In this section, we will be looking at how we can handle missing data in a pandas DataFrame. We have a few ways of detecting missing data that work for both series and DataFrames. We could use NumPy's isnan function; we could also use the isnull or notnull method supplied with series and DataFrames for detection. NaN detection could be useful for custom approaches for handling missing information.
In this Notebook, we're going to look at ways of managing missing information. First we generate a DataFrame containing missing data, illustrated in the following screenshot:
As mentioned before in pandas, missing information is encoded by NumPy's NaN. This is, obviously, not necessarily how missing information is encoded everywhere. For example, in some surveys, missing data is encoded by an impossible numeric value. Say, the...
