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import numpy as np
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- from pandas ._libs .tslib import iNaT
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-
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import pandas as pd
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from pandas import DataFrame , DatetimeIndex , Series , date_range , timedelta_range
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import pandas ._testing as tm
@@ -88,19 +86,6 @@ def test_series_ctor_datetime64(self):
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series = Series (dates )
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assert np .issubdtype (series .dtype , np .dtype ("M8[ns]" ))
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- def test_series_repr_nat (self ):
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- series = Series ([0 , 1000 , 2000 , iNaT ], dtype = "M8[ns]" )
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-
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- result = repr (series )
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- expected = (
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- "0 1970-01-01 00:00:00.000000\n "
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- "1 1970-01-01 00:00:00.000001\n "
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- "2 1970-01-01 00:00:00.000002\n "
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- "3 NaT\n "
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- "dtype: datetime64[ns]"
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- )
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- assert result == expected
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-
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def test_promote_datetime_date (self ):
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rng = date_range ("1/1/2000" , periods = 20 )
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ts = Series (np .random .randn (20 ), index = rng )
@@ -124,12 +109,6 @@ def test_promote_datetime_date(self):
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expected = rng .get_indexer (ts_slice .index )
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tm .assert_numpy_array_equal (result , expected )
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- def test_format_pre_1900_dates (self ):
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- rng = date_range ("1/1/1850" , "1/1/1950" , freq = "A-DEC" )
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- rng .format ()
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- ts = Series (1 , index = rng )
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- repr (ts )
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-
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def test_groupby_count_dateparseerror (self ):
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dr = date_range (start = "1/1/2012" , freq = "5min" , periods = 10 )
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