2020-05-27T03:46:00.4640450Z ______________ test_multiple_agg_funcs[rolling-2-expected_vals0] ______________ 2020-05-27T03:46:00.4640874Z [gw0] win32 -- Python 3.7.7 D:\a\1\s\test_venv\Scripts\python.exe 2020-05-27T03:46:00.4641216Z 2020-05-27T03:46:00.4641446Z func = 'rolling', window_size = 2 2020-05-27T03:46:00.4641974Z expected_vals = [[nan, nan, nan, nan], [15.0, 20.0, 25.0, 20.0], [25.0, 30.0, 35.0, 30.0], [nan, nan, nan, nan], [20.0, 30.0, 35.0, 30.0], [35.0, 40.0, 60.0, 40.0], ...] 2020-05-27T03:46:00.4642426Z 2020-05-27T03:46:00.4642646Z @pytest.mark.parametrize( 2020-05-27T03:46:00.4642970Z "func,window_size,expected_vals", 2020-05-27T03:46:00.4643468Z [ 2020-05-27T03:46:00.4643722Z ( 2020-05-27T03:46:00.4643997Z "rolling", 2020-05-27T03:46:00.4644235Z 2, 2020-05-27T03:46:00.4644500Z [ 2020-05-27T03:46:00.4644818Z [np.nan, np.nan, np.nan, np.nan], 2020-05-27T03:46:00.4645140Z [15.0, 20.0, 25.0, 20.0], 2020-05-27T03:46:00.4645482Z [25.0, 30.0, 35.0, 30.0], 2020-05-27T03:46:00.4645799Z [np.nan, np.nan, np.nan, np.nan], 2020-05-27T03:46:00.4646157Z [20.0, 30.0, 35.0, 30.0], 2020-05-27T03:46:00.4646503Z [35.0, 40.0, 60.0, 40.0], 2020-05-27T03:46:00.4646811Z [60.0, 80.0, 85.0, 80], 2020-05-27T03:46:00.4647113Z ], 2020-05-27T03:46:00.4647372Z ), 2020-05-27T03:46:00.4647589Z ( 2020-05-27T03:46:00.4647855Z "expanding", 2020-05-27T03:46:00.4648104Z None, 2020-05-27T03:46:00.4648376Z [ 2020-05-27T03:46:00.4648680Z [10.0, 10.0, 20.0, 20.0], 2020-05-27T03:46:00.4648983Z [15.0, 20.0, 25.0, 20.0], 2020-05-27T03:46:00.4649326Z [20.0, 30.0, 30.0, 20.0], 2020-05-27T03:46:00.4649670Z [10.0, 10.0, 30.0, 30.0], 2020-05-27T03:46:00.4649972Z [20.0, 30.0, 35.0, 30.0], 2020-05-27T03:46:00.4650322Z [26.666667, 40.0, 50.0, 30.0], 2020-05-27T03:46:00.4650683Z [40.0, 80.0, 60.0, 30.0], 2020-05-27T03:46:00.4650946Z ], 2020-05-27T03:46:00.4651208Z ), 2020-05-27T03:46:00.4651414Z ], 2020-05-27T03:46:00.4651646Z ) 2020-05-27T03:46:00.4651997Z def test_multiple_agg_funcs(func, window_size, expected_vals): 2020-05-27T03:46:00.4652328Z # GH 15072 2020-05-27T03:46:00.4652605Z df = pd.DataFrame( 2020-05-27T03:46:00.4652839Z [ 2020-05-27T03:46:00.4653114Z ["A", 10, 20], 2020-05-27T03:46:00.4653414Z ["A", 20, 30], 2020-05-27T03:46:00.4653669Z ["A", 30, 40], 2020-05-27T03:46:00.4653966Z ["B", 10, 30], 2020-05-27T03:46:00.4654266Z ["B", 30, 40], 2020-05-27T03:46:00.4654520Z ["B", 40, 80], 2020-05-27T03:46:00.4654822Z ["B", 80, 90], 2020-05-27T03:46:00.4655056Z ], 2020-05-27T03:46:00.4655354Z columns=["stock", "low", "high"], 2020-05-27T03:46:00.4655642Z ) 2020-05-27T03:46:00.4655833Z 2020-05-27T03:46:00.4656116Z f = getattr(df.groupby("stock"), func) 2020-05-27T03:46:00.4656545Z if window_size: 2020-05-27T03:46:00.4656817Z window = f(window_size) 2020-05-27T03:46:00.4657104Z else: 2020-05-27T03:46:00.4657337Z window = f() 2020-05-27T03:46:00.4657591Z 2020-05-27T03:46:00.4657872Z index = pd.MultiIndex.from_tuples( 2020-05-27T03:46:00.4658235Z [("A", 0), ("A", 1), ("A", 2), ("B", 3), ("B", 4), ("B", 5), ("B", 6)], 2020-05-27T03:46:00.4658625Z names=["stock", None], 2020-05-27T03:46:00.4658902Z ) 2020-05-27T03:46:00.4659155Z columns = pd.MultiIndex.from_tuples( 2020-05-27T03:46:00.4659548Z [("low", "mean"), ("low", "max"), ("high", "mean"), ("high", "min")] 2020-05-27T03:46:00.4659860Z ) 2020-05-27T03:46:00.4660195Z expected = pd.DataFrame(expected_vals, index=index, columns=columns) 2020-05-27T03:46:00.4660526Z 2020-05-27T03:46:00.4660750Z result = window.agg( 2020-05-27T03:46:00.4661122Z > OrderedDict((("low", ["mean", "max"]), ("high", ["mean", "min"]))) 2020-05-27T03:46:00.4661464Z ) 2020-05-27T03:46:00.4661630Z 2020-05-27T03:46:00.4661965Z test_venv\lib\site-packages\pandas\tests\window\test_api.py:346: 2020-05-27T03:46:00.4662381Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 2020-05-27T03:46:00.4662906Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2033: in aggregate 2020-05-27T03:46:00.4663339Z return super().aggregate(func, *args, **kwargs) 2020-05-27T03:46:00.4663730Z test_venv\lib\site-packages\pandas\core\window\rolling.py:603: in aggregate 2020-05-27T03:46:00.4664169Z result, how = self._aggregate(func, *args, **kwargs) 2020-05-27T03:46:00.4664599Z test_venv\lib\site-packages\pandas\core\base.py:417: in _aggregate 2020-05-27T03:46:00.4664941Z result = _agg(arg, _agg_1dim) 2020-05-27T03:46:00.4665315Z test_venv\lib\site-packages\pandas\core\base.py:384: in _agg 2020-05-27T03:46:00.4665658Z result[fname] = func(fname, agg_how) 2020-05-27T03:46:00.4666056Z test_venv\lib\site-packages\pandas\core\base.py:368: in _agg_1dim 2020-05-27T03:46:00.4666433Z return colg.aggregate(how) 2020-05-27T03:46:00.4666793Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2033: in aggregate 2020-05-27T03:46:00.4667225Z return super().aggregate(func, *args, **kwargs) 2020-05-27T03:46:00.4667657Z test_venv\lib\site-packages\pandas\core\window\rolling.py:603: in aggregate 2020-05-27T03:46:00.4668054Z result, how = self._aggregate(func, *args, **kwargs) 2020-05-27T03:46:00.4668471Z test_venv\lib\site-packages\pandas\core\base.py:475: in _aggregate 2020-05-27T03:46:00.4668902Z return self._aggregate_multiple_funcs(arg, _axis=_axis), None 2020-05-27T03:46:00.4669286Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 2020-05-27T03:46:00.4669590Z 2020-05-27T03:46:00.4669874Z self = RollingGroupby [window=2,center=False,axis=0], arg = ['mean', 'max'] 2020-05-27T03:46:00.4670218Z _axis = 0 2020-05-27T03:46:00.4670378Z 2020-05-27T03:46:00.4670690Z def _aggregate_multiple_funcs(self, arg, _axis): 2020-05-27T03:46:00.4671087Z from pandas.core.reshape.concat import concat 2020-05-27T03:46:00.4671345Z 2020-05-27T03:46:00.4671605Z if _axis != 0: 2020-05-27T03:46:00.4671974Z raise NotImplementedError("axis other than 0 is not supported") 2020-05-27T03:46:00.4672267Z 2020-05-27T03:46:00.4672562Z if self._selected_obj.ndim == 1: 2020-05-27T03:46:00.4672862Z obj = self._selected_obj 2020-05-27T03:46:00.4673151Z else: 2020-05-27T03:46:00.4673452Z obj = self._obj_with_exclusions 2020-05-27T03:46:00.4673697Z 2020-05-27T03:46:00.4673941Z results = [] 2020-05-27T03:46:00.4674209Z keys = [] 2020-05-27T03:46:00.4674412Z 2020-05-27T03:46:00.4674664Z # degenerate case 2020-05-27T03:46:00.4674921Z if obj.ndim == 1: 2020-05-27T03:46:00.4675282Z for a in arg: 2020-05-27T03:46:00.4675640Z colg = self._gotitem(obj.name, ndim=1, subset=obj) 2020-05-27T03:46:00.4675954Z try: 2020-05-27T03:46:00.4676277Z new_res = colg.aggregate(a) 2020-05-27T03:46:00.4676528Z 2020-05-27T03:46:00.4676805Z except TypeError: 2020-05-27T03:46:00.4677111Z pass 2020-05-27T03:46:00.4677359Z else: 2020-05-27T03:46:00.4677673Z results.append(new_res) 2020-05-27T03:46:00.4677958Z 2020-05-27T03:46:00.4678210Z # make sure we find a good name 2020-05-27T03:46:00.4678584Z name = com.get_callable_name(a) or a 2020-05-27T03:46:00.4678891Z keys.append(name) 2020-05-27T03:46:00.4679164Z 2020-05-27T03:46:00.4679405Z # multiples 2020-05-27T03:46:00.4679630Z else: 2020-05-27T03:46:00.4679957Z for index, col in enumerate(obj): 2020-05-27T03:46:00.4680401Z colg = self._gotitem(col, ndim=1, subset=obj.iloc[:, index]) 2020-05-27T03:46:00.4680756Z try: 2020-05-27T03:46:00.4681075Z new_res = colg.aggregate(arg) 2020-05-27T03:46:00.4681407Z except (TypeError, DataError): 2020-05-27T03:46:00.4681737Z pass 2020-05-27T03:46:00.4682119Z except ValueError as err: 2020-05-27T03:46:00.4682422Z # cannot aggregate 2020-05-27T03:46:00.4682818Z if "Must produce aggregated value" in str(err): 2020-05-27T03:46:00.4683257Z # raised directly in _aggregate_named 2020-05-27T03:46:00.4683562Z pass 2020-05-27T03:46:00.4683915Z elif "no results" in str(err): 2020-05-27T03:46:00.4684337Z # raised directly in _aggregate_multiple_funcs 2020-05-27T03:46:00.4684653Z pass 2020-05-27T03:46:00.4684954Z else: 2020-05-27T03:46:00.4685220Z raise 2020-05-27T03:46:00.4685512Z else: 2020-05-27T03:46:00.4685823Z results.append(new_res) 2020-05-27T03:46:00.4686109Z keys.append(col) 2020-05-27T03:46:00.4686378Z 2020-05-27T03:46:00.4686628Z # if we are empty 2020-05-27T03:46:00.4686893Z if not len(results): 2020-05-27T03:46:00.4687219Z > raise ValueError("no results") 2020-05-27T03:46:00.4687514Z E ValueError: no results 2020-05-27T03:46:00.4687767Z 2020-05-27T03:46:00.4688056Z test_venv\lib\site-packages\pandas\core\base.py:540: ValueError 2020-05-27T03:46:00.4900327Z ________________ test_rolling_apply_args_kwargs[args_kwargs0] _________________ 2020-05-27T03:46:00.4910069Z [gw0] win32 -- Python 3.7.7 D:\a\1\s\test_venv\Scripts\python.exe 2020-05-27T03:46:00.4913683Z 2020-05-27T03:46:00.4916587Z args_kwargs = [None, {'par': 10}] 2020-05-27T03:46:00.4920984Z 2020-05-27T03:46:00.4932595Z @pytest.mark.parametrize("args_kwargs", [[None, {"par": 10}], [(10,), None]]) 2020-05-27T03:46:00.4934841Z def test_rolling_apply_args_kwargs(args_kwargs): 2020-05-27T03:46:00.4935692Z # GH 33433 2020-05-27T03:46:00.4936381Z def foo(x, par): 2020-05-27T03:46:00.4937029Z return np.sum(x + par) 2020-05-27T03:46:00.4937700Z 2020-05-27T03:46:00.4938401Z df = DataFrame({"gr": [1, 1], "a": [1, 2]}) 2020-05-27T03:46:00.4939052Z 2020-05-27T03:46:00.4939885Z idx = Index(["gr", "a"]) 2020-05-27T03:46:00.4940561Z expected = DataFrame([[11.0, 11.0], [11.0, 12.0]], columns=idx) 2020-05-27T03:46:00.4941155Z 2020-05-27T03:46:00.4941805Z result = df.rolling(1).apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1]) 2020-05-27T03:46:00.4942523Z tm.assert_frame_equal(result, expected) 2020-05-27T03:46:00.4943122Z 2020-05-27T03:46:00.4943684Z result = df.rolling(1).apply(foo, args=(10,)) 2020-05-27T03:46:00.4945113Z 2020-05-27T03:46:00.4945760Z midx = MultiIndex.from_tuples([(1, 0), (1, 1)], names=["gr", None]) 2020-05-27T03:46:00.4946452Z expected = Series([11.0, 12.0], index=midx, name="a") 2020-05-27T03:46:00.4947078Z 2020-05-27T03:46:00.4947685Z gb_rolling = df.groupby("gr")["a"].rolling(1) 2020-05-27T03:46:00.4948255Z 2020-05-27T03:46:00.4949192Z > result = gb_rolling.apply(foo, args=args_kwargs[0], kwargs=args_kwargs[1]) 2020-05-27T03:46:00.4949774Z 2020-05-27T03:46:00.4950430Z test_venv\lib\site-packages\pandas\tests\window\test_apply.py:165: 2020-05-27T03:46:00.4951199Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 2020-05-27T03:46:00.4951938Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2067: in apply 2020-05-27T03:46:00.4952625Z kwargs=kwargs, 2020-05-27T03:46:00.4953353Z test_venv\lib\site-packages\pandas\core\window\rolling.py:1391: in apply 2020-05-27T03:46:00.4954036Z kwargs=kwargs, 2020-05-27T03:46:00.4954689Z test_venv\lib\site-packages\pandas\core\window\rolling.py:2196: in _apply 2020-05-27T03:46:00.4955365Z **kwargs, 2020-05-27T03:46:00.4956044Z test_venv\lib\site-packages\pandas\core\window\rolling.py:589: in _apply 2020-05-27T03:46:00.4956693Z result = calc(values) 2020-05-27T03:46:00.4957386Z test_venv\lib\site-packages\pandas\core\window\rolling.py:575: in calc 2020-05-27T03:46:00.4958198Z closed=self.closed, 2020-05-27T03:46:00.4958846Z _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 2020-05-27T03:46:00.4959456Z 2020-05-27T03:46:00.4960053Z self = <pandas.core.window.indexers.GroupbyRollingIndexer object at 0x3A34FA30> 2020-05-27T03:46:00.4960788Z num_values = 2, min_periods = None, center = False, closed = None 2020-05-27T03:46:00.4961381Z 2020-05-27T03:46:00.4961909Z @Appender(get_window_bounds_doc) 2020-05-27T03:46:00.4962523Z def get_window_bounds( 2020-05-27T03:46:00.4963100Z self, 2020-05-27T03:46:00.4963652Z num_values: int = 0, 2020-05-27T03:46:00.4964290Z min_periods: Optional[int] = None, 2020-05-27T03:46:00.4964946Z center: Optional[bool] = None, 2020-05-27T03:46:00.4965552Z closed: Optional[str] = None, 2020-05-27T03:46:00.4966208Z ) -> Tuple[np.ndarray, np.ndarray]: 2020-05-27T03:46:00.4966908Z # 1) For each group, get the indices that belong to the group 2020-05-27T03:46:00.4967605Z # 2) Use the indices to calculate the start & end bounds of the window 2020-05-27T03:46:00.4968322Z # 3) Append the window bounds in group order 2020-05-27T03:46:00.4968991Z start_arrays = [] 2020-05-27T03:46:00.4969583Z end_arrays = [] 2020-05-27T03:46:00.4970143Z window_indicies_start = 0 2020-05-27T03:46:00.4970823Z for key, indicies in self.groupby_indicies.items(): 2020-05-27T03:46:00.4971539Z if self.index_array is not None: 2020-05-27T03:46:00.4972196Z index_array = self.index_array.take(indicies) 2020-05-27T03:46:00.4972843Z else: 2020-05-27T03:46:00.4973467Z index_array = self.index_array 2020-05-27T03:46:00.4974075Z indexer = self.rolling_indexer( 2020-05-27T03:46:00.4974759Z index_array=index_array, window_size=self.window_size, 2020-05-27T03:46:00.4975407Z ) 2020-05-27T03:46:00.4975981Z start, end = indexer.get_window_bounds( 2020-05-27T03:46:00.4976661Z len(indicies), min_periods, center, closed 2020-05-27T03:46:00.4977281Z ) 2020-05-27T03:46:00.4977908Z # Cannot use groupby_indicies as they might not be monotonic with the object 2020-05-27T03:46:00.4978599Z # we're rolling over 2020-05-27T03:46:00.4979224Z window_indicies = np.arange( 2020-05-27T03:46:00.4979816Z window_indicies_start, 2020-05-27T03:46:00.4980472Z window_indicies_start + len(indicies), 2020-05-27T03:46:00.4981202Z dtype=np.int64, 2020-05-27T03:46:00.4981747Z ) 2020-05-27T03:46:00.4982364Z window_indicies_start += len(indicies) 2020-05-27T03:46:00.4983051Z # Extend as we'll be slicing window like [start, end) 2020-05-27T03:46:00.4983789Z window_indicies = np.append(window_indicies, [window_indicies[-1] + 1]) 2020-05-27T03:46:00.4984532Z > start_arrays.append(window_indicies.take(start)) 2020-05-27T03:46:00.4985326Z E TypeError: Cannot cast array data from dtype('int64') to dtype('int32') according to the rule 'safe' 2020-05-27T03:46:00.4986013Z