def test_le(self): with option_context("compute.ops_on_diff_frames", True): for pser, psser in self.fractional_extension_pser_psser_pairs: self.check_extension(pser <= pser, (psser <= psser).sort_index())
def test_ge(self): with option_context("compute.ops_on_diff_frames", True): self.check_extension(self.pser >= self.other_pser, (self.psser >= self.other_psser).sort_index()) self.check_extension(self.pser >= self.pser, (self.psser >= self.psser).sort_index())
def test_ne(self): with option_context("compute.ops_on_diff_frames", True): for pser, psser in self.numeric_pser_psser_pairs: self.assert_eq(pser != pser, (psser != psser).sort_index())
def test_gt(self): with option_context("compute.ops_on_diff_frames", True): self.assert_eq(self.pser > self.other_pser, (self.psser > self.other_psser).sort_index()) self.assert_eq(self.pser > self.pser, (self.psser > self.psser).sort_index())
def test_axis_on_dataframe(self): # The number of each count is intentionally big # because when data is small, it executes a shortcut. # Less than 'compute.shortcut_limit' will execute a shortcut # by using collected pandas dataframe directly. # now we set the 'compute.shortcut_limit' as 1000 explicitly with option_context("compute.shortcut_limit", 1000): pdf = pd.DataFrame( { "A": [1, -2, 3, -4, 5] * 300, "B": [1.0, -2, 3, -4, 5] * 300, "C": [-6.0, -7, -8, -9, 10] * 300, "D": [True, False, True, False, False] * 300, }, index=range(10, 15001, 10), ) kdf = ps.from_pandas(pdf) self.assert_eq(kdf.count(axis=1), pdf.count(axis=1)) self.assert_eq(kdf.var(axis=1), pdf.var(axis=1)) self.assert_eq(kdf.var(axis=1, ddof=0), pdf.var(axis=1, ddof=0)) self.assert_eq(kdf.std(axis=1), pdf.std(axis=1)) self.assert_eq(kdf.std(axis=1, ddof=0), pdf.std(axis=1, ddof=0)) self.assert_eq(kdf.max(axis=1), pdf.max(axis=1)) self.assert_eq(kdf.min(axis=1), pdf.min(axis=1)) self.assert_eq(kdf.sum(axis=1), pdf.sum(axis=1)) self.assert_eq(kdf.product(axis=1), pdf.product(axis=1)) self.assert_eq(kdf.kurtosis(axis=1), pdf.kurtosis(axis=1)) self.assert_eq(kdf.skew(axis=1), pdf.skew(axis=1)) self.assert_eq(kdf.mean(axis=1), pdf.mean(axis=1)) self.assert_eq(kdf.sem(axis=1), pdf.sem(axis=1)) self.assert_eq(kdf.sem(axis=1, ddof=0), pdf.sem(axis=1, ddof=0)) self.assert_eq(kdf.count(axis=1, numeric_only=True), pdf.count(axis=1, numeric_only=True)) self.assert_eq(kdf.var(axis=1, numeric_only=True), pdf.var(axis=1, numeric_only=True)) self.assert_eq( kdf.var(axis=1, ddof=0, numeric_only=True), pdf.var(axis=1, ddof=0, numeric_only=True), ) self.assert_eq(kdf.std(axis=1, numeric_only=True), pdf.std(axis=1, numeric_only=True)) self.assert_eq( kdf.std(axis=1, ddof=0, numeric_only=True), pdf.std(axis=1, ddof=0, numeric_only=True), ) self.assert_eq(kdf.max(axis=1, numeric_only=True), pdf.max(axis=1, numeric_only=True).astype(float)) self.assert_eq(kdf.min(axis=1, numeric_only=True), pdf.min(axis=1, numeric_only=True).astype(float)) self.assert_eq(kdf.sum(axis=1, numeric_only=True), pdf.sum(axis=1, numeric_only=True).astype(float)) self.assert_eq( kdf.product(axis=1, numeric_only=True), pdf.product(axis=1, numeric_only=True).astype(float), ) self.assert_eq(kdf.kurtosis(axis=1, numeric_only=True), pdf.kurtosis(axis=1, numeric_only=True)) self.assert_eq(kdf.skew(axis=1, numeric_only=True), pdf.skew(axis=1, numeric_only=True)) self.assert_eq(kdf.mean(axis=1, numeric_only=True), pdf.mean(axis=1, numeric_only=True)) self.assert_eq(kdf.sem(axis=1, numeric_only=True), pdf.sem(axis=1, numeric_only=True)) self.assert_eq( kdf.sem(axis=1, ddof=0, numeric_only=True), pdf.sem(axis=1, ddof=0, numeric_only=True), )