cleaned references to pandas v0.15 and v0.16 in docs (#17442)

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:
@@ -638,12 +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
 

doc/source/basics.rst

@@ -719,8 +719,6 @@ on an entire ``DataFrame`` or ``Series``, row- or column-wise, or elementwise.
 Tablewise Function Application
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.16.2
-
 ``DataFrames`` and ``Series`` can of course just be passed into functions.
 However, if the function needs to be called in a chain, consider using the :meth:`~DataFrame.pipe` method.
 Compare the following
@@ -1860,8 +1858,10 @@ dtypes
 ------
 
 The main types stored in pandas objects are ``float``, ``int``, ``bool``,
-``datetime64[ns]`` and ``datetime64[ns, tz]`` (in >= 0.17.0), ``timedelta[ns]``, ``category`` (in >= 0.15.0), 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.
+``datetime64[ns]`` and ``datetime64[ns, tz]`` (in >= 0.17.0), ``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.
 
 A convenient :attr:`~DataFrame.dtypes` attribute for DataFrames returns a Series with the data type of each column.
 

doc/source/categorical.rst

@@ -16,13 +16,6 @@
 Categorical Data
 ****************
 
-.. versionadded:: 0.15
-
-.. note::
- While there was `pandas.Categorical` in earlier versions, the ability to use
- categorical data in `Series` and `DataFrame` is new.
-
-
 This is an introduction to pandas categorical data type, including a short comparison
 with R's ``factor``.
 
@@ -295,10 +288,6 @@ Sorting and Order
 
 .. _categorical.sort:
 
-.. warning::
-
- The default for construction has changed in v0.16.0 to ``ordered=False``, from the prior implicit ``ordered=True``
-
 If categorical data is ordered (``s.cat.ordered == True``), then the order of the categories has a
 meaning and certain operations are possible. If the categorical is unordered, ``.min()/.max()`` will raise a `TypeError`.
 
@@ -803,13 +792,11 @@ Following table summarizes the results of ``Categoricals`` related concatenation
 Getting Data In/Out
 -------------------
 
-.. versionadded:: 0.15.2
+You can write data that contains ``category`` dtypes to a ``HDFStore``.
+See :ref:`here <io.hdf5-categorical>` for an example and caveats.
 
-Writing data (`Series`, `Frames`) to a HDF store that contains a ``category`` dtype was implemented
-in 0.15.2. See :ref:`here <io.hdf5-categorical>` for an example and caveats.
-
-Writing data to and reading data from *Stata* format files was implemented in
-0.15.2. See :ref:`here <io.stata-categorical>` for an example and caveats.
+It is also possible to write data to and reading data from *Stata* format files.
+See :ref:`here <io.stata-categorical>` for an example and caveats.
 
 Writing to a CSV file will convert the data, effectively removing any information about the
 categorical (categories and ordering). So if you read back the CSV file you have to convert the
@@ -928,32 +915,6 @@ an ``object`` dtype is a constant times the length of the data.
  s.astype('category').nbytes
 
 
-Old style constructor usage
-~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-In earlier versions than pandas 0.15, a `Categorical` could be constructed by passing in precomputed
-`codes` (called then `labels`) instead of values with categories. The `codes` were interpreted as
-pointers to the categories with `-1` as `NaN`. This type of constructor usage is replaced by
-the special constructor :func:`Categorical.from_codes`.
-
-Unfortunately, in some special cases, using code which assumes the old style constructor usage
-will work with the current pandas version, resulting in subtle bugs:
-
-.. code-block:: python
-
- >>> cat = pd.Categorical([1,2], [1,2,3])
- >>> # old version
- >>> cat.get_values()
- array([2, 3], dtype=int64)
- >>> # new version
- >>> cat.get_values()
- array([1, 2], dtype=int64)
-
-.. warning::
- If you used `Categoricals` with older versions of pandas, please audit your code before
- upgrading and change your code to use the :func:`~pandas.Categorical.from_codes`
- constructor.
-
 `Categorical` is not a `numpy` array
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -982,8 +943,7 @@ Dtype comparisons work:
  dtype == np.str_
  np.str_ == dtype
 
-To check if a Series contains Categorical data, with pandas 0.16 or later, use
-``hasattr(s, 'cat')``:
+To check if a Series contains Categorical data, use ``hasattr(s, 'cat')``:
 
 .. ipython:: python
 
@@ -1023,13 +983,13 @@ basic type) and applying along columns will also convert to object.
 Categorical Index
 ~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.16.1
-
-A new ``CategoricalIndex`` index type is introduced in version 0.16.1. See the
-:ref:`advanced indexing docs <indexing.categoricalindex>` for a more detailed
+``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.
+See the :ref:`advanced indexing docs <indexing.categoricalindex>` for a more detailed
 explanation.
 
-Setting the index, will create create a ``CategoricalIndex``
+Setting the index will create a ``CategoricalIndex``
 
 .. ipython:: python
 
@@ -1041,10 +1001,6 @@ Setting the index, will create create a ``CategoricalIndex``
  # This now sorts by the categories order
  df.sort_index()
 
-In previous versions (<0.16.1) there is no index of type ``category``, so
-setting the index to categorical column will convert the categorical data to a
-"normal" dtype first and therefore remove any custom ordering of the categories.
-
 Side Effects
 ~~~~~~~~~~~~
 

doc/source/comparison_with_r.rst

@@ -505,8 +505,6 @@ For more details and examples see :ref:`the reshaping documentation
 |factor|_
 ~~~~~~~~~
 
-.. versionadded:: 0.15
-
 pandas has a data type for categorical data.
 
 .. code-block:: r

doc/source/computation.rst

@@ -924,15 +924,12 @@ EWM has a ``min_periods`` argument, which has the same
 meaning it does for all the ``.expanding`` and ``.rolling`` methods:
 no output values will be set until at least ``min_periods`` non-null values
 are encountered in the (expanding) window.
-(This is a change from versions prior to 0.15.0, in which the ``min_periods``
-argument affected only the ``min_periods`` consecutive entries starting at the
-first non-null value.)
 
-EWM also has an ``ignore_na`` argument, which deterines how
+EWM also has an ``ignore_na`` argument, which determines how
 intermediate null values affect the calculation of the weights.
 When ``ignore_na=False`` (the default), weights are calculated based on absolute
 positions, so that intermediate null values affect the result.
-When ``ignore_na=True`` (which reproduces the behavior in versions prior to 0.15.0),
+When ``ignore_na=True``,
 weights are calculated by ignoring intermediate null values.
 For example, assuming ``adjust=True``, if ``ignore_na=False``, the weighted
 average of ``3, NaN, 5`` would be calculated as

doc/source/cookbook.rst

@@ -256,12 +256,6 @@ Panels
 
  pf = pd.Panel({'df1':df1,'df2':df2,'df3':df3});pf
 
- #Assignment using Transpose (pandas < 0.15)
- pf = pf.transpose(2,0,1)
- pf['E'] = pd.DataFrame(data, rng, cols)
- pf = pf.transpose(1,2,0);pf
-
- #Direct assignment (pandas > 0.15)
  pf.loc[:,:,'F'] = pd.DataFrame(data, rng, cols);pf
 
 `Mask a panel by using np.where and then reconstructing the panel with the new masked values

doc/source/dsintro.rst

@@ -453,8 +453,6 @@ available to insert at a particular location in the columns:
 Assigning New Columns in Method Chains
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-.. versionadded:: 0.16.0
-
 Inspired by `dplyr's
 <http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html#mutate>`__
 ``mutate`` verb, DataFrame has an :meth:`~pandas.DataFrame.assign`

doc/source/gotchas.rst

@@ -22,8 +22,8 @@ Frequently Asked Questions (FAQ)
 
 DataFrame memory usage
 ----------------------
-As of pandas version 0.15.0, the memory usage of a dataframe (including
-the index) is shown when accessing the ``info`` method of a dataframe. A
+The memory usage of a dataframe (including the index)
+is shown when accessing the ``info`` method of a dataframe. A
 configuration option, ``display.memory_usage`` (see :ref:`options`),
 specifies if the dataframe's memory usage will be displayed when
 invoking the ``df.info()`` method.

doc/source/indexing.rst

@@ -47,12 +47,6 @@ advanced indexing.
  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 subclass ``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>`)
-
 .. warning::
 
  Indexing on an integer-based Index with floats has been clarified in 0.18.0, for a summary of the changes, see :ref:`here <whatsnew_0180.float_indexers>`.
@@ -660,7 +654,6 @@ For getting *multiple* indexers, using ``.get_indexer``
 
 Selecting Random Samples
 ------------------------
-.. versionadded::0.16.1
 
 A random selection of rows or columns from a Series, DataFrame, or Panel with the :meth:`~DataFrame.sample` method. The method will sample rows by default, and accepts a specific number of rows/columns to return, or a fraction of rows.
 
@@ -1510,8 +1503,6 @@ See :ref:`Advanced Indexing <advanced>` for usage of MultiIndexes.
  ind.name = "bob"
  ind
 
-.. versionadded:: 0.15.0
-
 ``set_names``, ``set_levels``, and ``set_labels`` also take an optional
 `level`` argument
 
@@ -1527,11 +1518,6 @@ Set operations on Index objects
 
 .. _indexing.set_ops:
 
-.. warning::
-
- In 0.15.0. the set operations ``+`` and ``-`` were deprecated in order to provide these for numeric type operations on certain
- index types. ``+`` can be replace by ``.union()`` or ``|``, and ``-`` by ``.difference()``.
-
 The two main operations are ``union (|)``, ``intersection (&)``
 These can be directly called as instance methods or used via overloaded
 operators. Difference is provided via the ``.difference()`` method.

doc/source/install.rst

@@ -18,7 +18,7 @@ Instructions for installing from source,
 Python version support
 ----------------------
 
-Officially Python 2.7, 3.4, 3.5, and 3.6
+Officially Python 2.7, 3.5, and 3.6.
 
 Installing pandas
 -----------------
@@ -183,21 +183,17 @@ installed), make sure you have `pytest
 
  >>> import pandas as pd
  >>> pd.test()
- Running unit tests for pandas
- pandas version 0.18.0
- numpy version 1.10.2
- pandas is installed in pandas
- Python version 2.7.11 |Continuum Analytics, Inc.|
- (default, Dec 6 2015, 18:57:58) [GCC 4.2.1 (Apple Inc. build 5577)]
- nose version 1.3.7
+ running: pytest --skip-slow --skip-network C:\Users\TP\Anaconda3\envs\py36\lib\site-packages\pandas
+ ============================= test session starts =============================
+ platform win32 -- Python 3.6.2, pytest-3.2.1, py-1.4.34, pluggy-0.4.0
+ rootdir: C:\Users\TP\Documents\Python\pandasdev\pandas, inifile: setup.cfg
+ collected 12145 items / 3 skipped
+
  ..................................................................S......
  ........S................................................................
  .........................................................................
 
- ----------------------------------------------------------------------
- Ran 9252 tests in 368.339s
-
- OK (SKIP=117)
+ ==================== 12130 passed, 12 skipped in 368.339 seconds =====================
 
 Dependencies
 ------------