A Philosophical Primer in Pandas

Because the fact is, any true expert in the Microsoft Office Suite is a nascent Python linguist, whether they know it or not.

Each program in the MS Office suite is really nothing more than a clever shell — a mask — that makes common programming operations accessible and intuitive for a massive audience of contemporary workers.

At some level, this should strike you as a really, really obvious statement.

After all, the MS office programs are just that: programs.

Each one is a product of one programming language or another, and even the least-technically inclined reader will grasp that the overlay of an Excel spreadsheet is just a facade that assists information storage and recovery.

But the core of my argument is this; if you are proficient with that facade, and you can use it as a platform to manipulate data using the functions and logic built into Excel, you are only a very short conceptual step away from Python- fluency.

The gap between your waning “MS Office” skill, and the much more desirable “Python language” skill is more cosmetic than material, and you can make that upgrade more easily than you might think!The best approach for translating your Excel mastery into Python, or at least the approach I am adamantly in favor of, is to pursue a specific discipline in Python that corresponds to your existing skills.

If Excel is your particular field, look no further than the Pandas data manipulation library, an extension of standard Python functionality.

Pandas is an excellent — if peculiarly named — tool that uses a logical framework intuitively familiar to Excel users, with a different, vastly more powerful vocabulary and range of applications.

Before I began using Python and the Pandas Library, the largest Excel file I ever worked on contained about 50,000 rows across 80 columns — approximately 400 thousand cells rich with important content.

I plied that file with every scrap of MS Office talent I had, and I took great pride in the lumbering, overloaded Pivot Tables, graphs, and analyses that I was able to eke out of it with Excel.

In a span of 4 days — lighting fast for my aging MacBook Pro, I cobbled together a working analysis of that file that I still regard as the pinnacle of my career in Microsoft Office.

My work on that file was excellent, it was sophisticated by the standards of MS Excel, and it was grindingly inefficient by the standards of what I can achieve now, 3 months later, with an understanding of Pandas.

I revisited that data file recently, and performed a similar analysis in 45 minutes.

In the end, my point is only this; upgrading your professional skillset from MS office to Python, or any programming language, entails little more than a transition from one set of similar tools to another.

This is much more than a cosmetic difference, and while it will require dedicated work on your part, the benefits for your career could be the difference between gradual obsolescence and cleaving to the cutting edge!.I exhort you; don’t be a Typist in twenty-twenty.

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