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.. _tutorials:
*********
Tutorials
*********
This is a guide to many pandas tutorials, geared mainly for new users.
Internal Guides
---------------
pandas own :ref:`10 Minutes to pandas<10min>`
More complex recipes are in the :ref:`Cookbook<cookbook>`
pandas Cookbook
---------------
The goal of this 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
that entails.
Here are links to the v0.1 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.ipython.org/github/jvns/pandas-cookbook/blob/v0.1/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/v0.1/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/v0.1/cookbook/Chapter%202%20-%20Selecting%20data%20&%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/v0.1/cookbook/Chapter%203%20-%20Which%20borough%20has%20the%20most%20noise%20complaints%3F%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/v0.1/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/v0.1/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/v0.1/cookbook/Chapter%206%20-%20String%20operations%21%20Which%20month%20was%20the%20snowiest%3F.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/v0.1/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/v0.1/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.
Lessons for New pandas Users
----------------------------
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
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>`_
.. _tutorial-modern:
Modern Pandas
-------------
- `Modern Pandas <http://tomaugspurger.github.io/modern-1.html>`_
- `Method Chaining <http://tomaugspurger.github.io/method-chaining.html>`_
- `Indexes <http://tomaugspurger.github.io/modern-3-indexes.html>`_
- `Performance <http://tomaugspurger.github.io/modern-4-performance.html>`_
- `Tidy Data <http://tomaugspurger.github.io/modern-5-tidy.html>`_
- `Visualization <http://tomaugspurger.github.io/modern-6-visualization.html>`_
Excel charts with pandas, vincent and xlsxwriter
------------------------------------------------
- `Using Pandas and XlsxWriter to create Excel charts <https://pandas-xlsxwriter-charts.readthedocs.io/>`_
Various Tutorials
-----------------
- `Wes McKinney's (pandas BDFL) blog <http://blog.wesmckinney.com/>`_
- `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 <http://conference.scipy.org/scipy2013/tutorial_detail.php?id=109>`_
- `Financial analysis in python, by Thomas Wiecki <http://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 <http://manishamde.github.io/blog/2013/03/07/pandas-and-python-top-10/>`_
- `Pandas Tutorial, by Mikhail Semeniuk <http://www.bearrelroll.com/2013/05/python-pandas-tutorial>`_
|
