DOC: Added examples to pd.Index.get_loc

appveyor.yml

@@ -74,12 +74,18 @@ install:
   # create our env
   - cmd: conda create -n pandas python=%PYTHON_VERSION% cython pytest>=3.1.0 pytest-xdist
   - cmd: activate pandas
+  - cmd: pip install moto
   - SET REQ=ci\requirements-%PYTHON_VERSION%_WIN.run
   - cmd: echo "installing requirements from %REQ%"
   - cmd: conda install -n pandas --file=%REQ%
   - cmd: conda list -n pandas
   - cmd: echo "installing requirements from %REQ% - done"
 
+  # add some pip only reqs to the env
+  - SET REQ=ci\requirements-%PYTHON_VERSION%_WIN.pip
+  - cmd: echo "installing requirements from %REQ%"
+  - cmd: pip install -Ur %REQ%
+
   # build em using the local source checkout in the correct windows env
   - cmd: '%CMD_IN_ENV% python setup.py build_ext --inplace'
 

asv_bench/benchmarks/categoricals.py

@@ -67,6 +67,9 @@ def time_value_counts_dropna(self):
     def time_rendering(self):
         str(self.sel)
 
+    def time_set_categories(self):
+        self.ts.cat.set_categories(self.ts.cat.categories[::2])
+
 
 class Categoricals3(object):
     goal_time = 0.2

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
 
@@ -49,6 +78,65 @@ def time_value_counts_pindex(self):
         self.i.value_counts()
 
 
+class Properties(object):
+    def setup(self):
+        self.per = Period('2017-09-06 08:28', freq='min')
+
+    def time_year(self):
+        self.per.year
+
+    def time_month(self):
+        self.per.month
+
+    def time_day(self):
+        self.per.day
+
+    def time_hour(self):
+        self.per.hour
+
+    def time_minute(self):
+        self.per.minute
+
+    def time_second(self):
+        self.per.second
+
+    def time_is_leap_year(self):
+        self.per.is_leap_year
+
+    def time_quarter(self):
+        self.per.quarter
+
+    def time_qyear(self):
+        self.per.qyear
+
+    def time_week(self):
+        self.per.week
+
+    def time_daysinmonth(self):
+        self.per.daysinmonth
+
+    def time_dayofweek(self):
+        self.per.dayofweek
+
+    def time_dayofyear(self):
+        self.per.dayofyear
+
+    def time_start_time(self):
+        self.per.start_time
+
+    def time_end_time(self):
+        self.per.end_time
+
+    def time_to_timestamp():
+        self.per.to_timestamp()
+
+    def time_now():
+        self.per.now()
+
+    def time_asfreq():
+        self.per.asfreq('A')
+
+
 class period_standard_indexing(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/install_circle.sh

@@ -67,6 +67,7 @@ time conda create -n pandas -q --file=${REQ_BUILD} || exit 1
 time conda install -n pandas pytest>=3.1.0 || exit 1
 
 source activate pandas
+time pip install moto || exit 1
 
 # build but don't install
 echo "[build em]"

ci/install_travis.sh

@@ -104,7 +104,7 @@ if [ -e ${REQ} ]; then
 fi
 
 time conda install -n pandas pytest>=3.1.0
-time pip install pytest-xdist
+time pip install pytest-xdist moto
 
 if [ "$LINT" ]; then
    conda install flake8

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

ci/requirements_dev.txt

@@ -5,3 +5,4 @@ cython
 pytest>=3.1.0
 pytest-cov
 flake8
+moto

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
 
@@ -636,19 +625,16 @@ Index Types
 We have discussed ``MultiIndex`` in the previous sections pretty extensively. ``DatetimeIndex`` and ``PeriodIndex``
 are shown :ref:`here <timeseries.overview>`. ``TimedeltaIndex`` are :ref:`here <timedeltas.timedeltas>`.
 
-In the following sub-sections we will highlite some other index types.
+In the following sub-sections we will highlight some other index types.
 
 .. _indexing.categoricalindex:
 
 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
 
@@ -659,7 +645,7 @@ setting the index of a ``DataFrame/Series`` with a ``category`` dtype would conv
    df.dtypes
    df.B.cat.categories
 
-Setting the index, will create create a ``CategoricalIndex``
+Setting the index, will create a ``CategoricalIndex``
 
 .. ipython:: python
 
@@ -695,7 +681,7 @@ Groupby operations on the index will preserve the index nature as well
 Reindexing operations, will return a resulting index based on the type of the passed
 indexer, meaning that passing a list will return a plain-old-``Index``; indexing with
 a ``Categorical`` will return a ``CategoricalIndex``, indexed according to the categories
-of the PASSED ``Categorical`` dtype. This allows one to arbitrarly index these even with
+of the PASSED ``Categorical`` dtype. This allows one to arbitrarily index these even with
 values NOT in the categories, similarly to how you can reindex ANY pandas index.
 
 .. ipython :: python
@@ -736,23 +722,13 @@ Int64Index and RangeIndex
 Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``NDFrame`` objects.
 
 ``RangeIndex`` is a sub-class of ``Int64Index`` added in version 0.18.0, now providing the default index for all ``NDFrame`` objects.
-``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analagous to python `range types <https://docs.python.org/3/library/stdtypes.html#typesseq-range>`__.
+``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analogous to python `range types <https://docs.python.org/3/library/stdtypes.html#typesseq-range>`__.
 
 .. _indexing.float64index:
 
 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.
@@ -987,7 +963,7 @@ index can be somewhat complicated. For example, the following does not work:
     s.loc['c':'e'+1]
 
 A very common use case is to limit a time series to start and end at two
-specific dates. To enable this, we made the design design to make label-based
+specific dates. To enable this, we made the design to make label-based
 slicing include both endpoints:
 
 .. ipython:: python