Python Pandas: How to insert a new column which is a sum of next 'n' (can be a fraction also) values of another column? - python-3.x

Question

I've got a DataFrame, let's say the name is 'test' storing data as below:
Week Stock(In Number of Weeks) Demand (In Units)
0 W01 2.4 37
1 W02 3.6 33
2 W03 2.0 46
3 W04 5.8 45
4 W05 4.6 56
5 W06 3.0 38
6 W07 5.0 45
7 W08 7.5 54
8 W09 4.3 35
9 W10 2.2 38
10 W11 2.0 50
11 W12 6.0 37
I want to insert a new column in this dataframe which for every row, is the sum of "No. of weeks" rows of column "Demand(In Units)".
That is, in the case of this dataframe,
for 0th row that new column should be the sum of 2.4 rows of column "Demand(In Units)" which would be 37+33+ 0.4*46
for 1st row, the value should be 33+46+45+ 0.6*56
for 2nd row, it should be 46+45
.
.
.
for 7th row, it should be 54+35+38+50+37 (since number of rows left are smaller than the value 7.5, all the remaining rows get summed up)
.
.
.
and so on.
Effectively, I want my dataframe to have a new column as follows:
Week Stock(In Number of Weeks) Demand (In Units) Stock (In Units)
0 W01 2.4 37 88.4
1 W02 3.6 33 157.6
2 W03 2.0 46 91.0
3 W04 5.8 45 266.0
4 W05 4.6 56 214.0
5 W06 3.0 38 137.0
6 W07 5.0 45 222.0
7 W08 7.5 54 214.0
8 W09 4.3 35 160.0
9 W10 2.2 38 95.4
10 W11 2.0 50 87.0
11 W12 6.0 37 37.0
Can somebody suggest some way to achieve this?
I can achieve it through iterating over each row but it would be very slow for millions of rows which I want to process at a time.
The code which I am using right now is:
for i in range(len(test)):
if int(np.floor(test.loc[i, 'Stock(In Number of Weeks)'])) >= len(test[i:]):
number_of_full_rows = len(test[i:])
fraction_of_last_row = 0
y = 0
else:
number_of_full_rows = int(np.floor(test.loc[i, 'Stock(In Number of Weeks)']))
fraction_of_last_row = test.loc[i, 'Stock(In Number of Weeks)'] - number_of_full_rows
y = test.loc[i+number_of_full_rows, 'Demand (In Units)'] * fraction_of_last_row
x = np.sum(test[i:i+number_of_full_rows]['Demand (In Units)'])
test.loc[i, 'Stock (In Units)'] = x+y

Answer 1

I tried with some test data:
def func(r, col):
n = int(r['Stock(In Number of Weeks)'])
f = float(r['Stock(In Number of Weeks)'] - n)
i = r.name # row index value
z = np.zeros(len(df)) #initialize all zeros
v = np.hstack((np.ones(n), np.array(f))) # vecotor of ones and fraction part
e = min(len(v), len(z[i:]))
z[i:i+e] = v[:len(z[i:])] #change z starting at index until lenght
r['Stock (In Units)'] = col # z #compute scalar product
return r
df = df.apply(lambda r: func(df['Demand (In Units)'].values, r), axis=1)