**http://www.ceda.ac.uk/static/media/uploads/ncas-reading-2015/10_read_netcdf_python.pdf

**https://www.youtube.com/watch?v=xu7FAYnwGTc

You need to show some code for us to be able to help you. How are you calling the functions using netcdf4? Show a snippet of your code.

• My code is below :

import numpy as np

from netCDF4 import Dataset

nc = Dataset('F:\\Research\\Data\\World wide monthly rainfall data\\precip.mon.total.2.5x2.5.v6.nc','r')

for i in nc.variables:

print(i, nc.variables[i].units, nc.variables[i].shape)

d = np.array(nc.variables['precip'], dtype=type(nc.variables))

print(d[:,23.75,91.25])

• Output :

lat degrees_north (72,)

lon degrees_east (144,)

time days since 1800-1-1 00:00:00 (1320,)

precip mm (1320, 72, 144)

[-9.96921e+36 -9.96921e+36 -9.96921e+36 ..., -9.96921e+36 -9.96921e+36 -9.96921e+36]

"print(d[:,23.75,91.25])"

From my experience you can not specify the longitude and latitude values directly in the array. You need integer values that represent these values. You can get the integer values from your lat and lon variables. (Maybe this is a poor man's solutions and there is a more elegant way to solve it, but at least this works for me.)

Thanks, Daniel Kunkel. I have already try integer values like below :

print(d[:,71,143])

But, the result is same :

[-9.96921e+36 -9.96921e+36 -9.96921e+36 ..., -9.96921e+36 -9.96921e+36 -9.96921e+36]

Did you build the package with OPeNDAP?:

"Remote OPeNDAP-hosted datasets can be accessed for reading over http if a URL is provided to the Dataset constructor instead of a filename. However, this requires that the netCDF library be built with OPenDAP support, via the --enable-dap configure option (added in version 4.0.1)."

may be that is the issue. Also make sure that the format is correct or try different formats in the Dataset constructor.

Also check this http://schubert.atmos.colostate.edu/~cslocum/netcdf_example.html#code code to see if it helps.

Cheers

I once had problems with a netcdf4/hdf5 file which I could only access properly with the h5py package for hdf files. Usually, I use scipy for netcdf files. 

Thanks, everyone. I have solved it.

Could you share how did you resolve it?

My main problem was in the data calling system. I was always calling by lat, lon value (Example: d[:,23.75,91.25]). But, calling by index is the main calling format for this  (Daniel Kunkel mention it previous comment). Funny to say, when I called by index, result remained the same since all my index had null values coincidentally. Therefore, the result showed -9.96921e+36, which means 0.

I need precipitation values for 23.75, 91.25 (lat, lon). of which having index 26, 36 respectively. So, when I call by index like d[:,26,36]). The result is fine and similar to ArcGIS result.

My ultimate code is below :

import numpy as np

from netCDF4 import Dataset

nc = Dataset('F:\\Research\\Data\\World wide monthly rainfall data\\precip.mon.total.2.5x2.5.v6.nc','r')

for i in nc.variables:

print(i, nc.variables[i].units, nc.variables[i].shape)

d = np.array(nc.variables['precip'], dtype=type(nc.variables))

print(d[:, 26, 36])

Hi

You can easily extract your time series and save to excel text csv and json file

Http:\\www.AgriMetSoft.com

Thanks Topper. But, I already solve this problem.

Hi

By Netcdf-Extractor you can merge many nc files and extract from all times in all files once

https://agrimetsoft.com/Netcdf-Extractor.aspx

http://netcdf-extractor.com/

Thanks. But, "This site can’t be reached"

Hi

I've corrected it again

www.agrimetsoft.com

Please try it again.

Regards,

Nasrin

I used this for netcdf that comes from WRF

import numpy as np

from netCDF4 import Dataset # http://code.google.com/p/netcdf4-python/

import pandas as pd

import os

f = Dataset('wrfout_d01_2007-01-01_10_00_00', 'r') # Read es para leer

latbounds = [ 4.691417 ]# Punctual values

lonbounds = [ -74.209 ] # degrees east ? # Valores límites

cor_lat = pd.DataFrame(f.variables['XLAT'][0][:])

cor_lat2 = pd.DataFrame({'a':cor_lat.iloc[:,0], 'b':abs(cor_lat.iloc[:,0] - latbounds)})

a = cor_lat2[cor_lat2.b == min(cor_lat2.b)].index.get_values()[0]

cor_lon = pd.DataFrame(f.variables['XLONG'][0][:])

cor_lon2 = pd.DataFrame({'a':cor_lon.iloc[0,:], 'b':abs(cor_lon.iloc[0,:] - lonbounds)})

b = cor_lon2[cor_lon2.b == min(cor_lon2.b)].index.get_values()[0]

vlr = (f.variables['T2'][ : , a , b ] - 273.15)[0]

vlr#This is the result

**http://www.ceda.ac.uk/static/media/uploads/ncas-reading-2015/10_read_netcdf_python.pdf

**https://www.youtube.com/watch?v=xu7FAYnwGTc

http://www.ceda.ac.uk/static/media/uploads/ncas-reading-2015/10_read_netcdf_python.pdf

https://stackoverflow.com/questions/45582344/extracting-data-from-netcdf-by-python

https://www.youtube.com/watch?v=d6diNBEDPfM

https://geosci.uchicago.edu/~rtp1/itr/PythonLectures/ReadWriteNC.html