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In [153]: df1
Out[153]:
time username
0 2016-07-15 13:30:00.030 bob
In [154]: df2
Out[154]:
time version
0 2016-07-15 13:30:00.000 1
1 2016-07-15 13:30:00.030 2
In [155]: pd.merge_asof(df1, df2, on='time')
Out[155]:
time username version
0 2016-07-15 13:30:00.030 bob 2
If I disable the exact matches, then I get the prior entry as expected:
In [156]: pd.merge_asof(df1, df2, on='time', allow_exact_matches=False)
Out[156]:
time username version
0 2016-07-15 13:30:00.030 bob 1
But the tolerance isn't respected when I disable the exact matches!
In [157]: pd.merge_asof(df1, df2, on='time', allow_exact_matches=False, tolerance=pd.Timedelta('10ms'))
Out[157]:
time username version
0 2016-07-15 13:30:00.030 bob 1
I expect to get a null here.
This is pandas version 0.18.0+408.gd8f3d73.
The text was updated successfully, but these errors were encountered:
Using these DataFrames for this example:
I can perform the
pd.merge_asof()
as expected:If I disable the exact matches, then I get the prior entry as expected:
But the tolerance isn't respected when I disable the exact matches!
I expect to get a null here.
This is pandas version
0.18.0+408.gd8f3d73
.The text was updated successfully, but these errors were encountered: