Pandas one group run twice when use .apply() function

Code Sample, a copy-pastable example if possible

# Your code here
import pandas as pd
df = pd.DataFrame({'animal': 'cat dog cat fish dog cat cat'.split(),'size': list('SSMMMLL'),'weight': [8, 10, 11, 1, 20, 12, 12],'adult' : [False] * 5 + [True] * 2})
gb = df.groupby(['animal'])
def GrowUp(x):
    avg_weight = sum(x[x['size'] == 'S'].weight * 1.5)
    avg_weight += sum(x[x['size'] == 'M'].weight * 1.25)
    avg_weight += sum(x[x['size'] == 'L'].weight)
    avg_weight /= len(x)
    print(x)
    return pd.Series(['L',avg_weight,True], index=['size', 'weight', 'adult'])

expected_df = gb.apply(GrowUp)

print(expected_df)

Problem description

From output printed, 'cat' group was printed twice

adult animal size weight 0 False cat S 8 2 False cat M 11 5 True cat L 12 6 True cat L 12 adult animal size weight 0 False cat S 8 2 False cat M 11 5 True cat L 12 6 True cat L 12 adult animal size weight 1 False dog S 10 4 False dog M 20 adult animal size weight 3 False fish M 1 size weight adult animal
cat L 12.4375 True dog L 20.0000 True fish L 1.2500 True [this should explain why the current behaviour is a problem and why the expected output is a better solution.] 'cat' group should only call .apply() once Note: We receive a lot of issues on our GitHub tracker, so it is very possible that your issue has been posted before. Please check first before submitting so that we do not have to handle and close duplicates!

Note: Many problems can be resolved by simply upgrading pandas to the latest version. Before submitting, please check if that solution works for you. If possible, you may want to check if master addresses this issue, but that is not necessary.

For documentation-related issues, you can check the latest versions of the docs on master here:

https://pandas-docs.github.io/pandas-docs-travis/

If the issue has not been resolved there, go ahead and file it in the issue tracker.

Expected Output

adult animal size weight 0 False cat S 8 2 False cat M 11 5 True cat L 12 6 True cat L 12 adult animal size weight 1 False dog S 10 4 False dog M 20 adult animal size weight 3 False fish M 1 size weight adult animal
cat L 12.4375 True dog L 20.0000 True fish L 1.2500 True

Output of `pd.show_versions()`

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS

commit: None python: 3.6.4.final.0 python-bits: 32 OS: Windows OS-release: 7 machine: x86 processor: x86 Family 6 Model 42 Stepping 7, GenuineIntel byteorder: little LC_ALL: None LANG: None LOCALE: Chinese (Simplified)_People's Republic of China.936

pandas: 0.22.0 pytest: None pip: 9.0.1 setuptools: 28.8.0 Cython: None numpy: 1.14.0 scipy: None pyarrow: None xarray: None IPython: None sphinx: None patsy: None dateutil: 2.6.1 pytz: 2017.3 blosc: None bottleneck: None tables: None numexpr: None feather: None matplotlib: 2.1.1 openpyxl: None xlrd: None xlwt: None xlsxwriter: None lxml: None bs4: None html5lib: None sqlalchemy: None pymysql: None psycopg2: None jinja2: None s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None

Comment From: jreback

big red box : http://pandas.pydata.org/pandas-docs/stable/groupby.html#flexible-apply

this is an implementation detail

Comment From: Qiaoyoufeng

Hi, Jeff: I get it, i am just learning about pandas, and have not read that part yet, sorry about the ticket, thanks

At 2018-01-20 23:41:00, "Jeff Reback" notifications@github.com wrote: