Xml Matters #28: The Elementtree Api

Another Native Python Approach to XML

David Mertz, Ph.D.
Comparator, Gnosis Software, Inc.
June 2003

Fredrik Lundh's ElementTree module is an increasingly popular API for light-weight and fast manipulation of XML documents within Python. In this installment, I contrast ElementTree with several other libraries devoted to processing XML instances as object trees, especially with my own gnosis.xml.objectify module.


I have written several installments of this column in the past that have looked at XML libraries whose aim is to emulate the most familiar native operations in a given programming language. The first of these that I covered is my own gnosis.xml.objectify, for Python. I also dedicated installments to Haskell's HaXml and Ruby's REXML. Although I have not discussed them here, Java's JDOM and Perl's XML::Grove also have similar goals.

Lately I have noticed a number of posters to the comp.lang.python newsgroup mentioning Fredrik Lundh's ElementTree as a "native" XML library for Python. Of course, Python already has several XML API's included in its standard distribution--there is a DOM module, a SAX module, an expat wrapper, and the deprecated xmllib. Of these, only xml.dom converts an XML document into an in-memory object that can be manipulated with method calls on nodes. Actually, there are several different Python DOM implementation, each with somewhat different properties: xml.minidom is a basic one; xml.pulldom builds accessed subtrees only as needed, 4Suite's cDomlette (Ft.Xml.Domlette) builds a DOM tree in C, avoiding Python callbacks for speed.

Of course, appealing to my author's vanity, I am most curious to compare ElementTree to my own gnosis.xml.objectify, to which it is closest in purpose and behavior. The goal of ElementTree is to store representations of XML documents in data structures that behave in pretty much the way you think about data in Python. The focus here is on programming in Python, not on adapting your programming style to XML.

Some Benchmarks

My colleague Uche Ogbuji has previously written a short article on ElementTree for another publication. One of the tests he ran was comparing the relative speed and memory consumption of ElementTree versus DOM. Uche chose to use his own cDomlette for the comparison. Unfortunately, I am unable to install 4Suite 1.0a1 on the Mac OSX machine I am working on (I have mentioned this issue to Uche before, I am not sure if there is yet a resolution or workaround). However, we can use Uche's estimates to guess likely performance--he indicates that ElementTree is 30% slower, but 30% more memory-friendly, than Ft.Xml.Domlette.

Mostly I was curious how ElementTree compares in speed and memory to gnosis.xml.objectify. I have actually never benchmarked my module very precisely before, since I never had anything concrete to compare it to. I selected two documents that I have used for benchmarking in the base: a 289 kB XML version of Hamlet and a 3 mB XML web log. I created scripts that simply parse an XML document into the object models of the various tools, but do not perform any additional manipulation:

Scripts to time XML object models for Python

% cat time_xo.py
import sys
from gnosis.xml.objectify import XML_Objectify,EXPAT
doc = XML_Objectify(sys.stdin,EXPAT).make_instance()
% cat time_et.py
import sys
from elementtree import ElementTree
doc = ElementTree.parse(sys.stdin).getroot()
% cat time_minidom.py
import sys
from xml.dom import minidom
doc = minidom.parse(sys.stdin)

Creating the program object is quite similar in all three cases, and also with Ft.Xml.Domlette. I estimated memory usage by watching the output of top in another window; each test was run 3 times to make sure they were consistent, and the median value was used (memory was identical across runs).

Benchmarks of XMl object models in Python

One thing that is clear is that xml.minidom quickly becomes quite impractical for moderately large XML documents. The rest stay (fairly) reasonable. gnosis.xml.objectify is the most memory-friendly, but that is likely because it does not preserve all the information in the original XML instance (data content is kept, but not all structural information).

I also ran the following test of Ruby's REXML, using the following script:

Ruby REXML parsing script, time_rexml.rb

require "rexml/document"
include REXML
doc = (Document.new File.new ARGV.shift).root

REXML proved about as resource intensive as xml.minidom: parsing Hamlet.xml took 10 seconds and used 14 MB; parsing Weblog.xml took 190 seconds and used 150 MB. Obviously, the choice of programming language usually comes before the comparison of libraries for them.

Working With An Xml Document Object

A nice thing about ElementTree is that it can be round-tripped. That is, you can read in an XML instance, modify fairly native-feeling data structures, then call the .write() method to re-seralize to well-formed XML. DOM does this, of course, but gnosis.xml.objectify does not. It is not all that difficult to construct a custom output function for gnosis.xml.objectify that produces XML--but doing so is not automatic. With ElementTree, along with the .write() method of ElementTree instances, individual Element instances can be serialized with the convenience function elementtree.ElementTree.dump(). This lets you write XML fragments from individual object nodes--including from the root node of the XML instance.

I present a simple task that contrasts the ElementTree and gnosis.xml.objectify APIs. The large weblog.xml document used for benchmark tests contains about 8500 <entry> elements, each having the same collection of child fields--a typical arrangement for a data-oriented XML document. A particular task in processing this file might be to collect a few fields from each entry, but only if some other fields have particular values (or ranges, or match regexen, etc). Of course, if you really only want to perform this one task, using a streaming API like SAX avoids the need to model the whole document in memory--but let us assume that this task is one of several being performed on the large data structure by an application. One <entry> element looks something like:


Using gnosis.xml.objectify, I might write a filter-and-extract application as:


from gnosis.xml.objectify import XML_Objectify, EXPAT
weblog = XML_Objectify('weblog.xml',EXPAT).make_instance()
interesting = [entry for entry in weblog.entry
               if entry.host.PCDATA==''
               and entry.statusCode.PCDATA=='200']
for e in interesting:
    print "%s (%s)" % (e.resource.PCDATA,

List comprehensions are quite convenient as data filters. In essence, ElementTree works the same way:


from elementtree import ElementTree
weblog = ElementTree.parse('weblog.xml').getroot()
interesting = [entry for entry in weblog.findall('entry')
               if entry.find('host').text==''
               and entry.find('statusCode').text=='200']
for e in interesting:
    print "%s (%s)" % (e.findtext('resource'),

There are a few differences to note above. gnosis.xml.objectify attaches subelement nodes directly as attributes of nodes (every node is of a custom class named after the tag name). ElementTree, on the other hand, uses methods of the Element class to find child nodes. The method .findall() returns a list of all matching nodes; .find() returns just the first match; .findtext() returns the text content of a node. If you only want the first "match" on a gnosis.xml.objectify subelement, you just need to index it, e.g.: node.tag[0]. But if there is only one such sublement, you can also refer to it without the explicit indexing.

But in the ElementTree example, we do not really need to find all the <entry> elements explicitly, Element instances behave in a list-like way when iterated over. A point to note is that iteration is over all child nodes, whatever tag they may have. In contrast, a gnosis.xml.objectify node has no built-in method to step through all of its subelements. Still, it is easy to construct a one-line children() function (I will include one in future releases). Contrast:

>>> open('simple.xml','w.').write('''<root>
... <foo>this</foo>
... <bar>that</bar>
... <foo>more</foo></root>''')
>>> from elementtree import ElementTree
>>> root = ElementTree.parse('simple.xml').getroot()
>>> for node in root:
...     print node.text,
this that more
>>> for node in root.findall('foo'):
...     print node.text,
this more


>>> children=lambda o: [x for x in o.__dict__ if x!='__parent__']
>>> from gnosis.xml.objectify import XML_Objectify
>>> root = XML_Objectify('simple.xml').make_instance()
>>> for tag in children(root):
...     for node in getattr(root,tag):
...         print node.PCDATA,
this more that
>>> for node in root.foo:
...     print node.PCDATA,
this more

As you can see, gnosis.xml.objectify currently discards information about the original order of interpersed <foo> and <bar> elements (it could be remembered in another magic attribute, like .__parent__ is, but no one has had the need and/or sent a patch to do this).

ElementTree stores XML attributes in a node attribute called .attrib. The attributes are stored in a dictionary. gnosis.xml.objectify puts the XML attributes directly into node attributes of corresponding name. The style I use tends to flatten the distinction between XML attributes and element contents--to my mind that is something for XML to worry about, not for my native data structure to worrry about. For example:

>>> xml = '<root foo="this"><bar>that</bar></root>'
>>> open('attrs.xml','w').write(xml)
>>> et = ElementTree.parse('attrs.xml').getroot()
>>> xo = XML_Objectify('attrs.xml').make_instance()
>>> et.find('bar').text, et.attrib['foo']
('that', 'this')
>>> xo.bar.PCDATA, xo.foo
(u'that', u'this')

There is still some distinction in gnosis.xml.objectify between XML attributes that create node attributes containing text, and XML element contents that create node attributes containing objects (perhaps with subnodes having .PCDATA).

Xpaths And Tails

ElementTree implements a subset of XPATH in its .find*() methods. Using this style can be much more concise than nesting code to look within levels of subnodes, especially for XPATHs containing wildcards. For example, if I was interested in all the timestamps of hits to my web server, I could examine weblog.xml using:

>>> from elementtree import ElementTree
>>> weblog = ElementTree.parse('weblog.xml').getroot()
>>> timestamps = weblog.findall('entry/dateTime')
>>> for ts in timestamps:
...     if ts.text.startswith('19/Aug'):
...         print ts.text

Of course, for a shallow and regular document like weblog.xml, it is easy to do the same thing with list comprehensions:

>>> for ts in [ts.text for e in weblog
...            for ts in e.findall('dateTime')
...            if ts.text.startswith('19/Aug')]:
...     print ts

Prose-oriented XML documents, however, tend to have much more variable document structure, and typically nest tags at least five or six levels deep. An XML schema like DocBook or TEI, for example, might have citations in sections, subsections, bibliographies, sometimes within italics tags, or in blockquotes, and so on. Finding every <citation> element would require a cumbersome (probably recursive) search across levels. Or using XPATH, you could just write:

>>> from elementtree import ElementTree
>>> weblog = ElementTree.parse('weblog.xml').getroot()
>>> cites = weblog.findall('.//citation')

The XPATH support, however, in ElementTree is limited. You cannot use the various functions contained in full XPATH, nor can you search on attributes. In what it does though, the XPATH subset in ElementTree greatly aids readability and expressiveness.

I want to mention one more quirk of ElementTree before I wrap up. XML documents can be mixed content. Prose-oriented XML, in particular, tends to intersperse PCDATA and tags rather freely. But where exactly should you store the text that comes between child nodes. Since an ElementTree Element instance has a single .text attribute--which contains a string--that does not really leave space for a broken sequence of strings. The solution ElementTree adopts is to give each node a .tail attribute which contains all the text after a closing tag, but before the next element begins or the parent element is closed. For example:

>>> xml = '<a>begin<b>inside</b>middle<c>inside</c>end</a>'
>>> open('doc.xml','w').write(xml)
>>> doc = ElementTree.parse('doc.xml').getroot()
>>> doc.text, doc.tail
('begin', None)
>>> doc.find('b').text, doc.find('b').tail
('inside', 'middle')
>>> doc.find('c').text, doc.find('c').tail
('inside', 'end')


ElementTree is a nice effort to bring a much lighter weight object model to XML processing in Python than that provided by DOM. Although I have not addressed it in this article, but ElementTree is equally good at generating XML documents from scratch as it is at manipulating existing XML data.

As author of a similar library, gnosis.xml.objectify, I cannot be entirely objective in evaluating ElementTree; but nonetheless, I continue to find my own approach somewhat more natural in Python programs than that provided by ElementTree. The latter still usually utilizes node methods to manipulate data structures rather than directly accessing node attributes as one usually does with data structures built within an application.

However, in several areas, ElementTree shines. It is far easier to access deeply nested elements using XPATH than with manual recursive searches. Obviously, DOM also gives you XPATH, but at the cost of a far heavier and less uniform API. All the Element nodes of ElementTree act in a consistent manner, unlike DOMs panoply of node types.


The home page for ElementTree is:


IBM developerWorks columnist Uche Ogbuji discussed ElementTree for XML.com in a February 2003 article:


XML Matters #2 introduced gnosis.xml.objectify, then called simply xml_objectify.

XML Matters #11 updates readers to some early improvements to gnosis.xml.objectify. Some newer features have not been covered in this column, but are in the module's HISTORY and other documentation files.

XML Matters #14 discussed the HaXml module for the Haskell lazy pure-functional programming language.

XML Matters #18 discussed Ruby's REXML library.

Dave Kuhlman has developed another Python XML API/library called generateDS. He has written a very nice essay comparing generateDS with gnosis.xml.objectify at:


In brief, the idea behind generateDS is to use an XML Schema as the basis for Python classes that properly handle the elements in an XML instance. Rather than handle XML trees generically, generateDS is code generator for Python modules to handle specific XML document schemas--autogenerated code can easily be specialized to quickly form a custom application. Read more about the library at:


About The Author

Picture of Author For David Mertz an atomic object is a combination of facts. David may be reached at mertz@gnosis.cx; his life pored over at http://gnosis.cx/publish/. Suggestions and recommendations on this, past, or future, columns are welcomed. Check out David's new book Text Processing in Python.