if negative then with weighted average

Question

I have a DataFrame:

a = {'Price': [10, 15, 20, 25, 30], 'Total': [10000, 12000, 15000, 14000, 10000],
 'Previous Quarter': [0, 10000, 12000, 15000, 14000]}
a = pd.DataFrame(a)
print (a)

With this raw data, i have added a number of additional columns including a weighted average price (WAP)

a['Change'] = a['Total'] - a['Previous Quarter']
a['Amount'] = a['Price']*a['Change']
a['Cum Sum Amount'] = np.cumsum(a['Amount'])
a['WAP'] = a['Cum Sum Amount'] / a['Total']

This is fine, however as the total starts to decrease this brings down the weighted average price.

my question is, if Total decreases how would i get WAP to reflect the row above? For instance in row 3, Total is 1000, which is lower than in row 2. This brings WAP down from 12.6 to 11.78, but i would like it to say 12.6 instead of 11.78.

I have tried looping through a['Total'] < 0 then a['WAP'] = 0 but this impacts the whole column.

Ultimately i am looking for a WAP column which reads: 10, 10.83, 12.6, 12.6, 12.6

Answer 1

You could use cummax :

a['WAP'] = (a['Cum Sum Amount'] / a['Total']).cummax()

print (a['WAP'])

0    10.000000
1    10.833333
2    12.666667
3    12.666667
4    12.666667
Name: WAP, dtype: float64

Answer 2

As a total Python beginner, here are two options I could think of

Either

a['WAP'] = np.maximum.accumulate(a['Cum Sum Amount'] / a['Total'])

Or after you've already created WAP you could modify only the subset using the diff method (thanks to @ayhan for the loc which will modify a in place)

a.loc[a['WAP'].diff() < 0, 'WAP'] = max(a['WAP'])