BUG: Automatic change of color when the plot type is "line" but not when it is "scatter"

[TinoDerb]

Pandas version checks

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

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

• [x] I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import matplotlib.pyplot as plt
# I am using Jupyter. and my backend is notebook, for this, I use %matplotlib notebook
data = {'y': [1,2,3,4,5,6,7,8,9,10],
        'x': [1,2,3,4,5,6,7,8,9,10],
        'layer' : ['a','a','a','b','b','b','c','c','c','c']}
df = pd.DataFrame(data)
plt.figure()
df.groupby("layer").plot(x='x', y='y', ax= plt.gca(), kind='line') # this changes the color automatically

plt.figure()
df.groupby("layer").plot(x='x', y='y', ax= plt.gca(), kind='scatter') # this does not

Issue Description

I was trying to write an asnwer on stackoverflow and I noticed the following behaviour:

Using df.groupby("layer").plot(x='x', y='y', ax= plt.gca(), kind='line') changes the color of every new group automatically.

This is however not seen when using kind="scatter".

I think my version is a bit older than the latest one, but I could not find any similar issue on github.

Expected Behavior

I expect that the color should also change for the scatter kind. For this, here is the output of the following code:

plt.figure()
for layer in df['layer'].unique(): # group by layer
    subDf = df[df['layer'] == layer] # get subset of dataframe
    plt.scatter(subDf['x'], subDf['y'], label=layer) # plot
# add labels and such
plt.xlabel('x')
plt.ylabel('y')

Installed Versions

commit : 0f437949513225922d851e9581723d82120684a6 python : 3.11.5.final.0 python-bits : 64 OS : Windows OS-release : 10 Version : 10.0.22631 machine : AMD64 processor : Intel64 Family 6 Model 186 Stepping 3, GenuineIntel byteorder : little LC_ALL : None LANG : None LOCALE : English_United States.1252 pandas : 2.0.3 numpy : 1.24.3 pytz : 2023.3.post1 dateutil : 2.8.2 setuptools : 68.0.0 pip : 23.2.1 Cython : 3.0.6 pytest : 7.4.0 hypothesis : None sphinx : 5.0.2 blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.3 html5lib : 1.1 pymysql : None psycopg2 : None jinja2 : 3.1.2 IPython : 8.15.0 pandas_datareader: None bs4 : 4.12.2 bottleneck : 1.3.5 brotli : fastparquet : None fsspec : 2023.4.0 gcsfs : None matplotlib : 3.7.2 numba : 0.57.1 numexpr : 2.8.4 odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : 11.0.0 pyreadstat : None pyxlsb : None s3fs : 2023.4.0 scipy : 1.11.1 snappy : sqlalchemy : 1.4.39 tables : 3.8.0 tabulate : 0.8.10 xarray : 2023.6.0 xlrd : None zstandard : 0.19.0 tzdata : 2023.3 qtpy : 2.2.0 pyqt5 : None

[abhisin-07]

df.groupby("layer").plot(x='x', y='y', ax=plt.gca(), kind='line', color='blue'). DEFINE COLOR EXPICITLY BY PASSING COLOR PARAMETER IN THE FUNCTION PLOT BECAUSE WHEN WE USE groupby(layer) it divides the plot data in groups and uses default matplotlib color cycle to different groups sets

[TinoDerb]

@abhisin-07 Hey, it seems you didn't really read the issue. The desired results is to have the automatic matplotlib color cycle for kind='scatter', just like when plotting with kind='line'.

P.S.: no need for all caps :)

[yuanx749]

Hmm, with the line plot, even the color is correct, the legend is problematic.