Ryan Neve wrote: > Hello, > > [...] > This works, but is very slow for something that will be on the back end > of a web page.
Iterating in python is usually slow, so you should use numpy array methods if possible. I've made a faster version. It gives the same result for your test case, but you should test it further to see if it treats all cases properly. Regards, João Silva ------------------------------------------------ import numpy as np sample_array = np.array(([np.nan,1,2,3,np.nan,5,6,7,8,np.nan,np.nan,11,12,np.nan,np.nan,np.nan])) #Replace single nan with the neighbours average sample_array[1:-1] = np.where(np.isnan(sample_array[1:-1]),(sample_array[:-2]+sample_array[2:])/2.0,sample_array[1:-1]) #Fix ... Number nan ... sample_array[1:]= np.where(np.logical_and(np.logical_not(np.isnan(sample_array[:-1])),np.isnan(sample_array[1:])),sample_array[:-1],sample_array[1:]) #Fix ... nan Number ... sample_array[:-1]= np.where(np.logical_and(np.logical_not(np.isnan(sample_array[1:])),np.isnan(sample_array[:-1])),sample_array[1:],sample_array[:-1]) print sample_array ------------------------------------------------ ------------------------------------------------------------------------------ Let Crystal Reports handle the reporting - Free Crystal Reports 2008 30-Day trial. Simplify your report design, integration and deployment - and focus on what you do best, core application coding. Discover what's new with Crystal Reports now. http://p.sf.net/sfu/bobj-july _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
