# -*- coding: utf-8 -*- from pandas import compat, Timestamp import pytest from distutils.version import LooseVersion from numpy import nan import numpy as np from pandas import Series, date_range, NaT from pandas.api.types import CategoricalDtype from pandas.compat import product from pandas.util.testing import assert_series_equal import pandas.util.testing as tm from pandas.tests.series.common import TestData from pandas._libs.tslib import iNaT from pandas._libs.algos import Infinity, NegInfinity from itertools import chain import pandas.util._test_decorators as td class TestSeriesRank(TestData): s = Series([1, 3, 4, 2, nan, 2, 1, 5, nan, 3]) results = { 'average': np.array([1.5, 5.5, 7.0, 3.5, nan, 3.5, 1.5, 8.0, nan, 5.5]), 'min': np.array([1, 5, 7, 3, nan, 3, 1, 8, nan, 5]), 'max': np.array([2, 6, 7, 4, nan, 4, 2, 8, nan, 6]), 'first': np.array([1, 5, 7, 3, nan, 4, 2, 8, nan, 6]), 'dense': np.array([1, 3, 4, 2, nan, 2, 1, 5, nan, 3]), } def test_rank(self): pytest.importorskip('scipy.stats.special') rankdata = pytest.importorskip('scipy.stats.rankdata') self.ts[::2] = np.nan self.ts[:10][::3] = 4. ranks = self.ts.rank() oranks = self.ts.astype('O').rank() assert_series_equal(ranks, oranks) mask = np.isnan(self.ts) filled = self.ts.fillna(np.inf) # rankdata returns a ndarray exp = Series(rankdata(filled), index=filled.index, name='ts') exp[mask] = np.nan tm.assert_series_equal(ranks, exp) iseries = Series(np.arange(5).repeat(2)) iranks = iseries.rank() exp = iseries.astype(float).rank() assert_series_equal(iranks, exp) iseries = Series(np.arange(5)) + 1.0 exp = iseries / 5.0 iranks = iseries.rank(pct=True) assert_series_equal(iranks, exp) iseries = Series(np.repeat(1, 100)) exp = Series(np.repeat(0.505, 100)) iranks = iseries.rank(pct=True) assert_series_equal(iranks, exp) iseries[1] = np.nan exp = Series(np.repeat(50.0 / 99.0, 100)) exp[1] = np.nan iranks = iseries.rank(pct=True) assert_series_equal(iranks, exp) iseries = Series(np.arange(5)) + 1.0 iseries[4] = np.nan exp = iseries / 4.0 iranks = iseries.rank(pct=True) assert_series_equal(iranks, exp) iseries = Series(np.repeat(np.nan, 100)) exp = iseries.copy() iranks = iseries.rank(pct=True) assert_series_equal(iranks, exp) iseries = Series(np.arange(5)) + 1 iseries[4] = np.nan exp = iseries / 4.0 iranks = iseries.rank(pct=True) assert_series_equal(iranks, exp) rng = date_range('1/1/1990', periods=5) iseries = Series(np.arange(5), rng) + 1 iseries.iloc[4] = np.nan exp = iseries / 4.0 iranks = iseries.rank(pct=True) assert_series_equal(iranks, exp) iseries = Series([1e-50, 1e-100, 1e-20, 1e-2, 1e-20 + 1e-30, 1e-1]) exp = Series([2, 1, 3, 5, 4, 6.0]) iranks = iseries.rank() assert_series_equal(iranks, exp) # GH 5968 iseries = Series(['3 day', '1 day 10m', '-2 day', NaT], dtype='m8[ns]') exp = Series([3, 2, 1, np.nan]) iranks = iseries.rank() assert_series_equal(iranks, exp) values = np.array( [-50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40 ], dtype='float64') random_order = np.random.permutation(len(values)) iseries = Series(values[random_order]) exp = Series(random_order + 1.0, dtype='float64') iranks = iseries.rank() assert_series_equal(iranks, exp) def test_rank_categorical(self): # GH issue #15420 rank incorrectly orders ordered categories # Test ascending/descending ranking for ordered categoricals exp = Series([1., 2., 3., 4., 5., 6.]) exp_desc = Series([6., 5., 4., 3., 2., 1.]) ordered = Series( ['first', 'second', 'third', 'fourth', 'fifth', 'sixth'] ).astype(CategoricalDtype(categories=['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], ordered=True)) assert_series_equal(ordered.rank(), exp) assert_series_equal(ordered.rank(ascending=False), exp_desc) # Unordered categoricals should be ranked as objects unordered = Series(['first', 'second', 'third', 'fourth', 'fifth', 'sixth']).astype( CategoricalDtype(categories=['first', 'second', 'third', 'fourth', 'fifth', 'sixth'], ordered=False)) exp_unordered = Series([2., 4., 6., 3., 1., 5.]) res = unordered.rank() assert_series_equal(res, exp_unordered) unordered1 = Series( [1, 2, 3, 4, 5, 6], ).astype(CategoricalDtype([1, 2, 3, 4, 5, 6], False)) exp_unordered1 = Series([1., 2., 3., 4., 5., 6.]) res1 = unordered1.rank() assert_series_equal(res1, exp_unordered1) # Test na_option for rank data na_ser = Series( ['first', 'second', 'third', 'fourth', 'fifth', 'sixth', np.NaN] ).astype(CategoricalDtype(['first', 'second', 'third', 'fourth', 'fifth', 'sixth', 'seventh'], True)) exp_top = Series([2., 3., 4., 5., 6., 7., 1.]) exp_bot = Series([1., 2., 3., 4., 5., 6., 7.]) exp_keep = Series([1., 2., 3., 4., 5., 6., np.NaN]) assert_series_equal(na_ser.rank(na_option='top'), exp_top) assert_series_equal(na_ser.rank(na_option='bottom'), exp_bot) assert_series_equal(na_ser.rank(na_option='keep'), exp_keep) # Test na_option for rank data with ascending False exp_top = Series([7., 6., 5., 4., 3., 2., 1.]) exp_bot = Series([6., 5., 4., 3., 2., 1., 7.]) exp_keep = Series([6., 5., 4., 3., 2., 1., np.NaN]) assert_series_equal( na_ser.rank(na_option='top', ascending=False), exp_top ) assert_series_equal( na_ser.rank(na_option='bottom', ascending=False), exp_bot ) assert_series_equal( na_ser.rank(na_option='keep', ascending=False), exp_keep ) # Test with pct=True na_ser = Series(['first', 'second', 'third', 'fourth', np.NaN]).astype( CategoricalDtype(['first', 'second', 'third', 'fourth'], True)) exp_top = Series([0.4, 0.6, 0.8, 1., 0.2]) exp_bot = Series([0.2, 0.4, 0.6, 0.8, 1.]) exp_keep = Series([0.25, 0.5, 0.75, 1., np.NaN]) assert_series_equal(na_ser.rank(na_option='top', pct=True), exp_top) assert_series_equal(na_ser.rank(na_option='bottom', pct=True), exp_bot) assert_series_equal(na_ser.rank(na_option='keep', pct=True), exp_keep) def test_rank_signature(self): s = Series([0, 1]) s.rank(method='average') pytest.raises(ValueError, s.rank, 'average') @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'), pytest.param([np.iinfo(np.int64).min, -100, 0, 1, 9999, 100000, 1e10, np.iinfo(np.int64).max], 'int64', marks=pytest.mark.xfail( reason="iNaT is equivalent to minimum value of dtype" "int64 pending issue #16674")), ([NegInfinity(), '1', 'A', 'BA', 'Ba', 'C', Infinity()], 'object') ]) def test_rank_inf(self, contents, dtype): dtype_na_map = { 'float64': np.nan, 'float32': np.nan, 'int64': iNaT, 'object': None } # 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)) iseries = Series(values[random_order]) exp = Series(exp_order[random_order], dtype='float64') iranks = iseries.rank() assert_series_equal(iranks, exp) def test_rank_tie_methods(self): s = self.s def _check(s, expected, method='average'): result = s.rank(method=method) tm.assert_series_equal(result, Series(expected)) dtypes = [None, object] disabled = set([(object, 'first')]) results = self.results for method, dtype in product(results, dtypes): if (dtype, method) in disabled: continue series = s if dtype is None else s.astype(dtype) _check(series, results[method], method=method) @td.skip_if_no_scipy @pytest.mark.parametrize('ascending', [True, False]) @pytest.mark.parametrize('method', ['average', 'min', 'max', 'first', 'dense']) @pytest.mark.parametrize('na_option', ['top', 'bottom', 'keep']) def test_rank_tie_methods_on_infs_nans(self, method, na_option, ascending): dtypes = [('object', None, Infinity(), NegInfinity()), ('float64', np.nan, np.inf, -np.inf)] chunk = 3 disabled = set([('object', 'first')]) def _check(s, method, na_option, ascending): exp_ranks = { 'average': ([2, 2, 2], [5, 5, 5], [8, 8, 8]), 'min': ([1, 1, 1], [4, 4, 4], [7, 7, 7]), 'max': ([3, 3, 3], [6, 6, 6], [9, 9, 9]), 'first': ([1, 2, 3], [4, 5, 6], [7, 8, 9]), 'dense': ([1, 1, 1], [2, 2, 2], [3, 3, 3]) } ranks = exp_ranks[method] if na_option == 'top': order = [ranks[1], ranks[0], ranks[2]] elif na_option == 'bottom': order = [ranks[0], ranks[2], ranks[1]] else: order = [ranks[0], [np.nan] * chunk, ranks[1]] expected = order if ascending else order[::-1] expected = list(chain.from_iterable(expected)) result = s.rank(method=method, na_option=na_option, ascending=ascending) tm.assert_series_equal(result, Series(expected, dtype='float64')) for dtype, na_value, pos_inf, neg_inf in dtypes: in_arr = [neg_inf] * chunk + [na_value] * chunk + [pos_inf] * chunk iseries = Series(in_arr, dtype=dtype) if (dtype, method) in disabled: continue _check(iseries, method, na_option, ascending) def test_rank_desc_mix_nans_infs(self): # GH 19538 # check descending ranking when mix nans and infs iseries = Series([1, np.nan, np.inf, -np.inf, 25]) result = iseries.rank(ascending=False) exp = Series([3, np.nan, 1, 4, 2], dtype='float64') tm.assert_series_equal(result, exp) def test_rank_methods_series(self): pytest.importorskip('scipy.stats.special') rankdata = pytest.importorskip('scipy.stats.rankdata') import scipy xs = np.random.randn(9) xs = np.concatenate([xs[i:] for i in range(0, 9, 2)]) # add duplicates np.random.shuffle(xs) index = [chr(ord('a') + i) for i in range(len(xs))] for vals in [xs, xs + 1e6, xs * 1e-6]: ts = Series(vals, index=index) for m in ['average', 'min', 'max', 'first', 'dense']: result = ts.rank(method=m) sprank = rankdata(vals, m if m != 'first' else 'ordinal') expected = Series(sprank, index=index) if LooseVersion(scipy.__version__) >= LooseVersion('0.17.0'): expected = expected.astype('float64') tm.assert_series_equal(result, expected) def test_rank_dense_method(self): dtypes = ['O', 'f8', 'i8'] in_out = [([1], [1]), ([2], [1]), ([0], [1]), ([2, 2], [1, 1]), ([1, 2, 3], [1, 2, 3]), ([4, 2, 1], [3, 2, 1],), ([1, 1, 5, 5, 3], [1, 1, 3, 3, 2]), ([-5, -4, -3, -2, -1], [1, 2, 3, 4, 5])] for ser, exp in in_out: for dtype in dtypes: s = Series(ser).astype(dtype) result = s.rank(method='dense') expected = Series(exp).astype(result.dtype) assert_series_equal(result, expected) def test_rank_descending(self): dtypes = ['O', 'f8', 'i8'] for dtype, method in product(dtypes, self.results): if 'i' in dtype: s = self.s.dropna() else: s = self.s.astype(dtype) res = s.rank(ascending=False) expected = (s.max() - s).rank() assert_series_equal(res, expected) if method == 'first' and dtype == 'O': continue expected = (s.max() - s).rank(method=method) res2 = s.rank(method=method, ascending=False) assert_series_equal(res2, expected) def test_rank_int(self): s = self.s.dropna().astype('i8') for method, res in compat.iteritems(self.results): result = s.rank(method=method) expected = Series(res).dropna() expected.index = result.index assert_series_equal(result, expected) def test_rank_object_bug(self): # GH 13445 # smoke tests Series([np.nan] * 32).astype(object).rank(ascending=True) Series([np.nan] * 32).astype(object).rank(ascending=False) def test_rank_modify_inplace(self): # GH 18521 # Check rank does not mutate series s = Series([Timestamp('2017-01-05 10:20:27.569000'), NaT]) expected = s.copy() s.rank() result = s assert_series_equal(result, expected) # GH15630, pct should be on 100% basis when method='dense' @pytest.mark.parametrize('dtype', ['O', 'f8', 'i8']) @pytest.mark.parametrize('ser, exp', [ ([1], [1.]), ([1, 2], [1. / 2, 2. / 2]), ([2, 2], [1., 1.]), ([1, 2, 3], [1. / 3, 2. / 3, 3. / 3]), ([1, 2, 2], [1. / 2, 2. / 2, 2. / 2]), ([4, 2, 1], [3. / 3, 2. / 3, 1. / 3],), ([1, 1, 5, 5, 3], [1. / 3, 1. / 3, 3. / 3, 3. / 3, 2. / 3]), ([1, 1, 3, 3, 5, 5], [1. / 3, 1. / 3, 2. / 3, 2. / 3, 3. / 3, 3. / 3]), ([-5, -4, -3, -2, -1], [1. / 5, 2. / 5, 3. / 5, 4. / 5, 5. / 5])]) def test_rank_dense_pct(dtype, ser, exp): s = Series(ser).astype(dtype) result = s.rank(method='dense', pct=True) expected = Series(exp).astype(result.dtype) assert_series_equal(result, expected) @pytest.mark.parametrize('dtype', ['O', 'f8', 'i8']) @pytest.mark.parametrize('ser, exp', [ ([1], [1.]), ([1, 2], [1. / 2, 2. / 2]), ([2, 2], [1. / 2, 1. / 2]), ([1, 2, 3], [1. / 3, 2. / 3, 3. / 3]), ([1, 2, 2], [1. / 3, 2. / 3, 2. / 3]), ([4, 2, 1], [3. / 3, 2. / 3, 1. / 3],), ([1, 1, 5, 5, 3], [1. / 5, 1. / 5, 4. / 5, 4. / 5, 3. / 5]), ([1, 1, 3, 3, 5, 5], [1. / 6, 1. / 6, 3. / 6, 3. / 6, 5. / 6, 5. / 6]), ([-5, -4, -3, -2, -1], [1. / 5, 2. / 5, 3. / 5, 4. / 5, 5. / 5])]) def test_rank_min_pct(dtype, ser, exp): s = Series(ser).astype(dtype) result = s.rank(method='min', pct=True) expected = Series(exp).astype(result.dtype) assert_series_equal(result, expected) @pytest.mark.parametrize('dtype', ['O', 'f8', 'i8']) @pytest.mark.parametrize('ser, exp', [ ([1], [1.]), ([1, 2], [1. / 2, 2. / 2]), ([2, 2], [1., 1.]), ([1, 2, 3], [1. / 3, 2. / 3, 3. / 3]), ([1, 2, 2], [1. / 3, 3. / 3, 3. / 3]), ([4, 2, 1], [3. / 3, 2. / 3, 1. / 3],), ([1, 1, 5, 5, 3], [2. / 5, 2. / 5, 5. / 5, 5. / 5, 3. / 5]), ([1, 1, 3, 3, 5, 5], [2. / 6, 2. / 6, 4. / 6, 4. / 6, 6. / 6, 6. / 6]), ([-5, -4, -3, -2, -1], [1. / 5, 2. / 5, 3. / 5, 4. / 5, 5. / 5])]) def test_rank_max_pct(dtype, ser, exp): s = Series(ser).astype(dtype) result = s.rank(method='max', pct=True) expected = Series(exp).astype(result.dtype) assert_series_equal(result, expected) @pytest.mark.parametrize('dtype', ['O', 'f8', 'i8']) @pytest.mark.parametrize('ser, exp', [ ([1], [1.]), ([1, 2], [1. / 2, 2. / 2]), ([2, 2], [1.5 / 2, 1.5 / 2]), ([1, 2, 3], [1. / 3, 2. / 3, 3. / 3]), ([1, 2, 2], [1. / 3, 2.5 / 3, 2.5 / 3]), ([4, 2, 1], [3. / 3, 2. / 3, 1. / 3],), ([1, 1, 5, 5, 3], [1.5 / 5, 1.5 / 5, 4.5 / 5, 4.5 / 5, 3. / 5]), ([1, 1, 3, 3, 5, 5], [1.5 / 6, 1.5 / 6, 3.5 / 6, 3.5 / 6, 5.5 / 6, 5.5 / 6]), ([-5, -4, -3, -2, -1], [1. / 5, 2. / 5, 3. / 5, 4. / 5, 5. / 5])]) def test_rank_average_pct(dtype, ser, exp): s = Series(ser).astype(dtype) result = s.rank(method='average', pct=True) expected = Series(exp).astype(result.dtype) assert_series_equal(result, expected) @pytest.mark.parametrize('dtype', ['f8', 'i8']) @pytest.mark.parametrize('ser, exp', [ ([1], [1.]), ([1, 2], [1. / 2, 2. / 2]), ([2, 2], [1. / 2, 2. / 2.]), ([1, 2, 3], [1. / 3, 2. / 3, 3. / 3]), ([1, 2, 2], [1. / 3, 2. / 3, 3. / 3]), ([4, 2, 1], [3. / 3, 2. / 3, 1. / 3],), ([1, 1, 5, 5, 3], [1. / 5, 2. / 5, 4. / 5, 5. / 5, 3. / 5]), ([1, 1, 3, 3, 5, 5], [1. / 6, 2. / 6, 3. / 6, 4. / 6, 5. / 6, 6. / 6]), ([-5, -4, -3, -2, -1], [1. / 5, 2. / 5, 3. / 5, 4. / 5, 5. / 5])]) def test_rank_first_pct(dtype, ser, exp): s = Series(ser).astype(dtype) result = s.rank(method='first', pct=True) expected = Series(exp).astype(result.dtype) assert_series_equal(result, expected)