@@ -18,8 +18,7 @@ kernelspec:

1818</div>1919```202021-```{index}22-single: python21+```{index} single: python2322```24232524# About Python

@@ -122,8 +121,7 @@ Other features of Python:

122121123122### Syntax and Design124123125-```{index}126-single: Python; syntax and design124+```{index} single: Python; syntax and design127125```128126129127One nice feature of Python is its elegant syntax --- we'll see many examples later on.

@@ -140,8 +138,7 @@ Features like iterators, generators, decorators and list comprehensions make Pyt

140138141139## Scientific Programming142140143-```{index}144-single: scientific programming141+```{index} single: scientific programming145142```146143147144Python has become one of the core languages of scientific computing.

@@ -163,8 +160,7 @@ This section briefly showcases some examples of Python for scientific programmin

163160164161### Numerical Programming165162166-```{index}167-single: scientific programming; numeric163+```{index} single: scientific programming; numeric168164```169165170166

Fundamental matrix and array processing capabilities are provided by the excellent [NumPy](http://www.numpy.org/) library.

@@ -218,8 +214,7 @@ See them all [here](http://docs.scipy.org/doc/scipy/reference/index.html).

218214219215### Graphics220216221-```{index}222-single: Matplotlib217+```{index} single: Matplotlib223218```224219225220

The most popular and comprehensive Python library for creating figures and graphs is [Matplotlib](http://matplotlib.org/), with functionality including

@@ -258,8 +253,7 @@ Other graphics libraries include

258253259254It's useful to be able to manipulate symbolic expressions, as in Mathematica or Maple.260255261-```{index}262-single: SymPy256+```{index} single: SymPy263257```264258265259The [SymPy](http://www.sympy.org/) library provides this functionality from within the Python shell.

@@ -315,8 +309,7 @@ the last few years.

315309316310#### Pandas317311318-```{index}319-single: Pandas312+```{index} single: Pandas320313```321314322315One of the most popular libraries for working with data is [pandas](http://pandas.pydata.org/).

@@ -343,26 +336,22 @@ df.mean()

343336344337#### Other Useful Statistics Libraries345338346-```{index}347-single: statsmodels339+```{index} single: statsmodels348340```349341350342* [statsmodels](http://statsmodels.sourceforge.net/) --- various statistical routines351343352-```{index}353-single: scikit-learn344+```{index} single: scikit-learn354345```355346356347* [scikit-learn](http://scikit-learn.org/) --- machine learning in Python (sponsored by Google, among others)357348358-```{index}359-single: pyMC349+```{index} single: pyMC360350```361351362352* [pyMC](http://pymc-devs.github.io/pymc/) --- for Bayesian data analysis363353364-```{index}365-single: pystan354+```{index} single: pystan366355```367356368357* [pystan](https://pystan.readthedocs.org/en/latest/) Bayesian analysis based on [stan](http://mc-stan.org/)

@@ -371,8 +360,7 @@ single: pystan

371360372361Python has many libraries for studying graphs.373362374-```{index}375-single: NetworkX363+```{index} single: NetworkX376364```377365378366One well-known example is [NetworkX](http://networkx.github.io/).

@@ -414,70 +402,59 @@ plt.show()

414402415403### Cloud Computing416404417-```{index}418-single: cloud computing405+```{index} single: cloud computing419406```420407421408Running your Python code on massive servers in the cloud is becoming easier and easier.422409423-```{index}424-single: cloud computing; anaconda enterprise410+```{index} single: cloud computing; anaconda enterprise425411```426412427413A nice example is [Anaconda Enterprise](https://www.anaconda.com/enterprise/).428414429415See also430416431-```{index}432-single: cloud computing; amazon ec2417+```{index} single: cloud computing; amazon ec2433418```434419435420* [Amazon Elastic Compute Cloud](http://aws.amazon.com/ec2/)436421437-```{index}438-single: cloud computing; google app engine422+```{index} single: cloud computing; google app engine439423```440424441425* The [Google App Engine](https://cloud.google.com/appengine/) (Python, Java, PHP or Go)442426443-```{index}444-single: cloud computing; pythonanywhere427+```{index} single: cloud computing; pythonanywhere445428```446429447430* [Pythonanywhere](https://www.pythonanywhere.com/)448431449-```{index}450-single: cloud computing; sagemath cloud432+```{index} single: cloud computing; sagemath cloud451433```452434453435* [Sagemath Cloud](https://cloud.sagemath.com/)454436455437### Parallel Processing456438457-```{index}458-single: parallel computing439+```{index} single: parallel computing459440```460441461442Apart from the cloud computing options listed above, you might like to consider462443463-```{index}464-single: parallel computing; ipython444+```{index} single: parallel computing; ipython465445```466446467447* [Parallel computing through IPython clusters](http://ipython.org/ipython-doc/stable/parallel/parallel_demos.html).468448469-```{index}470-single: parallel computing; starcluster449+```{index} single: parallel computing; starcluster471450```472451473452* The [Starcluster](http://star.mit.edu/cluster/) interface to Amazon's EC2.474453475-```{index}476-single: parallel computing; copperhead454+```{index} single: parallel computing; copperhead477455```478456479-```{index}480-single: parallel computing; pycuda457+```{index} single: parallel computing; pycuda481458```482459483460

* GPU programming through [PyCuda](https://wiki.tiker.net/PyCuda), [PyOpenCL](https://mathema.tician.de/software/pyopencl/), [Theano](http://deeplearning.net/software/theano/) or similar.

@@ -489,32 +466,27 @@ There are many other interesting developments with scientific programming in Pyt

489466490467Some representative examples include491468492-```{index}493-single: scientific programming; Jupyter469+```{index} single: scientific programming; Jupyter494470```495471496472

* [Jupyter](http://jupyter.org/) --- Python in your browser with interactive code cells,  embedded images and other useful features.

497473498-```{index}499-single: scientific programming; Numba474+```{index} single: scientific programming; Numba500475```501476502477* [Numba](http://numba.pydata.org/) --- Make Python run at the same speed as native machine code!503478504-```{index}505-single: scientific programming; Blaze479+```{index} single: scientific programming; Blaze506480```507481508482* [Blaze](http://blaze.pydata.org/) --- a generalization of NumPy.509483510-```{index}511-single: scientific programming; PyTables484+```{index} single: scientific programming; PyTables512485```513486514487* [PyTables](http://www.pytables.org) --- manage large data sets.515488516-```{index}517-single: scientific programming; CVXPY489+```{index} single: scientific programming; CVXPY518490```519491520492* [CVXPY](https://github.com/cvxgrp/cvxpy) --- convex optimization in Python.

@@ -525,8 +497,7 @@ single: scientific programming; CVXPY

525497

* Read more about [Python's history and rise in popularity](https://www.welcometothejungle.com/en/articles/btc-python-popular) .

526498

* Have a look at [some of the Jupyter notebooks](http://nbviewer.jupyter.org/) people have shared on various scientific topics.

527499528-```{index}529-single: Python; PyPI500+```{index} single: Python; PyPI530501```531502532503* Visit the [Python Package Index](https://pypi.org/).