Merge branch 'master' into validate_docstrings-add-ignore_known_fail-argument

Author: dcreekp

README.md

@@ -26,8 +26,9 @@
   <td>Package Status</td>
   <td>
 		<a href="https://pypi.org/project/pandas/">
-		<img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" /></td>
+		<img src="https://img.shields.io/pypi/status/pandas.svg" alt="status" />
 		</a>
+  </td>
 </tr>
 <tr>
   <td>License</td>
@@ -73,8 +74,8 @@
 	<td>Gitter</td>
 	<td>
 		<a href="https://gitter.im/pydata/pandas">
-		<img src="https://badges.gitter.im/Join%20Chat.svg"
-	</a>
+		<img src="https://badges.gitter.im/Join%20Chat.svg" />
+		</a>
 	</td>
 </tr>
 </table>

ci/code_checks.sh

@@ -241,8 +241,8 @@ fi
 ### DOCSTRINGS ###
 if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
 
-    MSG='Validate docstrings (GL06, GL07, GL09, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT04, RT05, SA05)' ; echo $MSG
-    $BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL06,GL07,GL09,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT04,RT05,SA05
+    MSG='Validate docstrings (GL03, GL06, GL07, GL09, SS04, SS05, PR03, PR04, PR05, PR10, EX04, RT04, RT05, SA05)' ; echo $MSG
+    $BASE_DIR/scripts/validate_docstrings.py --format=azure --errors=GL03,GL06,GL07,GL09,SS04,SS05,PR03,PR04,PR05,PR10,EX04,RT04,RT05,SA05
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
 fi

doc/source/getting_started/10min.rst

@@ -103,7 +103,7 @@ Display the index, columns:
    df.columns
 
 :meth:`DataFrame.to_numpy` gives a NumPy representation of the underlying data.
-Note that his can be an expensive operation when your :class:`DataFrame` has
+Note that this can be an expensive operation when your :class:`DataFrame` has
 columns with different data types, which comes down to a fundamental difference
 between pandas and NumPy: **NumPy arrays have one dtype for the entire array,
 while pandas DataFrames have one dtype per column**. When you call

doc/source/getting_started/dsintro.rst

@@ -576,14 +576,14 @@ To write code compatible with all versions of Python, split the assignment in tw
 
 .. warning::
 
-   Dependent assignment maybe subtly change the behavior of your code between
+   Dependent assignment may subtly change the behavior of your code between
    Python 3.6 and older versions of Python.
 
-   If you wish write code that supports versions of python before and after 3.6,
+   If you wish to write code that supports versions of python before and after 3.6,
    you'll need to take care when passing ``assign`` expressions that
 
-   * Updating an existing column
-   * Referring to the newly updated column in the same ``assign``
+   * Update an existing column
+   * Refer to the newly updated column in the same ``assign``
 
    For example, we'll update column "A" and then refer to it when creating "B".
 
@@ -665,8 +665,8 @@ row-wise. For example:
 
    df - df.iloc[0]
 
-In the special case of working with time series data, and the DataFrame index
-also contains dates, the broadcasting will be column-wise:
+In the special case of working with time series data, if the DataFrame index
+contains dates, the broadcasting will be column-wise:
 
 .. ipython:: python
    :okwarning:
@@ -1030,47 +1030,3 @@ method:
                     major_axis=pd.date_range('1/1/2000', periods=5),
                     minor_axis=['a', 'b', 'c', 'd'])
    panel.to_frame()
-
-
-.. _dsintro.deprecate_panel:
-
-Deprecate Panel
----------------
-
-Over the last few years, pandas has increased in both breadth and depth, with new features,
-datatype support, and manipulation routines. As a result, supporting efficient indexing and functional
-routines for ``Series``, ``DataFrame`` and ``Panel`` has contributed to an increasingly fragmented and
-difficult-to-understand code base.
-
-The 3-D structure of a ``Panel`` is much less common for many types of data analysis,
-than the 1-D of the ``Series`` or the 2-D of the ``DataFrame``. Going forward it makes sense for
-pandas to focus on these areas exclusively.
-
-Oftentimes, one can simply use a MultiIndex ``DataFrame`` for easily working with higher dimensional data.
-
-In addition, the ``xarray`` package was built from the ground up, specifically in order to
-support the multi-dimensional analysis that is one of ``Panel`` s main use cases.
-`Here is a link to the xarray panel-transition documentation <https://xarray.pydata.org/en/stable/pandas.html#panel-transition>`__.
-
-.. ipython:: python
-   :okwarning:
-
-   import pandas.util.testing as tm
-   p = tm.makePanel()
-   p
-
-Convert to a MultiIndex DataFrame.
-
-.. ipython:: python
-   :okwarning:
-
-   p.to_frame()
-
-Alternatively, one can convert to an xarray ``DataArray``.
-
-.. ipython:: python
-   :okwarning:
-
-   p.to_xarray()
-
-You can see the full-documentation for the `xarray package <https://xarray.pydata.org/en/stable/>`__.

doc/source/user_guide/indexing.rst

@@ -435,7 +435,7 @@ Selection By Position
    This is sometimes called ``chained assignment`` and should be avoided.
    See :ref:`Returning a View versus Copy <indexing.view_versus_copy>`.
 
-Pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely Python and NumPy slicing. These are ``0-based`` indexing. When slicing, the start bounds is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise an ``IndexError``.
+Pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely Python and NumPy slicing. These are ``0-based`` indexing. When slicing, the start bound is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise an ``IndexError``.
 
 The ``.iloc`` attribute is the primary access method. The following are valid inputs:
 
@@ -545,7 +545,7 @@ Selection By Callable
 .. versionadded:: 0.18.1
 
 ``.loc``, ``.iloc``, and also ``[]`` indexing can accept a ``callable`` as indexer.
-The ``callable`` must be a function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing.
+The ``callable`` must be a function with one argument (the calling Series, DataFrame or Panel) that returns valid output for indexing.
 
 .. ipython:: python
 
@@ -569,7 +569,7 @@ You can use callable indexing in ``Series``.
    df1.A.loc[lambda s: s > 0]
 
 Using these methods / indexers, you can chain data selection operations
-without using temporary variable.
+without using a temporary variable.
 
 .. ipython:: python
 
@@ -907,7 +907,7 @@ of the DataFrame):
 
    df[df['A'] > 0]
 
-List comprehensions and ``map`` method of Series can also be used to produce
+List comprehensions and the ``map`` method of Series can also be used to produce
 more complex criteria:
 
 .. ipython:: python
@@ -1556,7 +1556,7 @@ See :ref:`Advanced Indexing <advanced>` for usage of MultiIndexes.
   ind
 
 ``set_names``, ``set_levels``, and ``set_codes`` also take an optional
-`level`` argument
+``level`` argument
 
 .. ipython:: python
 

doc/source/user_guide/io.rst

@@ -1689,7 +1689,7 @@ The ``Series`` and ``DataFrame`` objects have an instance method ``to_csv`` whic
 allows storing the contents of the object as a comma-separated-values file. The
 function takes a number of arguments. Only the first is required.
 
-* ``path_or_buf``: A string path to the file to write or a StringIO
+* ``path_or_buf``: A string path to the file to write or a file object.  If a file object it must be opened with `newline=''`
 * ``sep`` : Field delimiter for the output file (default ",")
 * ``na_rep``: A string representation of a missing value (default '')
 * ``float_format``: Format string for floating point numbers
@@ -1702,7 +1702,7 @@ function takes a number of arguments. Only the first is required.
 * ``mode`` : Python write mode, default 'w'
 * ``encoding``: a string representing the encoding to use if the contents are
   non-ASCII, for Python versions prior to 3
-* ``line_terminator``: Character sequence denoting line end (default '\\n')
+* ``line_terminator``: Character sequence denoting line end (default `os.linesep`)
 * ``quoting``: Set quoting rules as in csv module (default csv.QUOTE_MINIMAL). Note that if you have set a `float_format` then floats are converted to strings and csv.QUOTE_NONNUMERIC will treat them as non-numeric
 * ``quotechar``: Character used to quote fields (default '"')
 * ``doublequote``: Control quoting of ``quotechar`` in fields (default True)

doc/source/whatsnew/v0.13.1.rst

@@ -222,60 +222,155 @@ Enhancements
 
 - Panel :meth:`~pandas.Panel.apply` will work on non-ufuncs. See :ref:`the docs<basics.apply>`.
 
-  .. ipython:: python
+  .. code-block:: ipython
+
+      In [28]: import pandas.util.testing as tm
+
+      In [29]: panel = tm.makePanel(5)
 
-     import pandas.util.testing as tm
-     panel = tm.makePanel(5)
-     panel
-     panel['ItemA']
+      In [30]: panel
+      Out[30]:
+      <class 'pandas.core.panel.Panel'>
+      Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
+      Items axis: ItemA to ItemC
+      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
+      Minor_axis axis: A to D
+
+      In [31]: panel['ItemA']
+      Out[31]:
+                         A         B         C         D
+      2000-01-03 -0.673690  0.577046 -1.344312 -1.469388
+      2000-01-04  0.113648 -1.715002  0.844885  0.357021
+      2000-01-05 -1.478427 -1.039268  1.075770 -0.674600
+      2000-01-06  0.524988 -0.370647 -0.109050 -1.776904
+      2000-01-07  0.404705 -1.157892  1.643563 -0.968914
+
+      [5 rows x 4 columns]
 
   Specifying an ``apply`` that operates on a Series (to return a single element)
 
-  .. ipython:: python
+  .. code-block:: ipython
+
+      In [32]: panel.apply(lambda x: x.dtype, axis='items')
+      Out[32]:
+                        A        B        C        D
+      2000-01-03  float64  float64  float64  float64
+      2000-01-04  float64  float64  float64  float64
+      2000-01-05  float64  float64  float64  float64
+      2000-01-06  float64  float64  float64  float64
+      2000-01-07  float64  float64  float64  float64
 
-     panel.apply(lambda x: x.dtype, axis='items')
+      [5 rows x 4 columns]
 
   A similar reduction type operation
 
-  .. ipython:: python
+  .. code-block:: ipython
+
+      In [33]: panel.apply(lambda x: x.sum(), axis='major_axis')
+      Out[33]:
+            ItemA     ItemB     ItemC
+      A -1.108775 -1.090118 -2.984435
+      B -3.705764  0.409204  1.866240
+      C  2.110856  2.960500 -0.974967
+      D -4.532785  0.303202 -3.685193
 
-     panel.apply(lambda x: x.sum(), axis='major_axis')
+      [4 rows x 3 columns]
 
   This is equivalent to
 
-  .. ipython:: python
+  .. code-block:: ipython
+
+      In [34]: panel.sum('major_axis')
+      Out[34]:
+            ItemA     ItemB     ItemC
+      A -1.108775 -1.090118 -2.984435
+      B -3.705764  0.409204  1.866240
+      C  2.110856  2.960500 -0.974967
+      D -4.532785  0.303202 -3.685193
 
-     panel.sum('major_axis')
+      [4 rows x 3 columns]
 
   A transformation operation that returns a Panel, but is computing
   the z-score across the major_axis
 
-  .. ipython:: python
+  .. code-block:: ipython
 
-     result = panel.apply(lambda x: (x - x.mean()) / x.std(),
-                          axis='major_axis')
-     result
-     result['ItemA']
+      In [35]: result = panel.apply(lambda x: (x - x.mean()) / x.std(),
+        ....:                      axis='major_axis')
+        ....:
+
+      In [36]: result
+      Out[36]:
+      <class 'pandas.core.panel.Panel'>
+      Dimensions: 3 (items) x 5 (major_axis) x 4 (minor_axis)
+      Items axis: ItemA to ItemC
+      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
+      Minor_axis axis: A to D
+
+      In [37]: result['ItemA']                           # noqa E999
+      Out[37]:
+                        A         B         C         D
+      2000-01-03 -0.535778  1.500802 -1.506416 -0.681456
+      2000-01-04  0.397628 -1.108752  0.360481  1.529895
+      2000-01-05 -1.489811 -0.339412  0.557374  0.280845
+      2000-01-06  0.885279  0.421830 -0.453013 -1.053785
+      2000-01-07  0.742682 -0.474468  1.041575 -0.075499
+
+      [5 rows x 4 columns]
 
 - Panel :meth:`~pandas.Panel.apply` operating on cross-sectional slabs. (:issue:`1148`)
 
-  .. ipython:: python
+  .. code-block:: ipython
 
-     def f(x):
-         return ((x.T - x.mean(1)) / x.std(1)).T
+      In [38]: def f(x):
+         ....:     return ((x.T - x.mean(1)) / x.std(1)).T
+         ....:
 
-     result = panel.apply(f, axis=['items', 'major_axis'])
-     result
-     result.loc[:, :, 'ItemA']
+      In [39]: result = panel.apply(f, axis=['items', 'major_axis'])
 
-  This is equivalent to the following
+      In [40]: result
+      Out[40]:
+      <class 'pandas.core.panel.Panel'>
+      Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
+      Items axis: A to D
+      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
+      Minor_axis axis: ItemA to ItemC
 
-  .. ipython:: python
+      In [41]: result.loc[:, :, 'ItemA']
+      Out[41]:
+                         A         B         C         D
+      2000-01-03  0.012922 -0.030874 -0.629546 -0.757034
+      2000-01-04  0.392053 -1.071665  0.163228  0.548188
+      2000-01-05 -1.093650 -0.640898  0.385734 -1.154310
+      2000-01-06  1.005446 -1.154593 -0.595615 -0.809185
+      2000-01-07  0.783051 -0.198053  0.919339 -1.052721
+
+      [5 rows x 4 columns]
 
-     result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})
+  This is equivalent to the following
+
+  .. code-block:: ipython
 
-     result
-     result.loc[:, :, 'ItemA']
+      In [42]: result = pd.Panel({ax: f(panel.loc[:, :, ax]) for ax in panel.minor_axis})
+
+      In [43]: result
+      Out[43]:
+      <class 'pandas.core.panel.Panel'>
+      Dimensions: 4 (items) x 5 (major_axis) x 3 (minor_axis)
+      Items axis: A to D
+      Major_axis axis: 2000-01-03 00:00:00 to 2000-01-07 00:00:00
+      Minor_axis axis: ItemA to ItemC
+
+      In [44]: result.loc[:, :, 'ItemA']
+      Out[44]:
+                         A         B         C         D
+      2000-01-03  0.012922 -0.030874 -0.629546 -0.757034
+      2000-01-04  0.392053 -1.071665  0.163228  0.548188
+      2000-01-05 -1.093650 -0.640898  0.385734 -1.154310
+      2000-01-06  1.005446 -1.154593 -0.595615 -0.809185
+      2000-01-07  0.783051 -0.198053  0.919339 -1.052721
+
+      [5 rows x 4 columns]
 
 Performance
 ~~~~~~~~~~~