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I do a lot of... some_pandas_object.values to get to the raw numpy, because often dealing with raw np buffer is more efficient or ergonomic. Hopefully losin

I do a lot of... some_pandas_object.values to get to the raw numpy, because often dealing with raw np buffer is more efficient or ergonomic. Hopefully losing numpy foundations will not affect (the efficiency of) code which does this.

hannibalhorn 4y ago
I do the same. Sounds like arrow is being implemented as an alternative, and you'll have to explicitly opt in via a dtype or a global setting, so numpy isn't going away.
PaulHoule 4y ago
… and what about interoperability with scikit-learn, PyTorch, etc?
- nerdponx 4y ago
  None of that has changed.
bsdz 4y ago
Looks like they're recommending using ".to_numpy()" instead of ".values" in dev (2.*) docs: https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.values.html?highlight=values#pandas.DataFrame.values https://pandas.pydata.org/docs/dev/reference/api/pandas.Data...
- nerdponx 4y ago
  .values has been deprecated for a long time now, with .to_numpy() as its recommended replacement for just as long.
  - eigenvalue 4y ago
    Wow, had no idea about this. Hopefully they can just automatically interpret .values as .to_numpy or else a ton of old code is going to break.
    - nerdponx 4y ago
      That's what they've been doing for a long time as well!
__mharrison__ 4y ago
I often use Numba for complicated looping code for Pandas manipulation. Will be interesting to see how Arrow works with Numba.