Dive into NumPy! Start organizing, rearranging, and sampling your data.

Python Data Analysis and Visualization

From Python to Numpy

If there are several additional packages for NumPy, there are a trillion
additional packages for SciPy. In fact, every domain of science probably has
its own package and most of the examples we've been studying until now could
have been solved in two or three calls to a method in the relevant package.


But of course, that was not the goal and programming things yourself is generally
a good exercise if you have some spare time. The biggest difficulty at this
point is to find these relevant packages. Here is a very short list of packages
that are well-maintained, well-tested and may simplify your scientific life
(depending on your domain). There are, of course, many more and depending on your
specific needs, chances are you do not have to program everything by
yourself. For an extensive list, have a look at the [Awesome python list](https://awesome-python.com).
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# scikit-learn
[scikit-learn](http://scikit-learn.org/stable/) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is
designed to inter-operate with the Python numerical and scientific libraries NumPy and SciPy.

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# scikit-image
[scikit-image](http://scikit-image.org) is a Python package dedicated to
image processing, and using natively NumPy arrays as image objects. This
chapter describes how to use scikit-image on various image processing tasks,
and insists on the link with other scientific Python modules such as NumPy and
SciPy.
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# SymPy
[SymPy](http://www.sympy.org/en/index.html) is a Python library for symbolic
mathematics. It aims to become a full-featured computer algebra system (CAS)
while keeping the code as simple as possible in order to be comprehensible and
easily extensible. SymPy is written entirely in Python.
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# Astropy
The [Astropy](http://www.astropy.org) project is a community effort to develop a single core package for astronomy in Python and foster interoperability between Python astronomy packages.
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# Cartopy
[Cartopy](http://scitools.org.uk/cartopy/) is a Python package designed to make drawing maps for data analysis and visualization as easy as possible. Cartopy makes use of the powerful PROJ.4, NumPy and shapely libraries and has a simple and intuitive drawing interface to matplotlib for creating
publication quality maps.

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# Brian
[Brian](http://www.briansimulator.org) is a free, open source simulator for
spiking neural networks. It is written in the Python programming language and
is available on almost all platforms. We believe that a simulator should not
only save the time of processors, but also the time of scientists. Brian is
therefore designed to be easy to learn and use, highly flexible and easily
extensible.
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# Glumpy
[Glumpy](http://glumpy.github.io) is an OpenGL-based interactive
visualization library in Python. Its goal is to make it easy to create fast,
scalable, beautiful, interactive and dynamic visualizations.



This lesson gives a brief introduction to SciPy library and the additional packages that it offers.

Scipy & co