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The return of Python in data science

Python has returned – and in a big way. With the increased interest in big data and machine learning, it’s become a firm favourite among many data scientists and data analysts. But what makes this programming language so popular?

What is Python?

Named after the BBC classic comedy series Monty Python’s Flying Circus, Python is a multi-paradigm programming language and interpreter software.

It supports object-orientated programming, as well as other programming patterns including functional and structured styles, with high-level data structures. Using one unified language, it can handle every job, from app construction to data mining to running embedded systems.

What are the benefits of Python for data science?

Data science involves extrapolating useful data from massive stores, such as statistics and registers. These are often unsorted and difficult to make connections from. Companies like Google, Nasa and Cern are using Python for almost every programming purpose possible, including data science, due to its many benefits:

1. It’s universally usable

If organisations use the best-of solution for individual programmes, the result is often a confusing multitude of programming languages, from Java, to C#, to smaller languages like MATLAB. Each of these require specialist knowledge and have codebases which are incompatible. Instead, using Python as a solution for all programmes means they can all be maintained under one language.

Arguably, this one of the biggest benefits of Python: that it is universal. Available on Windows, Mac OS X, Linux and Unix operating systems, Python can integrate systems more effectively. The Python interpreter can be used as an extension language for customisable applications, but it is also easily extended with new functions and data type implemented in C or C++.

Python allows professionals to work with multiple data science applications, and not spend valuable time learning specialist programming languages.

2. It’s free

Python is open source and free to use, including for commercial purposes. This in itself has massive appeal. Couple this with Python needing less time to build a programme (compared to other language programmes), and this shorter development process also saves money.

3. Available support

Not only is Python free to use, but it also has vast libraries that can be freely distributed, which includes code for all major platforms. This rich internet resource bank is encouraging more people to adopt the language, creating an entire online community who are there to answer questions or suggest solutions.

4. Development

Python’s development is driven by its community, meaning it regularly receives updates and releases to its libraries. Sourcing the best package for your needs is only a quick search away, meaning data scientists in almost every sector can find downloadable, dedicated analytical libraries, tailored for their needs.

This is especially useful for data exploration, manipulation and extraction to deep learning models. A particular favourite for data science and analysis is the open source library called pandas. The highly recommended library scikit-learn focuses on machine learning and data mining.

As quickly as these emerging technologies develop, Python is similarly able to keep up and adapt.

5. Easy to learn

Python is relatively simple to learn. It has a clear, simple syntax and indentation structure, which makes it very readable. If you’re new to programming, this makes it easy to pick up, whereas programmers experienced in other languages can learn Python very quickly and enjoy how it has been designed to be beautiful and fun to use.

It is also consistent, allowing you to express complex operations in a single statement. Often, this means Python code is shorter than equivalent Java, C and C++ programmes.

More importantly for data science, Python has built-in high level data types, such as flexible arrays (known as lists in Python) and dictionaries. These can be indexed, sliced and manipulated with other built-in functions, making it perfect for data analysis, and can be used to construct fast runtime data structures.

 

Python’s popularity continues to rise, particularly due to its development capabilities with emerging technologies like machine learning and data science. Even with other languages available, Python’s universal application, ease of experimentation and numerous libraries make it a fun language to learn, whether you’re new to programming or an experienced data analyst.

If you’re interested in finding your next role in BI and data science, or are recruiting, please contact your local Reed Technology branch.

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