假设您有以下 DF:
In [124]: df
Out[124]:
animal_id animal_name number
0 623 dog 4
1 123 cat 2
2 623 cat 1
3 111 lion 6
In [125]: df.dtypes
Out[125]:
animal_id int64
animal_name category
number int64
dtype: object
第一个保存animal_name
列(如果您将来需要它):
In [126]: animal_name = df['animal_name']
将animal_name
列转换为分类(节省内存)数字列:
In [127]: df['animal_name'] = df['animal_name'].cat.codes.astype('category')
In [128]: df
Out[128]:
animal_id animal_name number
0 623 1 4
1 123 0 2
2 623 0 1
3 111 2 6
In [129]: df.dtypes
Out[129]:
animal_id int64
animal_name category
number int64
dtype: object
现在 OneHotEncoder 应该可以工作了:
In [130]: enc = OneHotEncoder()
In [131]: enc.fit(df)
Out[131]:
OneHotEncoder(categorical_features='all', dtype=<class 'numpy.float64'>,
handle_unknown='error', n_values='auto', sparse=True)
In [132]: X = enc.fit(df)
In [134]: X.n_values_
Out[134]: array([624, 3, 7])
In [135]: enc.feature_indices_
Out[135]: array([ 0, 624, 627, 634], dtype=int32)