Merge remote-tracking branch 'upstream/master' into groupby_tuples

Author: GuessWhoSamFoo

Makefile

@@ -12,6 +12,9 @@ clean_pyc:
 build: clean_pyc
 	python setup.py build_ext --inplace
 
+lint-diff:
+	git diff master --name-only -- "*.py" | grep "pandas" | xargs flake8
+
 develop: build
 	-python setup.py develop
 

asv_bench/benchmarks/categoricals.py

@@ -26,6 +26,9 @@ def setup(self):
         self.datetimes = pd.Series(pd.date_range(
             '1995-01-01 00:00:00', periods=10000, freq='s'))
 
+        self.values_some_nan = list(np.tile(self.categories + [np.nan], N))
+        self.values_all_nan = [np.nan] * len(self.values)
+
     def time_concat(self):
         concat([self.s, self.s])
 
@@ -46,6 +49,12 @@ def time_constructor_datetimes_with_nat(self):
         t.iloc[-1] = pd.NaT
         Categorical(t)
 
+    def time_constructor_with_nan(self):
+        Categorical(self.values_some_nan)
+
+    def time_constructor_all_nan(self):
+        Categorical(self.values_all_nan)
+
 
 class Categoricals2(object):
     goal_time = 0.2

asv_bench/benchmarks/index_object.py

@@ -219,3 +219,22 @@ def time_min(self):
 
     def time_min_trivial(self):
         self.idx_inc.min()
+
+
+class IndexOps(object):
+    goal_time = 0.2
+
+    def setup(self):
+        N = 10000
+        self.ridx = [RangeIndex(i * 100, (i + 1) * 100) for i in range(N)]
+        self.iidx = [idx.astype(int) for idx in self.ridx]
+        self.oidx = [idx.astype(str) for idx in self.iidx]
+
+    def time_concat_range(self):
+        self.ridx[0].append(self.ridx[1:])
+
+    def time_concat_int(self):
+        self.iidx[0].append(self.iidx[1:])
+
+    def time_concat_obj(self):
+        self.oidx[0].append(self.oidx[1:])

asv_bench/benchmarks/indexing.py

@@ -287,3 +287,19 @@ def setup(self):
 
     def time_subset(self):
         self.p.ix[(self.inds, self.inds, self.inds)]
+
+
+class IndexerLookup(object):
+    goal_time = 0.2
+
+    def setup(self):
+        self.s = Series(range(10))
+
+    def time_lookup_iloc(self):
+        self.s.iloc
+
+    def time_lookup_ix(self):
+        self.s.ix
+
+    def time_lookup_loc(self):
+        self.s.loc

ci/lint.sh

@@ -16,6 +16,13 @@ if [ "$LINT" ]; then
     fi
     echo "Linting *.py DONE"
 
+    echo "Linting setup.py"
+    flake8 setup.py
+    if [ $? -ne "0" ]; then
+        RET=1
+    fi
+    echo "Linting setup.py DONE"
+
     echo "Linting *.pyx"
     flake8 pandas --filename=*.pyx --select=E501,E302,E203,E111,E114,E221,E303,E128,E231,E126
     if [ $? -ne "0" ]; then

doc/source/advanced.rst

@@ -833,15 +833,15 @@ Of course if you need integer based selection, then use ``iloc``
 IntervalIndex
 ~~~~~~~~~~~~~
 
+.. versionadded:: 0.20.0
+
 :class:`IntervalIndex` together with its own dtype, ``interval`` as well as the
 :class:`Interval` scalar type,  allow first-class support in pandas for interval
 notation.
 
 The ``IntervalIndex`` allows some unique indexing and is also used as a
 return type for the categories in :func:`cut` and :func:`qcut`.
 
-.. versionadded:: 0.20.0
-
 .. warning::
 
    These indexing behaviors are provisional and may change in a future version of pandas.
@@ -862,7 +862,7 @@ selecting that particular interval.
    df.loc[2]
    df.loc[[2, 3]]
 
-If you select a lable *contained* within an interval, this will also select the interval.
+If you select a label *contained* within an interval, this will also select the interval.
 
 .. ipython:: python
 

doc/source/api.rst

@@ -1794,6 +1794,7 @@ Methods
     Timestamp.strftime
     Timestamp.strptime
     Timestamp.time
+    Timestamp.timestamp
     Timestamp.timetuple
     Timestamp.timetz
     Timestamp.to_datetime64

doc/source/basics.rst

@@ -1738,11 +1738,6 @@ description.
 Sorting
 -------
 
-.. warning::
-
-   The sorting API is substantially changed in 0.17.0, see :ref:`here <whatsnew_0170.api_breaking.sorting>` for these changes.
-   In particular, all sorting methods now return a new object by default, and **DO NOT** operate in-place (except by passing ``inplace=True``).
-
 There are two obvious kinds of sorting that you may be interested in: sorting
 by label and sorting by actual values.
 
@@ -1829,8 +1824,6 @@ faster than sorting the entire Series and calling ``head(n)`` on the result.
    s.nsmallest(3)
    s.nlargest(3)
 
-.. versionadded:: 0.17.0
-
 ``DataFrame`` also has the ``nlargest`` and ``nsmallest`` methods.
 
 .. ipython:: python
@@ -1881,7 +1874,7 @@ dtypes
 ------
 
 The main types stored in pandas objects are ``float``, ``int``, ``bool``,
-``datetime64[ns]`` and ``datetime64[ns, tz]`` (in >= 0.17.0), ``timedelta[ns]``,
+``datetime64[ns]`` and ``datetime64[ns, tz]``, ``timedelta[ns]``,
 ``category`` and ``object``. In addition these dtypes have item sizes, e.g.
 ``int64`` and ``int32``. See :ref:`Series with TZ <timeseries.timezone_series>`
 for more detail on ``datetime64[ns, tz]`` dtypes.

doc/source/categorical.rst

@@ -632,8 +632,6 @@ To get a single value `Series` of type ``category`` pass in a list with a single
 String and datetime accessors
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.17.1
-
 The accessors  ``.dt`` and ``.str`` will work if the ``s.cat.categories`` are of an appropriate
 type:
 

doc/source/computation.rst

@@ -206,8 +206,6 @@ Window Functions
    functions and are now deprecated. These are replaced by using the :class:`~pandas.core.window.Rolling`, :class:`~pandas.core.window.Expanding` and :class:`~pandas.core.window.EWM`. objects and a corresponding method call.
 
    The deprecation warning will show the new syntax, see an example :ref:`here <whatsnew_0180.window_deprecations>`
-   You can view the previous documentation
-   `here <http://pandas.pydata.org/pandas-docs/version/0.17.1/computation.html#moving-rolling-statistics-moments>`__
 
 For working with data, a number of windows functions are provided for
 computing common *window* or *rolling* statistics. Among these are count, sum,