Product of conditional cumsum of dataframe (stacked)

I have two data frames that are exactly the same size that were conditionally summed based on a third dataframe (sum everything to the left if the value is the same) using .stack and cumsum. I now want to multiply the summed values before the value changes. Have a table below that can probably explain better. So for x, since it doesn't change in the conditional table, the expected value is just the sum of everything. Since y changes in columns 01-Jan and 03-Jan, the expected value is the sums multiplied (250250). Since z changes in every column, the expected value is 300300*300.

Summing Table:

Conditional Table:

Expected Sum:

Expected Output:

Tried restacking them and seeing if they could be multiplied but it multiplied all the values regardless of conditional df. Tried looping to find multiply all values where the next row is <, but doesn't work if the Mult dataframe has negatives.

You can try (df is your "Mult" dataframe, df_cond is your "Cond" dataframe):

from itertools import groupby

m = df_cond.set_index('Condition').to_dict(orient='index')

def fn(x):
	out = []
	for _, g in groupby(zip(x.index, x), lambda k: m[x.name][k[0]]):
		out.append(sum(v for _, v in g))
	return np.prod(out)

df['Prod'] = df.set_index('Mult').apply(fn, axis=1).values
print(df)

Prints:

  Mult  01-Jan  02-Jan  03-Jan      Prod
0    x     100     100     100       300
1    y     250     100     150     62500
2    z     300     300     300  27000000