VLOOKUP equivalent function to look up value in pandas DataFrame

Question

I have a pandas dataframe with the following structure:

DF_Cell, DF_Site
C1,A
C2,A
C3,B
C4,B
C5,B

And I have a very long loop (100 million iterations) in which I treat one by one strings that correspond to the "DF_Cell" column in the DataFrame (first loop iteration creates C1, second iteration creates C2, etc...).

I would like to lookup in the dataframe the DF_Site corresponding to the cell (DF_Cell) treated in the loop.

One way I could think of was to put the treated cell in a one-cell DataFrame and then doing a left merge on it, but this is much too inefficient for such big data.

Is there a better way?

Answer 1

Perhaps you want to set DF_Cell as the index*:

In [11]: df = pd.read_csv('foo.csv', index_col='DF_Cell')
         # or df.set_index('DF_Cell', inplace=True)

In [12]: df
Out[12]: 
        DF_Site
DF_Cell        
C1            A
C2            A
C3            B
C4            B
C5            B

You can then refer to the row, or specific entry, using loc:

In [13]: df.loc['C1']
Out[13]: 
DF_Site    A
Name: C1, dtype: object

In [14]: df.loc['C1', 'DF_Site']
Out[14]: 'A'

*Assuming this has two columns, you could use squeeze=True.