这是一年半后的事实，但我也需要能够。transform()多个熊猫数据帧列一次(以及能够。inverse_transform()他们)。这扩展了上面@PriceHardman的优秀建议:

class MultiColumnLabelEncoder(LabelEncoder):
    """
    Wraps sklearn LabelEncoder functionality for use on multiple columns of a
    pandas dataframe.

    """
    def __init__(self, columns=None):
        self.columns = columns

    def fit(self, dframe):
        """
        Fit label encoder to pandas columns.

        Access individual column classes via indexig `self.all_classes_`

        Access individual column encoders via indexing
        `self.all_encoders_`
        """
        # if columns are provided, iterate through and get `classes_`
        if self.columns is not None:
            # ndarray to hold LabelEncoder().classes_ for each
            # column; should match the shape of specified `columns`
            self.all_classes_ = np.ndarray(shape=self.columns.shape,
                                           dtype=object)
            self.all_encoders_ = np.ndarray(shape=self.columns.shape,
                                            dtype=object)
            for idx, column in enumerate(self.columns):
                # fit LabelEncoder to get `classes_` for the column
                le = LabelEncoder()
                le.fit(dframe.loc[:, column].values)
                # append the `classes_` to our ndarray container
                self.all_classes_[idx] = (column,
                                          np.array(le.classes_.tolist(),
                                                  dtype=object))
                # append this column's encoder
                self.all_encoders_[idx] = le
        else:
            # no columns specified; assume all are to be encoded
            self.columns = dframe.iloc[:, :].columns
            self.all_classes_ = np.ndarray(shape=self.columns.shape,
                                           dtype=object)
            for idx, column in enumerate(self.columns):
                le = LabelEncoder()
                le.fit(dframe.loc[:, column].values)
                self.all_classes_[idx] = (column,
                                          np.array(le.classes_.tolist(),
                                                  dtype=object))
                self.all_encoders_[idx] = le
        return self

    def fit_transform(self, dframe):
        """
        Fit label encoder and return encoded labels.

        Access individual column classes via indexing
        `self.all_classes_`

        Access individual column encoders via indexing
        `self.all_encoders_`

        Access individual column encoded labels via indexing
        `self.all_labels_`
        """
        # if columns are provided, iterate through and get `classes_`
        if self.columns is not None:
            # ndarray to hold LabelEncoder().classes_ for each
            # column; should match the shape of specified `columns`
            self.all_classes_ = np.ndarray(shape=self.columns.shape,
                                           dtype=object)
            self.all_encoders_ = np.ndarray(shape=self.columns.shape,
                                            dtype=object)
            self.all_labels_ = np.ndarray(shape=self.columns.shape,
                                          dtype=object)
            for idx, column in enumerate(self.columns):
                # instantiate LabelEncoder
                le = LabelEncoder()
                # fit and transform labels in the column
                dframe.loc[:, column] =\
                    le.fit_transform(dframe.loc[:, column].values)
                # append the `classes_` to our ndarray container
                self.all_classes_[idx] = (column,
                                          np.array(le.classes_.tolist(),
                                                  dtype=object))
                self.all_encoders_[idx] = le
                self.all_labels_[idx] = le
        else:
            # no columns specified; assume all are to be encoded
            self.columns = dframe.iloc[:, :].columns
            self.all_classes_ = np.ndarray(shape=self.columns.shape,
                                           dtype=object)
            for idx, column in enumerate(self.columns):
                le = LabelEncoder()
                dframe.loc[:, column] = le.fit_transform(
                        dframe.loc[:, column].values)
                self.all_classes_[idx] = (column,
                                          np.array(le.classes_.tolist(),
                                                  dtype=object))
                self.all_encoders_[idx] = le
        return dframe.loc[:, self.columns].values

    def transform(self, dframe):
        """
        Transform labels to normalized encoding.
        """
        if self.columns is not None:
            for idx, column in enumerate(self.columns):
                dframe.loc[:, column] = self.all_encoders_[
                    idx].transform(dframe.loc[:, column].values)
        else:
            self.columns = dframe.iloc[:, :].columns
            for idx, column in enumerate(self.columns):
                dframe.loc[:, column] = self.all_encoders_[idx]\
                    .transform(dframe.loc[:, column].values)
        return dframe.loc[:, self.columns].values

    def inverse_transform(self, dframe):
        """
        Transform labels back to original encoding.
        """
        if self.columns is not None:
            for idx, column in enumerate(self.columns):
                dframe.loc[:, column] = self.all_encoders_[idx]\
                    .inverse_transform(dframe.loc[:, column].values)
        else:
            self.columns = dframe.iloc[:, :].columns
            for idx, column in enumerate(self.columns):
                dframe.loc[:, column] = self.all_encoders_[idx]\
                    .inverse_transform(dframe.loc[:, column].values)
        return dframe.loc[:, self.columns].values

例子:

如果df和df_copy()是混合类型的pandas数据帧，你可以将MultiColumnLabelEncoder()应用到dtype=object列上，方法如下:

# get `object` columns
df_object_columns = df.iloc[:, :].select_dtypes(include=['object']).columns
df_copy_object_columns = df_copy.iloc[:, :].select_dtypes(include=['object']).columns

# instantiate `MultiColumnLabelEncoder`
mcle = MultiColumnLabelEncoder(columns=object_columns)

# fit to `df` data
mcle.fit(df)

# transform the `df` data
mcle.transform(df)

# returns output like below
array([[1, 0, 0, ..., 1, 1, 0],
       [0, 5, 1, ..., 1, 1, 2],
       [1, 1, 1, ..., 1, 1, 2],
       ..., 
       [3, 5, 1, ..., 1, 1, 2],

# transform `df_copy` data
mcle.transform(df_copy)

# returns output like below (assuming the respective columns 
# of `df_copy` contain the same unique values as that particular 
# column in `df`
array([[1, 0, 0, ..., 1, 1, 0],
       [0, 5, 1, ..., 1, 1, 2],
       [1, 1, 1, ..., 1, 1, 2],
       ..., 
       [3, 5, 1, ..., 1, 1, 2],

# inverse `df` data
mcle.inverse_transform(df)

# outputs data like below
array([['August', 'Friday', '2013', ..., 'N', 'N', 'CA'],
       ['April', 'Tuesday', '2014', ..., 'N', 'N', 'NJ'],
       ['August', 'Monday', '2014', ..., 'N', 'N', 'NJ'],
       ..., 
       ['February', 'Tuesday', '2014', ..., 'N', 'N', 'NJ'],
       ['April', 'Tuesday', '2014', ..., 'N', 'N', 'NJ'],
       ['March', 'Tuesday', '2013', ..., 'N', 'N', 'NJ']], dtype=object)

# inverse `df_copy` data
mcle.inverse_transform(df_copy)

# outputs data like below
array([['August', 'Friday', '2013', ..., 'N', 'N', 'CA'],
       ['April', 'Tuesday', '2014', ..., 'N', 'N', 'NJ'],
       ['August', 'Monday', '2014', ..., 'N', 'N', 'NJ'],
       ..., 
       ['February', 'Tuesday', '2014', ..., 'N', 'N', 'NJ'],
       ['April', 'Tuesday', '2014', ..., 'N', 'N', 'NJ'],
       ['March', 'Tuesday', '2013', ..., 'N', 'N', 'NJ']], dtype=object)

你可以通过索引访问单独的列类、列标签和用于适合每个列的列编码器:

mcle.all_classes_ mcle.all_encoders_ mcle.all_labels_

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

如果我们有单列来做标签编码和它的逆变换，当python中有多列时，很容易做到这一点

def stringtocategory(dataset):
    '''
    @author puja.sharma
    @see The function label encodes the object type columns and gives label      encoded and inverse tranform of the label encoded data
    @param dataset dataframe on whoes column the label encoding has to be done
    @return label encoded and inverse tranform of the label encoded data.
   ''' 
   data_original = dataset[:]
   data_tranformed = dataset[:]
   for y in dataset.columns:
       #check the dtype of the column object type contains strings or chars
       if (dataset[y].dtype == object):
          print("The string type features are  : " + y)
          le = preprocessing.LabelEncoder()
          le.fit(dataset[y].unique())
          #label encoded data
          data_tranformed[y] = le.transform(dataset[y])
          #inverse label transform  data
          data_original[y] = le.inverse_transform(data_tranformed[y])
   return data_tranformed,data_original

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。

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。

在这里和其他地方进行了大量的搜索和实验后，我认为你的答案是:

pd.DataFrame(列= df.columns, data = LabelEncoder () .fit_transform (df.values.flatten ()) .reshape (df.shape))

这将跨列保留类别名称:

import pandas as pd
from sklearn.preprocessing import LabelEncoder

df = pd.DataFrame([['A','B','C','D','E','F','G','I','K','H'],
                   ['A','E','H','F','G','I','K','','',''],
                   ['A','C','I','F','H','G','','','','']], 
                  columns=['A1', 'A2', 'A3','A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10'])

pd.DataFrame(columns=df.columns, data=LabelEncoder().fit_transform(df.values.flatten()).reshape(df.shape))

    A1  A2  A3  A4  A5  A6  A7  A8  A9  A10
0   1   2   3   4   5   6   7   9   10  8
1   1   5   8   6   7   9   10  0   0   0
2   1   3   9   6   8   7   0   0   0   0