How to split an integer in n parts (each integer)?

Question

I have data similar to this:

Here, I have used the python function ffill() to fill the last finite value to the next nan cells.That's why the quantity is same for all the dates under a flag. Count is just the number of rows where flag is same.

Now, I need to split the quantity column like this:

So here 6 is divided as 2+2+1+1 because 6 needs to be divided in 4 rows. And similarly, 5 as 1+1+1+1+1 because I have more number of rows(9) than the value(5). So I can evenly distribute 5 as 1's in starting 5 rows. How can I do this in python?

Answer 1

First define a function with the logic to split a number x in to n parts.

def get_split(x, n):
    if x < n:
        return [1]*x + [0]*(n-x)
    q = x//n
    r = x%n
    c = n-r
    if r == 0:
        return [q]*n
    else:
        return [q+1 if i>=c else q for i in range(n)]

This function will return a list of the split

Then group the df using the df['flag'] column and for each group get the split using the above function and update the df['new qty'] column.

df['new qty'] = 0
groups = df.groupby('flag')
for key, grp in groups:  
    x, n = grp.head(1)[['qty', 'count']].values[0]   
    splits = sorted(get_split(x, n), reverse=True)   
    j=0
    for i, row in grp.iterrows():    
        df.loc[i, 'new qty'] = splits[j]
        j+=1

print(df)

Result:

         Date Desciption  qty  flag  count  new qty
0  2019-09-18          A    6     3      4        2
1  2019-09-19          A    6     3      4        2
2  2019-09-20          A    6     3      4        1
3  2019-09-21          A    6     3      4        1
4  2019-09-22          A    5     7      9        1
5  2019-09-23          A    5     7      9        1
6  2019-09-24          A    5     7      9        1
7  2019-09-25          A    5     7      9        1
8  2019-09-26          A    5     7      9        1
9  2019-09-27          A    5     7      9        0
10 2019-09-28          A    5     7      9        0
11 2019-09-29          A    5     7      9        0
12 2019-09-30          A    5     7      9        0