Is it possible to select pandas dataframe with row indices and column names?

Question

For datasets with meaningless row indices, I found it more useful to select data by row numbers but at the same time using column names. I know .iloc only takes in row/column numbers (integers) and .loc only takes in names. But is there a workaround to do a combination of row number and column name at the same time?

    A   B
1   1   a
5   2   a
6   3   a
4   4   b
9   5   b
3   6   b

For example, I would like to select the entry at row 2 and column B - I do not necessarily know that row 2 has row name 5 nor column B is the second column. What's the best way to reference that cell then?

(The row names are usually a filtered result or a random sample of a bigger dataset)

Answer 1

You can use faster iat as iloc:

print df
    A  B
1   1  a
5   2  a
6   3  c
8   4  b
9   5  b
10  6  b

print df['B'].iat[2]
c

print df['B'].iloc[2]
c

Timing:

In [266]: %timeit df['B'].iat[2]
The slowest run took 31.55 times longer than the fastest. This could mean that an intermediate result is being cached 
100000 loops, best of 3: 7.28 µs per loop

In [267]: %timeit df['B'].iloc[2]
The slowest run took 24.47 times longer than the fastest. This could mean that an intermediate result is being cached 
100000 loops, best of 3: 11.5 µs per loop