Python在使用函数时不能接受输入

我正在研究 Kaggle 上托管的房价问题。在构建我的模型时，我认为在测试集上重用我一直用于训练数据集的一些代码是有意义的，因此我将执行相互操作的代码合并到一个函数定义中。在此函数中，我正在处理缺失值并使用其返回值执行单热编码并将其用于随机森林回归。但是，它抛出以下错误：

Traceback (most recent call last):
  File "C:/Users/security/Downloads/AP/Boston-Kaggle/Model.py", line 56, in <module>
    sel.fit(x_train, y_train)
  File "C:\Users\security\AppData\Roaming\Python\Python37\site-packages\sklearn\feature_selection\from_model.py", line 196, in fit
    self.estimator_.fit(X, y, **fit_params)
  File "C:\Users\security\AppData\Roaming\Python\Python37\site-packages\sklearn\ensemble\forest.py", line 249, in fit
    X = check_array(X, accept_sparse="csc", dtype=DTYPE)
  File "C:\Users\security\AppData\Roaming\Python\Python37\site-packages\sklearn\utils\validation.py", line 542, in check_array
    allow_nan=force_all_finite == 'allow-nan')
  File "C:\Users\security\AppData\Roaming\Python\Python37\site-packages\sklearn\utils\validation.py", line 56, in _assert_all_finite
    raise ValueError(msg_err.format(type_err, X.dtype))
ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

在使用相同的代码而不将其组织成函数时，我没有遇到这个问题。def feature_selection_and_engineering(df)是有问题的功能。以下是我的全部代码：

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor

train = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Boston-Kaggle/master/test.csv")

def feature_selection_and_engineering(df):
    # Creating a series of how many NaN's are in each column
    nan_counts = df.isna().sum()

    # Creating a template list
    nan_columns = []

    # Iterating over the series and if the value is more than 0 (i.e there are some NaN's present)
    for i in range(0, len(nan_counts)):
        if nan_counts[i] > 0:
            nan_columns.append(df.columns[i])

    # Iterating through all the columns which are known to have NaN's
    for i in nan_columns:
        if df[nan_columns][i].dtypes == 'float64':
            df[i] = df[i].fillna(df[i].mean())
        elif df[nan_columns][i].dtypes == 'object':
            df[i] = df[i].fillna('XX')

    # Creating a template list
    categorical_columns = []

    # Iterating across all the columns,
    # checking if they're of the object datatype and if they are, appending them to the categorical list
    for i in range(0, len(df.dtypes)):
        if df.dtypes[i] == 'object':
            categorical_columns.append(df.columns[i])

    return categorical_columns

# take one-hot encoding
OHE_sdf = pd.get_dummies(feature_selection_and_engineering(train))

# drop the old categorical column from original df
train.drop(columns = feature_selection_and_engineering(train), axis = 1, inplace = True)

# attach one-hot encoded columns to original data frame
train = pd.concat([train, OHE_sdf], axis = 1, ignore_index = False)

# Dividing the training dataset into train/test sets with the test size being 20% of the overall dataset.
x_train, x_test, y_train, y_test = train_test_split(train, train['SalePrice'], test_size = 0.2, random_state = 42)

randomForestRegressor = RandomForestRegressor(n_estimators=1000)

# Invoking the Random Forest Classifier with a 1.25x the mean threshold to select correlating features
sel = SelectFromModel(RandomForestClassifier(n_estimators = 100), threshold = '1.25*mean')
sel.fit(x_train, y_train)

selected = sel.get_support()

# linearRegression.fit(x_train, y_train)
randomForestRegressor.fit(x_train, y_train)

# Assigning the accuracy of the model to the variable "accuracy"
accuracy = randomForestRegressor.score(x_train, y_train)

# Predicting for the data in the test set
predictions = randomForestRegressor.predict(feature_selection_and_engineering(test))

# Writing the predictions to a new CSV file
submission = pd.DataFrame({'Id': test['PassengerId'], 'SalePrice': predictions})
filename = 'Boston-Submission.csv'
submission.to_csv(filename, index=False)

print(accuracy*100, "%")

新错误：

    Traceback (most recent call last):
  File "/home/onur/Documents/Boston-Kaggle/Model.py", line 76, in <module>
    x_train, encoder = feature_selection_and_engineering(x_train)
  File "/home/onur/Documents/Boston-Kaggle/Model.py", line 57, in feature_selection_and_engineering
    encoder = train_one_hot_encoder(df, categorical_columns)
  File "/home/onur/Documents/Boston-Kaggle/Model.py", line 30, in train_one_hot_encoder
    return enc.fit(categorical_df)
  File "/opt/anaconda/envs/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py", line 493, in fit
    self._fit(X, handle_unknown=self.handle_unknown)
  File "/opt/anaconda/envs/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py", line 80, in _fit
    X_list, n_samples, n_features = self._check_X(X)
  File "/opt/anaconda/envs/lib/python3.7/site-packages/sklearn/preprocessing/_encoders.py", line 67, in _check_X
    force_all_finite=needs_validation)
  File "/opt/anaconda/envs/lib/python3.7/site-packages/sklearn/utils/validation.py", line 542, in check_array
    allow_nan=force_all_finite == 'allow-nan')
  File "/opt/anaconda/envs/lib/python3.7/site-packages/sklearn/utils/validation.py", line 60, in _assert_all_finite
    raise ValueError("Input contains NaN")
ValueError: Input contains NaN