def insert(self: _T, loc: int, item) -> _T:
"""
Make new Index inserting new item at location. Follows
Python list.append semantics for negative values.
Parameters
----------
loc : int
item : object
Returns
-------
new_index : Index
Raises
------
ValueError if the item is not valid for this dtype.
"""
arr = self._data
try:
code = arr._validate_scalar(item)
except (ValueError, TypeError):
# e.g. trying to insert an integer into a DatetimeIndex
# We cannot keep the same dtype, so cast to the (often object)
# minimal shared dtype before doing the insert.
dtype, _ = infer_dtype_from(item, pandas_dtype=True)
dtype = find_common_type([self.dtype, dtype])
return self.astype(dtype).insert(loc, item)
else:
new_vals = np.concatenate((arr._ndarray[:loc], [code], arr._ndarray[loc:]))
new_arr = arr._from_backing_data(new_vals)
return type(self)._simple_new(new_arr, name=self.name)
def test_infer_dtype_from_scalar_zerodim_datetimelike(cls):
# ndarray.item() can incorrectly return int instead of td64/dt64
val = cls(1234, "ns")
arr = np.array(val)
dtype, res = infer_dtype_from_scalar(arr)
assert dtype.type is cls
assert isinstance(res, cls)
dtype, res = infer_dtype_from(arr)
assert dtype.type is cls
def mask_missing(arr: ArrayLike, values_to_mask) -> npt.NDArray[np.bool_]:
"""
Return a masking array of same size/shape as arr
with entries equaling any member of values_to_mask set to True
Parameters
----------
arr : ArrayLike
values_to_mask: list, tuple, or scalar
Returns
-------
np.ndarray[bool]
"""
# When called from Block.replace/replace_list, values_to_mask is a scalar
# known to be holdable by arr.
# When called from Series._single_replace, values_to_mask is tuple or list
dtype, values_to_mask = infer_dtype_from(values_to_mask)
# error: Argument "dtype" to "array" has incompatible type "Union[dtype[Any],
# ExtensionDtype]"; expected "Union[dtype[Any], None, type, _SupportsDType, str,
# Union[Tuple[Any, int], Tuple[Any, Union[int, Sequence[int]]], List[Any],
# _DTypeDict, Tuple[Any, Any]]]"
values_to_mask = np.array(values_to_mask,
dtype=dtype) # type: ignore[arg-type]
na_mask = isna(values_to_mask)
nonna = values_to_mask[~na_mask]
# GH 21977
mask = np.zeros(arr.shape, dtype=bool)
for x in nonna:
if is_numeric_v_string_like(arr, x):
# GH#29553 prevent numpy deprecation warnings
pass
else:
new_mask = arr == x
if not isinstance(new_mask, np.ndarray):
# usually BooleanArray
new_mask = new_mask.to_numpy(dtype=bool, na_value=False)
mask |= new_mask
if na_mask.any():
mask |= isna(arr)
return mask
def setitem_datetimelike_compat(values: np.ndarray, num_set: int, other):
"""
Parameters
----------
values : np.ndarray
num_set : int
For putmask, this is mask.sum()
other : Any
"""
if values.dtype == object:
dtype, _ = infer_dtype_from(other, pandas_dtype=True)
if isinstance(dtype, np.dtype) and dtype.kind in ["m", "M"]:
# https://github.com/numpy/numpy/issues/12550
# timedelta64 will incorrectly cast to int
if not is_list_like(other):
other = [other] * num_set
else:
other = list(other)
return other
def mask_missing(arr: ArrayLike, values_to_mask) -> np.ndarray:
"""
Return a masking array of same size/shape as arr
with entries equaling any member of values_to_mask set to True
Parameters
----------
arr : ArrayLike
values_to_mask: list, tuple, or scalar
Returns
-------
np.ndarray[bool]
"""
# When called from Block.replace/replace_list, values_to_mask is a scalar
# known to be holdable by arr.
# When called from Series._single_replace, values_to_mask is tuple or list
dtype, values_to_mask = infer_dtype_from(values_to_mask)
values_to_mask = np.array(values_to_mask, dtype=dtype)
na_mask = isna(values_to_mask)
nonna = values_to_mask[~na_mask]
# GH 21977
mask = np.zeros(arr.shape, dtype=bool)
for x in nonna:
if is_numeric_v_string_like(arr, x):
# GH#29553 prevent numpy deprecation warnings
pass
else:
mask |= arr == x
if na_mask.any():
mask |= isna(arr)
return mask
