Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
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364 lines
12 KiB
364 lines
12 KiB
from itertools import product
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from string import ascii_lowercase
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import numpy as np
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import pytest
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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MultiIndex,
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Period,
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Series,
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Timedelta,
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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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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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def test_count_groupby_column_with_nan_in_groupby_column(self):
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# https://github.com/pandas-dev/pandas/issues/32841
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df = DataFrame({"A": [1, 1, 1, 1, 1], "B": [5, 4, np.NaN, 3, 0]})
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res = df.groupby(["B"]).count()
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expected = DataFrame(
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index=Index([0.0, 3.0, 4.0, 5.0], name="B"), data={"A": [1, 1, 1, 1]}
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)
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tm.assert_frame_equal(expected, res)
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def test_groupby_timedelta_cython_count():
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df = DataFrame(
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{"g": list("ab" * 2), "delt": np.arange(4).astype("timedelta64[ns]")}
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)
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expected = Series([2, 2], index=pd.Index(["a", "b"], name="g"), name="delt")
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result = df.groupby("g").delt.count()
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tm.assert_series_equal(expected, result)
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def test_count():
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n = 1 << 15
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dr = date_range("2015-08-30", periods=n // 10, freq="T")
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df = DataFrame(
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{
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"1st": np.random.choice(list(ascii_lowercase), n),
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"2nd": np.random.randint(0, 5, n),
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"3rd": np.random.randn(n).round(3),
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"4th": np.random.randint(-10, 10, n),
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"5th": np.random.choice(dr, n),
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"6th": np.random.randn(n).round(3),
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"7th": np.random.randn(n).round(3),
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"8th": np.random.choice(dr, n) - np.random.choice(dr, 1),
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"9th": np.random.choice(list(ascii_lowercase), n),
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}
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)
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for col in df.columns.drop(["1st", "2nd", "4th"]):
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df.loc[np.random.choice(n, n // 10), col] = np.nan
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df["9th"] = df["9th"].astype("category")
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for key in ["1st", "2nd", ["1st", "2nd"]]:
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left = df.groupby(key).count()
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right = df.groupby(key).apply(DataFrame.count).drop(key, axis=1)
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tm.assert_frame_equal(left, right)
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def test_count_non_nulls():
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# GH#5610
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# count counts non-nulls
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df = pd.DataFrame(
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[[1, 2, "foo"], [1, np.nan, "bar"], [3, np.nan, np.nan]],
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columns=["A", "B", "C"],
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)
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count_as = df.groupby("A").count()
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count_not_as = df.groupby("A", as_index=False).count()
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expected = DataFrame([[1, 2], [0, 0]], columns=["B", "C"], index=[1, 3])
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expected.index.name = "A"
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tm.assert_frame_equal(count_not_as, expected.reset_index())
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tm.assert_frame_equal(count_as, expected)
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count_B = df.groupby("A")["B"].count()
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tm.assert_series_equal(count_B, expected["B"])
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def test_count_object():
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df = pd.DataFrame({"a": ["a"] * 3 + ["b"] * 3, "c": [2] * 3 + [3] * 3})
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result = df.groupby("c").a.count()
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expected = pd.Series([3, 3], index=pd.Index([2, 3], name="c"), name="a")
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tm.assert_series_equal(result, expected)
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df = pd.DataFrame({"a": ["a", np.nan, np.nan] + ["b"] * 3, "c": [2] * 3 + [3] * 3})
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result = df.groupby("c").a.count()
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expected = pd.Series([1, 3], index=pd.Index([2, 3], name="c"), name="a")
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tm.assert_series_equal(result, expected)
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def test_count_cross_type():
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# GH8169
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vals = np.hstack(
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(np.random.randint(0, 5, (100, 2)), np.random.randint(0, 2, (100, 2)))
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)
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df = pd.DataFrame(vals, columns=["a", "b", "c", "d"])
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df[df == 2] = np.nan
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expected = df.groupby(["c", "d"]).count()
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for t in ["float32", "object"]:
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df["a"] = df["a"].astype(t)
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df["b"] = df["b"].astype(t)
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result = df.groupby(["c", "d"]).count()
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tm.assert_frame_equal(result, expected)
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def test_lower_int_prec_count():
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df = DataFrame(
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{
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"a": np.array([0, 1, 2, 100], np.int8),
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"b": np.array([1, 2, 3, 6], np.uint32),
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"c": np.array([4, 5, 6, 8], np.int16),
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"grp": list("ab" * 2),
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}
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)
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result = df.groupby("grp").count()
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expected = DataFrame(
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{"a": [2, 2], "b": [2, 2], "c": [2, 2]}, index=pd.Index(list("ab"), name="grp")
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)
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tm.assert_frame_equal(result, expected)
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def test_count_uses_size_on_exception():
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class RaisingObjectException(Exception):
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pass
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class RaisingObject:
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def __init__(self, msg="I will raise inside Cython"):
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super().__init__()
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self.msg = msg
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def __eq__(self, other):
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# gets called in Cython to check that raising calls the method
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raise RaisingObjectException(self.msg)
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df = DataFrame({"a": [RaisingObject() for _ in range(4)], "grp": list("ab" * 2)})
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result = df.groupby("grp").count()
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expected = DataFrame({"a": [2, 2]}, index=pd.Index(list("ab"), name="grp"))
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tm.assert_frame_equal(result, expected)
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