pandas daily standar deviation
Dear Developers,
                              I use the below code to read a netcdf file and get standard deviation of hourly data for each day. Though I get the values they are not matching with the hand computed values. Can any one tell me what I need to do for this to work
with best regards,
Sudheer

``` python
from netCDF4 import Dataset as ncd
from netCDF4 import num2date
from jdcal import *
from pandas import  Series,tseries,date_range
import numpy as np
import datetime as dt
from matplotlib import pyplot as plt
ncf=ncd('cur0n90e_10m.cdf','r')
time=ncf.variables['time'][:]
ru=ncf.variables['U_320'][:]
ru[np.where(ru==1e+35)]='NaN'
ru=np.squeeze(ru[:,0,0,0])
torg=ncf.variables['time'].units
dates=num2date(time,torg,calendar='standard')
torg=str(torg[len(torg)-19:len(torg)])
yr=float(torg[0:4])
mn=float(torg[5:7])
dd=float(torg[8:10])
hh=torg[11:13]
mm=torg[14:16]
ss=torg[17:19]
tfrac=(float(hh)+float(mm)/60.0+float(ss)/3600.0)/24.0
tfrac=float("%.2f" % tfrac)
refdate=gcal2jd(yr,mn,dd)
dtref=dt.datetime(int(yr),int(mn),int(dd),int(hh),int(mm),int(ss))
gtime=[]
D1=dates[0]
D2=dates[len(dates)-1]
daily=date_range(D1,D2,freq='D')
#dtrng=date_range(dtref, periods=len(time),freq='10min')
dtrng=date_range(D1,D2,freq='10min')
ts = Series(ru, index=dtrng,dtype='float64')
std=ts.groupby(daily.asof).std()
```
