Wes McKinney

@shoyer the problem with `Series.where` is that it presumes you have a Series on the LHS. For example, you might want: `cond.ifelse('foo', cond2.ifelse(df.other_col, 'bar'))`

How do you envision this working from a user perspective as we don't have the code-rewriting benefits of non-standard evaluation (that R has). You could of course require users to...

In theory, yes, though I would love to see that "alternative dataframe" (more of a real "database table"-like object) built inside pandas. C++ API stability for 3rd party libraries may...

I'm interested in evaluating the Arrow memory layout for nested data structures. If you don't require mutation, then the "fully shredded" columnar layout is fairly ideal for Dremel-style analytics (any...

Missing data uniformity and removing all the implicit type casting is definitely a top 5 priority from my POV. Not being able to exchange data with file formats and databases...

Things like monotonicity are so cheap to check and provide such significant performance benefits when they are known, that I would support always checking when it may be advantageous. These...

@llllllllll when you create a pandas.Series from an pandas.Array you are actually obtaining a view on that array, so if the source array mutates itself, it triggers copy-on-write (because it...

Thanks — I’ll try to fix this.

Thanks -- I will have a closer look at updating this for the latest version of Python

Let us know what behavior change (if any) would be preferred in Apache Arrow -- it might be nice to accept schemas with a permutation of column order (but only...