pd.read_csv automatically casts strings into int/float
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import csv

d = pd.DataFrame([
    ['1', '2', 3], 
    ['3', 4, 5]])

d.to_csv('test_d', quoting=csv.QUOTE_NONNUMERIC, index=False)

dr = pd.read_csv('test_d')
dr.dtypes
```

> 0    int64
1             int64
2             int64
dtype: object

#### Problem description

I have string columns that I write as strings, but when I read them I find out that they have been converted to numeric even though the csv has all the necessary quoting characters. I do not understand why they are converted to numeric if they are strings, the fact that the characters reperesent numbers should make no difference.

The option of using the parameter `dtype=...` is not an option for me because I do not necessarily know the names of all the columns.

#### Expected Output

The expected output is finding string where there are strings and numbers where there are numbers.

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 142 Stepping 12, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.1
numpy            : 1.16.5
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 41.4.0
Cython           : 0.29.13
pytest           : 5.2.1
hypothesis       : None
sphinx           : 2.2.0
blosc            : None
feather          : None
xlsxwriter       : 1.2.1
lxml.etree       : 4.4.1
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.3
IPython          : 7.8.0
pandas_datareader: None
bs4              : 4.8.0
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : 2.7.0
odfpy            : None
openpyxl         : 3.0.0
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : 0.2.2
scipy            : 1.3.1
sqlalchemy       : 1.3.9
tables           : 3.5.2
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.2.1

</details>
