pandas-dev · TomAugspurger · Jun 7, 2018 · May 17, 2018 · May 18, 2018 · May 18, 2018
diff --git a/Makefile b/Makefile
@@ -23,3 +23,4 @@ doc:
 	cd doc; \
 	python make.py clean; \
 	python make.py html
+	python make.py spellcheck
diff --git a/doc/make.py b/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 +305,18 @@ def zip_html(self):
                      '-q',
                      *fnames)
 
+    def spellcheck(self):
+        """Spell check the documentation."""
+        self._sphinx_build('spelling')
+        output_location = os.path.join('build', 'spelling', 'output.txt')
+        with open(output_location) as output:
+            lines = output.readlines()
+            if lines:
+                raise SyntaxError(
+                    'Found misspelled words.'
+                    ' Check pandas/doc/build/spelling/output.txt'
+                    ' for more details.')
+
 
 def main():
     cmds = [method for method in dir(DocBuilder) if not method.startswith('_')]

diff --git a/doc/source/advanced.rst b/doc/source/advanced.rst
@@ -342,7 +342,7 @@ As usual, **both sides** of the slicers are included as this is label indexing.
                        columns=micolumns).sort_index().sort_index(axis=1)
    dfmi
 
-Basic multi-index slicing using slices, lists, and labels.
+Basic MultiIndex slicing using slices, lists, and labels.
 
 .. 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

diff --git a/doc/source/basics.rst b/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

diff --git a/doc/source/conf.py b/doc/source/conf.py
@@ -73,10 +73,14 @@
               'sphinx.ext.ifconfig',
               'sphinx.ext.linkcode',
               'nbsphinx',
+              'sphinxcontrib.spelling'
               ]
 
 exclude_patterns = ['**.ipynb_checkpoints']
 
+spelling_word_list_filename = ['spelling_wordlist.txt', 'names_wordlist.txt']
+spelling_ignore_pypi_package_names = True
+
 with open("index.rst") as f:
     index_rst_lines = f.readlines()
 

diff --git a/doc/source/contributing.rst b/doc/source/contributing.rst
@@ -436,6 +436,25 @@ the documentation are also built by Travis-CI. These docs are then hosted `here
 <http://pandas-docs.github.io/pandas-docs-travis>`__, see also
 the :ref:`Continuous Integration <contributing.ci>` section.
 
+Spell checking documentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+When contributing to documentation to **pandas** it's good to check if your work
+contains any spelling errors. Sphinx provides an easy way to spell check documentation
+and docstrings.
+
+Running the spell check is easy. Just navigate to your local ``pandas/doc/`` directory and run::
+
+    python make.py spellcheck
+
+The spellcheck will take a few minutes to run (between 1 to 6 minutes). Sphinx will alert you
+with warnings and misspelt words - these misspelt words will be added to a file called
+``output.txt`` and you can find it on your local directory ``pandas/doc/build/spelling/``.
+
+The Sphinx spelling extension uses an EN-US dictionary to correct words, what means that in
+some cases you might need to add a word to this dictionary. You can do so by adding the word to
+the bag-of-words file named ``spelling_wordlist.txt`` located in the folder ``pandas/doc/``.
+
 .. _contributing.code:
 
 Contributing to the code base

diff --git a/doc/source/contributing_docstring.rst b/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:
 
@@ -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 ::

diff --git a/doc/source/cookbook.rst b/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
 
@@ -307,7 +307,7 @@ MultiIndexing
 
 The :ref:`multindexing <advanced.hierarchical>` docs.
 
-`Creating a multi-index from a labeled frame
+`Creating a MultiIndex from a labeled frame
 <http://stackoverflow.com/questions/14916358/reshaping-dataframes-in-pandas-based-on-column-labels>`__
 
 .. ipython:: python
@@ -330,7 +330,7 @@ The :ref:`multindexing <advanced.hierarchical>` docs.
 Arithmetic
 **********
 
-`Performing arithmetic with a multi-index that needs broadcasting
+`Performing arithmetic with a MultiIndex that needs broadcasting
 <http://stackoverflow.com/questions/19501510/divide-entire-pandas-multiindex-dataframe-by-dataframe-variable/19502176#19502176>`__
 
 .. ipython:: python
@@ -342,7 +342,7 @@ Arithmetic
 Slicing
 *******
 
-`Slicing a multi-index with xs
+`Slicing a MultiIndex with xs
 <http://stackoverflow.com/questions/12590131/how-to-slice-multindex-columns-in-pandas-dataframes>`__
 
 .. ipython:: python
@@ -363,7 +363,7 @@ To take the cross section of the 1st level and 1st axis the index:
 
    df.xs('six',level=1,axis=0)
 
-`Slicing a multi-index with xs, method #2
+`Slicing a MultiIndex with xs, method #2
 <http://stackoverflow.com/questions/14964493/multiindex-based-indexing-in-pandas>`__
 
 .. ipython:: python
@@ -386,13 +386,13 @@ To take the cross section of the 1st level and 1st axis the index:
    df.loc[(All,'Math'),('Exams')]
    df.loc[(All,'Math'),(All,'II')]
 
-`Setting portions of a multi-index with xs
+`Setting portions of a MultiIndex with xs
 <http://stackoverflow.com/questions/19319432/pandas-selecting-a-lower-level-in-a-dataframe-to-do-a-ffill>`__
 
 Sorting
 *******
 
-`Sort by specific column or an ordered list of columns, with a multi-index
+`Sort by specific column or an ordered list of columns, with a MultiIndex
 <http://stackoverflow.com/questions/14733871/mutli-index-sorting-in-pandas>`__
 
 .. 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
 
@@ -677,7 +677,7 @@ To create year and month crosstabulation:
 Apply
 *****
 
-`Rolling Apply to Organize - Turning embedded lists into a multi-index frame
+`Rolling Apply to Organize - Turning embedded lists into a MultiIndex frame
 <http://stackoverflow.com/questions/17349981/converting-pandas-dataframe-with-categorical-values-into-binary-values>`__
 
 .. ipython:: python
@@ -1029,8 +1029,8 @@ 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
 

diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst
@@ -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

diff --git a/doc/source/ecosystem.rst b/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:

diff --git a/doc/source/enhancingperf.rst b/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
@@ -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

diff --git a/doc/source/extending.rst b/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()``.
 

diff --git a/doc/source/groupby.rst b/doc/source/groupby.rst
@@ -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
 

diff --git a/doc/source/indexing.rst b/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
 

diff --git a/doc/source/install.rst b/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:

diff --git a/doc/source/internals.rst b/doc/source/internals.rst
@@ -41,7 +41,7 @@ There are functions that make the creation of a regular index easy:
 - ``date_range``: fixed frequency date range generated from a time rule or
   DateOffset. An ndarray of Python datetime objects
 - ``period_range``: fixed frequency date range generated from a time rule or
-  DateOffset. An ndarray of ``Period`` objects, representing Timespans
+  DateOffset. An ndarray of ``Period`` objects, representing timespans
 
 The motivation for having an ``Index`` class in the first place was to enable
 different implementations of indexing. This means that it's possible for you,