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128 changes: 10 additions & 118 deletions doc/source/tutorials.rst
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Expand Up @@ -18,117 +18,28 @@ A handy pandas `cheat sheet <http://pandas.pydata.org/Pandas_Cheat_Sheet.pdf>`_.
Community Guides
================

pandas Cookbook
---------------
pandas Cookbook by Julia Evans
------------------------------

The goal of this 2015 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
entails.
For the table of contents, see the `pandas-cookbook GitHub
repository <http://github.com/jvns/pandas-cookbook>`_.

Here are links to the v0.2 release. For an up-to-date table of contents, see the `pandas-cookbook GitHub
repository <http://github.com/jvns/pandas-cookbook>`_. To run the examples in this tutorial, you'll need to
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.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.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.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.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.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.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.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.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.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
Learn Pandas by Hernan Rojas
----------------------------

For more resources, please visit the main `repository <https://bitbucket.org/hrojas/learn-pandas>`__.

* `01 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%20-%20Lesson.ipynb>`_
* Importing libraries
* Creating data sets
* Creating data frames
* Reading from CSV
* Exporting to CSV
* Finding maximums
* Plotting data

* `02 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%20-%20Lesson.ipynb>`_
* Reading from TXT
* Exporting to TXT
* Selecting top/bottom records
* Descriptive statistics
* Grouping/sorting data

* `03 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/03%20-%20Lesson.ipynb>`_
* Creating functions
* Reading from EXCEL
* Exporting to EXCEL
* Outliers
* Lambda functions
* Slice and dice data

* `04 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/04%20-%20Lesson.ipynb>`_
* Adding/deleting columns
* Index operations

* `05 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/05%20-%20Lesson.ipynb>`_
* Stack/Unstack/Transpose functions

* `06 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/06%20-%20Lesson.ipynb>`_
* GroupBy function

* `07 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/07%20-%20Lesson.ipynb>`_
* Ways to calculate outliers

* `08 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/08%20-%20Lesson.ipynb>`_
* Read from Microsoft SQL databases

* `09 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/09%20-%20Lesson.ipynb>`_
* Export to CSV/EXCEL/TXT

* `10 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/10%20-%20Lesson.ipynb>`_
* Converting between different kinds of formats

* `11 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/11%20-%20Lesson.ipynb>`_
* Combining data from various sources

A set of lesson for new pandas users: `https://bitbucket.org/hrojas/learn-pandas>`__.

Practical data analysis with Python
-----------------------------------

This `guide <http://wavedatalab.github.io/datawithpython>`_ is a comprehensive introduction to the data analysis process using the Python data ecosystem and an interesting open dataset.
There are four sections covering selected topics as follows:

* `Munging Data <http://wavedatalab.github.io/datawithpython/munge.html>`_

* `Aggregating Data <http://wavedatalab.github.io/datawithpython/aggregate.html>`_

* `Visualizing Data <http://wavedatalab.github.io/datawithpython/visualize.html>`_

* `Time Series <http://wavedatalab.github.io/datawithpython/timeseries.html>`_
This `guide <http://wavedatalab.github.io/datawithpython>`_ is an introduction to the data analysis process using the Python data ecosystem and an interesting open dataset.
There are four sections covering selected topics as `munging data <http://wavedatalab.github.io/datawithpython/munge.html>`__,
`aggregating data <http://wavedatalab.github.io/datawithpython/aggregate.html>`_, `visualizing data <http://wavedatalab.github.io/datawithpython/visualize.html>`_
and `time series <http://wavedatalab.github.io/datawithpython/timeseries.html>`_.

.. _tutorial-exercises-new-users:

Expand All @@ -137,25 +48,6 @@ Exercises for new users
Practice your skills with real data sets and exercises.
For more resources, please visit the main `repository <https://github.com/guipsamora/pandas_exercises>`__.

* `01 - Getting & Knowing Your Data <https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data>`_

* `02 - Filtering & Sorting <https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting>`_

* `03 - Grouping <https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping>`_

* `04 - Apply <https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply>`_

* `05 - Merge <https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge>`_

* `06 - Stats <https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats>`_

* `07 - Visualization <https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization>`_

* `08 - Creating Series and DataFrames <https://github.com/guipsamora/pandas_exercises/tree/master/08_Creating_Series_and_DataFrames/Pokemon>`_

* `09 - Time Series <https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series>`_

* `10 - Deleting <https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting>`_

.. _tutorial-modern:

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