Error"can Only Compare Identically-labeled Series Objects" And Sort_index

I have two dataframes df1 df2with the same numbers of rows and columns and variables, and I'm trying to compare the boolean variable choice in the two dataframes. Then use if/else

Solution 1:

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
57True60True77False82Trueprint (df2)
   v_100  choice
41False52True674True76True

---

df1 = df1.reset_index(drop=True)
df2 = df2.reset_index(drop=True)
print (df1)
   v_100  choice
07True10True27False32Trueprint (df2)
   v_100  choice
01False12True274True36True

df1['v_100'] = df1['choice'].mask(df1['choice'] != df2['choice'],
                                  (df1['choice'] | df2['choice']) * 0.5)

print (df1)
   v_100  choice
00.5True11.0True20.5False31.0True

Solution 2:

The error happens because you compare two pandas.Series objects with different indices. A simple solution would be to compare just the values in the series. Try it:

if df1['choice'].values!= df2['choice'].values