pandas 如何忽略熊猫的索引比较断言帧相等
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how to ignore index comparison for pandas assert frame equal
提问by Lisa
I try to compare below two dataframe with "check_index_type" set to False. According to the documentation, if it set to False, it shouldn't "check the Index class, dtype and inferred_type are identical". Did I misunderstood the documentation? how to compare ignoring the index and return True for below test?
我尝试将“check_index_type”设置为 False 的两个数据框进行比较。根据文档，如果设置为 False，则不应“检查索引类，dtype 和 inferred_type 是否相同”。我误解了文档吗？如何比较忽略索引并为下面的测试返回 True？
I know I can reset the index but prefer not to.
我知道我可以重置索引但不想这样做。
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.testing.assert_frame_equal.html
https://pandas.pydata.org/pandas-docs/stable/generated/pandas.testing.assert_frame_equal.html
from pandas.util.testing import assert_frame_equal
import pandas as pd
d1 = pd.DataFrame([[1,2], [10, 20]], index=[0,2])
d2 = pd.DataFrame([[1, 2], [10, 20]], index=[0, 1])
assert_frame_equal(d1, d2, check_index_type=False)
AssertionError: DataFrame.index are different
DataFrame.index values are different (50.0 %)
[left]: Int64Index([0, 2], dtype='int64')
[right]: Int64Index([0, 1], dtype='int64')
回答by YOBEN_S
Index is part of data frame , if the index are different , we should say the dataframes are different , even the value of dfs are same , so , if you want to check the value , using array_equalfrom numpy
索引是数据帧的一部分，如果索引不同，我们应该说数据帧是不同的，甚至dfs的值也一样，所以，如果你想检查值，使用array_equalfrom 。numpy
d1 = pd.DataFrame([[1,2], [10, 20]], index=[0,2])
d2 = pd.DataFrame([[1, 2], [10, 20]], index=[0, 1])
np.array_equal(d1.values,d2.values)
Out[759]: True
For more info about assert_frame_equalin git
有关assert_frame_equal在git 中的更多信息
回答by The Aelfinn
If you really don't care about the index being equal, you can drop the index as follows:
如果您真的不关心索引是否相等，则可以按如下方式删除索引：
assert_frame_equal(d1.reset_index(drop=True), d2.reset_index(drop=True))
