I saw a video that claimed the following mathematical summation…
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import urllib.request | |
import pymupdf # pip install PyMuPDF | |
url = ('https://pycon-assets.s3.amazonaws.com/2024/media/' | |
'documents/DLCC-Floorplan-Marked_up_PyCon_US_2024.pdf') | |
# url = 'file:DLCC-Floorplan-Marked_up_PyCon_US_2024.pdf' | |
with urllib.request.urlopen(url) as response: | |
pdfStream = response.read() |
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# MIT No Attribution | |
# | |
# Copyright 2022 Ben Kehoe | |
# | |
# Permission is hereby granted, free of charge, to any person obtaining a copy of this | |
# software and associated documentation files (the "Software"), to deal in the Software | |
# without restriction, including without limitation the rights to use, copy, modify, | |
# merge, publish, distribute, sublicense, and/or sell copies of the Software, and to | |
# permit persons to whom the Software is furnished to do so. | |
# |
Examples of processing QTI data with Python.
I attempted to use pyslet
, which was designed for this purpose,
but I found it awkward to use and its documentation unclear. Instead, I tried to use beautifulsoup4
, but I
learned that library doesn't support XPath to query for specific elements of the data. I turned to using the
simple XML processing library lxml
. It has similarities to other XML parsing libraries I've used before, but
it has many unique features of its own.
Note that of the examples below, each does something a little differently. They don't all have the same output.
That's because they were mostly tests to see whether we preferred working with one library over another. Some
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version: '3' | |
services: | |
jupyter: | |
container_name: my_jupyter | |
#image: jupyter/datascience-notebook | |
image: jupyter/minimal-notebook | |
ports: | |
- '127.0.0.1:8888:8888' | |
environment: |
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