Python |
Python
is an interpreted, interactive, object-oriented programming language. It
incorporates modules, exceptions, dynamic typing, very high level dynamic
data types, and classes. Python combines remarkable power with very clear
syntax. It has interfaces to many system calls and libraries, as well as to
various window systems, and is extensible in C or C++. It is also usable as
an extension language for applications that need a programmable interface.
Finally, Python is portable: it runs on many Unix variants, on the Mac, and
on PCs under MS-DOS, Windows, Windows NT, and OS/2.
|
Enthought Canopy |
Enthought
Canopy is a comprehensive Python
analysis environment that provides easy
installation of
over 450 core scientific analytic and Python packages, creating a robust
platform you can explore, develop, and visualize on. In addition to its pre-built,
tested Python distribution, Enthought Canopy has valuable tools for iterative data
analysis, visualization and application development including:
|
Spark |
Fast
and general engine for large-scale data processing. Lets you load data into
RDDs (resilient distributed databases), and auto optimally spreads it out to
a cluster of machines.
|
numpy |
NumPy is the fundamental package for
scientific computing in Python. It is a Python library that provides a
multidimensional array object, various derived objects (such as masked arrays
and matrices), and an assortment of routines for fast operations on arrays,
including mathematical, logical, shape manipulation, sorting, selecting, I/O,
discrete Fourier transforms, basic linear algebra, basic statistical
operations, random simulation and much more.
At the core of the NumPy package, is the ndarray object. This
encapsulates n-dimensional arrays of
homogeneous data types, with many operations being performed in compiled code
for performance.
|
matplotlib |
matplotlib is a library for making 2D plots of arrays
in Python.
Although it has its origins in emulating the MATLAB® [1] graphics commands, it is independent of MATLAB, and
can be used in a Pythonic, object oriented way. Although matplotlib is
written primarily in pure Python, it makes heavy use of NumPy and other
extension code to provide good performance even for large arrays.
matplotlib is designed with the philosophy that you should be
able to create simple plots with just a few commands, or just one! If you
want to see a histogram of your data, you shouldn’t need to instantiate
objects, call methods, set properties, and so on; it should just work.
|
SciPy |
SciPy is a collection of mathematical
algorithms and convenience functions built on the Numpy extension of Python.
It adds significant power to the interactive Python session by providing the
user with high-level commands and classes for manipulating and visualizing
data. With SciPy an interactive Python session becomes a data-processing and
system-prototyping environment rivaling systems such as MATLAB, IDL, Octave,
R-Lab, and SciLab
|
pandas |
pandas
is a Python package providing fast, flexible, and expressive data
structures designed to make working with “relational” or “labeled” data both
easy and intuitive. It aims to be the fundamental high-level building block
for doing practical, real
world data analysis in Python.
Additionally, it has the broader goal of becoming the most powerful and flexible open
source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
|
urllib3 |
urllib3
is a powerful, sanity-friendly HTTP client for
Python.
|
Thursday, January 5, 2017
Python and Core Python Packages for Data Science
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