Learning environments for data analysis software

Posted on February 2, 2013 by emisshula

Welcome to my blog

This is my first blog post using the IPython notebook. I am very excited about the things it can do.

Here is what I want to cover

  1. Who I am
  2. What the blog will cover
  3. Why I named it Measure of Justice

Evan Misshula

I am a PhD student in Criminal Justice. I try to use social networks and data mining to help people make rational decisions about public safety. I care passionately about people that the world writes off. It is no shock. There have been many times when I have been written off.

Math, Computing, Causality, Networks, Security and Ethics

Early in my graduate career, I was struck that we spend a great deal of effort policing minority communities for drug use which has little effect on the non-involved but spend way less effort protecting the banking system from hackers. I also thought that there was a lot to learn about managing threats from inside by looking at both intrusion detection and counter- intelligence. Not suprisingly, I believe in second chances. Who gets those chances and when they come are an area of great interest.

What’s in a name?

When I studied Stochastic Control, Girsanov’s Theorem governed which measures were deformable into each other. Two measures needed to have the same sets of measure zero, to equivilent. In other words it is what we think that is impossible, not unlikely that is important.

My favorite new toy

I am excited about blogging again because I can now put code and math in the blog. I have spent a lot of time in graduate school learning new tools. This blog will hopefully document some of the challenges and help others find their way. Others blogs have certainly helped me.

We can assign variables in the ipython notebook.

print a


b=9 a+b 

But you can also reach into the operating system and execute bash commands.




cp 120907-Blogging with the IPython Notebook.ipynb EvanNB1.html old/
cp 121120-Back from PyCon Canada 2012.ipynb EvanNB1.ipynb EvanNB1_header.html fig/

This is a markdown cell

You can italicize and use boldface. It allows us to comment code and create interactive presentations. You can build lists of your favorite tools. Here are mine.

  • linux
  • emacs
  • r statistical language
  • Emacs Speaks Statistics
  • Org-mode
  • LaTeX
  • Sweave
  • Ggplot

What is most important is to LaTeX support. My favorite math equation is [ e^{i\pi}+1=0 ]. It can also render math numbered equations:


The browser displays

The program can display the numeric or character output of programs.

print "hi Doug"

hi Doug



It can also display graphs:

%pylab inline

Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
For more information, type 'help(pylab)'.

x = linspace(0, 3*pi)
plot(x, 0.5*sin(x), label=r'$\sin(x)$') plot(x, cos(x), 'ro', label=r'$\cos(x)$') title(r'Two familiar functions')

Symbolic Manipulation

The IPython notebook can also make symbolic calculations and solve complex algebraic equations:

%load_ext sympyprinting import sympy as sym
from sympy import *
x, y, z = sym.symbols("x y z")

The sympyprinting extension is already loaded. To reload it, use:
%reload_ext sympyprinting

Rational(3,2)*pi + exp(I*x) / (x**2 + y**2)

\ \π + \

eq = ((x+y)**3 * (x+3)) eq

\\left(x + 3\\right) \\left(x + y\\right)^{3}


x4 + 3 x3 y + 3 x3 + 3 x2 y2 + 9 x2 y + x y3 + 9 x y2 + 3 y3

Ipython can even calculate the derivative!!

diff(cos(x**2)**2 / (1+x)**2, x)
  • 4 \ - 2 \

It can also display pictures and videos…

from IPython.display import Image


from IPython.display import YouTubeVideo

We can even use other languages (including R)!!

This is because ipython communicates between the kernel and the browser so it knows how to send data to another interpreter.

So we can process code from Ruby:

%%ruby puts "Hello from Ruby #{RUBY_VERSION}"

Hello from Ruby 1.9.3

We can run bash scripts:

%%bash echo "hello from $BASH" 

hello from /bin/bash

We can interact with an R environment:

import rpy2;
from rpy2 import robjects; robjects.r("version")

platform x86_64-unknown-linux-gnu
arch x86_64
os linux-gnu
system x86_64, linux-gnu
major 2
minor 15.2
year 2012
month 10
day 26
svn rev 61015
language R
version.string R version 2.15.2 (2012-10-26)
nickname Trick or Treat 

%load_ext rmagic

The rmagic extension is already loaded. To reload it, use: %reload_ext rmagic

We can return R objects to python

X = np.array([0,1,2,3,4]) Y = np.array([3,5,4,6,7])
%%R -i X,Y -o XYcoef
XYlm = lm(Y~X)
XYcoef = coef(XYlm)

lm(formula = Y ~ X)

1 2 3 4 5
-0.2 0.9 -1.0 0.1 0.2

Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.2000 0.6164 5.191 0.0139 *
X 0.9000 0.2517 3.576 0.0374 *
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7958 on 3 degrees of freedom
Multiple R-squared: 0.81,   Adjusted R-squared: 0.7467
F-statistic: 12.79 on 1 and 3 DF, p-value: 0.03739

There is more to come. Ipython does d3 interactive graphs but I have not been able to get them to work. It also handles cython (python wrapped c-code) and mpi parallel code. More later. It is time for bed.