DOC: Add sphinx spelling extension

doc/make.py

@@ -224,8 +224,9 @@ def _sphinx_build(self, kind):
         --------
         >>> DocBuilder(num_jobs=4)._sphinx_build('html')
         """
-        if kind not in ('html', 'latex'):
-            raise ValueError('kind must be html or latex, not {}'.format(kind))
+        if kind not in ('html', 'latex', 'spelling'):
+            raise ValueError('kind must be html, latex or '
+                             'spelling, not {}'.format(kind))
 
         self._run_os('sphinx-build',
                      '-j{}'.format(self.num_jobs),
@@ -304,6 +305,10 @@ def zip_html(self):
                      '-q',
                      *fnames)
 
+    def spellcheck(self):
+        """Spell check the documentation."""
+        self._sphinx_build('spelling')
+
 
 def main():
     cmds = [method for method in dir(DocBuilder) if not method.startswith('_')]

doc/source/10min.rst

@@ -645,7 +645,7 @@ the quarter end:
    ts.index = (prng.asfreq('M', 'e') + 1).asfreq('H', 's') + 9
    ts.head()
 
-Categoricals
+Categorical
 ------------
 
 pandas can include categorical data in a ``DataFrame``. For full docs, see the
@@ -663,7 +663,7 @@ Convert the raw grades to a categorical data type.
     df["grade"]
 
 Rename the categories to more meaningful names (assigning to 
-``Series.cat.categories`` is inplace!).
+``Series.cat.categories`` is in place!).
 
 .. ipython:: python
 

doc/source/advanced.rst

@@ -182,7 +182,7 @@ For example:
    df[['foo','qux']].columns  # sliced
 
 This is done to avoid a recomputation of the levels in order to make slicing
-highly performant. If you want to see only the used levels, you can use the
+highly efficient. If you want to see only the used levels, you can use the
 :func:`MultiIndex.get_level_values` method.
 
 .. ipython:: python
@@ -387,7 +387,7 @@ Furthermore you can *set* the values using the following methods.
    df2.loc(axis=0)[:, :, ['C1', 'C3']] = -10
    df2
 
-You can use a right-hand-side of an alignable object as well.
+You can use a right-hand-side of an align object as well.
 
 .. ipython:: python
 
@@ -559,7 +559,7 @@ return a copy of the data rather than a view:
 
 .. _advanced.unsorted:
 
-Furthermore if you try to index something that is not fully lexsorted, this can raise:
+Furthermore if you try to index something that is not fully lex-sorted, this can raise:
 
 .. code-block:: ipython
 
@@ -593,7 +593,7 @@ Take Methods
 
 Similar to NumPy ndarrays, pandas Index, Series, and DataFrame also provides
 the ``take`` method that retrieves elements along a given axis at the given
-indices. The given indices must be either a list or an ndarray of integer
+indexes. The given indexes must be either a list or an ndarray of integer
 index positions. ``take`` will also accept negative integers as relative positions to the end of the object.
 
 .. ipython:: python
@@ -611,7 +611,7 @@ index positions. ``take`` will also accept negative integers as relative positio
    ser.iloc[positions]
    ser.take(positions)
 
-For DataFrames, the given indices should be a 1d list or ndarray that specifies
+For DataFrames, the given indexes should be a 1d list or ndarray that specifies
 row or column positions.
 
 .. ipython:: python
@@ -623,7 +623,7 @@ row or column positions.
    frm.take([0, 2], axis=1)
 
 It is important to note that the ``take`` method on pandas objects are not
-intended to work on boolean indices and may return unexpected results.
+intended to work on boolean indexes and may return unexpected results.
 
 .. ipython:: python
 
@@ -711,7 +711,7 @@ order is ``cab``).
 
    df2.sort_index()
 
-Groupby operations on the index will preserve the index nature as well.
+Group by operations on the index will preserve the index nature as well.
 
 .. ipython:: python
 
@@ -990,7 +990,7 @@ On the other hand, if the index is not monotonic, then both slice bounds must be
     KeyError: 'Cannot get right slice bound for non-unique label: 3'
 
 :meth:`Index.is_monotonic_increasing` and :meth:`Index.is_monotonic_decreasing` only check that
-an index is weakly monotonic. To check for strict montonicity, you can combine one of those with
+an index is weakly monotonic. To check for strict monotonicity, you can combine one of those with
 :meth:`Index.is_unique`
 
 .. ipython:: python

doc/source/basics.rst

@@ -593,7 +593,7 @@ categorical columns:
     frame = pd.DataFrame({'a': ['Yes', 'Yes', 'No', 'No'], 'b': range(4)})
     frame.describe()
 
-This behaviour can be controlled by providing a list of types as ``include``/``exclude``
+This behavior can be controlled by providing a list of types as ``include``/``exclude``
 arguments. The special value ``all`` can also be used:
 
 .. ipython:: python

doc/source/categorical.rst

@@ -370,7 +370,7 @@ Renaming categories is done by assigning new values to the
 
 .. note::
 
-    Be aware that assigning new categories is an inplace operation, while most other operations
+    Be aware that assigning new categories is an in place operation, while most other operations
     under ``Series.cat`` per default return a new ``Series`` of dtype `category`.
 
 Categories must be unique or a `ValueError` is raised:
@@ -847,7 +847,7 @@ the categories being combined.
 
 By default, the resulting categories will be ordered as
 they appear in the data. If you want the categories to
-be lexsorted, use ``sort_categories=True`` argument.
+be lex-sorted, use ``sort_categories=True`` argument.
 
 .. ipython:: python
 

doc/source/comparison_with_sql.rst

@@ -228,9 +228,9 @@ Grouping by more than one column is done by passing a list of columns to the
 JOIN
 ----
 JOINs can be performed with :meth:`~pandas.DataFrame.join` or :meth:`~pandas.merge`. By default,
-:meth:`~pandas.DataFrame.join` will join the DataFrames on their indices. Each method has
+:meth:`~pandas.DataFrame.join` will join the DataFrames on their indexes. Each method has
 parameters allowing you to specify the type of join to perform (LEFT, RIGHT, INNER, FULL) or the
-columns to join on (column names or indices).
+columns to join on (column names or indexes).
 
 .. ipython:: python
 

doc/source/conf.py

@@ -73,10 +73,15 @@
               'sphinx.ext.ifconfig',
               'sphinx.ext.linkcode',
               'nbsphinx',
+              'sphinxcontrib.spelling'
               ]
 
 exclude_patterns = ['**.ipynb_checkpoints']
 
+spelling_word_list_filename = 'spelling_wordlist.txt'
+spelling_show_suggestions = True
+spelling_ignore_pypi_package_names = True
+
 with open("index.rst") as f:
     index_rst_lines = f.readlines()
 

doc/source/contributing_docstring.rst

@@ -103,7 +103,7 @@ left before or after the docstring. The text starts in the next line after the
 opening quotes. The closing quotes have their own line
 (meaning that they are not at the end of the last sentence).
 
-In rare occasions reST styles like bold text or itallics will be used in
+In rare occasions reST styles like bold text or italics will be used in
 docstrings, but is it common to have inline code, which is presented between
 backticks. It is considered inline code:
 
@@ -513,7 +513,7 @@ instead of at the beginning, it is good to let the users know about it.
 To give an intuition on what can be considered related, here there are some
 examples:
 
-* ``loc`` and ``iloc``, as they do the same, but in one case providing indices
+* ``loc`` and ``iloc``, as they do the same, but in one case providing indexes
   and in the other positions
 * ``max`` and ``min``, as they do the opposite
 * ``iterrows``, ``itertuples`` and ``iteritems``, as it is easy that a user
@@ -692,7 +692,7 @@ the standard library go first, followed by third-party libraries (like
 matplotlib).
 
 When illustrating examples with a single ``Series`` use the name ``s``, and if
-illustrating with a single ``DataFrame`` use the name ``df``. For indices,
+illustrating with a single ``DataFrame`` use the name ``df``. For indexes,
 ``idx`` is the preferred name. If a set of homogeneous ``Series`` or
 ``DataFrame`` is used, name them ``s1``, ``s2``, ``s3``...  or ``df1``,
 ``df2``, ``df3``... If the data is not homogeneous, and more than one structure
@@ -706,7 +706,7 @@ than 5, to show the example with the default values. If doing the ``mean``, we
 could use something like ``[1, 2, 3]``, so it is easy to see that the value
 returned is the mean.
 
-For more complex examples (groupping for example), avoid using data without
+For more complex examples (grouping for example), avoid using data without
 interpretation, like a matrix of random numbers with columns A, B, C, D...
 And instead use a meaningful example, which makes it easier to understand the
 concept. Unless required by the example, use names of animals, to keep examples
@@ -877,7 +877,7 @@ be tricky. Here are some attention points:
   the actual error only the error name is sufficient.
 
 * If there is a small part of the result that can vary (e.g. a hash in an object
-  represenation), you can use ``...`` to represent this part.
+  representation), you can use ``...`` to represent this part.
 
   If you want to show that ``s.plot()`` returns a matplotlib AxesSubplot object,
   this will fail the doctest ::

doc/source/cookbook.rst

@@ -286,7 +286,7 @@ New Columns
    df = pd.DataFrame(
         {'AAA' : [1,1,1,2,2,2,3,3], 'BBB' : [2,1,3,4,5,1,2,3]}); df
 
-Method 1 : idxmin() to get the index of the mins
+Method 1 : idxmin() to get the index of the minimums
 
 .. ipython:: python
 
@@ -664,7 +664,7 @@ The :ref:`Pivot <reshaping.pivot>` docs.
 `Plot pandas DataFrame with year over year data
 <http://stackoverflow.com/questions/30379789/plot-pandas-data-frame-with-year-over-year-data>`__
 
-To create year and month crosstabulation:
+To create year and month cross tabulation:
 
 .. ipython:: python
 
@@ -723,7 +723,7 @@ Rolling Apply to multiple columns where function returns a Scalar (Volume Weight
    s = pd.concat([ (pd.Series(vwap(df.iloc[i:i+window]), index=[df.index[i+window]])) for i in range(len(df)-window) ]);
    s.round(2)
 
-Timeseries
+Time series
 ----------
 
 `Between times
@@ -1029,7 +1029,7 @@ Skip row between header and data
     01.01.1990 05:00;21;11;12;13
     """
 
-Option 1: pass rows explicitly to skiprows
+Option 1: pass rows explicitly to skip rows
 """"""""""""""""""""""""""""""""""""""""""
 
 .. ipython:: python

doc/source/dsintro.rst

@@ -882,7 +882,7 @@ dictionary of DataFrames as above, and the following named parameters:
    :header: "Parameter", "Default", "Description"
    :widths: 10, 10, 40
 
-   intersect, ``False``, drops elements whose indices do not align
+   intersect, ``False``, drops elements whose indexes do not align
    orient, ``items``, use ``minor`` to use DataFrames' columns as panel items
 
 For example, compare to the construction above:
@@ -1014,7 +1014,7 @@ 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 codebase.
+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
@@ -1023,7 +1023,7 @@ 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 usecases.
+support the multi-dimensional analysis that is one of ``Panel`` s main use cases.
 `Here is a link to the xarray panel-transition documentation <http://xarray.pydata.org/en/stable/pandas.html#panel-transition>`__.
 
 .. ipython:: python

doc/source/ecosystem.rst

@@ -184,8 +184,8 @@ and metadata disseminated in
 `SDMX <http://www.sdmx.org>`_ 2.1, an ISO-standard
 widely used by institutions such as statistics offices, central banks,   
 and international organisations. pandaSDMX can expose datasets and related 
-structural metadata including dataflows, code-lists, 
-and datastructure definitions as pandas Series 
+structural metadata including data flows, code-lists,
+and data structure definitions as pandas Series
 or multi-indexed DataFrames.  
 
 `fredapi <https://github.com/mortada/fredapi>`__
@@ -260,7 +260,7 @@ Data validation
 `Engarde <http://engarde.readthedocs.io/en/latest/>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Engarde is a lightweight library used to explicitly state your assumptions abour your datasets
+Engarde is a lightweight library used to explicitly state your assumptions about your datasets
 and check that they're *actually* true.
 
 .. _ecosystem.extensions:

doc/source/enhancingperf.rst

@@ -32,7 +32,7 @@ Cython (Writing C extensions for pandas)
 ----------------------------------------
 
 For many use cases writing pandas in pure Python and NumPy is sufficient. In some
-computationally heavy applications however, it can be possible to achieve sizeable
+computationally heavy applications however, it can be possible to achieve sizable
 speed-ups by offloading work to `cython <http://cython.org/>`__.
 
 This tutorial assumes you have refactored as much as possible in Python, for example
@@ -601,7 +601,7 @@ on the original ``DataFrame`` or return a copy with the new column.
 
    For backwards compatibility, ``inplace`` defaults to ``True`` if not
    specified. This will change in a future version of pandas - if your
-   code depends on an inplace assignment you should update to explicitly
+   code depends on an in place assignment you should update to explicitly
    set ``inplace=True``.
 
 .. ipython:: python
@@ -806,7 +806,7 @@ truncate any strings that are more than 60 characters in length. Second, we
 can't pass ``object`` arrays to ``numexpr`` thus string comparisons must be
 evaluated in Python space.
 
-The upshot is that this *only* applies to object-dtype'd expressions. So, if
+The upshot is that this *only* applies to object-dtype expressions. So, if
 you have an expression--for example
 
 .. ipython:: python

doc/source/extending.rst

@@ -167,7 +167,7 @@ you can retain subclasses through ``pandas`` data manipulations.
 
 There are 3 constructor properties to be defined:
 
-- ``_constructor``: Used when a manipulation result has the same dimesions as the original.
+- ``_constructor``: Used when a manipulation result has the same dimensions as the original.
 - ``_constructor_sliced``: Used when a manipulation result has one lower dimension(s) as the original, such as ``DataFrame`` single columns slicing.
 - ``_constructor_expanddim``: Used when a manipulation result has one higher dimension as the original, such as ``Series.to_frame()`` and ``DataFrame.to_panel()``.
 

doc/source/groupby.rst

@@ -942,7 +942,7 @@ that is itself a series, and possibly upcast the result to a DataFrame:
 
    ``apply`` can act as a reducer, transformer, *or* filter function, depending on exactly what is passed to it.
    So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in
-   the output as well as set the indices.
+   the output as well as set the indexes.
 
 .. warning::
 
@@ -994,7 +994,7 @@ is only interesting over one column (here ``colname``), it may be filtered
 Handling of (un)observed Categorical values
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-When using a ``Categorical`` grouper (as a single grouper, or as part of multipler groupers), the ``observed`` keyword
+When using a ``Categorical`` grouper (as a single grouper, or as part of multiple groupers), the ``observed`` keyword
 controls whether to return a cartesian product of all possible groupers values (``observed=False``) or only those
 that are observed groupers (``observed=True``).
 
@@ -1010,7 +1010,7 @@ Show only the observed values:
 
    pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'], categories=['a', 'b']), observed=True).count()
 
-The returned dtype of the grouped will *always* include *all* of the catergories that were grouped.
+The returned dtype of the grouped will *always* include *all* of the categories that were grouped.
 
 .. ipython:: python
 
@@ -1328,11 +1328,11 @@ Groupby by Indexer to 'resample' data
 
 Resampling produces new hypothetical samples (resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples.
 
-In order to resample to work on indices that are non-datetimelike, the following procedure can be utilized.
+In order to resample to work on indexes that are non-datetimelike, the following procedure can be utilized.
 
 In the following examples, **df.index // 5** returns a binary array which is used to determine what gets selected for the groupby operation.
 
-.. note:: The below example shows how we can downsample by consolidation of samples into fewer samples. Here by using **df.index // 5**, we are aggregating the samples in bins. By applying **std()** function, we aggregate the information contained in many samples into a small subset of values which is their standard deviation thereby reducing the number of samples.
+.. note:: The below example shows how we can down-sample by consolidation of samples into fewer samples. Here by using **df.index // 5**, we are aggregating the samples in bins. By applying **std()** function, we aggregate the information contained in many samples into a small subset of values which is their standard deviation thereby reducing the number of samples.
 
 .. ipython:: python
 

doc/source/indexing.rst

@@ -700,7 +700,7 @@ Current Behavior
 Reindexing
 ~~~~~~~~~~
 
-The idiomatic way to achieve selecting potentially not-found elmenents is via ``.reindex()``. See also the section on :ref:`reindexing <basics.reindexing>`.
+The idiomatic way to achieve selecting potentially not-found elements is via ``.reindex()``. See also the section on :ref:`reindexing <basics.reindexing>`.
 
 .. ipython:: python
 

doc/source/install.rst

@@ -31,7 +31,7 @@ PyPI and through conda.
 Starting **January 1, 2019**, all releases will be Python 3 only.
 
 If there are people interested in continued support for Python 2.7 past December
-31, 2018 (either backporting bugfixes or funding) please reach out to the
+31, 2018 (either backporting bug fixes or funding) please reach out to the
 maintainers on the issue tracker.
 
 For more information, see the `Python 3 statement`_ and the `Porting to Python 3 guide`_.
@@ -199,7 +199,7 @@ Running the test suite
 ----------------------
 
 pandas is equipped with an exhaustive set of unit tests, covering about 97% of
-the codebase as of this writing. To run it on your machine to verify that
+the code base as of this writing. To run it on your machine to verify that
 everything is working (and that you have all of the dependencies, soft and hard,
 installed), make sure you have `pytest
 <http://doc.pytest.org/en/latest/>`__ and run: