from functools import partial import operator import warnings import numpy as np import pytest import pandas.util._test_decorators as td from pandas.core.dtypes.common import is_integer_dtype import pandas as pd from pandas import ( Series, isna, ) import pandas._testing as tm from pandas.core.arrays import DatetimeArray import pandas.core.nanops as nanops use_bn = nanops._USE_BOTTLENECK @pytest.fixture(params=[True, False]) def skipna(request): """ Fixture to pass skipna to nanops functions. """ return request.param class TestnanopsDataFrame: def setup_method(self): np.random.seed(11235) nanops._USE_BOTTLENECK = False arr_shape = (11, 7) self.arr_float = np.random.randn(*arr_shape) self.arr_float1 = np.random.randn(*arr_shape) self.arr_complex = self.arr_float + self.arr_float1 * 1j self.arr_int = np.random.randint(-10, 10, arr_shape) self.arr_bool = np.random.randint(0, 2, arr_shape) == 0 self.arr_str = np.abs(self.arr_float).astype("S") self.arr_utf = np.abs(self.arr_float).astype("U") self.arr_date = np.random.randint(0, 20000, arr_shape).astype("M8[ns]") self.arr_tdelta = np.random.randint(0, 20000, arr_shape).astype("m8[ns]") self.arr_nan = np.tile(np.nan, arr_shape) self.arr_float_nan = np.vstack([self.arr_float, self.arr_nan]) self.arr_float1_nan = np.vstack([self.arr_float1, self.arr_nan]) self.arr_nan_float1 = np.vstack([self.arr_nan, self.arr_float1]) self.arr_nan_nan = np.vstack([self.arr_nan, self.arr_nan]) self.arr_inf = self.arr_float * np.inf self.arr_float_inf = np.vstack([self.arr_float, self.arr_inf]) self.arr_nan_inf = np.vstack([self.arr_nan, self.arr_inf]) self.arr_float_nan_inf = np.vstack([self.arr_float, self.arr_nan, self.arr_inf]) self.arr_nan_nan_inf = np.vstack([self.arr_nan, self.arr_nan, self.arr_inf]) self.arr_obj = np.vstack( [ self.arr_float.astype("O"), self.arr_int.astype("O"), self.arr_bool.astype("O"), self.arr_complex.astype("O"), self.arr_str.astype("O"), self.arr_utf.astype("O"), self.arr_date.astype("O"), self.arr_tdelta.astype("O"), ] ) with np.errstate(invalid="ignore"): self.arr_nan_nanj = self.arr_nan + self.arr_nan * 1j self.arr_complex_nan = np.vstack([self.arr_complex, self.arr_nan_nanj]) self.arr_nan_infj = self.arr_inf * 1j self.arr_complex_nan_infj = np.vstack([self.arr_complex, self.arr_nan_infj]) self.arr_float_2d = self.arr_float self.arr_float1_2d = self.arr_float1 self.arr_nan_2d = self.arr_nan self.arr_float_nan_2d = self.arr_float_nan self.arr_float1_nan_2d = self.arr_float1_nan self.arr_nan_float1_2d = self.arr_nan_float1 self.arr_float_1d = self.arr_float[:, 0] self.arr_float1_1d = self.arr_float1[:, 0] self.arr_nan_1d = self.arr_nan[:, 0] self.arr_float_nan_1d = self.arr_float_nan[:, 0] self.arr_float1_nan_1d = self.arr_float1_nan[:, 0] self.arr_nan_float1_1d = self.arr_nan_float1[:, 0] def teardown_method(self): nanops._USE_BOTTLENECK = use_bn def check_results(self, targ, res, axis, check_dtype=True): res = getattr(res, "asm8", res) if ( axis != 0 and hasattr(targ, "shape") and targ.ndim and targ.shape != res.shape ): res = np.split(res, [targ.shape[0]], axis=0)[0] try: tm.assert_almost_equal(targ, res, check_dtype=check_dtype) except AssertionError: # handle timedelta dtypes if hasattr(targ, "dtype") and targ.dtype == "m8[ns]": raise # There are sometimes rounding errors with # complex and object dtypes. # If it isn't one of those, re-raise the error. if not hasattr(res, "dtype") or res.dtype.kind not in ["c", "O"]: raise # convert object dtypes to something that can be split into # real and imaginary parts if res.dtype.kind == "O": if targ.dtype.kind != "O": res = res.astype(targ.dtype) else: cast_dtype = "c16" if hasattr(np, "complex128") else "f8" res = res.astype(cast_dtype) targ = targ.astype(cast_dtype) # there should never be a case where numpy returns an object # but nanops doesn't, so make that an exception elif targ.dtype.kind == "O": raise tm.assert_almost_equal(np.real(targ), np.real(res), check_dtype=check_dtype) tm.assert_almost_equal(np.imag(targ), np.imag(res), check_dtype=check_dtype) def check_fun_data( self, testfunc, targfunc, testarval, targarval, skipna, check_dtype=True, empty_targfunc=None, **kwargs, ): for axis in list(range(targarval.ndim)) + [None]: targartempval = targarval if skipna else testarval if skipna and empty_targfunc and isna(targartempval).all(): targ = empty_targfunc(targartempval, axis=axis, **kwargs) else: targ = targfunc(targartempval, axis=axis, **kwargs) if targartempval.dtype == object and ( targfunc is np.any or targfunc is np.all ): # GH#12863 the numpy functions will retain e.g. floatiness if isinstance(targ, np.ndarray): targ = targ.astype(bool) else: targ = bool(targ) res = testfunc(testarval, axis=axis, skipna=skipna, **kwargs) self.check_results(targ, res, axis, check_dtype=check_dtype) if skipna: res = testfunc(testarval, axis=axis, **kwargs) self.check_results(targ, res, axis, check_dtype=check_dtype) if axis is None: res = testfunc(testarval, skipna=skipna, **kwargs) self.check_results(targ, res, axis, check_dtype=check_dtype) if skipna and axis is None: res = testfunc(testarval, **kwargs) self.check_results(targ, res, axis, check_dtype=check_dtype) if testarval.ndim <= 1: return # Recurse on lower-dimension testarval2 = np.take(testarval, 0, axis=-1) targarval2 = np.take(targarval, 0, axis=-1) self.check_fun_data( testfunc, targfunc, testarval2, targarval2, skipna=skipna, check_dtype=check_dtype, empty_targfunc=empty_targfunc, **kwargs, ) def check_fun( self, testfunc, targfunc, testar, skipna, empty_targfunc=None, **kwargs ): targar = testar if testar.endswith("_nan") and hasattr(self, testar[:-4]): targar = testar[:-4] testarval = getattr(self, testar) targarval = getattr(self, targar) self.check_fun_data( testfunc, targfunc, testarval, targarval, skipna=skipna, empty_targfunc=empty_targfunc, **kwargs, ) def check_funs( self, testfunc, targfunc, skipna, allow_complex=True, allow_all_nan=True, allow_date=True, allow_tdelta=True, allow_obj=True, **kwargs, ): self.check_fun(testfunc, targfunc, "arr_float", skipna, **kwargs) self.check_fun(testfunc, targfunc, "arr_float_nan", skipna, **kwargs) self.check_fun(testfunc, targfunc, "arr_int", skipna, **kwargs) self.check_fun(testfunc, targfunc, "arr_bool", skipna, **kwargs) objs = [ self.arr_float.astype("O"), self.arr_int.astype("O"), self.arr_bool.astype("O"), ] if allow_all_nan: self.check_fun(testfunc, targfunc, "arr_nan", skipna, **kwargs) if allow_complex: self.check_fun(testfunc, targfunc, "arr_complex", skipna, **kwargs) self.check_fun(testfunc, targfunc, "arr_complex_nan", skipna, **kwargs) if allow_all_nan: self.check_fun(testfunc, targfunc, "arr_nan_nanj", skipna, **kwargs) objs += [self.arr_complex.astype("O")] if allow_date: targfunc(self.arr_date) self.check_fun(testfunc, targfunc, "arr_date", skipna, **kwargs) objs += [self.arr_date.astype("O")] if allow_tdelta: try: targfunc(self.arr_tdelta) except TypeError: pass else: self.check_fun(testfunc, targfunc, "arr_tdelta", skipna, **kwargs) objs += [self.arr_tdelta.astype("O")] if allow_obj: self.arr_obj = np.vstack(objs) # some nanops handle object dtypes better than their numpy # counterparts, so the numpy functions need to be given something # else if allow_obj == "convert": targfunc = partial( self._badobj_wrap, func=targfunc, allow_complex=allow_complex ) self.check_fun(testfunc, targfunc, "arr_obj", skipna, **kwargs) def _badobj_wrap(self, value, func, allow_complex=True, **kwargs): if value.dtype.kind == "O": if allow_complex: value = value.astype("c16") else: value = value.astype("f8") return func(value, **kwargs) @pytest.mark.parametrize( "nan_op,np_op", [(nanops.nanany, np.any), (nanops.nanall, np.all)] ) def test_nan_funcs(self, nan_op, np_op, skipna): self.check_funs(nan_op, np_op, skipna, allow_all_nan=False, allow_date=False) def test_nansum(self, skipna): self.check_funs( nanops.nansum, np.sum, skipna, allow_date=False, check_dtype=False, empty_targfunc=np.nansum, ) def test_nanmean(self, skipna): self.check_funs( nanops.nanmean, np.mean, skipna, allow_obj=False, allow_date=False ) def test_nanmean_overflow(self): # GH 10155 # In the previous implementation mean can overflow for int dtypes, it # is now consistent with numpy for a in [2**55, -(2**55), 20150515061816532]: s = Series(a, index=range(500), dtype=np.int64) result = s.mean() np_result = s.values.mean() assert result == a assert result == np_result assert result.dtype == np.float64 @pytest.mark.parametrize( "dtype", [ np.int16, np.int32, np.int64, np.float32, np.float64, getattr(np, "float128", None), ], ) def test_returned_dtype(self, dtype): if dtype is None: # no float128 available return s = Series(range(10), dtype=dtype) group_a = ["mean", "std", "var", "skew", "kurt"] group_b = ["min", "max"] for method in group_a + group_b: result = getattr(s, method)() if is_integer_dtype(dtype) and method in group_a: assert result.dtype == np.float64 else: assert result.dtype == dtype def test_nanmedian(self, skipna): with warnings.catch_warnings(record=True): warnings.simplefilter("ignore", RuntimeWarning) self.check_funs( nanops.nanmedian, np.median, skipna, allow_complex=False, allow_date=False, allow_obj="convert", ) @pytest.mark.parametrize("ddof", range(3)) def test_nanvar(self, ddof, skipna): self.check_funs( nanops.nanvar, np.var, skipna, allow_complex=False, allow_date=False, allow_obj="convert", ddof=ddof, ) @pytest.mark.parametrize("ddof", range(3)) def test_nanstd(self, ddof, skipna): self.check_funs( nanops.nanstd, np.std, skipna, allow_complex=False, allow_date=False, allow_obj="convert", ddof=ddof, ) @td.skip_if_no_scipy @pytest.mark.parametrize("ddof", range(3)) def test_nansem(self, ddof, skipna): from scipy.stats import sem with np.errstate(invalid="ignore"): self.check_funs( nanops.nansem, sem, skipna, allow_complex=False, allow_date=False, allow_tdelta=False, allow_obj="convert", ddof=ddof, ) @pytest.mark.parametrize( "nan_op,np_op", [(nanops.nanmin, np.min), (nanops.nanmax, np.max)] ) def test_nanops_with_warnings(self, nan_op, np_op, skipna): with warnings.catch_warnings(record=True): warnings.simplefilter("ignore", RuntimeWarning) self.check_funs(nan_op, np_op, skipna, allow_obj=False) def _argminmax_wrap(self, value, axis=None, func=None): res = func(value, axis) nans = np.min(value, axis) nullnan = isna(nans) if res.ndim: res[nullnan] = -1 elif ( hasattr(nullnan, "all") and nullnan.all() or not hasattr(nullnan, "all") and nullnan ): res = -1 return res def test_nanargmax(self, skipna): with warnings.catch_warnings(record=True): warnings.simplefilter("ignore", RuntimeWarning) func = partial(self._argminmax_wrap, func=np.argmax) self.check_funs(nanops.nanargmax, func, skipna, allow_obj=False) def test_nanargmin(self, skipna): with warnings.catch_warnings(record=True): warnings.simplefilter("ignore", RuntimeWarning) func = partial(self._argminmax_wrap, func=np.argmin) self.check_funs(nanops.nanargmin, func, skipna, allow_obj=False) def _skew_kurt_wrap(self, values, axis=None, func=None): if not isinstance(values.dtype.type, np.floating): values = values.astype("f8") result = func(values, axis=axis, bias=False) # fix for handling cases where all elements in an axis are the same if isinstance(result, np.ndarray): result[np.max(values, axis=axis) == np.min(values, axis=axis)] = 0 return result elif np.max(values) == np.min(values): return 0.0 return result @td.skip_if_no_scipy def test_nanskew(self, skipna): from scipy.stats import skew func = partial(self._skew_kurt_wrap, func=skew) with np.errstate(invalid="ignore"): self.check_funs( nanops.nanskew, func, skipna, allow_complex=False, allow_date=False, allow_tdelta=False, ) @td.skip_if_no_scipy def test_nankurt(self, skipna): from scipy.stats import kurtosis func1 = partial(kurtosis, fisher=True) func = partial(self._skew_kurt_wrap, func=func1) with np.errstate(invalid="ignore"): self.check_funs( nanops.nankurt, func, skipna, allow_complex=False, allow_date=False, allow_tdelta=False, ) def test_nanprod(self, skipna): self.check_funs( nanops.nanprod, np.prod, skipna, allow_date=False, allow_tdelta=False, empty_targfunc=np.nanprod, ) def check_nancorr_nancov_2d(self, checkfun, targ0, targ1, **kwargs): res00 = checkfun(self.arr_float_2d, self.arr_float1_2d, **kwargs) res01 = checkfun( self.arr_float_2d, self.arr_float1_2d, min_periods=len(self.arr_float_2d) - 1, **kwargs, ) tm.assert_almost_equal(targ0, res00) tm.assert_almost_equal(targ0, res01) res10 = checkfun(self.arr_float_nan_2d, self.arr_float1_nan_2d, **kwargs) res11 = checkfun( self.arr_float_nan_2d, self.arr_float1_nan_2d, min_periods=len(self.arr_float_2d) - 1, **kwargs, ) tm.assert_almost_equal(targ1, res10) tm.assert_almost_equal(targ1, res11) targ2 = np.nan res20 = checkfun(self.arr_nan_2d, self.arr_float1_2d, **kwargs) res21 = checkfun(self.arr_float_2d, self.arr_nan_2d, **kwargs) res22 = checkfun(self.arr_nan_2d, self.arr_nan_2d, **kwargs) res23 = checkfun(self.arr_float_nan_2d, self.arr_nan_float1_2d, **kwargs) res24 = checkfun( self.arr_float_nan_2d, self.arr_nan_float1_2d, min_periods=len(self.arr_float_2d) - 1, **kwargs, ) res25 = checkfun( self.arr_float_2d, self.arr_float1_2d, min_periods=len(self.arr_float_2d) + 1, **kwargs, ) tm.assert_almost_equal(targ2, res20) tm.assert_almost_equal(targ2, res21) tm.assert_almost_equal(targ2, res22) tm.assert_almost_equal(targ2, res23) tm.assert_almost_equal(targ2, res24) tm.assert_almost_equal(targ2, res25) def check_nancorr_nancov_1d(self, checkfun, targ0, targ1, **kwargs): res00 = checkfun(self.arr_float_1d, self.arr_float1_1d, **kwargs) res01 = checkfun( self.arr_float_1d, self.arr_float1_1d, min_periods=len(self.arr_float_1d) - 1, **kwargs, ) tm.assert_almost_equal(targ0, res00) tm.assert_almost_equal(targ0, res01) res10 = checkfun(self.arr_float_nan_1d, self.arr_float1_nan_1d, **kwargs) res11 = checkfun( self.arr_float_nan_1d, self.arr_float1_nan_1d, min_periods=len(self.arr_float_1d) - 1, **kwargs, ) tm.assert_almost_equal(targ1, res10) tm.assert_almost_equal(targ1, res11) targ2 = np.nan res20 = checkfun(self.arr_nan_1d, self.arr_float1_1d, **kwargs) res21 = checkfun(self.arr_float_1d, self.arr_nan_1d, **kwargs) res22 = checkfun(self.arr_nan_1d, self.arr_nan_1d, **kwargs) res23 = checkfun(self.arr_float_nan_1d, self.arr_nan_float1_1d, **kwargs) res24 = checkfun( self.arr_float_nan_1d, self.arr_nan_float1_1d, min_periods=len(self.arr_float_1d) - 1, **kwargs, ) res25 = checkfun( self.arr_float_1d, self.arr_float1_1d, min_periods=len(self.arr_float_1d) + 1, **kwargs, ) tm.assert_almost_equal(targ2, res20) tm.assert_almost_equal(targ2, res21) tm.assert_almost_equal(targ2, res22) tm.assert_almost_equal(targ2, res23) tm.assert_almost_equal(targ2, res24) tm.assert_almost_equal(targ2, res25) def test_nancorr(self): targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1] targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1] self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1) targ0 = np.corrcoef(self.arr_float_1d, self.arr_float1_1d)[0, 1] targ1 = np.corrcoef(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1] self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="pearson") def test_nancorr_pearson(self): targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1] targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1] self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="pearson") targ0 = np.corrcoef(self.arr_float_1d, self.arr_float1_1d)[0, 1] targ1 = np.corrcoef(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1] self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="pearson") @td.skip_if_no_scipy def test_nancorr_kendall(self): from scipy.stats import kendalltau targ0 = kendalltau(self.arr_float_2d, self.arr_float1_2d)[0] targ1 = kendalltau(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0] self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="kendall") targ0 = kendalltau(self.arr_float_1d, self.arr_float1_1d)[0] targ1 = kendalltau(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0] self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="kendall") @td.skip_if_no_scipy def test_nancorr_spearman(self): from scipy.stats import spearmanr targ0 = spearmanr(self.arr_float_2d, self.arr_float1_2d)[0] targ1 = spearmanr(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0] self.check_nancorr_nancov_2d(nanops.nancorr, targ0, targ1, method="spearman") targ0 = spearmanr(self.arr_float_1d, self.arr_float1_1d)[0] targ1 = spearmanr(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0] self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="spearman") @td.skip_if_no_scipy def test_invalid_method(self): targ0 = np.corrcoef(self.arr_float_2d, self.arr_float1_2d)[0, 1] targ1 = np.corrcoef(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1] msg = "Unknown method 'foo', expected one of 'kendall', 'spearman'" with pytest.raises(ValueError, match=msg): self.check_nancorr_nancov_1d(nanops.nancorr, targ0, targ1, method="foo") def test_nancov(self): targ0 = np.cov(self.arr_float_2d, self.arr_float1_2d)[0, 1] targ1 = np.cov(self.arr_float_2d.flat, self.arr_float1_2d.flat)[0, 1] self.check_nancorr_nancov_2d(nanops.nancov, targ0, targ1) targ0 = np.cov(self.arr_float_1d, self.arr_float1_1d)[0, 1] targ1 = np.cov(self.arr_float_1d.flat, self.arr_float1_1d.flat)[0, 1] self.check_nancorr_nancov_1d(nanops.nancov, targ0, targ1) def check_nancomp(self, checkfun, targ0): arr_float = self.arr_float arr_float1 = self.arr_float1 arr_nan = self.arr_nan arr_nan_nan = self.arr_nan_nan arr_float_nan = self.arr_float_nan arr_float1_nan = self.arr_float1_nan arr_nan_float1 = self.arr_nan_float1 while targ0.ndim: res0 = checkfun(arr_float, arr_float1) tm.assert_almost_equal(targ0, res0) if targ0.ndim > 1: targ1 = np.vstack([targ0, arr_nan]) else: targ1 = np.hstack([targ0, arr_nan]) res1 = checkfun(arr_float_nan, arr_float1_nan) tm.assert_numpy_array_equal(targ1, res1, check_dtype=False) targ2 = arr_nan_nan res2 = checkfun(arr_float_nan, arr_nan_float1) tm.assert_numpy_array_equal(targ2, res2, check_dtype=False) # Lower dimension for next step in the loop arr_float = np.take(arr_float, 0, axis=-1) arr_float1 = np.take(arr_float1, 0, axis=-1) arr_nan = np.take(arr_nan, 0, axis=-1) arr_nan_nan = np.take(arr_nan_nan, 0, axis=-1) arr_float_nan = np.take(arr_float_nan, 0, axis=-1) arr_float1_nan = np.take(arr_float1_nan, 0, axis=-1) arr_nan_float1 = np.take(arr_nan_float1, 0, axis=-1) targ0 = np.take(targ0, 0, axis=-1) @pytest.mark.parametrize( "op,nanop", [ (operator.eq, nanops.naneq), (operator.ne, nanops.nanne), (operator.gt, nanops.nangt), (operator.ge, nanops.nange), (operator.lt, nanops.nanlt), (operator.le, nanops.nanle), ], ) def test_nan_comparison(self, op, nanop): targ0 = op(self.arr_float, self.arr_float1) self.check_nancomp(nanop, targ0) def check_bool(self, func, value, correct): while getattr(value, "ndim", True): res0 = func(value) if correct: assert res0 else: assert not res0 if not hasattr(value, "ndim"): break # Reduce dimension for next step in the loop value = np.take(value, 0, axis=-1) def test__has_infs(self): pairs = [ ("arr_complex", False), ("arr_int", False), ("arr_bool", False), ("arr_str", False), ("arr_utf", False), ("arr_complex", False), ("arr_complex_nan", False), ("arr_nan_nanj", False), ("arr_nan_infj", True), ("arr_complex_nan_infj", True), ] pairs_float = [ ("arr_float", False), ("arr_nan", False), ("arr_float_nan", False), ("arr_nan_nan", False), ("arr_float_inf", True), ("arr_inf", True), ("arr_nan_inf", True), ("arr_float_nan_inf", True), ("arr_nan_nan_inf", True), ] for arr, correct in pairs: val = getattr(self, arr) self.check_bool(nanops._has_infs, val, correct) for arr, correct in pairs_float: val = getattr(self, arr) self.check_bool(nanops._has_infs, val, correct) self.check_bool(nanops._has_infs, val.astype("f4"), correct) self.check_bool(nanops._has_infs, val.astype("f2"), correct) def test__bn_ok_dtype(self): assert nanops._bn_ok_dtype(self.arr_float.dtype, "test") assert nanops._bn_ok_dtype(self.arr_complex.dtype, "test") assert nanops._bn_ok_dtype(self.arr_int.dtype, "test") assert nanops._bn_ok_dtype(self.arr_bool.dtype, "test") assert nanops._bn_ok_dtype(self.arr_str.dtype, "test") assert nanops._bn_ok_dtype(self.arr_utf.dtype, "test") assert not nanops._bn_ok_dtype(self.arr_date.dtype, "test") assert not nanops._bn_ok_dtype(self.arr_tdelta.dtype, "test") assert not nanops._bn_ok_dtype(self.arr_obj.dtype, "test") class TestEnsureNumeric: def test_numeric_values(self): # Test integer assert nanops._ensure_numeric(1) == 1 # Test float assert nanops._ensure_numeric(1.1) == 1.1 # Test complex assert nanops._ensure_numeric(1 + 2j) == 1 + 2j def test_ndarray(self): # Test numeric ndarray values = np.array([1, 2, 3]) assert np.allclose(nanops._ensure_numeric(values), values) # Test object ndarray o_values = values.astype(object) assert np.allclose(nanops._ensure_numeric(o_values), values) # Test convertible string ndarray s_values = np.array(["1", "2", "3"], dtype=object) assert np.allclose(nanops._ensure_numeric(s_values), values) # Test non-convertible string ndarray s_values = np.array(["foo", "bar", "baz"], dtype=object) msg = r"Could not convert .* to numeric" with pytest.raises(TypeError, match=msg): nanops._ensure_numeric(s_values) def test_convertable_values(self): assert np.allclose(nanops._ensure_numeric("1"), 1.0) assert np.allclose(nanops._ensure_numeric("1.1"), 1.1) assert np.allclose(nanops._ensure_numeric("1+1j"), 1 + 1j) def test_non_convertable_values(self): msg = "Could not convert foo to numeric" with pytest.raises(TypeError, match=msg): nanops._ensure_numeric("foo") # with the wrong type, python raises TypeError for us msg = "argument must be a string or a number" with pytest.raises(TypeError, match=msg): nanops._ensure_numeric({}) with pytest.raises(TypeError, match=msg): nanops._ensure_numeric([]) class TestNanvarFixedValues: # xref GH10242 def setup_method(self): # Samples from a normal distribution. self.variance = variance = 3.0 self.samples = self.prng.normal(scale=variance**0.5, size=100000) def test_nanvar_all_finite(self): samples = self.samples actual_variance = nanops.nanvar(samples) tm.assert_almost_equal(actual_variance, self.variance, rtol=1e-2) def test_nanvar_nans(self): samples = np.nan * np.ones(2 * self.samples.shape[0]) samples[::2] = self.samples actual_variance = nanops.nanvar(samples, skipna=True) tm.assert_almost_equal(actual_variance, self.variance, rtol=1e-2) actual_variance = nanops.nanvar(samples, skipna=False) tm.assert_almost_equal(actual_variance, np.nan, rtol=1e-2) def test_nanstd_nans(self): samples = np.nan * np.ones(2 * self.samples.shape[0]) samples[::2] = self.samples actual_std = nanops.nanstd(samples, skipna=True) tm.assert_almost_equal(actual_std, self.variance**0.5, rtol=1e-2) actual_std = nanops.nanvar(samples, skipna=False) tm.assert_almost_equal(actual_std, np.nan, rtol=1e-2) def test_nanvar_axis(self): # Generate some sample data. samples_norm = self.samples samples_unif = self.prng.uniform(size=samples_norm.shape[0]) samples = np.vstack([samples_norm, samples_unif]) actual_variance = nanops.nanvar(samples, axis=1) tm.assert_almost_equal( actual_variance, np.array([self.variance, 1.0 / 12]), rtol=1e-2 ) def test_nanvar_ddof(self): n = 5 samples = self.prng.uniform(size=(10000, n + 1)) samples[:, -1] = np.nan # Force use of our own algorithm. variance_0 = nanops.nanvar(samples, axis=1, skipna=True, ddof=0).mean() variance_1 = nanops.nanvar(samples, axis=1, skipna=True, ddof=1).mean() variance_2 = nanops.nanvar(samples, axis=1, skipna=True, ddof=2).mean() # The unbiased estimate. var = 1.0 / 12 tm.assert_almost_equal(variance_1, var, rtol=1e-2) # The underestimated variance. tm.assert_almost_equal(variance_0, (n - 1.0) / n * var, rtol=1e-2) # The overestimated variance. tm.assert_almost_equal(variance_2, (n - 1.0) / (n - 2.0) * var, rtol=1e-2) @pytest.mark.parametrize("axis", range(2)) @pytest.mark.parametrize("ddof", range(3)) def test_ground_truth(self, axis, ddof): # Test against values that were precomputed with Numpy. samples = np.empty((4, 4)) samples[:3, :3] = np.array( [ [0.97303362, 0.21869576, 0.55560287], [0.72980153, 0.03109364, 0.99155171], [0.09317602, 0.60078248, 0.15871292], ] ) samples[3] = samples[:, 3] = np.nan # Actual variances along axis=0, 1 for ddof=0, 1, 2 variance = np.array( [ [ [0.13762259, 0.05619224, 0.11568816], [0.20643388, 0.08428837, 0.17353224], [0.41286776, 0.16857673, 0.34706449], ], [ [0.09519783, 0.16435395, 0.05082054], [0.14279674, 0.24653093, 0.07623082], [0.28559348, 0.49306186, 0.15246163], ], ] ) # Test nanvar. var = nanops.nanvar(samples, skipna=True, axis=axis, ddof=ddof) tm.assert_almost_equal(var[:3], variance[axis, ddof]) assert np.isnan(var[3]) # Test nanstd. std = nanops.nanstd(samples, skipna=True, axis=axis, ddof=ddof) tm.assert_almost_equal(std[:3], variance[axis, ddof] ** 0.5) assert np.isnan(std[3]) @pytest.mark.parametrize("ddof", range(3)) def test_nanstd_roundoff(self, ddof): # Regression test for GH 10242 (test data taken from GH 10489). Ensure # that variance is stable. data = Series(766897346 * np.ones(10)) result = data.std(ddof=ddof) assert result == 0.0 @property def prng(self): return np.random.RandomState(1234) class TestNanskewFixedValues: # xref GH 11974 def setup_method(self): # Test data + skewness value (computed with scipy.stats.skew) self.samples = np.sin(np.linspace(0, 1, 200)) self.actual_skew = -0.1875895205961754 def test_constant_series(self): # xref GH 11974 for val in [3075.2, 3075.3, 3075.5]: data = val * np.ones(300) skew = nanops.nanskew(data) assert skew == 0.0 def test_all_finite(self): alpha, beta = 0.3, 0.1 left_tailed = self.prng.beta(alpha, beta, size=100) assert nanops.nanskew(left_tailed) < 0 alpha, beta = 0.1, 0.3 right_tailed = self.prng.beta(alpha, beta, size=100) assert nanops.nanskew(right_tailed) > 0 def test_ground_truth(self): skew = nanops.nanskew(self.samples) tm.assert_almost_equal(skew, self.actual_skew) def test_axis(self): samples = np.vstack([self.samples, np.nan * np.ones(len(self.samples))]) skew = nanops.nanskew(samples, axis=1) tm.assert_almost_equal(skew, np.array([self.actual_skew, np.nan])) def test_nans(self): samples = np.hstack([self.samples, np.nan]) skew = nanops.nanskew(samples, skipna=False) assert np.isnan(skew) def test_nans_skipna(self): samples = np.hstack([self.samples, np.nan]) skew = nanops.nanskew(samples, skipna=True) tm.assert_almost_equal(skew, self.actual_skew) @property def prng(self): return np.random.RandomState(1234) class TestNankurtFixedValues: # xref GH 11974 def setup_method(self): # Test data + kurtosis value (computed with scipy.stats.kurtosis) self.samples = np.sin(np.linspace(0, 1, 200)) self.actual_kurt = -1.2058303433799713 @pytest.mark.parametrize("val", [3075.2, 3075.3, 3075.5]) def test_constant_series(self, val): # xref GH 11974 data = val * np.ones(300) kurt = nanops.nankurt(data) assert kurt == 0.0 def test_all_finite(self): alpha, beta = 0.3, 0.1 left_tailed = self.prng.beta(alpha, beta, size=100) assert nanops.nankurt(left_tailed) < 0 alpha, beta = 0.1, 0.3 right_tailed = self.prng.beta(alpha, beta, size=100) assert nanops.nankurt(right_tailed) > 0 def test_ground_truth(self): kurt = nanops.nankurt(self.samples) tm.assert_almost_equal(kurt, self.actual_kurt) def test_axis(self): samples = np.vstack([self.samples, np.nan * np.ones(len(self.samples))]) kurt = nanops.nankurt(samples, axis=1) tm.assert_almost_equal(kurt, np.array([self.actual_kurt, np.nan])) def test_nans(self): samples = np.hstack([self.samples, np.nan]) kurt = nanops.nankurt(samples, skipna=False) assert np.isnan(kurt) def test_nans_skipna(self): samples = np.hstack([self.samples, np.nan]) kurt = nanops.nankurt(samples, skipna=True) tm.assert_almost_equal(kurt, self.actual_kurt) @property def prng(self): return np.random.RandomState(1234) class TestDatetime64NaNOps: # Enabling mean changes the behavior of DataFrame.mean # See https://github.com/pandas-dev/pandas/issues/24752 def test_nanmean(self): dti = pd.date_range("2016-01-01", periods=3) expected = dti[1] for obj in [dti, DatetimeArray(dti), Series(dti)]: result = nanops.nanmean(obj) assert result == expected dti2 = dti.insert(1, pd.NaT) for obj in [dti2, DatetimeArray(dti2), Series(dti2)]: result = nanops.nanmean(obj) assert result == expected @pytest.mark.parametrize("dtype", ["M8[ns]", "m8[ns]"]) def test_nanmean_skipna_false(self, dtype): arr = np.arange(12).astype(np.int64).view(dtype).reshape(4, 3) arr[-1, -1] = "NaT" result = nanops.nanmean(arr, skipna=False) assert np.isnat(result) assert result.dtype == dtype result = nanops.nanmean(arr, axis=0, skipna=False) expected = np.array([4, 5, "NaT"], dtype=arr.dtype) tm.assert_numpy_array_equal(result, expected) result = nanops.nanmean(arr, axis=1, skipna=False) expected = np.array([arr[0, 1], arr[1, 1], arr[2, 1], arr[-1, -1]]) tm.assert_numpy_array_equal(result, expected) def test_use_bottleneck(): if nanops._BOTTLENECK_INSTALLED: with pd.option_context("use_bottleneck", True): assert pd.get_option("use_bottleneck") with pd.option_context("use_bottleneck", False): assert not pd.get_option("use_bottleneck") @pytest.mark.parametrize( "numpy_op, expected", [ (np.sum, 10), (np.nansum, 10), (np.mean, 2.5), (np.nanmean, 2.5), (np.median, 2.5), (np.nanmedian, 2.5), (np.min, 1), (np.max, 4), (np.nanmin, 1), (np.nanmax, 4), ], ) def test_numpy_ops(numpy_op, expected): # GH8383 result = numpy_op(Series([1, 2, 3, 4])) assert result == expected @pytest.mark.parametrize( "operation", [ nanops.nanany, nanops.nanall, nanops.nansum, nanops.nanmean, nanops.nanmedian, nanops.nanstd, nanops.nanvar, nanops.nansem, nanops.nanargmax, nanops.nanargmin, nanops.nanmax, nanops.nanmin, nanops.nanskew, nanops.nankurt, nanops.nanprod, ], ) def test_nanops_independent_of_mask_param(operation): # GH22764 s = Series([1, 2, np.nan, 3, np.nan, 4]) mask = s.isna() median_expected = operation(s) median_result = operation(s, mask=mask) assert median_expected == median_result @pytest.mark.parametrize("min_count", [-1, 0]) def test_check_below_min_count__negative_or_zero_min_count(min_count): # GH35227 result = nanops.check_below_min_count((21, 37), None, min_count) expected_result = False assert result == expected_result @pytest.mark.parametrize( "mask", [None, np.array([False, False, True]), np.array([True] + 9 * [False])] ) @pytest.mark.parametrize("min_count, expected_result", [(1, False), (101, True)]) def test_check_below_min_count__positive_min_count(mask, min_count, expected_result): # GH35227 shape = (10, 10) result = nanops.check_below_min_count(shape, mask, min_count) assert result == expected_result @td.skip_if_windows @td.skip_if_32bit @pytest.mark.parametrize("min_count, expected_result", [(1, False), (2812191852, True)]) def test_check_below_min_count__large_shape(min_count, expected_result): # GH35227 large shape used to show that the issue is fixed shape = (2244367, 1253) result = nanops.check_below_min_count(shape, mask=None, min_count=min_count) assert result == expected_result @pytest.mark.parametrize("func", ["nanmean", "nansum"]) @pytest.mark.parametrize( "dtype", [ np.uint8, np.uint16, np.uint32, np.uint64, np.int8, np.int16, np.int32, np.int64, np.float16, np.float32, np.float64, ], ) def test_check_bottleneck_disallow(dtype, func): # GH 42878 bottleneck sometimes produces unreliable results for mean and sum assert not nanops._bn_ok_dtype(dtype, func)