This is the second blog in a series. See the first blog here: Loading Data Into a Pandas DataFrame: The Hard Way, and The Easy Way
No dataset is perfect and most datasets that we have to deal with on a day-to-day basis have values missing, often represented by “NA” or “NaN”. One of the reasons why the Pandas library is as popular as it is in the data science community is because of its capabilities in handling data that contains NaN values.