Lean Code – Part 2

Our article on “Lean Code” strongly suggests that the principles of lean can also be applied to the realm of software development, applications, and more specifically, programming.

Python has evolved to become a very popular and powerful programming language.  However, as mentioned in “Lean Code“, the performance of your application or program is as dependent on the skills of the programmer as they are on the capabilities of the programming language itself.

An example of skill versus language can be found in “Python for Data Science – For Dummies – A Wiley Brand” by John Paul Mueller and Luca Massaron (ISBN:  978-1-118-84418-2).  Page 106 of the book states:

It’s essential to realize that developers built pandas on top of NumPy.  As a result, every task you perform using pandas also goes through NumPy.  To obtain the benefits of pandas, you pay a performance penalty that some testers say is 100 times slower than NumPy for a similar task.

The functionality offered by pandas makes writing code faster and easier for the programmer, however, the performance trade-off exists for the end user.  Knowing when to use one module over the other depends on the programmer’s understanding of the language as opposed to simply providing a specific functionality.

Python for Data Science provides sufficient information to decide the best fit case for either pandas or NumPy.  The relevance of sharing this is to stress the importance of continually reading, learning, and understanding as much as possible about your language of choice for a given application.

From the end user’s perspective, performance matters and everyone wants it “yesterday”.  So, the question is, “Do we code quickly and sacrifice performance or sacrifice delivery for quick code?  What would you do?

Until Next Time – STAY lean!

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