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NLTK vs. spaCy: Natural Language Processing in Python

The venerable NLTK has been the standard tool for natural language processing in Python for some time. It contains an amazing variety of tools, algorithms, and corpuses. Recently, a competitor has arisen in the form of spaCy, which has the goal of providing powerful, streamlined language processing. Let’s see how these toolkits compare.

Philosophy

board-2084777_960_720NLTK provides a number of algorithms to choose from. For a researcher, this is a great boon. Its nine different stemming libraries, for example, allow you to finely customize your model. For the developer who just wants a stemmer to use as part of a larger project, this tends to be a hindrance. Which algorithm performs the best? Which is the fastest? Which is being maintained?

In contrast, spaCy implements a single stemmer, the one that the spaCy developers feel to be best. They promise to keep it updated, and may replace it with an improved algorithm as the state of the art progresses. You may update your version of spaCy and find that improvements to the library have boosted your application without any work necessary. (The downside is that you may need to rewrite some test cases.)

As a quick glance through the NLTK documentation demonstrates, different languages may need different algorithms. NLTK lets you mix and match the algorithms you need, but spaCy has to make a choice for each language. This is a long process and spaCy currently only has support for English.  Continue reading

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Automatically Generating License Data from Python Dependencies

computer-1869236_960_720We all know how important keeping track of your open-source licensing is for the average startup.  While most people think of open-source licenses as all being the same, there are meaningful differences that could have potentially serious legal implications for your code base.  From permissive licenses like MIT or BSD to so-called “reciprocal” or “copyleft” licenses, keeping track of the alphabet soup of dependencies in your source code can be a pain.

Today, we’re releasing pylicense, a simple python module that will add license data as comments directly from your requirements.txt or environment.yml files.

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Painlessly Deploying Data Apps with Bokeh, Flask, and Heroku

laptop-in-the-office-1967479_960_720Here at The Data Incubator, our Fellows deploy their own fully functional, public-facing web app to showcase their data science skills to employers. This not only gives them valuable experience dynamically fetching and displaying data, but also encourages them to think about end user interaction. To demo the process, we decided to marry together some of our favorite technologies:

  • Flask, a slick web framework for Python
  • Heroku for cloud-based app deployment
  • Bokeh for interactive, D3.js-style visualizations
  • Git for version control and distributing code

The goal is to create some distant ancestor of Google Finance: a form capable of accepting a stock ticker as input and producing a plot of the daily close price. Here’s the finished product. So how do we get there?

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