AttributeError: 'DataFrame' object has no attribute 'data'. Did you mean: '_data'? <class 'pandas.core.frame.DataFrame'>

AttributeError: 'DataFrame' object has no attribute 'data'. Did you mean: '_data'? 这个错误是由于在使用DataFrame对象的属性时出现了错误。根据提供的引用内容，有以下解决方案：1. 错误原因是使用了已过期的ix属性，可以尝试使用loc或iloc属性来替代。2. 可以检查pandas的版本是否过低，如果是的话可以考虑升级pandas到最新版本。3. 可以检查是否导入了pandas库，或者是否正确地导入了pandas库。请根据具体情况尝试以上解决方案。

相关问题

AttributeError: DataFrame object has no attribute append . Did you mean: _append ?

This error occurs when you try to call the `append` method on a Pandas DataFrame object, but the object does not have an `append` attribute.

One possible reason for this error is that you are trying to append a DataFrame to another DataFrame using the `append` method, but you are not using it correctly. In Pandas, the `append` method does not modify the original DataFrame, but instead it returns a new DataFrame that contains the rows from both DataFrames. Therefore, you need to assign the result of the `append` method to a new variable or to the original DataFrame, like this:

df1 = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})
df2 = pd.DataFrame({'A': [5, 6], 'B': [7, 8]})
df3 = df1.append(df2)

In this example, `df3` will contain the rows from both `df1` and `df2`.

If you are still getting the AttributeError, it's possible that the DataFrame object you are trying to append to does not have an `append` attribute. In this case, you can try using the `_append` attribute instead, which is a private method that is used internally by Pandas. However, be aware that using private methods may not be supported in future versions of Pandas and may cause unexpected behavior.

df1._append(df2)

AttributeError: 'DataFrame' object has no attribute 'data'. Did you mean: '_data'?

对不起，我在代码示例中犯了一个错误。波士顿房价数据集不是一个DataFrame对象，而是一个Bunch对象，因此没有`data`属性。我会修正这个错误，将数据集加载和划分的代码修改如下：

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge
from sklearn.neural_network import MLPRegressor
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import matplotlib.pyplot as plt

# 加载波士顿房价数据集
boston = load_boston()
X = boston.data
y = boston.target

# 数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 特征归一化
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

非常抱歉给您带来了困惑！