def test_misc_quantiles(data, q): pdf_series = pd.Series(data) gdf_series = Series(data) expected = pdf_series.quantile(q) actual = gdf_series.quantile(q) assert_eq(expected, actual)
def test_misc_quantiles(data, q): from cudf.tests import utils pdf_series = pd.Series(data) gdf_series = Series(data) expected = pdf_series.quantile(q) actual = gdf_series.quantile(q) utils.assert_eq(expected, actual)
def test_approx_quantiles_int(): arr = np.asarray([1, 2, 3]) quant_values = [0.5] approx_results = [2] gdf_series = Series(arr) q1 = gdf_series.quantile(quant_values, exact=False) assert approx_results == q1.to_pandas().values
def test_approx_quantiles(): arr = np.asarray([6.8, 0.15, 3.4, 4.17, 2.13, 1.11, -1.01, 0.8, 5.7]) quant_values = [0.0, 0.25, 0.33, 0.5, 1.0] gdf_series = Series(arr) pdf_series = pd.Series(arr) q1 = gdf_series.quantile(quant_values, exact=False) q2 = pdf_series.quantile(quant_values) assert_eq(q1, q2)
def test_approx_quantiles(): arr = np.asarray([6.8, 0.15, 3.4, 4.17, 2.13, 1.11, -1.01, 0.8, 5.7]) quant_values = [0.0, 0.25, 0.33, 0.5, 1.0] approx_results = [-1.01, 0.8, 0.8, 2.13, 6.8] gdf_series = Series(arr) q1 = gdf_series.quantile(quant_values, exact=False) np.testing.assert_allclose(q1.to_pandas().values, approx_results, rtol=1e-10)
def test_exact_quantiles_int(int_method): arr = np.asarray([7, 0, 3, 4, 2, 1, -1, 1, 6]) quant_values = [0.0, 0.25, 0.33, 0.5, 1.0] df = pd.DataFrame(arr) gdf_series = Series(arr) q1 = gdf_series.quantile( quant_values, interpolation=int_method, exact=True ) q2 = df.quantile(quant_values, interpolation=int_method) np.testing.assert_allclose( q1.to_pandas().values, np.array(q2.values).T.flatten(), rtol=1e-10 )
def test_exact_quantiles(int_method): arr = np.asarray([6.8, 0.15, 3.4, 4.17, 2.13, 1.11, -1.01, 0.8, 5.7]) quant_values = [0.0, 0.25, 0.33, 0.5, 1.0] df = pd.DataFrame(arr) gdf_series = Series(arr) q1 = gdf_series.quantile( quant_values, interpolation=int_method, exact=True ) q2 = df.quantile(quant_values, interpolation=int_method) np.testing.assert_allclose( q1.to_pandas().values, np.array(q2.values).T.flatten(), rtol=1e-10 )