Indexing bug with Sparse DataFrames and column names not starting with `0`
#### Code Sample, a copy-pastable example if possible

```python
import pandas as pd
import numpy as np

ary = np.array([ [1, 0, 0, 3],
                 [1, 0, 2, 0],
                 [0, 4, 0 ,0] ])

df = pd.DataFrame(ary)
df.columns = [1, 2, 3, 4]

dfs = pd.SparseDataFrame(df,
                         default_fill_value=0)

# DOES NOT WORK:

dfs.to_coo() # raises KeyError: 0

# WORKS (1)

dfs2 = dfs.copy()
dfs2.columns = [0, 1, 2, 3]
dfs2.to_coo()

# WORKS (2)

dfs3 = dfs.copy()
dfs3.columns = [str(i) for i in dfs3.columns]
dfs3.to_coo()
```
#### Problem description

In the example above, the Pandas SparseDataFrame method `to_coo()` (and possibly others) cannot handle sparse dataframes if the column names are integer types and don't start at 0. If the column names start at 0 or are string types, this is not an issue. 

#### Expected Output

```python
<3x4 sparse matrix of type '<class 'numpy.int64'>'
	with 5 stored elements in COOrdinate format>
```

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.5.final.0
python-bits: 64
OS: Darwin
OS-release: 18.2.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.4
pytest: None
pip: 18.1
setuptools: 40.2.0
Cython: 0.28.5
numpy: 1.15.4
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 6.5.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: 3.4.4
numexpr: 2.6.9
feather: None
matplotlib: 2.2.3
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
</details>
