netcdf4 extract for subset of lat lon

Favo's answer works (I assume; haven't checked). A more direct and idiomatic way is to use numpy's where function to find the necessary indices.

lats = f.variables['latitude'][:] 
lons = f.variables['longitude'][:]
lat_bnds, lon_bnds = [40, 43], [-96, -89]

lat_inds = np.where((lats > lat_bnds[0]) & (lats < lat_bnds[1]))
lon_inds = np.where((lons > lon_bnds[0]) & (lons < lon_bnds[1]))

air_subset = f.variables['air'][:,lat_inds,lon_inds]

Well this is pretty easy, you have to find the index for the upper and lower bound in latitude and longitude. You can do it by finding the value that is closest to the ones you're looking for.

latbounds = [ 40 , 43 ]
lonbounds = [ -96 , -89 ] # degrees east ? 
lats = f.variables['latitude'][:] 
lons = f.variables['longitude'][:]

# latitude lower and upper index
latli = np.argmin( np.abs( lats - latbounds[0] ) )
latui = np.argmin( np.abs( lats - latbounds[1] ) ) 

# longitude lower and upper index
lonli = np.argmin( np.abs( lons - lonbounds[0] ) )
lonui = np.argmin( np.abs( lons - lonbounds[1] ) )  

Then just subset the variable array.

# Air (time, latitude, longitude) 
airSubset = f.variables['air'][ : , latli:latui , lonli:lonui ] 

• Note, i'm assuming the longitude dimension variable is in degrees east, and the air variable has time, latitude, longitude dimensions.