.. _communitytutorials:

{{ header }}

*******************
Community tutorials
*******************

This is a guide to many pandas tutorials by the community, geared mainly for new users.

pandas cookbook by Julia Evans
------------------------------

The goal of this 2015 cookbook (by `Julia Evans <https://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 <https://github.com/jvns/pandas-cookbook>`_.

pandas workshop by Stefanie Molin
---------------------------------

An introductory workshop by `Stefanie Molin <https://github.com/stefmolin>`_
designed to quickly get you up to speed with pandas using real-world datasets.
It covers getting started with pandas, data wrangling, and data visualization
(with some exposure to matplotlib and seaborn). The
`pandas-workshop GitHub repository <https://github.com/stefmolin/pandas-workshop>`_
features detailed environment setup instructions (including a Binder environment),
slides and notebooks for following along, and exercises to practice the concepts.
There is also a lab with new exercises on a dataset not covered in the workshop for
additional practice.

Learn pandas by Hernan Rojas
----------------------------

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

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

This `guide <https://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 <https://wavedatalab.github.io/datawithpython/munge.html>`__,
`aggregating data <https://wavedatalab.github.io/datawithpython/aggregate.html>`_, `visualizing data <https://wavedatalab.github.io/datawithpython/visualize.html>`_
and `time series <https://wavedatalab.github.io/datawithpython/timeseries.html>`_.

.. _tutorial-exercises-new-users:

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>`__.


.. _tutorial-modern:

Modern pandas
-------------

Tutorial series written in 2016 by
`Tom Augspurger <https://github.com/TomAugspurger>`_.
The source may be found in the GitHub repository
`TomAugspurger/effective-pandas <https://github.com/TomAugspurger/effective-pandas>`_.

* `Modern Pandas <https://tomaugspurger.github.io/modern-1-intro.html>`_
* `Method Chaining <https://tomaugspurger.github.io/method-chaining.html>`_
* `Indexes <https://tomaugspurger.github.io/modern-3-indexes.html>`_
* `Performance <https://tomaugspurger.github.io/modern-4-performance.html>`_
* `Tidy Data <https://tomaugspurger.github.io/modern-5-tidy.html>`_
* `Visualization <https://tomaugspurger.github.io/modern-6-visualization.html>`_
* `Timeseries <https://tomaugspurger.github.io/modern-7-timeseries.html>`_

Excel charts with pandas, vincent and xlsxwriter
------------------------------------------------

*  `Using Pandas and XlsxWriter to create Excel charts <https://pandas-xlsxwriter-charts.readthedocs.io/>`_

Video tutorials
---------------

* `Pandas From The Ground Up <https://www.youtube.com/watch?v=5JnMutdy6Fw>`_
  (2015) (2:24)
  `GitHub repo <https://github.com/brandon-rhodes/pycon-pandas-tutorial>`__
* `Introduction Into Pandas <https://www.youtube.com/watch?v=-NR-ynQg0YM>`_
  (2016) (1:28)
  `GitHub repo <https://github.com/chendaniely/2016-pydata-carolinas-pandas>`__
* `Pandas: .head() to .tail() <https://www.youtube.com/watch?v=7vuO9QXDN50>`_
  (2016) (1:26)
  `GitHub repo <https://github.com/TomAugspurger/pydata-chi-h2t>`__
* `Data analysis in Python with pandas <https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y>`_
  (2016-2018)
  `GitHub repo <https://github.com/justmarkham/pandas-videos>`__ and
  `Jupyter Notebook <https://nbviewer.org/github/justmarkham/pandas-videos/blob/master/pandas.ipynb>`__
* `Best practices with pandas <https://www.youtube.com/playlist?list=PL5-da3qGB5IBITZj_dYSFqnd_15JgqwA6>`_
  (2018)
  `GitHub repo <https://github.com/justmarkham/pycon-2018-tutorial>`__ and
  `Jupyter Notebook <https://nbviewer.org/github/justmarkham/pycon-2018-tutorial/blob/master/tutorial.ipynb>`__


Various tutorials
-----------------

* `Wes McKinney's (pandas BDFL) blog <https://wesmckinney.com/archives.html>`_
* `Statistical analysis made easy in Python with SciPy and pandas DataFrames, by Randal Olson <http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/>`_
* `Statistical Data Analysis in Python, tutorial videos, by Christopher Fonnesbeck from SciPy 2013 <https://conference.scipy.org/scipy2013/tutorial_detail.php?id=109>`_
* `Financial analysis in Python, by Thomas Wiecki <https://nbviewer.ipython.org/github/twiecki/financial-analysis-python-tutorial/blob/master/1.%20Pandas%20Basics.ipynb>`_
* `Intro to pandas data structures, by Greg Reda <http://www.gregreda.com/2013/10/26/intro-to-pandas-data-structures/>`_
* `Pandas and Python: Top 10, by Manish Amde <https://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/>`_
* `Pandas DataFrames Tutorial, by Karlijn Willems <https://www.datacamp.com/community/tutorials/pandas-tutorial-dataframe-python>`_
* `A concise tutorial with real life examples <https://tutswiki.com/pandas-cookbook/chapter1/>`_
