Data Frame in Python

Exploring some Python Packages and R packages to move /work with both Python and R without melting your brain or exceeding your project deadline


If you liked the data.frame structure in R, you have some way to work with them at a faster processing speed in Python.

Here are three packages that enable you to do so-

(1) pydataframe

An implemention of an almost R like DataFrame object. (install via Pypi/Pip: “pip install pydataframe”)


        u = DataFrame( { "Field1": [1, 2, 3],
                        "Field2": ['abc', 'def', 'hgi']},
                         ['Field1', 'Field2']
                         ["rowOne", "rowTwo", "thirdRow"])

A DataFrame is basically a table with rows and columns.

Columns are named, rows are numbered (but can be named) and can be easily selected and calculated upon. Internally, columns are stored as 1d numpy arrays. If you set row names, they’re converted into a dictionary for fast access. There is a rich subselection/slicing API, see help(DataFrame.get_item) (it also works for setting values). Please note that any slice get’s you another DataFrame, to access individual entries use get_row(), get_column(), get_value().

DataFrames also understand basic arithmetic and you can either add (multiply,…) a constant value, or another DataFrame of the same size / with the same column names, like this:

#multiply every value in ColumnA that is smaller than 5 by 6.
my_df[my_df[:,'ColumnA'] < 5, 'ColumnA'] *= 6

#you always need to specify both row and column selectors, use : to mean everything
my_df[:, 'ColumnB'] = my_df[:,'ColumnA'] + my_df[:, 'ColumnC']

#let's take every row that starts with Shu in ColumnA and replace it with a new list (comprehension)
select = my_df.where(lambda row: row['ColumnA'].startswith('Shu'))
my_df[select, 'ColumnA'] = [row['ColumnA'].replace('Shu', 'Sha') for row in my_df[select,:].iter_rows()]

Dataframes talk directly to R via rpy2 (rpy2 is not a prerequiste for the library!)


(2) pandas

Library Highlights

  • A fast and efficient DataFrame object for data manipulation with integrated indexing;
  • Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
  • Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
  • Flexible reshaping and pivoting of data sets;
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
  • Columns can be inserted and deleted from data structures for size mutability;
  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
  • High performance merging and joining of data sets;
  • Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
  • Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
  • The library has been ruthlessly optimized for performance, with critical code paths compiled to C;
  • Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.

Why not R?

First of all, we love open source R! It is the most widely-used open source environment for statistical modeling and graphics, and it provided some early inspiration for pandas features. R users will be pleased to find this library adopts some of the best concepts of R, like the foundational DataFrame (one user familiar with R has described pandas as “R data.frame on steroids”). But pandas also seeks to solve some frustrations common to R users:

  • R has barebones data alignment and indexing functionality, leaving much work to the user. pandas makes it easy and intuitive to work with messy, irregularly indexed data, like time series data. pandas also provides rich tools, like hierarchical indexing, not found in R;
  • R is not well-suited to general purpose programming and system development. pandas enables you to do large-scale data processing seamlessly when developing your production applications;
  • Hybrid systems connecting R to a low-productivity systems language like Java, C++, or C# suffer from significantly reduced agility and maintainability, and you’re still stuck developing the system components in a low-productivity language;
  • The “copyleft” GPL license of R can create concerns for commercial software vendors who want to distribute R with their software under another license. Python and pandas use more permissive licenses.

(3) datamatrix

datamatrix 0.8

A Pythonic implementation of R’s data.frame structure.

Latest Version: 0.9

This module allows access to comma- or other delimiter separated files as if they were tables, using a dictionary-like syntax. DataMatrix objects can be manipulated, rows and columns added and removed, or even transposed


Modeling in Python

Continue reading “Data Frame in Python”

Cloud Computing – can be evil

Cloud Computing can be evil because-

1) Most browsers are owned by for profit corporations . Corporations can be evil, sometimes

And corporations can go bankrupt. You can back up data locally, but try backing up a corporation.

2) The content on your web page can be changed using translator extensions . This has interesting ramifications as in George Orwell. You may not be even aware of subtle changes introduced in your browser in the way it renders the html or some words using keywords from a browser extension app.

Imagine a new form of language called Politically Correct Truthspeak, and that can be in English but using machine learning learn to substitute politically sensitive words with Govt sanctioned words.

3) Your DNS and IP settings can be redirected using extensions. This means if a Govt passes a law- you can be denied the websites using just the browser not even the ISP.

Thats an extreme scenario for a authoritative govt creating its own version of Mafiaafire Redirector.

So how to keep the cloud computer honest?Move some stuff to the desktop

How to keep desktop computing efficient?Use some more cloud computing

It is not an OR but an AND function in which some computing can be local, some shared and some in the cloud.


Analytics for Cyber Conflict -Part Deux

Part 1 in this series is avaiable at

The next articles in this series will cover-

  1. the kind of algorithms that are currently or being proposed for cyber conflict, as well as or detection

Cyber Conflict requires some basic elements of the following broad disciplines within Computer and Information Science (besides the obvious disciplines of heterogeneous database types for different kinds of data) –

1) Cryptography – particularly a cryptographic  hash function that maximizes cost and time of the enemy trying to break it.


The ideal cryptographic hash function has four main or significant properties:

  • it is easy (but not necessarily quick) to compute the hash value for any given message
  • it is infeasible to generate a message that has a given hash
  • it is infeasible to modify a message without changing the hash
  • it is infeasible to find two different messages with the same hash

A commercial spin off is to use this to anonymized all customer data stored in any database, such that no database (or data table) that is breached contains personally identifiable information. For example anonymizing the IP Addresses and DNS records with a mashup  (embedded by default within all browsers) of Tor and MafiaaFire extensions can help create better information privacy on the internet.

This can also help in creating better encryption between Instant Messengers in Communication

2) Data Disaster Planning for Data Storage (but also simulations for breaches)- including using cloud computing, time sharing, or RAID for backing up data. Planning and creating an annual (?) exercise for a simulated cyber breach of confidential just like a cyber audit- similar to an annual accounting audit

3) Basic Data Reduction Algorithms for visualizing large amounts of information. This can include

  1. K Means Clustering, , , and
  2. Topic Models (LDA),
  3. Social Network Analysis,
  4. Graph Analysis and
  5. MapReduce and Parallelization algorithms for computational boosting

In the next article we will examine

  1. the role of non state agents as well as state agents competing and cooperating,
  2. and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.



  1.  Any effort to combat online piracy must guard against the risk of online censorship of lawful activity and must not inhibit innovation by our dynamic businesses large and small (AJ-yup)
  2. We must avoid creating new cybersecurity risks or disrupting the underlying architecture of the Internet.  (AJ-note this may include peer-to-peer browsers, browser extensions for re-routing and newer forms of encryption, or even relocation of internet routers in newer geographies )

We must avoid legislation that drives users to dangerous, unreliable DNS servers and puts next-generation security policies, such as the deployment of DNSSEC, at risk.

While we are strongly committed to the vigorous enforcement of intellectual property rights, existing tools are not strong enough to root out the worst online pirates beyond our borders.

We should never let criminals hide behind a hollow embrace of legitimate American values


We should all be committed to working with all interested constituencies to develop new legal tools to protect global intellectual property rights without jeopardizing the openness of the Internet. Our hope is that you will bring enthusiasm and know-how to this important challenge

Authored by

Victoria Espinel is Intellectual Property Enforcement Coordinator at Office of Management and Budget

Aneesh Chopra is the U.S. Chief Technology Officer and Assistant to the President and Associate Director for Technology at the Office of Science and Technology Policy
Howard Schmidt is Special Assistant to the President and Cybersecurity Coordinator for National Security Staff


AJ-Why not sponser a hackathon, White House and create a monetary incentive for hackers to suggest secure ways? Atleast a secure dialogue between policy makers and policy  breakers could be a way forward. 

SOPA in its current form is dead. We live to fight another day.



Let us never negotiate out of fear. But let us never fear to negotiate. John F K

Topic Models

Some stuff on Topic Models-

In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract “topics” that occur in a collection of documents. An early topic model was probabilistic latent semantic indexing (PLSI), created by Thomas Hofmann in 1999.[1] Latent Dirichlet allocation (LDA), perhaps the most common topic model currently in use, is a generalization of PLSI developed by David Blei, Andrew Ng, and Michael Jordan in 2002, allowing documents to have a mixture of topics.[2] Other topic models are generally extensions on LDA, such as Pachinko allocation, which improves on LDA by modeling correlations between topics in addition to the word correlations which constitute topics. Although topic models were first described and implemented in the context of natural language processing, they have applications in other fields such as bioinformatics.

In statistics, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word’s creation is attributable to one of the document’s topics. LDA is an example of a topic model

David M Blei’s page on Topic Models-

The topic models mailing list is a good forum for discussing topic modeling.

In R,

Some resources I compiled on Slideshare based on the above- Continue reading “Topic Models”

Funny HTML hack in on Twitter

Just saw a funny hack/spam

scroll down to see the title tag that shows the link to my blog when I mouse-hover on the  url.

Now you get this error message if you go here- because I changed my url structures. Note the url is uATQ13 (:-)

But if you go to and type in

You first get a redirect to

and then a redirect to

What surprised me is the hacking of the link

which changed the title in html (html newbies refer to

text from to (see bottom of the picture) when my mousetip hovered over it. How did this happen? Is it due to my chrome extensions ..hmm..or is it my alternate identity (cyber Jekyl and Cyber Hyde)….hmmm

Now I know it has just been two days since I wrote on chrome redirect extension called mediaafire (which could be one possible reason for this since I installed it too on my chrome browser besides having the adblock extension)


But nice hack-huh- two days is fast!!! Someone help figure this out.😉


Chrome Extension- MafiaaFire

The chrome extension MafiaaWire basically gives you an updated list of redirected websites. So the next time , your evil highness shuts down your favorite website- the list promises to give you an update.  While obviously entertainment intellectual property is a very obvious site category for such redirects, in some cases these extensions can be used for simple things like hosting dissents or protesters against govt corruption in non US countries .

Basically under the new SOPA act (an oline version of pepper spray even browsers like Firefox and Chrome would be liable for any such extension that can be used to download American Intellectual property illegally.

In the meantime – this is an interesting and creative use case of technology and sociology merging in the brave new world.

You can read about it here-

MAFIAAFire works by downloading a list which contains the names of the “blocked” sites as well as the sites to redirect to. This list is downloaded every time Firefox starts up or every two days on the Chrome version (although the user has the choice to force an update on the Chrome version instead of waiting for two days).

When a user types in a domain name from the list of blocked domains, the add-on recognizes this and automatically redirects the user to the secondary site. Since this happens before the browser connects to the DNS server, this renders any DNS blocks useless.

Although the add-on checks for which sites are entered into the address bar every time (as it needs to check if that site is on its block list), it does not log these requests nor send these requests to any central server. In other words: it does not track the user.


Download it from

Interesting times indeed!