Question

I like to think I’m not an idiot, but maybe I’m wrong. Can anyone explain to me why this isn’t working? I can achieve the desired results using ‘merge’. But I eventually need to join multiple pandas DataFrames so I need to get this method working.

In [2]: left = pandas.DataFrame({'ST_NAME': ['Oregon', 'Nebraska'], 'value': [4.685, 2.491]})

In [3]: right = pandas.DataFrame({'ST_NAME': ['Oregon', 'Nebraska'], 'value2': [6.218, 0.001]})

In [4]: left.join(right, on='ST_NAME', lsuffix='_left', rsuffix='_right')
Out[4]: 
  ST_NAME_left  value ST_NAME_right  value2
0       Oregon  4.685           NaN     NaN
1     Nebraska  2.491           NaN     NaN

Answer 1

Try using merge:

In [14]: right
Out[14]: 
    ST_NAME  value2
0    Oregon   6.218
1  Nebraska   0.001

In [15]: merge(left, right)
Out[15]: 
    ST_NAME  value  value2
0  Nebraska  2.491   0.001
1    Oregon  4.685   6.218

In [18]: merge(left, right, on='ST_NAME', sort=False)
Out[18]: 
    ST_NAME  value  value2
0    Oregon  4.685   6.218
1  Nebraska  2.491   0.001

DataFrame.join is a bit of legacy method and apparently doesn’t do column-on-column joins (originally it did index on column using the on parameter, hence the “legacy” designation).