Error"Can only compare identically-labeled Series objects" and sort_index
I think you need reset_index
for same index values and then comapare - for create new column is better use mask
or numpy.where
:
Also instead +
use |
because working with booleans.
df1 = df1.reset_index(drop=True)
df2 = df2.reset_index(drop=True)
df1['v_100'] = df1['choice'].mask(df1['choice'] != df2['choice'],
(df1['choice'] + df2['choice']) * 0.5)
df1['v_100'] = np.where(df1['choice'] != df2['choice'],
(df1['choice'] | df2['choice']) * 0.5,
df1['choice'])
Samples:
print (df1)
v_100 choice
5 7 True
6 0 True
7 7 False
8 2 True
print (df2)
v_100 choice
4 1 False
5 2 True
6 74 True
7 6 True
df1 = df1.reset_index(drop=True)
df2 = df2.reset_index(drop=True)
print (df1)
v_100 choice
0 7 True
1 0 True
2 7 False
3 2 True
print (df2)
v_100 choice
0 1 False
1 2 True
2 74 True
3 6 True
df1['v_100'] = df1['choice'].mask(df1['choice'] != df2['choice'],
(df1['choice'] | df2['choice']) * 0.5)
print (df1)
v_100 choice
0 0.5 True
1 1.0 True
2 0.5 False
3 1.0 True
The error happens because you compare two pandas.Series objects with different indices. A simple solution could be to compare just the values in the series. Try it:
if df1['choice'].values != df2['choice'].values