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8 changes: 4 additions & 4 deletions doc/source/enhancingperf.rst
Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand All @@ -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:

Expand Down Expand Up @@ -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::
Expand Down Expand Up @@ -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:

Expand Down
25 changes: 12 additions & 13 deletions doc/source/remote_data.rst
Original file line number Diff line number Diff line change
Expand Up @@ -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:
Expand All @@ -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
Expand All @@ -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.
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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:
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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/>`__
Expand Down
10 changes: 5 additions & 5 deletions doc/source/timedeltas.rst
Original file line number Diff line number Diff line change
Expand Up @@ -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

Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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``,
Expand Down