ENH: Integer NA Extension Array #21160
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jreback
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pandas/core/arrays/base.py
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| Parameters | ||
| ---------- | ||
| other : ExtenionArray or list/tuple of ExtenionArrays |
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## master #21160 +/- ##
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+ Coverage 91.96% 91.98% +0.01%
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Files 166 167 +1
Lines 50329 50606 +277
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+ Hits 46287 46551 +264
- Misses 4042 4055 +13
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There's a lot here, so preliminary thoughts based on a quick pass:
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@jreback Here is my take on the operators implementation, based on what I did in #20889. Looking at your implementation in |
pandas/core/dtypes/base.py
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| @@ -156,6 +156,12 @@ def name(self): | |||
| """ | |||
| raise AbstractMethodError(self) | |||
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| @property | |||
| def array_type(self): | |||
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Add this to the list of abstract methods / properties on line 110
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This change limits us to 1 array type per extension dtype. Is everyone OK with that? I don't see any downsides to that.
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this is not a problem, you simply have multiple arrays and multiple dtypes (as I did here).
pandas/core/dtypes/dtypes.py
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| from pandas import compat | ||
| from pandas.core.dtypes.generic import ABCIndexClass, ABCCategoricalIndex | ||
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| from .base import ExtensionDtype, _DtypeOpsMixin | ||
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| class Registry(object): | ||
| """ class to register our dtypes for inference |
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just "Registry for dtype inference"
jreback
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I split out #21185 with the changes to EA. To answer some of @jbrockmendel questions
I think this would overcomplicate things.
how so, the implementation is barely changed here
yes this is tricky, we are matching on the current impl. if we were to change this we should adjust.
sure, but we don't really support the concept of an ExtensionIndex yet. |
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| if mask is None: | ||
| mask = isna(values) | ||
| else: | ||
| assert len(mask) == len(values) |
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should this raise a ValueError instead of AssertionError?
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no, this is an internal construction error, need to satisfy the input guarantees
pandas/core/ops.py
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| is_extension_array_dtype(y) and not | ||
| is_scalar(y)): | ||
| y = x.__class__._from_sequence(y) | ||
| return op(x, y) |
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overlap in comment in #21191 since IntegerArray has an __init__ that I'm pretty sure can take left here (actually below in wrapper), I'm hopeful that in this case we can save a lot of trouble by using dispatch_to_index_op directly (passing the EA subclass rather than an Index subclass)
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latest push remove the type specific arrays, IOW everything is now just a |
| def _from_factorized(cls, values, original): | ||
| return cls(values, dtype=original.dtype) | ||
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| def __getitem__(self, item): |
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Without trying to overcomplicated things have we considered moving some of these items to a MaskedEAMixin? I'm thinking of taking a stab at the Boolean EA next and can see this being generalizable along with a few other methods (__iter__, __setitem__, perhaps take, etc...)
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certainly could, though I think might be better just to just directly subclass IntegerArray and the dtype, but that's for another PR :>
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anyhow any final comments @pandas-dev/pandas-core certainly will be follows in any event. |
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Made a couple doc follow-up notes in #22003 |
I am not necessarily saying that we should use None, but the fact is that we currently somewhat 'misuse' |
you can certainly make an issue to discuss this. and I agree we should prob move to a singular Null type. But this is not the PR for it. I disagree that |
Again, I didn't say we should use None for this. I also think we should not use that, it is already used in many other contexts. |
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ok, let's certainly discuss. In reality is a pretty cosmetic detail (mostly for actual printing). |
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bombs away |
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Ahum, I was actually reviewing this now |
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It would make it easier to review if you do not rebase your commits, once you do that github's features to automatically let me see what has changed since the last review does not work anymore.
To come back to my previous comment: can you add a section to the actual documentation? (not only whatsnew)
| expected = pd.Series([True, True, False, False], | ||
| index=list('ABCD')) | ||
| result = df.dtypes.apply(str) == str(dtype) | ||
| self.assert_series_equal(result, expected) |
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also test with the dtype itself? (result = df.dtypes == dtype)
| IntegerArray | ||
| """ | ||
| self._data, self._mask = coerce_to_array( | ||
| values, dtype=dtype, mask=mask, copy=copy) |
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Can you respond here further?
| # coerce when needed | ||
| s + 0.01 | ||
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| These dtypes can operate as part of of ``DataFrame``. |
| """ | ||
| We represent an IntegerArray with 2 numpy arrays | ||
| - data: contains a numpy integer array of the appropriate dtype | ||
| - mask: a boolean array holding a mask on the data, False is missing |
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False is missing
I don't think this is true currently?
But given the discussion earlier, I think it would be good to actually implement what you stated there to follow the example of arrow?
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| # coerce | ||
| data = self._coerce_to_ndarray() | ||
| return data.astype(dtype=dtype, copy=False) |
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Should we treat converting to float separately here? (that could be easily made more performant, as a probably common use case for astype)
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| def _maybe_mask_result(self, result, mask, other, op_name): | ||
| """ | ||
| Parameters |
| raise NotImplementedError( | ||
| "can only perform ops with 1-d structures") | ||
| elif is_list_like(other): | ||
| other = np.asarray(other) |
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Should we try to convert to IntegerArray here if possible?
eg s + s.tolist() gives floats (in case s is a series with int-na dtype)
| # otherwise perform the op | ||
| if isinstance(right, compat.string_types): | ||
| raise TypeError("{typ} cannot perform the operation mod".format( | ||
| typ=type(left).__name__)) |
| if isinstance(right, np.ndarray): | ||
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| # handle numpy scalars, this is a PITA | ||
| # TODO(jreback) |
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Can you clarify this to do comment?
| # assert our expected result | ||
| self.assert_series_equal(result, expected) | ||
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| def test_arith_integer_array(self, data, all_arithmetic_operators): |
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We need to discuss how to handle this (it will pop-up in all the other internal extension dtypes as well), so now is a good time.
I would personally move all specific tests that you add here that are not in the parent base tests to tests/arrays/integer, and only subclass here to check that the base tests make sense
| ExtensionScalarOpsMixin) | ||
| from .categorical import Categorical # noqa | ||
| from .datetimes import DatetimeArrayMixin # noqa | ||
| from .interval import IntervalArray # noqa | ||
| from .period import PeriodArrayMixin # noqa | ||
| from .timedeltas import TimedeltaArrayMixin # noqa | ||
| from .integer import ( # noqa | ||
| IntegerArray, to_integer_array) |
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What was the goal of exposing to_integer_array here? Is it used somewhere else?
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well I'll adress these in a followup. and for the record I didn't actually rebase this. |
your comment about the init. Well I already address this and it is simply not possible to have the init do nothing. I am -1 on adding from_* methods. This is how it is implemented. If you want to change it you are welcome to submit a PR. |
Sorry, but I don't think this should be an acceptable response. I think we should either roll back this PR or disable all public interfaces for this functionality until the (collective) development team is happy with the new features. As of now, I would consider this a blocker for new releases. As a general policy, I think we should require explicit approval from at least one other core developer before merging pull requests. I am all for avoiding large PRs and doing collaborative development in master, but we should not merge PRs unless they pass code review (certainly not for large changes like this one). |
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@shoyer that is a disengenuos response. If you want to do code review on the 100's of open PR's be my guest. This PR has been in a ready state for weeks. |
If folks respond in a timely manner, sure. If you haven't noticed I approve every other PR in a pretty timely manner. After round and rounds of comments. I believe I have earned, and deserve the same consideration. |
This PR is not just "one of the 100's of open PRs", but a major change that needs more attention than smaller PRs. I agree with Stephan that it would be good for such PRs to have the explicit +1 of other core devs. But OK, although I would have preferred to iterate further in this PR, let's move forward in follow-up PRs! |
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| .. warning:: | ||
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| The Integer NA support currently uses the captilized dtype version, e.g. ``Int8`` as compared to the traditional ``int8``. This may be changed at a future date. |
This misses the point. I spend an inordinate amount of time reviewing practically everything. I don't expect everyone to do this, rather for my PR's, which I DO expect review time from other folks. At the same time, endless comments, while certainly debatable, we don't force on anyone. Many many times, we have done follow up PR's just to cut down on the endless review cycle. In any event I will address your comments. |
* ENH: add integer-na support via an ExtensionArray closes pandas-dev#20700 closes pandas-dev#20747
closes #20700
closes #20747