On 11/12/06, Erin Sheldon <[EMAIL PROTECTED]> wrote: > On 11/12/06, Pierre GM <[EMAIL PROTECTED]> wrote: > > > > You could try the fromarrays function of numpy.core.records > > > > >>> mydescriptor = {'names': (a','b','c','d'), 'formats':('f4', 'f4', 'f4', > > 'f4')} > > >>> a = N.core.records.fromarrays(N.transpose(yourlist), dtype=mydescriptor) > > > > The 'transpose' function ensures that 'fromarrays' sees 4 arrays (one for > > each > > column).
Actually, there is a problem with that approach. It first converts the entire array to a single type, by default a floating type. For very large integers this precision is insufficient. For example, I have the following integer in my arrays: 94137100072000193L which ends up as 94137100072000192 after going to a float and then back to an integer. Erin ------------------------------------------------------------------------- Using Tomcat but need to do more? Need to support web services, security? Get stuff done quickly with pre-integrated technology to make your job easier Download IBM WebSphere Application Server v.1.0.1 based on Apache Geronimo http://sel.as-us.falkag.net/sel?cmd=lnk&kid=120709&bid=263057&dat=121642 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion