We see that the number 1 that was formerly at the top of the x1 column has been replaced by NaN -- "not a number". This particular example is somewhat silly, as we probably do not want to pretend that the number 1 is actually missing data. However, as we will see throughout the course, real data is messy and often has missing values. We've come across all these markers (and more) for noting that some data is missing: -, null, n/a, NA, N/A, nan, NaN, ... Being able to tell pandas which values to treat as missing will allow us to read in some otherwise troublesome data.