Python pandas cumsum with reset everytime there is a 0

You can use:

a = df != 0
df1 = a.cumsum()-a.cumsum().where(~a).ffill().fillna(0).astype(int)
print (df1)
   a  b
0  0  1
1  1  2
2  0  3
3  1  0
4  2  1
5  0  2

You may also try the following naive but reliable approach.

Per every column - create groups to count within. Group starts once sequential value difference by row appears and lasts while value is being constant: (x != x.shift()).cumsum().
Example:

    a   b
0   1   1
1   2   1
2   3   1
3   4   2
4   4   3
5   5   3

Calculate cummulative sums within groups per columns using pd.DataFrame's apply and groupby methods and you get cumsum with the zero reset in one line:

import pandas as pd
df = pd.DataFrame([[0,1],[1,1],[0,1],[1,0],[1,1],[0,1]],columns = ['a','b'])

cs = df.apply(lambda x: x.groupby((x != x.shift()).cumsum()).cumsum())
print(cs)

   a  b
0  0  1
1  1  2
2  0  3
3  1  0
4  2  1
5  0  2

Try this

df = pd.DataFrame([[0,1],[1,1],[0,1],[1,0],[1,1],[0,1]],columns = ['a','b'])
df['groupId1']=df.a.eq(0).cumsum()
df['groupId2']=df.b.eq(0).cumsum()
New=pd.DataFrame()
New['a']=df.groupby('groupId1').a.transform('cumsum')
New['b']=df.groupby('groupId2').b.transform('cumsum')

New
Out[1184]: 
   a  b
0  0  1
1  1  2
2  0  3
3  1  0
4  2  1
5  0  2