Updated script that can be controled by Nodejs web app
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mac OS
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import numpy as np
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import pytest
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from pandas import (
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Index,
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NaT,
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Period,
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PeriodIndex,
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Series,
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date_range,
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offsets,
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period_range,
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)
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import pandas._testing as tm
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class TestPeriodIndex:
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def test_view_asi8(self):
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idx = PeriodIndex([], freq="M")
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exp = np.array([], dtype=np.int64)
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tm.assert_numpy_array_equal(idx.view("i8"), exp)
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tm.assert_numpy_array_equal(idx.asi8, exp)
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idx = PeriodIndex(["2011-01", NaT], freq="M")
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exp = np.array([492, -9223372036854775808], dtype=np.int64)
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tm.assert_numpy_array_equal(idx.view("i8"), exp)
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tm.assert_numpy_array_equal(idx.asi8, exp)
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exp = np.array([14975, -9223372036854775808], dtype=np.int64)
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idx = PeriodIndex(["2011-01-01", NaT], freq="D")
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tm.assert_numpy_array_equal(idx.view("i8"), exp)
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tm.assert_numpy_array_equal(idx.asi8, exp)
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def test_values(self):
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idx = PeriodIndex([], freq="M")
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exp = np.array([], dtype=object)
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tm.assert_numpy_array_equal(idx.values, exp)
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tm.assert_numpy_array_equal(idx.to_numpy(), exp)
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exp = np.array([], dtype=np.int64)
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tm.assert_numpy_array_equal(idx.asi8, exp)
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idx = PeriodIndex(["2011-01", NaT], freq="M")
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exp = np.array([Period("2011-01", freq="M"), NaT], dtype=object)
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tm.assert_numpy_array_equal(idx.values, exp)
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tm.assert_numpy_array_equal(idx.to_numpy(), exp)
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exp = np.array([492, -9223372036854775808], dtype=np.int64)
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tm.assert_numpy_array_equal(idx.asi8, exp)
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idx = PeriodIndex(["2011-01-01", NaT], freq="D")
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exp = np.array([Period("2011-01-01", freq="D"), NaT], dtype=object)
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tm.assert_numpy_array_equal(idx.values, exp)
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tm.assert_numpy_array_equal(idx.to_numpy(), exp)
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exp = np.array([14975, -9223372036854775808], dtype=np.int64)
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tm.assert_numpy_array_equal(idx.asi8, exp)
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@pytest.mark.parametrize(
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"field",
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[
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"year",
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"month",
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"day",
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"hour",
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"minute",
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"second",
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"weekofyear",
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"week",
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"dayofweek",
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"day_of_week",
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"dayofyear",
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"day_of_year",
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"quarter",
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"qyear",
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"days_in_month",
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],
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)
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@pytest.mark.parametrize(
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"periodindex",
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[
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period_range(freq="Y", start="1/1/2001", end="12/1/2005"),
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period_range(freq="Q", start="1/1/2001", end="12/1/2002"),
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period_range(freq="M", start="1/1/2001", end="1/1/2002"),
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period_range(freq="D", start="12/1/2001", end="6/1/2001"),
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period_range(freq="h", start="12/31/2001", end="1/1/2002 23:00"),
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period_range(freq="Min", start="12/31/2001", end="1/1/2002 00:20"),
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period_range(
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freq="s", start="12/31/2001 00:00:00", end="12/31/2001 00:05:00"
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),
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period_range(end=Period("2006-12-31", "W"), periods=10),
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],
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)
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def test_fields(self, periodindex, field):
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periods = list(periodindex)
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ser = Series(periodindex)
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field_idx = getattr(periodindex, field)
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assert len(periodindex) == len(field_idx)
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for x, val in zip(periods, field_idx):
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assert getattr(x, field) == val
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if len(ser) == 0:
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return
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field_s = getattr(ser.dt, field)
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assert len(periodindex) == len(field_s)
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for x, val in zip(periods, field_s):
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assert getattr(x, field) == val
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def test_is_(self):
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create_index = lambda: period_range(freq="Y", start="1/1/2001", end="12/1/2009")
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index = create_index()
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assert index.is_(index)
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assert not index.is_(create_index())
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assert index.is_(index.view())
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assert index.is_(index.view().view().view().view().view())
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assert index.view().is_(index)
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ind2 = index.view()
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index.name = "Apple"
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assert ind2.is_(index)
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assert not index.is_(index[:])
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assert not index.is_(index.asfreq("M"))
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assert not index.is_(index.asfreq("Y"))
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assert not index.is_(index - 2)
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assert not index.is_(index - 0)
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def test_index_unique(self):
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idx = PeriodIndex([2000, 2007, 2007, 2009, 2009], freq="Y-JUN")
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expected = PeriodIndex([2000, 2007, 2009], freq="Y-JUN")
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tm.assert_index_equal(idx.unique(), expected)
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assert idx.nunique() == 3
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def test_pindex_fieldaccessor_nat(self):
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idx = PeriodIndex(
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["2011-01", "2011-02", "NaT", "2012-03", "2012-04"], freq="D", name="name"
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)
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exp = Index([2011, 2011, -1, 2012, 2012], dtype=np.int64, name="name")
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tm.assert_index_equal(idx.year, exp)
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exp = Index([1, 2, -1, 3, 4], dtype=np.int64, name="name")
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tm.assert_index_equal(idx.month, exp)
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def test_pindex_multiples(self):
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expected = PeriodIndex(
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["2011-01", "2011-03", "2011-05", "2011-07", "2011-09", "2011-11"],
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freq="2M",
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)
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pi = period_range(start="1/1/11", end="12/31/11", freq="2M")
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tm.assert_index_equal(pi, expected)
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assert pi.freq == offsets.MonthEnd(2)
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assert pi.freqstr == "2M"
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pi = period_range(start="1/1/11", periods=6, freq="2M")
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tm.assert_index_equal(pi, expected)
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assert pi.freq == offsets.MonthEnd(2)
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assert pi.freqstr == "2M"
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@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning")
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@pytest.mark.filterwarnings("ignore:Period with BDay freq:FutureWarning")
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def test_iteration(self):
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index = period_range(start="1/1/10", periods=4, freq="B")
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result = list(index)
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assert isinstance(result[0], Period)
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assert result[0].freq == index.freq
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def test_with_multi_index(self):
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# #1705
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index = date_range("1/1/2012", periods=4, freq="12h")
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index_as_arrays = [index.to_period(freq="D"), index.hour]
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s = Series([0, 1, 2, 3], index_as_arrays)
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assert isinstance(s.index.levels[0], PeriodIndex)
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assert isinstance(s.index.values[0][0], Period)
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def test_map(self):
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# test_map_dictlike generally tests
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index = PeriodIndex([2005, 2007, 2009], freq="Y")
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result = index.map(lambda x: x.ordinal)
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exp = Index([x.ordinal for x in index])
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tm.assert_index_equal(result, exp)
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def test_maybe_convert_timedelta():
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pi = PeriodIndex(["2000", "2001"], freq="D")
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offset = offsets.Day(2)
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assert pi._maybe_convert_timedelta(offset) == 2
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assert pi._maybe_convert_timedelta(2) == 2
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offset = offsets.BusinessDay()
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msg = r"Input has different freq=B from PeriodIndex\(freq=D\)"
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with pytest.raises(ValueError, match=msg):
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pi._maybe_convert_timedelta(offset)
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@pytest.mark.parametrize("array", [True, False])
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def test_dunder_array(array):
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obj = PeriodIndex(["2000-01-01", "2001-01-01"], freq="D")
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if array:
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obj = obj._data
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expected = np.array([obj[0], obj[1]], dtype=object)
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result = np.array(obj)
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tm.assert_numpy_array_equal(result, expected)
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result = np.asarray(obj)
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tm.assert_numpy_array_equal(result, expected)
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expected = obj.asi8
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for dtype in ["i8", "int64", np.int64]:
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result = np.array(obj, dtype=dtype)
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tm.assert_numpy_array_equal(result, expected)
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result = np.asarray(obj, dtype=dtype)
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tm.assert_numpy_array_equal(result, expected)
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for dtype in ["float64", "int32", "uint64"]:
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msg = "argument must be"
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with pytest.raises(TypeError, match=msg):
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np.array(obj, dtype=dtype)
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with pytest.raises(TypeError, match=msg):
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np.array(obj, dtype=getattr(np, dtype))
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