512 lines
17 KiB
Python
512 lines
17 KiB
Python
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from itertools import chain
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import operator
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import numpy as np
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import pytest
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from pandas._libs.algos import (
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Infinity,
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NegInfinity,
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)
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import pandas.util._test_decorators as td
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from pandas import (
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NA,
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NaT,
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Series,
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Timestamp,
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date_range,
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)
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import pandas._testing as tm
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from pandas.api.types import CategoricalDtype
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@pytest.fixture
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def ser():
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return Series([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])
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@pytest.fixture(
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params=[
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["average", np.array([1.5, 5.5, 7.0, 3.5, np.nan, 3.5, 1.5, 8.0, np.nan, 5.5])],
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["min", np.array([1, 5, 7, 3, np.nan, 3, 1, 8, np.nan, 5])],
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["max", np.array([2, 6, 7, 4, np.nan, 4, 2, 8, np.nan, 6])],
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["first", np.array([1, 5, 7, 3, np.nan, 4, 2, 8, np.nan, 6])],
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["dense", np.array([1, 3, 4, 2, np.nan, 2, 1, 5, np.nan, 3])],
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]
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)
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def results(request):
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return request.param
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@pytest.fixture(
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params=[
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"object",
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"float64",
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"int64",
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"Float64",
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"Int64",
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pytest.param("float64[pyarrow]", marks=td.skip_if_no("pyarrow")),
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pytest.param("int64[pyarrow]", marks=td.skip_if_no("pyarrow")),
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]
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)
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def dtype(request):
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return request.param
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class TestSeriesRank:
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@td.skip_if_no_scipy
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def test_rank(self, datetime_series):
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from scipy.stats import rankdata
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datetime_series[::2] = np.nan
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datetime_series[:10:3] = 4.0
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ranks = datetime_series.rank()
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oranks = datetime_series.astype("O").rank()
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tm.assert_series_equal(ranks, oranks)
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mask = np.isnan(datetime_series)
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filled = datetime_series.fillna(np.inf)
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# rankdata returns a ndarray
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exp = Series(rankdata(filled), index=filled.index, name="ts")
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exp[mask] = np.nan
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tm.assert_series_equal(ranks, exp)
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iseries = Series(np.arange(5).repeat(2))
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iranks = iseries.rank()
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exp = iseries.astype(float).rank()
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tm.assert_series_equal(iranks, exp)
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iseries = Series(np.arange(5)) + 1.0
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exp = iseries / 5.0
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iranks = iseries.rank(pct=True)
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tm.assert_series_equal(iranks, exp)
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iseries = Series(np.repeat(1, 100))
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exp = Series(np.repeat(0.505, 100))
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iranks = iseries.rank(pct=True)
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tm.assert_series_equal(iranks, exp)
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# Explicit cast to float to avoid implicit cast when setting nan
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iseries = iseries.astype("float")
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iseries[1] = np.nan
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exp = Series(np.repeat(50.0 / 99.0, 100))
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exp[1] = np.nan
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iranks = iseries.rank(pct=True)
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tm.assert_series_equal(iranks, exp)
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iseries = Series(np.arange(5)) + 1.0
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iseries[4] = np.nan
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exp = iseries / 4.0
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iranks = iseries.rank(pct=True)
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tm.assert_series_equal(iranks, exp)
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iseries = Series(np.repeat(np.nan, 100))
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exp = iseries.copy()
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iranks = iseries.rank(pct=True)
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tm.assert_series_equal(iranks, exp)
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# Explicit cast to float to avoid implicit cast when setting nan
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iseries = Series(np.arange(5), dtype="float") + 1
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iseries[4] = np.nan
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exp = iseries / 4.0
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iranks = iseries.rank(pct=True)
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tm.assert_series_equal(iranks, exp)
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rng = date_range("1/1/1990", periods=5)
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# Explicit cast to float to avoid implicit cast when setting nan
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iseries = Series(np.arange(5), rng, dtype="float") + 1
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iseries.iloc[4] = np.nan
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exp = iseries / 4.0
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iranks = iseries.rank(pct=True)
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tm.assert_series_equal(iranks, exp)
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iseries = Series([1e-50, 1e-100, 1e-20, 1e-2, 1e-20 + 1e-30, 1e-1])
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exp = Series([2, 1, 3, 5, 4, 6.0])
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iranks = iseries.rank()
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tm.assert_series_equal(iranks, exp)
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# GH 5968
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iseries = Series(["3 day", "1 day 10m", "-2 day", NaT], dtype="m8[ns]")
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exp = Series([3, 2, 1, np.nan])
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iranks = iseries.rank()
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tm.assert_series_equal(iranks, exp)
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values = np.array(
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[-50, -1, -1e-20, -1e-25, -1e-50, 0, 1e-40, 1e-20, 1e-10, 2, 40],
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dtype="float64",
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)
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random_order = np.random.permutation(len(values))
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iseries = Series(values[random_order])
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exp = Series(random_order + 1.0, dtype="float64")
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iranks = iseries.rank()
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tm.assert_series_equal(iranks, exp)
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def test_rank_categorical(self):
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# GH issue #15420 rank incorrectly orders ordered categories
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# Test ascending/descending ranking for ordered categoricals
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exp = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
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exp_desc = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
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ordered = Series(
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["first", "second", "third", "fourth", "fifth", "sixth"]
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).astype(
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CategoricalDtype(
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categories=["first", "second", "third", "fourth", "fifth", "sixth"],
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ordered=True,
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)
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)
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tm.assert_series_equal(ordered.rank(), exp)
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tm.assert_series_equal(ordered.rank(ascending=False), exp_desc)
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# Unordered categoricals should be ranked as objects
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unordered = Series(
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["first", "second", "third", "fourth", "fifth", "sixth"]
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).astype(
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CategoricalDtype(
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categories=["first", "second", "third", "fourth", "fifth", "sixth"],
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ordered=False,
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)
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)
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exp_unordered = Series([2.0, 4.0, 6.0, 3.0, 1.0, 5.0])
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res = unordered.rank()
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tm.assert_series_equal(res, exp_unordered)
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unordered1 = Series([1, 2, 3, 4, 5, 6]).astype(
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CategoricalDtype([1, 2, 3, 4, 5, 6], False)
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)
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exp_unordered1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
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res1 = unordered1.rank()
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tm.assert_series_equal(res1, exp_unordered1)
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# Test na_option for rank data
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na_ser = Series(
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["first", "second", "third", "fourth", "fifth", "sixth", np.NaN]
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).astype(
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CategoricalDtype(
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["first", "second", "third", "fourth", "fifth", "sixth", "seventh"],
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True,
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)
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)
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exp_top = Series([2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 1.0])
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exp_bot = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
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exp_keep = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, np.NaN])
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tm.assert_series_equal(na_ser.rank(na_option="top"), exp_top)
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tm.assert_series_equal(na_ser.rank(na_option="bottom"), exp_bot)
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tm.assert_series_equal(na_ser.rank(na_option="keep"), exp_keep)
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# Test na_option for rank data with ascending False
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exp_top = Series([7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0])
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exp_bot = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 7.0])
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exp_keep = Series([6.0, 5.0, 4.0, 3.0, 2.0, 1.0, np.NaN])
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tm.assert_series_equal(na_ser.rank(na_option="top", ascending=False), exp_top)
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tm.assert_series_equal(
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na_ser.rank(na_option="bottom", ascending=False), exp_bot
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)
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tm.assert_series_equal(na_ser.rank(na_option="keep", ascending=False), exp_keep)
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# Test invalid values for na_option
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msg = "na_option must be one of 'keep', 'top', or 'bottom'"
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with pytest.raises(ValueError, match=msg):
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na_ser.rank(na_option="bad", ascending=False)
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# invalid type
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with pytest.raises(ValueError, match=msg):
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na_ser.rank(na_option=True, ascending=False)
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# Test with pct=True
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na_ser = Series(["first", "second", "third", "fourth", np.NaN]).astype(
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CategoricalDtype(["first", "second", "third", "fourth"], True)
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)
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exp_top = Series([0.4, 0.6, 0.8, 1.0, 0.2])
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exp_bot = Series([0.2, 0.4, 0.6, 0.8, 1.0])
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exp_keep = Series([0.25, 0.5, 0.75, 1.0, np.NaN])
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tm.assert_series_equal(na_ser.rank(na_option="top", pct=True), exp_top)
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tm.assert_series_equal(na_ser.rank(na_option="bottom", pct=True), exp_bot)
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tm.assert_series_equal(na_ser.rank(na_option="keep", pct=True), exp_keep)
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def test_rank_signature(self):
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s = Series([0, 1])
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s.rank(method="average")
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msg = "No axis named average for object type Series"
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with pytest.raises(ValueError, match=msg):
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s.rank("average")
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@pytest.mark.parametrize("dtype", [None, object])
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def test_rank_tie_methods(self, ser, results, dtype):
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method, exp = results
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ser = ser if dtype is None else ser.astype(dtype)
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result = ser.rank(method=method)
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tm.assert_series_equal(result, Series(exp))
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@td.skip_if_no_scipy
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@pytest.mark.parametrize("ascending", [True, False])
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@pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
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@pytest.mark.parametrize("na_option", ["top", "bottom", "keep"])
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@pytest.mark.parametrize(
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"dtype, na_value, pos_inf, neg_inf",
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[
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("object", None, Infinity(), NegInfinity()),
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("float64", np.nan, np.inf, -np.inf),
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("Float64", NA, np.inf, -np.inf),
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pytest.param(
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"float64[pyarrow]",
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NA,
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np.inf,
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-np.inf,
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marks=td.skip_if_no("pyarrow"),
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),
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],
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)
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def test_rank_tie_methods_on_infs_nans(
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self, method, na_option, ascending, dtype, na_value, pos_inf, neg_inf
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):
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if dtype == "float64[pyarrow]":
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if method == "average":
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exp_dtype = "float64[pyarrow]"
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else:
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exp_dtype = "uint64[pyarrow]"
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else:
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exp_dtype = "float64"
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chunk = 3
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in_arr = [neg_inf] * chunk + [na_value] * chunk + [pos_inf] * chunk
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iseries = Series(in_arr, dtype=dtype)
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exp_ranks = {
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"average": ([2, 2, 2], [5, 5, 5], [8, 8, 8]),
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"min": ([1, 1, 1], [4, 4, 4], [7, 7, 7]),
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"max": ([3, 3, 3], [6, 6, 6], [9, 9, 9]),
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"first": ([1, 2, 3], [4, 5, 6], [7, 8, 9]),
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"dense": ([1, 1, 1], [2, 2, 2], [3, 3, 3]),
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}
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ranks = exp_ranks[method]
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if na_option == "top":
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order = [ranks[1], ranks[0], ranks[2]]
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elif na_option == "bottom":
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order = [ranks[0], ranks[2], ranks[1]]
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else:
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order = [ranks[0], [np.nan] * chunk, ranks[1]]
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expected = order if ascending else order[::-1]
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expected = list(chain.from_iterable(expected))
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result = iseries.rank(method=method, na_option=na_option, ascending=ascending)
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tm.assert_series_equal(result, Series(expected, dtype=exp_dtype))
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def test_rank_desc_mix_nans_infs(self):
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# GH 19538
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# check descending ranking when mix nans and infs
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iseries = Series([1, np.nan, np.inf, -np.inf, 25])
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result = iseries.rank(ascending=False)
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exp = Series([3, np.nan, 1, 4, 2], dtype="float64")
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tm.assert_series_equal(result, exp)
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@td.skip_if_no_scipy
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@pytest.mark.parametrize("method", ["average", "min", "max", "first", "dense"])
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@pytest.mark.parametrize(
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"op, value",
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[
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[operator.add, 0],
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[operator.add, 1e6],
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[operator.mul, 1e-6],
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],
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)
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def test_rank_methods_series(self, method, op, value):
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from scipy.stats import rankdata
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xs = np.random.randn(9)
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xs = np.concatenate([xs[i:] for i in range(0, 9, 2)]) # add duplicates
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np.random.shuffle(xs)
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index = [chr(ord("a") + i) for i in range(len(xs))]
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vals = op(xs, value)
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ts = Series(vals, index=index)
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result = ts.rank(method=method)
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sprank = rankdata(vals, method if method != "first" else "ordinal")
|
||
|
|
expected = Series(sprank, index=index).astype("float64")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser, exp",
|
||
|
|
[
|
||
|
|
([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]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_rank_dense_method(self, dtype, ser, exp):
|
||
|
|
s = Series(ser).astype(dtype)
|
||
|
|
result = s.rank(method="dense")
|
||
|
|
expected = Series(exp).astype(result.dtype)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_rank_descending(self, ser, results, dtype):
|
||
|
|
method, _ = results
|
||
|
|
if "i" in dtype:
|
||
|
|
s = ser.dropna()
|
||
|
|
else:
|
||
|
|
s = ser.astype(dtype)
|
||
|
|
|
||
|
|
res = s.rank(ascending=False)
|
||
|
|
expected = (s.max() - s).rank()
|
||
|
|
tm.assert_series_equal(res, expected)
|
||
|
|
|
||
|
|
expected = (s.max() - s).rank(method=method)
|
||
|
|
res2 = s.rank(method=method, ascending=False)
|
||
|
|
tm.assert_series_equal(res2, expected)
|
||
|
|
|
||
|
|
def test_rank_int(self, ser, results):
|
||
|
|
method, exp = results
|
||
|
|
s = ser.dropna().astype("i8")
|
||
|
|
|
||
|
|
result = s.rank(method=method)
|
||
|
|
expected = Series(exp).dropna()
|
||
|
|
expected.index = result.index
|
||
|
|
tm.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
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
# GH15630, pct should be on 100% basis when method='dense'
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser, exp",
|
||
|
|
[
|
||
|
|
([1], [1.0]),
|
||
|
|
([1, 2], [1.0 / 2, 2.0 / 2]),
|
||
|
|
([2, 2], [1.0, 1.0]),
|
||
|
|
([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
|
||
|
|
([1, 2, 2], [1.0 / 2, 2.0 / 2, 2.0 / 2]),
|
||
|
|
([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
|
||
|
|
([1, 1, 5, 5, 3], [1.0 / 3, 1.0 / 3, 3.0 / 3, 3.0 / 3, 2.0 / 3]),
|
||
|
|
([1, 1, 3, 3, 5, 5], [1.0 / 3, 1.0 / 3, 2.0 / 3, 2.0 / 3, 3.0 / 3, 3.0 / 3]),
|
||
|
|
([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 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)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser, exp",
|
||
|
|
[
|
||
|
|
([1], [1.0]),
|
||
|
|
([1, 2], [1.0 / 2, 2.0 / 2]),
|
||
|
|
([2, 2], [1.0 / 2, 1.0 / 2]),
|
||
|
|
([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
|
||
|
|
([1, 2, 2], [1.0 / 3, 2.0 / 3, 2.0 / 3]),
|
||
|
|
([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
|
||
|
|
([1, 1, 5, 5, 3], [1.0 / 5, 1.0 / 5, 4.0 / 5, 4.0 / 5, 3.0 / 5]),
|
||
|
|
([1, 1, 3, 3, 5, 5], [1.0 / 6, 1.0 / 6, 3.0 / 6, 3.0 / 6, 5.0 / 6, 5.0 / 6]),
|
||
|
|
([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 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)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser, exp",
|
||
|
|
[
|
||
|
|
([1], [1.0]),
|
||
|
|
([1, 2], [1.0 / 2, 2.0 / 2]),
|
||
|
|
([2, 2], [1.0, 1.0]),
|
||
|
|
([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
|
||
|
|
([1, 2, 2], [1.0 / 3, 3.0 / 3, 3.0 / 3]),
|
||
|
|
([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
|
||
|
|
([1, 1, 5, 5, 3], [2.0 / 5, 2.0 / 5, 5.0 / 5, 5.0 / 5, 3.0 / 5]),
|
||
|
|
([1, 1, 3, 3, 5, 5], [2.0 / 6, 2.0 / 6, 4.0 / 6, 4.0 / 6, 6.0 / 6, 6.0 / 6]),
|
||
|
|
([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 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)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser, exp",
|
||
|
|
[
|
||
|
|
([1], [1.0]),
|
||
|
|
([1, 2], [1.0 / 2, 2.0 / 2]),
|
||
|
|
([2, 2], [1.5 / 2, 1.5 / 2]),
|
||
|
|
([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
|
||
|
|
([1, 2, 2], [1.0 / 3, 2.5 / 3, 2.5 / 3]),
|
||
|
|
([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
|
||
|
|
([1, 1, 5, 5, 3], [1.5 / 5, 1.5 / 5, 4.5 / 5, 4.5 / 5, 3.0 / 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], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 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)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ser, exp",
|
||
|
|
[
|
||
|
|
([1], [1.0]),
|
||
|
|
([1, 2], [1.0 / 2, 2.0 / 2]),
|
||
|
|
([2, 2], [1.0 / 2, 2.0 / 2.0]),
|
||
|
|
([1, 2, 3], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
|
||
|
|
([1, 2, 2], [1.0 / 3, 2.0 / 3, 3.0 / 3]),
|
||
|
|
([4, 2, 1], [3.0 / 3, 2.0 / 3, 1.0 / 3]),
|
||
|
|
([1, 1, 5, 5, 3], [1.0 / 5, 2.0 / 5, 4.0 / 5, 5.0 / 5, 3.0 / 5]),
|
||
|
|
([1, 1, 3, 3, 5, 5], [1.0 / 6, 2.0 / 6, 3.0 / 6, 4.0 / 6, 5.0 / 6, 6.0 / 6]),
|
||
|
|
([-5, -4, -3, -2, -1], [1.0 / 5, 2.0 / 5, 3.0 / 5, 4.0 / 5, 5.0 / 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)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.single_cpu
|
||
|
|
def test_pct_max_many_rows():
|
||
|
|
# GH 18271
|
||
|
|
s = Series(np.arange(2**24 + 1))
|
||
|
|
result = s.rank(pct=True).max()
|
||
|
|
assert result == 1
|