DEPR: rename Timestamp.offset to .freq

closes pandas-dev#12160

Author: sinhrks <[email protected]>

Closes pandas-dev#13593 from sinhrks/depr_timestamp_offset and squashes the following commits:

c7749d5 [sinhrks] DEPR: rename Timestamp.offset to .freq

Author: sinhrks

doc/source/whatsnew/v0.19.0.txt

@@ -194,15 +194,15 @@ Other enhancements
      pd.to_numeric(s, downcast='unsigned')
      pd.to_numeric(s, downcast='integer')
 
-- ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, see :ref:`documentation here <text.extractall>` (:issue:`10008`, :issue:`13156`)
+- ``Index`` now supports ``.str.extractall()`` which returns a ``DataFrame``, the see :ref:`docs here <text.extractall>` (:issue:`10008`, :issue:`13156`)
 - ``.to_hdf/read_hdf()`` now accept path objects (e.g. ``pathlib.Path``, ``py.path.local``) for the file path (:issue:`11773`)
 
   .. ipython:: python
 
      idx = pd.Index(["a1a2", "b1", "c1"])
      idx.str.extractall("[ab](?P<digit>\d)")
 
-- ``Timestamp`` s can now accept positional and keyword parameters like :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`)
+- ``Timestamp`` can now accept positional and keyword parameters similar to :func:`datetime.datetime` (:issue:`10758`, :issue:`11630`)
 
   .. ipython:: python
 
@@ -227,8 +227,7 @@ Other enhancements
 - Consistent with the Python API, ``pd.read_csv()`` will now interpret ``+inf`` as positive infinity (:issue:`13274`)
 - The ``DataFrame`` constructor will now respect key ordering if a list of ``OrderedDict`` objects are passed in (:issue:`13304`)
 - ``pd.read_html()`` has gained support for the ``decimal`` option (:issue:`12907`)
-- A ``union_categorical`` function has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`)
-- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules.  New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`)
+- A top-level function :func:`union_categorical` has been added for combining categoricals, see :ref:`Unioning Categoricals<categorical.union>` (:issue:`13361`)
 - ``Series`` has gained the properties ``.is_monotonic``, ``.is_monotonic_increasing``, ``.is_monotonic_decreasing``, similar to ``Index`` (:issue:`13336`)
 
 .. _whatsnew_0190.api:
@@ -238,9 +237,16 @@ API changes
 
 
 - Non-convertible dates in an excel date column will be returned without conversion and the column will be ``object`` dtype, rather than raising an exception  (:issue:`10001`)
+- ``eval``'s upcasting rules for ``float32`` types have been updated to be more consistent with NumPy's rules.  New behavior will not upcast to ``float64`` if you multiply a pandas ``float32`` object by a scalar float64. (:issue:`12388`)
 - An ``UnsupportedFunctionCall`` error is now raised if NumPy ufuncs like ``np.mean`` are called on groupby or resample objects (:issue:`12811`)
 - Calls to ``.sample()`` will respect the random seed set via ``numpy.random.seed(n)`` (:issue:`13161`)
 - ``Styler.apply`` is now more strict about the outputs your function must return. For ``axis=0`` or ``axis=1``, the output shape must be identical. For ``axis=None``, the output must be a DataFrame with identical columns and index labels. (:issue:`13222`)
+- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`)
+- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`)
+- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`)
+- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`)
+- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`)
+
 
 .. _whatsnew_0190.api.tolist:
 
@@ -361,7 +367,7 @@ We are able to preserve the join keys
    pd.merge(df1, df2, how='outer').dtypes
 
 Of course if you have missing values that are introduced, then the
-resulting dtype will be upcast (unchanged from previous).
+resulting dtype will be upcast, which is unchanged from previous.
 
 .. ipython:: python
 
@@ -419,17 +425,6 @@ Furthermore:
 - Passing duplicated ``percentiles`` will now raise a ``ValueError``.
 - Bug in ``.describe()`` on a DataFrame with a mixed-dtype column index, which would previously raise a ``TypeError`` (:issue:`13288`)
 
-.. _whatsnew_0190.api.other:
-
-Other API changes
-^^^^^^^^^^^^^^^^^
-
-- ``Float64Index.astype(int)`` will now raise ``ValueError`` if ``Float64Index`` contains ``NaN`` values (:issue:`13149`)
-- ``TimedeltaIndex.astype(int)`` and ``DatetimeIndex.astype(int)`` will now return ``Int64Index`` instead of ``np.array`` (:issue:`13209`)
-- ``.filter()`` enforces mutual exclusion of the keyword arguments. (:issue:`12399`)
-- ``PeridIndex`` can now accept ``list`` and ``array`` which contains ``pd.NaT`` (:issue:`13430`)
-- ``__setitem__`` will no longer apply a callable rhs as a function instead of storing it. Call ``where`` directly to get the previous behavior. (:issue:`13299`)
-
 .. _whatsnew_0190.deprecations:
 
 Deprecations
@@ -439,6 +434,7 @@ Deprecations
 - ``buffer_lines`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13360`)
 - ``as_recarray`` has been deprecated in ``pd.read_csv()`` and will be removed in a future version (:issue:`13373`)
 - top-level ``pd.ordered_merge()`` has been renamed to ``pd.merge_ordered()`` and the original name will be removed in a future version (:issue:`13358`)
+- ``Timestamp.offset`` property (and named arg in the constructor), has been deprecated in favor of ``freq`` (:issue:`12160`)
 
 .. _whatsnew_0190.performance:
 
@@ -503,7 +499,7 @@ Bug Fixes
 - Bug in ``pd.read_csv()`` in which the ``nrows`` argument was not properly validated for both engines (:issue:`10476`)
 - Bug in ``pd.read_csv()`` with ``engine='python'`` in which infinities of mixed-case forms were not being interpreted properly (:issue:`13274`)
 - Bug in ``pd.read_csv()`` with ``engine='python'`` in which trailing ``NaN`` values were not being parsed (:issue:`13320`)
-- Bug in ``pd.read_csv()`` with ``engine='python'`` when reading from a tempfile.TemporaryFile on Windows with Python 3 (:issue:`13398`)
+- Bug in ``pd.read_csv()`` with ``engine='python'`` when reading from a ``tempfile.TemporaryFile`` on Windows with Python 3 (:issue:`13398`)
 - Bug in ``pd.read_csv()`` that prevents ``usecols`` kwarg from accepting single-byte unicode strings (:issue:`13219`)
 - Bug in ``pd.read_csv()`` that prevents ``usecols`` from being an empty set (:issue:`13402`)
 - Bug in ``pd.read_csv()`` with ``engine=='c'`` in which null ``quotechar`` was not accepted even though ``quoting`` was specified as ``None`` (:issue:`13411`)
@@ -516,7 +512,7 @@ Bug Fixes
 
 
 - Bug in ``pd.to_datetime()`` when passing invalid datatypes (e.g. bool); will now respect the ``errors`` keyword (:issue:`13176`)
-- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, `int16`` dtypes (:issue:`13451`)
+- Bug in ``pd.to_datetime()`` which overflowed on ``int8``, and ``int16`` dtypes (:issue:`13451`)
 - Bug in extension dtype creation where the created types were not is/identical (:issue:`13285`)
 
 - Bug in ``NaT`` - ``Period`` raises ``AttributeError`` (:issue:`13071`)

pandas/io/packers.py

@@ -481,12 +481,12 @@ def encode(obj):
             tz = obj.tzinfo
             if tz is not None:
                 tz = u(tz.zone)
-            offset = obj.offset
-            if offset is not None:
-                offset = u(offset.freqstr)
+            freq = obj.freq
+            if freq is not None:
+                freq = u(freq.freqstr)
             return {u'typ': u'timestamp',
                     u'value': obj.value,
-                    u'offset': offset,
+                    u'freq': freq,
                     u'tz': tz}
         if isinstance(obj, NaTType):
             return {u'typ': u'nat'}
@@ -556,7 +556,8 @@ def decode(obj):
     if typ is None:
         return obj
     elif typ == u'timestamp':
-        return Timestamp(obj[u'value'], tz=obj[u'tz'], offset=obj[u'offset'])
+        freq = obj[u'freq'] if 'freq' in obj else obj[u'offset']
+        return Timestamp(obj[u'value'], tz=obj[u'tz'], freq=freq)
     elif typ == u'nat':
         return NaT
     elif typ == u'period':

pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_2.7.12.msgpack

@@ -5,7 +5,7 @@
                     SparseSeries, SparseDataFrame,
                     Index, MultiIndex, bdate_range, to_msgpack,
                     date_range, period_range,
-                    Timestamp, Categorical, Period)
+                    Timestamp, NaT, Categorical, Period)
 from pandas.compat import u
 import os
 import sys
@@ -140,6 +140,13 @@ def create_data():
                int16=Categorical(np.arange(1000)),
                int32=Categorical(np.arange(10000)))
 
+    timestamp = dict(normal=Timestamp('2011-01-01'),
+                     nat=NaT,
+                     tz=Timestamp('2011-01-01', tz='US/Eastern'),
+                     freq=Timestamp('2011-01-01', freq='D'),
+                     both=Timestamp('2011-01-01', tz='Asia/Tokyo',
+                                    freq='M'))
+
     return dict(series=series,
                 frame=frame,
                 panel=panel,
@@ -149,7 +156,8 @@ def create_data():
                 sp_series=dict(float=_create_sp_series(),
                                ts=_create_sp_tsseries()),
                 sp_frame=dict(float=_create_sp_frame()),
-                cat=cat)
+                cat=cat,
+                timestamp=timestamp)
 
 
 def create_pickle_data():

pandas/io/tests/data/legacy_msgpack/0.18.1/0.18.1_x86_64_darwin_3.5.2.msgpack

@@ -8,7 +8,7 @@
 from distutils.version import LooseVersion
 
 from pandas import compat
-from pandas.compat import u
+from pandas.compat import u, PY3
 from pandas import (Series, DataFrame, Panel, MultiIndex, bdate_range,
                     date_range, period_range, Index, Categorical)
 from pandas.core.common import PerformanceWarning
@@ -58,6 +58,19 @@ def check_arbitrary(a, b):
         assert_series_equal(a, b)
     elif isinstance(a, Index):
         assert_index_equal(a, b)
+    elif isinstance(a, Categorical):
+        # Temp,
+        # Categorical.categories is changed from str to bytes in PY3
+        # maybe the same as GH 13591
+        if PY3 and b.categories.inferred_type == 'string':
+            pass
+        else:
+            tm.assert_categorical_equal(a, b)
+    elif a is NaT:
+        assert b is NaT
+    elif isinstance(a, Timestamp):
+        assert a == b
+        assert a.freq == b.freq
     else:
         assert(a == b)
 
@@ -815,8 +828,8 @@ def check_min_structure(self, data):
         for typ, v in self.minimum_structure.items():
             assert typ in data, '"{0}" not found in unpacked data'.format(typ)
             for kind in v:
-                assert kind in data[
-                    typ], '"{0}" not found in data["{1}"]'.format(kind, typ)
+                msg = '"{0}" not found in data["{1}"]'.format(kind, typ)
+                assert kind in data[typ], msg
 
     def compare(self, vf, version):
         # GH12277 encoding default used to be latin-1, now utf-8
@@ -839,8 +852,8 @@ def compare(self, vf, version):
 
                 # use a specific comparator
                 # if available
-                comparator = getattr(
-                    self, "compare_{typ}_{dt}".format(typ=typ, dt=dt), None)
+                comp_method = "compare_{typ}_{dt}".format(typ=typ, dt=dt)
+                comparator = getattr(self, comp_method, None)
                 if comparator is not None:
                     comparator(result, expected, typ, version)
                 else:
@@ -872,9 +885,8 @@ def read_msgpacks(self, version):
         n = 0
         for f in os.listdir(pth):
             # GH12142 0.17 files packed in P2 can't be read in P3
-            if (compat.PY3 and
-                    version.startswith('0.17.') and
-                    f.split('.')[-4][-1] == '2'):
+            if (compat.PY3 and version.startswith('0.17.') and
+               f.split('.')[-4][-1] == '2'):
                 continue
             vf = os.path.join(pth, f)
             try:

pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_2.7.12.pickle

@@ -46,6 +46,12 @@ def compare_element(self, result, expected, typ, version=None):
         if typ.startswith('sp_'):
             comparator = getattr(tm, "assert_%s_equal" % typ)
             comparator(result, expected, exact_indices=False)
+        elif typ == 'timestamp':
+            if expected is pd.NaT:
+                assert result is pd.NaT
+            else:
+                tm.assert_equal(result, expected)
+                tm.assert_equal(result.freq, expected.freq)
         else:
             comparator = getattr(tm, "assert_%s_equal" %
                                  typ, tm.assert_almost_equal)

pandas/io/tests/data/legacy_pickle/0.18.1/0.18.1_x86_64_darwin_3.5.2.pickle

@@ -1,3 +1,4 @@
 # prototypes for sharing
 
 cdef bint is_null_datetimelike(v)
+cpdef bint is_period(val)

pandas/io/tests/generate_legacy_storage_files.py

@@ -33,7 +33,7 @@ def is_bool(object obj):
 def is_complex(object obj):
     return util.is_complex_object(obj)
 
-def is_period(object val):
+cpdef bint is_period(object val):
     """ Return a boolean if this is a Period object """
     return util.is_period_object(val)
 
@@ -538,9 +538,6 @@ def is_time_array(ndarray[object] values):
             return False
     return True
 
-def is_period(object o):
-    from pandas import Period
-    return isinstance(o,Period)
 
 def is_period_array(ndarray[object] values):
     cdef Py_ssize_t i, n = len(values)

pandas/io/tests/test_packers.py

@@ -24,7 +24,7 @@ cimport cython
 from datetime cimport *
 cimport util
 cimport lib
-from lib cimport is_null_datetimelike
+from lib cimport is_null_datetimelike, is_period
 import lib
 from pandas import tslib
 from tslib import Timedelta, Timestamp, iNaT, NaT
@@ -484,8 +484,11 @@ def extract_freq(ndarray[object] values):
 
     for i in range(n):
         p = values[i]
+
         try:
-            return p.freq
+            # now Timestamp / NaT has freq attr
+            if is_period(p):
+                return p.freq
         except AttributeError:
             pass
 

pandas/io/tests/test_pickle.py

@@ -965,7 +965,7 @@ def test_indexing_with_datetime_tz(self):
         # indexing - fast_xs
         df = DataFrame({'a': date_range('2014-01-01', periods=10, tz='UTC')})
         result = df.iloc[5]
-        expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', offset='D')
+        expected = Timestamp('2014-01-06 00:00:00+0000', tz='UTC', freq='D')
         self.assertEqual(result, expected)
 
         result = df.loc[5]

pandas/lib.pxd

@@ -426,10 +426,10 @@ def test_constructor_with_datetime_tz(self):
         # indexing
         result = s.iloc[0]
         self.assertEqual(result, Timestamp('2013-01-01 00:00:00-0500',
-                                           tz='US/Eastern', offset='D'))
+                                           tz='US/Eastern', freq='D'))
         result = s[0]
         self.assertEqual(result, Timestamp('2013-01-01 00:00:00-0500',
-                                           tz='US/Eastern', offset='D'))
+                                           tz='US/Eastern', freq='D'))
 
         result = s[Series([True, True, False], index=s.index)]
         assert_series_equal(result, s[0:2])

pandas/src/inference.pyx

@@ -2365,7 +2365,7 @@ def test_reset_index_datetime(self):
                                      'a': np.arange(6, dtype='int64')},
                                     columns=['level_0', 'level_1', 'a'])
             expected['level_1'] = expected['level_1'].apply(
-                lambda d: pd.Timestamp(d, offset='D', tz=tz))
+                lambda d: pd.Timestamp(d, freq='D', tz=tz))
             assert_frame_equal(df.reset_index(), expected)
 
     def test_reset_index_period(self):

pandas/src/period.pyx

@@ -558,7 +558,7 @@ def _generate(cls, start, end, periods, name, offset,
 
     @property
     def _box_func(self):
-        return lambda x: Timestamp(x, offset=self.offset, tz=self.tz)
+        return lambda x: Timestamp(x, freq=self.offset, tz=self.tz)
 
     def _convert_for_op(self, value):
         """ Convert value to be insertable to ndarray """
@@ -1199,8 +1199,9 @@ def __iter__(self):
         for i in range(chunks):
             start_i = i * chunksize
             end_i = min((i + 1) * chunksize, l)
-            converted = tslib.ints_to_pydatetime(
-                data[start_i:end_i], tz=self.tz, offset=self.offset, box=True)
+            converted = tslib.ints_to_pydatetime(data[start_i:end_i],
+                                                 tz=self.tz, freq=self.freq,
+                                                 box=True)
             for v in converted:
                 yield v
 

pandas/tests/indexing/test_indexing.py

@@ -124,10 +124,11 @@ def test_minmax(self):
 
     def test_numpy_minmax(self):
         dr = pd.date_range(start='2016-01-15', end='2016-01-20')
-        self.assertEqual(np.min(dr), Timestamp(
-            '2016-01-15 00:00:00', offset='D'))
-        self.assertEqual(np.max(dr), Timestamp(
-            '2016-01-20 00:00:00', offset='D'))
+
+        self.assertEqual(np.min(dr),
+                         Timestamp('2016-01-15 00:00:00', freq='D'))
+        self.assertEqual(np.max(dr),
+                         Timestamp('2016-01-20 00:00:00', freq='D'))
 
         errmsg = "the 'out' parameter is not supported"
         tm.assertRaisesRegexp(ValueError, errmsg, np.min, dr, out=0)
@@ -148,11 +149,11 @@ def test_round(self):
             elt = rng[1]
 
             expected_rng = DatetimeIndex([
-                Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 01:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 02:00:00', tz=tz, offset='30T'),
+                Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 01:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 02:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 02:00:00', tz=tz, freq='30T'),
             ])
             expected_elt = expected_rng[1]
 
@@ -175,10 +176,10 @@ def test_repeat(self):
                                 freq='30Min', tz=tz)
 
             expected_rng = DatetimeIndex([
-                Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'),
+                Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'),
             ])
 
             tm.assert_index_equal(rng.repeat(reps), expected_rng)
@@ -192,10 +193,10 @@ def test_numpy_repeat(self):
                                 freq='30Min', tz=tz)
 
             expected_rng = DatetimeIndex([
-                Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 00:00:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'),
-                Timestamp('2016-01-01 00:30:00', tz=tz, offset='30T'),
+                Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 00:00:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'),
+                Timestamp('2016-01-01 00:30:00', tz=tz, freq='30T'),
             ])
 
             tm.assert_index_equal(np.repeat(rng, reps), expected_rng)