Merge branch 'master' of https://github.com/pandas-dev/pandas into tslibs-parsing

Author: jbrockmendel

MANIFEST.in

@@ -3,6 +3,7 @@ include LICENSE
 include RELEASE.md
 include README.rst
 include setup.py
+include pyproject.toml
 
 graft doc
 prune doc/build

asv_bench/benchmarks/period.py

@@ -2,6 +2,35 @@
 from pandas import Series, Period, PeriodIndex, date_range
 
 
+class PeriodProperties(object):
+    def setup(self):
+        self.per = Period('2012-06-01', freq='M')
+
+    def time_year(self):
+        self.per.year
+
+    def time_month(self):
+        self.per.month
+
+    def time_quarter(self):
+        self.per.quarter
+
+    def time_day(self):
+        self.per.day
+
+    def time_hour(self):
+        self.per.hour
+
+    def time_minute(self):
+        self.per.second
+
+    def time_second(self):
+        self.per.second
+
+    def time_leap_year(self):
+        self.per.is_leapyear
+
+
 class Constructor(object):
     goal_time = 0.2
 

asv_bench/benchmarks/timestamp.py

@@ -0,0 +1,60 @@
+from .pandas_vb_common import *
+from pandas import to_timedelta, Timestamp
+
+
+class TimestampProperties(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.ts = Timestamp('2017-08-25 08:16:14')
+
+    def time_tz(self):
+        self.ts.tz
+
+    def time_offset(self):
+        self.ts.offset
+
+    def time_dayofweek(self):
+        self.ts.dayofweek
+
+    def time_weekday_name(self):
+        self.ts.weekday_name
+
+    def time_dayofyear(self):
+        self.ts.dayofyear
+
+    def time_week(self):
+        self.ts.week
+
+    def time_quarter(self):
+        self.ts.quarter
+
+    def time_days_in_month(self):
+        self.ts.days_in_month
+
+    def time_freqstr(self):
+        self.ts.freqstr
+
+    def time_is_month_start(self):
+        self.ts.is_month_start
+
+    def time_is_month_end(self):
+        self.ts.is_month_end
+
+    def time_is_quarter_start(self):
+        self.ts.is_quarter_start
+
+    def time_is_quarter_end(self):
+        self.ts.is_quarter_end
+
+    def time_is_year_start(self):
+        self.ts.is_quarter_end
+
+    def time_is_year_end(self):
+        self.ts.is_quarter_end
+
+    def time_is_leap_year(self):
+        self.ts.is_quarter_end
+
+    def time_microsecond(self):
+        self.ts.microsecond

ci/requirements-3.5.sh

@@ -8,4 +8,4 @@ echo "install 35"
 conda remove -n pandas python-dateutil --force
 pip install python-dateutil
 
-conda install -n pandas -c conda-forge feather-format pyarrow=0.4.1
+conda install -n pandas -c conda-forge feather-format pyarrow=0.5.0

doc/source/10min.rst

@@ -655,7 +655,7 @@ the quarter end:
 Categoricals
 ------------
 
-Since version 0.15, pandas can include categorical data in a ``DataFrame``. For full docs, see the
+pandas can include categorical data in a ``DataFrame``. For full docs, see the
 :ref:`categorical introduction <categorical>` and the :ref:`API documentation <api.categorical>`.
 
 .. ipython:: python

doc/source/advanced.rst

@@ -26,12 +26,6 @@ See the :ref:`Indexing and Selecting Data <indexing>` for general indexing docum
    should be avoided.  See :ref:`Returning a View versus Copy
    <indexing.view_versus_copy>`
 
-.. warning::
-
-   In 0.15.0 ``Index`` has internally been refactored to no longer sub-class ``ndarray``
-   but instead subclass ``PandasObject``, similarly to the rest of the pandas objects. This should be
-   a transparent change with only very limited API implications (See the :ref:`Internal Refactoring <whatsnew_0150.refactoring>`)
-
 See the :ref:`cookbook<cookbook.selection>` for some advanced strategies
 
 .. _advanced.hierarchical:
@@ -270,9 +264,6 @@ Passing a list of labels or tuples works similar to reindexing:
 Using slicers
 ~~~~~~~~~~~~~
 
-.. versionadded:: 0.14.0
-
-In 0.14.0 we added a new way to slice multi-indexed objects.
 You can slice a multi-index by providing multiple indexers.
 
 You can provide any of the selectors as if you are indexing by label, see :ref:`Selection by Label <indexing.label>`,
@@ -384,7 +375,7 @@ selecting data at a particular level of a MultiIndex easier.
 
 .. ipython:: python
 
-   # using the slicers (new in 0.14.0)
+   # using the slicers
    df.loc[(slice(None),'one'),:]
 
 You can also select on the columns with :meth:`~pandas.MultiIndex.xs`, by
@@ -397,7 +388,7 @@ providing the axis argument
 
 .. ipython:: python
 
-   # using the slicers (new in 0.14.0)
+   # using the slicers
    df.loc[:,(slice(None),'one')]
 
 :meth:`~pandas.MultiIndex.xs` also allows selection with multiple keys
@@ -408,11 +399,9 @@ providing the axis argument
 
 .. ipython:: python
 
-   # using the slicers (new in 0.14.0)
+   # using the slicers
    df.loc[:,('bar','one')]
 
-.. versionadded:: 0.13.0
-
 You can pass ``drop_level=False`` to :meth:`~pandas.MultiIndex.xs` to retain
 the level that was selected
 
@@ -643,12 +632,9 @@ In the following sub-sections we will highlite some other index types.
 CategoricalIndex
 ~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.16.1
-
-We introduce a ``CategoricalIndex``, a new type of index object that is useful for supporting
-indexing with duplicates. This is a container around a ``Categorical`` (introduced in v0.15.0)
-and allows efficient indexing and storage of an index with a large number of duplicated elements. Prior to 0.16.1,
-setting the index of a ``DataFrame/Series`` with a ``category`` dtype would convert this to regular object-based ``Index``.
+``CategoricalIndex`` is a type of index that is useful for supporting
+indexing with duplicates. This is a container around a ``Categorical``
+and allows efficient indexing and storage of an index with a large number of duplicated elements.
 
 .. ipython:: python
 
@@ -743,16 +729,6 @@ Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``ND
 Float64Index
 ~~~~~~~~~~~~
 
-.. note::
-
-   As of 0.14.0, ``Float64Index`` is backed by a native ``float64`` dtype
-   array. Prior to 0.14.0, ``Float64Index`` was backed by an ``object`` dtype
-   array. Using a ``float64`` dtype in the backend speeds up arithmetic
-   operations by about 30x and boolean indexing operations on the
-   ``Float64Index`` itself are about 2x as fast.
-
-.. versionadded:: 0.13.0
-
 By default a ``Float64Index`` will be automatically created when passing floating, or mixed-integer-floating values in index creation.
 This enables a pure label-based slicing paradigm that makes ``[],ix,loc`` for scalar indexing and slicing work exactly the
 same.