请考虑以下方法：
首先让我们对所有非数值列进行one-hot编码：

In [220]: from sklearn.preprocessing import LabelEncoder

In [221]: x = df.select_dtypes(exclude=['number']) \
                .apply(LabelEncoder().fit_transform) \
                .join(df.select_dtypes(include=['number']))

In [228]: x
Out[228]:
        status  country  city      datetime  amount
601766       0        0     1  1.453916e+09     4.5
669244       0        1     0  1.454109e+09     6.9

现在我们可以使用LinearRegression分类器：

In [230]: classifier.fit(x.drop('status',1), x['status'])
Out[230]: LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

要在scikit-learn项目中进行one-hot编码，你可能会发现使用scikit-learn-contrib项目category_encoders更简洁：https://github.com/scikit-learn-contrib/categorical-encoding，其包括许多常见的分类变量编码方法，包括one-hot。

替代（因为你真的应该avoid usingLabelEncoder上的功能）。
ColumnTransformer和OneHotEncoder可以对 Dataframe 中的特征进行独热编码：

ct = ColumnTransformer(
    transformers=[
        ("ohe", OneHotEncoder(sparse_output=False), ["country", "city"]),
    ],
    remainder="passthrough",
).set_output(transform="pandas")

print(ct.fit_transform(X))

ohe__country_France  ohe__country_Italy  ohe__city_Naples  ohe__city_Paris  remainder__datetime  remainder__amount
0                  1.0                 0.0               0.0              1.0               1.4539                4.5
1                  0.0                 1.0               1.0              0.0               1.4541                6.9
2                  1.0                 0.0               0.0              1.0               1.4561                5.0

使用LogisticRegression的完整管道：

import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

raw_data = pd.DataFrame([["received", 1.4539, "France", 4.5, "Paris"], ["received", 1.4541, "Italy", 6.9, "Naples"], ["not-received", 1.4561, "France", 5.0, "Paris"]], columns=["status", "datetime", "country", "amount", "city"])

# X features include all variables except 'status', y label is 'status':
X = raw_data.drop(["status"], axis=1)
y = raw_data["status"]

# Create a pipeline with OHE for "country" and "city", then fits Logistic Regression:
pipe = make_pipeline(
    ColumnTransformer(
        transformers=[
            ("one-hot-encode", OneHotEncoder(), ["country", "city"]),
        ],
        remainder="passthrough",
    ),
    LogisticRegression(),
)

pipe.fit(X, y)