176 lines
5.3 KiB
Python
176 lines
5.3 KiB
Python
import numpy as np
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import pytest
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from pandas.core.dtypes.dtypes import DatetimeTZDtype
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import pandas as pd
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from pandas import NaT
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import pandas._testing as tm
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from pandas.core.arrays import DatetimeArray
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class TestReductions:
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@pytest.fixture
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def arr1d(self, tz_naive_fixture):
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tz = tz_naive_fixture
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dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
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arr = DatetimeArray._from_sequence(
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[
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"2000-01-03",
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"2000-01-03",
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"NaT",
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"2000-01-02",
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"2000-01-05",
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"2000-01-04",
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],
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dtype=dtype,
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)
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return arr
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def test_min_max(self, arr1d):
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arr = arr1d
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tz = arr.tz
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result = arr.min()
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expected = pd.Timestamp("2000-01-02", tz=tz)
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assert result == expected
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result = arr.max()
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expected = pd.Timestamp("2000-01-05", tz=tz)
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assert result == expected
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result = arr.min(skipna=False)
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assert result is NaT
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result = arr.max(skipna=False)
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assert result is NaT
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@pytest.mark.parametrize("tz", [None, "US/Central"])
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@pytest.mark.parametrize("skipna", [True, False])
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def test_min_max_empty(self, skipna, tz):
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dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
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arr = DatetimeArray._from_sequence([], dtype=dtype)
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result = arr.min(skipna=skipna)
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assert result is NaT
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result = arr.max(skipna=skipna)
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assert result is NaT
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@pytest.mark.parametrize("tz", [None, "US/Central"])
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@pytest.mark.parametrize("skipna", [True, False])
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def test_median_empty(self, skipna, tz):
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dtype = DatetimeTZDtype(tz=tz) if tz is not None else np.dtype("M8[ns]")
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arr = DatetimeArray._from_sequence([], dtype=dtype)
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result = arr.median(skipna=skipna)
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assert result is NaT
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arr = arr.reshape(0, 3)
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result = arr.median(axis=0, skipna=skipna)
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expected = type(arr)._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
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tm.assert_equal(result, expected)
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result = arr.median(axis=1, skipna=skipna)
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expected = type(arr)._from_sequence([], dtype=arr.dtype)
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tm.assert_equal(result, expected)
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def test_median(self, arr1d):
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arr = arr1d
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result = arr.median()
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assert result == arr[0]
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result = arr.median(skipna=False)
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assert result is NaT
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result = arr.dropna().median(skipna=False)
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assert result == arr[0]
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result = arr.median(axis=0)
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assert result == arr[0]
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def test_median_axis(self, arr1d):
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arr = arr1d
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assert arr.median(axis=0) == arr.median()
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assert arr.median(axis=0, skipna=False) is NaT
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msg = r"abs\(axis\) must be less than ndim"
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with pytest.raises(ValueError, match=msg):
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arr.median(axis=1)
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@pytest.mark.filterwarnings("ignore:All-NaN slice encountered:RuntimeWarning")
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def test_median_2d(self, arr1d):
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arr = arr1d.reshape(1, -1)
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# axis = None
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assert arr.median() == arr1d.median()
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assert arr.median(skipna=False) is NaT
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# axis = 0
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result = arr.median(axis=0)
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expected = arr1d
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tm.assert_equal(result, expected)
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# Since column 3 is all-NaT, we get NaT there with or without skipna
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result = arr.median(axis=0, skipna=False)
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expected = arr1d
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tm.assert_equal(result, expected)
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# axis = 1
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result = arr.median(axis=1)
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expected = type(arr)._from_sequence([arr1d.median()])
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tm.assert_equal(result, expected)
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result = arr.median(axis=1, skipna=False)
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expected = type(arr)._from_sequence([NaT], dtype=arr.dtype)
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tm.assert_equal(result, expected)
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def test_mean(self, arr1d):
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arr = arr1d
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# manually verified result
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expected = arr[0] + 0.4 * pd.Timedelta(days=1)
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result = arr.mean()
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assert result == expected
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result = arr.mean(skipna=False)
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assert result is NaT
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result = arr.dropna().mean(skipna=False)
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assert result == expected
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result = arr.mean(axis=0)
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assert result == expected
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def test_mean_2d(self):
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dti = pd.date_range("2016-01-01", periods=6, tz="US/Pacific")
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dta = dti._data.reshape(3, 2)
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result = dta.mean(axis=0)
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expected = dta[1]
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tm.assert_datetime_array_equal(result, expected)
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result = dta.mean(axis=1)
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expected = dta[:, 0] + pd.Timedelta(hours=12)
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tm.assert_datetime_array_equal(result, expected)
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result = dta.mean(axis=None)
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expected = dti.mean()
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assert result == expected
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@pytest.mark.parametrize("skipna", [True, False])
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def test_mean_empty(self, arr1d, skipna):
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arr = arr1d[:0]
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assert arr.mean(skipna=skipna) is NaT
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arr2d = arr.reshape(0, 3)
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result = arr2d.mean(axis=0, skipna=skipna)
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expected = DatetimeArray._from_sequence([NaT, NaT, NaT], dtype=arr.dtype)
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tm.assert_datetime_array_equal(result, expected)
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result = arr2d.mean(axis=1, skipna=skipna)
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expected = arr # i.e. 1D, empty
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tm.assert_datetime_array_equal(result, expected)
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result = arr2d.mean(axis=None, skipna=skipna)
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assert result is NaT
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