You can submit a comment below Posted under: The narrow passes block GPS reception and the resulting errors bounce over low resolution elevation maps giving us credit for riding a sawtooth shaped road. If my tracks were inaccurate due to trees or narrow passages, that would only compound the error. The result was not the m that I believe is correct, but instead was m because it mapped the meandering path that the GPS recorded in the deep mountains, instead of magically mapping the path I physically took.
Running the same iterparse method in Listing 4 on the Open Directory data takes seconds per run, or slightly more than five times longer than parsing the copyright data.
As the Open Directory data is also slightly more than five times as large at 1. Serialization If all you need to do with an XML file is grab some text from within a single node, it might be possible to use a simple regular expression that will probably operate faster than any XML parser. In practice, though, this is nearly impossible to get right when the data is at all complex, and I do not recommend it.
XML libraries are invaluable when true data manipulation is required. Serializing XML to a string or file is where lxml excels because it relies on libxml2 C code directly.
If your task requires any serialization at all, lxml is a clear choice, but there are some tricks to get the best performance out of the library.
Use deepcopy when serializing subtrees lxml retains references between child nodes and their parents. One effect of this is that a node lxml etree write a check lxml can have one and only one parent. Output a new tree like: See Related topics for a link.
Finding elements quickly After parsing, the most common XML task is to locate specific data of interest inside the parsed tree. As a user, you should be aware of the performance characteristics and optimization techniques for each approach.
Avoid use of find and findall The find and findall methods, inherited from the ElementTree API, locate one or more descendant nodes using a simplified XPath-like expression language called ElementPath. In cases where the expression should match a node name, it is far faster in some cases twice as fast to use the iterchildren or iterdescendants methods with their optional tag parameter when compared to their equivalent ElementPath expressions.
For more complex patterns, use the XPath class to precompile search patterns. Simple patterns that mimic the behavior of iterchildren with tag arguments for example, etree. Title" execute in effectively the same time as their iterchildren equivalents.
Compiling the pattern in each execution of the loop or using the xpath method on an element described in the lxml documentation, see Resources can be almost twice as slow as compiling once and then using that pattern repeatedly. XPath evaluation in lxml is fast.
If only a subset of nodes needs to be serialized, it is much better to limit with precise XPath expressions up front than to inspect all the nodes later.
For example, limiting the sample serialization to include only titles containing the word night, as in Listing 8takes 60 percent of the time to serialize the full set. Return a node only if the first expression matches. Other ways to increase performance In addition to the use of specific methods within lxml, you can use approaches outside of the library to influence execution speed.
Some of these are simple code changes; others require new thinking about how to handle large data problems. Psyco The Psyco module is an often-missed way to increase the speed of Python applications with minimal work.
Typical gains for a pure Python program are between two and four times, but lxml does most of its work in C, so the difference is unusually small. When I ran Listing 4 with Psyco enabled, I reduced runtime by only three seconds Psyco has a large memory overhead which might even negate any gains if the machine has to go to swap.
For more information about Psyco, see Related topics. Threading If, instead, your application relies mostly on internal, C-driven lxml features, it might be to your advantage to run it as a threaded application in a multiprocessor environment.
There are restrictions on how to start the threads—especially with XSLT. Consult the FAQ section on threads in the lxml documentation for more information. Employing on-demand virtual servers is an increasingly popular solution for executing central processing unit CPU bound offline tasks.
General strategies for any high-volume XML task The specific code samples presented here might not apply to your project, but consider a few principles—borne out by testing and the lxml documentation—when faced with XML data measured in gigabytes or more: Use an iterative parsing strategy to incrementally process large documents.
If searching the entire document in random order is required, move to an indexed XML database. Be extremely conservative in the data that you select. If you are only interested in particular nodes, use methods that select by those names. If you require predicate syntax, try one of the XPath classes and methods available.
Consider the task at hand and the comfort level of the developer. Take the time to do even simple benchmarking. When processing millions of records, small differences add up, and it is not always obvious which methods are the most efficient. Conclusion Many software products come with the pick-two caveat, meaning that you must choose only two:Python lxml is the most feature-rich and easy-to-use library for processing XML and HTML data.
Python scripts are written to perform many . The caninariojana.comtTree module implements a simple and efficient API for parsing and creating XML data.
Changed in version This module will use a fast implementation whenever available. The caninariojana.comntTree module is deprecated. Not all elements of . For XML schema validation, we need the etree module from the lxml package.
Let’s also import StringIO from the io package for passing strings as files to etree, We can write this to a file check the incorrect lines and tags: # parse xml try: doc = etree. parse (StringIO. Introduction¶. OWSLib is a Python package for client programming with Open Geospatial Consortium (OGC) web service (hence OWS) interface standards, and their related content models..
OWSLib was buried down inside PCL, but has been brought out as a separate project in r I want to check whether the tag exists, and, if so, to get the contents.
Edit edit: Ok, I am going to combine two of the answers, but I can only vote for one. Sorry. Parsing XML with Python using caninariojana.comify June 6, Cross-Platform, Python, Web Python, XML, XML Parsing Series Mike A couple years ago I .