Can't use a lambda function in named aggregation
#### Code Sample, a copy-pastable example if possible

```python
animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
                            'height': [9.1, 6.0, 9.5, 34.0],
                            'weight': [7.9, 7.5, 9.9, 198.0]})

# Yields an error
animals.groupby('kind').agg(mean_height=('height', 'mean'),
                            perc90=('height', lambda s: np.percentile(s, q=0.90)))

def myfunc(s): return np.percentile(s, q=0.90)

# Works just fine
animals.groupby('kind').agg(mean_height=('height', 'mean'),
                            perc90=('height', myfunc))


```
#### Problem description

When I `groupby`+`agg` with a named function, the named aggregation works perfectly. However, when done with a lambda function instead, the following error is raised: 

    KeyError: "[('height', '<lambda>')] not in index"

Notice that his is not [error 7186](https://github.com/pandas-dev/pandas/issues/7186) because there are no more than one lambda here.

I've noticed that this error only happens when I mix `lambda` functions along with `str`-mapped functions, i.e. _just_

    animals.groupby('kind').agg(perc90=('height', lambda s: np.percentile(s, q=0.90)))

works just fine.

#### Expected Output

          mean_height  perc95
    kind
    cat           9.3  9.1038
    dog          20.0  6.2660

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

INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Linux
OS-release       : 4.15.0-1036-gcp
machine          : x86_64
processor        :
byteorder        : little
LC_ALL           : None
LANG             : C.UTF-8
LOCALE           : en_US.UTF-8

pandas           : 0.25.1
numpy            : 1.17.2
pytz             : 2019.2
dateutil         : 2.8.0
pip              : 19.0.3
setuptools       : 40.8.0
Cython           : None
pytest           : None
hypothesis       : None
sphinx           : None
blosc            : None
feather          : None
xlsxwriter       : None
lxml.etree       : None
html5lib         : None
pymysql          : None
psycopg2         : None
jinja2           : None
IPython          : None
pandas_datareader: None
bs4              : None
bottleneck       : None
fastparquet      : None
gcsfs            : None
lxml.etree       : None
matplotlib       : 3.1.1
numexpr          : None
odfpy            : None
openpyxl         : None
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.1
sqlalchemy       : None
tables           : None
xarray           : None
xlrd             : None
xlwt             : None
xlsxwriter       : None
[paste the output of ``pd.show_versions()`` here below this line]

</details>
