diff --git a/doc/source/dsintro.rst b/doc/source/dsintro.rst
index c6e919eb6096c..3c3c67092c8f1 100644
--- a/doc/source/dsintro.rst
+++ b/doc/source/dsintro.rst
@@ -26,7 +26,7 @@ objects. To get started, import numpy and load pandas into your namespace:
    randn = np.random.randn
    from pandas import *
 
-Here is a basic tenet to keep in mind: **data alignment is intrinsic**. Link
+Here is a basic tenet to keep in mind: **data alignment is intrinsic**. The link
 between labels and data will not be broken unless done so explicitly by you.
 
 We'll give a brief intro to the data structures, then consider all of the broad
diff --git a/doc/source/io.rst b/doc/source/io.rst
index 1494871b6262d..508f6076f075d 100644
--- a/doc/source/io.rst
+++ b/doc/source/io.rst
@@ -164,7 +164,7 @@ You can also use a list of columns to create a hierarchical index:
 
 The ``dialect`` keyword gives greater flexibility in specifying the file format.
 By default it uses the Excel dialect but you can specify either the dialect name
-or a :class:``python:csv.Dialect`` instance.
+or a :class:`python:csv.Dialect` instance.
 
 .. ipython:: python
    :suppress:
@@ -286,6 +286,13 @@ data columns:
                  index_col=0) #index is the nominal column
    df
 
+**Note**: When passing a dict as the `parse_dates` argument, the order of
+the columns prepended is not guaranteed, because `dict` objects do not impose
+an ordering on their keys. On Python 2.7+ you may use `collections.OrderedDict`
+instead of a regular `dict` if this matters to you. Because of this, when using a
+dict for 'parse_dates' in conjunction with the `index_col` argument, it's best to
+specify `index_col` as a column label rather then as an index on the resulting frame.
+
 Date Parsing Functions
 ~~~~~~~~~~~~~~~~~~~~~~
 Finally, the parser allows you can specify a custom ``date_parser`` function to
diff --git a/doc/source/visualization.rst b/doc/source/visualization.rst
index 3e7fa29806091..f4cbbe7a074a7 100644
--- a/doc/source/visualization.rst
+++ b/doc/source/visualization.rst
@@ -327,6 +327,8 @@ for Fourier series. By coloring these curves differently for each class
 it is possible to visualize data clustering. Curves belonging to samples
 of the same class will usually be closer together and form larger structures.
 
+**Note**: The "Iris" dataset is available `here <https://raw.github.com/pydata/pandas/master/pandas/tests/data/iris.csv>`__.
+
 .. ipython:: python
 
    from pandas import read_csv
@@ -440,6 +442,8 @@ forces acting on our sample are at an equilibrium) is where a dot representing
 our sample will be drawn. Depending on which class that sample belongs it will
 be colored differently.
 
+**Note**: The "Iris" dataset is available `here <https://raw.github.com/pydata/pandas/master/pandas/tests/data/iris.csv>`__.
+
 .. ipython:: python
 
    from pandas import read_csv