df.groupby(.).plot.scatter() creates multiple plots

[toobaz]

Code Sample, a copy-pastable example if possible

df = pd.DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], columns=['x', 'y'])
df['cat'] = [1, 2, 1, 2]
df.groupby('cat').plot.scatter(x='x', y='y')

Problem description

I just wanted to plot together different sets of points, with each set being assigned a color and (reason not to use c=) a label in the legend.

Unfortunately the above produces three separate plots.

I think I understand why it produces multiple plots: because pandas assumes that a df.groupby().plot.* will always result in multiple plots, since we have two dimensions (groups, and columns). However, scatterplots are different from e.g. simple line plots because they have already 2-dimensional data (x= and y= arguments) - or, seen from another point of view, because x is not inferred from the axis.

(Notice that while an additional initial spurious plot is currently being created, the issue for that is #21609, and #21943 is already working on solving it, so this issue is specifically about the fact of having one plot per category)

Expected Output

One plot only

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-6-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: it_IT.UTF-8
LOCALE: it_IT.UTF-8

pandas: 0.24.0.dev0+307.g446cd2cd0
pytest: 3.5.0
pip: 9.0.1
setuptools: 39.2.0
Cython: 0.28.4
numpy: 1.14.3
scipy: 0.19.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.5.6
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.0dev
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.2.2.post1634.dev0+ge8120cf6d
openpyxl: 2.3.0
xlrd: 1.0.0
xlwt: 1.3.0
xlsxwriter: 0.9.6
lxml: 4.1.1
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.2.1
gcsfs: None

[allieus]

In the code below, "groupby apply function" is called 3 times.
"groupby apply function" is in GroupByPlot.__getattr__('scatter').
So it seems to be Plot 3 times in total.

>>> df
   x  y  cat
0  1  2    1
1  3  4    2
2  5  6    1
3  7  8    2

df.groupby('cat').plot.scatter(x='x', y='y')

And, convert the result of groupby to a DataFrame. And, plot it.

def fn(series):
    return series.max()

df2 = df.groupby('cat').apply(fn)
df2.plot.scatter(x='x', y='y')