from datetime import ( datetime, timedelta, ) import numpy as np import pytest from pandas._libs.algos import ( Infinity, NegInfinity, ) import pandas.util._test_decorators as td from pandas import ( DataFrame, Series, ) import pandas._testing as tm class TestRank: s = Series([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3]) df = DataFrame({"A": s, "B": s}) results = { "average": np.array([1.5, 5.5, 7.0, 3.5, np.nan, 3.5, 1.5, 8.0, np.nan, 5.5]), "min": np.array([1, 5, 7, 3, np.nan, 3, 1, 8, np.nan, 5]), "max": np.array([2, 6, 7, 4, np.nan, 4, 2, 8, np.nan, 6]), "first": np.array([1, 5, 7, 3, np.nan, 4, 2, 8, np.nan, 6]), "dense": np.array([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3]), } @pytest.fixture(params=["average", "min", "max", "first", "dense"]) def method(self, request): """ Fixture for trying all rank methods """ return request.param @td.skip_if_no_scipy def test_rank(self, float_frame): import scipy.stats # noqa:F401 from scipy.stats import rankdata float_frame.loc[::2, "A"] = np.nan float_frame.loc[::3, "B"] = np.nan float_frame.loc[::4, "C"] = np.nan float_frame.loc[::5, "D"] = np.nan ranks0 = float_frame.rank() ranks1 = float_frame.rank(1) mask = np.isnan(float_frame.values) fvals = float_frame.fillna(np.inf).values exp0 = np.apply_along_axis(rankdata, 0, fvals) exp0[mask] = np.nan exp1 = np.apply_along_axis(rankdata, 1, fvals) exp1[mask] = np.nan tm.assert_almost_equal(ranks0.values, exp0) tm.assert_almost_equal(ranks1.values, exp1) # integers df = DataFrame(np.random.randint(0, 5, size=40).reshape((10, 4))) result = df.rank() exp = df.astype(float).rank() tm.assert_frame_equal(result, exp) result = df.rank(1) exp = df.astype(float).rank(1) tm.assert_frame_equal(result, exp) def test_rank2(self): df = DataFrame([[1, 3, 2], [1, 2, 3]]) expected = DataFrame([[1.0, 3.0, 2.0], [1, 2, 3]]) / 3.0 result = df.rank(1, pct=True) tm.assert_frame_equal(result, expected) df = DataFrame([[1, 3, 2], [1, 2, 3]]) expected = df.rank(0) / 2.0 result = df.rank(0, pct=True) tm.assert_frame_equal(result, expected) df = DataFrame([["b", "c", "a"], ["a", "c", "b"]]) expected = DataFrame([[2.0, 3.0, 1.0], [1, 3, 2]]) result = df.rank(1, numeric_only=False) tm.assert_frame_equal(result, expected) expected = DataFrame([[2.0, 1.5, 1.0], [1, 1.5, 2]]) result = df.rank(0, numeric_only=False) tm.assert_frame_equal(result, expected) df = DataFrame([["b", np.nan, "a"], ["a", "c", "b"]]) expected = DataFrame([[2.0, np.nan, 1.0], [1.0, 3.0, 2.0]]) result = df.rank(1, numeric_only=False) tm.assert_frame_equal(result, expected) expected = DataFrame([[2.0, np.nan, 1.0], [1.0, 1.0, 2.0]]) result = df.rank(0, numeric_only=False) tm.assert_frame_equal(result, expected) # f7u12, this does not work without extensive workaround data = [ [datetime(2001, 1, 5), np.nan, datetime(2001, 1, 2)], [datetime(2000, 1, 2), datetime(2000, 1, 3), datetime(2000, 1, 1)], ] df = DataFrame(data) # check the rank expected = DataFrame([[2.0, np.nan, 1.0], [2.0, 3.0, 1.0]]) result = df.rank(1, numeric_only=False, ascending=True) tm.assert_frame_equal(result, expected) expected = DataFrame([[1.0, np.nan, 2.0], [2.0, 1.0, 3.0]]) result = df.rank(1, numeric_only=False, ascending=False) tm.assert_frame_equal(result, expected) df = DataFrame({"a": [1e-20, -5, 1e-20 + 1e-40, 10, 1e60, 1e80, 1e-30]}) exp = DataFrame({"a": [3.5, 1.0, 3.5, 5.0, 6.0, 7.0, 2.0]}) tm.assert_frame_equal(df.rank(), exp) def test_rank_does_not_mutate(self): # GH#18521 # Check rank does not mutate DataFrame df = DataFrame(np.random.randn(10, 3), dtype="float64") expected = df.copy() df.rank() result = df tm.assert_frame_equal(result, expected) def test_rank_mixed_frame(self, float_string_frame): float_string_frame["datetime"] = datetime.now() float_string_frame["timedelta"] = timedelta(days=1, seconds=1) float_string_frame.rank(numeric_only=False) with pytest.raises(TypeError, match="not supported between instances of"): float_string_frame.rank(axis=1) @td.skip_if_no_scipy def test_rank_na_option(self, float_frame): import scipy.stats # noqa:F401 from scipy.stats import rankdata float_frame.loc[::2, "A"] = np.nan float_frame.loc[::3, "B"] = np.nan float_frame.loc[::4, "C"] = np.nan float_frame.loc[::5, "D"] = np.nan # bottom ranks0 = float_frame.rank(na_option="bottom") ranks1 = float_frame.rank(1, na_option="bottom") fvals = float_frame.fillna(np.inf).values exp0 = np.apply_along_axis(rankdata, 0, fvals) exp1 = np.apply_along_axis(rankdata, 1, fvals) tm.assert_almost_equal(ranks0.values, exp0) tm.assert_almost_equal(ranks1.values, exp1) # top ranks0 = float_frame.rank(na_option="top") ranks1 = float_frame.rank(1, na_option="top") fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values fval1 = float_frame.T fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T fval1 = fval1.fillna(np.inf).values exp0 = np.apply_along_axis(rankdata, 0, fval0) exp1 = np.apply_along_axis(rankdata, 1, fval1) tm.assert_almost_equal(ranks0.values, exp0) tm.assert_almost_equal(ranks1.values, exp1) # descending # bottom ranks0 = float_frame.rank(na_option="top", ascending=False) ranks1 = float_frame.rank(1, na_option="top", ascending=False) fvals = float_frame.fillna(np.inf).values exp0 = np.apply_along_axis(rankdata, 0, -fvals) exp1 = np.apply_along_axis(rankdata, 1, -fvals) tm.assert_almost_equal(ranks0.values, exp0) tm.assert_almost_equal(ranks1.values, exp1) # descending # top ranks0 = float_frame.rank(na_option="bottom", ascending=False) ranks1 = float_frame.rank(1, na_option="bottom", ascending=False) fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values fval1 = float_frame.T fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T fval1 = fval1.fillna(np.inf).values exp0 = np.apply_along_axis(rankdata, 0, -fval0) exp1 = np.apply_along_axis(rankdata, 1, -fval1) tm.assert_numpy_array_equal(ranks0.values, exp0) tm.assert_numpy_array_equal(ranks1.values, exp1) # bad values throw error msg = "na_option must be one of 'keep', 'top', or 'bottom'" with pytest.raises(ValueError, match=msg): float_frame.rank(na_option="bad", ascending=False) # invalid type with pytest.raises(ValueError, match=msg): float_frame.rank(na_option=True, ascending=False) def test_rank_axis(self): # check if using axes' names gives the same result df = DataFrame([[2, 1], [4, 3]]) tm.assert_frame_equal(df.rank(axis=0), df.rank(axis="index")) tm.assert_frame_equal(df.rank(axis=1), df.rank(axis="columns")) @td.skip_if_no_scipy def test_rank_methods_frame(self): import scipy.stats # noqa:F401 from scipy.stats import rankdata xs = np.random.randint(0, 21, (100, 26)) xs = (xs - 10.0) / 10.0 cols = [chr(ord("z") - i) for i in range(xs.shape[1])] for vals in [xs, xs + 1e6, xs * 1e-6]: df = DataFrame(vals, columns=cols) for ax in [0, 1]: for m in ["average", "min", "max", "first", "dense"]: result = df.rank(axis=ax, method=m) sprank = np.apply_along_axis( rankdata, ax, vals, m if m != "first" else "ordinal" ) sprank = sprank.astype(np.float64) expected = DataFrame(sprank, columns=cols).astype("float64") tm.assert_frame_equal(result, expected) @pytest.mark.parametrize("dtype", ["O", "f8", "i8"]) def test_rank_descending(self, method, dtype): if "i" in dtype: df = self.df.dropna().astype(dtype) else: df = self.df.astype(dtype) res = df.rank(ascending=False) expected = (df.max() - df).rank() tm.assert_frame_equal(res, expected) expected = (df.max() - df).rank(method=method) if dtype != "O": res2 = df.rank(method=method, ascending=False, numeric_only=True) tm.assert_frame_equal(res2, expected) res3 = df.rank(method=method, ascending=False, numeric_only=False) tm.assert_frame_equal(res3, expected) @pytest.mark.parametrize("axis", [0, 1]) @pytest.mark.parametrize("dtype", [None, object]) def test_rank_2d_tie_methods(self, method, axis, dtype): df = self.df def _check2d(df, expected, method="average", axis=0): exp_df = DataFrame({"A": expected, "B": expected}) if axis == 1: df = df.T exp_df = exp_df.T result = df.rank(method=method, axis=axis) tm.assert_frame_equal(result, exp_df) frame = df if dtype is None else df.astype(dtype) _check2d(frame, self.results[method], method=method, axis=axis) @pytest.mark.parametrize( "method,exp", [ ("dense", [[1.0, 1.0, 1.0], [1.0, 0.5, 2.0 / 3], [1.0, 0.5, 1.0 / 3]]), ( "min", [ [1.0 / 3, 1.0, 1.0], [1.0 / 3, 1.0 / 3, 2.0 / 3], [1.0 / 3, 1.0 / 3, 1.0 / 3], ], ), ( "max", [[1.0, 1.0, 1.0], [1.0, 2.0 / 3, 2.0 / 3], [1.0, 2.0 / 3, 1.0 / 3]], ), ( "average", [[2.0 / 3, 1.0, 1.0], [2.0 / 3, 0.5, 2.0 / 3], [2.0 / 3, 0.5, 1.0 / 3]], ), ( "first", [ [1.0 / 3, 1.0, 1.0], [2.0 / 3, 1.0 / 3, 2.0 / 3], [3.0 / 3, 2.0 / 3, 1.0 / 3], ], ), ], ) def test_rank_pct_true(self, method, exp): # see gh-15630. df = DataFrame([[2012, 66, 3], [2012, 65, 2], [2012, 65, 1]]) result = df.rank(method=method, pct=True) expected = DataFrame(exp) tm.assert_frame_equal(result, expected) @pytest.mark.single_cpu def test_pct_max_many_rows(self): # GH 18271 df = DataFrame( {"A": np.arange(2**24 + 1), "B": np.arange(2**24 + 1, 0, -1)} ) result = df.rank(pct=True).max() assert (result == 1).all() @pytest.mark.parametrize( "contents,dtype", [ ( [ -np.inf, -50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40, np.inf, ], "float64", ), ( [ -np.inf, -50, -1, -1e-20, -1e-25, -1e-45, 0, 1e-40, 1e-20, 1e-10, 2, 40, np.inf, ], "float32", ), ([np.iinfo(np.uint8).min, 1, 2, 100, np.iinfo(np.uint8).max], "uint8"), ( [ np.iinfo(np.int64).min, -100, 0, 1, 9999, 100000, 1e10, np.iinfo(np.int64).max, ], "int64", ), ([NegInfinity(), "1", "A", "BA", "Ba", "C", Infinity()], "object"), ( [datetime(2001, 1, 1), datetime(2001, 1, 2), datetime(2001, 1, 5)], "datetime64", ), ], ) def test_rank_inf_and_nan(self, contents, dtype, frame_or_series): dtype_na_map = { "float64": np.nan, "float32": np.nan, "object": None, "datetime64": np.datetime64("nat"), } # Insert nans at random positions if underlying dtype has missing # value. Then adjust the expected order by adding nans accordingly # This is for testing whether rank calculation is affected # when values are interwined with nan values. values = np.array(contents, dtype=dtype) exp_order = np.array(range(len(values)), dtype="float64") + 1.0 if dtype in dtype_na_map: na_value = dtype_na_map[dtype] nan_indices = np.random.choice(range(len(values)), 5) values = np.insert(values, nan_indices, na_value) exp_order = np.insert(exp_order, nan_indices, np.nan) # Shuffle the testing array and expected results in the same way random_order = np.random.permutation(len(values)) obj = frame_or_series(values[random_order]) expected = frame_or_series(exp_order[random_order], dtype="float64") result = obj.rank() tm.assert_equal(result, expected) def test_df_series_inf_nan_consistency(self): # GH#32593 index = [5, 4, 3, 2, 1, 6, 7, 8, 9, 10] col1 = [5, 4, 3, 5, 8, 5, 2, 1, 6, 6] col2 = [5, 4, np.nan, 5, 8, 5, np.inf, np.nan, 6, -np.inf] df = DataFrame( data={ "col1": col1, "col2": col2, }, index=index, dtype="f8", ) df_result = df.rank() series_result = df.copy() series_result["col1"] = df["col1"].rank() series_result["col2"] = df["col2"].rank() tm.assert_frame_equal(df_result, series_result) def test_rank_both_inf(self): # GH#32593 df = DataFrame({"a": [-np.inf, 0, np.inf]}) expected = DataFrame({"a": [1.0, 2.0, 3.0]}) result = df.rank() tm.assert_frame_equal(result, expected) @pytest.mark.parametrize( "na_option,ascending,expected", [ ("top", True, [3.0, 1.0, 2.0]), ("top", False, [2.0, 1.0, 3.0]), ("bottom", True, [2.0, 3.0, 1.0]), ("bottom", False, [1.0, 3.0, 2.0]), ], ) def test_rank_inf_nans_na_option( self, frame_or_series, method, na_option, ascending, expected ): obj = frame_or_series([np.inf, np.nan, -np.inf]) result = obj.rank(method=method, na_option=na_option, ascending=ascending) expected = frame_or_series(expected) tm.assert_equal(result, expected) @pytest.mark.parametrize( "na_option,ascending,expected", [ ("bottom", True, [1.0, 2.0, 4.0, 3.0]), ("bottom", False, [1.0, 2.0, 4.0, 3.0]), ("top", True, [2.0, 3.0, 1.0, 4.0]), ("top", False, [2.0, 3.0, 1.0, 4.0]), ], ) def test_rank_object_first(self, frame_or_series, na_option, ascending, expected): obj = frame_or_series(["foo", "foo", None, "foo"]) result = obj.rank(method="first", na_option=na_option, ascending=ascending) expected = frame_or_series(expected) tm.assert_equal(result, expected) @pytest.mark.parametrize( "data,expected", [ ({"a": [1, 2, "a"], "b": [4, 5, 6]}, DataFrame({"b": [1.0, 2.0, 3.0]})), ({"a": [1, 2, "a"]}, DataFrame(index=range(3), columns=[])), ], ) def test_rank_mixed_axis_zero(self, data, expected): df = DataFrame(data) with pytest.raises(TypeError, match="'<' not supported between instances of"): df.rank() result = df.rank(numeric_only=True) tm.assert_frame_equal(result, expected)