Fix many spelling mistakes in the docs.

Author: lexual

doc/README.rst

@@ -33,8 +33,8 @@ Some other important things to know about the docs:
   itself and the docs in this folder ``pandas/doc/``.
 
   The docstrings provide a clear explanation of the usage of the individual
-  functions, while the documentation in this filder consists of tutorial-like
-  overviews per topic together with some other information (whatsnew,
+  functions, while the documentation in this folder consists of tutorial-like
+  overviews per topic together with some other information (what's new,
   installation, etc).
 
 - The docstrings follow the **Numpy Docstring Standard** which is used widely
@@ -56,7 +56,7 @@ Some other important things to know about the docs:
           x = 2
           x**3
 
-  will be renderd as
+  will be rendered as
 
   ::
 
@@ -66,7 +66,7 @@ Some other important things to know about the docs:
       Out[2]: 8
 
   This means that almost all code examples in the docs are always run (and the
-  ouptut saved) during the doc build. This way, they will always be up to date,
+  output saved) during the doc build. This way, they will always be up to date,
   but it makes the doc building a bit more complex.
 
 
@@ -135,8 +135,8 @@ If you want to do a full clean build, do::
 
 Staring with 0.13.1 you can tell ``make.py`` to compile only a single section
 of the docs, greatly reducing the turn-around time for checking your changes.
-You will be prompted to delete unrequired `.rst` files, since the last commited
-version can always be restored from git.
+You will be prompted to delete `.rst` files that aren't required, since the
+last committed version can always be restored from git.
 
 ::
 

doc/source/10min.rst

@@ -260,7 +260,7 @@ For slicing columns explicitly
 
    df.iloc[:,1:3]
 
-For getting a value explicity
+For getting a value explicitly
 
 .. ipython:: python
 

doc/source/basics.rst

@@ -346,7 +346,7 @@ General DataFrame Combine
 The ``combine_first`` method above calls the more general DataFrame method
 ``combine``. This method takes another DataFrame and a combiner function,
 aligns the input DataFrame and then passes the combiner function pairs of
-Series (ie, columns whose names are the same).
+Series (i.e., columns whose names are the same).
 
 So, for instance, to reproduce ``combine_first`` as above:
 
@@ -1461,7 +1461,7 @@ from the current type (say ``int`` to ``float``)
    df3.dtypes
 
 The ``values`` attribute on a DataFrame return the *lower-common-denominator* of the dtypes, meaning
-the dtype that can accommodate **ALL** of the types in the resulting homogenous dtyped numpy array. This can
+the dtype that can accommodate **ALL** of the types in the resulting homogeneous dtyped numpy array. This can
 force some *upcasting*.
 
 .. ipython:: python

doc/source/cookbook.rst

@@ -499,7 +499,7 @@ The :ref:`HDFStores <io.hdf5>` docs
 `Merging on-disk tables with millions of rows
 <http://stackoverflow.com/questions/14614512/merging-two-tables-with-millions-of-rows-in-python/14617925#14617925>`__
 
-Deduplicating a large store by chunks, essentially a recursive reduction operation. Shows a function for taking in data from
+De-duplicating a large store by chunks, essentially a recursive reduction operation. Shows a function for taking in data from
 csv file and creating a store by chunks, with date parsing as well.
 `See here
 <http://stackoverflow.com/questions/16110252/need-to-compare-very-large-files-around-1-5gb-in-python/16110391#16110391>`__

doc/source/dsintro.rst

@@ -118,7 +118,7 @@ provided. The value will be repeated to match the length of **index**
 Series is ndarray-like
 ~~~~~~~~~~~~~~~~~~~~~~
 
-``Series`` acts very similary to a ``ndarray``, and is a valid argument to most NumPy functions.
+``Series`` acts very similarly to a ``ndarray``, and is a valid argument to most NumPy functions.
 However, things like slicing also slice the index.
 
 .. ipython :: python
@@ -474,7 +474,7 @@ DataFrame:
 
 For a more exhaustive treatment of more sophisticated label-based indexing and
 slicing, see the :ref:`section on indexing <indexing>`. We will address the
-fundamentals of reindexing / conforming to new sets of lables in the
+fundamentals of reindexing / conforming to new sets of labels in the
 :ref:`section on reindexing <basics.reindexing>`.
 
 Data alignment and arithmetic
@@ -892,7 +892,7 @@ Slicing
 ~~~~~~~
 
 Slicing works in a similar manner to a Panel. ``[]`` slices the first dimension.
-``.ix`` allows you to slice abitrarily and get back lower dimensional objects
+``.ix`` allows you to slice arbitrarily and get back lower dimensional objects
 
 .. ipython:: python
 

doc/source/enhancingperf.rst

@@ -553,7 +553,7 @@ standard Python.
 :func:`pandas.eval` Parsers
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-There are two different parsers and and two different engines you can use as
+There are two different parsers and two different engines you can use as
 the backend.
 
 The default ``'pandas'`` parser allows a more intuitive syntax for expressing

doc/source/faq.rst

@@ -144,7 +144,7 @@ Frequency conversion
 
 Frequency conversion is implemented using the ``resample`` method on TimeSeries
 and DataFrame objects (multiple time series). ``resample`` also works on panels
-(3D). Here is some code that resamples daily data to montly:
+(3D). Here is some code that resamples daily data to monthly:
 
 .. ipython:: python
 

doc/source/gotchas.rst

@@ -183,7 +183,7 @@ Why not make NumPy like R?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Many people have suggested that NumPy should simply emulate the ``NA`` support
-present in the more domain-specific statistical programming langauge `R
+present in the more domain-specific statistical programming language `R
 <http://r-project.org>`__. Part of the reason is the NumPy type hierarchy:
 
 .. csv-table::

doc/source/groupby.rst

@@ -969,7 +969,7 @@ Regroup columns of a DataFrame according to their sum, and sum the aggregated on
    df.groupby(df.sum(), axis=1).sum()
 
 
-Returning a Series to propogate names
+Returning a Series to propagate names
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Group DataFrame columns, compute a set of metrics and return a named Series.

doc/source/indexing.rst

@@ -88,10 +88,10 @@ of multi-axis indexing.
   See more at :ref:`Selection by Position <indexing.integer>`
 
 - ``.ix`` supports mixed integer and label based access. It is primarily label
-  based, but will fallback to integer positional access. ``.ix`` is the most
+  based, but will fall back to integer positional access. ``.ix`` is the most
   general and will support any of the inputs to ``.loc`` and ``.iloc``, as well
   as support for floating point label schemes. ``.ix`` is especially useful
-  when dealing with mixed positional and label based hierarchial indexes.
+  when dealing with mixed positional and label based hierarchical indexes.
   As using integer slices with ``.ix`` have different behavior depending on
   whether the slice is interpreted as position based or label based, it's
   usually better to be explicit and use ``.iloc`` or ``.loc``.
@@ -230,7 +230,7 @@ new column.
    - The ``Series/Panel`` accesses are available starting in 0.13.0.
 
 If you are using the IPython environment, you may also use tab-completion to
-see these accessable attributes.
+see these accessible attributes.
 
 Slicing ranges
 --------------
@@ -328,7 +328,7 @@ For getting values with a boolean array
    df1.loc['a']>0
    df1.loc[:,df1.loc['a']>0]
 
-For getting a value explicity (equiv to deprecated ``df.get_value('a','A')``)
+For getting a value explicitly (equiv to deprecated ``df.get_value('a','A')``)
 
 .. ipython:: python
 
@@ -415,7 +415,7 @@ For getting a cross section using an integer position (equiv to ``df.xs(1)``)
 
    df1.iloc[1]
 
-There is one signficant departure from standard python/numpy slicing semantics.
+There is one significant departure from standard python/numpy slicing semantics.
 python/numpy allow slicing past the end of an array without an associated error.
 
 .. ipython:: python
@@ -494,7 +494,7 @@ out what you're asking for. If you only want to access a scalar value, the
 fastest way is to use the ``at`` and ``iat`` methods, which are implemented on
 all of the data structures.
 
-Similary to ``loc``, ``at`` provides **label** based scalar lookups, while, ``iat`` provides **integer** based lookups analagously to ``iloc``
+Similarly to ``loc``, ``at`` provides **label** based scalar lookups, while, ``iat`` provides **integer** based lookups analogously to ``iloc``
 
 .. ipython:: python
 
@@ -643,7 +643,7 @@ To return a Series of the same shape as the original
 
    s.where(s > 0)
 
-Selecting values from a DataFrame with a boolean critierion now also preserves
+Selecting values from a DataFrame with a boolean criterion now also preserves
 input data shape. ``where`` is used under the hood as the implementation.
 Equivalent is ``df.where(df < 0)``
 
@@ -690,7 +690,7 @@ without creating a copy:
 **alignment**
 
 Furthermore, ``where`` aligns the input boolean condition (ndarray or DataFrame),
-such that partial selection with setting is possible. This is analagous to
+such that partial selection with setting is possible. This is analogous to
 partial setting via ``.ix`` (but on the contents rather than the axis labels)
 
 .. ipython:: python
@@ -756,7 +756,7 @@ between the values of columns ``a`` and ``c``. For example:
    # query
    df.query('(a < b) & (b < c)')
 
-Do the same thing but fallback on a named index if there is no column
+Do the same thing but fall back on a named index if there is no column
 with the name ``a``.
 
 .. ipython:: python
@@ -899,7 +899,7 @@ The ``in`` and ``not in`` operators
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 :meth:`~pandas.DataFrame.query` also supports special use of Python's ``in`` and
-``not in`` comparison operators, providing a succint syntax for calling the
+``not in`` comparison operators, providing a succinct syntax for calling the
 ``isin`` method of a ``Series`` or ``DataFrame``.
 
 .. ipython:: python
@@ -1416,7 +1416,7 @@ faster, and allows one to index *both* axes if so desired.
 Why does the assignment when using chained indexing fail!
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-So, why does this show the ``SettingWithCopy`` warning / and possibly not work when you do chained indexing and assignement:
+So, why does this show the ``SettingWithCopy`` warning / and possibly not work when you do chained indexing and assignment:
 
 .. code-block:: python
 
@@ -2149,7 +2149,7 @@ metadata, like the index ``name`` (or, for ``MultiIndex``, ``levels`` and
 
 You can use the ``rename``, ``set_names``, ``set_levels``, and ``set_labels``
 to set these attributes directly. They default to returning a copy; however,
-you can specify ``inplace=True`` to have the data change inplace.
+you can specify ``inplace=True`` to have the data change in place.
 
 .. ipython:: python