您可以这样做以摆脱弃用消息

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer

ct = ColumnTransformer(
    [('one_hot_encoder', OneHotEncoder(), [0])],    # The column numbers to be transformed (here is [0] but can be [0, 1, 3])
    remainder='passthrough'                         # Leave the rest of the columns untouched
)

x = np.array(ct.fit_transform(x), dtype=np.float)

# Importing the Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

#import dataset
dataset = pd.read_csv("Data.csv")
X = dataset.iloc[:,:-1].values
Y = dataset.iloc[:,3].values

#Taking care of Missing data
from sklearn.impute import SimpleImputer  
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
imputer = imputer.fit(X[:,1:3])
X[:,1:3] = imputer.transform(X[:,1:3])

#Encoding Categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
labelencoder_X = LabelEncoder()
X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
transformer = ColumnTransformer([('one_hot_encoder', OneHotEncoder(), [0])],remainder='passthrough')
X = np.array(transformer.fit_transform(X), dtype=np.float)
labelencoder_Y = LabelEncoder()
Y = labelencoder_Y.fit_transform(Y)

目前，您无需执行任何操作。即使出现这些警告，代码也应该可以工作。从技术上讲，它们不是错误。

如果您想基于此示例构建一些模型，您可能应该解决它们。您需要的大部分信息都在警告中。例如：

In case you used a LabelEncoder before this OneHotEncoder to convert the categories to integers, then you can now use the OneHotEncoder directly.

因此，当您迁移到sklearn0.22 版时，您不需要同时使用LabelEncoder()和OneHotEncoder()，您可以在 中完成所有操作OneHotEncoder()，但您可能需要查看特定于版本的文档以了解如何执行此操作并满足您的特定需求版本发布时需要。

暂时不要做任何事。

用于ColumnTransformer Example 的文档进行 检查。# TODO: 创建一个 LabelEncoder 对象并将其拟合到 X 中的每个特征

# import preprocessing from sklearn
from sklearn import preprocessing
# 1. INSTANTIATE
# encode labels with value between 0 and n_classes-1.
le = preprocessing.LabelEncoder()
# 2/3. FIT AND TRANSFORM
# use df.apply() to apply le.fit_transform to all columns
X_2 = X.apply(le.fit_transform)
X_2.head()

如果您希望查看端到端示例，请查看。