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BUG: multi-type SparseDataFrame fixes and improvements #13917
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BUG: multi-type SparseDataFrame fixes and improvements #13917
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Current coverage is 85.30% (diff: 100%)@@ master #13917 diff @@
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Files 139 139
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Hits 42785 42785
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pandas/core/internals.py
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doesn't np.find_common_type do this? (or .common_type)?
should create a pandas version of this to isolate (e.g. this won't handle non-numpy types)
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as sparse mainly supports |
Types were incorrectly determined when slicing SparseDataFrames with multiple dtypes (such as float and object). Also enables type inference for SparseArrays by default.
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I used numpy's find_common_type instead of that local function, this changed a test ( Another (maybe non-minor) change is the default parameter in I also added some new tests as suggested, but don't feel confident in adding the proposed pandas alternative to the numpy find_common_type - my knowledge of pandas dtypes isn't really great. It would likely be similar to |
| def _lcd_dtype(l): | ||
| """ find the lowest dtype that can accomodate the given types """ | ||
| m = l[0].dtype | ||
| for x in l[1:]: |
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what I meant was can start off by simply moving this (your version or new one with np.find_common_type to pandas.types.cast (and if we have specific tests move similarly; if not, ideally add some). we can add the pandas specific functionaility later.
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can u also add tests to check normal current default ( |
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It turns out this PR works without the default argument change, I was too hasty to change it. Your PR fixes that better, so I reverted the change. Common type discovery moved to |
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yep, need to fix this. @sstanovnik can you create a new issue for this. |
| self.assertEqual(values.dtype, np.int64) | ||
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| # guess all ints are cast to uints.... | ||
| # B uint64 forces float because there are other signed int types |
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this might fix another bug, can you search for uint64 issues and see?
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add the issue as a reference here
and in the whatsnew
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Opened issue. Moved the tests, added new tests. Found and processed #10364. |
pandas/tests/types/test_cast.py
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| class TestCommonTypes(tm.TestCase): | ||
| def setUp(self): | ||
| super(TestCommonTypes, self).setUp() |
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you don't need setUp here
pandas/tests/types/test_cast.py
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| self.assertEqual(_find_common_type([np.object]), np.object) | ||
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| self.assertEqual(_find_common_type([np.int16, np.int64]), | ||
| np.int64) |
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group them
eg ints
floats
easier to read
doc/source/whatsnew/v0.19.0.txt
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| - ``pd.Timedelta(None)`` is now accepted and will return ``NaT``, mirroring ``pd.Timestamp`` (:issue:`13687`) | ||
| - ``Timestamp``, ``Period``, ``DatetimeIndex``, ``PeriodIndex`` and ``.dt`` accessor have gained a ``.is_leap_year`` property to check whether the date belongs to a leap year. (:issue:`13727`) | ||
| - ``pd.read_hdf`` will now raise a ``ValueError`` instead of ``KeyError``, if a mode other than ``r``, ``r+`` and ``a`` is supplied. (:issue:`13623`) | ||
| - ``.values`` will now return ``np.float64`` with a ``DataFrame`` with ``np.int64`` and ``np.uint64`` dtypes, conforming to ``np.find_common_type`` (:issue:`10364`, :issue:`13917`) |
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DataFrame.values will now ....with a frame of mixed int64 and uint64 dtypes.....
doc/source/whatsnew/v0.19.0.txt
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| - Bug in ``SparseDataFrame`` doesn't respect passed ``SparseArray`` or ``SparseSeries`` 's dtype and ``fill_value`` (:issue:`13866`) | ||
| - Bug in ``SparseArray`` and ``SparseSeries`` don't apply ufunc to ``fill_value`` (:issue:`13853`) | ||
| - Bug in ``SparseSeries.abs`` incorrectly keeps negative ``fill_value`` (:issue:`13853`) | ||
| - Bug in single row slicing on multi-type ``SparseDataFrame``s: types were previously forced to float (:issue:`13917`) |
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, types where previously...
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This reads fine to me? There's a colon after SparseDataFrames
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change : to ,
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Oh wait sorry I misread your comment.
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lgtm. @sinhrks ? |
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Thanks for your patience. |
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ha! thanks for yours |
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lgtm, thx @sstanovnik ! |
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thanks! |
git diff upstream/master | flake8 --diffTypes were incorrectly determined when slicing SparseDataFrames with
multiple dtypes (such as float and object) into SparseSeries.
No existing issue covers this.