Merge branch 'master' of https://github.com/pandas-dev/pandas into doc-decorator

Author: HH-MWB

asv_bench/benchmarks/frame_methods.py

@@ -564,7 +564,7 @@ def setup(self):
 
     def time_frame_get_dtype_counts(self):
         with warnings.catch_warnings(record=True):
-            self.df._data.get_dtype_counts()
+            self.df.dtypes.value_counts()
 
     def time_info(self):
         self.df.info()

doc/source/getting_started/intro_tutorials/10_text_data.rst

@@ -199,7 +199,7 @@ names in the ``Name`` column. By using pandas string methods, the
 
 Next, we need to get the corresponding location, preferably the index
 label, in the table for which the name length is the largest. The
-:meth:`~Series.idxmax`` method does exactly that. It is not a string method and is
+:meth:`~Series.idxmax` method does exactly that. It is not a string method and is
 applied to integers, so no ``str`` is used.
 
 .. ipython:: python

doc/source/user_guide/computation.rst

@@ -312,15 +312,35 @@ We provide a number of common statistical functions:
     :meth:`~Rolling.median`, Arithmetic median of values
     :meth:`~Rolling.min`, Minimum
     :meth:`~Rolling.max`, Maximum
-    :meth:`~Rolling.std`, Bessel-corrected sample standard deviation
-    :meth:`~Rolling.var`, Unbiased variance
+    :meth:`~Rolling.std`, Sample standard deviation
+    :meth:`~Rolling.var`, Sample variance
     :meth:`~Rolling.skew`, Sample skewness (3rd moment)
     :meth:`~Rolling.kurt`, Sample kurtosis (4th moment)
     :meth:`~Rolling.quantile`, Sample quantile (value at %)
     :meth:`~Rolling.apply`, Generic apply
     :meth:`~Rolling.cov`, Unbiased covariance (binary)
     :meth:`~Rolling.corr`, Correlation (binary)
 
+.. _computation.window_variance.caveats:
+
+.. note::
+
+   Please note that :meth:`~Rolling.std` and :meth:`~Rolling.var` use the sample
+   variance formula by default, i.e. the sum of squared differences is divided by
+   ``window_size - 1`` and not by ``window_size`` during averaging. In statistics,
+   we use sample when the dataset is drawn from a larger population that we
+   don't have access to. Using it implies that the data in our window is a
+   random sample from the population, and we are interested not in the variance
+   inside the specific window but in the variance of some general window that
+   our windows represent. In this situation, using the sample variance formula
+   results in an unbiased estimator and so is preferred.
+
+   Usually, we are instead interested in the variance of each window as we slide
+   it over the data, and in this case we should specify ``ddof=0`` when calling
+   these methods to use population variance instead of sample variance. Using
+   sample variance under the circumstances would result in a biased estimator
+   of the variable we are trying to determine.
+
 .. _stats.rolling_apply:
 
 Rolling apply
@@ -848,15 +868,22 @@ Method summary
     :meth:`~Expanding.median`, Arithmetic median of values
     :meth:`~Expanding.min`, Minimum
     :meth:`~Expanding.max`, Maximum
-    :meth:`~Expanding.std`, Unbiased standard deviation
-    :meth:`~Expanding.var`, Unbiased variance
+    :meth:`~Expanding.std`, Sample standard deviation
+    :meth:`~Expanding.var`, Sample variance
     :meth:`~Expanding.skew`, Unbiased skewness (3rd moment)
     :meth:`~Expanding.kurt`, Unbiased kurtosis (4th moment)
     :meth:`~Expanding.quantile`, Sample quantile (value at %)
     :meth:`~Expanding.apply`, Generic apply
     :meth:`~Expanding.cov`, Unbiased covariance (binary)
     :meth:`~Expanding.corr`, Correlation (binary)
 
+.. note::
+
+   Using sample variance formulas for :meth:`~Expanding.std` and
+   :meth:`~Expanding.var` comes with the same caveats as using them with rolling
+   windows. See :ref:`this section <computation.window_variance.caveats>` for more
+   information.
+
 .. currentmodule:: pandas
 
 Aside from not having a ``window`` parameter, these functions have the same

doc/source/whatsnew/v0.14.0.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0140:
 
-v0.14.0 (May 31 , 2014)
------------------------
+Version 0.14.0 (May 31 , 2014)
+------------------------------
 
 {{ header }}
 
@@ -321,7 +321,7 @@ Text parsing API changes
 
 .. _whatsnew_0140.groupby:
 
-Groupby API changes
+GroupBy API changes
 ~~~~~~~~~~~~~~~~~~~
 
 More consistent behavior for some groupby methods:
@@ -473,8 +473,8 @@ Some other enhancements to the sql functions include:
 
 .. _whatsnew_0140.slicers:
 
-Multiindexing using slicers
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Multi-indexing using slicers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In 0.14.0 we added a new way to slice MultiIndexed objects.
 You can slice a MultiIndex by providing multiple indexers.

doc/source/whatsnew/v0.14.1.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0141:
 
-v0.14.1 (July 11, 2014)
------------------------
+Version 0.14.1 (July 11, 2014)
+------------------------------
 
 {{ header }}
 

doc/source/whatsnew/v0.15.0.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0150:
 
-v0.15.0 (October 18, 2014)
---------------------------
+Version 0.15.0 (October 18, 2014)
+---------------------------------
 
 {{ header }}
 
@@ -105,7 +105,7 @@ For full docs, see the :ref:`categorical introduction <categorical>` and the
 
 .. _whatsnew_0150.timedeltaindex:
 
-TimedeltaIndex/Scalar
+TimedeltaIndex/scalar
 ^^^^^^^^^^^^^^^^^^^^^
 
 We introduce a new scalar type ``Timedelta``, which is a subclass of ``datetime.timedelta``, and behaves in a similar manner,
@@ -247,8 +247,8 @@ Additionally :meth:`~pandas.DataFrame.memory_usage` is an available method for a
 
 .. _whatsnew_0150.dt:
 
-.dt accessor
-^^^^^^^^^^^^
+Series.dt accessor
+^^^^^^^^^^^^^^^^^^
 
 ``Series`` has gained an accessor to succinctly return datetime like properties for the *values* of the Series, if its a datetime/period like Series. (:issue:`7207`)
 This will return a Series, indexed like the existing Series. See the :ref:`docs <basics.dt_accessors>`
@@ -600,7 +600,7 @@ Rolling/expanding moments improvements
 
 .. _whatsnew_0150.sql:
 
-Improvements in the SQL io module
+Improvements in the SQL IO module
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 - Added support for a ``chunksize`` parameter to ``to_sql`` function. This allows DataFrame to be written in chunks and avoid packet-size overflow errors (:issue:`8062`).

doc/source/whatsnew/v0.15.1.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0151:
 
-v0.15.1 (November 9, 2014)
---------------------------
+Version 0.15.1 (November 9, 2014)
+---------------------------------
 
 {{ header }}
 

doc/source/whatsnew/v0.15.2.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0152:
 
-v0.15.2 (December 12, 2014)
----------------------------
+Version 0.15.2 (December 12, 2014)
+----------------------------------
 
 {{ header }}
 

doc/source/whatsnew/v0.16.0.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0160:
 
-v0.16.0 (March 22, 2015)
-------------------------
+Version 0.16.0 (March 22, 2015)
+-------------------------------
 
 {{ header }}
 
@@ -218,7 +218,7 @@ Backwards incompatible API changes
 
 .. _whatsnew_0160.api_breaking.timedelta:
 
-Changes in Timedelta
+Changes in timedelta
 ^^^^^^^^^^^^^^^^^^^^
 
 In v0.15.0 a new scalar type ``Timedelta`` was introduced, that is a

doc/source/whatsnew/v0.16.1.rst

@@ -1,7 +1,7 @@
 .. _whatsnew_0161:
 
-v0.16.1 (May 11, 2015)
-----------------------
+Version 0.16.1 (May 11, 2015)
+-----------------------------
 
 {{ header }}