Python for Big Data
===================

.. sidebar:: Page Contents

   .. contents::
      :local:



Managing Data
-------------

Scipy
~~~~~

* https://www.scipy.org/

According to the SciPy Web page, "SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of
open-source software for mathematics, science, and engineering. In
particular, these are some of the core packages:

* NumPy
* IPython
* Pandas
* Matplotlib
* Sympy
* SciPy library

It is thus an agglomeration of useful pacakes and will prbably sufice
for your projects in case you use Python.



Pandas
~~~~~~

* http://pandas.pydata.org/

According to the Pandas Web page, "Pandas is a library library providing
high-performance, easy-to-use data structures and data analysis tools
for the Python programming language."

In addition to access to charts via matplotlib it has elementary
functionality for conduction data analysis. Pandas may be very
suitable for your projects.

Tutorial: http://pandas.pydata.org/pandas-docs/stable/10min.html


Numpy
-----

* http://www.numpy.org/

According to the Numpy Web page "NumPy is a package for scientific
computing with Python. It contains a powerful N-dimensional array
object, sophisticated (broadcasting) functions, tools for integrating
C/C++ and Fortran code, useful linear algebra, Fourier transform, and
random number capabilities

Tutorial: https://docs.scipy.org/doc/numpy-dev/user/quickstart.html




Graphics Libraries
----------------------------------------------------------------------

MatplotLib
~~~~~~~~~~

* http://matplotlib.org/

According the the Matplotlib Web page, "matplotlib is a python 2D
plotting library which produces publication quality figures in a
variety of hardcopy formats and interactive environments across
platforms. matplotlib can be used in python scripts, the python and
ipython shell (ala MATLAB®* or Mathematica®†), web application
servers, and six graphical user interface toolkits."


ggplot
~~~~~~

* http://ggplot.yhathq.com/
  
According to the ggplot python Web page ggplot is a plotting system
for Python based on R's ggplot2. It allows to quickly generate some
plots quickly with little effort. Often it may be easier to use than
matplotlib directly.


seaborn
~~~~~~~~

http://www.data-analysis-in-python.org/t_seaborn.html

The good library for plotting is called seaborn which is build on top
of matplotlib. It provides high level templates for common
statistical plots.

* Gallery: http://stanford.edu/~mwaskom/software/seaborn/examples/index.html
* Original Tutorial: http://stanford.edu/~mwaskom/software/seaborn/tutorial.html
* Additional Tutorial: https://stanford.edu/~mwaskom/software/seaborn/tutorial/distributions.html

Bokeh
~~~~~

Bokeh is an interactive visualization library with focus on 
web browsers for display. Its goal is to provide a similar experience
as D3.js
  
* URL: http://bokeh.pydata.org/
* Gallery: http://bokeh.pydata.org/en/latest/docs/gallery.html

pygal
~~~~~

Pygal is a simple API to produce graphs that can be easily
embedded into your Web pages. It contains annotations when you hover
over data points. It also allows to present the data in a table.

* URL: http://pygal.org/

  
Network and Graphs
------------------

* igraph: http://www.pythonforsocialscientists.org/t_igraph.html
* networkx: https://networkx.github.io/



Examples
----------------------------------------------------------------------

- :doc:`Fingerprint Analysis <python_lesson1>`