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

Author: tommyod

doc/source/tutorials.rst

@@ -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