huge memory allocation and slowdown when indexing
#### Code Sample, a copy-pastable example if possible

To run this code you'll need a somewhat large matrix.  You can get my `expression.tsv` from [here](https://figshare.com/articles/Transcriptomic_analysis_of_CA1_inhibitory_interneurons/6198656).  Then run this:

```
import numpy as np
import pandas as pd
expr=pd.read_csv('expression.tsv',sep='\t',index_col=0).T

expr.loc['Tulip',['Sst', 'Pvalb', 'Npy', 'Calm2']] # <-- this is very fast

labels=np.array(['filler' for i in range(len(expr))])
df=expr.assign(celltype=labels)
df.loc['Tulip',['Sst', 'Pvalb', 'Npy', 'Calm2']] # <-- this is insanely slow and allocates lots of RAM

```
#### Problem description

For some reason indexing into a dataframe is sometimes really slow and results in large memory allocation.  

#### Expected Output
I feel like it shouldn't be necessary to allocate a huge amount of memory every time you want to index a dataframe.  

<details>

```
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 4.4.0-112-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 38.5.1
Cython: 0.25.2
numpy: 1.14.2
scipy: 1.0.0
pyarrow: 0.9.0
xarray: None
IPython: 6.2.1
sphinx: None
patsy: 0.4.1
dateutil: 2.7.0
pytz: 2018.3
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.4
feather: 0.4.0
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
```

</details>
