AttributeError: '_iLocIndexer' object has no attribute 'append'

AttributeError是一个常见的Python错误，它发生在试图访问或调用一个对象上不存在的属性时。在这个特定的例子中，"_iLocIndexer"对象尝试执行"append"操作，但这个对象实际上并没有'append'这个属性。_iLocIndexer可能是pandas DataFrame或Series的一个内置索引器，用于数据操作，但它不是一个可以直接添加元素的容器。

通常，这种错误会出现在以下几种情况：
1. 你可能误将数据结构的某个部分当作列表来操作，而实际上它是不可变的。
2. 对象已经被修改，不再支持某个方法。
3. 导入的库函数有误，或者是版本更新导致API发生变化。

解决这个问题需要检查代码，确认是否试图对正确的对象类型执行append操作，并确保该对象支持该操作。如果需要追加元素，应该检查对象是否应该是列表、数组或DataFrame的某一列。

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 'append' AttributeError: 'DataFrame' object has no attribute 'append'

这个错误通常是因为pandas DataFrame对象没有append方法导致的。相反，您可以使用concat方法将两个DataFrame对象连接在一起。以下是一个示例代码：

import pandas as pd

# 创建两个DataFrame对象
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [7, 8, 9], 'B': [10, 11, 12]})

# 使用concat方法将两个DataFrame对象连接在一起
df3 = pd.concat([df1, df2])

# 打印结果
print(df3)