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

"DataFrame"对象没有"append"属性的错误通常是由于使用了错误的方法或属性名称导致的。根据引用，正确的属性名称可能是"_append"而不是"append"。你可以尝试使用"_append"方法来解决这个问题。

此外，根据引用，可能是因为在PyCharm中使用了"data.score"这个属性，而实际上"data"是一个DataFrame对象，它没有"score"属性。你可以通过查看数据框的列名来确定应该使用哪个属性来获取具体的数据。

另外，引用提到了一个包含报告器模块的路径，但与当前问题似乎没有直接关联。

所以，根据以上引用内容，你可以尝试使用"_append"方法并检查你的DataFrame对象的属性名称来解决"'DataFrame' object has no attribute 'append'. Did you mean: '_append'?"的问题。

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)

return object.__getattribute__(self, name) AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?

如果您的pandas版本比较老，可能会出现`DataFrame`对象没有`append`属性的错误。这时可以使用`concat`函数来实现在原有的DataFrame中添加一行数据。下面是一个示例代码：

import pandas as pd

# 假设原有的DataFrame为df，如下所示
df = pd.DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'c']})

# 添加新的一行数据
new_row = pd.DataFrame({'A': [4], 'B': ['d']})
df = pd.concat([df, new_row], ignore_index=True)

# 打印添加后的DataFrame
print(df)

上述代码中，我们首先创建了一个名为`df`的DataFrame，然后定义了一个新的DataFrame`new_row`，代表要添加的新行数据。接着，我们使用`pd.concat()`函数将新行数据添加到原有的DataFrame中，并将结果重新赋值给`df`变量。最后，我们使用`print()`函数打印添加后的DataFrame。需要注意的是，我们在`pd.concat()`函数中传入了`ignore_index=True`参数，这是为了使新行数据的索引自动递增，避免出现重复的索引。