Asish Mahapatra

> We intend to change this to return false (discussed in Basel), should probably get this into 3.0 @phofl Would this change only apply for boolean ops or do you...

`df.groupby.first` has a `skipna` argument that defaults to `True` https://pandas.pydata.org/docs/reference/api/pandas.core.groupby.DataFrameGroupBy.first.html Try setting that to `False`. ```py In [190]: pd.DataFrame({'a': [1, 1, 2, 2], 'b': [np.nan, '2', '3', '4']}).groupby('a').first(skipna=False) Out[190]: b...

on main it's about 5x faster than on 2.2.2 but still extremely slow compared to 2.0.3 on 2.0.3 -> 17ms 2.2.2 -> 5.4 s main -> 1.08 s

Is there an issue linked with this?

@Tom-Evers you'll have to build from the git repo I believe. The last pypi release was way back in 2015. Gohlke's site won't work since there are no wheels there...

So something like `map(lambda cond: lambda sent_index, word_indices: cond, conds)` ?

@sfc-gh-mvashishtha xref- https://github.com/snowflakedb/snowflake-sqlalchemy/issues/380 - it looks like the latest release on Apr 11 (EDIT: That release got yanked - no reason provided) might have addressed it. `pip install snowflake-sqlalchemy==1.5.1 'sqlalchemy>2.0'`...

I'd agree in general with @rhshadrach's comments in the linked issue (specifically around the edge cases where say most of the entries are May but one is April etc.). In...

A 2-pass approach might work. If the first pass errors out, then re-run with `format='mixed'` - and should also raise a `PerformanceWarning`

I'm not necessarily a fan of using first K because there's nothing fundamentally different from K=1 (which is today's behavior) and you would still run into issues if "April" pops...