我有一段代碼是我的資料的預處理檔案。一切都是 kosher 的，直到我必須將預處理過的資料輸入到采用 Pandas 資料幀和陣列的 fit 函式中。如何將這些訓練資料轉換為用于喂養的資料框？對于pipeline.fit()函式，資料型別是列轉換器而不是熊貓 df。
代碼：
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
# generate the data
data = pd.DataFrame({
'y': [1, 2, 3, 4, 5],
'x1': [6, 7, 8, np.nan, np.nan],
'x2': [9, 10, 11, np.nan, np.nan],
'x3': ['a', 'b', 'c', np.nan, np.nan],
'x4': [np.nan, np.nan, 'd', 'e', 'f']
})
# extract the features and target
x = data.drop(labels=['y'], axis=1)
y = data['y']
# split the data
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)
# map the features to the corresponding types (numerical or categorical)
numerical_features = x_train.select_dtypes(include=['int64', 'float64']).columns.tolist()
categorical_features = x_train.select_dtypes(include=['object']).columns.tolist()
# define the numerical features pipeline
numerical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())
])
# define the categorical features pipeline
categorical_transformer = Pipeline(steps=[
('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
('onehot', OneHotEncoder(handle_unknown='ignore'))
])
# define the overall pipeline
preprocessor_pipeline = ColumnTransformer(transformers=[
('num', numerical_transformer, numerical_features),
('cat', categorical_transformer, categorical_features)
])
# fit the pipeline to the training data
preprocessor_pipeline.fit(x_train)
# apply the pipeline to the training and test data
x_train_ = preprocessor_pipeline.transform(x_train)
x_test_ = preprocessor_pipeline.transform(x_test)
獎勵：我還需要預處理我的標簽 (y_train) 嗎？
uj5u.com熱心網友回復：
要將您的管道結果轉換為資料幀，您只需要這樣：
x_train_df = pd.DataFrame(data=x_train_)
x_test_df = pd.DataFrame(data=x_test_)
由于您的標簽 y 在大多數情況下已經是數字，因此不需要進一步的預處理。但這也取決于您要在下一步中使用的 ML 模型。
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