235 lines
7.1 KiB
Python
235 lines
7.1 KiB
Python
from datetime import timedelta
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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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Int64Index,
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Series,
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Timedelta,
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TimedeltaIndex,
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date_range,
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timedelta_range,
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)
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import pandas._testing as tm
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from ..datetimelike import DatetimeLike
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randn = np.random.randn
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class TestTimedeltaIndex(DatetimeLike):
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_holder = TimedeltaIndex
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@pytest.fixture
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def index(self):
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return tm.makeTimedeltaIndex(10)
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def create_index(self) -> TimedeltaIndex:
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index = pd.to_timedelta(range(5), unit="d")._with_freq("infer")
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assert index.freq == "D"
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ret = index + pd.offsets.Hour(1)
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assert ret.freq == "D"
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return ret
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def test_numeric_compat(self):
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# Dummy method to override super's version; this test is now done
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# in test_arithmetic.py
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pass
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def test_shift(self):
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pass # this is handled in test_arithmetic.py
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def test_pickle_compat_construction(self):
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pass
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def test_pickle_after_set_freq(self):
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tdi = timedelta_range("1 day", periods=4, freq="s")
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tdi = tdi._with_freq(None)
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res = tm.round_trip_pickle(tdi)
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tm.assert_index_equal(res, tdi)
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def test_isin(self):
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index = tm.makeTimedeltaIndex(4)
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result = index.isin(index)
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assert result.all()
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result = index.isin(list(index))
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assert result.all()
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tm.assert_almost_equal(
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index.isin([index[2], 5]), np.array([False, False, True, False])
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)
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def test_factorize(self):
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idx1 = TimedeltaIndex(["1 day", "1 day", "2 day", "2 day", "3 day", "3 day"])
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exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
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exp_idx = TimedeltaIndex(["1 day", "2 day", "3 day"])
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arr, idx = idx1.factorize()
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, exp_idx)
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assert idx.freq == exp_idx.freq
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arr, idx = idx1.factorize(sort=True)
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, exp_idx)
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assert idx.freq == exp_idx.freq
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def test_factorize_preserves_freq(self):
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# GH#38120 freq should be preserved
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idx3 = timedelta_range("1 day", periods=4, freq="s")
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exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
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arr, idx = idx3.factorize()
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, idx3)
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assert idx.freq == idx3.freq
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arr, idx = pd.factorize(idx3)
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tm.assert_numpy_array_equal(arr, exp_arr)
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tm.assert_index_equal(idx, idx3)
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assert idx.freq == idx3.freq
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def test_sort_values(self):
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idx = TimedeltaIndex(["4d", "1d", "2d"])
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ordered = idx.sort_values()
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assert ordered.is_monotonic
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ordered = idx.sort_values(ascending=False)
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assert ordered[::-1].is_monotonic
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ordered, dexer = idx.sort_values(return_indexer=True)
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assert ordered.is_monotonic
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tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0]), check_dtype=False)
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ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
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assert ordered[::-1].is_monotonic
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tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1]), check_dtype=False)
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def test_argmin_argmax(self):
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idx = TimedeltaIndex(["1 day 00:00:05", "1 day 00:00:01", "1 day 00:00:02"])
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assert idx.argmin() == 1
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assert idx.argmax() == 0
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def test_misc_coverage(self):
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rng = timedelta_range("1 day", periods=5)
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result = rng.groupby(rng.days)
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assert isinstance(list(result.values())[0][0], Timedelta)
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def test_map(self):
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# test_map_dictlike generally tests
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rng = timedelta_range("1 day", periods=10)
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f = lambda x: x.days
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result = rng.map(f)
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exp = Int64Index([f(x) for x in rng])
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tm.assert_index_equal(result, exp)
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def test_pass_TimedeltaIndex_to_index(self):
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rng = timedelta_range("1 days", "10 days")
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idx = Index(rng, dtype=object)
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expected = Index(rng.to_pytimedelta(), dtype=object)
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tm.assert_numpy_array_equal(idx.values, expected.values)
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def test_append_numpy_bug_1681(self):
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td = timedelta_range("1 days", "10 days", freq="2D")
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a = DataFrame()
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c = DataFrame({"A": "foo", "B": td}, index=td)
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str(c)
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result = a.append(c)
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assert (result["B"] == td).all()
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def test_fields(self):
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rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s")
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tm.assert_index_equal(rng.days, Index([1, 1], dtype="int64"))
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tm.assert_index_equal(
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rng.seconds,
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Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype="int64"),
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)
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tm.assert_index_equal(
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rng.microseconds, Index([100 * 1000 + 123, 100 * 1000 + 123], dtype="int64")
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)
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tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype="int64"))
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msg = "'TimedeltaIndex' object has no attribute '{}'"
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with pytest.raises(AttributeError, match=msg.format("hours")):
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rng.hours
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with pytest.raises(AttributeError, match=msg.format("minutes")):
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rng.minutes
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with pytest.raises(AttributeError, match=msg.format("milliseconds")):
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rng.milliseconds
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# with nat
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s = Series(rng)
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s[1] = np.nan
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tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=[0, 1]))
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tm.assert_series_equal(
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s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=[0, 1])
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)
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# preserve name (GH15589)
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rng.name = "name"
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assert rng.days.name == "name"
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def test_freq_conversion(self):
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# doc example
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# series
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td = Series(date_range("20130101", periods=4)) - Series(
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date_range("20121201", periods=4)
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)
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td[2] += timedelta(minutes=5, seconds=3)
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td[3] = np.nan
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result = td / np.timedelta64(1, "D")
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expected = Series([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan])
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tm.assert_series_equal(result, expected)
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result = td.astype("timedelta64[D]")
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expected = Series([31, 31, 31, np.nan])
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tm.assert_series_equal(result, expected)
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result = td / np.timedelta64(1, "s")
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expected = Series([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan])
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tm.assert_series_equal(result, expected)
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result = td.astype("timedelta64[s]")
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tm.assert_series_equal(result, expected)
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# tdi
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td = TimedeltaIndex(td)
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result = td / np.timedelta64(1, "D")
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expected = Index([31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan])
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tm.assert_index_equal(result, expected)
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result = td.astype("timedelta64[D]")
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expected = Index([31, 31, 31, np.nan])
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tm.assert_index_equal(result, expected)
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result = td / np.timedelta64(1, "s")
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expected = Index([31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan])
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tm.assert_index_equal(result, expected)
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result = td.astype("timedelta64[s]")
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tm.assert_index_equal(result, expected)
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