>>> import netCDF4 as nc >>> import numpy as np >>> import sys >>> >>> print("Python version:", sys.version) Python version: 3.12.12 | packaged by conda-forge | (main, Oct 13 2025, 14:34:15) [GCC 14.3.0] >>> print("NetCDF4 version:", nc.__version__) NetCDF4 version: 1.7.2 >>> >>> filepath = "tmp.nc" >>> nx, n_strlen = 3, 10 >>> >>> ds = nc.Dataset(filepath, "w") >>> ds.createDimension("x", nx) "<class 'netCDF4.Dimension'>": name = 'x', size = 3 >>> ds.createDimension("nstr", n_strlen) "<class 'netCDF4.Dimension'>": name = 'nstr', size = 10 >>> v = ds.createVariable("v", "S1", dimensions=("x", "nstr",)) >>> >>> strings = ['Münster', 'London', 'Amsterdam'] >>> print( ... "Strings for variable content:" ... f"\n{strings}" ... "\n" ... ) Strings for variable content: ['Münster', 'London', 'Amsterdam'] >>> >>> bbytes = [text.encode("utf-8") for text in strings] >>> pad = b'\0' * n_strlen >>> bbytes = [(x + pad)[:n_strlen] for x in bbytes] >>> print( ... "Equal-length byte objects with utf-8 encoding:" ... f"\n{bbytes}" ... "\n" ... ) Equal-length byte objects with utf-8 encoding: [b'M\xc3\xbcnster\x00\x00', b'London\x00\x00\x00\x00', b'Amsterdam\x00'] >>> >>> bytesarray = np.array(bbytes, dtype=f"S{n_strlen}") >>> print( ... "\nBytes(Sxx) array:" ... f"\n{bytesarray}" ... f"\n :: shape={bytesarray.shape} dtype ={bytesarray.dtype}" ... "\n" ... ) Bytes(Sxx) array: [b'M\xc3\xbcnster' b'London' b'Amsterdam'] :: shape=(3,) dtype =|S10 >>> >>> chararray = np.array([[bb[i:i+1] for i in range(n_strlen)] for bb in bbytes]) >>> print( ... "\nCharacter(S1) array:" ... f"\n{chararray}" ... f" :: shape={chararray.shape} dtype ={chararray.dtype}" ... "\n" ... ) Character(S1) array: [[b'M' b'\xc3' b'\xbc' b'n' b's' b't' b'e' b'r' b'' b''] [b'L' b'o' b'n' b'd' b'o' b'n' b'' b'' b'' b''] [b'A' b'm' b's' b't' b'e' b'r' b'd' b'a' b'm' b'']] :: shape=(3, 10) dtype =|S1 >>> >>> # Store chararray in variable, and mark as UTF8 encoded >>> v._Encoding = "UTF-8" >>> v[:] = chararray >>> >>> ds.close() >>> >>> from os import system as run >>> print("\nNCDUMP of file:") NCDUMP of file: >>> run(f"ncdump {filepath}") netcdf tmp { dimensions: x = 3 ; nstr = 10 ; variables: char v(x, nstr) ; v:_Encoding = "UTF-8" ; data: v = "M\303\274nster", "London", "Amsterdam" ; } 0 >>> >>> ds2 = nc.Dataset(filepath) >>> v = ds2.variables['v'] >>> data = v[:] >>> print( ... "\nData read back from file variable:" ... f"\n{data}" ... f" :: shape={data.shape} dtype ={data.dtype}" ... "\n" ... ) Data read back from file variable: ['Münster\x00\x00L' 'ondon\x00\x00\x00\x00A' 'msterdam'] :: shape=(3,) dtype =<U10 >>> >>> print( ... "individual elements..." ... f"{''.join(f"\n {elem}" for elem in data)}" ... "\n" ... ) individual elements... MünsterL ondonA msterdam >>> >>> v.set_auto_chartostring(False) >>> data = v[:] >>> >>> print( ... "\nSame variable data, read *without* encoding interpretation:", ... '\n', data, ... f"\n :: shape={data.shape} dtype ={data.dtype}" ... "\n" ... ) Same variable data, read *without* encoding interpretation: [[b'M' b'\xc3' b'\xbc' b'n' b's' b't' b'e' b'r' -- --] [b'L' b'o' b'n' b'd' b'o' b'n' -- -- -- --] [b'A' b'm' b's' b't' b'e' b'r' b'd' b'a' b'm' --]] :: shape=(3, 10) dtype =|S1