TYP/REF: define comparison methods non-dynamically (pandas-dev#36930)

pandas/core/arraylike.py

@@ -0,0 +1,43 @@
+"""
+Methods that can be shared by many array-like classes or subclasses:
+    Series
+    Index
+    ExtensionArray
+"""
+import operator
+
+from pandas.errors import AbstractMethodError
+
+from pandas.core.ops.common import unpack_zerodim_and_defer
+
+
+class OpsMixin:
+    # -------------------------------------------------------------
+    # Comparisons
+
+    def _cmp_method(self, other, op):
+        raise AbstractMethodError(self)
+
+    @unpack_zerodim_and_defer("__eq__")
+    def __eq__(self, other):
+        return self._cmp_method(other, operator.eq)
+
+    @unpack_zerodim_and_defer("__ne__")
+    def __ne__(self, other):
+        return self._cmp_method(other, operator.ne)
+
+    @unpack_zerodim_and_defer("__lt__")
+    def __lt__(self, other):
+        return self._cmp_method(other, operator.lt)
+
+    @unpack_zerodim_and_defer("__le__")
+    def __le__(self, other):
+        return self._cmp_method(other, operator.le)
+
+    @unpack_zerodim_and_defer("__gt__")
+    def __gt__(self, other):
+        return self._cmp_method(other, operator.gt)
+
+    @unpack_zerodim_and_defer("__ge__")
+    def __ge__(self, other):
+        return self._cmp_method(other, operator.ge)

pandas/core/arrays/datetimelike.py

@@ -24,7 +24,6 @@
     round_nsint64,
 )
 from pandas._typing import DatetimeLikeScalar, DtypeObj
-from pandas.compat import set_function_name
 from pandas.compat.numpy import function as nv
 from pandas.errors import AbstractMethodError, NullFrequencyError, PerformanceWarning
 from pandas.util._decorators import Appender, Substitution, cache_readonly
@@ -51,8 +50,8 @@
 
 from pandas.core import nanops, ops
 from pandas.core.algorithms import checked_add_with_arr, unique1d, value_counts
+from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
-from pandas.core.arrays.base import ExtensionOpsMixin
 import pandas.core.common as com
 from pandas.core.construction import array, extract_array
 from pandas.core.indexers import check_array_indexer, check_setitem_lengths
@@ -73,46 +72,6 @@ class InvalidComparison(Exception):
     pass
 
 
-def _datetimelike_array_cmp(cls, op):
-    """
-    Wrap comparison operations to convert Timestamp/Timedelta/Period-like to
-    boxed scalars/arrays.
-    """
-    opname = f"__{op.__name__}__"
-    nat_result = opname == "__ne__"
-
-    @unpack_zerodim_and_defer(opname)
-    def wrapper(self, other):
-        if self.ndim > 1 and getattr(other, "shape", None) == self.shape:
-            # TODO: handle 2D-like listlikes
-            return op(self.ravel(), other.ravel()).reshape(self.shape)
-
-        try:
-            other = self._validate_comparison_value(other, opname)
-        except InvalidComparison:
-            return invalid_comparison(self, other, op)
-
-        dtype = getattr(other, "dtype", None)
-        if is_object_dtype(dtype):
-            # We have to use comp_method_OBJECT_ARRAY instead of numpy
-            #  comparison otherwise it would fail to raise when
-            #  comparing tz-aware and tz-naive
-            with np.errstate(all="ignore"):
-                result = ops.comp_method_OBJECT_ARRAY(op, self.astype(object), other)
-            return result
-
-        other_i8 = self._unbox(other)
-        result = op(self.asi8, other_i8)
-
-        o_mask = isna(other)
-        if self._hasnans | np.any(o_mask):
-            result[self._isnan | o_mask] = nat_result
-
-        return result
-
-    return set_function_name(wrapper, opname, cls)
-
-
 class AttributesMixin:
     _data: np.ndarray
 
@@ -426,9 +385,7 @@ def _with_freq(self, freq):
 DatetimeLikeArrayT = TypeVar("DatetimeLikeArrayT", bound="DatetimeLikeArrayMixin")
 
 
-class DatetimeLikeArrayMixin(
-    ExtensionOpsMixin, AttributesMixin, NDArrayBackedExtensionArray
-):
+class DatetimeLikeArrayMixin(OpsMixin, AttributesMixin, NDArrayBackedExtensionArray):
     """
     Shared Base/Mixin class for DatetimeArray, TimedeltaArray, PeriodArray
 
@@ -1093,7 +1050,35 @@ def _is_unique(self):
 
     # ------------------------------------------------------------------
     # Arithmetic Methods
-    _create_comparison_method = classmethod(_datetimelike_array_cmp)
+
+    def _cmp_method(self, other, op):
+        if self.ndim > 1 and getattr(other, "shape", None) == self.shape:
+            # TODO: handle 2D-like listlikes
+            return op(self.ravel(), other.ravel()).reshape(self.shape)
+
+        try:
+            other = self._validate_comparison_value(other, f"__{op.__name__}__")
+        except InvalidComparison:
+            return invalid_comparison(self, other, op)
+
+        dtype = getattr(other, "dtype", None)
+        if is_object_dtype(dtype):
+            # We have to use comp_method_OBJECT_ARRAY instead of numpy
+            #  comparison otherwise it would fail to raise when
+            #  comparing tz-aware and tz-naive
+            with np.errstate(all="ignore"):
+                result = ops.comp_method_OBJECT_ARRAY(op, self.astype(object), other)
+            return result
+
+        other_i8 = self._unbox(other)
+        result = op(self.asi8, other_i8)
+
+        o_mask = isna(other)
+        if self._hasnans | np.any(o_mask):
+            nat_result = op is operator.ne
+            result[self._isnan | o_mask] = nat_result
+
+        return result
 
     # pow is invalid for all three subclasses; TimedeltaArray will override
     #  the multiplication and division ops
@@ -1582,8 +1567,6 @@ def median(self, axis: Optional[int] = None, skipna: bool = True, *args, **kwarg
         return self._from_backing_data(result.astype("i8"))
 
 
-DatetimeLikeArrayMixin._add_comparison_ops()
-
 # -------------------------------------------------------------------
 # Shared Constructor Helpers
 

pandas/core/base.py

@@ -30,6 +30,7 @@
 from pandas.core import algorithms, common as com
 from pandas.core.accessor import DirNamesMixin
 from pandas.core.algorithms import duplicated, unique1d, value_counts
+from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import ExtensionArray
 from pandas.core.construction import create_series_with_explicit_dtype
 import pandas.core.nanops as nanops
@@ -587,7 +588,7 @@ def _is_builtin_func(self, arg):
         return self._builtin_table.get(arg, arg)
 
 
-class IndexOpsMixin:
+class IndexOpsMixin(OpsMixin):
     """
     Common ops mixin to support a unified interface / docs for Series / Index
     """

pandas/core/generic.py

@@ -1764,7 +1764,7 @@ def _drop_labels_or_levels(self, keys, axis: int = 0):
     # ----------------------------------------------------------------------
     # Iteration
 
-    def __hash__(self):
+    def __hash__(self) -> int:
         raise TypeError(
             f"{repr(type(self).__name__)} objects are mutable, "
             f"thus they cannot be hashed"

pandas/core/indexes/base.py

@@ -121,41 +121,6 @@
 str_t = str
 
 
-def _make_comparison_op(op, cls):
-    def cmp_method(self, other):
-        if isinstance(other, (np.ndarray, Index, ABCSeries, ExtensionArray)):
-            if other.ndim > 0 and len(self) != len(other):
-                raise ValueError("Lengths must match to compare")
-
-        if is_object_dtype(self.dtype) and isinstance(other, ABCCategorical):
-            left = type(other)(self._values, dtype=other.dtype)
-            return op(left, other)
-        elif is_object_dtype(self.dtype) and isinstance(other, ExtensionArray):
-            # e.g. PeriodArray
-            with np.errstate(all="ignore"):
-                result = op(self._values, other)
-
-        elif is_object_dtype(self.dtype) and not isinstance(self, ABCMultiIndex):
-            # don't pass MultiIndex
-            with np.errstate(all="ignore"):
-                result = ops.comp_method_OBJECT_ARRAY(op, self._values, other)
-
-        elif is_interval_dtype(self.dtype):
-            with np.errstate(all="ignore"):
-                result = op(self._values, np.asarray(other))
-
-        else:
-            with np.errstate(all="ignore"):
-                result = ops.comparison_op(self._values, np.asarray(other), op)
-
-        if is_bool_dtype(result):
-            return result
-        return ops.invalid_comparison(self, other, op)
-
-    name = f"__{op.__name__}__"
-    return set_function_name(cmp_method, name, cls)
-
-
 def _make_arithmetic_op(op, cls):
     def index_arithmetic_method(self, other):
         if isinstance(other, (ABCSeries, ABCDataFrame, ABCTimedeltaIndex)):
@@ -5400,17 +5365,38 @@ def drop(self, labels, errors: str_t = "raise"):
     # --------------------------------------------------------------------
     # Generated Arithmetic, Comparison, and Unary Methods
 
-    @classmethod
-    def _add_comparison_methods(cls):
+    def _cmp_method(self, other, op):
         """
-        Add in comparison methods.
+        Wrapper used to dispatch comparison operations.
         """
-        cls.__eq__ = _make_comparison_op(operator.eq, cls)
-        cls.__ne__ = _make_comparison_op(operator.ne, cls)
-        cls.__lt__ = _make_comparison_op(operator.lt, cls)
-        cls.__gt__ = _make_comparison_op(operator.gt, cls)
-        cls.__le__ = _make_comparison_op(operator.le, cls)
-        cls.__ge__ = _make_comparison_op(operator.ge, cls)
+        if isinstance(other, (np.ndarray, Index, ABCSeries, ExtensionArray)):
+            if other.ndim > 0 and len(self) != len(other):
+                raise ValueError("Lengths must match to compare")
+
+        if is_object_dtype(self.dtype) and isinstance(other, ABCCategorical):
+            left = type(other)(self._values, dtype=other.dtype)
+            return op(left, other)
+        elif is_object_dtype(self.dtype) and isinstance(other, ExtensionArray):
+            # e.g. PeriodArray
+            with np.errstate(all="ignore"):
+                result = op(self._values, other)
+
+        elif is_object_dtype(self.dtype) and not isinstance(self, ABCMultiIndex):
+            # don't pass MultiIndex
+            with np.errstate(all="ignore"):
+                result = ops.comp_method_OBJECT_ARRAY(op, self._values, other)
+
+        elif is_interval_dtype(self.dtype):
+            with np.errstate(all="ignore"):
+                result = op(self._values, np.asarray(other))
+
+        else:
+            with np.errstate(all="ignore"):
+                result = ops.comparison_op(self._values, np.asarray(other), op)
+
+        if is_bool_dtype(result):
+            return result
+        return ops.invalid_comparison(self, other, op)
 
     @classmethod
     def _add_numeric_methods_binary(cls):
@@ -5594,7 +5580,6 @@ def shape(self):
 
 Index._add_numeric_methods()
 Index._add_logical_methods()
-Index._add_comparison_methods()
 
 
 def ensure_index_from_sequences(sequences, names=None):

pandas/core/ops/__init__.py

@@ -20,13 +20,13 @@
 
 from pandas.core import algorithms
 from pandas.core.construction import extract_array
-from pandas.core.ops.array_ops import (
+from pandas.core.ops.array_ops import (  # noqa:F401
     arithmetic_op,
+    comp_method_OBJECT_ARRAY,
     comparison_op,
     get_array_op,
     logical_op,
 )
-from pandas.core.ops.array_ops import comp_method_OBJECT_ARRAY  # noqa:F401
 from pandas.core.ops.common import unpack_zerodim_and_defer
 from pandas.core.ops.docstrings import (
     _arith_doc_FRAME,
@@ -323,33 +323,6 @@ def wrapper(left, right):
     return wrapper
 
 
-def comp_method_SERIES(cls, op, special):
-    """
-    Wrapper function for Series arithmetic operations, to avoid
-    code duplication.
-    """
-    assert special  # non-special uses flex_method_SERIES
-    op_name = _get_op_name(op, special)
-
-    @unpack_zerodim_and_defer(op_name)
-    def wrapper(self, other):
-
-        res_name = get_op_result_name(self, other)
-
-        if isinstance(other, ABCSeries) and not self._indexed_same(other):
-            raise ValueError("Can only compare identically-labeled Series objects")
-
-        lvalues = extract_array(self, extract_numpy=True)
-        rvalues = extract_array(other, extract_numpy=True)
-
-        res_values = comparison_op(lvalues, rvalues, op)
-
-        return self._construct_result(res_values, name=res_name)
-
-    wrapper.__name__ = op_name
-    return wrapper
-
-
 def bool_method_SERIES(cls, op, special):
     """
     Wrapper function for Series arithmetic operations, to avoid

pandas/core/ops/methods.py

@@ -48,7 +48,6 @@ def _get_method_wrappers(cls):
         arith_method_SERIES,
         bool_method_SERIES,
         comp_method_FRAME,
-        comp_method_SERIES,
         flex_comp_method_FRAME,
         flex_method_SERIES,
     )
@@ -58,7 +57,7 @@ def _get_method_wrappers(cls):
         arith_flex = flex_method_SERIES
         comp_flex = flex_method_SERIES
         arith_special = arith_method_SERIES
-        comp_special = comp_method_SERIES
+        comp_special = None
         bool_special = bool_method_SERIES
     elif issubclass(cls, ABCDataFrame):
         arith_flex = arith_method_FRAME
@@ -189,16 +188,18 @@ def _create_methods(cls, arith_method, comp_method, bool_method, special):
         new_methods["divmod"] = arith_method(cls, divmod, special)
         new_methods["rdivmod"] = arith_method(cls, rdivmod, special)
 
-    new_methods.update(
-        dict(
-            eq=comp_method(cls, operator.eq, special),
-            ne=comp_method(cls, operator.ne, special),
-            lt=comp_method(cls, operator.lt, special),
-            gt=comp_method(cls, operator.gt, special),
-            le=comp_method(cls, operator.le, special),
-            ge=comp_method(cls, operator.ge, special),
+    if comp_method is not None:
+        # Series already has this pinned
+        new_methods.update(
+            dict(
+                eq=comp_method(cls, operator.eq, special),
+                ne=comp_method(cls, operator.ne, special),
+                lt=comp_method(cls, operator.lt, special),
+                gt=comp_method(cls, operator.gt, special),
+                le=comp_method(cls, operator.le, special),
+                ge=comp_method(cls, operator.ge, special),
+            )
         )
-    )
 
     if bool_method:
         new_methods.update(