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4 changes: 2 additions & 2 deletions doc/source/indexing.rst
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Expand Up @@ -69,7 +69,7 @@ Different Choices for Indexing
.. versionadded:: 0.11.0

Object selection has had a number of user-requested additions in order to
support more explicit location based indexing. pandas now supports three types
support more explicit location based indexing. Pandas now supports three types
of multi-axis indexing.

- ``.loc`` is primarily label based, but may also be used with a boolean array. ``.loc`` will raise ``KeyError`` when the items are not found. Allowed inputs are:
Expand Down Expand Up @@ -401,7 +401,7 @@ Selection By Position
This is sometimes called ``chained assignment`` and should be avoided.
See :ref:`Returning a View versus Copy <indexing.view_versus_copy>`

pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely python and numpy slicing. These are ``0-based`` indexing. When slicing, the start bounds is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise a ``IndexError``.
Pandas provides a suite of methods in order to get **purely integer based indexing**. The semantics follow closely python and numpy slicing. These are ``0-based`` indexing. When slicing, the start bounds is *included*, while the upper bound is *excluded*. Trying to use a non-integer, even a **valid** label will raise an ``IndexError``.

The ``.iloc`` attribute is the primary access method. The following are valid inputs:

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