Gropuby using pd.Series.mode throws error when by column contains values with same starting value

[princekf]

Groupby with pd.Series.mode throws error, if by values starts with a value from previous row.

df2 = pd.DataFrame({
    "Name" : ['Thomas', 'Thomas', 'Thomas John'],
    "Credit" : [1200, 1300, 900],
    "Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
   'Mood':pd.Series.mode,
   'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)

Problem description

When I execute the above code I got error

Exception: Must produce aggregated value
.....
....
and a lot of stack traces..

If I change the third name to John instead of Thomas John, it works as expected. ie The following code works.

df2 = pd.DataFrame({
    "Name" : ['Thomas', 'Thomas', 'John'],
    "Credit" : [1200, 1300, 900],
    "Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
   'Mood':pd.Series.mode,
   'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)

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Expected Output

Groupby should work, even if first letters of column values are same

Output of pd.show_versions()

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

commit: None
python: 2.7.16.final.0
python-bits: 64
OS: Darwin
OS-release: 19.0.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.23.3
pytest: None
pip: 10.0.1
setuptools: 40.0.0
Cython: None
numpy: 1.14.5
scipy: 0.13.0b1
pyarrow: None
xarray: None
IPython: 5.7.0
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: 1.1.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 1.0.1
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[wesbarnett]

This bug also occurs even in this situation where the names have no overlap:

df2 = pd.DataFrame({
    "Name" : ['A', 'A', 'B'],
    "Credit" : [1200, 1300, 900],
    "Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
   'Mood':pd.Series.mode,
   'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)

But this works:

df2 = pd.DataFrame({
    "Name" : ['B', 'A', 'B'],
    "Credit" : [1200, 1300, 900],
    "Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
   'Mood':pd.Series.mode,
   'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)

And this works:

df2 = pd.DataFrame({
    "Name" : ['B', 'B', 'A'],
    "Credit" : [1200, 1300, 900],
    "Mood" : ['sad', 'happy', 'happy']
})
aggrFDColumnDetails = {
   'Mood':pd.Series.mode,
   'Credit':'sum'
}
df2.groupby(['Name']).agg(aggrFDColumnDetails)

I actually tried several combinations of what labels are where and some work and some don't.

Installed versions

``` INSTALLED VERSIONS ------------------ commit : None python : 3.7.4.final.0 python-bits : 64 OS : Darwin OS-release : 18.7.0 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.2
dateutil : 2.8.0
pip : 19.3
setuptools : 41.2.0
Cython : 0.29.13
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.4.1
html5lib : 1.0.1
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.10.1
IPython : 7.8.0
pandas_datareader: None
bs4 : 4.8.0
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.4.1
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.8
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None

</details>

[mroeschke]

I suppose this works on master now. Could use a test

In [6]: df2 = pd.DataFrame({
   ...:     "Name" : ['Thomas', 'Thomas', 'Thomas John'],
   ...:     "Credit" : [1200, 1300, 900],
   ...:     "Mood" : ['sad', 'happy', 'happy']
   ...: })
   ...: aggrFDColumnDetails = {
   ...:    'Mood':pd.Series.mode,
   ...:    'Credit':'sum'
   ...: }
   ...: df2.groupby(['Name']).agg(aggrFDColumnDetails)
Out[6]:
                     Mood  Credit
Name
Thomas       [happy, sad]    2500
Thomas John         happy     900

[jxb4892]

take
+) Does this issue only need more tests?

[FactorizeD]

take