1014 lines
34 KiB
Python
1014 lines
34 KiB
Python
# coding=utf-8
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# pylint: disable-msg=E1101,W0612
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import pytest
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import numpy as np
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from datetime import datetime, timedelta, time
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import pandas as pd
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import pandas.util.testing as tm
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import pandas.util._test_decorators as td
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from pandas._libs.tslib import iNaT
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from pandas.compat import lrange, StringIO, product
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from pandas.errors import NullFrequencyError
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from pandas.core.indexes.timedeltas import TimedeltaIndex
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from pandas.core.indexes.datetimes import DatetimeIndex
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from pandas.tseries.offsets import BDay, BMonthEnd
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from pandas import (Index, Series, date_range, NaT, concat, DataFrame,
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Timestamp, to_datetime, offsets,
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timedelta_range)
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from pandas.util.testing import (assert_series_equal, assert_almost_equal,
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assert_frame_equal)
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from pandas.tests.series.common import TestData
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def _simple_ts(start, end, freq='D'):
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rng = date_range(start, end, freq=freq)
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return Series(np.random.randn(len(rng)), index=rng)
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def assert_range_equal(left, right):
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assert (left.equals(right))
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assert (left.freq == right.freq)
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assert (left.tz == right.tz)
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class TestTimeSeries(TestData):
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def test_shift(self):
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shifted = self.ts.shift(1)
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unshifted = shifted.shift(-1)
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tm.assert_index_equal(shifted.index, self.ts.index)
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tm.assert_index_equal(unshifted.index, self.ts.index)
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tm.assert_numpy_array_equal(unshifted.dropna().values,
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self.ts.values[:-1])
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offset = BDay()
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shifted = self.ts.shift(1, freq=offset)
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unshifted = shifted.shift(-1, freq=offset)
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assert_series_equal(unshifted, self.ts)
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unshifted = self.ts.shift(0, freq=offset)
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assert_series_equal(unshifted, self.ts)
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shifted = self.ts.shift(1, freq='B')
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unshifted = shifted.shift(-1, freq='B')
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assert_series_equal(unshifted, self.ts)
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# corner case
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unshifted = self.ts.shift(0)
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assert_series_equal(unshifted, self.ts)
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# Shifting with PeriodIndex
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ps = tm.makePeriodSeries()
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shifted = ps.shift(1)
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unshifted = shifted.shift(-1)
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tm.assert_index_equal(shifted.index, ps.index)
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tm.assert_index_equal(unshifted.index, ps.index)
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tm.assert_numpy_array_equal(unshifted.dropna().values, ps.values[:-1])
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shifted2 = ps.shift(1, 'B')
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shifted3 = ps.shift(1, BDay())
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assert_series_equal(shifted2, shifted3)
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assert_series_equal(ps, shifted2.shift(-1, 'B'))
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pytest.raises(ValueError, ps.shift, freq='D')
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# legacy support
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shifted4 = ps.shift(1, freq='B')
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assert_series_equal(shifted2, shifted4)
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shifted5 = ps.shift(1, freq=BDay())
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assert_series_equal(shifted5, shifted4)
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# 32-bit taking
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# GH 8129
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index = date_range('2000-01-01', periods=5)
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for dtype in ['int32', 'int64']:
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s1 = Series(np.arange(5, dtype=dtype), index=index)
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p = s1.iloc[1]
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result = s1.shift(periods=p)
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expected = Series([np.nan, 0, 1, 2, 3], index=index)
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assert_series_equal(result, expected)
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# xref 8260
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# with tz
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s = Series(date_range('2000-01-01 09:00:00', periods=5,
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tz='US/Eastern'), name='foo')
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result = s - s.shift()
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exp = Series(TimedeltaIndex(['NaT'] + ['1 days'] * 4), name='foo')
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assert_series_equal(result, exp)
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# incompat tz
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s2 = Series(date_range('2000-01-01 09:00:00', periods=5,
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tz='CET'), name='foo')
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pytest.raises(TypeError, lambda: s - s2)
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def test_shift2(self):
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ts = Series(np.random.randn(5),
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index=date_range('1/1/2000', periods=5, freq='H'))
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result = ts.shift(1, freq='5T')
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exp_index = ts.index.shift(1, freq='5T')
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tm.assert_index_equal(result.index, exp_index)
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# GH #1063, multiple of same base
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result = ts.shift(1, freq='4H')
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exp_index = ts.index + offsets.Hour(4)
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tm.assert_index_equal(result.index, exp_index)
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idx = DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-04'])
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pytest.raises(NullFrequencyError, idx.shift, 1)
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def test_shift_dst(self):
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# GH 13926
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dates = date_range('2016-11-06', freq='H', periods=10, tz='US/Eastern')
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s = Series(dates)
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res = s.shift(0)
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tm.assert_series_equal(res, s)
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assert res.dtype == 'datetime64[ns, US/Eastern]'
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res = s.shift(1)
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exp_vals = [NaT] + dates.astype(object).values.tolist()[:9]
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exp = Series(exp_vals)
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tm.assert_series_equal(res, exp)
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assert res.dtype == 'datetime64[ns, US/Eastern]'
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res = s.shift(-2)
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exp_vals = dates.astype(object).values.tolist()[2:] + [NaT, NaT]
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exp = Series(exp_vals)
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tm.assert_series_equal(res, exp)
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assert res.dtype == 'datetime64[ns, US/Eastern]'
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for ex in [10, -10, 20, -20]:
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res = s.shift(ex)
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exp = Series([NaT] * 10, dtype='datetime64[ns, US/Eastern]')
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tm.assert_series_equal(res, exp)
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assert res.dtype == 'datetime64[ns, US/Eastern]'
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def test_tshift(self):
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# PeriodIndex
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ps = tm.makePeriodSeries()
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shifted = ps.tshift(1)
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unshifted = shifted.tshift(-1)
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assert_series_equal(unshifted, ps)
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shifted2 = ps.tshift(freq='B')
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assert_series_equal(shifted, shifted2)
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shifted3 = ps.tshift(freq=BDay())
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assert_series_equal(shifted, shifted3)
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pytest.raises(ValueError, ps.tshift, freq='M')
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# DatetimeIndex
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shifted = self.ts.tshift(1)
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unshifted = shifted.tshift(-1)
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assert_series_equal(self.ts, unshifted)
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shifted2 = self.ts.tshift(freq=self.ts.index.freq)
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assert_series_equal(shifted, shifted2)
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inferred_ts = Series(self.ts.values, Index(np.asarray(self.ts.index)),
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name='ts')
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shifted = inferred_ts.tshift(1)
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unshifted = shifted.tshift(-1)
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assert_series_equal(shifted, self.ts.tshift(1))
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assert_series_equal(unshifted, inferred_ts)
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no_freq = self.ts[[0, 5, 7]]
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pytest.raises(ValueError, no_freq.tshift)
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def test_truncate(self):
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offset = BDay()
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ts = self.ts[::3]
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start, end = self.ts.index[3], self.ts.index[6]
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start_missing, end_missing = self.ts.index[2], self.ts.index[7]
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# neither specified
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truncated = ts.truncate()
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assert_series_equal(truncated, ts)
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# both specified
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expected = ts[1:3]
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truncated = ts.truncate(start, end)
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assert_series_equal(truncated, expected)
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truncated = ts.truncate(start_missing, end_missing)
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assert_series_equal(truncated, expected)
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# start specified
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expected = ts[1:]
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truncated = ts.truncate(before=start)
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assert_series_equal(truncated, expected)
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truncated = ts.truncate(before=start_missing)
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assert_series_equal(truncated, expected)
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# end specified
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expected = ts[:3]
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truncated = ts.truncate(after=end)
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assert_series_equal(truncated, expected)
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truncated = ts.truncate(after=end_missing)
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assert_series_equal(truncated, expected)
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# corner case, empty series returned
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truncated = ts.truncate(after=self.ts.index[0] - offset)
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assert (len(truncated) == 0)
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truncated = ts.truncate(before=self.ts.index[-1] + offset)
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assert (len(truncated) == 0)
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pytest.raises(ValueError, ts.truncate,
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before=self.ts.index[-1] + offset,
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after=self.ts.index[0] - offset)
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def test_truncate_nonsortedindex(self):
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# GH 17935
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s = pd.Series(['a', 'b', 'c', 'd', 'e'],
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index=[5, 3, 2, 9, 0])
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with tm.assert_raises_regex(ValueError,
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'truncate requires a sorted index'):
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s.truncate(before=3, after=9)
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rng = pd.date_range('2011-01-01', '2012-01-01', freq='W')
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ts = pd.Series(np.random.randn(len(rng)), index=rng)
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with tm.assert_raises_regex(ValueError,
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'truncate requires a sorted index'):
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ts.sort_values(ascending=False).truncate(before='2011-11',
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after='2011-12')
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def test_asfreq(self):
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ts = Series([0., 1., 2.], index=[datetime(2009, 10, 30), datetime(
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2009, 11, 30), datetime(2009, 12, 31)])
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daily_ts = ts.asfreq('B')
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monthly_ts = daily_ts.asfreq('BM')
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tm.assert_series_equal(monthly_ts, ts)
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daily_ts = ts.asfreq('B', method='pad')
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monthly_ts = daily_ts.asfreq('BM')
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tm.assert_series_equal(monthly_ts, ts)
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daily_ts = ts.asfreq(BDay())
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monthly_ts = daily_ts.asfreq(BMonthEnd())
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tm.assert_series_equal(monthly_ts, ts)
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result = ts[:0].asfreq('M')
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assert len(result) == 0
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assert result is not ts
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daily_ts = ts.asfreq('D', fill_value=-1)
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result = daily_ts.value_counts().sort_index()
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expected = Series([60, 1, 1, 1],
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index=[-1.0, 2.0, 1.0, 0.0]).sort_index()
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tm.assert_series_equal(result, expected)
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def test_asfreq_datetimeindex_empty_series(self):
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# GH 14320
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expected = Series(index=pd.DatetimeIndex(
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["2016-09-29 11:00"])).asfreq('H')
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result = Series(index=pd.DatetimeIndex(["2016-09-29 11:00"]),
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data=[3]).asfreq('H')
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tm.assert_index_equal(expected.index, result.index)
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def test_diff(self):
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# Just run the function
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self.ts.diff()
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# int dtype
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a = 10000000000000000
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b = a + 1
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s = Series([a, b])
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rs = s.diff()
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assert rs[1] == 1
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# neg n
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rs = self.ts.diff(-1)
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xp = self.ts - self.ts.shift(-1)
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assert_series_equal(rs, xp)
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# 0
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rs = self.ts.diff(0)
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xp = self.ts - self.ts
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assert_series_equal(rs, xp)
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# datetime diff (GH3100)
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s = Series(date_range('20130102', periods=5))
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rs = s - s.shift(1)
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xp = s.diff()
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assert_series_equal(rs, xp)
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# timedelta diff
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nrs = rs - rs.shift(1)
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nxp = xp.diff()
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assert_series_equal(nrs, nxp)
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# with tz
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s = Series(
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date_range('2000-01-01 09:00:00', periods=5,
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tz='US/Eastern'), name='foo')
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result = s.diff()
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assert_series_equal(result, Series(
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TimedeltaIndex(['NaT'] + ['1 days'] * 4), name='foo'))
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def test_pct_change(self):
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rs = self.ts.pct_change(fill_method=None)
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assert_series_equal(rs, self.ts / self.ts.shift(1) - 1)
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rs = self.ts.pct_change(2)
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filled = self.ts.fillna(method='pad')
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assert_series_equal(rs, filled / filled.shift(2) - 1)
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rs = self.ts.pct_change(fill_method='bfill', limit=1)
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filled = self.ts.fillna(method='bfill', limit=1)
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assert_series_equal(rs, filled / filled.shift(1) - 1)
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rs = self.ts.pct_change(freq='5D')
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filled = self.ts.fillna(method='pad')
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assert_series_equal(rs,
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(filled / filled.shift(freq='5D') - 1)
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.reindex_like(filled))
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def test_pct_change_shift_over_nas(self):
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s = Series([1., 1.5, np.nan, 2.5, 3.])
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chg = s.pct_change()
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expected = Series([np.nan, 0.5, 0., 2.5 / 1.5 - 1, .2])
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assert_series_equal(chg, expected)
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@pytest.mark.parametrize("freq, periods, fill_method, limit",
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[('5B', 5, None, None),
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('3B', 3, None, None),
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('3B', 3, 'bfill', None),
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('7B', 7, 'pad', 1),
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('7B', 7, 'bfill', 3),
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('14B', 14, None, None)])
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def test_pct_change_periods_freq(self, freq, periods, fill_method, limit):
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# GH 7292
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rs_freq = self.ts.pct_change(freq=freq,
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fill_method=fill_method,
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limit=limit)
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rs_periods = self.ts.pct_change(periods,
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fill_method=fill_method,
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limit=limit)
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assert_series_equal(rs_freq, rs_periods)
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empty_ts = Series(index=self.ts.index)
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rs_freq = empty_ts.pct_change(freq=freq,
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fill_method=fill_method,
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limit=limit)
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rs_periods = empty_ts.pct_change(periods,
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fill_method=fill_method,
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limit=limit)
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assert_series_equal(rs_freq, rs_periods)
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def test_autocorr(self):
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# Just run the function
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corr1 = self.ts.autocorr()
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# Now run it with the lag parameter
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corr2 = self.ts.autocorr(lag=1)
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# corr() with lag needs Series of at least length 2
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if len(self.ts) <= 2:
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assert np.isnan(corr1)
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assert np.isnan(corr2)
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else:
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assert corr1 == corr2
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# Choose a random lag between 1 and length of Series - 2
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# and compare the result with the Series corr() function
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n = 1 + np.random.randint(max(1, len(self.ts) - 2))
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corr1 = self.ts.corr(self.ts.shift(n))
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corr2 = self.ts.autocorr(lag=n)
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# corr() with lag needs Series of at least length 2
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if len(self.ts) <= 2:
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assert np.isnan(corr1)
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assert np.isnan(corr2)
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else:
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assert corr1 == corr2
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def test_first_last_valid(self):
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ts = self.ts.copy()
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ts[:5] = np.NaN
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index = ts.first_valid_index()
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assert index == ts.index[5]
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ts[-5:] = np.NaN
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index = ts.last_valid_index()
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assert index == ts.index[-6]
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ts[:] = np.nan
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assert ts.last_valid_index() is None
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assert ts.first_valid_index() is None
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ser = Series([], index=[])
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assert ser.last_valid_index() is None
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assert ser.first_valid_index() is None
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# GH12800
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empty = Series()
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assert empty.last_valid_index() is None
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assert empty.first_valid_index() is None
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# GH20499: its preserves freq with holes
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ts.index = date_range("20110101", periods=len(ts), freq="B")
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ts.iloc[1] = 1
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ts.iloc[-2] = 1
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assert ts.first_valid_index() == ts.index[1]
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assert ts.last_valid_index() == ts.index[-2]
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assert ts.first_valid_index().freq == ts.index.freq
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assert ts.last_valid_index().freq == ts.index.freq
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def test_mpl_compat_hack(self):
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result = self.ts[:, np.newaxis]
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expected = self.ts.values[:, np.newaxis]
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assert_almost_equal(result, expected)
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def test_timeseries_coercion(self):
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idx = tm.makeDateIndex(10000)
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ser = Series(np.random.randn(len(idx)), idx.astype(object))
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assert ser.index.is_all_dates
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assert isinstance(ser.index, DatetimeIndex)
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def test_empty_series_ops(self):
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# see issue #13844
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a = Series(dtype='M8[ns]')
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b = Series(dtype='m8[ns]')
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assert_series_equal(a, a + b)
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assert_series_equal(a, a - b)
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assert_series_equal(a, b + a)
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pytest.raises(TypeError, lambda x, y: x - y, b, a)
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def test_contiguous_boolean_preserve_freq(self):
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rng = date_range('1/1/2000', '3/1/2000', freq='B')
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mask = np.zeros(len(rng), dtype=bool)
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mask[10:20] = True
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masked = rng[mask]
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expected = rng[10:20]
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assert expected.freq is not None
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assert_range_equal(masked, expected)
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mask[22] = True
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masked = rng[mask]
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assert masked.freq is None
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def test_to_datetime_unit(self):
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epoch = 1370745748
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s = Series([epoch + t for t in range(20)])
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result = to_datetime(s, unit='s')
|
|
expected = Series([Timestamp('2013-06-09 02:42:28') + timedelta(
|
|
seconds=t) for t in range(20)])
|
|
assert_series_equal(result, expected)
|
|
|
|
s = Series([epoch + t for t in range(20)]).astype(float)
|
|
result = to_datetime(s, unit='s')
|
|
expected = Series([Timestamp('2013-06-09 02:42:28') + timedelta(
|
|
seconds=t) for t in range(20)])
|
|
assert_series_equal(result, expected)
|
|
|
|
s = Series([epoch + t for t in range(20)] + [iNaT])
|
|
result = to_datetime(s, unit='s')
|
|
expected = Series([Timestamp('2013-06-09 02:42:28') + timedelta(
|
|
seconds=t) for t in range(20)] + [NaT])
|
|
assert_series_equal(result, expected)
|
|
|
|
s = Series([epoch + t for t in range(20)] + [iNaT]).astype(float)
|
|
result = to_datetime(s, unit='s')
|
|
expected = Series([Timestamp('2013-06-09 02:42:28') + timedelta(
|
|
seconds=t) for t in range(20)] + [NaT])
|
|
assert_series_equal(result, expected)
|
|
|
|
# GH13834
|
|
s = Series([epoch + t for t in np.arange(0, 2, .25)] +
|
|
[iNaT]).astype(float)
|
|
result = to_datetime(s, unit='s')
|
|
expected = Series([Timestamp('2013-06-09 02:42:28') + timedelta(
|
|
seconds=t) for t in np.arange(0, 2, .25)] + [NaT])
|
|
assert_series_equal(result, expected)
|
|
|
|
s = concat([Series([epoch + t for t in range(20)]
|
|
).astype(float), Series([np.nan])],
|
|
ignore_index=True)
|
|
result = to_datetime(s, unit='s')
|
|
expected = Series([Timestamp('2013-06-09 02:42:28') + timedelta(
|
|
seconds=t) for t in range(20)] + [NaT])
|
|
assert_series_equal(result, expected)
|
|
|
|
result = to_datetime([1, 2, 'NaT', pd.NaT, np.nan], unit='D')
|
|
expected = DatetimeIndex([Timestamp('1970-01-02'),
|
|
Timestamp('1970-01-03')] + ['NaT'] * 3)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
with pytest.raises(ValueError):
|
|
to_datetime([1, 2, 'foo'], unit='D')
|
|
with pytest.raises(ValueError):
|
|
to_datetime([1, 2, 111111111], unit='D')
|
|
|
|
# coerce we can process
|
|
expected = DatetimeIndex([Timestamp('1970-01-02'),
|
|
Timestamp('1970-01-03')] + ['NaT'] * 1)
|
|
result = to_datetime([1, 2, 'foo'], unit='D', errors='coerce')
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
result = to_datetime([1, 2, 111111111], unit='D', errors='coerce')
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
def test_series_ctor_datetime64(self):
|
|
rng = date_range('1/1/2000 00:00:00', '1/1/2000 1:59:50', freq='10s')
|
|
dates = np.asarray(rng)
|
|
|
|
series = Series(dates)
|
|
assert np.issubdtype(series.dtype, np.dtype('M8[ns]'))
|
|
|
|
def test_series_repr_nat(self):
|
|
series = Series([0, 1000, 2000, iNaT], dtype='M8[ns]')
|
|
|
|
result = repr(series)
|
|
expected = ('0 1970-01-01 00:00:00.000000\n'
|
|
'1 1970-01-01 00:00:00.000001\n'
|
|
'2 1970-01-01 00:00:00.000002\n'
|
|
'3 NaT\n'
|
|
'dtype: datetime64[ns]')
|
|
assert result == expected
|
|
|
|
def test_asfreq_keep_index_name(self):
|
|
# GH #9854
|
|
index_name = 'bar'
|
|
index = pd.date_range('20130101', periods=20, name=index_name)
|
|
df = pd.DataFrame([x for x in range(20)], columns=['foo'], index=index)
|
|
|
|
assert index_name == df.index.name
|
|
assert index_name == df.asfreq('10D').index.name
|
|
|
|
def test_promote_datetime_date(self):
|
|
rng = date_range('1/1/2000', periods=20)
|
|
ts = Series(np.random.randn(20), index=rng)
|
|
|
|
ts_slice = ts[5:]
|
|
ts2 = ts_slice.copy()
|
|
ts2.index = [x.date() for x in ts2.index]
|
|
|
|
result = ts + ts2
|
|
result2 = ts2 + ts
|
|
expected = ts + ts[5:]
|
|
assert_series_equal(result, expected)
|
|
assert_series_equal(result2, expected)
|
|
|
|
# test asfreq
|
|
result = ts2.asfreq('4H', method='ffill')
|
|
expected = ts[5:].asfreq('4H', method='ffill')
|
|
assert_series_equal(result, expected)
|
|
|
|
result = rng.get_indexer(ts2.index)
|
|
expected = rng.get_indexer(ts_slice.index)
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
def test_asfreq_normalize(self):
|
|
rng = date_range('1/1/2000 09:30', periods=20)
|
|
norm = date_range('1/1/2000', periods=20)
|
|
vals = np.random.randn(20)
|
|
ts = Series(vals, index=rng)
|
|
|
|
result = ts.asfreq('D', normalize=True)
|
|
norm = date_range('1/1/2000', periods=20)
|
|
expected = Series(vals, index=norm)
|
|
|
|
assert_series_equal(result, expected)
|
|
|
|
vals = np.random.randn(20, 3)
|
|
ts = DataFrame(vals, index=rng)
|
|
|
|
result = ts.asfreq('D', normalize=True)
|
|
expected = DataFrame(vals, index=norm)
|
|
|
|
assert_frame_equal(result, expected)
|
|
|
|
def test_first_subset(self):
|
|
ts = _simple_ts('1/1/2000', '1/1/2010', freq='12h')
|
|
result = ts.first('10d')
|
|
assert len(result) == 20
|
|
|
|
ts = _simple_ts('1/1/2000', '1/1/2010')
|
|
result = ts.first('10d')
|
|
assert len(result) == 10
|
|
|
|
result = ts.first('3M')
|
|
expected = ts[:'3/31/2000']
|
|
assert_series_equal(result, expected)
|
|
|
|
result = ts.first('21D')
|
|
expected = ts[:21]
|
|
assert_series_equal(result, expected)
|
|
|
|
result = ts[:0].first('3M')
|
|
assert_series_equal(result, ts[:0])
|
|
|
|
def test_first_raises(self):
|
|
# GH20725
|
|
ser = pd.Series('a b c'.split())
|
|
with pytest.raises(TypeError): # index is not a DatetimeIndex
|
|
ser.first('1D')
|
|
|
|
def test_last_subset(self):
|
|
ts = _simple_ts('1/1/2000', '1/1/2010', freq='12h')
|
|
result = ts.last('10d')
|
|
assert len(result) == 20
|
|
|
|
ts = _simple_ts('1/1/2000', '1/1/2010')
|
|
result = ts.last('10d')
|
|
assert len(result) == 10
|
|
|
|
result = ts.last('21D')
|
|
expected = ts['12/12/2009':]
|
|
assert_series_equal(result, expected)
|
|
|
|
result = ts.last('21D')
|
|
expected = ts[-21:]
|
|
assert_series_equal(result, expected)
|
|
|
|
result = ts[:0].last('3M')
|
|
assert_series_equal(result, ts[:0])
|
|
|
|
def test_last_raises(self):
|
|
# GH20725
|
|
ser = pd.Series('a b c'.split())
|
|
with pytest.raises(TypeError): # index is not a DatetimeIndex
|
|
ser.last('1D')
|
|
|
|
def test_format_pre_1900_dates(self):
|
|
rng = date_range('1/1/1850', '1/1/1950', freq='A-DEC')
|
|
rng.format()
|
|
ts = Series(1, index=rng)
|
|
repr(ts)
|
|
|
|
def test_at_time(self):
|
|
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
rs = ts.at_time(rng[1])
|
|
assert (rs.index.hour == rng[1].hour).all()
|
|
assert (rs.index.minute == rng[1].minute).all()
|
|
assert (rs.index.second == rng[1].second).all()
|
|
|
|
result = ts.at_time('9:30')
|
|
expected = ts.at_time(time(9, 30))
|
|
assert_series_equal(result, expected)
|
|
|
|
df = DataFrame(np.random.randn(len(rng), 3), index=rng)
|
|
|
|
result = ts[time(9, 30)]
|
|
result_df = df.loc[time(9, 30)]
|
|
expected = ts[(rng.hour == 9) & (rng.minute == 30)]
|
|
exp_df = df[(rng.hour == 9) & (rng.minute == 30)]
|
|
|
|
# expected.index = date_range('1/1/2000', '1/4/2000')
|
|
|
|
assert_series_equal(result, expected)
|
|
tm.assert_frame_equal(result_df, exp_df)
|
|
|
|
chunk = df.loc['1/4/2000':]
|
|
result = chunk.loc[time(9, 30)]
|
|
expected = result_df[-1:]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# midnight, everything
|
|
rng = date_range('1/1/2000', '1/31/2000')
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
|
|
result = ts.at_time(time(0, 0))
|
|
assert_series_equal(result, ts)
|
|
|
|
# time doesn't exist
|
|
rng = date_range('1/1/2012', freq='23Min', periods=384)
|
|
ts = Series(np.random.randn(len(rng)), rng)
|
|
rs = ts.at_time('16:00')
|
|
assert len(rs) == 0
|
|
|
|
def test_at_time_raises(self):
|
|
# GH20725
|
|
ser = pd.Series('a b c'.split())
|
|
with pytest.raises(TypeError): # index is not a DatetimeIndex
|
|
ser.at_time('00:00')
|
|
|
|
def test_between(self):
|
|
series = Series(date_range('1/1/2000', periods=10))
|
|
left, right = series[[2, 7]]
|
|
|
|
result = series.between(left, right)
|
|
expected = (series >= left) & (series <= right)
|
|
assert_series_equal(result, expected)
|
|
|
|
def test_between_time(self):
|
|
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
stime = time(0, 0)
|
|
etime = time(1, 0)
|
|
|
|
close_open = product([True, False], [True, False])
|
|
for inc_start, inc_end in close_open:
|
|
filtered = ts.between_time(stime, etime, inc_start, inc_end)
|
|
exp_len = 13 * 4 + 1
|
|
if not inc_start:
|
|
exp_len -= 5
|
|
if not inc_end:
|
|
exp_len -= 4
|
|
|
|
assert len(filtered) == exp_len
|
|
for rs in filtered.index:
|
|
t = rs.time()
|
|
if inc_start:
|
|
assert t >= stime
|
|
else:
|
|
assert t > stime
|
|
|
|
if inc_end:
|
|
assert t <= etime
|
|
else:
|
|
assert t < etime
|
|
|
|
result = ts.between_time('00:00', '01:00')
|
|
expected = ts.between_time(stime, etime)
|
|
assert_series_equal(result, expected)
|
|
|
|
# across midnight
|
|
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
|
stime = time(22, 0)
|
|
etime = time(9, 0)
|
|
|
|
close_open = product([True, False], [True, False])
|
|
for inc_start, inc_end in close_open:
|
|
filtered = ts.between_time(stime, etime, inc_start, inc_end)
|
|
exp_len = (12 * 11 + 1) * 4 + 1
|
|
if not inc_start:
|
|
exp_len -= 4
|
|
if not inc_end:
|
|
exp_len -= 4
|
|
|
|
assert len(filtered) == exp_len
|
|
for rs in filtered.index:
|
|
t = rs.time()
|
|
if inc_start:
|
|
assert (t >= stime) or (t <= etime)
|
|
else:
|
|
assert (t > stime) or (t <= etime)
|
|
|
|
if inc_end:
|
|
assert (t <= etime) or (t >= stime)
|
|
else:
|
|
assert (t < etime) or (t >= stime)
|
|
|
|
def test_between_time_raises(self):
|
|
# GH20725
|
|
ser = pd.Series('a b c'.split())
|
|
with pytest.raises(TypeError): # index is not a DatetimeIndex
|
|
ser.between_time(start_time='00:00', end_time='12:00')
|
|
|
|
def test_between_time_types(self):
|
|
# GH11818
|
|
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
|
|
pytest.raises(ValueError, rng.indexer_between_time,
|
|
datetime(2010, 1, 2, 1), datetime(2010, 1, 2, 5))
|
|
|
|
frame = DataFrame({'A': 0}, index=rng)
|
|
pytest.raises(ValueError, frame.between_time,
|
|
datetime(2010, 1, 2, 1), datetime(2010, 1, 2, 5))
|
|
|
|
series = Series(0, index=rng)
|
|
pytest.raises(ValueError, series.between_time,
|
|
datetime(2010, 1, 2, 1), datetime(2010, 1, 2, 5))
|
|
|
|
@td.skip_if_has_locale
|
|
def test_between_time_formats(self):
|
|
# GH11818
|
|
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
|
|
ts = DataFrame(np.random.randn(len(rng), 2), index=rng)
|
|
|
|
strings = [("2:00", "2:30"), ("0200", "0230"), ("2:00am", "2:30am"),
|
|
("0200am", "0230am"), ("2:00:00", "2:30:00"),
|
|
("020000", "023000"), ("2:00:00am", "2:30:00am"),
|
|
("020000am", "023000am")]
|
|
expected_length = 28
|
|
|
|
for time_string in strings:
|
|
assert len(ts.between_time(*time_string)) == expected_length
|
|
|
|
def test_to_period(self):
|
|
from pandas.core.indexes.period import period_range
|
|
|
|
ts = _simple_ts('1/1/2000', '1/1/2001')
|
|
|
|
pts = ts.to_period()
|
|
exp = ts.copy()
|
|
exp.index = period_range('1/1/2000', '1/1/2001')
|
|
assert_series_equal(pts, exp)
|
|
|
|
pts = ts.to_period('M')
|
|
exp.index = exp.index.asfreq('M')
|
|
tm.assert_index_equal(pts.index, exp.index.asfreq('M'))
|
|
assert_series_equal(pts, exp)
|
|
|
|
# GH 7606 without freq
|
|
idx = DatetimeIndex(['2011-01-01', '2011-01-02', '2011-01-03',
|
|
'2011-01-04'])
|
|
exp_idx = pd.PeriodIndex(['2011-01-01', '2011-01-02', '2011-01-03',
|
|
'2011-01-04'], freq='D')
|
|
|
|
s = Series(np.random.randn(4), index=idx)
|
|
expected = s.copy()
|
|
expected.index = exp_idx
|
|
assert_series_equal(s.to_period(), expected)
|
|
|
|
df = DataFrame(np.random.randn(4, 4), index=idx, columns=idx)
|
|
expected = df.copy()
|
|
expected.index = exp_idx
|
|
assert_frame_equal(df.to_period(), expected)
|
|
|
|
expected = df.copy()
|
|
expected.columns = exp_idx
|
|
assert_frame_equal(df.to_period(axis=1), expected)
|
|
|
|
def test_groupby_count_dateparseerror(self):
|
|
dr = date_range(start='1/1/2012', freq='5min', periods=10)
|
|
|
|
# BAD Example, datetimes first
|
|
s = Series(np.arange(10), index=[dr, lrange(10)])
|
|
grouped = s.groupby(lambda x: x[1] % 2 == 0)
|
|
result = grouped.count()
|
|
|
|
s = Series(np.arange(10), index=[lrange(10), dr])
|
|
grouped = s.groupby(lambda x: x[0] % 2 == 0)
|
|
expected = grouped.count()
|
|
|
|
assert_series_equal(result, expected)
|
|
|
|
def test_to_csv_numpy_16_bug(self):
|
|
frame = DataFrame({'a': date_range('1/1/2000', periods=10)})
|
|
|
|
buf = StringIO()
|
|
frame.to_csv(buf)
|
|
|
|
result = buf.getvalue()
|
|
assert '2000-01-01' in result
|
|
|
|
def test_series_map_box_timedelta(self):
|
|
# GH 11349
|
|
s = Series(timedelta_range('1 day 1 s', periods=5, freq='h'))
|
|
|
|
def f(x):
|
|
return x.total_seconds()
|
|
|
|
s.map(f)
|
|
s.apply(f)
|
|
DataFrame(s).applymap(f)
|
|
|
|
def test_asfreq_resample_set_correct_freq(self):
|
|
# GH5613
|
|
# we test if .asfreq() and .resample() set the correct value for .freq
|
|
df = pd.DataFrame({'date': ["2012-01-01", "2012-01-02", "2012-01-03"],
|
|
'col': [1, 2, 3]})
|
|
df = df.set_index(pd.to_datetime(df.date))
|
|
|
|
# testing the settings before calling .asfreq() and .resample()
|
|
assert df.index.freq is None
|
|
assert df.index.inferred_freq == 'D'
|
|
|
|
# does .asfreq() set .freq correctly?
|
|
assert df.asfreq('D').index.freq == 'D'
|
|
|
|
# does .resample() set .freq correctly?
|
|
assert df.resample('D').asfreq().index.freq == 'D'
|
|
|
|
def test_pickle(self):
|
|
|
|
# GH4606
|
|
p = tm.round_trip_pickle(NaT)
|
|
assert p is NaT
|
|
|
|
idx = pd.to_datetime(['2013-01-01', NaT, '2014-01-06'])
|
|
idx_p = tm.round_trip_pickle(idx)
|
|
assert idx_p[0] == idx[0]
|
|
assert idx_p[1] is NaT
|
|
assert idx_p[2] == idx[2]
|
|
|
|
# GH11002
|
|
# don't infer freq
|
|
idx = date_range('1750-1-1', '2050-1-1', freq='7D')
|
|
idx_p = tm.round_trip_pickle(idx)
|
|
tm.assert_index_equal(idx, idx_p)
|
|
|
|
def test_setops_preserve_freq(self):
|
|
for tz in [None, 'Asia/Tokyo', 'US/Eastern']:
|
|
rng = date_range('1/1/2000', '1/1/2002', name='idx', tz=tz)
|
|
|
|
result = rng[:50].union(rng[50:100])
|
|
assert result.name == rng.name
|
|
assert result.freq == rng.freq
|
|
assert result.tz == rng.tz
|
|
|
|
result = rng[:50].union(rng[30:100])
|
|
assert result.name == rng.name
|
|
assert result.freq == rng.freq
|
|
assert result.tz == rng.tz
|
|
|
|
result = rng[:50].union(rng[60:100])
|
|
assert result.name == rng.name
|
|
assert result.freq is None
|
|
assert result.tz == rng.tz
|
|
|
|
result = rng[:50].intersection(rng[25:75])
|
|
assert result.name == rng.name
|
|
assert result.freqstr == 'D'
|
|
assert result.tz == rng.tz
|
|
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nofreq = DatetimeIndex(list(rng[25:75]), name='other')
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result = rng[:50].union(nofreq)
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assert result.name is None
|
|
assert result.freq == rng.freq
|
|
assert result.tz == rng.tz
|
|
|
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result = rng[:50].intersection(nofreq)
|
|
assert result.name is None
|
|
assert result.freq == rng.freq
|
|
assert result.tz == rng.tz
|
|
|
|
def test_min_max(self):
|
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rng = date_range('1/1/2000', '12/31/2000')
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rng2 = rng.take(np.random.permutation(len(rng)))
|
|
|
|
the_min = rng2.min()
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|
the_max = rng2.max()
|
|
assert isinstance(the_min, Timestamp)
|
|
assert isinstance(the_max, Timestamp)
|
|
assert the_min == rng[0]
|
|
assert the_max == rng[-1]
|
|
|
|
assert rng.min() == rng[0]
|
|
assert rng.max() == rng[-1]
|
|
|
|
def test_min_max_series(self):
|
|
rng = date_range('1/1/2000', periods=10, freq='4h')
|
|
lvls = ['A', 'A', 'A', 'B', 'B', 'B', 'C', 'C', 'C', 'C']
|
|
df = DataFrame({'TS': rng, 'V': np.random.randn(len(rng)), 'L': lvls})
|
|
|
|
result = df.TS.max()
|
|
exp = Timestamp(df.TS.iat[-1])
|
|
assert isinstance(result, Timestamp)
|
|
assert result == exp
|
|
|
|
result = df.TS.min()
|
|
exp = Timestamp(df.TS.iat[0])
|
|
assert isinstance(result, Timestamp)
|
|
assert result == exp
|
|
|
|
def test_from_M8_structured(self):
|
|
dates = [(datetime(2012, 9, 9, 0, 0), datetime(2012, 9, 8, 15, 10))]
|
|
arr = np.array(dates,
|
|
dtype=[('Date', 'M8[us]'), ('Forecasting', 'M8[us]')])
|
|
df = DataFrame(arr)
|
|
|
|
assert df['Date'][0] == dates[0][0]
|
|
assert df['Forecasting'][0] == dates[0][1]
|
|
|
|
s = Series(arr['Date'])
|
|
assert isinstance(s[0], Timestamp)
|
|
assert s[0] == dates[0][0]
|
|
|
|
with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
|
|
s = Series.from_array(arr['Date'], Index([0]))
|
|
assert s[0] == dates[0][0]
|
|
|
|
def test_get_level_values_box(self):
|
|
from pandas import MultiIndex
|
|
|
|
dates = date_range('1/1/2000', periods=4)
|
|
levels = [dates, [0, 1]]
|
|
labels = [[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]]
|
|
|
|
index = MultiIndex(levels=levels, labels=labels)
|
|
|
|
assert isinstance(index.get_level_values(0)[0], Timestamp)
|