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

Author: jbrockmendel

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

@@ -78,6 +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
 

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-2.7_WIN.pip

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

ci/requirements-3.6_NUMPY_DEV.pip

@@ -625,7 +625,7 @@ 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:
 
@@ -645,7 +645,7 @@ and allows efficient indexing and storage of an index with a large number of dup
    df.dtypes
    df.B.cat.categories
 
-Setting the index, will create create a ``CategoricalIndex``
+Setting the index, will create a ``CategoricalIndex``
 
 .. ipython:: python
 
@@ -681,7 +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
@@ -722,7 +722,7 @@ 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:
 
@@ -963,7 +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