"Richard O'Keefe" <rao...@gmail.com> writes: > My difficulty is that from a statistics/data science perspective, > it doesn't seem terribly *useful*.
There are two common use cases in my experience: 1) Error checking, most frequently right after reading in a dataset. A quick look at the data types of all columns shows if it is coherent with your expectations. If you have a column called "data" of data type "Object", then most probably something went wrong with parsing some date format. 2) Type checking for specific operations. For example, you might want to compute an average over all rows for each numerical column in your dataset. That's easiest to do by selecting columns of the right data type. You are completely right that data type information is not sufficient for checking for all possible problems, such as unit mismatch. But it remains a useful tool. Cheers, Konrad.
