diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 1f9987d9d3f5b..8efe2fc090fc5 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -8595,6 +8595,7 @@ def _reduce( cols = self.columns[~dtype_is_dt] self = self[cols] + any_object = self.dtypes.apply(is_object_dtype).any() # TODO: Make other agg func handle axis=None properly GH#21597 axis = self._get_axis_number(axis) labels = self._get_agg_axis(axis) @@ -8621,7 +8622,17 @@ def _get_data() -> DataFrame: data = self._get_bool_data() return data - if numeric_only is not None: + if numeric_only is not None or ( + numeric_only is None + and axis == 0 + and not any_object + and not self._mgr.any_extension_types + ): + # For numeric_only non-None and axis non-None, we know + # which blocks to use and no try/except is needed. + # For numeric_only=None only the case with axis==0 and no object + # dtypes are unambiguous can be handled with BlockManager.reduce + # Case with EAs see GH#35881 df = self if numeric_only is True: df = _get_data() @@ -8629,14 +8640,18 @@ def _get_data() -> DataFrame: df = df.T axis = 0 + ignore_failures = numeric_only is None + # After possibly _get_data and transposing, we are now in the # simple case where we can use BlockManager.reduce - res = df._mgr.reduce(blk_func) - out = df._constructor(res).iloc[0].rename(None) + res, indexer = df._mgr.reduce(blk_func, ignore_failures=ignore_failures) + out = df._constructor(res).iloc[0] if out_dtype is not None: out = out.astype(out_dtype) if axis == 0 and is_object_dtype(out.dtype): - out[:] = coerce_to_dtypes(out.values, df.dtypes) + # GH#35865 careful to cast explicitly to object + nvs = coerce_to_dtypes(out.values, df.dtypes.iloc[np.sort(indexer)]) + out[:] = np.array(nvs, dtype=object) return out assert numeric_only is None diff --git a/pandas/core/internals/blocks.py b/pandas/core/internals/blocks.py index 09f276be7d64a..9b6c4b664285e 100644 --- a/pandas/core/internals/blocks.py +++ b/pandas/core/internals/blocks.py @@ -348,12 +348,18 @@ def apply(self, func, **kwargs) -> List["Block"]: return self._split_op_result(result) - def reduce(self, func) -> List["Block"]: + def reduce(self, func, ignore_failures: bool = False) -> List["Block"]: # We will apply the function and reshape the result into a single-row # Block with the same mgr_locs; squeezing will be done at a higher level assert self.ndim == 2 - result = func(self.values) + try: + result = func(self.values) + except (TypeError, NotImplementedError): + if ignore_failures: + return [] + raise + if np.ndim(result) == 0: # TODO(EA2D): special case not needed with 2D EAs res_values = np.array([[result]]) @@ -2454,6 +2460,34 @@ def is_bool(self): """ return lib.is_bool_array(self.values.ravel("K")) + def reduce(self, func, ignore_failures: bool = False) -> List[Block]: + """ + For object-dtype, we operate column-wise. + """ + assert self.ndim == 2 + + values = self.values + if len(values) > 1: + # split_and_operate expects func with signature (mask, values, inplace) + def mask_func(mask, values, inplace): + if values.ndim == 1: + values = values.reshape(1, -1) + return func(values) + + return self.split_and_operate(None, mask_func, False) + + try: + res = func(values) + except TypeError: + if not ignore_failures: + raise + return [] + + assert isinstance(res, np.ndarray) + assert res.ndim == 1 + res = res.reshape(1, -1) + return [self.make_block_same_class(res)] + def convert( self, copy: bool = True, diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py index f2480adce89b4..7f5e99c3348b7 100644 --- a/pandas/core/internals/managers.py +++ b/pandas/core/internals/managers.py @@ -2,6 +2,7 @@ import itertools from typing import ( Any, + Callable, DefaultDict, Dict, List, @@ -324,18 +325,44 @@ def _verify_integrity(self) -> None: f"tot_items: {tot_items}" ) - def reduce(self: T, func) -> T: + def reduce( + self: T, func: Callable, ignore_failures: bool = False + ) -> Tuple[T, np.ndarray]: + """ + Apply reduction function blockwise, returning a single-row BlockManager. + + Parameters + ---------- + func : reduction function + ignore_failures : bool, default False + Whether to drop blocks where func raises TypeError. + + Returns + ------- + BlockManager + np.ndarray + Indexer of mgr_locs that are retained. + """ # If 2D, we assume that we're operating column-wise assert self.ndim == 2 res_blocks: List[Block] = [] for blk in self.blocks: - nbs = blk.reduce(func) + nbs = blk.reduce(func, ignore_failures) res_blocks.extend(nbs) - index = Index([0]) # placeholder - new_mgr = BlockManager.from_blocks(res_blocks, [self.items, index]) - return new_mgr + index = Index([None]) # placeholder + if ignore_failures: + if res_blocks: + indexer = np.concatenate([blk.mgr_locs.as_array for blk in res_blocks]) + new_mgr = self._combine(res_blocks, copy=False, index=index) + else: + indexer = [] + new_mgr = type(self).from_blocks([], [Index([]), index]) + else: + indexer = np.arange(self.shape[0]) + new_mgr = type(self).from_blocks(res_blocks, [self.items, index]) + return new_mgr, indexer def operate_blockwise(self, other: "BlockManager", array_op) -> "BlockManager": """ @@ -700,7 +727,9 @@ def get_numeric_data(self, copy: bool = False) -> "BlockManager": """ return self._combine([b for b in self.blocks if b.is_numeric], copy) - def _combine(self: T, blocks: List[Block], copy: bool = True) -> T: + def _combine( + self: T, blocks: List[Block], copy: bool = True, index: Optional[Index] = None + ) -> T: """ return a new manager with the blocks """ if len(blocks) == 0: return self.make_empty() @@ -716,6 +745,8 @@ def _combine(self: T, blocks: List[Block], copy: bool = True) -> T: new_blocks.append(b) axes = list(self.axes) + if index is not None: + axes[-1] = index axes[0] = self.items.take(indexer) return type(self).from_blocks(new_blocks, axes)