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To start, we'll import Pandas, a very common data science library in Python, and the HTRC FeatureReader, a library written by HTRC to ease the use of the Extracted Features Dataset:
Next, we'll use each volume’s both volumes' HathiTrust IDs, their permanent, unique identifier in HathiTrust, to download their EF files and create FeatureReader Volume objects--which allow us to take advantage of volume-level data in the files--for each of our volumes of interest. A Volume in FeatureReader has specific properties and built-in methods that let us do a number of common tasks. We can create Volumes simply by using Volume()and adding the HathiTrust ID (HTID) in the parentheses:
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After running the above code, the print statements will return and we’ll have indicate that we have successfully created Volume objects for each work by Le Guin.
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We'll be focusing on one particular field of features in the EF Dataset: begin-line characters. This is because we’re working off of on the assumption that a page of poetry is likely to contain more capitalized letters at the start beginning of each line on a page, a common poetry convention that Le Guin is known to have usedfollowed. To start, we'll create a Pandas DataFrame with the first character of each line (begin_line character, _char) by page, for both of our volumes:
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