Code Sample, a copy-pastable example if possible

Example from https://stackoverflow.com/questions/49053692/csv-to-netcdf-produces-nc-files-4x-larger-than-the-original-csv

```{python} import pandas as pd import xarray as xr import numpy as np import os

Create pandas DataFrame

df = pd.DataFrame(np.random.randint(low=0, high=10, size=(100000,5)), columns=['a', 'b', 'c', 'd', 'e'])

Make 'e' a column of strings

df['e'] = df['e'].astype(str)

Save to csv

df.to_csv('df.csv')

Convert to an xarray's Dataset

ds = xr.Dataset.from_dataframe(df)

Save NetCDF file

ds.to_netcdf('ds.nc')

Compute stats

stats1 = os.stat('df.csv') stats2 = os.stat('ds.nc') print('csv=',str(stats1.st_size)) print('nc =',str(stats2.st_size)) print('nc/csv=',str(stats2.st_size/stats1.st_size)) ```

The result:

```

csv = 1688902 bytes nc = 6432441 bytes nc/csv = 3.8086526038811015 ```

Problem description

NetCDF can store numerical data, as well as some other data, such as categorical data, in a much more efficient way than CSV, do to it's ability to store numbers (integers, limited precision floats) in smaller encodings (e.g. 8 bit integers), as well as it's ability to compress data using zlib.

The answers in the stack exchange link at the top of the page give some examples of how this can be done. The second one is particularly useful, and it would be nice if xarray provided an encoding={'dtype': 'auto'} option to automatically select the most compact dtype able to store a given DataArray before saving the file.

Expected Output

NetCDF from should be equal to or smaller than a CSV full of numerical data in most cases.

Output of xr.show_versions()

``` INSTALLED VERSIONS ------------------ commit: None python: 3.6.7 | packaged by conda-forge | (default, Nov 21 2018, 03:09:43) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.18.0-15-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_AU.UTF-8 LOCALE: en_AU.UTF-8 libhdf5: 1.10.4 libnetcdf: 4.6.2 xarray: 0.11.3 pandas: 0.24.1 netCDF4: 1.4.2 h5netcdf: 0.6.2 h5py: 2.9.0 dask: 1.0.0 ```