TextBlob is a Python (2 and 3) library for processing textual data. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, translation, and more.

Last week, while working on new features for our product, I had to find a quick and efficient way to extract the main topics/objects from a sentence. Since I’m using Python, I initially thought that it’s going to be a very easy task to achieve with NLTK. However, when I tried its default tools (POS tagger, Parser…), I indeed got quite accurate results, but performance was pretty bad. So I had to find a better way. Like I did in my previous post, I’ll start with the bottom line – Here you can find my code for extracting the main topics/noun phrases from a given sentence. It works fine with real sentences (from a blog/news article). It’s a bit less accurate compared to the default NLTK tools, but it works much faster!

We prefer Pyramid to Django, Flask, and Bottle due to its flexibility, scalability and speed. It gives us more control than Django and is easy to create a small app that can scale later without being rewritten. These are many of the same reasons for Why We Choose Python in general. Recently we provided some training on how Pyramid works that was recorded. It provides a great overview of why Pyramid is ideal and how to setup a basic app with scaffolds, routes, and persistence. We also built a ToDo App for a web shootout we organized in Indianapolis through IndyPy. Putting these together turned out to be a great introduction to Pyramid, so I wrote this post.

Zipline is a financial backtester for trading algorithms written in Python. The system is fundamentally event-driven and a close approximation of how live-trading systems operate.

Zipline is currently used in production as the backtesting engine powering Quantopian (https://www.quantopian.com) -- a free, community-centered platform that allows development and real-time backtesting of trading algorithms in the web browser.

mincss (code on github) is a tool that when given a URL (or multiple URLs) downloads that page and all its CSS and compares each and every selector in the CSS and finds out which ones aren't used. The outcome is a copy of the original CSS but with the selectors not found in the document(s) removed.

lc-tools is a set of command line tools to control various clouds. It uses libcloud for cloud related stuff so should support as much cloud providers as libcloud does.

Fabric is a Python (2.5 or higher) library and command-line tool for streamlining the use of SSH for application deployment or systems administration tasks.

It provides a basic suite of operations for executing local or remote shell commands (normally or via sudo) and uploading/downloading files, as well as auxiliary functionality such as prompting the running user for input, or aborting execution.

Typical use involves creating a Python module containing one or more functions, then executing them via the fab command-line tool.

youtube-dl is a small command-line program to download videos from YouTube.com and a few more sites. It requires the Python interpreter, version 2.x (x being at least 5), and it is not platform specific. It should work in your Unix box, in Windows or in Mac OS X. It is released to the public domain, which means you can modify it, redistribute it or use it however you like.

scikits.learn is a Python module integrating classic machine learning algorithms in the tightly-knit world of scientific Python packages (numpy, scipy, matplotlib). It aims to provide simple and efficient solutions to learning problems that are accessible to everybody and reusable in various contexts: machine-learning as a versatile tool for science and engineering.

Given our development processes we found the average productivity of a single Django developer to be equivalent to the output generated by two C# ASP.NET developers. Given equal-sized teams, Django allowed our developers to be twice as productive as our ASP.NET team.

I suspect these results may actually reflect a lower bound of the productivity differences. It should be noted that about half of the Team Python developers, while fluent in Python, had not used Django before. They quickly learned Django, but it is possible this fluency disparity may have caused an unintended bias in results–handicapping overall Django velocity.