Working with missing data
Data is "missing" in pandas when it has a value of
NaN (also seen as
np.nan—the form from NumPy). The
NaN value represents that in a particular
Series that there is not a value specified for the particular index label.
In pandas, there are a number of reasons why a value can be
- A join of two sets of data does not have matched values
- Data that you retrieved from an external source is incomplete
NaN value is not known at a given point in time and will be filled in later
- There is a data collection error retrieving a value, but the event must still be recorded in the index
- Reindexing of data has resulted in an index that does not have a value
- The shape of data has changed and there are now additional rows or columns, which ...