Python's simplicity, combined with its practicality across a broad range of industry solutions, has made it one of the most popular development platforms in use today. With over 44% of software developers using it to build solutions, only JavaScript, HTML/CSS, and SQL beat it as the most used programming language.

The primary reason for Python's popularity is that it is easy to learn. The syntax of the language is not complicated, and its emphasis on natural language makes its code easy to understand. In addition, with a rich 30-year history, Python has a mature and supportive community. This large group of users has contributed to a broad range of libraries and frameworks that cover almost anything you need to solve with code. Finally, Python is cross-platform, giving developers the freedom to deploy their solutions on Linux, macOS, and Windows.

Where is Python used in Software Development?

Python is a popular language because it is so versatile. It is found in every industry and is well suited for building applications that range from data science and machine learning to web applications and cybersecurity.

Data Science

Python has an extensive range of libraries that support data science solutions. As this field of study primarily focuses on extracting value from raw data, Python’s mature data science capabilities are well suited to solving problems that require the analysis of large volumes of information. For example, the matplotlib library offers comprehensive support for creating static, animated, and interactive visualizations in Python. Python also excels at data analysis. The NumPy package provides the fundamentals data scientists need to perform scientific computing with Python.

In addition to NumPy and matploitlib, Python also offers many other data science libraries and packages, each crafted for a particular specialization. For example, the QuTIP library simulates the dynamics of open quantum systems, while AstroPy is a package for Astronomy related solutions. There is also BioPython for bioinformatics and SciPy for mathematical analysis. Needless to say, Python is the de-facto programming language for building solutions in this field. With additional libraries and packages ranging from statistical computing and signal processing to chemistry and geoscience, there is very little in data science that Python cannot do.

Machine Learning

Python is also well-suited for building machine learning software development platforms. Due to the complexity of creating solutions for this field, developers leverage Python's simplicity, consistency, and cross-platform support to accelerate their solution process. In addition, as its code is easy to learn and read, it helps developers cut through the inherent complexity of machine learning and artificial intelligence. Python also offers a broad range of libraries and packages that support the creation of these complex systems.

For example, TensorFlow and Scikit-learn are well-known machine learning libraries that seamlessly integrate with Python code. These libraries drive many AI-based solutions in use today with support for classification, regression, clustering, and neural networks. For instance, J.P. Morgan uses Scikit-learn for predictive analytics, and Spotify uses it for music recommendations. There are many other use cases for Python machine learning. Leveraging the language's capabilities and the libraries and packages available, software developers can build solutions such as spam filters, search engines, and virtual assistants.

Web Applications

Software developers that build web applications can leverage Python's capabilities to create robust server-side solutions. Typically, web developers use Python for the back-end services and solution logic and leverage HTML, CSS, and JavaScript for the web front end. There are libraries and packages such as pyjs that compile Python code to JavaScript. However, most web development projects use the relevant languages for their particular roles and specialty.

Most web development projects leverage frameworks that provide the basic functionality most solutions need. For example, PHP projects leverage Laravel and Symfony, and .NET projects use ASP.NET Core and Umbraco. Python also has several robust web frameworks that software developers can leverage to build their web apps. For example, Django is a Python web platform that offers a broad range of standard features. By leveraging this framework, software developers can rapidly create web apps as Django takes care of the base functionality. Other examples of popular Python web frameworks include Flask, web2py, and Bottle.

Cybersecurity

In addition to data science, machine learning, and web applications, Python's versatility and ease of use also make it a popular platform for cybersecurity. Since many information security-related tasks are repetitive and typically involve large amounts of data, leveraging Python's capabilities can automate many of these tasks. Cybersecurity professionals also utilize Python's functionality and extendibility to build tools used during penetration tests. For instance, libraries like Socket provide the low-level networking capabilities many information security apps need to connect to remote systems.

Another helpful Python cybersecurity library is Scapy. By calling its functions in their Python code, software developers can leverage its broad range of networking capabilities. One of this library's core features is its ability to manipulate network packets. Information security professionals have crafted tools to perform tasks such as port scanning, arp spoofing, packet capturing, and frame injection using this functionality. You can even build offensive penetration testing solutions with Python. A good example is Pupy which includes remote command execution, data upload and download, a keylogger, and persistence.

Python - Power and Versatility

Python's simplicity, combined with its versatility, makes it a great general-purpose language software developers can leverage to build a broad range of solutions. In addition, its support for Linux, macOS, and Windows environments delivers the flexibility developers need to create applications that need to run on multiple platforms. Furthermore, Python's vast libraries provide solutions for everything from data science and machine learning to web development and cybersecurity. This extendibility not only delivers the functionality developers need but also accelerates project delivery. Finally, Python's substantial development community provides continuous updates keeping its functionality relevant. All these benefits warrant that Python should always be near the top of your programming language shortlist.

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