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DOC: remove long listings in the tutorials.rst page (pandas-dev#24152)
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‎doc/source/tutorials.rst

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@@ -18,117 +18,28 @@ A handy pandas `cheat sheet <http://pandas.pydata.org/Pandas_Cheat_Sheet.pdf>`_.
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Community Guides
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================
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pandas Cookbook
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---------------
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pandas Cookbook by Julia Evans
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------------------------------
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The goal of this 2015 cookbook (by `Julia Evans <http://jvns.ca>`_) is to
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give you some concrete examples for getting started with pandas. These
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are examples with real-world data, and all the bugs and weirdness that
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entails.
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For the table of contents, see the `pandas-cookbook GitHub
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repository <http://github.com/jvns/pandas-cookbook>`_.
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Here are links to the v0.2 release. For an up-to-date table of contents, see the `pandas-cookbook GitHub
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repository <http://github.com/jvns/pandas-cookbook>`_. To run the examples in this tutorial, you'll need to
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clone the GitHub repository and get IPython Notebook running.
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See `How to use this cookbook <https://github.com/jvns/pandas-cookbook#how-to-use-this-cookbook>`_.
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* `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>`_
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Shows off IPython's awesome tab completion and magic functions.
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* `Chapter 1: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%201%20-%20Reading%20from%20a%20CSV.ipynb>`_
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Reading your data into pandas is pretty much the easiest thing. Even
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when the encoding is wrong!
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* `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>`_
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It's not totally obvious how to select data from a pandas dataframe.
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Here we explain the basics (how to take slices and get columns)
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* `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>`_
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Here we get into serious slicing and dicing and learn how to filter
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dataframes in complicated ways, really fast.
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* `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>`_
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Groupby/aggregate is seriously my favorite thing about pandas
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and I use it all the time. You should probably read this.
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* `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>`_
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Here you get to find out if it's cold in Montreal in the winter
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(spoiler: yes). Web scraping with pandas is fun! Here we combine dataframes.
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* `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>`_
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Strings with pandas are great. It has all these vectorized string
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operations and they're the best. We will turn a bunch of strings
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containing "Snow" into vectors of numbers in a trice.
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* `Chapter 7: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%207%20-%20Cleaning%20up%20messy%20data.ipynb>`_
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Cleaning up messy data is never a joy, but with pandas it's easier.
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* `Chapter 8: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%208%20-%20How%20to%20deal%20with%20timestamps.ipynb>`_
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Parsing Unix timestamps is confusing at first but it turns out
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to be really easy.
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* `Chapter 9: <http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/blob/v0.2/cookbook/Chapter%209%20-%20Loading%20data%20from%20SQL%20databases.ipynb>`_
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Reading data from SQL databases.
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Lessons for new pandas users
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Learn Pandas by Hernan Rojas
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----------------------------
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For more resources, please visit the main `repository <https://bitbucket.org/hrojas/learn-pandas>`__.
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* `01 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/01%20-%20Lesson.ipynb>`_
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* Importing libraries
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* Creating data sets
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* Creating data frames
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* Reading from CSV
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* Exporting to CSV
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* Finding maximums
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* Plotting data
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* `02 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/02%20-%20Lesson.ipynb>`_
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* Reading from TXT
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* Exporting to TXT
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* Selecting top/bottom records
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* Descriptive statistics
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* Grouping/sorting data
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* `03 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/03%20-%20Lesson.ipynb>`_
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* Creating functions
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* Reading from EXCEL
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* Exporting to EXCEL
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* Outliers
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* Lambda functions
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* Slice and dice data
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* `04 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/04%20-%20Lesson.ipynb>`_
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* Adding/deleting columns
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* Index operations
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* `05 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/05%20-%20Lesson.ipynb>`_
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* Stack/Unstack/Transpose functions
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* `06 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/06%20-%20Lesson.ipynb>`_
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* GroupBy function
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* `07 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/07%20-%20Lesson.ipynb>`_
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* Ways to calculate outliers
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* `08 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/08%20-%20Lesson.ipynb>`_
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* Read from Microsoft SQL databases
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* `09 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/09%20-%20Lesson.ipynb>`_
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* Export to CSV/EXCEL/TXT
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* `10 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/10%20-%20Lesson.ipynb>`_
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* Converting between different kinds of formats
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* `11 - Lesson: <http://nbviewer.ipython.org/urls/bitbucket.org/hrojas/learn-pandas/raw/master/lessons/11%20-%20Lesson.ipynb>`_
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* Combining data from various sources
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A set of lesson for new pandas users: `https://bitbucket.org/hrojas/learn-pandas>`__.
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Practical data analysis with Python
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-----------------------------------
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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.
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There are four sections covering selected topics as follows:
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* `Munging Data <http://wavedatalab.github.io/datawithpython/munge.html>`_
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* `Aggregating Data <http://wavedatalab.github.io/datawithpython/aggregate.html>`_
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* `Visualizing Data <http://wavedatalab.github.io/datawithpython/visualize.html>`_
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* `Time Series <http://wavedatalab.github.io/datawithpython/timeseries.html>`_
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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.
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There are four sections covering selected topics as `munging data <http://wavedatalab.github.io/datawithpython/munge.html>`__,
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`aggregating data <http://wavedatalab.github.io/datawithpython/aggregate.html>`_, `visualizing data <http://wavedatalab.github.io/datawithpython/visualize.html>`_
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and `time series <http://wavedatalab.github.io/datawithpython/timeseries.html>`_.
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.. _tutorial-exercises-new-users:
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Practice your skills with real data sets and exercises.
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For more resources, please visit the main `repository <https://github.com/guipsamora/pandas_exercises>`__.
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* `01 - Getting & Knowing Your Data <https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data>`_
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* `02 - Filtering & Sorting <https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting>`_
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* `03 - Grouping <https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping>`_
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* `04 - Apply <https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply>`_
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* `05 - Merge <https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge>`_
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* `06 - Stats <https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats>`_
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* `07 - Visualization <https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization>`_
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* `08 - Creating Series and DataFrames <https://github.com/guipsamora/pandas_exercises/tree/master/08_Creating_Series_and_DataFrames/Pokemon>`_
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* `09 - Time Series <https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series>`_
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* `10 - Deleting <https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting>`_
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.. _tutorial-modern:
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