REF: IntervalIndex[IntervalArray] #20611
more review fixes
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| Original file line number | Diff line number | Diff line change |
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@@ -454,11 +454,11 @@ def __getitem__(self, value): | |
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| def __setitem__(self, key, value): | ||
| # na value: need special casing to set directly on numpy arrays | ||
| needs_float_conversion = False | ||
| if is_scalar(value) and isna(value): | ||
| if is_integer_dtype(self.dtype.subtype): | ||
| # can't set NaN on a numpy integer array | ||
| needs_float_conversion = True | ||
| elif is_datetime64_any_dtype(self.dtype.subtype): | ||
| # need proper NaT to set directly on the numpy array | ||
| value = np.datetime64('NaT') | ||
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@@ -485,13 +485,13 @@ def __setitem__(self, key, value): | |
| # Need to ensure that left and right are updated atomically, so we're | ||
| # forced to copy, update the copy, and swap in the new values. | ||
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There was a problem hiding this comment. not sure I am clear why this is problematic. is there a failure possible when updating the 2nd one? There was a problem hiding this comment. Without the forcing the copy I'm seeing pandas/pandas/tests/extension/base/setitem.py Lines 17 to 23 in 1dd05cc |
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| left = self.left.copy(deep=True) | ||
| if needs_float_conversion: | ||
| left = left.astype('float') | ||
| left.values[key] = value_left | ||
| self._left = left | ||
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| right = self.right.copy(deep=True) | ||
| if needs_float_conversion: | ||
| right = right.astype('float') | ||
| right.values[key] = value_right | ||
| self._right = right | ||
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@@ -505,7 +505,7 @@ def fillna(self, value=None, method=None, limit=None): | |
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| if not isinstance(value, ABCInterval): | ||
| msg = ("'IntervalArray.fillna' only supports filling with a " | ||
| "scalar 'pandas.Interval'. Got a '{}' instead." | ||
| .format(type(value).__name__)) | ||
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There was a problem hiding this comment. Single quote is in the wrong place. Should be |
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| raise TypeError(msg) | ||
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@@ -580,7 +580,6 @@ def _concat_same_type(cls, to_concat): | |
| raise ValueError("Intervals must all be closed on the same side.") | ||
| closed = closed.pop() | ||
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| left = np.concatenate([interval.left for interval in to_concat]) | ||
| right = np.concatenate([interval.right for interval in to_concat]) | ||
| return cls._simple_new(left, right, closed=closed, copy=False) | ||
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Should this support the case where
valueisnp.nan? Doesn't look like that currently works.There was a problem hiding this comment.
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Yes, it currently fails. I'll see what can be done.
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Looks like there's also something strange going on with datetime data:
In [2]: ia = pd.interval_range(pd.Timestamp('20180101'), periods=3).values In [3]: ia Out[3]: IntervalArray([(2018-01-01, 2018-01-02], (2018-01-02, 2018-01-03], (2018-01-03, 2018-01-04]], closed='right', dtype='interval[datetime64[ns]]') In [4]: new_iv = pd.Interval(pd.Timestamp('20180101'), pd.Timestamp('20180105')) In [5]: new_iv Out[5]: Interval('2018-01-01', '2018-01-05', closed='right') In [6]: ia[0] = new_iv In [7]: ia Out[7]: --------------------------------------------------------------------------- ValueError: left side of interval must be <= right side In [8]: ia.left Out[8]: DatetimeIndex(['2018-01-01', '2018-01-05', '2018-01-03'], dtype='datetime64[ns]', freq='D') In [9]: ia.right Out[9]: DatetimeIndex(['2018-01-05', '2018-01-03', '2018-01-04'], dtype='datetime64[ns]', freq='D')