pandas.tslib -> pandas.libs.tslib

Author: jreback

Makefile

@@ -1,4 +1,4 @@
-tseries: pandas/lib.pyx pandas/tslib.pyx pandas/hashtable.pyx
+tseries: pandas/lib.pyx pandas/libs/tslib.pyx pandas/hashtable.pyx
 	python setup.py build_ext --inplace
 
 .PHONY : develop build clean clean_pyc tseries doc

doc/source/whatsnew/v0.20.0.txt

@@ -5,7 +5,7 @@ v0.20.0 (????, 2017)
 
 This is a major release from 0.19 and includes a small number of API changes, several new features,
 enhancements, and performance improvements along with a large number of bug fixes. We recommend that all
-users upgrade to this version.
+users upgrade to that version.
 
 Highlights include:
 
@@ -255,7 +255,7 @@ Possible incompat for HDF5 formats for pandas < 0.13.0
 been dropped in favor of ``pd.Series``. (:issue:``15098).
 
 This *may* cause HDF5 files that were created in prior versions to become unreadable if ``pd.TimeSeries``
-was used. This is most likely to be for pandas < 0.13.0. If you find yourself in this situation.
+was used. This is most likely to be for pandas < 0.13.0. If you find yourself in that situation.
 You can use a recent prior version of pandas to read in your HDF5 files,
 then write them out again after applying the procedure below.
 
@@ -465,6 +465,7 @@ If indicated, a deprecation warning will be issued if you reference that module.
     "pandas._testing", "pandas.util.libtesting", ""
     "pandas._sparse", "pandas.sparse.libsparse", ""
     "pandas._hash", "pandas.tools.libhash", ""
+    "pandas.tslib", "pandas.libs.tslib", ""
 
 
 .. _whatsnew_0200.api_breaking.groupby_describe:
@@ -525,7 +526,7 @@ New Behavior:
 Other API Changes
 ^^^^^^^^^^^^^^^^^
 
-- ``numexpr`` version is now required to be >= 2.4.6 and it will not be used at all if this requisite is not fulfilled (:issue:`15213`).
+- ``numexpr`` version is now required to be >= 2.4.6 and it will not be used at all if that requisite is not fulfilled (:issue:`15213`).
 - ``CParserError`` has been renamed to ``ParserError`` in ``pd.read_csv`` and will be removed in the future (:issue:`12665`)
 - ``SparseArray.cumsum()`` and ``SparseSeries.cumsum()`` will now always return ``SparseArray`` and ``SparseSeries`` respectively (:issue:`12855`)
 - ``DataFrame.applymap()`` with an empty ``DataFrame`` will return a copy of the empty ``DataFrame`` instead of a ``Series`` (:issue:`8222`)
@@ -591,7 +592,7 @@ Performance Improvements
 Bug Fixes
 ~~~~~~~~~
 
-- Bug in ``Timestamp.replace`` now raises ``TypeError`` when incorrect argument names are given; previously this raised ``ValueError`` (:issue:`15240`)
+- Bug in ``Timestamp.replace`` now raises ``TypeError`` when incorrect argument names are given; previously that raised ``ValueError`` (:issue:`15240`)
 - Bug in ``Index`` power operations with reversed operands (:issue:`14973`)
 - Bug in ``TimedeltaIndex`` addition where overflow was being allowed without error (:issue:`14816`)
 - Bug in ``TimedeltaIndex`` raising a ``ValueError`` when boolean indexing with ``loc`` (:issue:`14946`)
@@ -666,7 +667,7 @@ Bug Fixes
 - Bug in ``DataFrame.resample().median()`` if duplicate column names are present (:issue:`14233`)
 
 - Bug in ``DataFrame.groupby().describe()`` when grouping on ``Index`` containing tuples (:issue:`14848`)
-- Bug in creating a ``MultiIndex`` with tuples and not passing a list of names; this will now raise ``ValueError`` (:issue:`15110`)
+- Bug in creating a ``MultiIndex`` with tuples and not passing a list of names; that will now raise ``ValueError`` (:issue:`15110`)
 - Bug in ``groupby().nunique()`` with a datetimelike-grouper where bins counts were incorrect (:issue:`13453`)
 
 - Bug in catching an overflow in ``Timestamp`` + ``Timedelta/Offset`` operations (:issue:`15126`)

pandas/__init__.py

@@ -23,7 +23,8 @@
 from pandas.compat.numpy import *
 
 try:
-    from pandas import hashtable, tslib, lib
+    from pandas import hashtable, lib
+    from pandas.libs import tslib as _tslib
 except ImportError as e:  # pragma: no cover
     # hack but overkill to use re
     module = str(e).lstrip('cannot import name ')

pandas/core/algorithms.py

@@ -37,7 +37,7 @@
 import pandas.algos as algos
 import pandas.hashtable as htable
 from pandas.compat import string_types
-from pandas.tslib import iNaT
+from pandas.libs.tslib import iNaT
 
 
 # --------------- #

pandas/core/common.py

@@ -9,7 +9,7 @@
 
 import numpy as np
 import pandas.lib as lib
-import pandas.tslib as tslib
+import pandas.libs.tslib as tslib
 from pandas import compat
 from pandas.compat import long, zip, iteritems
 from pandas.core.config import get_option
@@ -476,7 +476,7 @@ def _where_compat(mask, arr1, arr2):
         new_vals = np.where(mask, arr1.view('i8'), arr2.view('i8'))
         return new_vals.view(_NS_DTYPE)
 
-    import pandas.tslib as tslib
+    import pandas.libs.tslib as tslib
     if arr1.dtype == _NS_DTYPE:
         arr1 = tslib.ints_to_pydatetime(arr1.view('i8'))
     if arr2.dtype == _NS_DTYPE:

pandas/core/generic.py

@@ -7,6 +7,7 @@
 
 import numpy as np
 import pandas.lib as lib
+from pandas.libs import tslib
 
 import pandas as pd
 
@@ -6037,7 +6038,7 @@ def cum_func(self, axis=None, skipna=True, *args, **kwargs):
                 issubclass(y.dtype.type, (np.datetime64, np.timedelta64))):
             result = accum_func(y, axis)
             mask = isnull(self)
-            np.putmask(result, mask, pd.tslib.iNaT)
+            np.putmask(result, mask, tslib.iNaT)
         elif skipna and not issubclass(y.dtype.type, (np.integer, np.bool_)):
             mask = isnull(self)
             np.putmask(y, mask, mask_a)

pandas/core/groupby.py

@@ -60,7 +60,7 @@
 from pandas.core.config import option_context
 import pandas.lib as lib
 from pandas.lib import Timestamp
-import pandas.tslib as tslib
+import pandas.libs.tslib as tslib
 import pandas.algos as _algos
 
 _doc_template = """

pandas/core/internals.py

@@ -54,12 +54,12 @@
 import pandas.core.missing as missing
 from pandas.sparse.array import _maybe_to_sparse, SparseArray
 import pandas.lib as lib
-import pandas.tslib as tslib
+import pandas.libs.tslib as tslib
 import pandas.computation.expressions as expressions
 from pandas.util.decorators import cache_readonly
 from pandas.util.validators import validate_bool_kwarg
 
-from pandas.tslib import Timedelta
+from pandas.libs.tslib import Timedelta
 from pandas import compat, _np_version_under1p9
 from pandas.compat import range, map, zip, u
 

pandas/core/ops.py

@@ -15,7 +15,7 @@
 from pandas.util.decorators import Appender
 import pandas.computation.expressions as expressions
 from pandas.lib import isscalar
-from pandas.tslib import iNaT
+from pandas.libs.tslib import iNaT
 from pandas.compat import bind_method
 import pandas.core.missing as missing
 import pandas.algos as _algos

pandas/core/series.py

@@ -69,7 +69,7 @@
 from pandas.util.validators import validate_bool_kwarg
 
 import pandas.lib as lib
-import pandas.tslib as tslib
+import pandas.libs.tslib as tslib
 import pandas.index as _index
 
 from pandas.core.config import get_option

pandas/formats/format.py

@@ -34,7 +34,7 @@
 from pandas.formats.printing import adjoin, justify, pprint_thing
 import pandas.core.common as com
 import pandas.lib as lib
-from pandas.tslib import iNaT, Timestamp, Timedelta, format_array_from_datetime
+from pandas.libs.tslib import iNaT, Timestamp, Timedelta, format_array_from_datetime
 from pandas.tseries.index import DatetimeIndex
 from pandas.tseries.period import PeriodIndex
 import pandas as pd

pandas/index.pyx

@@ -15,10 +15,11 @@ cimport util
 
 import numpy as np
 
-cimport tslib
+cimport libs.tslib
 from hashtable cimport *
-from pandas import algos, tslib, hashtable as _hash
-from pandas.tslib import Timestamp, Timedelta
+from pandas import algos, hashtable as _hash
+from pandas.libs import tslib
+from pandas.libs.tslib import Timestamp, Timedelta
 
 from datetime cimport (get_datetime64_value, _pydatetime_to_dts,
                        pandas_datetimestruct)

pandas/indexes/base.py

@@ -3,7 +3,7 @@
 import operator
 
 import numpy as np
-import pandas.tslib as tslib
+import pandas.libs.tslib as tslib
 import pandas.lib as lib
 import pandas._join as _join
 import pandas.algos as _algos

pandas/io/json/json.py

@@ -4,7 +4,7 @@
 import numpy as np
 
 from pandas.io.json import libjson
-from pandas.tslib import iNaT
+from pandas.libs.tslib import iNaT
 from pandas.compat import StringIO, long, u
 from pandas import compat, isnull
 from pandas import Series, DataFrame, to_datetime

pandas/io/packers.py

@@ -55,7 +55,7 @@
                     Index, MultiIndex, Float64Index, Int64Index,
                     Panel, RangeIndex, PeriodIndex, DatetimeIndex, NaT,
                     Categorical, CategoricalIndex)
-from pandas.tslib import NaTType
+from pandas.libs.tslib import NaTType
 from pandas.sparse.api import SparseSeries, SparseDataFrame
 from pandas.sparse.array import BlockIndex, IntIndex
 from pandas.core.generic import NDFrame

pandas/io/pytables.py

@@ -45,8 +45,8 @@
 from pandas.computation.pytables import Expr, maybe_expression
 
 import pandas.lib as lib
+import pandas.libs.tslib as tslib
 import pandas.algos as algos
-import pandas.tslib as tslib
 
 from distutils.version import LooseVersion
 

pandas/io/stata.py

@@ -31,7 +31,7 @@
 
 from pandas.io.common import get_filepath_or_buffer, BaseIterator
 from pandas.lib import max_len_string_array, infer_dtype
-from pandas.tslib import NaT, Timestamp
+from pandas.libs.tslib import NaT, Timestamp
 
 _version_error = ("Version of given Stata file is not 104, 105, 108, "
                   "111 (Stata 7SE), 113 (Stata 8/9), 114 (Stata 10/11), "

pandas/lib.pyx

@@ -55,10 +55,10 @@ from datetime import datetime as pydatetime
 # this is our tseries.pxd
 from datetime cimport *
 
-from tslib cimport (convert_to_tsobject, convert_to_timedelta64,
+from libs.tslib cimport (convert_to_tsobject, convert_to_timedelta64,
                     _check_all_nulls)
-import tslib
-from tslib import NaT, Timestamp, Timedelta
+import libs.tslib
+from libs.tslib import NaT, Timestamp, Timedelta
 
 cdef int64_t NPY_NAT = util.get_nat()
 

pandas/libs/__init__.pxd

@@ -26,10 +26,10 @@ cimport util
 cimport lib
 from lib cimport is_null_datetimelike, is_period
 import lib
-from pandas import tslib
-from tslib import Timedelta, Timestamp, iNaT, NaT
-from tslib import have_pytz, _get_utcoffset
-from tslib cimport (
+from pandas.libs import tslib
+from pandas.libs.tslib import (Timedelta, Timestamp, iNaT,
+                               NaT, have_pytz, _get_utcoffset)
+from libs.tslib cimport (
     maybe_get_tz,
     _is_utc,
     _is_tzlocal,

pandas/tslib.pxd renamed to pandas/libs/tslib.pxd

@@ -34,12 +34,11 @@ class TestPDApi(Base, tm.TestCase):
     lib = ['api', 'compat', 'computation', 'core',
            'indexes', 'formats', 'pandas',
            'test', 'tools', 'tseries', 'sparse',
-           'types', 'util', 'options', 'io']
+           'types', 'util', 'options', 'io', 'libs']
 
     # top-level packages that are c-imports, should rename to _*
     # to avoid naming conflicts
-    lib_to_rename = ['algos', 'hashtable', 'tslib',
-                     'lib', 'index']
+    lib_to_rename = ['algos', 'hashtable', 'lib', 'index']
 
     # these are already deprecated; awaiting removal
     deprecated_modules = ['stats', 'datetools', 'parser', 'json']

pandas/tslib.pyx renamed to pandas/libs/tslib.pyx

@@ -10,6 +10,7 @@
                     TimedeltaIndex, PeriodIndex, notnull, isnull)
 from pandas.types.common import needs_i8_conversion
 from pandas.util.testing import assertRaisesRegexp
+from pandas.libs.tslib import iNaT
 
 import pandas.util.testing as tm
 
@@ -322,7 +323,7 @@ def test_get_unique_index(self):
 
             if needs_i8_conversion(ind):
                 vals = ind.asi8[[0] * 5]
-                vals[0] = pd.tslib.iNaT
+                vals[0] = iNaT
             else:
                 vals = ind.values[[0] * 5]
                 vals[0] = np.nan
@@ -836,7 +837,7 @@ def test_hasnans_isnans(self):
                 if len(index) == 0:
                     continue
                 elif isinstance(index, pd.tseries.base.DatetimeIndexOpsMixin):
-                    values[1] = pd.tslib.iNaT
+                    values[1] = iNaT
                 elif isinstance(index, (Int64Index, UInt64Index)):
                     continue
                 else:
@@ -876,7 +877,7 @@ def test_fillna(self):
                 values = idx.values
 
                 if isinstance(index, pd.tseries.base.DatetimeIndexOpsMixin):
-                    values[1] = pd.tslib.iNaT
+                    values[1] = iNaT
                 elif isinstance(index, (Int64Index, UInt64Index)):
                     continue
                 else:

pandas/src/period.pyx

@@ -4,7 +4,7 @@
 import pandas as pd
 from pandas import tslib, offsets, lib
 import pandas.util.testing as tm
-from pandas.tslib import OutOfBoundsDatetime
+from pandas.libs.tslib import OutOfBoundsDatetime
 from pandas import (DatetimeIndex, Index, Timestamp, datetime, date_range,
                     to_datetime)
 

pandas/tests/api/test_api.py

@@ -350,7 +350,7 @@ def test_range_tz_dateutil(self):
         # GH 2906
         tm._skip_if_no_dateutil()
         # Use maybe_get_tz to fix filename in tz under dateutil.
-        from pandas.tslib import maybe_get_tz
+        from pandas.libs.tslib import maybe_get_tz
         tz = lambda x: maybe_get_tz('dateutil/' + x)
 
         start = datetime(2011, 1, 1, tzinfo=tz('US/Eastern'))

pandas/tests/indexes/common.py

@@ -5,7 +5,7 @@
 
 from itertools import product
 import pandas as pd
-import pandas.tslib as tslib
+import pandas.libs.tslib as tslib
 import pandas.util.testing as tm
 from pandas.core.common import PerformanceWarning
 from pandas.tseries.index import cdate_range

pandas/tests/indexes/datetimes/test_construction.py

@@ -326,7 +326,7 @@ def test_month_range_union_tz_pytz(self):
     def test_month_range_union_tz_dateutil(self):
         tm._skip_if_windows_python_3()
         tm._skip_if_no_dateutil()
-        from pandas.tslib import _dateutil_gettz as timezone
+        from pandas.libs.tslib import _dateutil_gettz as timezone
         tz = timezone('US/Eastern')
 
         early_start = datetime(2011, 1, 1)

pandas/tests/indexes/datetimes/test_date_range.py

@@ -2,7 +2,7 @@
 from datetime import timedelta
 
 import pandas as pd
-import pandas.tslib as tslib
+import pandas.libs.tslib as tslib
 import pandas.util.testing as tm
 import pandas.tseries.period as period
 from pandas import (DatetimeIndex, PeriodIndex, period_range, Series, Period,

pandas/tests/indexes/datetimes/test_ops.py

@@ -9,7 +9,7 @@
                                  is_float_dtype,
                                  is_scalar)
 from pandas.compat import range, lrange, lzip, StringIO, lmap
-from pandas.tslib import NaT
+from pandas.libs.tslib import NaT
 from numpy import nan
 from numpy.random import randn
 import numpy as np

pandas/tests/indexes/datetimes/test_setops.py

@@ -1 +1 @@
-{u'status': {u'state': u'DONE'}, u'kind': u'bigquery#job', u'statistics': {u'query': {u'cacheHit': True, u'totalBytesProcessed': u'0'}, u'endTime': u'1377668744674', u'totalBytesProcessed': u'0', u'startTime': u'1377668744466'}, u'jobReference': {u'projectId': u'57288129629', u'jobId': u'bqjob_r5f956972f0190bdf_00000140c374bf42_2'}, u'etag': u'"4PTsVxg68bQkQs1RJ1Ndewqkgg4/oO4VmgFrAku4N6FWci9s7iFIftc"', u'configuration': {u'query': {u'createDisposition': u'CREATE_IF_NEEDED', u'query': u'SELECT * FROM [publicdata:samples.shakespeare]', u'writeDisposition': u'WRITE_TRUNCATE', u'destinationTable': {u'projectId': u'57288129629', u'tableId': u'anonb5ec450da88eeeb78a27784ea482ee75a146d442', u'datasetId': u'_d0b4f5f0d50dc68a3eb0fa6cba66a9a8687d9253'}}}, u'id': u'57288129629:bqjob_r5f956972f0190bdf_00000140c374bf42_2', u'selfLink': u'https://www.googleapis.com/bigquery/v2/projects/57288129629/jobs/bqjob_r5f956972f0190bdf_00000140c374bf42_2'}
+{u'status': {u'state': u'DONE'}, u'kind': u'bigquery#job', u'statistics': {u'query': {u'cacheHit': True, u'totalBytesProcessed': u'0'}, u'endTime': u'1377668744674', u'totalBytesProcessed': u'0', u'startTime': u'1377668744466'}, u'jobReference': {u'projectId': u'57288129629', u'jobId': u'bqjob_r5f956972f0190bdf_00000140c374bf42_2'}, u'etag': u'"4PTsVxg68bQkQs1RJ1Ndewqkgg4/oO4VmgFrAku4N6FWci9s7iFIftc"', u'configuration': {u'query': {u'createDisposition': u'CREATE_IF_NEEDED', u'query': u'SELECT * FROM [publicdata:samples.shakespeare]', u'writeDisposition': u'WRITE_TRUNCATE', u'destinationTable': {u'projectId': u'57288129629', u'tableId': u'anonb5ec450da88eeeb78a27784ea482ee75a146d442', u'datasetId': u'_d0b4f5f0d50dc68a3eb0fa6cba66a9a8687d9253'}}}, u'id': u'57288129629:bqjob_r5f956972f0190bdf_00000140c374bf42_2', u'selfLink': u'https://www.googleapis.com/bigquery/v2/projects/57288129629/jobs/bqjob_r5f956972f0190bdf_00000140c374bf42_2'}

pandas/tests/indexes/period/test_ops.py

@@ -5238,7 +5238,7 @@ def test_append_with_timezones_dateutil(self):
 
         # use maybe_get_tz instead of dateutil.tz.gettz to handle the windows
         # filename issues.
-        from pandas.tslib import maybe_get_tz
+        from pandas.libs.tslib import maybe_get_tz
         gettz = lambda x: maybe_get_tz('dateutil/' + x)
 
         # as columns

pandas/tests/indexing/test_indexing.py

@@ -19,7 +19,7 @@
 from pandas.io.parsers import read_csv
 from pandas.io.stata import (read_stata, StataReader, InvalidColumnName,
                              PossiblePrecisionLoss, StataMissingValue)
-from pandas.tslib import NaT
+from pandas.libs.tslib import NaT
 from pandas.types.common import is_categorical_dtype
 
 

pandas/tests/io/data/gbq_fake_job.txt

@@ -256,8 +256,8 @@ def test_timestamp_tz_arg(self):
             self.assertEqual(p.tz, exp.tz)
 
     def test_timestamp_tz_arg_dateutil(self):
-        from pandas.tslib import _dateutil_gettz as gettz
-        from pandas.tslib import maybe_get_tz
+        from pandas.libs.tslib import _dateutil_gettz as gettz
+        from pandas.libs.tslib import maybe_get_tz
         for case in ['dateutil/Europe/Brussels', 'dateutil/Asia/Tokyo',
                      'dateutil/US/Pacific']:
             p = Period('1/1/2005', freq='M').to_timestamp(
@@ -275,7 +275,7 @@ def test_timestamp_tz_arg_dateutil(self):
             self.assertEqual(p.tz, exp.tz)
 
     def test_timestamp_tz_arg_dateutil_from_string(self):
-        from pandas.tslib import _dateutil_gettz as gettz
+        from pandas.libs.tslib import _dateutil_gettz as gettz
         p = Period('1/1/2005',
                    freq='M').to_timestamp(tz='dateutil/Europe/Brussels')
         self.assertEqual(p.tz, gettz('Europe/Brussels'))