Merge pull request pandas-dev#9986 from mortada/doc_cleanup

DOC/CLN: HTML docs fixes

Author: shoyer

doc/source/enhancingperf.rst

@@ -66,7 +66,7 @@ Here's the function in pure python:
            s += f(a + i * dx)
        return s * dx
 
-We achieve our result by by using ``apply`` (row-wise):
+We achieve our result by using ``apply`` (row-wise):
 
 .. ipython:: python
 
@@ -86,7 +86,7 @@ hence we'll concentrate our efforts cythonizing these two functions.
 .. note::
 
   In python 2 replacing the ``range`` with its generator counterpart (``xrange``)
-  would mean the ``range`` line would vanish. In python 3 range is already a generator.
+  would mean the ``range`` line would vanish. In python 3 ``range`` is already a generator.
 
 .. _enhancingperf.plain:
 
@@ -248,7 +248,7 @@ efforts here.
 More advanced techniques
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-There is still scope for improvement, here's an example of using some more
+There is still hope for improvement. Here's an example of using some more
 advanced cython techniques:
 
 .. ipython::
@@ -373,7 +373,7 @@ This Python syntax is **not** allowed:
 :func:`~pandas.eval` Examples
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-:func:`pandas.eval` works well with expressions containing large arrays
+:func:`pandas.eval` works well with expressions containing large arrays.
 
 First let's create a few decent-sized arrays to play with:
 

doc/source/remote_data.rst

@@ -49,7 +49,7 @@ Yahoo! Finance
     import datetime
     start = datetime.datetime(2010, 1, 1)
     end = datetime.datetime(2013, 1, 27)
-    f=web.DataReader("F", 'yahoo', start, end)
+    f = web.DataReader("F", 'yahoo', start, end)
     f.ix['2010-01-04']
 
 .. _remote_data.yahoo_options:
@@ -58,10 +58,10 @@ Yahoo! Finance Options
 ----------------------
 ***Experimental***
 
-The Options class allows the download of options data from Yahoo! Finance.
+The ``Options`` class allows the download of options data from Yahoo! Finance.
 
 The ``get_all_data`` method downloads and caches option data for all expiry months
-and provides a formatted ``DataFrame`` with a hierarchical index, so its easy to get
+and provides a formatted ``DataFrame`` with a hierarchical index, so it is easy to get
 to the specific option you want.
 
 .. ipython:: python
@@ -71,10 +71,10 @@ to the specific option you want.
       data = aapl.get_all_data()
       data.iloc[0:5, 0:5]
 
-      #Show the $100 strike puts at all expiry dates:
+      # Show the $100 strike puts at all expiry dates:
       data.loc[(100, slice(None), 'put'),:].iloc[0:5, 0:5]
 
-      #Show the volume traded of $100 strike puts at all expiry dates:
+      # Show the volume traded of $100 strike puts at all expiry dates:
       data.loc[(100, slice(None), 'put'),'Vol'].head()
 
 If you don't want to download all the data, more specific requests can be made.
@@ -121,7 +121,7 @@ Google Finance
     import datetime
     start = datetime.datetime(2010, 1, 1)
     end = datetime.datetime(2013, 1, 27)
-    f=web.DataReader("F", 'google', start, end)
+    f = web.DataReader("F", 'google', start, end)
     f.ix['2010-01-04']
 
 .. _remote_data.fred:
@@ -152,7 +152,7 @@ Dataset names are listed at `Fama/French Data Library
 .. ipython:: python
 
     import pandas.io.data as web
-    ip=web.DataReader("5_Industry_Portfolios", "famafrench")
+    ip = web.DataReader("5_Industry_Portfolios", "famafrench")
     ip[4].ix[192607]
 
 .. _remote_data.wb:
@@ -302,9 +302,8 @@ Problematic Country Codes & Indicators
    :func:`wb.download()` is more flexible.  To achieve this, the warning
    and exception logic changed.
    
-The world bank converts some country codes,
-in their response, which makes error checking by pandas difficult.
-Retired indicators still persist in the search.
+The world bank converts some country codes in their response, which makes error
+checking by pandas difficult. Retired indicators still persist in the search.
 
 Given the new flexibility of 0.15.1, improved error handling by the user
 may be necessary for fringe cases.
@@ -377,13 +376,13 @@ The following will fetch users and pageviews (metrics) data per day of the week,
         filters     = "pagePath=~aboutus;ga:country==France",
     )
 
-The only mandatory arguments are ``metrics,`` ``dimensions`` and ``start_date``. We can only strongly recommend you to always specify the ``account_id``, ``profile_id`` and ``property_id`` to avoid accessing the wrong data bucket in Google Analytics.
+The only mandatory arguments are ``metrics,`` ``dimensions`` and ``start_date``. We strongly recommend that you always specify the ``account_id``, ``profile_id`` and ``property_id`` to avoid accessing the wrong data bucket in Google Analytics.
 
 The ``index_col`` argument indicates which dimension(s) has to be taken as index.
 
-The ``filters`` argument indicates the filtering to apply to the query. In the above example, the page has URL has to contain ``aboutus`` AND the visitors country has to be France.
+The ``filters`` argument indicates the filtering to apply to the query. In the above example, the page URL has to contain ``aboutus`` AND the visitors country has to be France.
 
-Detailed informations in the followings:
+Detailed information in the following:
 
 * `pandas & google analytics, by yhat <http://blog.yhathq.com/posts/pandas-google-analytics.html>`__
 * `Google Analytics integration in pandas, by Chang She <http://quantabee.wordpress.com/2012/12/17/google-analytics-pandas/>`__

doc/source/timedeltas.rst

@@ -29,13 +29,13 @@ Time Deltas
    Starting in v0.15.0, we introduce a new scalar type ``Timedelta``, which is a subclass of ``datetime.timedelta``, and behaves in a similar manner,
    but allows compatibility with ``np.timedelta64`` types as well as a host of custom representation, parsing, and attributes.
 
-Timedeltas are differences in times, expressed in difference units, e.g. days,hours,minutes,seconds.
+Timedeltas are differences in times, expressed in difference units, e.g. days, hours, minutes, seconds.
 They can be both positive and negative.
 
 Parsing
 -------
 
-You can construct a ``Timedelta`` scalar thru various arguments:
+You can construct a ``Timedelta`` scalar through various arguments:
 
 .. ipython:: python
 
@@ -46,7 +46,7 @@ You can construct a ``Timedelta`` scalar thru various arguments:
    Timedelta('-1 days 2 min 3us')
 
    # like datetime.timedelta
-   # note: these MUST be specified as keyword argments
+   # note: these MUST be specified as keyword arguments
    Timedelta(days=1,seconds=1)
 
    # integers with a unit
@@ -100,7 +100,7 @@ It will construct Series if the input is a Series, a scalar if the input is scal
 Operations
 ----------
 
-You can operate on Series/DataFrames and construct ``timedelta64[ns]`` Series thru
+You can operate on Series/DataFrames and construct ``timedelta64[ns]`` Series through
 subtraction operations on ``datetime64[ns]`` Series, or ``Timestamps``.
 
 .. ipython:: python
@@ -290,7 +290,7 @@ TimedeltaIndex
 
 .. versionadded:: 0.15.0
 
-To generate an index with time delta, you can use either the TimedeltaIndex or
+To generate an index with time delta, you can use either the ``TimedeltaIndex`` or
 the ``timedelta_range`` constructor.
 
 Using ``TimedeltaIndex`` you can pass string-like, ``Timedelta``, ``timedelta``,