Data Cleaning–The Bain of Existence

Often we find datasets in the wild they are rittled with NaNs, malformed strings, and inconsistent data.  The inexperienced data scientist is often too quick just to drop rows upon rows of data, rather than preserve the data that which they so meticulously gathered.

In this series, we will focus on data preservation methods for cleaning our datasets.   We will use basic examples to illustrate each method, and then try out our techniques on a dataset that we find in the wild.