pandas merge dataframe with NaN (or “unknown”) for missing values

Question

I have 2 dataframes, one of which has supplemental information for some (but not all) of the rows in the other.

names = df({\'names\':[\'bob\',\'frank\',\'james\         


        

Answer 1

For outer or inner join also join function can be used. In the case above let's suppose that names is the main table (all rows from this table must occur in result). Then to run left outer join use:

what = names.set_index('names').join(info.set_index('names'), how='left')

resp.

what = names.set_index('names').join(info.set_index('names'), how='left').fillna("unknown")

set_index functions are used to create temporary index column (same in both tables). When dataframes would have contain such index column, then this step wouldn't be necessary. For example:

# define index when create dataframes
names = pd.DataFrame({'names':['bob',...],'position':['dev',...]}).set_index('names')
info = pd.DataFrame({'names':['joe',...],'classification':['thief',...]}).set_index('names')

what = names.join(info, how='left')

To perform other types of join just change how attribute (left/right/inner/outer are allowed). More info here