pd.get_dummies()不需要使用LabelEncoder + OneHotEncoder，它们可以存储原始值，然后在新数据上使用它们。
像下面那样更改代码将为您提供所需的结果。
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
input_df = pd.DataFrame(dict(fruit=['Apple', 'Orange', 'Pine'],
color=['Red', 'Orange','Green'],
is_sweet = [0,0,1],
country=['USA','India','Asia']))
filtered_df = input_df.apply(pd.to_numeric, errors='ignore')
# This is what you need
le_dict = {}
for col in filtered_df.columns:
le_dict[col] = LabelEncoder().fit(filtered_df[col])
filtered_df[col] = le_dict[col].transform(filtered_df[col])
enc = OneHotEncoder()
enc.fit(filtered_df)
refreshed_df = enc.transform(filtered_df).toarray()
new_df = pd.DataFrame(dict(fruit=['Apple'],
color=['Red'],
is_sweet = [0],
country=['USA']))
for col in new_df.columns:
new_df[col] = le_dict[col].transform(new_df[col])
new_refreshed_df = enc.transform(new_df).toarray()
print(filtered_df)
color country fruit is_sweet
0 2 2 0 0
1 1 1 1 0
2 0 0 2 1
print(refreshed_df)
[[ 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0.]
[ 0. 1. 0. 0. 1. 0. 0. 1. 0. 1. 0.]
[ 1. 0. 0. 1. 0. 0. 0. 0. 1. 0. 1.]]
print(new_df)
color country fruit is_sweet
0 2 2 0 0
print(new_refreshed_df)
[[ 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 0.]]
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