iloc indexing ~10 times slower than direct column indexing +no documention of it
#### Code Sample, a copy-pastable example if possible
Updated without yfinance as it was a lousy choice of example - requires only standard numpy and datetime.  A DateTimeIndex exacerbates the time slowness.
```python
import timeit
timeit.timeit(stmt='for x in range(len(hist)): _ = hist.iloc[x].Col1', setup='import numpy as np; import pandas as pd; col = np.random.uniform(low=-1, high=1, size=(10000)); hist = pd.DataFrame({"Col1":col,"Col2":col,"Col3":col,"Col4":col,"Col5":col})', number=3)
#3.553613900000073
timeit.timeit(stmt='for x in range(len(hist)): _ = hist.Col1[x]', setup='import numpy as np; import pandas as pd; col = np.random.uniform(low=-1, high=1, size=(10000)); hist = pd.DataFrame({"Col1":col,"Col2":col,"Col3":col,"Col4":col,"Col5":col})', number=3)
#0.5215671999999358
timeit.timeit(stmt='for x in range(len(hist)): _ = hist.iloc[x].Col1', setup='import datetime; import numpy as np; import pandas as pd; col = np.random.uniform(low=-1, high=1, size=(10000)); hist = pd.DataFrame({"Col1":col,"Col2":col,"Col3":col,"Col4":col,"Col5":col}, [datetime.datetime.now() + datetime.timedelta(seconds=x) for x in range(10000)])', number=3)
#4.485623600000508
timeit.timeit(stmt='for x in range(len(hist)): _ = hist.Col1[x]', setup='import datetime; import numpy as np; import pandas as pd; col = np.random.uniform(low=-1, high=1, size=(10000)); hist = pd.DataFrame({"Col1":col,"Col2":col,"Col3":col,"Col4":col,"Col5":col}, [datetime.datetime.now() + datetime.timedelta(seconds=x) for x in range(10000)])', number=3)
#0.7185356999998476
```
~~Old code example:~~
~~#requires pip install yfinance for this example - could substitute with any data set~~
~~timeit.timeit(stmt='for x in range(len(hist)): _ = hist.iloc[x].Close', setup='import yfinance as yf; hist = yf.Ticker("MSFT").history(period="max")', number=3)~~
~~#5.178573199999846~~
~~timeit.timeit(stmt='for x in range(len(hist)): _ = hist.iloc[x].Close', setup='import yfinance as yf; hist = yf.Ticker("MSFT").history(period="max")', number=3)~~
~~#0.5886300999998184~~


#### Problem description

One would naturally expect iloc to have close to identical efficiency as column indexing.  This is a dramatic blunder in a lot of code more than likely if it is not well documented and known.  I would expect some strange inefficiency is present as logically it could be slightly slower to return a row rather than a data point.  But 10 times slower is so dramatic that this function should be avoided.

#### Expected Output

Less than double the speed of single column value access.  Or at least thorough documentation of this limitation.

#### 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 60 Stepping 3, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : en
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             : None
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>
