Reputation: 4426
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?
I know I can reset the index but prefer not to.
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')
Upvotes: 66
Views: 65762
Reputation: 3968
For those who came to this question because they're interested in using pd.testing.assert_series_equal
(operates on pd.Series
), pandas 1.1.0 has introduced an argument check_index
:
import pandas as pd
s1 = pd.Series({"a": 1})
s2 = pd.Series({"b": 1})
pd.testing.assert_series_equal(s1, s2, check_index=False)
This argument does not yet exist for pd.testing.assert_frame_equals
.
Upvotes: 13
Reputation: 16968
If you really don't care about the index being equal, you can drop the index as follows:
assert_frame_equal(d1.reset_index(drop=True), d2.reset_index(drop=True))
Upvotes: 64
Reputation: 323286
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_equal
from 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_equal
in git
Upvotes: 57