204 lines
6.7 KiB
Python
204 lines
6.7 KiB
Python
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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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Series,
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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.core.arrays import PeriodArray
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class TestSeriesIsIn:
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def test_isin(self):
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s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
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result = s.isin(["A", "C"])
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expected = Series([True, False, True, False, False, False, True, True])
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tm.assert_series_equal(result, expected)
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# GH#16012
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# This specific issue has to have a series over 1e6 in len, but the
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# comparison array (in_list) must be large enough so that numpy doesn't
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# do a manual masking trick that will avoid this issue altogether
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s = Series(list("abcdefghijk" * 10**5))
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# If numpy doesn't do the manual comparison/mask, these
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# unorderable mixed types are what cause the exception in numpy
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in_list = [-1, "a", "b", "G", "Y", "Z", "E", "K", "E", "S", "I", "R", "R"] * 6
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assert s.isin(in_list).sum() == 200000
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def test_isin_with_string_scalar(self):
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# GH#4763
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s = Series(["A", "B", "C", "a", "B", "B", "A", "C"])
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msg = (
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r"only list-like objects are allowed to be passed to isin\(\), "
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r"you passed a \[str\]"
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)
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with pytest.raises(TypeError, match=msg):
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s.isin("a")
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s = Series(["aaa", "b", "c"])
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with pytest.raises(TypeError, match=msg):
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s.isin("aaa")
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def test_isin_with_i8(self):
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# GH#5021
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expected = Series([True, True, False, False, False])
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expected2 = Series([False, True, False, False, False])
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# datetime64[ns]
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s = Series(date_range("jan-01-2013", "jan-05-2013"))
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result = s.isin(s[0:2])
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tm.assert_series_equal(result, expected)
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result = s.isin(s[0:2].values)
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tm.assert_series_equal(result, expected)
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# fails on dtype conversion in the first place
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result = s.isin(np.asarray(s[0:2].values).astype("datetime64[D]"))
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tm.assert_series_equal(result, expected)
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result = s.isin([s[1]])
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tm.assert_series_equal(result, expected2)
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result = s.isin([np.datetime64(s[1])])
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tm.assert_series_equal(result, expected2)
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result = s.isin(set(s[0:2]))
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tm.assert_series_equal(result, expected)
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# timedelta64[ns]
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s = Series(pd.to_timedelta(range(5), unit="d"))
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result = s.isin(s[0:2])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("empty", [[], Series(dtype=object), np.array([])])
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def test_isin_empty(self, empty):
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# see GH#16991
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s = Series(["a", "b"])
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expected = Series([False, False])
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result = s.isin(empty)
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tm.assert_series_equal(expected, result)
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def test_isin_read_only(self):
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# https://github.com/pandas-dev/pandas/issues/37174
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arr = np.array([1, 2, 3])
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arr.setflags(write=False)
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s = Series([1, 2, 3])
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result = s.isin(arr)
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expected = Series([True, True, True])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("dtype", [object, None])
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def test_isin_dt64_values_vs_ints(self, dtype):
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# GH#36621 dont cast integers to datetimes for isin
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dti = date_range("2013-01-01", "2013-01-05")
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ser = Series(dti)
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comps = np.asarray([1356998400000000000], dtype=dtype)
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res = dti.isin(comps)
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expected = np.array([False] * len(dti), dtype=bool)
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tm.assert_numpy_array_equal(res, expected)
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res = ser.isin(comps)
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tm.assert_series_equal(res, Series(expected))
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res = pd.core.algorithms.isin(ser, comps)
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tm.assert_numpy_array_equal(res, expected)
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def test_isin_tzawareness_mismatch(self):
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dti = date_range("2013-01-01", "2013-01-05")
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ser = Series(dti)
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other = dti.tz_localize("UTC")
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res = dti.isin(other)
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expected = np.array([False] * len(dti), dtype=bool)
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tm.assert_numpy_array_equal(res, expected)
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res = ser.isin(other)
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tm.assert_series_equal(res, Series(expected))
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res = pd.core.algorithms.isin(ser, other)
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tm.assert_numpy_array_equal(res, expected)
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def test_isin_period_freq_mismatch(self):
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dti = date_range("2013-01-01", "2013-01-05")
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pi = dti.to_period("M")
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ser = Series(pi)
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# We construct another PeriodIndex with the same i8 values
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# but different dtype
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dtype = dti.to_period("Y").dtype
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other = PeriodArray._simple_new(pi.asi8, dtype=dtype)
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res = pi.isin(other)
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expected = np.array([False] * len(pi), dtype=bool)
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tm.assert_numpy_array_equal(res, expected)
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res = ser.isin(other)
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tm.assert_series_equal(res, Series(expected))
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res = pd.core.algorithms.isin(ser, other)
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tm.assert_numpy_array_equal(res, expected)
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@pytest.mark.parametrize("values", [[-9.0, 0.0], [-9, 0]])
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def test_isin_float_in_int_series(self, values):
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# GH#19356 GH#21804
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ser = Series(values)
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result = ser.isin([-9, -0.5])
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expected = Series([True, False])
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("dtype", ["boolean", "Int64", "Float64"])
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@pytest.mark.parametrize(
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"data,values,expected",
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[
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([0, 1, 0], [1], [False, True, False]),
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([0, 1, 0], [1, pd.NA], [False, True, False]),
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([0, pd.NA, 0], [1, 0], [True, False, True]),
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([0, 1, pd.NA], [1, pd.NA], [False, True, True]),
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([0, 1, pd.NA], [1, np.nan], [False, True, False]),
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([0, pd.NA, pd.NA], [np.nan, pd.NaT, None], [False, False, False]),
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],
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)
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def test_isin_masked_types(self, dtype, data, values, expected):
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# GH#42405
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ser = Series(data, dtype=dtype)
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result = ser.isin(values)
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expected = Series(expected, dtype="boolean")
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tm.assert_series_equal(result, expected)
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@pytest.mark.slow
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def test_isin_large_series_mixed_dtypes_and_nan():
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# https://github.com/pandas-dev/pandas/issues/37094
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# combination of object dtype for the values and > 1_000_000 elements
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ser = Series([1, 2, np.nan] * 1_000_000)
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result = ser.isin({"foo", "bar"})
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expected = Series([False] * 3 * 1_000_000)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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"array,expected",
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[
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(
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[0, 1j, 1j, 1, 1 + 1j, 1 + 2j, 1 + 1j],
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Series([False, True, True, False, True, True, True], dtype=bool),
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)
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],
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)
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def test_isin_complex_numbers(array, expected):
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# GH 17927
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result = Series(array).isin([1j, 1 + 1j, 1 + 2j])
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tm.assert_series_equal(result, expected)
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