netcdf4

CDO can crop a netcdf file in terms of latitude and longitude as long as they are defined in a standard way, and I know that NCO can cut out a subset of a netcdf file along any dimension if you know the range of indices that you want, as stated in the answers to this related question: Is there a way to crop a NETCDF file? However, I was wondering if the ncks hyperslabber can work directly on the values of the dimension, rather than the index values? Yes, using a decimal indicates the range of actual values (eg, latitudes) to extract over, while using integers indicates the range of indices

I have recently started to work with netcdf in R. Sample data is here: http://www.earthstat.org/data-download/ > Harvested area and yield for 175 crops > individual crops > soybean_HarvAreaYield2000_NetCDF In this folder, there is a netcdf file called soybean_AreaYieldProduction.nc This is how I open the netcdf library(ncdf4) dat <- nc_open("soybean_AreaYieldProduction.nc") print(soy) 1 variables (excluding dimension variables): float soybeanData[longitude,latitude,level,time] LayerDescriptions: struct(5).Data(:,:,1/2/3/4/5/6) to access data layer: 1=Harvested Area fraction, 2=Yield 3

I'm trying to convert a netCDF file to either a CSV or text file using Python. I have read this post but I am still missing a step (I'm new to Python). It's a dataset including latitude, longitude, time and precipitation data. This is my code so far: import netCDF4 import pandas as pd precip_nc_file = 'file_path' nc = netCDF4.Dataset(precip_nc_file, mode='r') nc.variables.keys() lat = nc.variables['lat'][:] lon = nc.variables['lon'][:] time_var = nc.variables['time'] dtime = netCDF4.num2date(time_var[:],time_var.units) precip = nc.variables['precip'][:] I am not sure how to proceed from here,