In-memory to_csv compression
#### Code Sample, a copy-pastable example if possible

```python
# Attempt 1
import pandas as pd

df = pd.DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8], "C": [9, 10, 11, 12]})
test = df.to_csv(compression="gzip")
type(test)
```

```
RuntimeWarning: compression has no effect when passing file-like object as input.
Out: str
```

```python
# Attempt 2
from io import BytesIO
b_buf = BytesIO()
df.to_csv(b_buf, compression="gzip")
```

```
Out: TypeError: a bytes-like object is required, not 'str'
```
#### Problem description

I am trying to gzip compress a dataframe in memory (as opposed to directly to a named file location). The use case for this is (I imagine) similar to the reason by to_csv now allows not specifying a path in other cases to create an in memory representation, but specifically my case is that I need to save the compressed df to a cloud location using a custom URI, and I'm temporarily keeping it in memory for that purpose.

#### Expected Output
I would expect the compression option to result in a compressed, bytes object (similar to the gzip library).

Thank you in advance for your help!

Note: I originally saw #21227 (df.to_csv ignores compression when provided with a file handle), and thought it might have also been a fix, but looks like it just stopped a little short of fixing my issue as well.

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 17.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 9.0.3
setuptools: 39.0.1
Cython: None
numpy: 1.15.1
scipy: None
pyarrow: None
xarray: None
IPython: 6.4.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
