Merge pull request pandas-dev#7745 from lexual/docs_fixes

Docs fixes

Author: jorisvandenbossche

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.
 
 
@@ -135,12 +135,12 @@ 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.
 
 ::
 
-    #omit autosummary and api section
+    #omit autosummary and API section
     python make.py clean
     python make.py --no-api
 

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::
@@ -500,7 +500,7 @@ parse HTML tables in the top-level pandas io function ``read_html``.
        molasses.  However consider the fact that many tables on the web are not
        big enough for the parsing algorithm runtime to matter. It is more
        likely that the bottleneck will be in the process of reading the raw
-       text from the url over the web, i.e., IO (input-output). For very large
+       text from the URL over the web, i.e., IO (input-output). For very large
        tables, this might not be true.
 
 **Issues with using** |Anaconda|_

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