Merge pull request #7206 from jreback/ops

API: remove datetime-like index ops from Series

Author: jreback

doc/source/release.rst

@@ -86,21 +86,10 @@ API Changes
 - ``df.to_html`` will now print out the header of an empty dataframe (:issue:`6062`)
 - The ``interpolate`` ``downcast`` keyword default has been changed from ``infer`` to
   ``None``. This is to preseve the original dtype unless explicitly requested otherwise (:issue:`6290`).
-- allow a Series to utilize index methods depending on its index type, e.g. ``Series.year`` is now defined
-  for a Series with a ``DatetimeIndex`` or a ``PeriodIndex``; trying this on a non-supported Index type will
-  now raise a ``TypeError``. (:issue:`4551`, :issue:`4056`, :issue:`5519`)
-
-  The following are affected:
-
-  - ``date,time,year,month,day``
-  - ``hour,minute,second,weekofyear``
-  - ``week,dayofweek,dayofyear,quarter``
-  - ``microsecond,nanosecond,qyear``
-  - ``is_month_start,is_month_end``
-  - ``is_quarter_start,is_quarter_end``
-  - ``is_year_start,is_year_end``
-  - ``min(),max()``
-  - ``pd.infer_freq()``
+- allow ``Series`` and ``Index`` to share common ops. remove the ``Series.weekday`` property from Series;
+  Using a ``DatetimeIndex/PeriodIndex`` method on a Series will now raise a ``TypeError``.
+  support ``min(),max(),factorize(),unique(),nunique(),value_counts()`` on ``Index`` types.
+  (:issue:`4551`, :issue:`4056`, :issue:`5519`, :issue:`6380`, :issue:`7206`).
 
 - Add ``is_month_start``, ``is_month_end``, ``is_quarter_start``, ``is_quarter_end``,
   ``is_year_start``, ``is_year_end`` accessors for ``DateTimeIndex`` / ``Timestamp`` which return a boolean array

doc/source/v0.14.0.txt

@@ -73,28 +73,10 @@ API changes
   ``None``. This is to preseve the original dtype unless explicitly requested otherwise (:issue:`6290`).
 - When converting a dataframe to HTML it used to return `Empty DataFrame`. This special case has
   been removed, instead a header with the column names is returned (:issue:`6062`).
-- allow a Series to utilize index methods depending on its index type, e.g. ``Series.year`` is now defined
-  for a Series with a ``DatetimeIndex`` or a ``PeriodIndex``; trying this on a non-supported Index type will
-  now raise a ``TypeError``. (:issue:`4551`, :issue:`4056`, :issue:`5519`)
-
-  The following are affected:
-
-  - ``date,time,year,month,day``
-  - ``hour,minute,second,weekofyear``
-  - ``week,dayofweek,dayofyear,quarter``
-  - ``microsecond,nanosecond,qyear``
-  - ``is_month_start,is_month_end``
-  - ``is_quarter_start,is_quarter_end``
-  - ``is_year_start,is_year_end``
-  - ``min(),max()``
-  - ``pd.infer_freq()``
-
-  .. ipython:: python
-
-     s = Series(np.random.randn(5),index=tm.makeDateIndex(5))
-     s
-     s.year
-     s.index.year
+- allow ``Series`` and ``Index`` to share common ops. remove the ``Series.weekday`` property from Series;
+  Using a ``DatetimeIndex/PeriodIndex`` method on a Series will now raise a ``TypeError``.
+  support ``min(),max(),factorize(),unique(),nunique(),value_counts()`` on ``Index`` types.
+  (:issue:`4551`, :issue:`4056`, :issue:`5519`, :issue:`6380`, :issue:`7206`).
 
 - Add ``is_month_start``, ``is_month_end``, ``is_quarter_start``, ``is_quarter_end``, ``is_year_start``, ``is_year_end`` accessors for ``DateTimeIndex`` / ``Timestamp`` which return a boolean array of whether the timestamp(s) are at the start/end of the month/quarter/year defined by the frequency of the ``DateTimeIndex`` / ``Timestamp`` (:issue:`4565`, :issue:`6998`)
 
@@ -355,7 +337,7 @@ are introduced. The function :func:`~pandas.read_sql` is kept as a convenience
 wrapper around the other two and will delegate to specific function depending on
 the provided input (database table name or sql query).
 
-In practice, you have to provide a SQLAlchemy ``engine`` to the sql functions. 
+In practice, you have to provide a SQLAlchemy ``engine`` to the sql functions.
 To connect with SQLAlchemy you use the :func:`create_engine` function to create an engine
 object from database URI. You only need to create the engine once per database you are
 connecting to. For an in-memory sqlite database:
@@ -384,7 +366,7 @@ or by specifying a sql query:
 .. ipython:: python
 
    pd.read_sql_query('SELECT * FROM db_table', engine)
-   
+
 Some other enhancements to the sql functions include:
 
 - support for writing the index. This can be controlled with the ``index``
@@ -397,7 +379,7 @@ Some other enhancements to the sql functions include:
 
     Some of the existing functions or function aliases have been deprecated
     and will be removed in future versions. This includes: ``tquery``, ``uquery``,
-    ``read_frame``, ``frame_query``, ``write_frame``. 
+    ``read_frame``, ``frame_query``, ``write_frame``.
 
 .. warning::
 
@@ -638,7 +620,7 @@ Deprecations
 - The support for the 'mysql' flavor when using DBAPI connection objects has been deprecated.
   MySQL will be further supported with SQLAlchemy engines (:issue:`6900`).
 
-  - The following ``io.sql`` functions have been deprecated: ``tquery``, ``uquery``, ``read_frame``, ``frame_query``, ``write_frame``. 
+  - The following ``io.sql`` functions have been deprecated: ``tquery``, ``uquery``, ``read_frame``, ``frame_query``, ``write_frame``.
 
 - The `percentile_width` keyword argument in :meth:`~DataFrame.describe` has been deprecated.
   Use the `percentiles` keyword instead, which takes a list of percentiles to display. The

pandas/core/base.py

@@ -261,12 +261,10 @@ def _wrap_access_object(self, obj):
 
     def max(self):
         """ The maximum value of the object """
-        self._is_allowed_index_op('max')
         return self.values.max()
 
     def min(self):
         """ The minimum value of the object """
-        self._is_allowed_index_op('min')
         return self.values.min()
 
     def value_counts(self, normalize=False, sort=True, ascending=False,

pandas/core/series.py

@@ -121,11 +121,15 @@ class Series(base.IndexOpsMixin, generic.NDFrame):
 
     @property
     def _allow_datetime_index_ops(self):
-        return self.index.is_all_dates and isinstance(self.index, DatetimeIndex)
+        # disabling to invalidate datetime index ops (GH7206)
+        # return self.index.is_all_dates and isinstance(self.index, DatetimeIndex)
+        return False
 
     @property
     def _allow_period_index_ops(self):
-        return self.index.is_all_dates and isinstance(self.index, PeriodIndex)
+        # disabling to invalidate period index ops (GH7206)
+        # return self.index.is_all_dates and isinstance(self.index, PeriodIndex)
+        return False
 
     def __init__(self, data=None, index=None, dtype=None, name=None,
                  copy=False, fastpath=False):

pandas/tests/test_base.py

@@ -452,7 +452,15 @@ def setUp(self):
 
     def test_ops_properties(self):
         self.check_ops_properties(['year','month','day','hour','minute','second','weekofyear','week','dayofweek','dayofyear','quarter'])
-        self.check_ops_properties(['date','time','microsecond','nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start', 'is_quarter_end', 'is_year_start', 'is_year_end'], lambda x: isinstance(x,DatetimeIndex))
+        self.check_ops_properties(['date','time','microsecond','nanosecond', 'is_month_start', 'is_month_end', 'is_quarter_start',
+                                   'is_quarter_end', 'is_year_start', 'is_year_end'], lambda x: isinstance(x,DatetimeIndex))
+
+    def test_ops_properties_basic(self):
+
+        # sanity check that the behavior didn't change
+        # GH7206
+        for op in ['year','day','second','weekday']:
+            self.assertRaises(TypeError, lambda x: getattr(self.dt_series,op))
 
 class TestPeriodIndexOps(Ops):
     _allowed = '_allow_period_index_ops'

pandas/tests/test_series.py

@@ -5619,7 +5619,9 @@ def test_asfreq(self):
 
     def test_weekday(self):
         # Just run the function
-        weekdays = self.ts.weekday
+        def f():
+            self.ts.weekday
+        self.assertRaises(TypeError, f)
 
     def test_diff(self):
         # Just run the function
@@ -5715,7 +5717,7 @@ def test_select(self):
         assert_series_equal(result, expected)
 
         result = self.ts.select(lambda x: x.weekday() == 2)
-        expected = self.ts[self.ts.weekday == 2]
+        expected = self.ts[self.ts.index.weekday == 2]
         assert_series_equal(result, expected)
 
 #------------------------------------------------------------------------------