Superhuman Level - Pandas DataFrame Reshaping because of Duplicates - python-2.7

Question

Do you like puzzles that only superhumans can solve? This is the final test to prove such an ability.
A single company might get different levels of funding (seed, a) from multiple banks possibly at different times.
Let's look at the data then the story to get a better picture.
import pandas as pd
data = {'id':[1,2,2,3,4],'company':['alpha','beta','beta','alpha','alpha'],'bank':['z', 'x', 'y', 'z', 'j'],
'rd': ['seed', 'seed', 'seed', 'a', 'a'], 'funding': [100, 200, 200, 300, 50],
'date': ['2006-12-01', '2004-09-01', '2004-09-01', '2007-05-01', '2007-09-01']}
df = pd.DataFrame(data, columns = ['id','company', 'round', 'bank', 'funding', 'date'])
df
Yields:
id company rd bank funding date
0 1 alpha seed z 100 2006-12-01
1 2 beta seed x 200 2004-09-01
2 2 beta seed y 200 2004-09-01
3 3 alpha a z 300 2007-05-01
4 4 alpha a j 50 2007-09-01
Desired Output:
company bank_seed funding_seed date_seed bank_a funding_a date_a
0 alpha z 100 2006-12-01 [z,j] 350 2007-09-01
1 beta [x,y] 200 2004-09-01 None None None
As you can see, I am not a superhuman but shall try to explain my thought process.
Let's look at company alpha
Company alpha first got seed money for $100 from bank z in late 2006. A few months later, their investors were very happy with their progress so bank z gave them money ($300 more!). However, Company alpha needed a little more cash but had to go to some random Swiss bank j to stay alive. Bank j reluctantly gave $50 more. Yay! They now have $350 from their updated 'a' round ending in September 2007.
Company beta is pretty new. They got funding totaling $200 from two different banks. But wait... there's nothing in here about their round 'a'. That's okay we'll put None for now and check back with them later.
The issue is that Company alpha sucks and got money from the Swiss...
This is my non-working code that had worked on a subset of my data - it won't work here.
import itertools
unique_company = df.company.unique()
df_indexed = df.set_index(['company', 'rd'])
index = pd.MultiIndex.from_tuples(list(itertools.product(unique_company, list(df.rd.unique()))))
reindexed = df_indexed.reindex(index, fill_value=0)
reindexed = reindexed.unstack().applymap(lambda cell: 0 if '1970-01-01' in str(cell) else cell)
working_df = pd.DataFrame(reindexed.iloc[:,
reindexed.columns.get_level_values(0).isin(['company', 'funding'])].to_records())
If you know how to solve part of this problem, go ahead and put it below. Thank you in advance for taking the time to look at this! :)
Lastly, if you want to see how my code does work. Then, do this but you lose so much valuable info...
df = df.drop_duplicates(subset='id')
df = df.drop_duplicates(subset='rd')

Answer 1

Take a pre-processing step to spread out the funding across records with the same 'id' and 'date'
df.funding /= df.groupby(['id', 'date']).funding.transform('count')
Then process
d1 = df.groupby(['company', 'round']).agg(
dict(bank=lambda x: tuple(x), funding='sum', date='last')
).unstack().sort_index(1, 1)
d1.columns = d1.columns.to_series().map('{0[0]}_{0[1]}'.format)
d1
bank funding date bank funding date
round a a a seed seed seed
company
alpha (z, j) 350.0 2007-09-01 (z,) 100.0 2006-12-01
beta None NaN NaT (x, y) 200.0 2004-09-01

Answer 2

Groupby, aggregate and unstack will get you close to what you want
df.groupby(['company', 'round']).agg({'bank': lambda x: ','.join(x), 'funding': 'sum', 'date': 'max'}).unstack().reset_index()
df.columns = ['_'.join(col).strip() for col in df.columns.values]
You get
company_ bank_a bank_seed funding_a funding_seed date_a date_seed
0 alpha z,j z 350.0 100.0 2007-09-01 2006-12-01
1 beta None x,y NaN 400.0 None 2004-09-01