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