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Reproducible Example

import pandas as pd df = pd.DataFrame( dict( Date=pd.period_range("2000-01-01", periods=10, name="Date"), Value=range(10), ), ) result1 = df[df.Date == "2000-01-06"] # Correct result2 = df.query("Date == '2000-01-06'") # Empty! # To show that the period type is the problem df.Date = df.Date.dt.to_timestamp() result3 = df[df.Date == "2000-01-06"] # Correct result4 = df.query("Date == '2000-01-06'") # Correct

Issue Description

I would expect query("Date == '2000-01-06'") to return the same regardless of whether the Date column was datetime or period, given that df.Date == "2000-01-06" returns the same in both cases.

Also, >=, <, etc. work as expected for the period column, it's just == that is wrong.

I dug a little deeper and it seems that query in this case is using .isin to evaluate ==, so the issue also shows in this code without any eval going on.

pd.period_range("2000-01-01", periods=3).isin(['2000-01-02']) # False, False, False pd.date_range("2000-01-01", periods=3).isin(['2000-01-02']) # False, True, False pd.period_range("2000-01-01", periods=3) == '2000-01-02' # False, True, False

From what I can tell, isin will attempt to turn the passed list into a PeriodArray here:

But it will fail because it can't work out the freq, so reverts to just doing element-wise equality which compares periods and strings and so everything is False.

Maybe a check here to see if this is PeriodArray and if so pass self.freq, or is there something like period_array_like that will copy the freq/dtype from one period array to a new one.

Is it worth questioning why .query() uses isin to handle == in the first place? I have no idea, I'm sure there's a good reason.

Expected Behavior

Clear from the above?

Installed Versions

pd.show_versions() is still buggy, doesn't work on my machine.

I'm on version 2.0.3.