BUG: passing a SparseArray to pd.Index - overly eager FutureWarning

[jsignell]

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Reproducible Example

In [1]: import pandas as pd
   ...: 
   ...: s = pd.Series(pd.Categorical(["a", "b", "a"], categories=["a", "b", "c"]))
   ...: exp = pd.get_dummies(s, sparse=True)
   ...: exp.sort_values(by=exp.columns.tolist())
<ipython-input-1-312167088340>:5: FutureWarning: In a future version, passing a SparseArray to pd.Index will store that array directly instead of converting to a dense numpy ndarray. To retain the old behavior, use pd.Index(arr.to_numpy()) instead
  exp.sort_values(by=exp.columns.tolist())
<ipython-input-1-312167088340>:5: FutureWarning: In a future version, passing a SparseArray to pd.Index will store that array directly instead of converting to a dense numpy ndarray. To retain the old behavior, use pd.Index(arr.to_numpy()) instead
  exp.sort_values(by=exp.columns.tolist())
<ipython-input-1-312167088340>:5: FutureWarning: In a future version, passing a SparseArray to pd.Index will store that array directly instead of converting to a dense numpy ndarray. To retain the old behavior, use pd.Index(arr.to_numpy()) instead
  exp.sort_values(by=exp.columns.tolist())
Out[1]: 
   a  b  c
1  0  1  0
0  1  0  0
2  1  0  0

Issue Description

In pandas 1.4.0 there is a FutureWarning that I believe is too aggressive. In the example above it is not clear what to change in the code snippet. For context this came up in the dask test suite.

Expected Behavior

I imagine there might be some internal code that is coercing the array that I provided into an index. It this is the expected behavior then I think the error message needs to be changed to make it clear how to fix the code snippet.

Installed Versions

INSTALLED VERSIONS

commit : bb1f651
python : 3.9.9.final.0
python-bits : 64
OS : Linux
OS-release : 5.11.0-7633-generic
Version : #35163010093020.04~ae2753e-Ubuntu SMP Mon Aug 30 18:23:52 UTC
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.4.0
numpy : 1.21.5
pytz : 2021.3
dateutil : 2.8.2
pip : 21.3.1
setuptools : 59.8.0
Cython : None
pytest : 6.2.5
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 8.0.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : 0.7.2
fsspec : 2022.01.0
gcsfs : None
matplotlib : 3.5.1
numba : 0.55.0
numexpr : 2.8.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 6.0.1
pyreadstat : None
pyxlsb : None
s3fs : 2022.01.0
scipy : 1.7.3
sqlalchemy : None
tables : 3.6.1
tabulate : None
xarray : 0.20.2
xlrd : None
xlwt : None
zstandard : None

[jorisvandenbossche]

This seems to be because in the lexsort code, we create a Categorical from the column to sort by, which in turn will create an Index from the unique values of that column, in this case from a sparse array:

pandas/pandas/core/sorting.py

Lines 322 to 323 in 6659da4

	for k, order in zip(keys, orders):
	cat = Categorical(k, ordered=True)

So we will need to silence the warnings there.