From 4c5eddd63e94bacddb96bf61f81a6a8fcd9c33f0 Mon Sep 17 00:00:00 2001 From: Brock Date: Thu, 20 Aug 2020 21:19:10 -0700 Subject: [PATCH 1/6] REF: remove unnecesary try/except --- pandas/core/groupby/generic.py | 69 ++++++++++++++++------------------ 1 file changed, 33 insertions(+), 36 deletions(-) diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py index 166631e69f523..51532a75d2d4a 100644 --- a/pandas/core/groupby/generic.py +++ b/pandas/core/groupby/generic.py @@ -31,7 +31,7 @@ import numpy as np from pandas._libs import lib -from pandas._typing import FrameOrSeries, FrameOrSeriesUnion +from pandas._typing import ArrayLike, FrameOrSeries, FrameOrSeriesUnion from pandas.util._decorators import Appender, Substitution, doc from pandas.core.dtypes.cast import ( @@ -60,6 +60,7 @@ validate_func_kwargs, ) import pandas.core.algorithms as algorithms +from pandas.core.arrays import ExtensionArray from pandas.core.base import DataError, SpecificationError import pandas.core.common as com from pandas.core.construction import create_series_with_explicit_dtype @@ -1034,32 +1035,31 @@ def _cython_agg_blocks( no_result = object() - def cast_result_block(result, block: "Block", how: str) -> "Block": - # see if we can cast the block to the desired dtype + def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike: + # see if we can cast the values to the desired dtype # this may not be the original dtype assert not isinstance(result, DataFrame) assert result is not no_result - dtype = maybe_cast_result_dtype(block.dtype, how) + dtype = maybe_cast_result_dtype(values.dtype, how) result = maybe_downcast_numeric(result, dtype) - if block.is_extension and isinstance(result, np.ndarray): - # e.g. block.values was an IntegerArray - # (1, N) case can occur if block.values was Categorical + if isinstance(values, ExtensionArray) and isinstance(result, np.ndarray): + # e.g. values was an IntegerArray + # (1, N) case can occur if values was Categorical # and result is ndarray[object] # TODO(EA2D): special casing not needed with 2D EAs assert result.ndim == 1 or result.shape[0] == 1 try: # Cast back if feasible - result = type(block.values)._from_sequence( - result.ravel(), dtype=block.values.dtype + result = type(values)._from_sequence( + result.ravel(), dtype=values.dtype ) except (ValueError, TypeError): # reshape to be valid for non-Extension Block result = result.reshape(1, -1) - agg_block: "Block" = block.make_block(result) - return agg_block + return result def blk_func(block: "Block") -> List["Block"]: new_blocks: List["Block"] = [] @@ -1093,33 +1093,30 @@ def blk_func(block: "Block") -> List["Block"]: # Categoricals. This will done by later self._reindex_output() # Doing it here creates an error. See GH#34951 sgb = get_groupby(obj, self.grouper, observed=True) - try: - result = sgb.aggregate(lambda x: alt(x, axis=self.axis)) - except TypeError: - # we may have an exception in trying to aggregate - # continue and exclude the block - raise + result = sgb.aggregate(lambda x: alt(x, axis=self.axis)) + + result = cast(DataFrame, result) + # unwrap DataFrame to get array + if len(result._mgr.blocks) != 1: + # We've split an object block! Everything we've assumed + # about a single block input returning a single block output + # is a lie. To keep the code-path for the typical non-split case + # clean, we choose to clean up this mess later on. + assert len(locs) == result.shape[1] + for i, loc in enumerate(locs): + agg_block = result.iloc[:, [i]]._mgr.blocks[0] + agg_block.mgr_locs = [loc] + new_blocks.append(agg_block) else: - result = cast(DataFrame, result) - # unwrap DataFrame to get array - if len(result._mgr.blocks) != 1: - # We've split an object block! Everything we've assumed - # about a single block input returning a single block output - # is a lie. To keep the code-path for the typical non-split case - # clean, we choose to clean up this mess later on. - assert len(locs) == result.shape[1] - for i, loc in enumerate(locs): - agg_block = result.iloc[:, [i]]._mgr.blocks[0] - agg_block.mgr_locs = [loc] - new_blocks.append(agg_block) - else: - result = result._mgr.blocks[0].values - if isinstance(result, np.ndarray) and result.ndim == 1: - result = result.reshape(1, -1) - agg_block = cast_result_block(result, block, how) - new_blocks = [agg_block] + result = result._mgr.blocks[0].values + if isinstance(result, np.ndarray) and result.ndim == 1: + result = result.reshape(1, -1) + res_values = cast_agg_result(result, block.values, how) + agg_block = block.make_block(res_values) + new_blocks = [agg_block] else: - agg_block = cast_result_block(result, block, how) + res_values = cast_agg_result(result, block.values, how) + agg_block = block.make_block(res_values) new_blocks = [agg_block] return new_blocks From 42649fbb855a895ee5818d7dc80bdbd0ce0e9f5a Mon Sep 17 00:00:00 2001 From: Karthik Mathur <22126205+mathurk1@users.noreply.github.com> Date: Fri, 21 Aug 2020 17:34:51 -0500 Subject: [PATCH 2/6] TST: add test for agg on ordered categorical cols (#35630) --- .../tests/groupby/aggregate/test_aggregate.py | 79 +++++++++++++++++++ 1 file changed, 79 insertions(+) diff --git a/pandas/tests/groupby/aggregate/test_aggregate.py b/pandas/tests/groupby/aggregate/test_aggregate.py index ce9d4b892d775..8fe450fe6abfc 100644 --- a/pandas/tests/groupby/aggregate/test_aggregate.py +++ b/pandas/tests/groupby/aggregate/test_aggregate.py @@ -1063,6 +1063,85 @@ def test_groupby_get_by_index(): pd.testing.assert_frame_equal(res, expected) +@pytest.mark.parametrize( + "grp_col_dict, exp_data", + [ + ({"nr": "min", "cat_ord": "min"}, {"nr": [1, 5], "cat_ord": ["a", "c"]}), + ({"cat_ord": "min"}, {"cat_ord": ["a", "c"]}), + ({"nr": "min"}, {"nr": [1, 5]}), + ], +) +def test_groupby_single_agg_cat_cols(grp_col_dict, exp_data): + # test single aggregations on ordered categorical cols GHGH27800 + + # create the result dataframe + input_df = pd.DataFrame( + { + "nr": [1, 2, 3, 4, 5, 6, 7, 8], + "cat_ord": list("aabbccdd"), + "cat": list("aaaabbbb"), + } + ) + + input_df = input_df.astype({"cat": "category", "cat_ord": "category"}) + input_df["cat_ord"] = input_df["cat_ord"].cat.as_ordered() + result_df = input_df.groupby("cat").agg(grp_col_dict) + + # create expected dataframe + cat_index = pd.CategoricalIndex( + ["a", "b"], categories=["a", "b"], ordered=False, name="cat", dtype="category" + ) + + expected_df = pd.DataFrame(data=exp_data, index=cat_index) + + tm.assert_frame_equal(result_df, expected_df) + + +@pytest.mark.parametrize( + "grp_col_dict, exp_data", + [ + ({"nr": ["min", "max"], "cat_ord": "min"}, [(1, 4, "a"), (5, 8, "c")]), + ({"nr": "min", "cat_ord": ["min", "max"]}, [(1, "a", "b"), (5, "c", "d")]), + ({"cat_ord": ["min", "max"]}, [("a", "b"), ("c", "d")]), + ], +) +def test_groupby_combined_aggs_cat_cols(grp_col_dict, exp_data): + # test combined aggregations on ordered categorical cols GH27800 + + # create the result dataframe + input_df = pd.DataFrame( + { + "nr": [1, 2, 3, 4, 5, 6, 7, 8], + "cat_ord": list("aabbccdd"), + "cat": list("aaaabbbb"), + } + ) + + input_df = input_df.astype({"cat": "category", "cat_ord": "category"}) + input_df["cat_ord"] = input_df["cat_ord"].cat.as_ordered() + result_df = input_df.groupby("cat").agg(grp_col_dict) + + # create expected dataframe + cat_index = pd.CategoricalIndex( + ["a", "b"], categories=["a", "b"], ordered=False, name="cat", dtype="category" + ) + + # unpack the grp_col_dict to create the multi-index tuple + # this tuple will be used to create the expected dataframe index + multi_index_list = [] + for k, v in grp_col_dict.items(): + if isinstance(v, list): + for value in v: + multi_index_list.append([k, value]) + else: + multi_index_list.append([k, v]) + multi_index = pd.MultiIndex.from_tuples(tuple(multi_index_list)) + + expected_df = pd.DataFrame(data=exp_data, columns=multi_index, index=cat_index) + + tm.assert_frame_equal(result_df, expected_df) + + def test_nonagg_agg(): # GH 35490 - Single/Multiple agg of non-agg function give same results # TODO: agg should raise for functions that don't aggregate From 47121ddc1c655f428c6c3fcea8fbf02eba85600a Mon Sep 17 00:00:00 2001 From: tkmz-n <60312218+tkmz-n@users.noreply.github.com> Date: Sat, 22 Aug 2020 07:42:50 +0900 Subject: [PATCH 3/6] TST: resample does not yield empty groups (#10603) (#35799) --- pandas/tests/resample/test_timedelta.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/pandas/tests/resample/test_timedelta.py b/pandas/tests/resample/test_timedelta.py index 0fbb60c176b30..3fa85e62d028c 100644 --- a/pandas/tests/resample/test_timedelta.py +++ b/pandas/tests/resample/test_timedelta.py @@ -150,3 +150,18 @@ def test_resample_timedelta_edge_case(start, end, freq, resample_freq): tm.assert_index_equal(result.index, expected_index) assert result.index.freq == expected_index.freq assert not np.isnan(result[-1]) + + +def test_resample_with_timedelta_yields_no_empty_groups(): + # GH 10603 + df = pd.DataFrame( + np.random.normal(size=(10000, 4)), + index=pd.timedelta_range(start="0s", periods=10000, freq="3906250n"), + ) + result = df.loc["1s":, :].resample("3s").apply(lambda x: len(x)) + + expected = pd.DataFrame( + [[768.0] * 4] * 12 + [[528.0] * 4], + index=pd.timedelta_range(start="1s", periods=13, freq="3s"), + ) + tm.assert_frame_equal(result, expected) From 1decb3e0ee1923a29b8eded7507bcb783b3870d0 Mon Sep 17 00:00:00 2001 From: Brock Date: Fri, 21 Aug 2020 18:48:02 -0700 Subject: [PATCH 4/6] revert accidental rebase --- pandas/core/groupby/generic.py | 61 ++++++++++++++++++---------------- 1 file changed, 32 insertions(+), 29 deletions(-) diff --git a/pandas/core/groupby/generic.py b/pandas/core/groupby/generic.py index 4b1f6cfe0a662..60e23b14eaf09 100644 --- a/pandas/core/groupby/generic.py +++ b/pandas/core/groupby/generic.py @@ -30,7 +30,7 @@ import numpy as np from pandas._libs import lib -from pandas._typing import ArrayLike, FrameOrSeries, FrameOrSeriesUnion +from pandas._typing import FrameOrSeries, FrameOrSeriesUnion from pandas.util._decorators import Appender, Substitution, doc from pandas.core.dtypes.cast import ( @@ -59,7 +59,6 @@ validate_func_kwargs, ) import pandas.core.algorithms as algorithms -from pandas.core.arrays import ExtensionArray from pandas.core.base import DataError, SpecificationError import pandas.core.common as com from pandas.core.construction import create_series_with_explicit_dtype @@ -1034,31 +1033,32 @@ def _cython_agg_blocks( no_result = object() - def cast_agg_result(result, values: ArrayLike, how: str) -> ArrayLike: - # see if we can cast the values to the desired dtype + def cast_result_block(result, block: "Block", how: str) -> "Block": + # see if we can cast the block to the desired dtype # this may not be the original dtype assert not isinstance(result, DataFrame) assert result is not no_result - dtype = maybe_cast_result_dtype(values.dtype, how) + dtype = maybe_cast_result_dtype(block.dtype, how) result = maybe_downcast_numeric(result, dtype) - if isinstance(values, ExtensionArray) and isinstance(result, np.ndarray): - # e.g. values was an IntegerArray - # (1, N) case can occur if values was Categorical + if block.is_extension and isinstance(result, np.ndarray): + # e.g. block.values was an IntegerArray + # (1, N) case can occur if block.values was Categorical # and result is ndarray[object] # TODO(EA2D): special casing not needed with 2D EAs assert result.ndim == 1 or result.shape[0] == 1 try: # Cast back if feasible - result = type(values)._from_sequence( - result.ravel(), dtype=values.dtype + result = type(block.values)._from_sequence( + result.ravel(), dtype=block.values.dtype ) except (ValueError, TypeError): # reshape to be valid for non-Extension Block result = result.reshape(1, -1) - return result + agg_block: "Block" = block.make_block(result) + return agg_block def blk_func(block: "Block") -> List["Block"]: new_blocks: List["Block"] = [] @@ -1092,25 +1092,28 @@ def blk_func(block: "Block") -> List["Block"]: # Categoricals. This will done by later self._reindex_output() # Doing it here creates an error. See GH#34951 sgb = get_groupby(obj, self.grouper, observed=True) - result = sgb.aggregate(lambda x: alt(x, axis=self.axis)) - - assert isinstance(result, (Series, DataFrame)) # for mypy - # In the case of object dtype block, it may have been split - # in the operation. We un-split here. - result = result._consolidate() - assert isinstance(result, (Series, DataFrame)) # for mypy - assert len(result._mgr.blocks) == 1 - - # unwrap DataFrame to get array - result = result._mgr.blocks[0].values - if isinstance(result, np.ndarray) and result.ndim == 1: - result = result.reshape(1, -1) - res_values = cast_agg_result(result, block.values, how) - agg_block = block.make_block(res_values) - new_blocks = [agg_block] + try: + result = sgb.aggregate(lambda x: alt(x, axis=self.axis)) + except TypeError: + # we may have an exception in trying to aggregate + # continue and exclude the block + raise + else: + assert isinstance(result, (Series, DataFrame)) # for mypy + # In the case of object dtype block, it may have been split + # in the operation. We un-split here. + result = result._consolidate() + assert isinstance(result, (Series, DataFrame)) # for mypy + assert len(result._mgr.blocks) == 1 + + # unwrap DataFrame to get array + result = result._mgr.blocks[0].values + if isinstance(result, np.ndarray) and result.ndim == 1: + result = result.reshape(1, -1) + agg_block = cast_result_block(result, block, how) + new_blocks = [agg_block] else: - res_values = cast_agg_result(result, block.values, how) - agg_block = block.make_block(res_values) + agg_block = cast_result_block(result, block, how) new_blocks = [agg_block] return new_blocks From 010935e9bdd113dd99c4c2f53f73eb52e8024668 Mon Sep 17 00:00:00 2001 From: Brock Date: Sat, 22 Aug 2020 20:41:22 -0700 Subject: [PATCH 5/6] REF: make apply_blockwise _actually_ blockwise --- pandas/core/window/rolling.py | 69 ++++++++++++++++++++++------------- 1 file changed, 44 insertions(+), 25 deletions(-) diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py index f7e81f41b8675..062d174bffe71 100644 --- a/pandas/core/window/rolling.py +++ b/pandas/core/window/rolling.py @@ -66,7 +66,8 @@ from pandas.core.window.numba_ import generate_numba_apply_func if TYPE_CHECKING: - from pandas import Series + from pandas import DataFrame, Series + from pandas.core.internals import Block # noqa:F401 def calculate_center_offset(window) -> int: @@ -418,35 +419,40 @@ def _wrap_results(self, results, obj, skipped: List[int]) -> FrameOrSeriesUnion: for i in skipped: exclude.extend(orig_blocks[i].columns) - kept_blocks = [blk for i, blk in enumerate(orig_blocks) if i not in skipped] - - final = [] - for result, block in zip(results, kept_blocks): - - result = type(obj)(result, index=obj.index, columns=block.columns) - final.append(result) - - exclude = exclude or [] columns = [c for c in self._selected_obj.columns if c not in exclude] - if not columns and not len(final) and exclude: + if not columns and not len(results) and exclude: raise DataError("No numeric types to aggregate") - elif not len(final): + elif not len(results): return obj.astype("float64") - df = concat(final, axis=1).reindex(columns=columns, copy=False) + df = concat(results, axis=1).reindex(columns=columns, copy=False) + self._insert_on_column(df, obj) + return df + def _insert_on_column(self, result: "DataFrame", obj: "DataFrame"): # if we have an 'on' column we want to put it back into # the results in the same location + from pandas import Series + if self.on is not None and not self._on.equals(obj.index): name = self._on.name extra_col = Series(self._on, index=obj.index, name=name) - if name not in df.columns and name not in df.index.names: - new_loc = len(df.columns) - df.insert(new_loc, name, extra_col) - elif name in df.columns: + if name in result.columns: # TODO: sure we want to overwrite results? - df[name] = extra_col - return df + result[name] = extra_col + elif name in result.index.names: + pass + elif name in self._selected_obj.columns: + # insert in the same location as we had in _selected_obj + old_cols = self._selected_obj.columns + new_cols = result.columns + old_loc = old_cols.get_loc(name) + overlap = new_cols.intersection(old_cols[:old_loc]) + new_loc = len(overlap) + result.insert(new_loc, name, extra_col) + else: + # insert at the end + result[name] = extra_col def _center_window(self, result, window) -> np.ndarray: """ @@ -530,21 +536,34 @@ def _apply_blockwise( # This isn't quite blockwise, since `blocks` is actually a collection # of homogenenous DataFrames. blocks, obj = self._create_blocks(self._selected_obj) + mgr = obj._mgr + + def hfunc(bvalues: ArrayLike) -> ArrayLike: + values = self._prep_values(bvalues.T) + return homogeneous_func(values).T skipped: List[int] = [] - results: List[ArrayLike] = [] - for i, b in enumerate(blocks): + res_blocks: List["Block"] = [] + for i, blk in enumerate(mgr.blocks): try: - values = self._prep_values(b.values) + nbs = blk.apply(hfunc) except (TypeError, NotImplementedError): skipped.append(i) continue - result = homogeneous_func(values) - results.append(result) + res_blocks.extend(nbs) + + if not len(res_blocks) and skipped: + raise DataError("No numeric types to aggregate") + elif not len(res_blocks): + return obj.astype("float64") - return self._wrap_results(results, obj, skipped) + new_cols = mgr.reset_dropped_locs(res_blocks, skipped) + new_mgr = type(mgr).from_blocks(res_blocks, [new_cols, obj.index]) + out = obj._constructor(new_mgr) + self._insert_on_column(out, obj) + return out def _apply( self, From 24e7936288de1a6ea7bc6d0058da8bac4bbab0c0 Mon Sep 17 00:00:00 2001 From: Brock Date: Sat, 22 Aug 2020 21:13:44 -0700 Subject: [PATCH 6/6] mypy fixup --- pandas/core/window/rolling.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/pandas/core/window/rolling.py b/pandas/core/window/rolling.py index 062d174bffe71..a70247d9f7f9c 100644 --- a/pandas/core/window/rolling.py +++ b/pandas/core/window/rolling.py @@ -539,8 +539,10 @@ def _apply_blockwise( mgr = obj._mgr def hfunc(bvalues: ArrayLike) -> ArrayLike: - values = self._prep_values(bvalues.T) - return homogeneous_func(values).T + # TODO(EA2D): getattr unnecessary with 2D EAs + values = self._prep_values(getattr(bvalues, "T", bvalues)) + res_values = homogeneous_func(values) + return getattr(res_values, "T", res_values) skipped: List[int] = [] res_blocks: List["Block"] = []