Odd behavior when broadcasting between DataFrame with MultiIndex and DataFrame with Index #19606
Description
Code Sample, a copy-pastable example if possible
d1 = pd.DataFrame({'C' : [i+1 for i in range(6)], 'D' : [(i+1)*10 for i in range(6)]},
index=pd.MultiIndex.from_product([[1,2],[10,20,30]], names=['A','B']))
d2 = pd.DataFrame({'E': [100,200]}, index=pd.Index([1,2], name='A'))
print(d1+d2) # Gives odd result
print(d1.C + d2) # Gives error
print(d1.C + d2.E) # Works!Problem description
What I am trying to do is broadcast the values in the DataFrame d2, using the shared name and values in the index with d1 (i.e., the index column 'A'). The expression d1+d2 gives this surprising result:
C D E
A B
1 10 NaN NaN NaN
20 NaN NaN NaN
30 NaN NaN NaN
2 10 NaN NaN NaN
20 NaN NaN NaN
30 NaN NaN NaN
The expression d1.C + d2 gives the error:
ValueError: cannot join with no level specified and no overlapping names
The final expression d1.C + d2.E gives an expected result
A B
1 10 101
20 102
30 103
2 10 204
20 205
30 206
dtype: int64
Expected Output
Since the broadcast works with adding the two series, for the expression d1+d2, I would have expected this result, or maybe an error:
C D
A B
1 10 101 110
20 102 120
30 103 130
2 10 204 240
20 205 250
30 206 260
I don't see why the first result happens at all. Either the correct addition should happen, or an error should be raised.
Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
pandas: 0.22.0
pytest: 3.0.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.2.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None