Fix groupby behaviour on columns with missing values #3391
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samukweku
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It appears as though we never initialized `na_position_` in the case of `dt.by()`, and this resulted in some random data corruption for columns, that contain missing values. As of this PR, we initialize `na_position_` to `NaPosition::FIRST` to be consistent with what we declare in `dt.by()` [documentation](https://datatable.readthedocs.io/en/latest/api/dt/by.html): ``` The default behavior of groupby is to sort the groups in the ascending order, with NA values appearing before any other values. ``` Also, we switch to python 3.8 for testing debug wheels, so that we keep track of the status of the mentioned groupby tests. Closes #3331
samukweku
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It appears as though we never initialized `na_position_` in the case of `dt.by()`, and this resulted in some random data corruption for columns, that contain missing values. As of this PR, we initialize `na_position_` to `NaPosition::FIRST` to be consistent with what we declare in `dt.by()` [documentation](https://datatable.readthedocs.io/en/latest/api/dt/by.html): ``` The default behavior of groupby is to sort the groups in the ascending order, with NA values appearing before any other values. ``` Also, we switch to python 3.8 for testing debug wheels, so that we keep track of the status of the mentioned groupby tests. Closes #3331
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It appears as though we never initialized
na_position_in the case ofdt.by(), and this resulted in some random data corruption for columns, that contain missing values. As of this PR, we initializena_position_toNaPosition::FIRSTto be consistent with what we declare indt.by()documentation:Also, we switch to python 3.8 for testing debug wheels, so that we keep track of the status of the mentioned groupby tests.
Closes #3331