read_sql_query convert TEXT looking like floats to float
#### Code Sample, you need to create a database with "float looking" strings.

```python
import pandas as pd
query = "SELECT MYSTR FROM TABLE"
df = pd.read_sql_query(query, engine)
df.info()
```
returns
```
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 524 entries, 0 to 523
Data columns (total 1 columns):
MYSTR          524 non-null float64
dtypes:  float64(1)
```
#### Problem description

I have a MySQL table with a column saved as datatype `TEXT`. These strings may be interpreted as floats because they contain only digits and dots. If I have the right filter happening, the query may return ONLY float-looking strings, meaning either looking like normal integers or integers separated by a decimal dot.

read_sql_query converts without asking anything looking like a float to a float. This is unfortunate and breaks the application.

It is not a solution to reconvert back to string with df.astype(str). Indeed, a four-digit string will be returned as a 6-character string, e.g. `4906 `will return as `4906.0`. Also, a string having left trailing zeros will have these zeros disappear.

On another machine, the column stays a string column. It may be a version difference, either python (good: 3.6 vs bad: 3.7), or sqlalchemy (good: 1.3.12 bad: 1.3.5).

I could not update sqlalchemy to a the higher version using `conda update sqlalchemy`, it stays at 1.3.5.
The python and pandas have however a higher version (6.7 & 0.25). I tried reverting to 0.23.4 as on the functional machine, however the bug (or "feature"?) was still there.

Is python 3.7 the root cause?

#### Expected Output
TEXT data type should stay strings in pandas.

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

Machine converting to float:
<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 7
machine          : AMD64
processor        : Intel64 Family 6 Model 63 Stepping 2, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.0
numpy            : 1.16.4
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 41.0.1
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.1.8
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.0
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : 1.3.5
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : 1.1.8
</details>

Machine keeping string format:
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.10.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 78 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.4
pytest: 5.0.1
pip: 19.1.1
setuptools: 44.0.0.post20200106
Cython: 0.28.2
numpy: 1.17.4
scipy: 1.3.2
pyarrow: None
xarray: 0.11.3
IPython: 7.11.1
sphinx: 1.7.9
patsy: 0.5.1
dateutil: 2.8.1
pytz: 2019.3
blosc: None
bottleneck: 1.3.1
tables: 3.6.1
numexpr: 2.7.0
feather: None
matplotlib: 3.1.1
openpyxl: 2.5.4
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.2.7
lxml: 4.4.2
bs4: 4.8.2
html5lib: 1.0.1
sqlalchemy: 1.3.12
pymysql: None
psycopg2: None
jinja2: 2.10.3
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
