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Python is a widely used, general-purpose, high-level back-end programming language that’s highly valued by startups who need to quickly prototype and develop applications, as well as data-driven companies that need to integrate data analysis and statistical techniques into their workflows.

Its combination of readability, flexibility, and suitability to data science operations have made Python one of the most popular and beloved languages according to developers on Stack Overflow. In this article, we’ll explore what sets Python apart from other programming languages, why it’s popular with data scientists, and what you should look for in a Python engineer.

High-level, Readable, and Efficient

One of Python’s defining characteristics is its efficiency. Every programming language has to balance the programmer’s time and the machine’s resources. Python is biased toward the former, with a guiding philosophy that comes down to “there should be one—and preferably only one—obvious way to do something.” That can mean there’s a bit of a learning curve as developers learn the ins and outs of Python syntax, but the upside is that developers can do more with fewer lines of code compared to more lower-level implementation languages like Java or C++. This efficiency is especially valuable for startups who need to quickly prototype applications and get them to market.

Python is also famous for its code readability, meaning that an application written by a developer in Python is more likely to be intelligible to the developers who have to maintain it months and years down the line.

Beloved by Data Scientists

Along with R and Java, Python is one of the most popular languages for data science and statistical analysis. For data scientists, Python combines Java’s suitability for building high-traffic web applications with R’s focus on executing complex statistical functions.

Another one of Python’s strongest assets is its extensive set of libraries. These libraries can make it easier for developers to perform complex machine learning or statistical analysis tasks without having to rewrite many lines of code. Some of the most popular libraries include tools for data manipulation and visualization (NumPy, SciPy, and matplotlib), data mining and Natural Language Processing (Pattern, NLTK). Perhaps unsurprisingly, Python is the language of choice for organizations with data-heavy workflows, from YouTube to the New York Stock Exchange to the National Web Service.

Python Basics

  • It’s object-oriented.
  • It’s cross-platform, working on Linux, Windows, Mac, and most other operating systems.
  • Python’s standard library supports:
    • HTML & XML
    • JSON
    • E-mail processing
    • HTTP Server libraries, easy for developing servers, and support for FTP, IMAP, and other Internet protocols
  • It’s free and supported by an active open-source community.
  • It’s often substituted for PHP in the LAMP software stack.

Python engineers have a number of options when it comes to frameworks. Frameworks are collections of packages that take care of the implementation details so you can quickly write applications. Which framework is best for your project depends on the scale of your application, its complexity, and your data needs.

  • Django: A very structured, all-in-one framework with lots of “scaffolding,” it’s designed for large-scale, complex applications. Lots of components and elegant database management make it a good choice for data-heavy sites.
  • Flask: A lightweight, minimalist framework, it gives developers a more flexible approach to using Python. Similar to Pyramid, it has a loose development style and is ideal for smaller, less complicated applications.
  • Pyramid: The middle road of Flask and Django, this framework offers a mix of flexibility and structure and is also good for complicated, bigger applications.
  • Twisted: A low-level networking Python framework.
  • Tornado: A framework that’s good for web servers and web apps.

The Python Developer’s Toolbox

What should you look for in a Python engineer? Experience on large-scale, high-traffic applications is at the top of the list, along with fluency in SQL and database optimization. Common duties and core skills of Python developers include modular programming, object-oriented programming, and extensive experience with SQL.

Other related skills and technologies a Python engineer should know include:

Explore more about Python and how it stacks up to other data programming languages in this article: R vs. Java vs. Python: Which is Right For Your Project?