According to Stack Overflow's 2022 Developer Survey, Python is one of the top programming languages today - its relatively simple learning curve and versatility make it an attractive choice for coders of all levels (even those with no coding experience at all). Developers use Python to build websites, mobile apps, data science and machine learning applications, and automation programs.
With its wide range of libraries and frameworks, it can power a wide variety of projects at varying complexities. In this article, we'll cover what Python is, its use cases, and the best practices to get you up and running.
What Is Python?
Python is a general-purpose programming language with a strong emphasis on readability and code simplicity. It uses structured, object-oriented, and functional programming paradigms to make coding more efficient.
It is a backend language (it runs in the background to power websites and applications) but can also be used for desktop applications, mobile apps, scripting, data science, and machine learning projects.
One key distinction between Python and other coding languages is its use of whitespace: tabs and spaces are used to organize code rather than curly braces or keywords. This allows developers to create more readable and concise code that is easier to debug.
Why Is Python such a popular language?
Python is mainly popular because of its ease of use. It's easy to learn and comprehensible for new coders, while still providing advanced features like libraries and frameworks that more experienced developers can appreciate. Here are a few of the main benefits of Python:
Its syntax mimics natural language: Developers can build projects far more easily and quickly than with other languages.
It's cross-platform: Python can create programs for multiple platforms, including Windows, MacOS, Linux, Raspberry Pi, iOS, and Android.
It has a massive archive of libraries and frameworks: The Python Package Index features over 300,000 packages for developers to use in their projects.
It is highly extensible: For optimized performance, developers can write code in other languages like C/C++ and Java.
There is a large and active community of Python developers: Python's open-source nature and its popularity mean plenty of resources are available to help you with coding challenges and find new solutions.
Common Use Cases for Python
Programmers can use Python for server-side development, web development, GUI (graphical user interface) application development, game development, automation scripts, and data science projects (including machine learning). Let's take a look at how those use cases work in reality:
Data Analysis and Machine Learning
Python's vast libraries make it the ideal for data analysis. With packages like NumPy and SciPy, developers can manipulate large datasets quickly and efficiently. Developers can use Python to build all sorts of data visualizations, such as:
Bar charts
Pie charts
Scatter plots
Bubble charts
Histograms
Word clouds
Its machine learning capabilities are also highly advanced - scikit-learn is a popular library that supports supervised and unsupervised algorithms.
Web Development
Python can be used to develop websites using frameworks like Django (a fully-fledged web framework that supports frontend and backend development) and Flask (a microframework designed for quickly building APIs or web applications). Both come with features like URL routing, authentication, and unit testing. Python can also be used to create server-side web applications. With frameworks like Tornado or Pyramid, developers can create scalable projects with asynchronous programming capabilities.
Scripting and Automation
With the right libraries, developers can automate simple tasks like moving files around or more complex processes like web scraping, data mining, and API integrations. The best example of this is the automation library Selenium, which allows developers to create automated test scripts and easily manage browser activities.
Python's automation scripts are known for being relatively simple - non-developers (accountants, marketers) use them to automate simple tasks like data entry, finding online content, or sending emails and texts at specific cadences.
Prototyping and Testing
Python helps developers build control, bug tracking, and testing throughout the iterative product development process. Developers can create quick prototypes with Python's readability and write code to test software. For product development teams whose members have varying coding abilities, Python is popular because it simplifies the process of communication between technical and non-technical staff.
Everyday Tasks
While Python is commonly used to build new products or test existing ones, it can also be used for simple day-to-day tasks. For example, Python can help you:
Generate Excel spreadsheets
Compress or extract files
Organize folders and images
Convert text to PDFs
Send emails
Track stock market prices
Send yourself a reminder at 8:00 every morning
Automatically populate online form fields
Randomly select a winner for a social media giveaway
Python Best Practices for Beginner Developers
If you're just getting started with coding in Python, here are some best practices to help you get the most out of your projects:
Follow the PEP 8 style guide: The PEP 8 style guide is a comprehensive set of coding conventions and best practices designed to improve the readability and consistency of Python code. Adherence will significantly enhance your coding experience and make it easier for others to understand and contribute to your projects.
Centralize your log data: Make sure you centralize Python logs when it comes to debugging and logging. Centralizing log data is a key best practice for coding in Python as it ensures that all log messages are stored in a single, reliable place and handled uniformly across the application.
Write meaningful docstrings: A docstring is a block of text that describes the purpose of a function or module in Python code. Good docstrings help developers understand your code from the get-go, without needing to refer to external documentation.
Use Python virtual environments: A Python virtual environment is an isolated and secure workspace where you can install packages and work with different versions of the same software in a safe way. Using virtual environments keeps projects from interfering with each other.
Endnote
Python is a powerful and versatile programming language that seasoned software developers and non-coders alike use for everything from web development and data science to everyday automations and tasks. With its vast libraries and resources, Python is one of the most popular languages today, and following a few best practices specific to Python, like following PEP 8 style guidelines, centralizing log data, writing meaningful docstrings, and using virtual environments will help you get the most out of your development experience as you build on your Python skills.
