performance of loc and iloc
```python
import pandas as pd 
import numpy as np
import time

# print(pd.show_versions())

# create the columns and rows of the df
assets = np.array(range(3400))
dates = np.array(range(5000))
df_bool = pd.DataFrame(False, index=dates, columns=assets)

# create the array from the df
a = df_bool.to_numpy()

valid_since = {asset: np.random.choice(dates, size=1)[0] for asset in assets}

h = time.time()
for asset in assets:
	start = valid_since[asset]
	df_bool.loc[start].loc[asset] = True
print(time.time() - h)

h = time.time()
for asset in assets:
	start = valid_since[asset]
	df_bool.iloc[start].iloc[asset] = True

print(time.time() - h)

h = time.time()
for asset in assets:
	start = valid_since[asset]
	df_bool.loc[start, asset] = True 

print(time.time() - h)

h = time.time()
for asset in assets:
	start = valid_since[asset]
	df_bool.iloc[start, asset] = True 

print(time.time() - h)

h = time.time()
for asset in assets:
	start = valid_since[asset]
	a[start, asset] = True 

print(time.time() - h)

```
#### Problem description
Hi, happy new year xD. I have a problem with the performance using the loc and iloc when it has the comma, for instance the df.iloc [1000, 1000] takes more time in the code of example that df.iloc [1000] .iloc [1000] and I don't understand why this is happen, and Why the access of df.loc [1000, 1000] is so slow, the code take a lot of time when use this part. 
Note: All in the code use direct accessing is not doing any kind of slicing or taking multiple assets or dates at the same time it only needs to update one cells at time.



<details>

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 32
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.0
numpy            : 1.17.0
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.2.3
setuptools       : 40.8.0
Cython           : None
pytest           : 5.0.1
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : 1.1.8
lxml.etree       : 4.4.1
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: 0.8.1
bs4              : 4.8.0
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.4.1
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : 1.2.0
xlwt             : None
xlsxwriter       : 1.1.8
None

</details>
