UserWarning about columns and attribute while plotting in Jupyter

[jrbrodie77]

Code Sample, a copy-pastable example if possible

vibe_df.plot(x='Time', y='Fz')     # This works just fine.
vibe_df.plot(x='Time', y=['Fz'])     # This raises a UserWarning:

Problem description

I get a UserWarning while plotting a DataFrame. It only occurs if I pass a list of columns for y.

UserWarning is:
C:\Program Files\Anaconda3\lib\site-packages\pandas\plotting_core.py:1714: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access
series.name = label

Expected Output

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.5.4.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: 0.21.0
pytest: 2.9.2
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.24.1
numpy: 1.11.3
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.4.6
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.1.0
tables: 3.2.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.0
openpyxl: 2.3.2
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
sqlalchemy: 1.0.13
pymysql: None
psycopg2: None
jinja2: 2.8
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[TomAugspurger]

This is the line

pandas/pandas/plotting/_core.py

Line 1718 in fdba133

series.name = label

That code pretty clearly assumes that 'y' is a scalar, and that's how it's documented

Parameters
----------
data : DataFrame
x : label or position, default None
y : label or position, default None
    Allows plotting of one column versus another

So we can either validate that data[y] is a Series, and raise if not, or see if we can support and document multiple values for 'y'.

btw, here's a reproducible example

In [12]: df = pd.DataFrame({"A": [1, 2], 'B': [3, 4], 'C': [5, 6]})

In [13]: df.plot(x='A', y=['B', 'C'])