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Python Library

To install:

$ pip install -e python/
# Optional requirements:
$ pip install -r python/requirements.txt


You'll probably want to get an instance of Archive:

from pha import Archive
archive = Archive.default_location()

Or Archive(path), but normal installation always puts the data into the data/ directory.

The key objects are all implemented in Archive, Activity, and Page.

  • Activity is one visit in the browser. This includes any changes to the location hash. This represents both old activity fetched from browser history (from HistoryItem and VisitItem), as well as new activity (with more complete information available).
  • Page is a fetched page. By default only one version a page will be created for a given URL (though the code/database allows for multiple pages fetched over time). A page is both stored in the database, as well as in a JSON file in data/pages/ (the library tries to be resilient when the two sources don't match).

Note that URLs do include the fragment/hash, so and are treated as different.

Typically you'll call:

  • archive.get_activity(url): get a list of activities for the URL
  • archive.activity(): get a list of ALL activities
  • archive.activity_with_page(): get a list of all activity that also have a fetched page
  • archive.sample_activity_with_page(number, unique_url=True, unique_domain=False): fetch a random sample of pages. Because there tend to be lots of pages from some domains (e.g., this tries to get a sampling of "unique" pages. If you ask for unique_url then it will look at the entire URL, normalize segments of the URL, and treat number and non-number segments differently. So it would include a homepage and an article page, but probably not multiple article pages from the same site. unique_domain gets only one page per domain.
  • archive.get_activity_by_source( get every activity that came from the given activity (typically through navigation).


You might spend most of your time with the Page objects, at least if you are interested in content parsing and interpretation.

A few highlights:

  • page.html: returns a viewable HTML representation of the page.
  • page.lxml: returns the page, having been parsed with lxml.html.
  • page.full_text: tries to get the text of page.
  • page.readable_text: if the page was parseable with Readability then this will contain the text extracted as part of the article view (excluding navigation, etc).
  • page.readable_html: an HTML view of the readable portion of the page.
  • page.display_page(): run in a Jupyter Notebook, this will show the page in an iframe (see also notebooktools).


There's several helper modules:


I'm collecting notebooks in this directory as examples, and hopefully they'll grow into simultaneously documentation and interesting data interpretation. It would be cool to have more!

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