Pandas Group By With Running Total - pandas

Question

My granny has some strange ideas. Every birthday she takes me shopping.
She has some strict rules. If I buy a present less than $20 she wont contribute anything. If I spend over $20 she will contribute up to $30.
So if a present costs $27 she would contribute $7.
That now leaves me with $23 to spend on extra presents that birthday; the same rules as above apply on any additional presents.
Once the $30 are spent there are no more contributions from granny and I must pay the rest myself.
Here is an example table of my 11th, 12th and 13th birthday.
DollarsSpent granny_pays
BirthDayAge PresentNum
11 1 25.00 5.00 -- I used up $5
2 100.00 25.00 -- I used up last $20
3 10.00 0.00
4 50.00 0.00
12 1 39.00 19.00 -- I used up $19 only $11 left
2 7.00 0.00
3 32.00 11.00 -- I used up the last $11 despite $12 of $32 above the $20 starting point
4 19.00 0.00
13 1 21.00 1.00 -- used up $1
2 27.00 7.00 -- used up $7, total used up $8 and never spent last $22
So in pandas I have gotten this far.
import pandas as pd
granny_wont_pay_first = 20.
granny_limit = 30.
df = pd.DataFrame({'BirthDayAge' : ['11','11','11','11','12','12','12','12','13','13']
,'PresentNum' : [1,2,3,4,1,2,3,4,1,2]
,'DollarsSpent' : [25.,100.,10.,50.,39.,7.,32.,19.,21.,27.]
})
df.set_index(['BirthDayAge','PresentNum'],inplace=True)
df['granny_pays'] = df['DollarsSpent'] - granny_wont_pay_first
df['granny_limit'] = granny_limit
df['zero'] = 0.0
df['granny_pays'] = df[['granny_pays','zero','granny_limit']].apply(np.median,axis=1)
df.drop(['granny_limit','zero'], axis=1, inplace=True)
print df.head(len(df))
And this is the output. Using the median on the 3 numbers is a nice way to work out what granny will contribute.
The problem is that you can see each present is treated in isolation and I don't correctly erode my $30 each present within each BirthDayAge.
DollarsSpent granny_pays
BirthDayAge PresentNum
11 1 25.00 5.00
2 100.00 30.00 -- should be 25.0
3 10.00 0.00
4 50.00 30.00 -- should be 0.0
12 1 39.00 19.00
2 7.00 0.00
3 32.00 12.00 -- should be 11.0
4 19.00 0.00
13 1 21.00 1.00
2 27.00 7.00
Trying to think of a nice pandas way to do this erosion.
Hopefully no loops please.

Answer 1

I don't know if there is a more concise way, but this should work and does avoid loops as requested.
df['per_gift'] = df.DollarsSpent - 20
df['per_gift'] = np.where( df.per_gift > 0, df.per_gift, 0 )
df['per_bday'] = df.groupby('BirthDayAge').per_gift.cumsum()
df['per_bday'] = np.where( df.per_bday > 30, 30, df.per_bday )
df['granny_pays'] = df.groupby('BirthDayAge').per_bday.diff()
df['granny_pays'] = df.granny_pays.fillna(df.per_bday)
Note that 'per_gift' ignores the maximum subsidy of $30 and 'per_bday' is the cumulative subsidy (capped at $30) per 'BirthDayAge'.
BirthDayAge DollarsSpent PresentNum per_gift per_bday granny_pays
0 11 25 1 5 5 5
1 11 100 2 80 30 25
2 11 10 3 0 30 0
3 11 50 4 30 30 0
4 12 39 1 19 19 19
5 12 7 2 0 19 0
6 12 32 3 12 30 11
7 12 19 4 0 30 0
8 13 21 1 1 1 1
9 13 27 2 7 8 7