But the original question was answered so thank you very much!

Sorry for the silence! I got pulled away to another project. Unfortunately I wasn't able to finish completing the task in xarray but I found that the easiest way around the problem was to use a combination of two functions:

```python def change_missing_vals_to_9999f(ds, variable): """ Change the missing values from np.nan to -9999.0f""" arr = ds[variable].values

# set the values to -9999
arr[np.isnan(arr)] = -9999

# reassign the values back to the array
ds[variable] = (ds[variable].dims, arr)

return ds

def change_missing_data_values(filename): """ change the values INSIDE the .nc file to -9999.0f """ assert ( filename.split(".")[-1] == "nc" ), "This function only works with .nc files. Filename: {}".format(filename) print(" Processing {} ").format(filename)

# ONLY OPEN THE DATASET ONCE
ds = xr.open_dataset(filename)
variables = ds.data_vars.keys()

for variable in variables:
    print("** Working on variable {} **".format(variable))
    ds = change_missing_vals_to_9999f(ds, variable)

# ds.map(change_missing_vals_to_9999f, variable)

# rewrite to netcdf file
ds.to_netcdf(filename)
print("** Written variables {} to filename {} **").format(variables, filename)

return

```

and then another function using the NCO command:

def change_nc_FillValue(filename): """ use the NCO command to change the fillvalue metadata in the .nc files""" command = "ncatted -a _FillValue,,m,f,-9999.0 {}".format(filename) os.system(command) print("** _FillValue changed on {} file **".format(filename)) return

RUN HERE: ``` @click.command() @click.argument("filename", type=str) def main(filename): """ Run the two commands a) change the Values INSIDE the .nc file [python, numpy, xarray] b) change the associated METADATA for the .nc file headers [nco] """ change_missing_data_values(filename) change_nc_FillValue(filename)

print("**** PROCESS DONE FOR {} ****").format(filename)

return

```

@spencerkclark Thanks very much that is awesome!

One final Q: How do I set the fill_value not to nan but to -1 (this is what I need for the Land Surface Model)?

The current output of ncdump -h Rg_dummy.nc is:

... variables: double time(time) ; time:_FillValue = NaN ; time:standard_name = "time" ; time:units = "day as %Y%m%d.%f" ; time:calendar = "proleptic_gregorian" ; short Rg(time, y, x) ; Rg:_FillValue = NaN ; Rg:long_name = "HWSD sub sum content" ; Rg:units = "percent wt" ; Rg:valid_range = 97., 103. ; double latitude(y, x) ; latitude:_FillValue = -99999. ; double longitude(y, x) ; longitude:_FillValue = -99999. ;

What I want is:

ncdump -h Rg_dummy.nc ... variables: double time(time) ; time:_FillValue = -1s ; time:standard_name = "time" ; time:units = "day as %Y%m%d.%f" ; time:calendar = "proleptic_gregorian" ; short Rg(time, y, x) ; Rg:_FillValue = -1s ; Rg:long_name = "HWSD sub sum content" ; Rg:units = "percent wt" ; Rg:valid_range = 97., 103. ; double latitude(y, x) ; latitude:_FillValue = -99999. ; double longitude(y, x) ; longitude:_FillValue = -99999. ;

I want to do something like: ds2.to_netcdf(filename, set_fill_value=-1)

I saw these: Issue #1598 Issue #1865

But failed to understand where/how to use them

Thank you so much for helping me out with xarray. It's crazy powerful. It's also just very big!

That all worked great until I tried to write out to a .nc file.

```python data_dir = "./" filename = "Rg_dummy.nc"

get the datetime range

times = pd.date_range("2000-01-01", "2000-12-31", name="time") var = "Rg"

copyfile(data_dir + filename, "temp.nc") ds = xr.open_dataset("temp.nc") print("Temporary Data read to Python")

FORWARD FILL FROM THE ORIGINAL DATA to new timesteps

ds['time'] = np.array([times[0]]) ds.reindex({"time":times}) ds.ffill("time")

ds.to_netcdf(filename, format="NETCDF3_CLASSIC") print(filename, "Written!")

remove temporary file

os.remove(data_dir+"temp.nc") print("Temporary Data Removed")

del ds ```

I get the following Error message: ``` Temporary Data read to Python

TypeError Traceback (most recent call last) <ipython-input-228-e3d645224353> in <module>() 15 ds.ffill("time") 16 ---> 17 ds.to_netcdf(filename, format="NETCDF3_CLASSIC") 18 print(filename, "Written!") 19

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute) 1148 engine=engine, encoding=encoding, 1149 unlimited_dims=unlimited_dims, -> 1150 compute=compute) 1151 1152 def to_zarr(self, store=None, mode='w-', synchronizer=None, group=None,

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/api.pyc in to_netcdf(dataset, path_or_file, mode, format, group, engine, writer, encoding, unlimited_dims, compute) 721 try: 722 dataset.dump_to_store(store, sync=sync, encoding=encoding, --> 723 unlimited_dims=unlimited_dims, compute=compute) 724 if path_or_file is None: 725 return target.getvalue()

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in dump_to_store(self, store, encoder, sync, encoding, unlimited_dims, compute) 1073 1074 store.store(variables, attrs, check_encoding, -> 1075 unlimited_dims=unlimited_dims) 1076 if sync: 1077 store.sync(compute=compute)

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in store(self, variables, attributes, check_encoding_set, unlimited_dims) 366 self.set_dimensions(variables, unlimited_dims=unlimited_dims) 367 self.set_variables(variables, check_encoding_set, --> 368 unlimited_dims=unlimited_dims) 369 370 def set_attributes(self, attributes):

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in set_variables(self, args, kwargs) 405 def set_variables(self, args, kwargs): 406 with self.ensure_open(autoclose=False): --> 407 super(NetCDF4DataStore, self).set_variables(*args, kwargs) 408 409 def encode_variable(self, variable):

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in set_variables(self, variables, check_encoding_set, unlimited_dims) 403 check = vn in check_encoding_set 404 target, source = self.prepare_variable( --> 405 name, v, check, unlimited_dims=unlimited_dims) 406 407 self.writer.add(source, target)

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in prepare_variable(self, name, variable, check_encoding, unlimited_dims) 451 least_significant_digit=encoding.get( 452 'least_significant_digit'), --> 453 fill_value=fill_value) 454 _disable_auto_decode_variable(nc4_var) 455

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.createVariable()

netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Variable.init()

TypeError: illegal primitive data type, must be one of ['i8', 'f4', 'f8', 'S1', 'i2', 'i4', 'u8', 'u4', 'u1', 'u2', 'i1'], got datetime64[ns] ```

and if I try with the default netcdf writing options

python ds.to_netcdf(filename) print(filename, "Written!")

I get this error message: ``` Temporary Data read to Python

ValueError Traceback (most recent call last) <ipython-input-229-453d5f074d33> in <module>() 15 ds.ffill("time") 16 ---> 17 ds.to_netcdf(filename) 18 print(filename, "Written!") 19

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in to_netcdf(self, path, mode, format, group, engine, encoding, unlimited_dims, compute) 1148 engine=engine, encoding=encoding, 1149 unlimited_dims=unlimited_dims, -> 1150 compute=compute) 1151 1152 def to_zarr(self, store=None, mode='w-', synchronizer=None, group=None,

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/api.pyc in to_netcdf(dataset, path_or_file, mode, format, group, engine, writer, encoding, unlimited_dims, compute) 721 try: 722 dataset.dump_to_store(store, sync=sync, encoding=encoding, --> 723 unlimited_dims=unlimited_dims, compute=compute) 724 if path_or_file is None: 725 return target.getvalue()

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/core/dataset.pyc in dump_to_store(self, store, encoder, sync, encoding, unlimited_dims, compute) 1073 1074 store.store(variables, attrs, check_encoding, -> 1075 unlimited_dims=unlimited_dims) 1076 if sync: 1077 store.sync(compute=compute)

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in store(self, variables, attributes, check_encoding_set, unlimited_dims) 366 self.set_dimensions(variables, unlimited_dims=unlimited_dims) 367 self.set_variables(variables, check_encoding_set, --> 368 unlimited_dims=unlimited_dims) 369 370 def set_attributes(self, attributes):

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in set_variables(self, args, kwargs) 405 def set_variables(self, args, kwargs): 406 with self.ensure_open(autoclose=False): --> 407 super(NetCDF4DataStore, self).set_variables(*args, kwargs) 408 409 def encode_variable(self, variable):

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/common.pyc in set_variables(self, variables, check_encoding_set, unlimited_dims) 403 check = vn in check_encoding_set 404 target, source = self.prepare_variable( --> 405 name, v, check, unlimited_dims=unlimited_dims) 406 407 self.writer.add(source, target)

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in prepare_variable(self, name, variable, check_encoding, unlimited_dims) 418 unlimited_dims=None): 419 datatype = _get_datatype(variable, self.format, --> 420 raise_on_invalid_encoding=check_encoding) 421 attrs = variable.attrs.copy() 422

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in _get_datatype(var, nc_format, raise_on_invalid_encoding) 99 def _get_datatype(var, nc_format='NETCDF4', raise_on_invalid_encoding=False): 100 if nc_format == 'NETCDF4': --> 101 datatype = _nc4_dtype(var) 102 else: 103 if 'dtype' in var.encoding:

/home/mpim/m300690/miniconda3/envs/holaps/lib/python2.7/site-packages/xarray/backends/netCDF4_.pyc in _nc4_dtype(var) 122 else: 123 raise ValueError('unsupported dtype for netCDF4 variable: {}' --> 124 .format(var.dtype)) 125 return dtype 126

ValueError: unsupported dtype for netCDF4 variable: datetime64[ns] ```

Version information

If this is useful:

``` In [230]: xr.show_versions()

INSTALLED VERSIONS

commit: None python: 2.7.15.final.0 python-bits: 64 OS: Linux OS-release: 2.6.32-696.18.7.el6.x86_64 machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: None.None

xarray: 0.10.7 pandas: 0.23.0 numpy: 1.11.3 scipy: 1.1.0 netCDF4: 1.4.0 h5netcdf: None h5py: None Nio: None zarr: None bottleneck: 1.2.1 cyordereddict: None dask: None distributed: None matplotlib: 1.5.1 cartopy: None seaborn: None setuptools: 39.1.0 pip: 18.1 conda: None pytest: None IPython: 5.7.0 sphinx: None ```

The ds.time[0] won't let me set it's value to a datetime. Instead it returns a float: python In [19]: ds.time[0].item() Out[19]: 10509.0

And none of the following work: ```python

doesn't change the time value

ds.time[0].values = times[0]

returns an error because I can't assign to a function call

ds.time[0].item() = times[0]

returns ValueError: replacement data must match the Variable's shape

ds['time'].values = np.array(times[0]) ```

Thanks for your help!

Data: netcdf_files.zip

Code below:

```python import numpy as np import pandas as pd import xarray as xr from shutil import copyfile import os

data_dir = "./" filename = "Rg_dummy.nc"

get the datetime range

times = pd.date_range("2000-01-01", "2000-12-31", name="time") var = "Rg"

copyfile(filename, "temp.nc") ds = xr.open_dataset("temp.nc") print("Temporary Data read to Python")

FORWARD FILL FROM THE ORIGINAL DATA to new timesteps

ds.reindex({"time":times}) ds.ffill("time")

ds.to_netcdf(filename, format="NETCDF3_CLASSIC")

print(filename, "Written!")

remove temporary file

os.remove(data_dir+"temp.nc") print("Temporary Data Removed")

del ds

```

Thanks for your help! you definitely understood me correctly!

This doesn't seem to work as it fills myRg arrays with nan values.

python ds = ds.reindex({"time":pd.date_range("2000-01-01","2000-12-31")}) ds = ds.ffill("time")