223 lines
7.5 KiB
Python
223 lines
7.5 KiB
Python
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from itertools import product
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import numpy as np
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import pytest
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from pandas import DataFrame, MultiIndex, Period, Series, Timedelta, Timestamp
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import pandas._testing as tm
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class TestCounting:
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def test_cumcount(self):
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df = DataFrame([["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"])
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g = df.groupby("A")
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sg = g.A
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expected = Series([0, 1, 2, 0, 3])
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tm.assert_series_equal(expected, g.cumcount())
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tm.assert_series_equal(expected, sg.cumcount())
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def test_cumcount_empty(self):
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ge = DataFrame().groupby(level=0)
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se = Series(dtype=object).groupby(level=0)
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# edge case, as this is usually considered float
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e = Series(dtype="int64")
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tm.assert_series_equal(e, ge.cumcount())
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tm.assert_series_equal(e, se.cumcount())
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def test_cumcount_dupe_index(self):
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df = DataFrame(
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[["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=[0] * 5
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)
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g = df.groupby("A")
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sg = g.A
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expected = Series([0, 1, 2, 0, 3], index=[0] * 5)
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tm.assert_series_equal(expected, g.cumcount())
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tm.assert_series_equal(expected, sg.cumcount())
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def test_cumcount_mi(self):
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mi = MultiIndex.from_tuples([[0, 1], [1, 2], [2, 2], [2, 2], [1, 0]])
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df = DataFrame([["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=mi)
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g = df.groupby("A")
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sg = g.A
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expected = Series([0, 1, 2, 0, 3], index=mi)
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tm.assert_series_equal(expected, g.cumcount())
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tm.assert_series_equal(expected, sg.cumcount())
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def test_cumcount_groupby_not_col(self):
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df = DataFrame(
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[["a"], ["a"], ["a"], ["b"], ["a"]], columns=["A"], index=[0] * 5
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)
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g = df.groupby([0, 0, 0, 1, 0])
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sg = g.A
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expected = Series([0, 1, 2, 0, 3], index=[0] * 5)
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tm.assert_series_equal(expected, g.cumcount())
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tm.assert_series_equal(expected, sg.cumcount())
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def test_ngroup(self):
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df = DataFrame({"A": list("aaaba")})
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g = df.groupby("A")
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sg = g.A
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expected = Series([0, 0, 0, 1, 0])
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tm.assert_series_equal(expected, g.ngroup())
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tm.assert_series_equal(expected, sg.ngroup())
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def test_ngroup_distinct(self):
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df = DataFrame({"A": list("abcde")})
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g = df.groupby("A")
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sg = g.A
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expected = Series(range(5), dtype="int64")
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tm.assert_series_equal(expected, g.ngroup())
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tm.assert_series_equal(expected, sg.ngroup())
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def test_ngroup_one_group(self):
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df = DataFrame({"A": [0] * 5})
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g = df.groupby("A")
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sg = g.A
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expected = Series([0] * 5)
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tm.assert_series_equal(expected, g.ngroup())
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tm.assert_series_equal(expected, sg.ngroup())
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def test_ngroup_empty(self):
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ge = DataFrame().groupby(level=0)
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se = Series(dtype=object).groupby(level=0)
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# edge case, as this is usually considered float
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e = Series(dtype="int64")
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tm.assert_series_equal(e, ge.ngroup())
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tm.assert_series_equal(e, se.ngroup())
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def test_ngroup_series_matches_frame(self):
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df = DataFrame({"A": list("aaaba")})
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s = Series(list("aaaba"))
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tm.assert_series_equal(df.groupby(s).ngroup(), s.groupby(s).ngroup())
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def test_ngroup_dupe_index(self):
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df = DataFrame({"A": list("aaaba")}, index=[0] * 5)
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g = df.groupby("A")
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sg = g.A
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expected = Series([0, 0, 0, 1, 0], index=[0] * 5)
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tm.assert_series_equal(expected, g.ngroup())
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tm.assert_series_equal(expected, sg.ngroup())
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def test_ngroup_mi(self):
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mi = MultiIndex.from_tuples([[0, 1], [1, 2], [2, 2], [2, 2], [1, 0]])
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df = DataFrame({"A": list("aaaba")}, index=mi)
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g = df.groupby("A")
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sg = g.A
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expected = Series([0, 0, 0, 1, 0], index=mi)
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tm.assert_series_equal(expected, g.ngroup())
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tm.assert_series_equal(expected, sg.ngroup())
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def test_ngroup_groupby_not_col(self):
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df = DataFrame({"A": list("aaaba")}, index=[0] * 5)
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g = df.groupby([0, 0, 0, 1, 0])
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sg = g.A
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expected = Series([0, 0, 0, 1, 0], index=[0] * 5)
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tm.assert_series_equal(expected, g.ngroup())
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tm.assert_series_equal(expected, sg.ngroup())
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def test_ngroup_descending(self):
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df = DataFrame(["a", "a", "b", "a", "b"], columns=["A"])
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g = df.groupby(["A"])
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ascending = Series([0, 0, 1, 0, 1])
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descending = Series([1, 1, 0, 1, 0])
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tm.assert_series_equal(descending, (g.ngroups - 1) - ascending)
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tm.assert_series_equal(ascending, g.ngroup(ascending=True))
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tm.assert_series_equal(descending, g.ngroup(ascending=False))
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def test_ngroup_matches_cumcount(self):
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# verify one manually-worked out case works
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df = DataFrame(
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[["a", "x"], ["a", "y"], ["b", "x"], ["a", "x"], ["b", "y"]],
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columns=["A", "X"],
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)
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g = df.groupby(["A", "X"])
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g_ngroup = g.ngroup()
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g_cumcount = g.cumcount()
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expected_ngroup = Series([0, 1, 2, 0, 3])
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expected_cumcount = Series([0, 0, 0, 1, 0])
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tm.assert_series_equal(g_ngroup, expected_ngroup)
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tm.assert_series_equal(g_cumcount, expected_cumcount)
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def test_ngroup_cumcount_pair(self):
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# brute force comparison for all small series
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for p in product(range(3), repeat=4):
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df = DataFrame({"a": p})
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g = df.groupby(["a"])
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order = sorted(set(p))
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ngroupd = [order.index(val) for val in p]
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cumcounted = [p[:i].count(val) for i, val in enumerate(p)]
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tm.assert_series_equal(g.ngroup(), Series(ngroupd))
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tm.assert_series_equal(g.cumcount(), Series(cumcounted))
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def test_ngroup_respects_groupby_order(self):
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np.random.seed(0)
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df = DataFrame({"a": np.random.choice(list("abcdef"), 100)})
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for sort_flag in (False, True):
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g = df.groupby(["a"], sort=sort_flag)
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df["group_id"] = -1
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df["group_index"] = -1
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for i, (_, group) in enumerate(g):
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df.loc[group.index, "group_id"] = i
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for j, ind in enumerate(group.index):
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df.loc[ind, "group_index"] = j
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tm.assert_series_equal(Series(df["group_id"].values), g.ngroup())
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tm.assert_series_equal(Series(df["group_index"].values), g.cumcount())
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@pytest.mark.parametrize(
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"datetimelike",
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[
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[Timestamp(f"2016-05-{i:02d} 20:09:25+00:00") for i in range(1, 4)],
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[Timestamp(f"2016-05-{i:02d} 20:09:25") for i in range(1, 4)],
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[Timedelta(x, unit="h") for x in range(1, 4)],
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[Period(freq="2W", year=2017, month=x) for x in range(1, 4)],
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],
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)
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def test_count_with_datetimelike(self, datetimelike):
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# test for #13393, where DataframeGroupBy.count() fails
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# when counting a datetimelike column.
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df = DataFrame({"x": ["a", "a", "b"], "y": datetimelike})
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res = df.groupby("x").count()
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expected = DataFrame({"y": [2, 1]}, index=["a", "b"])
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expected.index.name = "x"
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tm.assert_frame_equal(expected, res)
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def test_count_with_only_nans_in_first_group(self):
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# GH21956
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df = DataFrame({"A": [np.nan, np.nan], "B": ["a", "b"], "C": [1, 2]})
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result = df.groupby(["A", "B"]).C.count()
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mi = MultiIndex(levels=[[], ["a", "b"]], codes=[[], []], names=["A", "B"])
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expected = Series([], index=mi, dtype=np.int64, name="C")
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tm.assert_series_equal(result, expected, check_index_type=False)
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