From 9cbaf20138eadf745317010d7b1608225515beeb Mon Sep 17 00:00:00 2001 From: Valentin Haenel Date: Thu, 21 Feb 2013 22:26:12 +0100 Subject: [PATCH] mention the Python Scientific Lecture Notes --- docs/scenarios/scientific.rst | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/docs/scenarios/scientific.rst b/docs/scenarios/scientific.rst index 2c515b0..a8cddde 100644 --- a/docs/scenarios/scientific.rst +++ b/docs/scenarios/scientific.rst @@ -10,9 +10,12 @@ is widely used in academia and scientific projects because it is easy to write, and it performs really well. Due to its high performance nature, scientific computing in python often refers -to external libraries, typically written in faster languages (like C, or FORTRAN -for matrix operations). The main libraries used are `NumPy`_, -`SciPy`_ and `Matplotlib`_. +to external libraries, typically written in faster languages (like C, or +FORTRAN for matrix operations). The main libraries used are `NumPy`_, `SciPy`_ +and `Matplotlib`_. Going into detail about these libraries is beyond the scope +of the Python guide. However, a comprehensive introduction to the scientific +Python ecosystem can be found in the `Python Scientific Lecture Notes +`_ Libraries :::::::::