Docs request: Specifying behavior of `dtype` parameter
#### Code Sample, a copy-pastable example if possible

```python
# My data comes in with some empty IDs, but these rows are still usable.
data_with_nan = {'ID':['1000','1001','1002', np.nan],
                 'Price':['900','100','150', '600']}

# I set dtype to str.
df_with_nan = pd.DataFrame(data_with_nan,dtype=str)

# That nan cell is still arriving as a float,
type(df_with_nan.iloc[3,0])

# type(df_with_nan.iloc[3,0])
# <class 'float'>

# and perceived as a nan in pandas.
df_with_nan.notnull()

#       ID  Price
# 0   True   True
# 1   True   True
# 2   True   True
# 3  False   True

```
#### Problem description
`dtype` does not explicitly say that it ignores NaN in the documentation of

-  `pd.DataFrame`
-  `read_excel`
-  `read_csv`

For instance, in `read_csv` docs [here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html), this is the description of the `dtype` param:

> dtype : Type name or dict of column -> type, optional
> 
>     Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32, ‘c’: ‘Int64’} Use str or object together with suitable na_values settings to preserve and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion.

For the longest time, I kept reading in my dataframes as `str` in the belief that it would convert `nan` to "NaN". This, obviously, drained performance. The docs don't explicitly say that specifying a dtype ignores the null values. I don't know if I'm just oblivious, but that would've helped me several months ago.

#### Expected Output

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

<details>

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 85 Stepping 4, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None
pandas           : 0.25.3
numpy            : 1.17.4
pytz             : 2019.3
dateutil         : 2.8.1
pip              : 19.0.3
setuptools       : 40.8.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : None
numexpr          : None
odfpy            : None
openpyxl         : 3.0.0
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : None
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : None

</details>
