Merge pull request #5842 from jvns/patch-1

Add pandas cookbook to tutorials (for #5837)

Author: jreback

doc/source/tutorials.rst

@@ -16,3 +16,43 @@ More complex recipes are in the :ref:`Cookbook<cookbook>`
 Tutorials
 ---------
 
+
+Pandas Cookbook
+---------------
+
+The goal of this cookbook (by `Julia Evans <http://jvns.ca>`_) is to
+give you some concrete examples for getting started with pandas. These
+are examples with real-world data, and all the bugs and weirdness that
+that entails.
+
+Here are links to the v0.1 release. For an up-to-date table of contents, see the `pandas-cookbook GitHub
+repository <http://github.com/jvns/pandas-cookbook>`_.
+
+*  | `A quick tour of the IPython
+     Notebook <http://nbviewer.ipython.org/github/jvns/pandas-c|%2055ookbook/blob/v0.1/cookbook/A%20quick%20tour%20of%20IPython%20Notebook.ipynb>`_
+   |  Shows off IPython's awesome tab completion and magic functions.
+*  | `Chapter 1: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%201%20-%20Reading%20from%20a%20CSV.ipynb>`_
+      Reading your data into pandas is pretty much the easiest thing. Even
+     when the encoding is wrong!
+*  | `Chapter 2: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%202%20-%20Selecting%20data%20&%20finding%20the%20most%20common%20complaint%20type.ipynb>`_
+     It's not totally obvious how to select data from a pandas dataframe.
+     Here we explain the basics (how to take slices and get columns)
+*  | `Chapter 3: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%3F%20%28or%2C%20more%20selecting%20data%29.ipynb>`_
+    Here we get into serious slicing and dicing and learn how to filter
+     dataframes in complicated ways, really fast.
+*  | `Chapter 4: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%204%20-%20Find%20out%20on%20which%20weekday%20people%20bike%20the%20most%20with%20groupby%20and%20aggregate.ipynb>`_
+      Groupby/aggregate is seriously my favorite thing about pandas
+     and I use it all the time. You should probably read this.
+*  | `Chapter 5:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%205%20-%20Combining%20dataframes%20and%20scraping%20Canadian%20weather%20data.ipynb>`_
+     Here you get to find out if it's cold in Montreal in the winter
+     (spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes.
+*  | `Chapter 6:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%206%20-%20String%20operations%21%20Which%20month%20was%20the%20snowiest%3F.ipynb>`_
+      Strings with pandas are great. It has all these vectorized string
+     operations and they're the best. We will turn a bunch of strings
+     containing "Snow" into vectors of numbers in a trice.
+*  | `Chapter 7: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb>`_
+      Cleaning up messy data is never a joy, but with pandas it's easier.
+*  | `Chapter 8:  <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/cookbook/Chapter%208%20-%20How%20to%20deal%20with%20timestamps.ipynb>`_
+      Parsing Unix timestamps is confusing at first but it turns out
+      to be really easy.
+