Merge branch 'master' into PR_TOOL_MERGE_PR_19762

Author: jreback

ci/lint.sh

@@ -111,6 +111,15 @@ if [ "$LINT" ]; then
         RET=1
     fi
 
+    # Check for the following code in the extension array base tests
+    # tm.assert_frame_equal
+    # tm.assert_series_equal
+    grep -r -E --include '*.py' --exclude base.py 'tm.assert_(series|frame)_equal' pandas/tests/extension/base
+
+    if [ $? = "0" ]; then
+        RET=1
+    fi
+
     echo "Check for invalid testing DONE"
 
     # Check for imports from pandas.core.common instead
@@ -156,6 +165,7 @@ if [ "$LINT" ]; then
         RET=1
     fi
     echo "Check for deprecated messages without sphinx directive DONE"
+    
 else
     echo "NOT Linting"
 fi

doc/source/basics.rst

@@ -2312,4 +2312,4 @@ All NumPy dtypes are subclasses of ``numpy.generic``:
 .. note::
 
     Pandas also defines the types ``category``, and ``datetime64[ns, tz]``, which are not integrated into the normal
-    NumPy hierarchy and wont show up with the above function.
+    NumPy hierarchy and won't show up with the above function.

doc/source/dsintro.rst

@@ -364,6 +364,19 @@ and returns a DataFrame. It operates like the ``DataFrame`` constructor except
 for the ``orient`` parameter which is ``'columns'`` by default, but which can be
 set to ``'index'`` in order to use the dict keys as row labels.
 
+
+.. ipython:: python
+
+   pd.DataFrame.from_dict(dict([('A', [1, 2, 3]), ('B', [4, 5, 6])]))
+
+If you pass ``orient='index'``, the keys will be the row labels. In this
+case, you can also pass the desired column names:
+
+.. ipython:: python
+
+   pd.DataFrame.from_dict(dict([('A', [1, 2, 3]), ('B', [4, 5, 6])]),
+                          orient='index', columns=['one', 'two', 'three'])
+
 .. _basics.dataframe.from_records:
 
 **DataFrame.from_records**
@@ -378,28 +391,6 @@ dtype. For example:
    data
    pd.DataFrame.from_records(data, index='C')
 
-.. _basics.dataframe.from_items:
-
-**DataFrame.from_items**
-
-``DataFrame.from_items`` works analogously to the form of the ``dict``
-constructor that takes a sequence of ``(key, value)`` pairs, where the keys are
-column (or row, in the case of ``orient='index'``) names, and the value are the
-column values (or row values). This can be useful for constructing a DataFrame
-with the columns in a particular order without having to pass an explicit list
-of columns:
-
-.. ipython:: python
-
-   pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])])
-
-If you pass ``orient='index'``, the keys will be the row labels. But in this
-case you must also pass the desired column names:
-
-.. ipython:: python
-
-   pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],
-                           orient='index', columns=['one', 'two', 'three'])
 
 Column selection, addition, deletion
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -539,7 +530,7 @@ To write code compatible with all versions of Python, split the assignment in tw
    you'll need to take care when passing ``assign`` expressions that
 
    * Updating an existing column
-   * Refering to the newly updated column in the same ``assign``
+   * Referring to the newly updated column in the same ``assign``
 
    For example, we'll update column "A" and then refer to it when creating "B".
 

doc/source/gotchas.rst

@@ -22,22 +22,22 @@ Frequently Asked Questions (FAQ)
 
 DataFrame memory usage
 ----------------------
-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.
+The memory usage of a ``DataFrame`` (including the index) is shown when calling
+the :meth:`~DataFrame.info`. A configuration option, ``display.memory_usage`` 
+(see :ref:`the list of options <options.available>`), specifies if the 
+``DataFrame``'s memory usage will be displayed when invoking the ``df.info()`` 
+method.
 
-For example, the memory usage of the dataframe below is shown
-when calling ``df.info()``:
+For example, the memory usage of the ``DataFrame`` below is shown
+when calling :meth:`~DataFrame.info`:
 
 .. ipython:: python
 
     dtypes = ['int64', 'float64', 'datetime64[ns]', 'timedelta64[ns]',
               'complex128', 'object', 'bool']
     n = 5000
-    data = dict([ (t, np.random.randint(100, size=n).astype(t))
-                    for t in dtypes])
+    data = dict([(t, np.random.randint(100, size=n).astype(t))
+                  for t in dtypes])
     df = pd.DataFrame(data)
     df['categorical'] = df['object'].astype('category')
 
@@ -48,7 +48,7 @@ pandas does not count the memory used by values in columns with
 ``dtype=object``.
 
 Passing ``memory_usage='deep'`` will enable a more accurate memory usage report,
-that accounts for the full usage of the contained objects. This is optional
+accounting for the full usage of the contained objects. This is optional
 as it can be expensive to do this deeper introspection.
 
 .. ipython:: python
@@ -58,11 +58,11 @@ as it can be expensive to do this deeper introspection.
 By default the display option is set to ``True`` but can be explicitly
 overridden by passing the ``memory_usage`` argument when invoking ``df.info()``.
 
-The memory usage of each column can be found by calling the ``memory_usage``
-method. This returns a Series with an index represented by column names
-and memory usage of each column shown in bytes. For the dataframe above,
-the memory usage of each column and the total memory usage of the
-dataframe can be found with the memory_usage method:
+The memory usage of each column can be found by calling the 
+:meth:`~DataFrame.memory_usage` method. This returns a ``Series`` with an index 
+represented by column names and memory usage of each column shown in bytes. For 
+the ``DataFrame`` above, the memory usage of each column and the total memory 
+usage can be found with the ``memory_usage`` method:
 
 .. ipython:: python
 
@@ -71,18 +71,18 @@ dataframe can be found with the memory_usage method:
     # total memory usage of dataframe
     df.memory_usage().sum()
 
-By default the memory usage of the dataframe's index is shown in the
-returned Series, the memory usage of the index can be suppressed by passing
+By default the memory usage of the ``DataFrame``'s index is shown in the
+returned ``Series``, the memory usage of the index can be suppressed by passing
 the ``index=False`` argument:
 
 .. ipython:: python
 
     df.memory_usage(index=False)
 
-The memory usage displayed by the ``info`` method utilizes the
-``memory_usage`` method to determine the memory usage of a dataframe
-while also formatting the output in human-readable units (base-2
-representation; i.e., 1KB = 1024 bytes).
+The memory usage displayed by the :meth:`~DataFrame.info` method utilizes the
+:meth:`~DataFrame.memory_usage` method to determine the memory usage of a 
+``DataFrame`` while also formatting the output in human-readable units (base-2
+representation; i.e. 1KB = 1024 bytes).
 
 See also :ref:`Categorical Memory Usage <categorical.memory>`.
 
@@ -91,17 +91,18 @@ See also :ref:`Categorical Memory Usage <categorical.memory>`.
 Using If/Truth Statements with pandas
 -------------------------------------
 
-pandas follows the NumPy convention of raising an error when you try to convert something to a ``bool``.
-This happens in a ``if`` or when using the boolean operations, ``and``, ``or``, or ``not``.  It is not clear
-what the result of
+pandas follows the NumPy convention of raising an error when you try to convert 
+something to a ``bool``. This happens in an ``if``-statement or when using the 
+boolean operations: ``and``, ``or``, and ``not``. It is not clear what the result
+of the following code should be:
 
 .. code-block:: python
 
     >>> if pd.Series([False, True, False]):
          ...
 
-should be. Should it be ``True`` because it's not zero-length? ``False`` because there are ``False`` values?
-It is unclear, so instead, pandas raises a ``ValueError``:
+Should it be ``True`` because it's not zero-length, or ``False`` because there 
+are ``False`` values? It is unclear, so instead, pandas raises a ``ValueError``:
 
 .. code-block:: python
 
@@ -111,9 +112,9 @@ It is unclear, so instead, pandas raises a ``ValueError``:
         ...
     ValueError: The truth value of an array is ambiguous. Use a.empty, a.any() or a.all().
 
-
-If you see that, you need to explicitly choose what you want to do with it (e.g., use `any()`, `all()` or `empty`).
-or, you might want to compare if the pandas object is ``None``
+You need to explicitly choose what you want to do with the ``DataFrame``, e.g.
+use :meth:`~DataFrame.any`, :meth:`~DataFrame.all` or :meth:`~DataFrame.empty`.
+Alternatively, you might want to compare if the pandas object is ``None``:
 
 .. code-block:: python
 
@@ -122,15 +123,16 @@ or, you might want to compare if the pandas object is ``None``
     >>> I was not None
 
 
-or return if ``any`` value is ``True``.
+Below is how to check if any of the values are ``True``:
 
 .. code-block:: python
 
     >>> if pd.Series([False, True, False]).any():
            print("I am any")
     >>> I am any
 
-To evaluate single-element pandas objects in a boolean context, use the method ``.bool()``:
+To evaluate single-element pandas objects in a boolean context, use the method 
+:meth:`~DataFrame.bool`:
 
 .. ipython:: python
 
@@ -161,25 +163,25 @@ See :ref:`boolean comparisons<basics.compare>` for more examples.
 Using the ``in`` operator
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Using the Python ``in`` operator on a Series tests for membership in the
+Using the Python ``in`` operator on a ``Series`` tests for membership in the
 index, not membership among the values.
 
-.. ipython::
+.. ipython:: python
 
     s = pd.Series(range(5), index=list('abcde'))
     2 in s
     'b' in s
 
 If this behavior is surprising, keep in mind that using ``in`` on a Python
-dictionary tests keys, not values, and Series are dict-like.
-To test for membership in the values, use the method :func:`~pandas.Series.isin`:
+dictionary tests keys, not values, and ``Series`` are dict-like.
+To test for membership in the values, use the method :meth:`~pandas.Series.isin`:
 
-.. ipython::
+.. ipython:: python
 
     s.isin([2])
     s.isin([2]).any()
 
-For DataFrames, likewise, ``in`` applies to the column axis,
+For ``DataFrames``, likewise, ``in`` applies to the column axis,
 testing for membership in the list of column names.
 
 ``NaN``, Integer ``NA`` values and ``NA`` type promotions
@@ -189,12 +191,12 @@ Choice of ``NA`` representation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 For lack of ``NA`` (missing) support from the ground up in NumPy and Python in
-general, we were given the difficult choice between either
+general, we were given the difficult choice between either:
 
 - A *masked array* solution: an array of data and an array of boolean values
-  indicating whether a value is there or is missing
+  indicating whether a value is there or is missing.
 - Using a special sentinel value, bit pattern, or set of sentinel values to
-  denote ``NA`` across the dtypes
+  denote ``NA`` across the dtypes.
 
 For many reasons we chose the latter. After years of production use it has
 proven, at least in my opinion, to be the best decision given the state of
@@ -226,15 +228,16 @@ arrays. For example:
    s2.dtype
 
 This trade-off is made largely for memory and performance reasons, and also so
-that the resulting Series continues to be "numeric". One possibility is to use
-``dtype=object`` arrays instead.
+that the resulting ``Series`` continues to be "numeric". One possibility is to 
+use ``dtype=object`` arrays instead.
 
 ``NA`` type promotions
 ~~~~~~~~~~~~~~~~~~~~~~
 
-When introducing NAs into an existing Series or DataFrame via ``reindex`` or
-some other means, boolean and integer types will be promoted to a different
-dtype in order to store the NAs. These are summarized by this table:
+When introducing NAs into an existing ``Series`` or ``DataFrame`` via 
+:meth:`~Series.reindex` or some other means, boolean and integer types will be 
+promoted to a different dtype in order to store the NAs. The promotions are 
+summarized in this table:
 
 .. csv-table::
    :header: "Typeclass","Promotion dtype for storing NAs"
@@ -289,19 +292,19 @@ integer arrays to floating when NAs must be introduced.
 
 Differences with NumPy
 ----------------------
-For Series and DataFrame objects, ``var`` normalizes by ``N-1`` to produce
-unbiased estimates of the sample variance, while NumPy's ``var`` normalizes
-by N, which measures the variance of the sample. Note that ``cov``
-normalizes by ``N-1`` in both pandas and NumPy.
+For ``Series`` and ``DataFrame`` objects, :meth:`~DataFrame.var` normalizes by 
+``N-1`` to produce unbiased estimates of the sample variance, while NumPy's 
+``var`` normalizes by N, which measures the variance of the sample. Note that 
+:meth:`~DataFrame.cov` normalizes by ``N-1`` in both pandas and NumPy.
 
 
 Thread-safety
 -------------
 
 As of pandas 0.11, pandas is not 100% thread safe. The known issues relate to
-the ``DataFrame.copy`` method. If you are doing a lot of copying of DataFrame
-objects shared among threads, we recommend holding locks inside the threads
-where the data copying occurs.
+the :meth:`~DataFrame.copy` method. If you are doing a lot of copying of 
+``DataFrame`` objects shared among threads, we recommend holding locks inside 
+the threads where the data copying occurs.
 
 See `this link <https://stackoverflow.com/questions/13592618/python-pandas-dataframe-thread-safe>`__
 for more information.
@@ -310,7 +313,8 @@ for more information.
 Byte-Ordering Issues
 --------------------
 Occasionally you may have to deal with data that were created on a machine with
-a different byte order than the one on which you are running Python. A common symptom of this issue is an error like
+a different byte order than the one on which you are running Python. A common 
+symptom of this issue is an error like:
 
 .. code-block:: python
 
@@ -320,8 +324,8 @@ a different byte order than the one on which you are running Python. A common sy
 
 To deal
 with this issue you should convert the underlying NumPy array to the native
-system byte order *before* passing it to Series/DataFrame/Panel constructors
-using something similar to the following:
+system byte order *before* passing it to ``Series`` or ``DataFrame`` 
+constructors using something similar to the following:
 
 .. ipython:: python
 

doc/source/tutorials.rst

@@ -26,32 +26,34 @@ repository <http://github.com/jvns/pandas-cookbook>`_. To run the examples in th
 clone the GitHub repository and get IPython Notebook running.
 See `How to use this cookbook <https://github.com/jvns/pandas-cookbook#how-to-use-this-cookbook>`_.
 
--  `A quick tour of the IPython Notebook: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/A%20quick%20tour%20of%20IPython%20Notebook.ipynb>`_
+-  `A quick tour of the IPython Notebook: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/A%20quick%20tour%20of%20IPython%20Notebook.ipynb>`_
    Shows off IPython's awesome tab completion and magic functions.
--  `Chapter 1: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%201%20-%20Reading%20from%20a%20CSV.ipynb>`_
+-  `Chapter 1: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%201%20-%20Reading%20from%20a%20CSV.ipynb>`_
    Reading your data into pandas is pretty much the easiest thing. Even
    when the encoding is wrong!
--  `Chapter 2: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%202%20-%20Selecting%20data%20&%20finding%20the%20most%20common%20complaint%20type.ipynb>`_
+-  `Chapter 2: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%202%20-%20Selecting%20data%20%26%20finding%20the%20most%20common%20complaint%20type.ipynb>`_
    It's not totally obvious how to select data from a pandas dataframe.
    Here we explain the basics (how to take slices and get columns)
--  `Chapter 3: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%3F%20%28or%2C%20more%20selecting%20data%29.ipynb>`_
+-  `Chapter 3: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%20%28or%2C%20more%20selecting%20data%29.ipynb>`_
    Here we get into serious slicing and dicing and learn how to filter
    dataframes in complicated ways, really fast.
--  `Chapter 4: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%204%20-%20Find%20out%20on%20which%20weekday%20people%20bike%20the%20most%20with%20groupby%20and%20aggregate.ipynb>`_
+-  `Chapter 4: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%204%20-%20Find%20out%20on%20which%20weekday%20people%20bike%20the%20most%20with%20groupby%20and%20aggregate.ipynb>`_
    Groupby/aggregate is seriously my favorite thing about pandas
    and I use it all the time. You should probably read this.
--  `Chapter 5:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%205%20-%20Combining%20dataframes%20and%20scraping%20Canadian%20weather%20data.ipynb>`_
+-  `Chapter 5:  <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%205%20-%20Combining%20dataframes%20and%20scraping%20Canadian%20weather%20data.ipynb>`_
    Here you get to find out if it's cold in Montreal in the winter
    (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes.
--  `Chapter 6:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%206%20-%20String%20operations%21%20Which%20month%20was%20the%20snowiest%3F.ipynb>`_
+-  `Chapter 6:  <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%206%20-%20String%20Operations-%20Which%20month%20was%20the%20snowiest.ipynb>`_
    Strings with pandas are great. It has all these vectorized string
    operations and they're the best. We will turn a bunch of strings
    containing "Snow" into vectors of numbers in a trice.
--  `Chapter 7: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb>`_
+-  `Chapter 7: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb>`_
    Cleaning up messy data is never a joy, but with pandas it's easier.
--  `Chapter 8:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%208%20-%20How%20to%20deal%20with%20timestamps.ipynb>`_
+-  `Chapter 8:  <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%208%20-%20How%20to%20deal%20with%20timestamps.ipynb>`_
    Parsing Unix timestamps is confusing at first but it turns out
    to be really easy.
+-  `Chapter 9:  <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%209%20-%20Loading%20data%20from%20SQL%20databases.ipynb>`_
+   Reading data from SQL databases.
 
 
 Lessons for new pandas users

doc/source/whatsnew/v0.14.1.txt

@@ -145,7 +145,7 @@ Performance
 ~~~~~~~~~~~
 - Improvements in dtype inference for numeric operations involving yielding performance gains for dtypes: ``int64``, ``timedelta64``, ``datetime64`` (:issue:`7223`)
 - Improvements in Series.transform for significant performance gains (:issue:`6496`)
-- Improvements in DataFrame.transform with ufuncs and built-in grouper functions for signifcant performance gains (:issue:`7383`)
+- Improvements in DataFrame.transform with ufuncs and built-in grouper functions for significant performance gains (:issue:`7383`)
 - Regression in groupby aggregation of datetime64 dtypes (:issue:`7555`)
 - Improvements in `MultiIndex.from_product` for large iterables (:issue:`7627`)