Dataframe.groupby aggregations with categorical columns lead to incorrect results.
#### Code Sample

```python
# In[2]:

import pandas as pd

def create_df():
    df = pd.DataFrame(
        {
            'major_id': [1, 2, 1, 2, 2],
            'minor_id': ['a', 'b', 'c', 'd', 'e'],
            'values': [1, 2, 3, 4, 5]
        }
    )
    return df

def groupby(df):
    df['max_value'] = (
        df
        .groupby(['major_id', 'minor_id'])
        ['values']
        .transform('max')
    )
    
    return df


# In[3]:

# correct result
df = create_df()
groupby(df)

# Out[3]
       "   major_id minor_id  values  max_value\n",
       "0         1        a       1          1\n",
       "1         2        b       2          2\n",
       "2         1        c       3          3\n",
       "3         2        d       4          4\n",
       "4         2        e       5          5"

# In[4]:

# incorrect result: groupby with one non-categorical column and one categorical column
df = create_df()
df = df.astype({'minor_id': 'category'})
groupby(df)

# Out[4]

       "   major_id minor_id  values  max_value\n",
       "0         1        a       1        1.0\n",
       "1         2        b       2        3.0\n",
       "2         1        c       3        NaN\n",
       "3         2        d       4        NaN\n",
       "4         2        e       5        NaN"
```
#### Problem description

`groupby` with one non-categorical column and one categorical column leads to incorrect aggregations (wrong values, or `NAN`s).

#### Output of ``pd.show_versions()``

<details>

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_GB.UTF-8
LOCALE           : en_GB.UTF-8

pandas           : 1.0.1
numpy            : 1.17.3
pytz             : 2019.3
dateutil         : 2.8.0
pip              : 19.2.2
setuptools       : 41.0.1
Cython           : None
pytest           : 5.2.1
hypothesis       : 5.5.4
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : 2.8.4 (dt dec pq3 ext lo64)
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : 0.3.3
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : 0.16.0
pytables         : None
pytest           : 5.2.1
pyxlsb           : None
s3fs             : 0.4.0
scipy            : 1.3.1
sqlalchemy       : 1.3.10
tables           : None
tabulate         : 0.8.5
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
numba            : 0.48.0
</details>
