which means it divides its code into self-contained entities called "objects". Each object includes data and the code that processes that data.

Objects make code more maintainable and easy to port from one project to another. An object-oriented language is part of what makes Python efficient and user-friendly.

What are Python packages and modules?

Portable, well-organized code is the foundation of Python. Instead of writing everything from scratch, users can take ready-made Python code from resources known as packages or modules.

Modules help programmers:

Organize complex programs into smaller manageable blocks.
Minimize the amount of source code needed for a project.

What are Python libraries and frameworks?

Python programmers can package modules into libraries and frameworks. At the simplest level:

Module is a piece of reusable code.
Library is a collection of modules.
Framework consists of libraries.

You can use a module and a library to create your own code structure. A framework is more like scaffolding - something you can take as a whole and add your details.

Frameworks are popular in website and application development because they eliminate repetitive work, allowing developers to start creating faster.

What are the use cases of Python?

Example of object-oriented programming in Python used in video game development.

Python was designed from the start for mass use. Guido van Rossum wanted his language to be:

Intuitive to use.
At least as powerful as competitors like C++ and JavaScript.
Open source.
As understandable as everyday English.

In many ways, he succeeded. Python's simple syntax and readability made the language popular among both developers and non-developers. Today, a large community of Python programmers spans many industries.

Backend Web Development

Any web application consists of two parts: front-end and back-end:

Front-end is the user interface you see as a visitor.
Back-end manages behind-the-scenes functions, like databases storing user login information.

For web developers, Python is primarily a back-end language. It provides code that retrieves requested user information from necessary databases and returns it to the user.

Python's strength lies in its popular web frameworks.

Django speeds up web development so programmers can launch new applications or sites in hours.
Flask is a microframework that works as an application programming interface, also called an API. It optimizes and simplifies creating web applications.
Pyramid is for projects that "start small and end big." It helps applications scale and remain functional regardless of growth.

Some frameworks, including Django and Pyramid, are full-stack. They work on both front-end and back-end, offering developers broader functionality.

Data Science

Data science uses technology and analytics to derive business insights from big data. Data scientists spend their days finding, organizing, and modeling data to gain deeper knowledge and solve business problems.

Data scientists use Python to create complex machine learning algorithms capable of processing large amounts of data. Python is the most preferred language because it is simple, scalable, and offers many machine learning and data processing libraries. For example:

Keras builds neural networks and advanced data models.
Pandas can manipulate and analyze large amounts of data.
NumPy allows work with special multi-valued variables called arrays.

Python also has several ready-made automation frameworks, including PYUnit and UnitTest, which allow data scientists to run experiments quickly and easily. The faster the test, the faster the results.

Data Analytics

While data scientists spend time working with raw data, analysts take existing data points and extract useful insights. Over the past few years, data analytics has become a core business function, driving more business decisions than ever before.

Python helps data analysts perform their work more efficiently and extract more value from large datasets. Analysts use many of the same standard libraries as data scientists, including:

Pandas
NumPy
Scrapy
BeautifulSoup.

Python libraries for data analysts typically focus on workflow automation, data finding, and reformatting to make data more useful.

Data Visualization

Data is only valuable if decision-makers can look at it and understand trends, patterns, and relationships. Data visualization makes this easier by converting raw data into graphs, charts, diagrams, and other accessible graphical representations.

Python offers many plotting libraries that allow you to create any kind of visualization, from basic bar charts to interactive plots. Some of the most popular data visualization libraries include:

Matplotlib: Great for creating basic visualizations like bar charts, line charts, and scatter plots.
Seaborn: Based on Matplotlib but has a more advanced interface that allows creating beautiful plots faster.
Plotly: Ideal for creating interactive plots of all kinds, ready for publication.

You can create simple graphs and charts as a basic user, and also refine your skills to create very complex visualizations for expert audiences.

Machine Learning and Artificial Intelligence

Over the past few years, the world has made significant advances in artificial intelligence - computers capable of mimicking human intelligence. One of the most popular forms of AI today is machine learning, which teaches computers to learn from patterns.

Machine learning can:

Predict patient outcomes in healthcare.
Suggest music and movies based on past choices.
Handle customer service questions online.
Translate languages with greater accuracy than ever before.

Python is the number one programming language for machine learning. It is stable and flexible, so developers can rely on it for complex projects. It is also easy to learn, so developers can start deep work faster.

Most importantly, Python offers a wide range of libraries specifically designed for machine learning. Among the most popular are:

SciKit-Learn: Simplifies the interface of machine learning algorithms.
Tensorflow: Builds advanced neural networks for companies like Dropbox and Google.
MlPy: Predicts outcomes by analyzing patterns.

Using these libraries, scientists implement new algorithms and expand machine learning capabilities.

Scientific Computing

Scientific computing uses computers to solve real-world problems, typically in science or engineering. Researchers and engineers use scientific computing to develop mathematical models and test hypotheses, often in scenarios where real-world experiments would be dangerous or impossible.

Along with MATLAB, Python is one of the main languages for developing such mathematical models. It is easy to learn, has extensive documentation, and offers many input/output options to simplify data processing.

Python also has several libraries designed for computational tasks:

NumPy - the "fundamental language for scientific computing in Python."
SciPy provides many algorithms for scientific programming.
Matplotlib is essential for data visualization.

Such tools are essential in the modern world of scientific computing, which processes ever-growing data volumes.

Python also supports more advanced scientific computing. Its machine learning capabilities allow researchers to identify new patterns and correlations. Additionally, the language is very flexible for creating APIs that connect to cloud systems and even cutting-edge quantum computers.

Task Automation

Programmers, engineers, and researchers are always looking for ways to automate repetitive tasks, such as checking files for errors or cleaning data. The process of creating such automatic systems is called scripting.

Python is an ideal language for scripting. With its simple syntax and readability, even relative beginners can use it to write basic automations. The language has many built-in scripting functions, and many libraries contain scripting resources.

With Python, you can automate almost any repetitive task, including:

Sending email
Reading or writing files
Filling PDF and CSV files
Extracting data from web pages
Getting operating system information

Thanks to Python's extensive libraries and support for various data structures, there are many other automation possibilities. And if your automation goal does not match any existing Python structure, you can create your own.

Finance

Python is widely recognized as one of the best and most useful programming languages for the financial industry. It is accessible to non-tech specialists and capable of handling both basic and complex tasks, such as:

Predicting stock prices and other assets.
Analyzing industry trends and patterns.
Automating data analysis workflows.

Python is especially popular in quantitative finance, which deals with analyzing large datasets. Machine learning libraries, including PyBrain and SciKit, enable advanced machine analytics, while libraries like Pandas and Matplotlib simplify creating complex visualizations.

Teaching Programming

Python is an important part of the programming teacher's toolkit. According to reports, 71% of educators use Python to teach machine learning and data science, and 88% of students learn Python to enter these fields.

Python is a valuable language for teaching for several reasons:

Its syntax is simple, so students spend less time learning commands and more time coding.
Its detailed documentation allows students to practice problem-solving independently.
Since the learning curve is not as steep as in other languages, teachers can cover complex topics more quickly and deeply.
Teachers can use Python to teach multiple types of programming.
Python computer programming has real-world applications in many fields, from software development to data analysis.

Game Development

Video game development is not the most popular use of Python, mainly because competitors like C++ and C# are faster and more powerful. However, popular video games like Battlefield 2 and Civilization IV used Python for certain elements.

Additionally, the Python library PyGame has become a staple resource for many aspiring game developers. PyGame is free, works on any platform, and is accessible to anyone with basic knowledge of Python programming.

Interestingly, the popular game engine Godot bases its native scripting language GDScript on Python. People who have learned Python and want to create video games will feel right at home in Godot.