Wes McKinney

DESIGN: Wishlist from scikit-learn, keras, tensorflow?

1

What can pandas provide in the way of a C/C++/Cython API to better enable upstack ML / statistical libraries? @ogrisel @amueller, who might have some good perspectives?

DEV: Publishing benchmarks vs. trunk?

2

We'll want to develop a microbenchmark suite to compare pandas 2.0 perf versus 1.x, especially in microbenchmarks. What's the right tool for this? asv, vbench?

Placeholder for this part of the project

Copy on write for views

6

I will work on a full document for this to get the conversation started, but this can be the placeholder for our discussion about COW

A better vectorized ternary operator

3

I was poking around pandas to see if someone had implemented the equivalent of a vectorized if-then-else. For example, similar to `np.where`, but pandas friendly ``` python df['category'].isin(values).ifelse(df.group_a, df.group_b) ```...

Make NA/null a first-class citizen in groupby operations

3

xref #9 Maybe we can collect a list of pandas issues that have happened in and around this. - https://github.com/pydata/pandas/issues/14170 I've found it's valuable to be able to consistently compute...

pandas would potentially benefit from a more efficient decimal dtype, possibly using libmpdec (what CPython uses internally) for the internal implementation.

"Predicate pushdown" in group-bys

2

xref #15 I brought this up at SciPy 2015, but there's a significant performance win available in expressions like: ``` df[boolean_cond].groupby(grouping_exprs).agg(agg_expr) ``` If you do this currently, it will produce...

Alternate groupby API that is more functionally consistent with databases or systems like dplyr

2

pandas's row indexes introduces a level of semantic incompatibility with other systems that occasionally causes problems for users who are using both pandas and some other system. Functionally, this mainly...