BUG: MultiIndex with nan values obtained by groupby behaves different to MultiIndex.from_tuples()

[ssche]

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

• I have confirmed this bug exists on the latest version of pandas.
  I checked in 1.1.1

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

---

Code Sample, a copy-pastable example

In [11]: import pandas as pd
    ...: import numpy as np
    ...: 
    ...: df = pd.DataFrame({
    ...:     'animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
    ...:     'type': [np.nan, np.nan, np.nan, np.nan],
    ...:     'speed': [380., 370., 24., 26.]
    ...: })
    ...: speed = df.groupby(['animal', 'type'], dropna=False)['speed'].first()

In [12]: speed
Out[12]: 
animal  type
Falcon  NaN     380.0
Parrot  NaN      24.0
Name: speed, dtype: float64

In [13]: speed.index.levels
Out[13]: FrozenList([['Falcon', 'Parrot'], [nan]])

In [14]: speed.index.codes
Out[14]: FrozenList([[0, 1], [0, 0]])

In [15]: # Reconstruct same index to allow for multiplication.
    ...: ix_wing = pd.MultiIndex.from_tuples(
    ...:     [('Falcon', np.nan), ('Parrot', np.nan)], names=['animal', 'type']
    ...: )
    ...: wing = pd.Series([42, 44], index=ix_wing)

In [16]: wing
Out[16]: 
animal  type
Falcon  NaN     42
Parrot  NaN     44
dtype: int64

In [17]: wing.index.levels
Out[17]: FrozenList([['Falcon', 'Parrot'], []])

In [18]: wing.index.codes
Out[18]: FrozenList([[0, 1], [-1, -1]])

In [19]: 

In [19]: 

In [19]: speed * wing
Out[19]: 
animal  type
Falcon  NaN    NaN
        NaN    NaN
Parrot  NaN    NaN
        NaN    NaN
dtype: float64

Problem description

I'm trying to perform combine two series (say multiplication for now). One of them is obtained by a groupby aggregation (say first) and the other series is constructed manually. Both series have a MultiIndex which should be the same and a multiplication should work fine. However, it seems that groupby(..., dropna=False) creates a different MI which causes the operation to return an unexpected result.

Expected Output

I would expect the result of speed * wing to be

Falcon  NaN     15960.0
Parrot  NaN     1056.0

Output of pd.show_versions()

INSTALLED VERSIONS

commit : f2ca0a2
python : 3.8.5.final.0
python-bits : 64
OS : Linux
OS-release : 5.7.17-200.fc32.x86_64
Version : #1 SMP Fri Aug 21 15:23:46 UTC 2020
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_AU.UTF-8
LOCALE : en_AU.UTF-8

pandas : 1.1.1
numpy : 1.18.4
pytz : 2019.3
dateutil : 2.8.1
pip : 20.2.1
setuptools : 47.3.1
Cython : 0.29.17
pytest : 5.1.1
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : 0.9.6
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.2
IPython : 7.17.0
pandas_datareader: None
bs4 : None
bottleneck : 1.3.1
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : 2.7.1
odfpy : None
openpyxl : 1.8.6
pandas_gbq : None
pyarrow : 0.17.1
pytables : None
pyxlsb : None
s3fs : None
scipy : 1.4.1
sqlalchemy : 1.3.12
tables : 3.6.1
tabulate : None
xarray : None
xlrd : 1.2.0
xlwt : None
numba : None

[ssche]

When recreating the MI of the groupby operation, the multiplication works as expected, so I suspect the groupby grouping MI creation is where it fails.

In [59]: speed = df.groupby(['animal', 'type'], dropna=False)['speed'].first()
    ...: speed.index = pd.MultiIndex.from_tuples(speed.index)
    ...: 
    ...: speed * wing
Out[59]: 
Falcon  NaN    15960.0
Parrot  NaN     1056.0
dtype: float64

[TomAugspurger]

as a workaround

In [68]: wing.reindex_like(speed) * speed
Out[68]:
animal  type
Falcon  NaN     15960.0
Parrot  NaN      1056.0
dtype: float64

I'm not sure which representation is preferable (storing NaN in the levels and use regular codes, or not storing NaN and using -1). But we should be consistent. cc @topper-123.

[fish-face]

Unsure if this is the same underlying issue, but it fits the title:

In[3] = pd.DataFrame(data={"a": [1, 2, 3, np.nan, 4], "b": ["a", "b", "c", "d", np.nan], "c": [0, 12, 23, 45, 56]})
In[4] df
     a    b   c
0  1.0    a   0
1  2.0    b  12
2  3.0    c  23
3  NaN    d  45
4  4.0  NaN  56
In[5] df.groupby(["a", "c"], dropna=False).sum().groupby(["a", "c"], dropna=True).sum()
        b
a   c    
1.0 0   a
2.0 12  b
3.0 23  c
4.0 56  0
NaN 45  d
In [6]: idx = pd.MultiIndex.from_tuples([(1.0, 0), (2.0, 12), (3.0, 23), (4.0, 56), (np.nan, 45)], names=('a', 'b'))
In [8]: df2 = pd.DataFrame(["a", "b", "c", np.nan, "d"], index=idx)
In [9]: df2
          0
a   b      
1.0 0     a
2.0 12    b
3.0 23    c
4.0 56  NaN
NaN 45    d
In [12]: df2.groupby(["a", "b"], dropna=True).first()
          0
a   b      
1.0 0     a
2.0 12    b
3.0 23    c
4.0 56  NaN

After performing a groupby, a subsequent groupby with dropna=True will not drop NaNs from keys. I have also seen this in the opposite, where a subsequent groupby with dropna=False will drop NaNs, but I cannot reproduce this in a small example.

[ssche]

My rewriting of index workaround can cause another issue (reindex_like isn't applicable for my use case as the other operand of the arithmetic operation isn't available yet).

Pandas version: 1.1.2

In [12]: import pandas as pd
    ...: import numpy as np
    ...: 
    ...: df = pd.DataFrame({
    ...:   'a': [pd.NaT, pd.NaT],
    ...:   'b': [3, 4],
    ...:   'c': [2.0, 3.0],
    ...:   'd': [5, 4]
    ...: })
    ...: dfg = df.groupby(by=['a', 'b', 'c'], dropna=False).first()
    ...: new_index = pd.MultiIndex.from_tuples(dfg.index, names=dfg.index.names)
    ...: dfg.index = new_index
    ...: dfg.reset_index()
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-11-908344b5e83b> in <module>
     11 new_index = pd.MultiIndex.from_tuples(dfg.index, names=dfg.index.names)
     12 dfg.index = new_index
---> 13 dfg.reset_index()

~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/frame.py in reset_index(self, level, drop, inplace, col_level, col_fill)
   4851                     name = tuple(name_lst)
   4852                 # to ndarray and maybe infer different dtype
-> 4853                 level_values = _maybe_casted_values(lev, lab)
   4854                 new_obj.insert(0, name, level_values)
   4855 

~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/frame.py in _maybe_casted_values(index, labels)
   4784                     dtype = index.dtype
   4785                     fill_value = na_value_for_dtype(dtype)
-> 4786                     values = construct_1d_arraylike_from_scalar(
   4787                         fill_value, len(mask), dtype
   4788                     )

~/envs/pandas-test/lib/python3.8/site-packages/pandas/core/dtypes/cast.py in construct_1d_arraylike_from_scalar(value, length, dtype)
   1556 
   1557         subarr = np.empty(length, dtype=dtype)
-> 1558         subarr.fill(value)
   1559 
   1560     return subarr

ValueError: cannot convert float NaN to integer

I reopened another issue providing more detail on the above.