REF: simplify dispatch in groupby.ops

pandas/_libs/groupby.pyx

@@ -455,11 +455,11 @@ ctypedef fused complexfloating_t:
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def _group_add(complexfloating_t[:, ::1] out,
-               int64_t[::1] counts,
-               ndarray[complexfloating_t, ndim=2] values,
-               const intp_t[:] labels,
-               Py_ssize_t min_count=0):
+def group_add(complexfloating_t[:, ::1] out,
+              int64_t[::1] counts,
+              ndarray[complexfloating_t, ndim=2] values,
+              const intp_t[:] labels,
+              Py_ssize_t min_count=0):
     """
     Only aggregates on axis=0 using Kahan summation
     """
@@ -506,19 +506,13 @@ def _group_add(complexfloating_t[:, ::1] out,
                     out[i, j] = sumx[i, j]
 
 
-group_add_float32 = _group_add['float32_t']
-group_add_float64 = _group_add['float64_t']
-group_add_complex64 = _group_add['float complex']
-group_add_complex128 = _group_add['double complex']
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def _group_prod(floating[:, ::1] out,
-                int64_t[::1] counts,
-                ndarray[floating, ndim=2] values,
-                const intp_t[:] labels,
-                Py_ssize_t min_count=0):
+def group_prod(floating[:, ::1] out,
+               int64_t[::1] counts,
+               ndarray[floating, ndim=2] values,
+               const intp_t[:] labels,
+               Py_ssize_t min_count=0):
     """
     Only aggregates on axis=0
     """
@@ -560,19 +554,15 @@ def _group_prod(floating[:, ::1] out,
                     out[i, j] = prodx[i, j]
 
 
-group_prod_float32 = _group_prod['float']
-group_prod_float64 = _group_prod['double']
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
 @cython.cdivision(True)
-def _group_var(floating[:, ::1] out,
-               int64_t[::1] counts,
-               ndarray[floating, ndim=2] values,
-               const intp_t[:] labels,
-               Py_ssize_t min_count=-1,
-               int64_t ddof=1):
+def group_var(floating[:, ::1] out,
+              int64_t[::1] counts,
+              ndarray[floating, ndim=2] values,
+              const intp_t[:] labels,
+              Py_ssize_t min_count=-1,
+              int64_t ddof=1):
     cdef:
         Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
         floating val, ct, oldmean
@@ -619,17 +609,13 @@ def _group_var(floating[:, ::1] out,
                     out[i, j] /= (ct - ddof)
 
 
-group_var_float32 = _group_var['float']
-group_var_float64 = _group_var['double']
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def _group_mean(floating[:, ::1] out,
-                int64_t[::1] counts,
-                ndarray[floating, ndim=2] values,
-                const intp_t[::1] labels,
-                Py_ssize_t min_count=-1):
+def group_mean(floating[:, ::1] out,
+               int64_t[::1] counts,
+               ndarray[floating, ndim=2] values,
+               const intp_t[::1] labels,
+               Py_ssize_t min_count=-1):
     cdef:
         Py_ssize_t i, j, N, K, lab, ncounts = len(counts)
         floating val, count, y, t
@@ -675,10 +661,6 @@ def _group_mean(floating[:, ::1] out,
                     out[i, j] = sumx[i, j] / count
 
 
-group_mean_float32 = _group_mean['float']
-group_mean_float64 = _group_mean['double']
-
-
 @cython.wraparound(False)
 @cython.boundscheck(False)
 def group_ohlc(floating[:, ::1] out,

pandas/core/groupby/groupby.py

@@ -1602,7 +1602,7 @@ def std(self, ddof: int = 1):
             Standard deviation of values within each group.
         """
         return self._get_cythonized_result(
-            "group_var_float64",
+            "group_var",
             aggregate=True,
             needs_counts=True,
             needs_values=True,

pandas/core/groupby/ops.py

@@ -129,31 +129,22 @@ def _get_cython_function(kind: str, how: str, dtype: np.dtype, is_numeric: bool)
     # see if there is a fused-type version of function
     # only valid for numeric
     f = getattr(libgroupby, ftype, None)
-    if f is not None and is_numeric:
-        return f
-
-    # otherwise find dtype-specific version, falling back to object
-    for dt in [dtype_str, "object"]:
-        f2 = getattr(libgroupby, f"{ftype}_{dt}", None)
-        if f2 is not None:
-            return f2
-
-    if hasattr(f, "__signatures__"):
-        # inspect what fused types are implemented
-        if dtype_str == "object" and "object" not in f.__signatures__:
-            # disallow this function so we get a NotImplementedError below
-            #  instead of a TypeError at runtime
-            f = None
-
-    func = f
-
-    if func is None:
-        raise NotImplementedError(
-            f"function is not implemented for this dtype: "
-            f"[how->{how},dtype->{dtype_str}]"
-        )
+    if f is not None:
+        if is_numeric:
+            return f
+        elif dtype == object:
+            if "object" not in f.__signatures__:
+                # raise NotImplementedError here rather than TypeError later
+                raise NotImplementedError(
+                    f"function is not implemented for this dtype: "
+                    f"[how->{how},dtype->{dtype_str}]"
+                )
+            return f
 
-    return func
+    raise NotImplementedError(
+        f"function is not implemented for this dtype: "
+        f"[how->{how},dtype->{dtype_str}]"
+    )
 
 
 class BaseGrouper:
@@ -475,25 +466,24 @@ def _get_cython_func_and_vals(
         func : callable
         values : np.ndarray
         """
-        try:
-            func = _get_cython_function(kind, how, values.dtype, is_numeric)
-        except NotImplementedError:
+        if how in ["median", "cumprod"]:
+            # these two only have float64 implementations
             if is_numeric:
-                try:
-                    values = ensure_float64(values)
-                except TypeError:
-                    if lib.infer_dtype(values, skipna=False) == "complex":
-                        values = values.astype(complex)
-                    else:
-                        raise
-                func = _get_cython_function(kind, how, values.dtype, is_numeric)
+                values = ensure_float64(values)
             else:
-                raise
-        else:
-            if values.dtype.kind in ["i", "u"]:
-                if how in ["ohlc"]:
-                    # The output may still include nans, so we have to cast
-                    values = ensure_float64(values)
+                raise NotImplementedError(
+                    f"function is not implemented for this dtype: "
+                    f"[how->{how},dtype->{values.dtype.name}]"
+                )
+            func = getattr(libgroupby, f"group_{how}_float64")
+            return func, values
+
+        func = _get_cython_function(kind, how, values.dtype, is_numeric)
+
+        if values.dtype.kind in ["i", "u"]:
+            if how in ["add", "var", "prod", "mean", "ohlc"]:
+                # result may still include NaN, so we have to cast
+                values = ensure_float64(values)
 
         return func, values
 
@@ -643,10 +633,9 @@ def _cython_operation(
                 values = ensure_float64(values)
             else:
                 values = ensure_int_or_float(values)
-        elif is_numeric and not is_complex_dtype(dtype):
-            values = ensure_float64(values)
-        else:
-            values = values.astype(object)
+        elif is_numeric:
+            if not is_complex_dtype(dtype):
+                values = ensure_float64(values)
 
         arity = self._cython_arity.get(how, 1)
         ngroups = self.ngroups

pandas/tests/groupby/test_libgroupby.py

@@ -4,8 +4,7 @@
 from pandas._libs.groupby import (
     group_cumprod_float64,
     group_cumsum,
-    group_var_float32,
-    group_var_float64,
+    group_var,
 )
 
 from pandas.core.dtypes.common import ensure_platform_int
@@ -102,7 +101,7 @@ def test_group_var_constant(self):
 class TestGroupVarFloat64(GroupVarTestMixin):
     __test__ = True
 
-    algo = staticmethod(group_var_float64)
+    algo = staticmethod(group_var)
     dtype = np.float64
     rtol = 1e-5
 
@@ -124,7 +123,7 @@ def test_group_var_large_inputs(self):
 class TestGroupVarFloat32(GroupVarTestMixin):
     __test__ = True
 
-    algo = staticmethod(group_var_float32)
+    algo = staticmethod(group_var)
     dtype = np.float32
     rtol = 1e-2