review updates

pandas-dev · jreback · Oct 2, 2017 · Sep 27, 2017 · Sep 29, 2017 · Oct 2, 2017
commit 3d58a512d99c82c6b0346c5d90cc2d9c791709b4
diff --git a/doc/source/timeseries.rst b/doc/source/timeseries.rst
@@ -88,7 +88,7 @@ Class              Remarks                         How to create
 .. _timeseries.representation:
 
 Timestamps vs. Time Spans
---------------------------
+-------------------------
 
 Timestamped data is the most basic type of time series data that associates
 values with points in time. For pandas objects it means using the points in
@@ -263,12 +263,10 @@ pandas supports converting integer or float epoch times to ``Timestamp`` and
 ``DatetimeIndex``. The default unit is nanoseconds, since that is how ``Timestamp``
 objects are stored internally. However, epochs are often stored in another ``unit``
 which can be specified. These are computed from the starting point specified by the
-:ref:`Origin Parameter <timeseries.origin>`.
+``origin`` parameter.
 
 .. ipython:: python
 
-   pd.Timestamp(1349720105, unit='s')
-
    pd.to_datetime([1349720105, 1349806505, 1349892905,
                    1349979305, 1350065705], unit='s')
 
@@ -292,6 +290,10 @@ which can be specified. These are computed from the starting point specified by
       pd.to_datetime([1490195805.433, 1490195805.433502912], unit='s')
       pd.to_datetime(1490195805433502912, unit='ns')
 
+.. seealso::
+
+   :ref:`timeseries.origin`
+
 .. _timeseries.converting.epoch_inverse:
 
 From Timestamps to Epoch
@@ -312,8 +314,8 @@ We convert the ``DatetimeIndex`` to an ``int64`` array, then divide by the conve
 
 .. _timeseries.origin:
 
-Using the Origin Parameter
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+Using the ``origin`` Parameter
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. versionadded:: 0.20.0
 
@@ -353,7 +355,7 @@ To generate an index with timestamps, you can use either the ``DatetimeIndex`` o
 
 In practice this becomes very cumbersome because we often need a very long
 index with a large number of timestamps. If we need timestamps on a regular
-frequency, we can use the ``date_range`` and ``bdate_range`` functions
+frequency, we can use the :func:`date_range` and :func:`bdate_range` functions
 to create a ``DatetimeIndex``. The default frequency for ``date_range`` is a
 **calendar day** while the default for ``bdate_range`` is a **business day**:
 
@@ -392,11 +394,16 @@ of those specified will not be generated:
 
    pd.bdate_range(start=start, periods=20)
 
+.. _timeseries.custom-freq-ranges:
+
+Custom Frequency Ranges
+~~~~~~~~~~~~~~~~~~~~~~~
+
 .. versionadded:: 0.21.0
 
 ``bdate_range`` can also generate a range of custom frequency dates by using
 the ``weekmask`` and ``holidays`` parameters.  These parameters will only be
-used if a custom frequency string is passed:
+used if a custom frequency string is passed.
 
 .. ipython:: python
 
@@ -406,8 +413,19 @@ used if a custom frequency string is passed:
 
    pd.bdate_range(start, end, freq='C', weekmask=weekmask, holidays=holidays)
 
-See the :ref:`Custom Business Days <timeseries.custombusinessdays>` section for
-additional information on custom frequencies.
+   pd.bdate_range(start, end, freq='CBMS', weekmask=weekmask)
+
+.. warning::
+
+   This functionality was originally exclusive to ``cdate_range``, which is
+   deprecated as of version 0.21.0 in favor of ``bdate_range``.  Note that
+   ``cdate_range`` only utilizes the ``weekmask`` and ``holidays`` parameters
+   when custom business day, 'C', is passed as the frequency string. Support has 
+   been expanded with ``bdate_range`` to work with any custom frequency string.
+
+.. seealso::
+
+   :ref:`timeseries.custombusinessdays`
 
 .. _timeseries.timestamp-limits:
 
@@ -422,7 +440,9 @@ can be represented using a 64-bit integer is limited to approximately 584 years:
    pd.Timestamp.min
    pd.Timestamp.max
 
-See :ref:`here <timeseries.oob>` for ways to represent data outside these bound.
+.. seealso::
+
+   :ref:`timeseries.oob`
 
 .. _timeseries.datetimeindex:
 

diff --git a/doc/source/whatsnew/v0.21.0.txt b/doc/source/whatsnew/v0.21.0.txt
@@ -164,7 +164,6 @@ Other Enhancements
 - :func:`read_excel` raises ``ImportError`` with a better message if ``xlrd`` is not installed. (:issue:`17613`)
 - :func:`read_json` now accepts a ``chunksize`` parameter that can be used when ``lines=True``. If ``chunksize`` is passed, read_json now returns an iterator which reads in ``chunksize`` lines with each iteration. (:issue:`17048`)
 - :meth:`DataFrame.assign` will preserve the original order of ``**kwargs`` for Python 3.6+ users instead of sorting the column names
-- :func:`bdate_range` has gained ``weekmask`` and ``holidays`` parameters for constructing custom frequency date ranges (:issue:`17596`)
 
 
 .. _whatsnew_0210.api_breaking:
@@ -572,9 +571,9 @@ Deprecations
 - :func:`SeriesGroupBy.nth` has deprecated ``True`` in favor of ``'all'`` for its kwarg ``dropna`` (:issue:`11038`).
 - :func:`DataFrame.as_blocks` is deprecated, as this is exposing the internal implementation (:issue:`17302`)
 - ``pd.TimeGrouper`` is deprecated in favor of :class:`pandas.Grouper` (:issue:`16747`)
-- ``cdate_range`` has been deprecated in favor of :func:`bdate_range` (:issue:`17596`)
+- ``cdate_range`` has been deprecated in favor of :func:`bdate_range`, which has gained ``weekmask`` and ``holidays`` parameters for building custom frequency date ranges. See the :ref:`documentation <timeseries.custom-freq-ranges>` for more details (:issue:`17596`)
 
-.. _whatsnew_0210.deprecations.argmin_min
+.. _whatsnew_0210.deprecations.argmin_min:
 
 Series.argmax and Series.argmin
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -740,9 +739,9 @@ Numeric
 
 Categorical
 ^^^^^^^^^^^
-- Bug in :func:`Series.isin` when called with a categorical (:issue`16639`)
+- Bug in :func:`Series.isin` when called with a categorical (:issue:`16639`)
 - Bug in the categorical constructor with empty values and categories causing the ``.categories`` to be an empty ``Float64Index`` rather than an empty ``Index`` with object dtype (:issue:`17248`)
-- Bug in categorical operations with :ref:`Series.cat <categorical.cat>' not preserving the original Series' name (:issue:`17509`)
+- Bug in categorical operations with :ref:`Series.cat <categorical.cat>` not preserving the original Series' name (:issue:`17509`)
 
 PyPy
 ^^^^

diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py
@@ -2051,8 +2051,8 @@ def date_range(start=None, end=None, periods=None, freq='D', tz=None,
 
 
 def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
-                normalize=True, name=None, weekmask='Mon Tue Wed Thu Fri',
-                holidays=None, closed=None, **kwargs):
+                normalize=True, name=None, weekmask=None, holidays=None,
+                closed=None, **kwargs):
     """
     Return a fixed frequency DatetimeIndex, with business day as the default
     frequency
@@ -2074,16 +2074,17 @@ def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
         Normalize start/end dates to midnight before generating date range
     name : string, default None
         Name of the resulting DatetimeIndex
-    weekmask : string, default 'Mon Tue Wed Thu Fri'
-        weekmask of valid business days, passed to ``numpy.busdaycalendar``,
-        only used when custom frequency strings are passed
+    weekmask : string or None, default None
+        Weekmask of valid business days, passed to ``numpy.busdaycalendar``,
+        only used when custom frequency strings are passed.  The default
+        value None is equivalent to 'Mon Tue Wed Thu Fri'
 
         .. versionadded:: 0.21.0
 
     holidays : list-like or None, default None
-        list-like of dates to exclude from the set of valid business days,
-        passed to ``numpy.busdaycalendar``, only used when custom frequency
-        strings are passed
+        Dates to exclude from the set of valid business days, passed to
+        ``numpy.busdaycalendar``, only used when custom frequency strings
+        are passed
 
         .. versionadded:: 0.21.0
 
@@ -2106,13 +2107,15 @@ def bdate_range(start=None, end=None, periods=None, freq='B', tz=None,
 
     if is_string_like(freq) and freq.startswith('C'):
         try:
+            weekmask = weekmask or 'Mon Tue Wed Thu Fri'
             freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask)
         except (KeyError, TypeError):
             msg = 'invalid custom frequency string: {freq}'.format(freq=freq)
             raise ValueError(msg)
-    elif holidays or (weekmask != 'Mon Tue Wed Thu Fri'):
-        warnings.warn('a custom frequency string was not passed, ignoring '
-                      'parameters: holidays, weekmask', UserWarning)
+    elif holidays or weekmask:
+        msg = ('a custom frequency string is required when holidays or '
+               'weekmask are passed, got frequency {freq}').format(freq=freq)
+        raise ValueError(msg)
 
     return DatetimeIndex(start=start, end=end, periods=periods,
                          freq=freq, tz=tz, normalize=normalize, name=name,
@@ -2166,7 +2169,7 @@ def cdate_range(start=None, end=None, periods=None, freq='C', tz=None,
     rng : DatetimeIndex
     """
     warnings.warn("cdate_range is deprecated and will be removed in a future "
-                  "version, instead use bdate_range(..., freq='{freq}')"
+                  "version, instead use pd.bdate_range(..., freq='{freq}')"
                   .format(freq=freq), FutureWarning, stacklevel=2)
 
     if freq == 'C':

diff --git a/pandas/tests/indexes/datetimes/test_date_range.py b/pandas/tests/indexes/datetimes/test_date_range.py
@@ -589,8 +589,10 @@ def test_cdaterange_weekmask(self):
         expected = DatetimeIndex(['2013-05-01', '2013-05-02', '2013-05-05'])
         tm.assert_index_equal(result, expected)
 
-        # warning with non-custom freq
-        with tm.assert_produces_warning(UserWarning):
+        # raise with non-custom freq
+        msg = ('a custom frequency string is required when holidays or '
+               'weekmask are passed, got frequency B')
+        with tm.assert_raises_regex(ValueError, msg):
             bdate_range('2013-05-01', periods=3,
                         weekmask='Sun Mon Tue Wed Thu')
 
@@ -600,8 +602,10 @@ def test_cdaterange_holidays(self):
         expected = DatetimeIndex(['2013-05-02', '2013-05-03', '2013-05-06'])
         tm.assert_index_equal(result, expected)
 
-        # warning with non-custom freq
-        with tm.assert_produces_warning(UserWarning):
+        # raise with non-custom freq
+        msg = ('a custom frequency string is required when holidays or '
+               'weekmask are passed, got frequency B')
+        with tm.assert_raises_regex(ValueError, msg):
             bdate_range('2013-05-01', periods=3, holidays=['2013-05-01'])
 
     def test_cdaterange_weekmask_and_holidays(self):
@@ -611,8 +615,10 @@ def test_cdaterange_weekmask_and_holidays(self):
         expected = DatetimeIndex(['2013-05-02', '2013-05-05', '2013-05-06'])
         tm.assert_index_equal(result, expected)
 
-        # warning with non-custom freq
-        with tm.assert_produces_warning(UserWarning):
+        # raise with non-custom freq
+        msg = ('a custom frequency string is required when holidays or '
+               'weekmask are passed, got frequency B')
+        with tm.assert_raises_regex(ValueError, msg):
             bdate_range('2013-05-01', periods=3,
                         weekmask='Sun Mon Tue Wed Thu',
                         holidays=['2013-05-01'])
@@ -629,7 +635,7 @@ def test_all_custom_freq(self, freq):
         with tm.assert_raises_regex(ValueError, msg.format(freq=bad_freq)):
             bdate_range(START, END, freq=bad_freq)
 
-    def test_depr_cdaterange(self):
+    def test_deprecation_cdaterange(self):
         # GH17596
         with tm.assert_produces_warning(FutureWarning):
             cdate_range(START, END)