Download The Programming Historian (an open-access introduction to by William J. Turkel, Adam Crymble and Alan MacEachern PDF

By William J. Turkel, Adam Crymble and Alan MacEachern

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We turn to that now, starting with a discussion of some more of the things that you can do with Python strings. Python string formatting Python includes a special formatting operator that allows you to interpolate one string in another one. It is represented by a percent sign. Open a Python shell and try the following examples. frame = 'This is a %s' print frame -> This is a %s print frame % 'cat' -> This is a cat print frame % 'dog' -> This is a dog There is also a form which allows you to interpolate a list of strings into another one.

Py and execute it, to confirm that it works as expected. wrapStringInHTML("html-to-freq-3", url, outstring) Note that we interspersed our word-frequency pairs with the HTML break tag, which acts as a newline. If all went well, you should see the same word frequencies that you computed in the last section. Using word frequencies to refine a Google search Let's go through one more cycle of refinement. Start by doing a Google search for "dollard" and counting the number of hits on the first five pages that actually refer to Adam Dollard Des Ormeaux.

In this section we will start to make the transition to working with collections rather than individual texts. When you have a group of texts, you can do a number of different kinds of analysis which fall under the general heading of text mining. These include * Automatically classifying documents into categories * Measuring similarity and clustering texts * Identifying and extracting entities like names, dates, institutions, and events * Inferring relationships * Creating document summaries We will work through a number of text mining projects.

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