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https://github.com/PiBrewing/craftbeerpi4.git
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439 lines
13 KiB
Python
439 lines
13 KiB
Python
import numpy as np
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import pytest
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from pandas._libs.tslibs import iNaT
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from pandas._libs.tslibs.period import IncompatibleFrequency
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import pandas.util._test_decorators as td
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from pandas.core.dtypes.base import registry
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from pandas.core.dtypes.dtypes import PeriodDtype
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import pandas as pd
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import pandas._testing as tm
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from pandas.core.arrays import PeriodArray, period_array
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# ----------------------------------------------------------------------------
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# Dtype
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def test_registered():
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assert PeriodDtype in registry.dtypes
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result = registry.find("Period[D]")
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expected = PeriodDtype("D")
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assert result == expected
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# ----------------------------------------------------------------------------
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# period_array
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@pytest.mark.parametrize(
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"data, freq, expected",
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[
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([pd.Period("2017", "D")], None, [17167]),
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([pd.Period("2017", "D")], "D", [17167]),
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([2017], "D", [17167]),
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(["2017"], "D", [17167]),
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([pd.Period("2017", "D")], pd.tseries.offsets.Day(), [17167]),
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([pd.Period("2017", "D"), None], None, [17167, iNaT]),
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(pd.Series(pd.date_range("2017", periods=3)), None, [17167, 17168, 17169]),
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(pd.date_range("2017", periods=3), None, [17167, 17168, 17169]),
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(pd.period_range("2017", periods=4, freq="Q"), None, [188, 189, 190, 191]),
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],
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)
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def test_period_array_ok(data, freq, expected):
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result = period_array(data, freq=freq).asi8
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expected = np.asarray(expected, dtype=np.int64)
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tm.assert_numpy_array_equal(result, expected)
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def test_period_array_readonly_object():
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# https://github.com/pandas-dev/pandas/issues/25403
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pa = period_array([pd.Period("2019-01-01")])
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arr = np.asarray(pa, dtype="object")
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arr.setflags(write=False)
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result = period_array(arr)
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tm.assert_period_array_equal(result, pa)
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result = pd.Series(arr)
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tm.assert_series_equal(result, pd.Series(pa))
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result = pd.DataFrame({"A": arr})
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tm.assert_frame_equal(result, pd.DataFrame({"A": pa}))
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def test_from_datetime64_freq_changes():
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# https://github.com/pandas-dev/pandas/issues/23438
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arr = pd.date_range("2017", periods=3, freq="D")
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result = PeriodArray._from_datetime64(arr, freq="M")
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expected = period_array(["2017-01-01", "2017-01-01", "2017-01-01"], freq="M")
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tm.assert_period_array_equal(result, expected)
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@pytest.mark.parametrize(
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"data, freq, msg",
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[
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(
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[pd.Period("2017", "D"), pd.Period("2017", "A")],
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None,
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"Input has different freq",
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),
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([pd.Period("2017", "D")], "A", "Input has different freq"),
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],
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)
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def test_period_array_raises(data, freq, msg):
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with pytest.raises(IncompatibleFrequency, match=msg):
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period_array(data, freq)
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def test_period_array_non_period_series_raies():
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ser = pd.Series([1, 2, 3])
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with pytest.raises(TypeError, match="dtype"):
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PeriodArray(ser, freq="D")
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def test_period_array_freq_mismatch():
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arr = period_array(["2000", "2001"], freq="D")
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with pytest.raises(IncompatibleFrequency, match="freq"):
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PeriodArray(arr, freq="M")
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with pytest.raises(IncompatibleFrequency, match="freq"):
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PeriodArray(arr, freq=pd.tseries.offsets.MonthEnd())
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def test_asi8():
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result = period_array(["2000", "2001", None], freq="D").asi8
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expected = np.array([10957, 11323, iNaT])
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tm.assert_numpy_array_equal(result, expected)
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def test_take_raises():
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arr = period_array(["2000", "2001"], freq="D")
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with pytest.raises(IncompatibleFrequency, match="freq"):
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arr.take([0, -1], allow_fill=True, fill_value=pd.Period("2000", freq="W"))
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msg = "value should be a 'Period' or 'NaT'. Got 'str' instead"
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with pytest.raises(TypeError, match=msg):
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arr.take([0, -1], allow_fill=True, fill_value="foo")
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@pytest.mark.parametrize("dtype", [int, np.int32, np.int64, "uint32", "uint64"])
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def test_astype(dtype):
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# We choose to ignore the sign and size of integers for
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# Period/Datetime/Timedelta astype
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arr = period_array(["2000", "2001", None], freq="D")
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result = arr.astype(dtype)
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if np.dtype(dtype).kind == "u":
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expected_dtype = np.dtype("uint64")
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else:
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expected_dtype = np.dtype("int64")
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expected = arr.astype(expected_dtype)
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assert result.dtype == expected_dtype
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tm.assert_numpy_array_equal(result, expected)
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def test_astype_copies():
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arr = period_array(["2000", "2001", None], freq="D")
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result = arr.astype(np.int64, copy=False)
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# Add the `.base`, since we now use `.asi8` which returns a view.
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# We could maybe override it in PeriodArray to return ._data directly.
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assert result.base is arr._data
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result = arr.astype(np.int64, copy=True)
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assert result is not arr._data
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tm.assert_numpy_array_equal(result, arr._data.view("i8"))
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def test_astype_categorical():
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arr = period_array(["2000", "2001", "2001", None], freq="D")
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result = arr.astype("category")
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categories = pd.PeriodIndex(["2000", "2001"], freq="D")
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expected = pd.Categorical.from_codes([0, 1, 1, -1], categories=categories)
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tm.assert_categorical_equal(result, expected)
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def test_astype_period():
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arr = period_array(["2000", "2001", None], freq="D")
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result = arr.astype(PeriodDtype("M"))
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expected = period_array(["2000", "2001", None], freq="M")
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tm.assert_period_array_equal(result, expected)
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@pytest.mark.parametrize("other", ["datetime64[ns]", "timedelta64[ns]"])
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def test_astype_datetime(other):
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arr = period_array(["2000", "2001", None], freq="D")
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# slice off the [ns] so that the regex matches.
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with pytest.raises(TypeError, match=other[:-4]):
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arr.astype(other)
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def test_fillna_raises():
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arr = period_array(["2000", "2001", "2002"], freq="D")
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with pytest.raises(ValueError, match="Length"):
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arr.fillna(arr[:2])
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def test_fillna_copies():
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arr = period_array(["2000", "2001", "2002"], freq="D")
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result = arr.fillna(pd.Period("2000", "D"))
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assert result is not arr
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# ----------------------------------------------------------------------------
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# setitem
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@pytest.mark.parametrize(
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"key, value, expected",
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[
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([0], pd.Period("2000", "D"), [10957, 1, 2]),
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([0], None, [iNaT, 1, 2]),
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([0], np.nan, [iNaT, 1, 2]),
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([0, 1, 2], pd.Period("2000", "D"), [10957] * 3),
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(
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[0, 1, 2],
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[pd.Period("2000", "D"), pd.Period("2001", "D"), pd.Period("2002", "D")],
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[10957, 11323, 11688],
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),
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],
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)
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def test_setitem(key, value, expected):
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arr = PeriodArray(np.arange(3), freq="D")
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expected = PeriodArray(expected, freq="D")
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arr[key] = value
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tm.assert_period_array_equal(arr, expected)
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def test_setitem_raises_incompatible_freq():
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arr = PeriodArray(np.arange(3), freq="D")
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with pytest.raises(IncompatibleFrequency, match="freq"):
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arr[0] = pd.Period("2000", freq="A")
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other = period_array(["2000", "2001"], freq="A")
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with pytest.raises(IncompatibleFrequency, match="freq"):
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arr[[0, 1]] = other
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def test_setitem_raises_length():
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arr = PeriodArray(np.arange(3), freq="D")
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with pytest.raises(ValueError, match="length"):
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arr[[0, 1]] = [pd.Period("2000", freq="D")]
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def test_setitem_raises_type():
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arr = PeriodArray(np.arange(3), freq="D")
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with pytest.raises(TypeError, match="int"):
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arr[0] = 1
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# ----------------------------------------------------------------------------
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# Ops
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def test_sub_period():
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arr = period_array(["2000", "2001"], freq="D")
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other = pd.Period("2000", freq="M")
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with pytest.raises(IncompatibleFrequency, match="freq"):
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arr - other
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# ----------------------------------------------------------------------------
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# Methods
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@pytest.mark.parametrize(
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"other",
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[pd.Period("2000", freq="H"), period_array(["2000", "2001", "2000"], freq="H")],
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)
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def test_where_different_freq_raises(other):
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ser = pd.Series(period_array(["2000", "2001", "2002"], freq="D"))
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cond = np.array([True, False, True])
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with pytest.raises(IncompatibleFrequency, match="freq"):
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ser.where(cond, other)
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# ----------------------------------------------------------------------------
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# Printing
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def test_repr_small():
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arr = period_array(["2000", "2001"], freq="D")
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result = str(arr)
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expected = (
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"<PeriodArray>\n['2000-01-01', '2001-01-01']\nLength: 2, dtype: period[D]"
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)
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assert result == expected
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def test_repr_large():
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arr = period_array(["2000", "2001"] * 500, freq="D")
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result = str(arr)
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expected = (
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"<PeriodArray>\n"
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"['2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', "
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"'2000-01-01',\n"
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" '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', "
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"'2001-01-01',\n"
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" ...\n"
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" '2000-01-01', '2001-01-01', '2000-01-01', '2001-01-01', "
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"'2000-01-01',\n"
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" '2001-01-01', '2000-01-01', '2001-01-01', '2000-01-01', "
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"'2001-01-01']\n"
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"Length: 1000, dtype: period[D]"
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)
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assert result == expected
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# ----------------------------------------------------------------------------
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# Reductions
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class TestReductions:
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def test_min_max(self):
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arr = period_array(
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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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freq="D",
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)
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result = arr.min()
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expected = pd.Period("2000-01-02", freq="D")
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assert result == expected
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result = arr.max()
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expected = pd.Period("2000-01-05", freq="D")
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assert result == expected
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result = arr.min(skipna=False)
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assert result is pd.NaT
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result = arr.max(skipna=False)
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assert result is pd.NaT
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@pytest.mark.parametrize("skipna", [True, False])
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def test_min_max_empty(self, skipna):
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arr = period_array([], freq="D")
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result = arr.min(skipna=skipna)
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assert result is pd.NaT
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result = arr.max(skipna=skipna)
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assert result is pd.NaT
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# ----------------------------------------------------------------------------
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# Arrow interaction
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pyarrow_skip = pyarrow_skip = td.skip_if_no("pyarrow", min_version="0.15.1.dev")
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@pyarrow_skip
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def test_arrow_extension_type():
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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p1 = ArrowPeriodType("D")
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p2 = ArrowPeriodType("D")
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p3 = ArrowPeriodType("M")
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assert p1.freq == "D"
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assert p1 == p2
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assert not p1 == p3
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assert hash(p1) == hash(p2)
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assert not hash(p1) == hash(p3)
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@pyarrow_skip
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@pytest.mark.parametrize(
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"data, freq",
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[
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(pd.date_range("2017", periods=3), "D"),
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(pd.date_range("2017", periods=3, freq="A"), "A-DEC"),
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],
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)
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def test_arrow_array(data, freq):
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import pyarrow as pa
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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periods = period_array(data, freq=freq)
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result = pa.array(periods)
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assert isinstance(result.type, ArrowPeriodType)
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assert result.type.freq == freq
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expected = pa.array(periods.asi8, type="int64")
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assert result.storage.equals(expected)
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# convert to its storage type
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result = pa.array(periods, type=pa.int64())
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assert result.equals(expected)
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# unsupported conversions
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msg = "Not supported to convert PeriodArray to 'double' type"
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with pytest.raises(TypeError, match=msg):
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pa.array(periods, type="float64")
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with pytest.raises(TypeError, match="different 'freq'"):
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pa.array(periods, type=ArrowPeriodType("T"))
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@pyarrow_skip
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def test_arrow_array_missing():
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import pyarrow as pa
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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arr = PeriodArray([1, 2, 3], freq="D")
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arr[1] = pd.NaT
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result = pa.array(arr)
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assert isinstance(result.type, ArrowPeriodType)
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assert result.type.freq == "D"
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expected = pa.array([1, None, 3], type="int64")
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assert result.storage.equals(expected)
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@pyarrow_skip
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def test_arrow_table_roundtrip():
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import pyarrow as pa
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from pandas.core.arrays._arrow_utils import ArrowPeriodType
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arr = PeriodArray([1, 2, 3], freq="D")
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arr[1] = pd.NaT
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df = pd.DataFrame({"a": arr})
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table = pa.table(df)
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assert isinstance(table.field("a").type, ArrowPeriodType)
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, PeriodDtype)
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tm.assert_frame_equal(result, df)
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table2 = pa.concat_tables([table, table])
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result = table2.to_pandas()
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expected = pd.concat([df, df], ignore_index=True)
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tm.assert_frame_equal(result, expected)
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@pyarrow_skip
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def test_arrow_table_roundtrip_without_metadata():
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import pyarrow as pa
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arr = PeriodArray([1, 2, 3], freq="H")
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arr[1] = pd.NaT
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df = pd.DataFrame({"a": arr})
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table = pa.table(df)
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# remove the metadata
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table = table.replace_schema_metadata()
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assert table.schema.metadata is None
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result = table.to_pandas()
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assert isinstance(result["a"].dtype, PeriodDtype)
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tm.assert_frame_equal(result, df)
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