def _cmp_method(self, other, op): from pandas.arrays import BooleanArray if isinstance(other, StringArray): other = other._ndarray mask = isna(self) | isna(other) valid = ~mask if not lib.is_scalar(other): if len(other) != len(self): # prevent improper broadcasting when other is 2D raise ValueError( f"Lengths of operands do not match: {len(self)} != {len(other)}" ) other = np.asarray(other) other = other[valid] if op.__name__ in ops.ARITHMETIC_BINOPS: result = np.empty_like(self._ndarray, dtype="object") result[mask] = StringDtype.na_value result[valid] = op(self._ndarray[valid], other) return StringArray(result) else: # logical result = np.zeros(len(self._ndarray), dtype="bool") result[valid] = op(self._ndarray[valid], other) return BooleanArray(result, mask)
def _str_map(self, f, na_value=None, dtype: Optional[Dtype] = None): from pandas.arrays import ( BooleanArray, IntegerArray, StringArray, ) from pandas.core.arrays.string_ import StringDtype if dtype is None: dtype = StringDtype() if na_value is None: na_value = self.dtype.na_value mask = isna(self) arr = np.asarray(self) if is_integer_dtype(dtype) or is_bool_dtype(dtype): constructor: Union[Type[IntegerArray], Type[BooleanArray]] if is_integer_dtype(dtype): constructor = IntegerArray else: constructor = BooleanArray na_value_is_na = isna(na_value) if na_value_is_na: na_value = 1 result = lib.map_infer_mask( arr, f, mask.view("uint8"), convert=False, na_value=na_value, # error: Value of type variable "_DTypeScalar" of "dtype" cannot be # "object" # error: Argument 1 to "dtype" has incompatible type # "Union[ExtensionDtype, str, dtype[Any], Type[object]]"; expected # "Type[object]" dtype=np.dtype(dtype), # type: ignore[type-var,arg-type] ) if not na_value_is_na: mask[:] = False return constructor(result, mask) elif is_string_dtype(dtype) and not is_object_dtype(dtype): # i.e. StringDtype result = lib.map_infer_mask(arr, f, mask.view("uint8"), convert=False, na_value=na_value) return StringArray(result) else: # This is when the result type is object. We reach this when # -> We know the result type is truly object (e.g. .encode returns bytes # or .findall returns a list). # -> We don't know the result type. E.g. `.get` can return anything. return lib.map_infer_mask(arr, f, mask.view("uint8"))
def _str_map(self, f, na_value=None, dtype: Optional[Dtype] = None): from pandas.arrays import ( BooleanArray, IntegerArray, StringArray, ) from pandas.core.arrays.string_ import StringDtype if dtype is None: dtype = StringDtype() if na_value is None: na_value = self.dtype.na_value mask = isna(self) arr = np.asarray(self) if is_integer_dtype(dtype) or is_bool_dtype(dtype): constructor: Union[Type[IntegerArray], Type[BooleanArray]] if is_integer_dtype(dtype): constructor = IntegerArray else: constructor = BooleanArray na_value_is_na = isna(na_value) if na_value_is_na: na_value = 1 result = lib.map_infer_mask( arr, f, mask.view("uint8"), convert=False, na_value=na_value, dtype=np.dtype(dtype), ) if not na_value_is_na: mask[:] = False return constructor(result, mask) elif is_string_dtype(dtype) and not is_object_dtype(dtype): # i.e. StringDtype result = lib.map_infer_mask( arr, f, mask.view("uint8"), convert=False, na_value=na_value ) return StringArray(result) else: # This is when the result type is object. We reach this when # -> We know the result type is truly object (e.g. .encode returns bytes # or .findall returns a list). # -> We don't know the result type. E.g. `.get` can return anything. return lib.map_infer_mask(arr, f, mask.view("uint8"))
def peakmem_stringarray_construction(self): StringArray(self.series_arr)
def time_string_array_with_nan_construction(self): StringArray(self.series_arr_nan)
def time_string_array_construction(self): StringArray(self.series_arr)