Scikit学习OneHotEncoder拟合和变换错误：ValueError：X的形状与拟合期间不同

Scikit学习OneHotEncoder拟合和变换错误：ValueError：X的形状与拟合期间不同

pd.get_dummies()

不需要使用LabelEnprer +
OneHotEnprer，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 pdfrom sklearn.preprocessing import OneHotEnprer, LabelEnprerinput_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 needle_dict = {}for col in filtered_df.columns:    le_dict[col] = LabelEnprer().fit(filtered_df[col])    filtered_df[col] = le_dict[col].transform(filtered_df[col])enc = oneHotEnprer()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_sweet0      2        2      0         01      1        1      1         02      0        0      2         1print(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_sweet0      2        2      0         0print(new_refreshed_df)[[ 0.  0.  1.  0.  0.  1.  1.  0.  0.  1.  0.]]