Meet the man behind the most important tool in data science
Wes McKinney hates the idea of researchers wasting their time. “Scientists unnecessarily dealing with the drudgery of simple data manipulation tasks makes me feel terrible,” he says.
Perhaps more than any other person, McKinney has helped fix that problem. McKinney is the developer of “Pandas”, one of the main tools used by data analysts working in the popular programming language Python.
Millions of people around the world use Pandas. In October 2017 alone, Stack Overflow, a website for programmers, recorded 5 million visits to questions about Pandas from more than 1 million unique visitors. Data scientists at Google, Facebook, JP Morgan, and virtually other major company that analyze data uses Pandas. Most people haven’t heard of it, but for many people who do heavy data analysis—a rapidly growing group these days—life wouldn’t be the same without it. (Pandas is open source, so it’s free to use.)
So what does Pandas do that is so valuable? I asked McKinney how he explains it to non-programmer friends. “I tell them that it enables people to analyze and work with data who are not expert computer scientists,” he says. “You still have to write code, but it’s making the code intuitive and accessible. It helps people move beyond just using Excel for data analysis.”
Basically, Pandas makes it so that data analysis tasks that would have taken 50 complex lines of code in the past now only take 5 simple lines, because McKinney already did the heavy lifting.
McKinney, 32, grew up in Akron, Ohio. From an early age, he showed a penchant for math and technology—in high school he was a mathlete and ran a website dedicated to the video game GoldenEye 007. He went on to attend the Massachusetts Institute for Technology where he studied pure math.
Like many quants, after graduating McKinney headed to New York to work in finance at AQR Capital Management. At the hedge fund he found that the hard finance problems were more about dealing with data than math. The most valuable work involved gathering new sources of data, merging datasets together, an cleaning it all up. As anyone who works in data science knows, quality data is far more important than fancy analysis.
McKinney was frustrated with the tools available to complete these basic data tasks at the time—he was not a fan of Excel or R (another popular programming tool). A colleague suggested McKinney try the language Python. McKinney was smitten. “I fell in love pretty quickly with Python,” McKinney told me. “I loved it for its economy of expressions. You can express complicated ideas in Python with very little code, and it is very easy to read.”
But Python was missing some key features that would make it a good language for data analysis. For example, it was challenging to import CSV files (one of the most common formats for storing datasets). It also didn’t have an intuitive way of dealing with spreadsheet-like datasets with rows and columns, or a simple way to create a new column based on existing columns.
Pandas addressed these problems. David Robinson, a data scientist at Stack Overflow, explained the importance of it in technical terms.
