When we have a Int64Index, series[i] is treated as label-based. For consistency, series[i:j] should be too.
ser = pd.Series(range(5), index=pd.Index([1, 3, 5, 7, 9]))
ser2 = ser.copy()
ser2.index = ser2.index.astype('f8')
>>> ser[1] # <- label-based
0
>>> ser[1:3] # <- positional
3 1
5 2
dtype: int64
>>> ser2[1:3] # <- label-based
1.0 0
3.0 1
dtype: int64
The logic by which ser[i:j] is positional is not easy to track down and does not use modern idioms (i.e. _should_fallback_to_positional)
NumericIndex._convert_slice_indexer specifically special-cases floating dtypes
def _convert_slice_indexer(self, key: slice, kind: str):
if is_float_dtype(self.dtype):
assert kind in ["loc", "getitem"]
# We always treat __getitem__ slicing as label-based
# translate to locations
return self.slice_indexer(key.start, key.stop, key.step)
return super()._convert_slice_indexer(key, kind=kind)
while the base class ...
def _convert_slice_indexer(self, key: slice, kind: str_t):
# potentially cast the bounds to integers
start, stop, step = key.start, key.stop, key.step
# figure out if this is a positional indexer
def is_int(v):
return v is None or is_integer(v)
is_index_slice = is_int(start) and is_int(stop) and is_int(step)
is_positional = is_index_slice and not (self.is_integer() or self.is_categorical())
if kind == "getitem":
"""
called from the getitem slicers, validate that we are in fact
integers
"""
if self.is_integer() or is_index_slice:
self._validate_indexer("slice", key.start, "getitem")
self._validate_indexer("slice", key.stop, "getitem")
self._validate_indexer("slice", key.step, "getitem")
return key
AFAICT the is_positional = is_index_slice and not (self.is_integer() or self.is_categorical()) check should be is_positional = is_index_slice and self._should_fallback_to_positional. The if self.is_integer() or is_index_slice: should probably also be done in terms of _should_fallback_to_positional
>>> ser[1:3] # <- match ser.loc[1:3]
1 0
3 1
dtype: int64
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