I assume you have this data in a txt file, correct? You can load up all of it in a numpy array using
import numpy as np
data = np.loadtxt("climat_file.txt", skiprows = 1)

Then you can compute the mean you want by taking it on a slice of the data array. For example, if you want to compute the mean of your data in Jan for 1950-1970 (say including 1970)
mean1950_1970 = data[1950:1971,1].mean()

Then the std deviation you want could be computed using
my_std = np.sqrt(np.mean((data[:,1]-mean1950_1970)**2))

Hope this helps,

On Tue, Apr 12, 2011 at 1:48 PM, Climate Research <climateforu@gmail.com> wrote:
I am purely new to python and numpy..  I am using python for doing statistical calculations to Climate data..

I  have  a  data set in the following format..

Year  Jan      feb       Mar    Apr.................   Dec
1900  1000    1001       ,        ,                         ,
1901  1011    1012       ,        ,                         ,
1902  1009    1007       ,                                  ,
,,,,        ,           '          ,        ,                         ,
,,,,        ,           ,
2010  1008    1002       ,        ,                         ,

I actually want to standardize each of these values with corresponding standard deviations for  each monthly data column..
I have found out the standard deviations for each column..  but now i need to  find the standared deviation  only for a prescribed mean value
ie,  when i am finding the standared deviation for the January data column..  the mean should be calculated only for the january data, say from 1950-1970. With this mean  i  want to calculate the SD  for entire column.
Any help will be appreciated..

NumPy-Discussion mailing list

Jonathan Rocher, PhD
Scientific software developer
Enthought, Inc.