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DOC: Updated links to 2 tutorials in tutorials.rst #19857

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64a0d99
Changed links to 2 tutorials from v0.2 to v0.1, as v0.2 did not exist…
tommyod Feb 23, 2018
3f09bc3
Updated all pandas-tutorial links to v0.2, added chapter 9 of tutorial
tommyod Feb 24, 2018
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20 changes: 11 additions & 9 deletions doc/source/tutorials.rst
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Expand Up @@ -26,32 +26,34 @@ repository <http://github.com/jvns/pandas-cookbook>`_. To run the examples in th
clone the GitHub repository and get IPython Notebook running.
See `How to use this cookbook <https://github.com/jvns/pandas-cookbook#how-to-use-this-cookbook>`_.

- `A quick tour of the IPython Notebook: <http://nbviewer.ipython.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/A%20quick%20tour%20of%20IPython%20Notebook.ipynb>`_
- `A quick tour of the IPython Notebook: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/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/v0.2/cookbook/Chapter%201%20-%20Reading%20from%20a%20CSV.ipynb>`_
- `Chapter 1: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/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/v0.2/cookbook/Chapter%202%20-%20Selecting%20data%20&%20finding%20the%20most%20common%20complaint%20type.ipynb>`_
- `Chapter 2: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%202%20-%20Selecting%20data%20%26%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/v0.2/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%3F%20%28or%2C%20more%20selecting%20data%29.ipynb>`_
- `Chapter 3: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%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/v0.2/cookbook/Chapter%204%20-%20Find%20out%20on%20which%20weekday%20people%20bike%20the%20most%20with%20groupby%20and%20aggregate.ipynb>`_
- `Chapter 4: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/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/v0.2/cookbook/Chapter%205%20-%20Combining%20dataframes%20and%20scraping%20Canadian%20weather%20data.ipynb>`_
- `Chapter 5: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/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/v0.2/cookbook/Chapter%206%20-%20String%20operations%21%20Which%20month%20was%20the%20snowiest%3F.ipynb>`_
- `Chapter 6: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%206%20-%20String%20Operations-%20Which%20month%20was%20the%20snowiest.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/v0.2/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb>`_
- `Chapter 7: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/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/v0.2/cookbook/Chapter%208%20-%20How%20to%20deal%20with%20timestamps.ipynb>`_
- `Chapter 8: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/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.
- `Chapter 9: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%209%20-%20Loading%20data%20from%20SQL%20databases.ipynb>`_
Reading data from SQL databases.


Lessons for new pandas users
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