def logical_op(left: ArrayLike, right: Any, op) -> ArrayLike: """ Evaluate a logical operation `|`, `&`, or `^`. Parameters ---------- left : np.ndarray or ExtensionArray right : object Cannot be a DataFrame, Series, or Index. op : {operator.and_, operator.or_, operator.xor} Or one of the reversed variants from roperator. Returns ------- ndarray or ExtensionArray """ fill_int = lambda x: x def fill_bool(x, left=None): # if `left` is specifically not-boolean, we do not cast to bool if x.dtype.kind in ["c", "f", "O"]: # dtypes that can hold NA mask = isna(x) if mask.any(): x = x.astype(object) x[mask] = False if left is None or is_bool_dtype(left.dtype): x = x.astype(bool) return x is_self_int_dtype = is_integer_dtype(left.dtype) right = lib.item_from_zerodim(right) if is_list_like(right) and not hasattr(right, "dtype"): # e.g. list, tuple right = construct_1d_object_array_from_listlike(right) # NB: We assume extract_array has already been called on left and right lvalues = ensure_wrapped_if_datetimelike(left) rvalues = right if should_extension_dispatch(lvalues, rvalues): # Call the method on lvalues res_values = op(lvalues, rvalues) else: if isinstance(rvalues, np.ndarray): is_other_int_dtype = is_integer_dtype(rvalues.dtype) rvalues = rvalues if is_other_int_dtype else fill_bool( rvalues, lvalues) else: # i.e. scalar is_other_int_dtype = lib.is_integer(rvalues) # For int vs int `^`, `|`, `&` are bitwise operators and return # integer dtypes. Otherwise these are boolean ops filler = fill_int if is_self_int_dtype and is_other_int_dtype else fill_bool res_values = na_logical_op(lvalues, rvalues, op) # error: Cannot call function of unknown type res_values = filler(res_values) # type: ignore[operator] return res_values
def comparison_op( left: ArrayLike, right: Any, op, str_rep: Optional[str] = None, ) -> ArrayLike: """ Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`. Parameters ---------- left : np.ndarray or ExtensionArray right : object Cannot be a DataFrame, Series, or Index. op : {operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le} Returns ------- ndarray or ExtensionArray """ # NB: We assume extract_array has already been called on left and right lvalues = left rvalues = right rvalues = lib.item_from_zerodim(rvalues) if isinstance(rvalues, list): # TODO: same for tuples? rvalues = np.asarray(rvalues) if isinstance(rvalues, (np.ndarray, ABCExtensionArray)): # TODO: make this treatment consistent across ops and classes. # We are not catching all listlikes here (e.g. frozenset, tuple) # The ambiguous case is object-dtype. See GH#27803 if len(lvalues) != len(rvalues): if _can_broadcast(lvalues, rvalues): return _broadcast_comparison_op(lvalues, rvalues, op) raise ValueError("Lengths must match to compare", lvalues.shape, rvalues.shape) if should_extension_dispatch(lvalues, rvalues): res_values = dispatch_to_extension_op(op, lvalues, rvalues) elif is_scalar(rvalues) and isna(rvalues): # numpy does not like comparisons vs None if op is operator.ne: res_values = np.ones(lvalues.shape, dtype=bool) else: res_values = np.zeros(lvalues.shape, dtype=bool) elif is_object_dtype(lvalues.dtype): res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues) else: with np.errstate(all="ignore"): res_values = na_arithmetic_op(lvalues, rvalues, op, str_rep, is_cmp=True) return res_values
def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike: """ Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`. Note: the caller is responsible for ensuring that numpy warnings are suppressed (with np.errstate(all="ignore")) if needed. Parameters ---------- left : np.ndarray or ExtensionArray right : object Cannot be a DataFrame, Series, or Index. op : {operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le} Returns ------- ndarray or ExtensionArray """ # NB: We assume extract_array has already been called on left and right lvalues = ensure_wrapped_if_datetimelike(left) rvalues = ensure_wrapped_if_datetimelike(right) rvalues = lib.item_from_zerodim(rvalues) if isinstance(rvalues, list): # We don't catch tuple here bc we may be comparing e.g. MultiIndex # to a tuple that represents a single entry, see test_compare_tuple_strs rvalues = np.asarray(rvalues) if isinstance(rvalues, (np.ndarray, ABCExtensionArray)): # TODO: make this treatment consistent across ops and classes. # We are not catching all listlikes here (e.g. frozenset, tuple) # The ambiguous case is object-dtype. See GH#27803 if len(lvalues) != len(rvalues): raise ValueError( "Lengths must match to compare", lvalues.shape, rvalues.shape ) if should_extension_dispatch(lvalues, rvalues) or ( (isinstance(rvalues, (Timedelta, BaseOffset, Timestamp)) or right is NaT) and not is_object_dtype(lvalues.dtype) ): # Call the method on lvalues res_values = op(lvalues, rvalues) elif is_scalar(rvalues) and isna(rvalues): # TODO: but not pd.NA? # numpy does not like comparisons vs None if op is operator.ne: res_values = np.ones(lvalues.shape, dtype=bool) else: res_values = np.zeros(lvalues.shape, dtype=bool) elif is_numeric_v_string_like(lvalues, rvalues): # GH#36377 going through the numexpr path would incorrectly raise return invalid_comparison(lvalues, rvalues, op) elif is_object_dtype(lvalues.dtype) or isinstance(rvalues, str): res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues) else: res_values = _na_arithmetic_op(lvalues, rvalues, op, is_cmp=True) return res_values
def comparison_op(left: ArrayLike, right: Any, op) -> ArrayLike: """ Evaluate a comparison operation `=`, `!=`, `>=`, `>`, `<=`, or `<`. Parameters ---------- left : np.ndarray or ExtensionArray right : object Cannot be a DataFrame, Series, or Index. op : {operator.eq, operator.ne, operator.gt, operator.ge, operator.lt, operator.le} Returns ------- ndarray or ExtensionArray """ # NB: We assume extract_array has already been called on left and right lvalues = maybe_upcast_datetimelike_array(left) rvalues = right rvalues = lib.item_from_zerodim(rvalues) if isinstance(rvalues, list): # TODO: same for tuples? rvalues = np.asarray(rvalues) if isinstance(rvalues, (np.ndarray, ABCExtensionArray)): # TODO: make this treatment consistent across ops and classes. # We are not catching all listlikes here (e.g. frozenset, tuple) # The ambiguous case is object-dtype. See GH#27803 if len(lvalues) != len(rvalues): raise ValueError("Lengths must match to compare", lvalues.shape, rvalues.shape) if should_extension_dispatch(lvalues, rvalues): # Call the method on lvalues res_values = op(lvalues, rvalues) elif is_scalar(rvalues) and isna(rvalues): # numpy does not like comparisons vs None if op is operator.ne: res_values = np.ones(lvalues.shape, dtype=bool) else: res_values = np.zeros(lvalues.shape, dtype=bool) elif is_numeric_v_string_like(lvalues, rvalues): # GH#36377 going through the numexpr path would incorrectly raise return invalid_comparison(lvalues, rvalues, op) elif is_object_dtype(lvalues.dtype): res_values = comp_method_OBJECT_ARRAY(op, lvalues, rvalues) else: with warnings.catch_warnings(): # suppress warnings from numpy about element-wise comparison warnings.simplefilter("ignore", DeprecationWarning) with np.errstate(all="ignore"): res_values = _na_arithmetic_op(lvalues, rvalues, op, is_cmp=True) return res_values