REGR: to_csv problems with zip compression and large dataframes
#### Code Sample, a copy-pastable example

```python
import pandas as pd
import io
f = io.BytesIO()
d = pd.DataFrame({'a':[1]*5000})
d.to_csv(f, compression='zip')
f.seek(0)
pd.read_csv(f, compression='zip')
```

#### Problem description
Writing large (over 1163 rows) dataframes to csv with zip compression (inferred or explicit; to file or io.BytesIO) creates a corrupted zip file.
ValueError: Multiple files found in ZIP file. Only one file per ZIP: ['zip', 'zip', 'zip', 'zip', 'zip']

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

<details>

INSTALLED VERSIONS
------------------
commit           : 3e89b4c4b1580aa890023fc550774e63d499da25
python           : 3.8.6.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
Version          : 10.0.19041
machine          : AMD64
processor        : Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : Polish_Poland.1250

pandas           : 1.2.0
numpy            : 1.19.3
pytz             : 2020.5
dateutil         : 2.8.1
pip              : 20.3.3
setuptools       : 51.1.0.post20201221
Cython           : None
pytest           : 6.2.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.11.2
IPython          : 7.19.0
pandas_datareader: None
bs4              : None
bottleneck       : 1.3.2
fsspec           : 0.8.5
fastparquet      : None
gcsfs            : None
matplotlib       : 3.3.3
numexpr          : 2.7.1
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pyxlsb           : None
s3fs             : None
scipy            : 1.5.4
sqlalchemy       : None
tables           : None
tabulate         : None
xarray           : 0.16.2
xlrd             : None
xlwt             : None
numba            : 0.52.0

</details>
