Python 熊猫“只能比较相同标记的数据帧对象”错误
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/18548370/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas "Can only compare identically-labeled DataFrame objects" error
提问by user1804633
I'm using Pandas to compare the outputs of two files loaded into two data frames (uat, prod): ...
我正在使用 Pandas 来比较加载到两个数据帧（uat、prod）中的两个文件的输出：...
uat = uat[['Customer Number','Product']]
prod = prod[['Customer Number','Product']]
print uat['Customer Number'] == prod['Customer Number']
print uat['Product'] == prod['Product']
print uat == prod
The first two match exactly:
74357 True
74356 True
Name: Customer Number, dtype: bool
74357 True
74356 True
Name: Product, dtype: bool
For the third print, I get an error: Can only compare identically-labeled DataFrame objects. If the first two compared fine, what's wrong with the 3rd?
对于第三次打印，我收到一个错误：只能比较标记相同的 DataFrame 对象。如果前两个比较好，那么第三个有什么问题？
Thanks
谢谢
采纳答案by Andy Hayden
Here's a small example to demonstrate this (which only applied to DataFrames, not Series, until Pandas 0.19 where it applies to both):
这是一个演示这一点的小示例（它仅适用于 DataFrames，而不适用于 Series，直到 Pandas 0.19 适用于两者）：
In [1]: df1 = pd.DataFrame([[1, 2], [3, 4]])
In [2]: df2 = pd.DataFrame([[3, 4], [1, 2]], index=[1, 0])
In [3]: df1 == df2
Exception: Can only compare identically-labeled DataFrame objects
One solution is to sort the indexfirst (Note: some functions require sorted indexes):
一种解决方案是先对索引进行排序（注意：有些函数需要排序索引）：
In [4]: df2.sort_index(inplace=True)
In [5]: df1 == df2
Out[5]:
0 1
0 True True
1 True True
Note: ==is also sensitive to the order of columns, so you may have to use sort_index(axis=1):
注意：对列的顺序==也很敏感，因此您可能必须使用sort_index(axis=1)：
In [11]: df1.sort_index().sort_index(axis=1) == df2.sort_index().sort_index(axis=1)
Out[11]:
0 1
0 True True
1 True True
Note: This can still raise (if the index/columns aren't identically labelled after sorting).
注意：这仍然可以引发（如果排序后索引/列的标签不同）。
回答by CoreDump
You can also try dropping the index column if it is not needed to compare:
如果不需要比较，您也可以尝试删除索引列：
print(df1.reset_index(drop=True) == df2.reset_index(drop=True))
I have used this same technique in a unit test like so:
我在单元测试中使用了同样的技术，如下所示：
from pandas.util.testing import assert_frame_equal
assert_frame_equal(actual.reset_index(drop=True), expected.reset_index(drop=True))
