BUG: com.maybe_make_list does not resolve correctly for np.array

[erfannariman]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the master branch of pandas.

Reproducible Example

from pandas.core.common import maybe_make_list
import numpy as np

arr = np.array(["A", "C"])
result = maybe_make_list(arr)
print(result)

[array(['A', 'C'], dtype='<U1')]

Issue Description

This discussion started in this thread. Should com.maybe_make_list take np.array into account and maybe some other list like objects as well. For example Index objects.

Expected Behavior

from pandas.core.common import maybe_make_list
import numpy as np

arr = np.array(["A", "C"])
result = maybe_make_list(arr)
print(result)

array(['A', 'C'], dtype='<U1')

Installed Versions

INSTALLED VERSIONS

commit : 445bb9f
python : 3.8.12.final.0
python-bits : 64
OS : Darwin
OS-release : 20.6.0
Version : Darwin Kernel Version 20.6.0: Mon Aug 30 06:12:21 PDT 2021; root:xnu-7195.141.6~3/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8

pandas : 1.4.0.dev0+904.g445bb9f57c.dirty
numpy : 1.21.2
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.23.2
sphinx : 4.2.0
blosc : 1.10.6
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : 7.28.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.10.0
fastparquet : 0.7.1
gcsfs : 2021.10.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.10.0
scipy : 1.7.1
sqlalchemy : 1.4.25
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.2
xlrd : 2.0.1
xlwt : 1.3.0
numba : None

[rhshadrach]

I propose the following methods instead of the current maybe_make_list:

def maybe_make_list_like(obj):
    if obj is None or is_list_like(obj):
        return obj
    else:
        return [obj]

def maybe_make_list(obj) -> list:
    obj = maybe_make_list_like(obj)
    if obj is not None and not isinstance(obj, list):
        obj = list(obj)
    return obj

Currently, it is used in:

• crosstab: BUG: crosstab fails with lists/tuples #44076
• merge: keep current behavior (i.e. don't use the proposed functions); related issue: DOC: Merge/join treats lists/tuples differently than other list-likes #44077
• BaseWindowGroupby: BUG: groupby.rolling.corr incorrect/fails when grouping columns contain tuples #44078
• MPLPlot.__init__: Use maybe_make_list

[erfannariman]

I propose the following methods instead of the current maybe_make_list:

def maybe_make_list_like(obj):
    if obj is None or is_list_like(obj):
        return obj
    else:
        return [obj]

def maybe_make_list(obj) -> list:
    obj = maybe_make_list_like(obj)
    if obj is not None and not isinstance(obj, list):
        obj = list(obj)
    return obj

Currently, it is used in:

• crosstab: BUG: crosstab fails with lists/tuples #44076
• merge: keep current behavior (i.e. don't use the proposed functions); related issue: DOC: Merge/join treats lists/tuples differently than other list-likes #44077
• BaseWindowGroupby: BUG: groupby.rolling.corr incorrect/fails when grouping columns contain tuples #44078
• MPLPlot.__init__: Use maybe_make_list

Some quick tests, but this looks like behaving as expected:

In [8]: maybe_make_list(np.array(["A", "B"]))
Out[8]: ['A', 'B']

In [9]: maybe_make_list("A")
Out[9]: ['A']

In [10]: 

In [10]: maybe_make_list(("A",))
Out[10]: ['A']

In [11]: maybe_make_list(pd.Index(["A", "B"]))
Out[11]: ['A', 'B']

Looks good.