Scala: выполнение преобразований Array изменяется при наличии другого блока Array

Мне кажется, нужно:

df['Class'] = np.where(df['Class'].isin(['Individual','Group']), df['Class'], 'Other')
print (df)
        Class
0  Individual
1       Group
2       Other
3       Other
4       Other
5       Other
6       Group

Другое решение (медленнее):

m = (df['Class'] == 'Individual') | (df['Class'] == 'Group')
df['Class'] = np.where(m, df['Class'], 'Other')

Другое решение:

df['Class'] = df['Class'].map({'Individual':'Individual', 'Group':'Group'}).fillna('Other')

Производительность (в реальных данных зависит от количества замен):

#[700000 rows x 1 columns]
df = pd.concat([df] * 100000, ignore_index=True)
#print (df)

In [208]: %timeit df['Class1'] = np.where(df['Class'].isin(['Individual','Group']), df['Class'], 'Other')
25.9 ms ± 485 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [209]: %timeit df['Class2'] = np.where((df['Class'] == 'Individual') | (df['Class'] == 'Group'), df['Class'], 'Other')
120 ms ± 6.63 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [210]: %timeit df['Class3'] = df['Class'].map({'Individual':'Individual', 'Group':'Group'}).fillna('Other')
95.7 ms ± 3.85 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [211]: %timeit df.loc[~df['Class'].isin(['Individual', 'Group']), 'Class'] = 'Other'
97.8 ms ± 6.78 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)