这是我解决你问题的办法。为了将包含文本的数据帧列转换为编码值，只需使用我的函数text_to_numbers，它返回LE的字典。Key是列LabelEncoder()作为值的列名。

def text_to_numbers(df):
        le_dict = dict()
        for i in df.columns:
            if df[i].dtype not in ["float64", "bool", "int64"]:
                le_dict[i] = preprocessing.LabelEncoder()
                df[i] = le_dict[i].fit_transform(df[i])
    
        return df, le_dict

下面的函数将使保留原始的未编码数据帧成为可能。

 def numbers_to_text(df, le_dict):
        for i in le_dict.keys():
            df[i] = le_dict[i].inverse_transform(df[i])
    
        return df

下面是我一次性转换多列的解决方案，以及精确的inverse_transform

from sklearn import preprocessing
columns = ['buying','maint','lug_boot','safety','cls']  # columns names where transform is required
for X in columns:
  exec(f'le_{X} = preprocessing.LabelEncoder()')  #create label encoder with name "le_X", where X is column name
  exec(f'df.{X} = le_{X}.fit_transform(df.{X})')  #execute fit transform for column X with respective lable encoder "le_X", where X is column name
df.head()  # to display transformed results

for X in columns:
  exec(f'df.{X} = le_{X}.inverse_transform(df.{X})')  #execute inverse_transform for column X with respective lable encoder "le_X", where X is column name
df.head() # to display Inverse transformed results of df

这是我解决你问题的办法。为了将包含文本的数据帧列转换为编码值，只需使用我的函数text_to_numbers，它返回LE的字典。Key是列LabelEncoder()作为值的列名。

def text_to_numbers(df):
        le_dict = dict()
        for i in df.columns:
            if df[i].dtype not in ["float64", "bool", "int64"]:
                le_dict[i] = preprocessing.LabelEncoder()
                df[i] = le_dict[i].fit_transform(df[i])
    
        return df, le_dict

下面的函数将使保留原始的未编码数据帧成为可能。

 def numbers_to_text(df, le_dict):
        for i in le_dict.keys():
            df[i] = le_dict[i].inverse_transform(df[i])
    
        return df

import pandas as pd
from sklearn.preprocessing import LabelEncoder

train=pd.read_csv('.../train.csv')

#X=train.loc[:,['waterpoint_type_group','status','waterpoint_type','source_class']].values
# Create a label encoder object 
def MultiLabelEncoder(columnlist,dataframe):
    for i in columnlist:

        labelencoder_X=LabelEncoder()
        dataframe[i]=labelencoder_X.fit_transform(dataframe[i])
columnlist=['waterpoint_type_group','status','waterpoint_type','source_class','source_type']
MultiLabelEncoder(columnlist,train)

在这里，我正在从位置读取一个csv，在函数中，我正在传递列列表，我想要labelencode和dataframe，我想应用这个。

这并没有直接回答你的问题(Naputipulu Jon和PriceHardman对此有精彩的回答)

但是，对于一些分类任务等，您可以使用

pandas.get_dummies(input_df) 

这可以输入带有分类数据的数据框架，并返回带有二进制值的数据框架。变量值被编码到结果数据框架中的列名中。更多的

根据对@PriceHardman解决方案提出的意见，我将提出以下版本的类:

class LabelEncodingColoumns(BaseEstimator, TransformerMixin):
def __init__(self, cols=None):
    pdu._is_cols_input_valid(cols)
    self.cols = cols
    self.les = {col: LabelEncoder() for col in cols}
    self._is_fitted = False

def transform(self, df, **transform_params):
    """
    Scaling ``cols`` of ``df`` using the fitting

    Parameters
    ----------
    df : DataFrame
        DataFrame to be preprocessed
    """
    if not self._is_fitted:
        raise NotFittedError("Fitting was not preformed")
    pdu._is_cols_subset_of_df_cols(self.cols, df)

    df = df.copy()

    label_enc_dict = {}
    for col in self.cols:
        label_enc_dict[col] = self.les[col].transform(df[col])

    labelenc_cols = pd.DataFrame(label_enc_dict,
        # The index of the resulting DataFrame should be assigned and
        # equal to the one of the original DataFrame. Otherwise, upon
        # concatenation NaNs will be introduced.
        index=df.index
    )

    for col in self.cols:
        df[col] = labelenc_cols[col]
    return df

def fit(self, df, y=None, **fit_params):
    """
    Fitting the preprocessing

    Parameters
    ----------
    df : DataFrame
        Data to use for fitting.
        In many cases, should be ``X_train``.
    """
    pdu._is_cols_subset_of_df_cols(self.cols, df)
    for col in self.cols:
        self.les[col].fit(df[col])
    self._is_fitted = True
    return self

这个类适合编码器的训练集，并在转换时使用适合的版本。代码的初始版本可以在这里找到。