CLN: Numba internal routines

pandas/core/groupby/generic.py

@@ -74,12 +74,11 @@
     get_groupby,
     group_selection_context,
 )
-from pandas.core.groupby.numba_ import generate_numba_func, split_for_numba
 from pandas.core.indexes.api import Index, MultiIndex, all_indexes_same
 import pandas.core.indexes.base as ibase
 from pandas.core.internals import BlockManager
 from pandas.core.series import Series
-from pandas.core.util.numba_ import NUMBA_FUNC_CACHE, maybe_use_numba
+from pandas.core.util.numba_ import maybe_use_numba
 
 from pandas.plotting import boxplot_frame_groupby
 
@@ -518,29 +517,16 @@ def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
             result = getattr(self, func)(*args, **kwargs)
         return self._transform_fast(result)
 
-    def _transform_general(
-        self, func, *args, engine="cython", engine_kwargs=None, **kwargs
-    ):
+    def _transform_general(self, func, *args, **kwargs):
         """
         Transform with a non-str `func`.
         """
-        if maybe_use_numba(engine):
-            numba_func, cache_key = generate_numba_func(
-                func, engine_kwargs, kwargs, "groupby_transform"
-            )
-
         klass = type(self._selected_obj)
 
         results = []
         for name, group in self:
             object.__setattr__(group, "name", name)
-            if maybe_use_numba(engine):
-                values, index = split_for_numba(group)
-                res = numba_func(values, index, *args)
-                if cache_key not in NUMBA_FUNC_CACHE:
-                    NUMBA_FUNC_CACHE[cache_key] = numba_func
-            else:
-                res = func(group, *args, **kwargs)
+            res = func(group, *args, **kwargs)
 
             if isinstance(res, (ABCDataFrame, ABCSeries)):
                 res = res._values

pandas/core/groupby/groupby.py

@@ -1071,16 +1071,15 @@ def _transform_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs)
         sorted_labels = algorithms.take_nd(labels, sorted_index, allow_fill=False)
         sorted_data = data.take(sorted_index, axis=self.axis).to_numpy()
         starts, ends = lib.generate_slices(sorted_labels, n_groups)
-        cache_key = (func, "groupby_transform")
-        if cache_key in NUMBA_FUNC_CACHE:
-            numba_transform_func = NUMBA_FUNC_CACHE[cache_key]
-        else:
-            numba_transform_func = numba_.generate_numba_transform_func(
-                tuple(args), kwargs, func, engine_kwargs
-            )
+
+        numba_transform_func = numba_.generate_numba_transform_func(
+            tuple(args), kwargs, func, engine_kwargs
+        )
         result = numba_transform_func(
             sorted_data, sorted_index, starts, ends, len(group_keys), len(data.columns)
         )
+
+        cache_key = (func, "groupby_transform")
         if cache_key not in NUMBA_FUNC_CACHE:
             NUMBA_FUNC_CACHE[cache_key] = numba_transform_func
 
@@ -1106,16 +1105,15 @@ def _aggregate_with_numba(self, data, func, *args, engine_kwargs=None, **kwargs)
         sorted_labels = algorithms.take_nd(labels, sorted_index, allow_fill=False)
         sorted_data = data.take(sorted_index, axis=self.axis).to_numpy()
         starts, ends = lib.generate_slices(sorted_labels, n_groups)
-        cache_key = (func, "groupby_agg")
-        if cache_key in NUMBA_FUNC_CACHE:
-            numba_agg_func = NUMBA_FUNC_CACHE[cache_key]
-        else:
-            numba_agg_func = numba_.generate_numba_agg_func(
-                tuple(args), kwargs, func, engine_kwargs
-            )
+
+        numba_agg_func = numba_.generate_numba_agg_func(
+            tuple(args), kwargs, func, engine_kwargs
+        )
         result = numba_agg_func(
             sorted_data, sorted_index, starts, ends, len(group_keys), len(data.columns)
         )
+
+        cache_key = (func, "groupby_agg")
         if cache_key not in NUMBA_FUNC_CACHE:
             NUMBA_FUNC_CACHE[cache_key] = numba_agg_func
 

pandas/core/groupby/numba_.py

@@ -4,34 +4,17 @@
 
 import numpy as np
 
-from pandas._typing import FrameOrSeries, Scalar
+from pandas._typing import Scalar
 from pandas.compat._optional import import_optional_dependency
 
 from pandas.core.util.numba_ import (
     NUMBA_FUNC_CACHE,
     NumbaUtilError,
-    check_kwargs_and_nopython,
     get_jit_arguments,
     jit_user_function,
 )
 
 
-def split_for_numba(arg: FrameOrSeries) -> Tuple[np.ndarray, np.ndarray]:
-    """
-    Split pandas object into its components as numpy arrays for numba functions.
-
-    Parameters
-    ----------
-    arg : Series or DataFrame
-
-    Returns
-    -------
-    (ndarray, ndarray)
-        values, index
-    """
-    return arg.to_numpy(), arg.index.to_numpy()
-
-
 def validate_udf(func: Callable) -> None:
     """
     Validate user defined function for ops when using Numba with groupby ops.
@@ -67,46 +50,6 @@ def f(values, index, ...):
         )
 
 
-def generate_numba_func(
-    func: Callable,
-    engine_kwargs: Optional[Dict[str, bool]],
-    kwargs: dict,
-    cache_key_str: str,
-) -> Tuple[Callable, Tuple[Callable, str]]:
-    """
-    Return a JITed function and cache key for the NUMBA_FUNC_CACHE
-
-    This _may_ be specific to groupby (as it's only used there currently).
-
-    Parameters
-    ----------
-    func : function
-        user defined function
-    engine_kwargs : dict or None
-        numba.jit arguments
-    kwargs : dict
-        kwargs for func
-    cache_key_str : str
-        string representing the second part of the cache key tuple
-
-    Returns
-    -------
-    (JITed function, cache key)
-
-    Raises
-    ------
-    NumbaUtilError
-    """
-    nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
-    check_kwargs_and_nopython(kwargs, nopython)
-    validate_udf(func)
-    cache_key = (func, cache_key_str)
-    numba_func = NUMBA_FUNC_CACHE.get(
-        cache_key, jit_user_function(func, nopython, nogil, parallel)
-    )
-    return numba_func, cache_key
-
-
 def generate_numba_agg_func(
     args: Tuple,
     kwargs: Dict[str, Any],
@@ -120,7 +63,7 @@ def generate_numba_agg_func(
     2. Return a groupby agg function with the jitted function inline
 
     Configurations specified in engine_kwargs apply to both the user's
-    function _AND_ the rolling apply function.
+    function _AND_ the groupby evaluation loop.
 
     Parameters
     ----------
@@ -137,16 +80,15 @@ def generate_numba_agg_func(
     -------
     Numba function
     """
-    nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
-
-    check_kwargs_and_nopython(kwargs, nopython)
+    nopython, nogil, parallel = get_jit_arguments(engine_kwargs, kwargs)
 
     validate_udf(func)
+    cache_key = (func, "groupby_agg")
+    if cache_key in NUMBA_FUNC_CACHE:
+        return NUMBA_FUNC_CACHE[cache_key]
 
     numba_func = jit_user_function(func, nopython, nogil, parallel)
-
     numba = import_optional_dependency("numba")
-
     if parallel:
         loop_range = numba.prange
     else:
@@ -175,17 +117,17 @@ def group_agg(
 def generate_numba_transform_func(
     args: Tuple,
     kwargs: Dict[str, Any],
-    func: Callable[..., Scalar],
+    func: Callable[..., np.ndarray],
     engine_kwargs: Optional[Dict[str, bool]],
 ) -> Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int, int], np.ndarray]:
     """
     Generate a numba jitted transform function specified by values from engine_kwargs.
 
     1. jit the user's function
-    2. Return a groupby agg function with the jitted function inline
+    2. Return a groupby transform function with the jitted function inline
 
     Configurations specified in engine_kwargs apply to both the user's
-    function _AND_ the rolling apply function.
+    function _AND_ the groupby evaluation loop.
 
     Parameters
     ----------
@@ -202,16 +144,15 @@ def generate_numba_transform_func(
     -------
     Numba function
     """
-    nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
-
-    check_kwargs_and_nopython(kwargs, nopython)
+    nopython, nogil, parallel = get_jit_arguments(engine_kwargs, kwargs)
 
     validate_udf(func)
+    cache_key = (func, "groupby_transform")
+    if cache_key in NUMBA_FUNC_CACHE:
+        return NUMBA_FUNC_CACHE[cache_key]
 
     numba_func = jit_user_function(func, nopython, nogil, parallel)
-
     numba = import_optional_dependency("numba")
-
     if parallel:
         loop_range = numba.prange
     else:

pandas/core/util/numba_.py

@@ -24,37 +24,8 @@ def set_use_numba(enable: bool = False) -> None:
     GLOBAL_USE_NUMBA = enable
 
 
-def check_kwargs_and_nopython(
-    kwargs: Optional[Dict] = None, nopython: Optional[bool] = None
-) -> None:
-    """
-    Validate that **kwargs and nopython=True was passed
-    https://github.com/numba/numba/issues/2916
-
-    Parameters
-    ----------
-    kwargs : dict, default None
-        user passed keyword arguments to pass into the JITed function
-    nopython : bool, default None
-        nopython parameter
-
-    Returns
-    -------
-    None
-
-    Raises
-    ------
-    NumbaUtilError
-    """
-    if kwargs and nopython:
-        raise NumbaUtilError(
-            "numba does not support kwargs with nopython=True: "
-            "https://github.com/numba/numba/issues/2916"
-        )
-
-
 def get_jit_arguments(
-    engine_kwargs: Optional[Dict[str, bool]] = None
+    engine_kwargs: Optional[Dict[str, bool]] = None, kwargs: Optional[Dict] = None,
 ) -> Tuple[bool, bool, bool]:
     """
     Return arguments to pass to numba.JIT, falling back on pandas default JIT settings.
@@ -63,16 +34,27 @@ def get_jit_arguments(
     ----------
     engine_kwargs : dict, default None
         user passed keyword arguments for numba.JIT
+    kwargs : dict, default None
+        user passed keyword arguments to pass into the JITed function
 
     Returns
     -------
     (bool, bool, bool)
         nopython, nogil, parallel
+
+    Raises
+    ------
+    NumbaUtilError
     """
     if engine_kwargs is None:
         engine_kwargs = {}
 
     nopython = engine_kwargs.get("nopython", True)
+    if kwargs and nopython:
+        raise NumbaUtilError(
+            "numba does not support kwargs with nopython=True: "
+            "https://github.com/numba/numba/issues/2916"
+        )
     nogil = engine_kwargs.get("nogil", False)
     parallel = engine_kwargs.get("parallel", False)
     return nopython, nogil, parallel

pandas/core/window/numba_.py

@@ -6,7 +6,7 @@
 from pandas.compat._optional import import_optional_dependency
 
 from pandas.core.util.numba_ import (
-    check_kwargs_and_nopython,
+    NUMBA_FUNC_CACHE,
     get_jit_arguments,
     jit_user_function,
 )
@@ -42,14 +42,14 @@ def generate_numba_apply_func(
     -------
     Numba function
     """
-    nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
+    nopython, nogil, parallel = get_jit_arguments(engine_kwargs, kwargs)
 
-    check_kwargs_and_nopython(kwargs, nopython)
+    cache_key = (func, "rolling_apply")
+    if cache_key in NUMBA_FUNC_CACHE:
+        return NUMBA_FUNC_CACHE[cache_key]
 
     numba_func = jit_user_function(func, nopython, nogil, parallel)
-
     numba = import_optional_dependency("numba")
-
     if parallel:
         loop_range = numba.prange
     else:

pandas/core/window/rolling.py

@@ -1374,14 +1374,7 @@ def apply(
         if maybe_use_numba(engine):
             if raw is False:
                 raise ValueError("raw must be `True` when using the numba engine")
-            cache_key = (func, "rolling_apply")
-            if cache_key in NUMBA_FUNC_CACHE:
-                # Return an already compiled version of roll_apply if available
-                apply_func = NUMBA_FUNC_CACHE[cache_key]
-            else:
-                apply_func = generate_numba_apply_func(
-                    args, kwargs, func, engine_kwargs
-                )
+            apply_func = generate_numba_apply_func(args, kwargs, func, engine_kwargs)
             center = self.center
         elif engine in ("cython", None):
             if engine_kwargs is not None:
@@ -1403,7 +1396,7 @@ def apply(
             center=center,
             floor=0,
             name=func,
-            use_numba_cache=engine == "numba",
+            use_numba_cache=maybe_use_numba(engine),
             raw=raw,
             original_func=func,
             args=args,