The sunk-cost fallacy is one of many harmful cognitive biases to which humans fall prey. It refers to our tendency to continue to devote time and resources to a lost cause because we have already spent — sunk — so much time in the pursuit. The sunk-cost fallacy applies to stay in bad jobs longer than we should, slaving away at a project even when it’s clear it won’t work, and yes, continuing to use a tedious, outdated plotting library — matplotlib — when more efficient, interactive, and better-looking alternatives exist.
Over the past few months, I’ve realized the only reason I use matplotlib is the hundreds of hours I’ve sunk into learning the convoluted syntax. This complication leads to hours of frustration on StackOverflow figuring out how to format dates or add a second y-axis. Fortunately, this is a great time for Python plotting, and after exploring the options, a clear winner — in terms of ease-of-use, documentation, and functionality — is the plotly Python library.
Plotly Brief Overview:
The plotly Python package is an open-source library built on plotly.js which in turn is built on d3.js. We’ll be using a wrapper on plotly called cufflinks designed to work with Pandas dataframes. So, our entire stack is cufflinks > plotly > plotly.js > d3.js which means we get the efficiency of coding in Python with the incredible interactive graphics capabilities of d3.
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