SparseDataFrame to_csv returns IndexError: too many indices for array

[hannahlindsley]

Calling to_csv on a SparseDataFrame returns IndexError: too many indices for array.

import pandas as pd
from scipy.sparse import rand

x = rand(100, 10001, density=0.2, format='csr')
df = pd.SparseDataFrame(x)
df.to_csv("tmp.csv")

Calling .to_dense() on the df prior to writing out, of course, works fine.

df.to_dense().to_csv("tmp.csv")

I'd like to be able to save sparse data to disk. If for some reason it shouldn't be written out this way, is there any way to add that in the doc or the error message?

Expected Output

Nontype, no exception, and a csv on the file system.

INSTALLED VERSIONS

commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.21.0
pytest: None
pip: 9.0.1
setuptools: 28.8.0
Cython: None
numpy: 1.13.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.1.0
psycopg2: 2.7.3.2 (dt dec pq3 ext lo64)
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[hexgnu]

I think I found the issue here. Made a PR for it. The problem is that get_values needs to be called internally since it's assuming something is an ndarray instead of a sparse array.