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Merge branch 'timeseries'
* timeseries: (200 commits) TST: don't use deprecated DateRange BUG: fix buglets surfacing from merge RLS: set released to false, bump dev version to 0.8.0 BUG: fix major performance issue in DatetimeIndex.union affecting join performance on irregular indexes, remedying #1046 ENH: add to_datetime method to Index, close #208 ENH: legacy time rule support and refactoring, better alias handling. misc tests, #1041 ENH: to_datetime will convert array of strings and NAs to datetime64 with NaT, close #999 ENH: more datetime64 integration in core data algorithms per #996, close #1035 ENH: handle datetime64 in block formation from dict of arrays in DataFrame constructor, close #1037 BUG: fix broken time_rule usage in legacy DateRange, close #1036 BUG: name inline method something different ENH: initial version of convert_to_annual for pandas, #736 BUG: convert datetime64 -> datetime.datetime for matplotlib, close #1003 ENH: integrate cython ohlc in groupby and test, close #152 ENH: implement Cython OHLC function for groupby #152 ENH: use cython bin groupers, fix bug in DatetimeIndex.__getitem causing slowness, some timeseries vbenches ENH: enable to_datetime to be vectorized, handle NAs, close #858 TST: interactions between array of datetime objects and DatetimeIndex, bug fixes TST: remove errant foo and test_datetime64.py TST: moved test_datetime64.py tests to test_timeseries ...
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Diff for: .gitignore

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dist
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MANIFEST
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*.c
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!np_datetime.c
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!np_datetime_strings.c
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!skts.c
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*.cpp
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*.so
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*.pyd
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*flymake*
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scikits
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.coverage
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pandas.egg-info
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pandas.egg-info

Diff for: NP_LICENSE.txt

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Copyright (c) 2005-2011, NumPy Developers.
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are
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met:
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* Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above
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copyright notice, this list of conditions and the following
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disclaimer in the documentation and/or other materials provided
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with the distribution.
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* Neither the name of the NumPy Developers nor the names of any
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contributors may be used to endorse or promote products derived
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from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Diff for: bench/bench_dense_to_sparse.py

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N = 100000
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rng = DateRange('1/1/2000', periods=N, offset=datetools.Minute())
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rng2 = np.asarray(rng).astype('M8').astype('i8')
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rng2 = np.asarray(rng).astype('M8[us]').astype('i8')
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series = {}
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for i in range(1, K + 1):

Diff for: bench/bench_tseries.py

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Diff for: pandas/core/api.py

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import numpy as np
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from pandas.core.datetools import DateOffset
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from pandas.core.datetools import DateOffset, to_datetime
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import pandas.core.datetools as datetools
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from pandas.core.common import isnull, notnull, save, load
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from pandas.core.format import set_printoptions
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from pandas.core.index import Index, Int64Index, Factor, MultiIndex
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from pandas.core.index import (Index, Int64Index, Factor, MultiIndex,
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DatetimeIndex, IntervalIndex)
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# deprecated
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from pandas.core.daterange import DateRange
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from pandas.core.daterange import date_range, bdate_range
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from pandas.core.datetools import Timestamp, Interval
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from pandas.core.series import Series, TimeSeries
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from pandas.core.frame import DataFrame
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from pandas.core.panel import Panel
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from pandas.core.groupby import groupby
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from pandas.core.groupby import groupby, TimeGrouper
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from pandas.core.reshape import pivot_simple as pivot
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DataMatrix = DataFrame

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