toto-pdfquery 0.1.1

Concise and friendly PDF scraper using JQuery or XPath selectors. Forked from Jack Cushman's pdfquery as not maintained anymore.

Concise, friendly PDF scraping using JQuery or XPath syntax.

PDFQuery is a light wrapper around pdfminer, lxml and pyquery. It’s designed to reliably extract data from sets of PDFs with as little code as possible.

Table of Contents

• Concise, friendly PDF scraping using JQuery or XPath syntax.

  • Installation

  • Quick Start

• Usage

  • Data Models

  • Finding what you want

  • Custom Selectors

  • Caching

  • Bulk Data Scraping

    • Search Target

    • Formatting Functions

    • Filtering Functions

    • Special Keywords

      • with_parent

      • with_formatter

• Object Reference

  • Public Methods

  • Public But Less Useful Methods

• Documentation for Underlying Libraries

Installation

easy_install pdfquery or pip install pdfquery.

Quick Start

The basic idea is to transform a PDF document into an element tree so we can find items with JQuery-like selectors using pyquery. Suppose we’re trying to extract a name from a set of PDFs, but all we know is that it appears underneath the words “Your first name and initial” in each PDF:

>>> pdf = pdfquery.PDFQuery("tests/samples/IRS_1040A.pdf")
>>> pdf.load()
>>> label = pdf.pq('LTTextLineHorizontal:contains("Your first name and initial")')
>>> left_corner = float(label.attr('x0'))
>>> bottom_corner = float(label.attr('y0'))
>>> name = pdf.pq('LTTextLineHorizontal:in_bbox("%s, %s, %s, %s")' % (left_corner, bottom_corner-30, left_corner+150, bottom_corner)).text()
>>> name
'John E.'

Note that we don’t have to know where the name is on the page, or what page it’s on, or how the PDF has it stored internally.

Performance Note: The initial call to pdf.load() runs very slowly, because the underlying pdfminer library has to compare every element on the page to every other element. See the Caching section to avoid this on subsequent runs.

Now let’s extract and format a bunch of data all at once:

>>> pdf = pdfquery.PDFQuery("tests/samples/IRS_1040A.pdf")
>>> pdf.extract( [
     ('with_parent', 'LTPage[pageid="1"]'),
     ('with_formatter', 'text'),

     ('last_name', 'LTTextLineHorizontal:in_bbox("315,680,395,700")'),
     ('spouse', 'LTTextLineHorizontal:in_bbox("170,650,220,680")'),

     ('with_parent', 'LTPage[pageid="2"]'),

     ('oath', 'LTTextLineHorizontal:contains("perjury")', lambda match: match.text()[:30]+"..."),
     ('year', 'LTTextLineHorizontal:contains("Form 1040A (")', lambda match: int(match.text()[-5:-1]))
 ])

Result:

{'last_name': 'Michaels',
 'spouse': 'Susan R.',
 'year': 2007,
 'oath': 'Under penalties of perjury, I ...',}

Usage

Data Models

PDFQuery works by loading a PDF as a pdfminer layout, converting the layout to an etree with lxml.etree, and then applying a pyquery wrapper. All three underlying libraries are exposed, so you can use any of their interfaces to get at the data you want.

First pdfminer opens the document and reads its layout. You can access the pdfminer document at pdf.doc:

>>> pdf = pdfquery.PDFQuery("tests/samples/IRS_1040A.pdf")
>>> pdf.doc
<pdfminer.pdfparser.PDFDocument object at 0xd95c90>
>>> pdf.doc.catalog # fetch attribute of underlying pdfminer document
{'JT': <PDFObjRef:14>, 'PageLabels': <PDFObjRef:10>, 'Type': /Catalog, 'Pages': <PDFObjRef:12>, 'Metadata': <PDFObjRef:13>}

Next the layout is turned into an lxml.etree with a pyquery wrapper. After you call pdf.load() (by far the most expensive operation in the process), you can access the etree at pdf.tree, and the pyquery wrapper at pdf.pq:

>>> pdf.load()
>>> pdf.tree
<lxml.etree._ElementTree object at 0x106a285f0>
>>> pdf.tree.write("test2.xml", pretty_print=True, encoding="utf-8")
>>> pdf.tree.xpath('//*/LTPage')
[<Element LTPage at 0x994cb0>, <Element LTPage at 0x994a58>]
>>> pdf.pq('LTPage[pageid=1] :contains("Your first name")')
[<LTTextLineHorizontal>]

You’ll save some time and memory if you call load() with only the page numbers you need. For example:

>>> pdf.load(0, 2, 3, range(4,8))

Performance Note: The initial call to pdf.load() runs very slowly, because the underlying pdfminer library has to compare every element on the page to every other element. See the Caching section to avoid this on subsequent runs.

Under the hood, pdf.tree is basically an XML representation of the layout tree generated by pdfminer.pdfinterp. By default the tree is processed to combine individual character nodes, remove extra spaces, and sort the tree spatially. You can always get back to the original pdfminer Layout object from an element fetched by xpath or pyquery:

>>> pdf.pq(':contains("Your first name and initial")')[0].layout
<LTTextLineHorizontal 143.651,714.694,213.083,721.661 u'Your  first  name  and  initial\n'>

Finding what you want

PDFs are internally messy, so it’s usually not helpful to find things based on document structure or element classes the way you would with HTML. Instead the most reliable selectors are the static labels on the page, which you can find by searching for their text contents, and physical location on the page. PDF coordinates are given in points (72 to the inch) starting from the bottom left corner. PDFMiner (and so PDFQuery) describes page locations in terms of bounding boxes, or bboxes. A bbox consists of four coordinates: the X and Y of the lower left corner, and the X and Y of the upper right corner.

If you’re scraping text that’s always in the same place on the page, the easiest way is to use Acrobat Pro’s Measurement Tool, Photoshop, or a similar tool to measure distances (in points) from the lower left corner of the page, and use those distances to craft a selector like :in_bbox("x0,y0,x1,y1") (see below for more on in_bbox).

If you’re scraping text that might be in different parts of the page, the same basic technique applies, but you’ll first have to find an element with consistent text that appears a consistent distance from the text you want, and then calculate the bbox relative to that element. See the Quick Start for an example of that approach.

If both of those fail, your best bet is to dump the xml using pdf.tree.write(filename, pretty_print=True), and see if you can find any other structure, tags or elements that reliably identify the part you’re looking for. This is also helpful when you’re trying to figure out why your selectors don’t match …

Custom Selectors

The version of pyquery returned by pdf.pq supports some PDF-specific selectors to find elements by location on the page.

• :in_bbox(“x0,y0,x1,y1”): Matches only elements that fit entirely within the given bbox.

• :overlaps_bbox(“x0,y0,x1,y1”): Matches any elements that overlap the given bbox.

If you need a selector that isn’t supported, you can write a filtering function returning a boolean:

>>> def big_elements():
    return float(this.get('width',0)) * float(this.get('height',0)) > 40000
>>> pdf.pq('LTPage[page_index="1"] *').filter(big_elements)
[<LTTextBoxHorizontal>, <LTRect>, <LTRect>]

(If you come up with any particularly useful filters, patch them into pdfquery.py as selectors and submit a pull request …)

Caching

PDFQuery accepts an optional caching argument that will store the results of PDF parsing, so subsequent runs on the same file will be much quicker. For example:

from pdfquery.cache import FileCache
pdfquery.PDFQuery("tests/samples/IRS_1040A.pdf", parse_tree_cacher=FileCache("/tmp/"))

Bulk Data Scraping

Often you’re going to want to grab a bunch of different data from a PDF, using the same repetitive process: (1) find an element of the document using a pyquery selector or Xpath; (2) parse the resulting text; and (3) store it in a dict to be used later.

The extract method simplifies that process. Given a list of keywords and selectors:

>>> pdf.extract([
      ('last_name', ':in_bbox("315,680,395,700")'),
      ('year', ':contains("Form 1040A (")', lambda match: int(match.text()[-5:-1]))
 ])

the extract method returns a dictionary (by default) with a pyquery result set for each keyword, optionally processed through the supplied formatting function. In this example the result is:

{'last_name': [<LTTextLineHorizontal>], 'year': 2007}

(It’s often helpful to start with ('with_formatter', 'text') so you get results like “Michaels” instead of [<LTTextLineHorizontal>]. See Special Keywords below for more.)

Search Target

By default, extract searches the entire tree (or the part of the document loaded earlier by load(), if it was limited to particular pages). If you want to limit the search to a part of the tree that you fetched with pdf.pq() earlier, pass that in as the second parameter after the list of searches.

Formatting Functions

Notice that the ‘year’ example above contains an optional third paramater – a formatting function. The formatting function will be passed a pyquery match result, so lambda match: match.text() will return the text contents of the matched elements.

Filtering Functions

Instead of a string, the selector can be a filtering function returning a boolean:

>>> pdf.extract([('big', big_elements)])
{'big': [<LTPage>, <LTTextBoxHorizontal>, <LTRect>, <LTRect>, <LTPage>, <LTTextBoxHorizontal>, <LTRect>]}

(See Custom Selectors above for how to define functions like big_elements.)

Special Keywords

extract also looks for two special keywords in the list of searches that set defaults for the searches listed afterward. Note that you can include the same special keyword more than once to change the setting, as demonstrated in the Quick Start section. The keywords are:

with_parent

The with_parent keyword limits the following searches to children of the parent search. For example:

>>> pdf.extract([
     ('with_parent','LTPage[page_index="1"]'),
     ('last_name', ':in_bbox("315,680,395,700")') # only matches elements on page 1
 ])

with_formatter

The with_formatter keyword sets a default formatting function that will be called unless a specific one is supplied. For example:

('with_formatter', lambda match: int(match.text()))

will attempt to convert all of the following search results to integers. If you supply a string instead of a function, it will be interpreted as a method name to call on the pyquery search results. For example, the following two lines are equivalent:

('with_formatter', lambda match: match.text())
('with_formatter', 'text')

If you want to stop filtering results, you can use:

('with_formatter', None)

Object Reference

Public Methods

PDFQuery(   file,
            merge_tags=('LTChar', 'LTAnon'),
            round_floats=True,
            round_digits=3,
            input_text_formatter=None,
            normalize_spaces=True,
            resort=True,
            parse_tree_cacher=None,
            laparams={'all_texts':True, 'detect_vertical':True})

Initialization function. Usually you’ll only need to pass in the file (file object or path). The rest of the arguments control preprocessing of the element tree:

• merge_tags: consecutive runs of these elements will be merged together, with the text of following elements appended to the first element. This is useful for keeping the size of the tree down, but it might help to turn it off if you want to select individual characters regardless of their containers.

• round_floats and round_digits: if round_floats is True, numbers will be rounded to round_digits places. This is almost always good.

• input_text_formatter: a function that takes a string and returns a modified string, to be applied to the text content of elements.

• normalize_spaces: if True (and input_text_formatter isn’t otherwise set), sets input_text_formatter to replace s+ with a single space.

• resort: if True, elements will be sorted such that any element fully within the bounding box of another element becomes a child of that element, and elements on the same level are sorted top to bottom, left to right.

• parse_tree_cacher: an object that knows how to save and load results of parsing a given page range from a given PDF. Pass in FileCache(‘/tmp/’) to save caches to the filesystem.

• laparams: parameters for the pdfminer.layout.LAParams object used to initialize pdfminer.converter.PDFPageAggregator. Can be dict, LAParams(), or None.

extract(    searches,
            tree=None,
            as_dict=True)

See “Bulk Data Scraping.”

• searches: list of searches to run, each consisting of a keyword, selector, and optional formatting function.

• tree: pyquery tree to run searches against. By default, targets entire tree loaded by pdf.load()

• as_dict: if changed to False, will return a list instead of a dict to preserve the order of the results.

load(*page_numbers)

Initialize the pdf.tree and pdf.pq objects. This will be called implicitly by pdf.extract(), but it’s more efficient to call it explicitly with just the page numbers you need. Page numbers can be any combination of integers and lists, e.g. pdf.load(0,2,3,[4,5,6],range(10,15)).

You can call pdf.load(None) if for some reason you want to initialize without loading any pages (like you are only interested in the document info).

Public But Less Useful Methods

These are mostly used internally, but might be helpful sometimes …

get_layout(page)

Given a page number (zero-indexed) or pdfminer PDFPage object, return the LTPage layout object for that page.

get_layouts()

Return list of all layouts (equivalent to calling get_layout() for each page).

get_page(page_number)

Given a page number, return the appropriate pdfminer PDFPage object.

get_pyquery(tree=None, page_numbers=[])

Wrap a given lxml element tree in pyquery. If no tree is supplied, will generate one from given page numbers, or all page numbers.

get_tree(*page_numbers)

Generate an etree for the given page numbers. *page_numbers can be the same form as in load().

Documentation for Underlying Libraries

• PDFMiner (pdf.doc): pdfminer_homepage, pdfminer_documentation.

• LXML.etree (pdf.tree): lxml_homepage, tutorial.

• PyQuery (pdf.pq): pyquery_documentation.