forwardfill combined with calculation (method='ffill' * xyz) in python pandas -

i need fill nan spaces calculation, depends on previous values in dataframe = df. have far this:

df = pd.dataframe({'a': [none] * 6, 'b': [2, 3, 10, 3, 5, 8]}) df["c"] =np.nan  df["c"][0] = 1 df["c"][2] = 3  = 1 while i<10:     df.c.fillna(df.c.shift(i)*df.b,inplace=true)     i+1 

unfortunately solution while loop not work , bad solution pandas. looking kind of a

df.c.fillna(method='ffill'*df.b,inplace=true) 

i know doesn't work, think makes clearer looking for.

before filling dataframe looks this:

   b   c 0  2   1 1  3 nan 2 10   3 3  3 nan 4  5 nan 5  8 nan 

the desired outcome should this:

      b   c 0     2   1  # nothing filled in since data set df["c"][0] = 1 1     3   3  # fill in previous c * b = 1 * 3 = 3 2    10   3  # nothing filled in since data set df["c"][2] = 3 3     3   9  # fill in previous c * b = 3 * 3 = 9 4     5  45  # fill in previous c * b = 9 * 5 = 45 5     8 360  # fill in previous c * b = 45 * 8 = 360 

so basically: if there no data availabe, should filled caculation.

i can't figure out way in single loop, problem here want kind of rolling apply can @ previous row, problem here previous row update not observable until apply finishes instance following works because in run apply 3 times. isn't great imo:

in [103]: def func(x):     if pd.notnull(x['c']):         return x['c']     else:         return df.iloc[x.name - 1]['c'] * x['b'] df['c'] = df.apply(func, axis =1) df['c'] = df.apply(func, axis =1) df['c'] = df.apply(func, axis =1) df  out[103]:         b    c 0  none   2    1 1  none   3    3 2  none  10    3 3  none   3    9 4  none   5   45 5  none   8  360