pandas-dev
diff --git a/‎doc/source/advanced.rst
+133 b/‎doc/source/advanced.rst
+133
diff --git a/‎doc/source/basics.rst
+1-1 b/‎doc/source/basics.rst
+1-1
diff --git a/‎doc/source/ecosystem.rst
+4-3 b/‎doc/source/ecosystem.rst
+4-3
diff --git a/‎doc/source/faq.rst
-115 b/‎doc/source/faq.rst
-115
@@ -844,3 +844,136 @@ Of course if you need integer based selection, then use ``iloc``
 .. ipython:: python
 
    dfir.iloc[0:5]
+
+Miscellaneous indexing FAQ
+--------------------------
+
+Integer indexing
+~~~~~~~~~~~~~~~~
+
+Label-based indexing with integer axis labels is a thorny topic. It has been
+discussed heavily on mailing lists and among various members of the scientific
+Python community. In pandas, our general viewpoint is that labels matter more
+than integer locations. Therefore, with an integer axis index *only*
+label-based indexing is possible with the standard tools like ``.loc``. The
+following code will generate exceptions:
+
+.. code-block:: python
+
+   s = pd.Series(range(5))
+   s[-1]
+   df = pd.DataFrame(np.random.randn(5, 4))
+   df
+   df.loc[-2:]
+
+This deliberate decision was made to prevent ambiguities and subtle bugs (many
+users reported finding bugs when the API change was made to stop "falling back"
+on position-based indexing).
+
+Non-monotonic indexes require exact matches
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+If the index of a ``Series`` or ``DataFrame`` is monotonically increasing or decreasing, then the bounds
+of a label-based slice can be outside the range of the index, much like slice indexing a
+normal Python ``list``. Monotonicity of an index can be tested with the ``is_monotonic_increasing`` and
+``is_monotonic_decreasing`` attributes.
+
+.. ipython:: python
+
+    df = pd.DataFrame(index=[2,3,3,4,5], columns=['data'], data=range(5))
+    df.index.is_monotonic_increasing
+
+    # no rows 0 or 1, but still returns rows 2, 3 (both of them), and 4:
+    df.loc[0:4, :]
+
+    # slice is are outside the index, so empty DataFrame is returned
+    df.loc[13:15, :]
+
+On the other hand, if the index is not monotonic, then both slice bounds must be
+*unique* members of the index.
+
+.. ipython:: python
+
+    df = pd.DataFrame(index=[2,3,1,4,3,5], columns=['data'], data=range(6))
+    df.index.is_monotonic_increasing
+
+    # OK because 2 and 4 are in the index
+    df.loc[2:4, :]
+
+.. code-block:: python
+
+    # 0 is not in the index
+    In [9]: df.loc[0:4, :]
+    KeyError: 0
+
+    # 3 is not a unique label
+    In [11]: df.loc[2:3, :]
+    KeyError: 'Cannot get right slice bound for non-unique label: 3'
+
+
+Endpoints are inclusive
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Compared with standard Python sequence slicing in which the slice endpoint is
+not inclusive, label-based slicing in pandas **is inclusive**. The primary
+reason for this is that it is often not possible to easily determine the
+"successor" or next element after a particular label in an index. For example,
+consider the following Series:
+
+.. ipython:: python
+
+   s = pd.Series(np.random.randn(6), index=list('abcdef'))
+   s
+
+Suppose we wished to slice from ``c`` to ``e``, using integers this would be
+
+.. ipython:: python
+
+   s[2:5]
+
+However, if you only had ``c`` and ``e``, determining the next element in the
+index can be somewhat complicated. For example, the following does not work:
+
+::
+
+    s.loc['c':'e'+1]
+
+A very common use case is to limit a time series to start and end at two
+specific dates. To enable this, we made the design design to make label-based
+slicing include both endpoints:
+
+.. ipython:: python
+
+    s.loc['c':'e']
+
+This is most definitely a "practicality beats purity" sort of thing, but it is
+something to watch out for if you expect label-based slicing to behave exactly
+in the way that standard Python integer slicing works.
+
+
+Indexing potentially changes underlying Series dtype
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The different indexing operation can potentially change the dtype of a ``Series``.
+
+.. ipython:: python
+
+   series1 = pd.Series([1, 2, 3])
+   series1.dtype
+   res = series1[[0,4]]
+   res.dtype
+   res
+
+.. ipython:: python
+   series2 = pd.Series([True])
+   series2.dtype
+   res = series2.reindex_like(series1)
+   res.dtype
+   res
+
+This is because the (re)indexing operations above silently inserts ``NaNs`` and the ``dtype``
+changes accordingly.  This can cause some issues when using ``numpy`` ``ufuncs``
+such as ``numpy.logical_and``.
+
+See the `this old issue <https://github.com/pydata/pandas/issues/2388>`__ for a more
+detailed discussion.
@@ -1889,7 +1889,7 @@ gotchas
 
 Performing selection operations on ``integer`` type data can easily upcast the data to ``floating``.
 The dtype of the input data will be preserved in cases where ``nans`` are not introduced (starting in 0.11.0)
-See also :ref:`integer na gotchas <gotchas.intna>`
+See also :ref:`Support for integer ``NA`` <gotchas.intna>`
 
 .. ipython:: python
 
 
@@ -93,12 +93,13 @@ targets the IPython Notebook environment.
 
 `Plotly’s <https://plot.ly/>`__ `Python API <https://plot.ly/python/>`__ enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and `D3.js <http://d3js.org/>`__. The library supports plotting directly from a pandas DataFrame and cloud-based collaboration. Users of `matplotlib, ggplot for Python, and Seaborn <https://plot.ly/python/matplotlib-to-plotly-tutorial/>`__ can convert figures into interactive web-based plots. Plots can be drawn in `IPython Notebooks <https://plot.ly/ipython-notebooks/>`__ , edited with R or MATLAB, modified in a GUI, or embedded in apps and dashboards. Plotly is free for unlimited sharing, and has `cloud <https://plot.ly/product/plans/>`__, `offline <https://plot.ly/python/offline/>`__, or `on-premise <https://plot.ly/product/enterprise/>`__ accounts for private use.
 
-`Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Visualizing Data in Qt applications
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Spun off from the main pandas library, the `Pandas-Qt <https://github.com/datalyze-solutions/pandas-qt>`__
+Spun off from the main pandas library, the `qtpandas <https://github.com/draperjames/qtpandas>`__
 library enables DataFrame visualization and manipulation in PyQt4 and PySide applications.
 
+
 .. _ecosystem.ide:
 
 IDE