id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
376370028,MDU6SXNzdWUzNzYzNzAwMjg=,2534,to_dataframe() excessive memory usage,1665346,closed,0,,,3,2018-11-01T12:20:39Z,2022-05-01T22:04:51Z,2022-05-01T22:04:43Z,NONE,,,,"#### Code Sample, a copy-pastable example if possible

```python
import xarray as xr
from glob import glob

# This refers to a large multi-file NetCDF dataset
file_list = sorted(glob('~/Data/**/**/*.nc'))

dataset = xr.open_mfdataset(file_list,
                            decode_times=True,
                            autoclose=True,
                            decode_cf=True,
                            cache=False,
                            concat_dim='time')

# At this point, the total RAM used by the python process is ~1.4G

# Select a timeseries at a single point
# This is near instantaneous and uses no additional memory
ts = dataset.sel({'lat': 10, 'lon': 10}, method='nearest')

# Convert that timeseries to a pandas dataframe.
# This is where the actual data reading happens, and reads the data into memory
df = ts.to_dataframe()
# At this point, the total RAM used by the python process is ~10.5G
```
#### Problem description

Despite the fact that the resulting dataframe only has a single lat/lon point's worth of data, a huge amount of RAM is used.  I can get (what appears to be) an identical pandas DataFrame by changing the final line to:

```python
df = (ts * 1.0).to_dataframe()
```
which reduces the total RAM to ~2.2G (i.e. 0.6G additional RAM for that single line vs 9G additional RAM).  No type conversion is taking place (i.e. `ts` and `ts * 1.0` both have identical data types)

#### Expected Output

I would expect that `to_dataframe()` would require the same amount of memory whether or not it was multiplied by 1.0.  I'm aware there could be a good reason for this, but it took me by surprise somewhat.

#### Output of ``xr.show_versions()``

<details>

commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

xarray: 0.10.7
pandas: 0.23.1
numpy: 1.13.3
scipy: 0.17.0
netCDF4: 1.4.1
h5netcdf: None
h5py: None
Nio: None
zarr: None
bottleneck: None
cyordereddict: None
dask: 0.19.0
distributed: None
matplotlib: 1.5.1
cartopy: None
seaborn: 0.8.1
setuptools: 20.7.0
pip: 18.0
conda: None
pytest: None
IPython: 2.4.1
sphinx: None

</details>
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