在现有 pandas df 中添加缺少观测值的变量，而不会丢失较大 df 中的观测值

Question

I have two dataframes. One called ENFORE with 139 observations:

citation    Enfore
0170/0952   1
0175/0686   1
0184/0521   1
0183/0726   1
0178/0595   0

And another data frame called CITATIONS with 668 observations that also have the column citation, but not Enfore. All the citations in ENFORE are in the CITATIONS data frame.

I would like to add the column Enfore to the CITATIONS data frame and fill in observations that are not in the ENFORE data frame with an 'X'.

Using various variations of this code (merge and join)

enfore_merged = pd.merge(enfore , harrington_citations, on = 'citation')

I have not been able to create the data frame I describe above.

Answer 1

You are almost there:

enfore_merged = harrington_citations.merge(enfore, how='left', on='citation')
enfore_merged['Enfore'] = enfore_merged['Enfore'].fillna('X')

Answer 2

Use Series.map :

harrington_citations['Enfore']=harrington_citations['citation'].map(enfore.set_index('citation')['Enfore']).fillna('X')

This creates a new Enfore column in its CITATIONS data frame that I understand is harrington_citations by mapping the citation column of this data frame with a series obtained from the ENFORE data frame whose index is citation and its values are those of the Enfore column