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* y-p/doc_fix: DOC: fix intersphinx ref to python doc in io.rst DOC: closes #1709, add link to iris data set DOC: grammer DOC: add caveat regarding the use of dicts as arg to read_csv()
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doc/source/dsintro.rst

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@@ -26,7 +26,7 @@ objects. To get started, import numpy and load pandas into your namespace:
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randn = np.random.randn
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from pandas import *
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Here is a basic tenet to keep in mind: **data alignment is intrinsic**. Link
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Here is a basic tenet to keep in mind: **data alignment is intrinsic**. The link
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between labels and data will not be broken unless done so explicitly by you.
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We'll give a brief intro to the data structures, then consider all of the broad

doc/source/io.rst

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@@ -164,7 +164,7 @@ You can also use a list of columns to create a hierarchical index:
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The ``dialect`` keyword gives greater flexibility in specifying the file format.
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By default it uses the Excel dialect but you can specify either the dialect name
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or a :class:``python:csv.Dialect`` instance.
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or a :class:`python:csv.Dialect` instance.
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.. ipython:: python
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:suppress:
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index_col=0) #index is the nominal column
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df
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**Note**: When passing a dict as the `parse_dates` argument, the order of
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the columns prepended is not guaranteed, because `dict` objects do not impose
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an ordering on their keys. On Python 2.7+ you may use `collections.OrderedDict`
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instead of a regular `dict` if this matters to you. Because of this, when using a
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dict for 'parse_dates' in conjunction with the `index_col` argument, it's best to
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specify `index_col` as a column label rather then as an index on the resulting frame.
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Date Parsing Functions
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~~~~~~~~~~~~~~~~~~~~~~
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Finally, the parser allows you can specify a custom ``date_parser`` function to

doc/source/visualization.rst

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@@ -327,6 +327,8 @@ for Fourier series. By coloring these curves differently for each class
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it is possible to visualize data clustering. Curves belonging to samples
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of the same class will usually be closer together and form larger structures.
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**Note**: The "Iris" dataset is available `here <https://raw.github.com/pydata/pandas/master/pandas/tests/data/iris.csv>`__.
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.. ipython:: python
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from pandas import read_csv
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our sample will be drawn. Depending on which class that sample belongs it will
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be colored differently.
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**Note**: The "Iris" dataset is available `here <https://raw.github.com/pydata/pandas/master/pandas/tests/data/iris.csv>`__.
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.. ipython:: python
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from pandas import read_csv

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