299 lines
10 KiB
Python
299 lines
10 KiB
Python
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from datetime import (
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time,
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timedelta,
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)
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import numpy as np
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import pytest
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from pandas.errors import OutOfBoundsTimedelta
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import pandas as pd
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from pandas import (
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Series,
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TimedeltaIndex,
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isna,
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to_timedelta,
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)
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import pandas._testing as tm
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from pandas.core.arrays import TimedeltaArray
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class TestTimedeltas:
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@pytest.mark.parametrize("readonly", [True, False])
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def test_to_timedelta_readonly(self, readonly):
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# GH#34857
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arr = np.array([], dtype=object)
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if readonly:
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arr.setflags(write=False)
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result = to_timedelta(arr)
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expected = to_timedelta([])
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tm.assert_index_equal(result, expected)
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def test_to_timedelta_null(self):
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result = to_timedelta(["", ""])
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assert isna(result).all()
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def test_to_timedelta_same_np_timedelta64(self):
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# pass thru
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result = to_timedelta(np.array([np.timedelta64(1, "s")]))
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expected = pd.Index(np.array([np.timedelta64(1, "s")]))
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tm.assert_index_equal(result, expected)
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def test_to_timedelta_series(self):
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# Series
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expected = Series([timedelta(days=1), timedelta(days=1, seconds=1)])
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result = to_timedelta(Series(["1d", "1days 00:00:01"]))
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tm.assert_series_equal(result, expected)
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def test_to_timedelta_units(self):
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# with units
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result = TimedeltaIndex(
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[np.timedelta64(0, "ns"), np.timedelta64(10, "s").astype("m8[ns]")]
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)
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expected = to_timedelta([0, 10], unit="s")
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize(
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"dtype, unit",
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[
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["int64", "s"],
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["int64", "m"],
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["int64", "h"],
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["timedelta64[s]", "s"],
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["timedelta64[D]", "D"],
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],
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)
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def test_to_timedelta_units_dtypes(self, dtype, unit):
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# arrays of various dtypes
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arr = np.array([1] * 5, dtype=dtype)
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result = to_timedelta(arr, unit=unit)
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exp_dtype = "m8[ns]" if dtype == "int64" else "m8[s]"
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expected = TimedeltaIndex([np.timedelta64(1, unit)] * 5, dtype=exp_dtype)
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tm.assert_index_equal(result, expected)
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def test_to_timedelta_oob_non_nano(self):
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arr = np.array([pd.NaT._value + 1], dtype="timedelta64[m]")
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msg = (
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"Cannot convert -9223372036854775807 minutes to "
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r"timedelta64\[s\] without overflow"
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)
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with pytest.raises(OutOfBoundsTimedelta, match=msg):
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to_timedelta(arr)
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with pytest.raises(OutOfBoundsTimedelta, match=msg):
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TimedeltaIndex(arr)
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with pytest.raises(OutOfBoundsTimedelta, match=msg):
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TimedeltaArray._from_sequence(arr)
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@pytest.mark.parametrize(
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"arg", [np.arange(10).reshape(2, 5), pd.DataFrame(np.arange(10).reshape(2, 5))]
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)
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@pytest.mark.parametrize("errors", ["ignore", "raise", "coerce"])
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def test_to_timedelta_dataframe(self, arg, errors):
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# GH 11776
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with pytest.raises(TypeError, match="1-d array"):
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to_timedelta(arg, errors=errors)
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def test_to_timedelta_invalid_errors(self):
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# bad value for errors parameter
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msg = "errors must be one of"
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with pytest.raises(ValueError, match=msg):
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to_timedelta(["foo"], errors="never")
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@pytest.mark.parametrize("arg", [[1, 2], 1])
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def test_to_timedelta_invalid_unit(self, arg):
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# these will error
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msg = "invalid unit abbreviation: foo"
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with pytest.raises(ValueError, match=msg):
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to_timedelta(arg, unit="foo")
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def test_to_timedelta_time(self):
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# time not supported ATM
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msg = (
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"Value must be Timedelta, string, integer, float, timedelta or convertible"
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)
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with pytest.raises(ValueError, match=msg):
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to_timedelta(time(second=1))
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assert to_timedelta(time(second=1), errors="coerce") is pd.NaT
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def test_to_timedelta_bad_value(self):
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msg = "Could not convert 'foo' to NumPy timedelta"
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with pytest.raises(ValueError, match=msg):
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to_timedelta(["foo", "bar"])
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def test_to_timedelta_bad_value_coerce(self):
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tm.assert_index_equal(
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TimedeltaIndex([pd.NaT, pd.NaT]),
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to_timedelta(["foo", "bar"], errors="coerce"),
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)
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tm.assert_index_equal(
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TimedeltaIndex(["1 day", pd.NaT, "1 min"]),
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to_timedelta(["1 day", "bar", "1 min"], errors="coerce"),
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)
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def test_to_timedelta_invalid_errors_ignore(self):
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# gh-13613: these should not error because errors='ignore'
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invalid_data = "apple"
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assert invalid_data == to_timedelta(invalid_data, errors="ignore")
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invalid_data = ["apple", "1 days"]
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tm.assert_numpy_array_equal(
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np.array(invalid_data, dtype=object),
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to_timedelta(invalid_data, errors="ignore"),
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)
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invalid_data = pd.Index(["apple", "1 days"])
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tm.assert_index_equal(invalid_data, to_timedelta(invalid_data, errors="ignore"))
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invalid_data = Series(["apple", "1 days"])
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tm.assert_series_equal(
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invalid_data, to_timedelta(invalid_data, errors="ignore")
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)
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@pytest.mark.parametrize(
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"val, errors",
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[
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("1M", True),
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("1 M", True),
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("1Y", True),
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("1 Y", True),
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("1y", True),
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("1 y", True),
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("1m", False),
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("1 m", False),
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("1 day", False),
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("2day", False),
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],
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)
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def test_unambiguous_timedelta_values(self, val, errors):
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# GH36666 Deprecate use of strings denoting units with 'M', 'Y', 'm' or 'y'
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# in pd.to_timedelta
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msg = "Units 'M', 'Y' and 'y' do not represent unambiguous timedelta"
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if errors:
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with pytest.raises(ValueError, match=msg):
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to_timedelta(val)
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else:
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# check it doesn't raise
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to_timedelta(val)
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def test_to_timedelta_via_apply(self):
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# GH 5458
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expected = Series([np.timedelta64(1, "s")])
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result = Series(["00:00:01"]).apply(to_timedelta)
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tm.assert_series_equal(result, expected)
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result = Series([to_timedelta("00:00:01")])
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tm.assert_series_equal(result, expected)
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def test_to_timedelta_inference_without_warning(self):
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# GH#41731 inference produces a warning in the Series constructor,
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# but _not_ in to_timedelta
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vals = ["00:00:01", pd.NaT]
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with tm.assert_produces_warning(None):
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result = to_timedelta(vals)
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expected = TimedeltaIndex([pd.Timedelta(seconds=1), pd.NaT])
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tm.assert_index_equal(result, expected)
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def test_to_timedelta_on_missing_values(self):
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# GH5438
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timedelta_NaT = np.timedelta64("NaT")
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actual = to_timedelta(Series(["00:00:01", np.nan]))
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expected = Series(
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[np.timedelta64(1000000000, "ns"), timedelta_NaT],
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dtype=f"{tm.ENDIAN}m8[ns]",
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)
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tm.assert_series_equal(actual, expected)
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ser = Series(["00:00:01", pd.NaT], dtype="m8[ns]")
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actual = to_timedelta(ser)
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tm.assert_series_equal(actual, expected)
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@pytest.mark.parametrize("val", [np.nan, pd.NaT])
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def test_to_timedelta_on_missing_values_scalar(self, val):
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actual = to_timedelta(val)
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assert actual._value == np.timedelta64("NaT").astype("int64")
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def test_to_timedelta_float(self):
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# https://github.com/pandas-dev/pandas/issues/25077
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arr = np.arange(0, 1, 1e-6)[-10:]
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result = to_timedelta(arr, unit="s")
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expected_asi8 = np.arange(999990000, 10**9, 1000, dtype="int64")
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tm.assert_numpy_array_equal(result.asi8, expected_asi8)
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def test_to_timedelta_coerce_strings_unit(self):
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arr = np.array([1, 2, "error"], dtype=object)
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result = to_timedelta(arr, unit="ns", errors="coerce")
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expected = to_timedelta([1, 2, pd.NaT], unit="ns")
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tm.assert_index_equal(result, expected)
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def test_to_timedelta_ignore_strings_unit(self):
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arr = np.array([1, 2, "error"], dtype=object)
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result = to_timedelta(arr, unit="ns", errors="ignore")
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tm.assert_numpy_array_equal(result, arr)
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@pytest.mark.parametrize(
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"expected_val, result_val", [[timedelta(days=2), 2], [None, None]]
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)
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def test_to_timedelta_nullable_int64_dtype(self, expected_val, result_val):
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# GH 35574
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expected = Series([timedelta(days=1), expected_val])
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result = to_timedelta(Series([1, result_val], dtype="Int64"), unit="days")
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize(
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("input", "expected"),
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[
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("8:53:08.71800000001", "8:53:08.718"),
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("8:53:08.718001", "8:53:08.718001"),
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("8:53:08.7180000001", "8:53:08.7180000001"),
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("-8:53:08.71800000001", "-8:53:08.718"),
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("8:53:08.7180000089", "8:53:08.718000008"),
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],
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)
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@pytest.mark.parametrize("func", [pd.Timedelta, to_timedelta])
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def test_to_timedelta_precision_over_nanos(self, input, expected, func):
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# GH: 36738
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expected = pd.Timedelta(expected)
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result = func(input)
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assert result == expected
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def test_to_timedelta_zerodim(self, fixed_now_ts):
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# ndarray.item() incorrectly returns int for dt64[ns] and td64[ns]
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dt64 = fixed_now_ts.to_datetime64()
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arg = np.array(dt64)
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msg = (
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"Value must be Timedelta, string, integer, float, timedelta "
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"or convertible, not datetime64"
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)
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with pytest.raises(ValueError, match=msg):
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to_timedelta(arg)
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arg2 = arg.view("m8[ns]")
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result = to_timedelta(arg2)
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assert isinstance(result, pd.Timedelta)
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assert result._value == dt64.view("i8")
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def test_to_timedelta_numeric_ea(self, any_numeric_ea_dtype):
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# GH#48796
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ser = Series([1, pd.NA], dtype=any_numeric_ea_dtype)
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result = to_timedelta(ser)
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expected = Series([pd.Timedelta(1, unit="ns"), pd.NaT])
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tm.assert_series_equal(result, expected)
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def test_from_numeric_arrow_dtype(any_numeric_ea_dtype):
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# GH 52425
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pytest.importorskip("pyarrow")
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ser = Series([1, 2], dtype=f"{any_numeric_ea_dtype.lower()}[pyarrow]")
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result = to_timedelta(ser)
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expected = Series([1, 2], dtype="timedelta64[ns]")
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tm.assert_series_equal(result, expected)
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