Vectorized lookup on a pandas dataframe

Question

I have two DataFrames . . .

df1 is a table I need to pull values from using index, column pairs retrieved from multiple columns in df2.

I see t

Answer 1

lookup and get_value are great answers if your values exist in lookup dataframe.

However, if you've (row, column) pairs not present in the lookup dataframe, and want the lookup value be NaN -- merge and stack is one way to do it

In [206]: df2.merge(df1.stack().reset_index().rename(columns={0: 'looked_up'}),
                    left_on=['animal', 'letter'], right_on=['level_0', 'level_1'],
                    how='left').drop(['level_0', 'level_1'], 1)
Out[206]:
    0   1   2   3   4 animal letter  looked_up
0   0   1   2   3   4    cat      a          0
1   5   6   7   8   9    dog      b          6
2  10  11  12  13  14   fish      c         12
3  15  16  17  18  19   bird      d         18

Test with adding non-existing (animal, letter) pair

In [207]: df22
Out[207]:
      0     1     2     3     4 animal letter
0   0.0   1.0   2.0   3.0   4.0    cat      a
1   5.0   6.0   7.0   8.0   9.0    dog      b
2  10.0  11.0  12.0  13.0  14.0   fish      c
3  15.0  16.0  17.0  18.0  19.0   bird      d
4   NaN   NaN   NaN   NaN   NaN  dummy    NaN

In [208]: df22.merge(df1.stack().reset_index().rename(columns={0: 'looked_up'}),
                    left_on=['animal', 'letter'], right_on=['level_0', 'level_1'],
                    how='left').drop(['level_0', 'level_1'], 1)
Out[208]:
      0     1     2     3     4 animal letter  looked_up
0   0.0   1.0   2.0   3.0   4.0    cat      a        0.0
1   5.0   6.0   7.0   8.0   9.0    dog      b        6.0
2  10.0  11.0  12.0  13.0  14.0   fish      c       12.0
3  15.0  16.0  17.0  18.0  19.0   bird      d       18.0
4   NaN   NaN   NaN   NaN   NaN  dummy    NaN        NaN