DataFrame Groupby Named Aggregation Using Lambda Results in a KeyError, Dict-of-Dicts Approach Works Fine
#### Code Sample, a copy-pastable example if possible

```python
import numpy as np
import pandas as pd
data = [['tom', 10, 'blue', 1000, 'a'], ['nick', 15, 'blue', 2000, 'b'], ['julie', 14, 'green', 3000, 'a'], ['bob', 11, 'green', 4000, 'a'], ['cindy', 16, 'red', 5000, 'b']]
df = pd.DataFrame(data, columns = ['Name', 'Age', 'Color', 'Num', 'Letter'])

# Dict-style renaming works fine:
df.groupby(by='Color').agg({'Num': {'SumNum'  : np.sum, 'SumNumIfLetterA': lambda x: x[df.iloc[x.index].Letter=='a'].sum()}})

Out[2]: 
         Num                
      SumNum SumNumIfLetterA
Color                       
blue    3000            1000
green   7000            7000
red     5000               0

# Named Aggregation using function also works fine:
def simplefunc(x):
    return x[df.iloc[x.index].Letter=='a'].sum()
df.groupby(by='Color').agg(SumNum = ('Num', np.sum), SumNumIfLetterA = ('Num', simplefunc))

Out[3]: 
       SumNum  SumNumIfLetterA
Color                         
blue     3000             1000
green    7000             7000
red      5000                0

# Named Aggregation using identical lambda throws a KeyError:
df.groupby(by='Color').agg(SumNum = ('Num', np.sum), SumNumIfLetterA = ('Num', lambda x: x[df.iloc[x.index].Letter=='a'].sum()))

```
#### Problem description

A lambda function that previously worked fine (using a Dict-of-Dict) no longer works with Named Aggregation.  Using a function with Named Aggregation works fine but replacing the function with an identical lambda does not work with Named Aggregation.  Is this expected behavior or it is a bug?

#### Expected Output
~~~
       SumNum  SumNumIfLetterA
Color                         
blue     3000             1000
green    7000             7000
red      5000                0
~~~
#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit           : None
python           : 3.7.4.final.0
python-bits      : 64
OS               : Windows
OS-release       : 10
machine          : AMD64
processor        : Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder        : little
LC_ALL           : None
LANG             : None
LOCALE           : None.None

pandas           : 0.25.0
numpy            : 1.16.4
pytz             : 2019.1
dateutil         : 2.8.0
pip              : 19.1.1
setuptools       : 41.0.1
Cython           : 0.29.12
pytest           : 5.0.1
hypothesis       : None
sphinx           : 2.1.2
blosc            : None
feather          : None
xlsxwriter       : 1.1.8
lxml.etree       : 4.3.4
html5lib         : 1.0.1
pymysql          : None
psycopg2         : None
jinja2           : 2.10.1
IPython          : 7.7.0
pandas_datareader: None
bs4              : 4.7.1
bottleneck       : 1.2.1
fastparquet      : None
gcsfs            : None
lxml.etree       : 4.3.4
matplotlib       : 3.1.0
numexpr          : 2.6.9
odfpy            : None
openpyxl         : 2.6.2
pandas_gbq       : None
pyarrow          : None
pytables         : None
s3fs             : None
scipy            : 1.3.0
sqlalchemy       : 1.3.5
tables           : 3.5.2
xarray           : None
xlrd             : 1.2.0
xlwt             : 1.3.0
xlsxwriter       : 1.1.8
</details>
