Quality assurance

I know we have spent a lot of time cleaning the data, but there is still one last task we need to perform – quality assurance. Proper quality assurance is a very important practice. In a nutshell, you need to define certain assumptions about the dataset (for example, minimum and maximum values, the acceptable number of missing values, standard deviation, medians, the number of unique values, and many more). The key is to start with something that is somewhat reasonable, and then run tests to check whether the data fits your assumptions. If not, investigate specific data points to check whether your assumptions were incorrect (and update them), or whether there are still some issues with the data. It just gets a little more tricky for the multilevel columns. Consider the following code:

assumptions = {
 'killed': [0, 1_500_000],
 'wounded&apos...