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| author | scoder <none@none> | 2007-05-08 13:43:58 +0200 |
|---|---|---|
| committer | scoder <none@none> | 2007-05-08 13:43:58 +0200 |
| commit | 84f7e8acaf332e4aefa43f73ed4cdd539377acfa (patch) | |
| tree | 94e3e37a114b72f377d8d7eb9f9af2f448c79458 /doc/performance.txt | |
| parent | 94326dbfde287c0a8f644875c4bb535ab0d415aa (diff) | |
| download | python-lxml-84f7e8acaf332e4aefa43f73ed4cdd539377acfa.tar.gz | |
[svn r2267] numpy
--HG--
branch : trunk
Diffstat (limited to 'doc/performance.txt')
| -rw-r--r-- | doc/performance.txt | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/doc/performance.txt b/doc/performance.txt index 787c1d45..099f1c58 100644 --- a/doc/performance.txt +++ b/doc/performance.txt @@ -83,7 +83,7 @@ overhead than the simpler top-down structure of ElementTree. What this means is: the more of your code runs in Python, the less you can benefit from the speed of lxml and libxml2. Note, however, that this is true for most performance critical Python applications. No one would implement complex -matrix calculations in pure Python when you can use Numeric. +matrix calculations in pure Python when you can use NumPy. The up side then is that lxml provides powerful tools like tree iterators, XPath and XSLT, that can handle complex operations at the speed of C. Their |