Adjust timeseries.rst

Author: Matt Roeschke

doc/source/timeseries.rst

@@ -414,7 +414,7 @@ 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 :func:`date_range` and :func:`bdate_range` functions
 to create a ``DatetimeIndex``. The default frequency for ``date_range`` is a
-**day** while the default for ``bdate_range`` is a **business day**:
+**calendar day** while the default for ``bdate_range`` is a **business day**:
 
 .. ipython:: python
 
@@ -937,26 +937,6 @@ time information included in the operation.
    hour.apply(ts).normalize()
    hour.apply(pd.Timestamp("2014-01-01 23:30")).normalize()
 
-.. _timeseries.dayvscalendarday:
-
-Day vs. CalendarDay
-~~~~~~~~~~~~~~~~~~~
-
-:class:`Day` (``'D'``) is a timedelta-like offset that respects absolute time
-arithmetic and is an alias for 24 :class:`Hour`. This offset is the default
-argument to many pandas time related function like :func:`date_range` and :func:`timedelta_range`.
-
-:class:`CalendarDay` (``'CD'``) is a relativedelta-like offset that respects
-calendar time arithmetic. :class:`CalendarDay` is useful preserving calendar day
-semantics with date times with have day light savings transitions, i.e. :class:`CalendarDay`
-will preserve the hour before the day light savings transition.
-
-.. ipython:: python
-
-   ts = pd.Timestamp('2016-10-30 00:00:00', tz='Europe/Helsinki')
-   ts + pd.offsets.Day(1)
-   ts + pd.offsets.CalendarDay(1)
-
 
 Parametric Offsets
 ~~~~~~~~~~~~~~~~~~
@@ -1248,8 +1228,7 @@ frequencies. We will refer to these aliases as *offset aliases*.
 
     "B", "business day frequency"
     "C", "custom business day frequency"
-    "D", "day frequency"
-    "CD", "calendar day frequency"
+    "D", "calendar day frequency"
     "W", "weekly frequency"
     "M", "month end frequency"
     "SM", "semi-month end frequency (15th and end of month)"