pandas.read_parquet cause OOM in a 16GB machine, it's a 200MB parquet file, 6M rows.
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd

# query from db
datalist = ...      # 6M rows, 4 fields(string string string datetime) every row.
df = pd.from_dict(datalist)
df.to_parquet("test.pq")
df = pd.read_parquet("test.pq")  # OOM
```
#### Problem description
> df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6741512 entries, 0 to 6741511
Data columns (total 5 columns):
index        int64
_id          object
wxOpenID     object
channel      object
createdAt    datetime64[ns]
dtypes: datetime64[ns](1), int64(1), object(3)
memory usage: 257.2+ MB

It have 6M rows writed to disk(a parquet file), and the file size was 200MB.
then, I read it from the parquet file, it causes a OOM.

#### Expected Output
do not make OOM.

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

<details>

 pd.show_versions()                                                                                                                                                                           

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.6.8.final.0
python-bits      : 64
OS               : Linux
OS-release       : 3.10.0-957.5.1.el7.x86_64
machine          : x86_64
processor        : x86_64
byteorder        : little
LC_ALL           : None
LANG             : en_US.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.1
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 40.6.2
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.8.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : 2.6.3
pandas_gbq       : None
pyarrow          : 0.14.1
pytables         : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
