Running sums based on another column in Pandas

Question

I have a dataframe like the following:

   col1   col2
0     1   True
1     3   True
2     3   True
3     1  False
4     2   True
5     3   True
6     2  False
7     2   True

I want to get a running sum of True values. Whenever I see a False value in col2 , I need to take the cumulative sum of col1 up to that point. So, the DataFrame would look like the following:

   col1   col2  col3
0     1   True     0
1     3   True     0
2     3   True     0
3     1  False     7
4     2   True     0
5     3   True     0
6     2  False     5
7     2   True     0

How can I do this?

Answer 1

You can create a group variable with cumsum on col2 and then calculate the sum per group:

df.loc[~df.col2, 'col3'] = (df.col1 * df.col2).groupby(by = (~df.col2).cumsum()).cumsum().shift()
df.fillna(0)

Answer 2

You can use more general solution which works nice with multiple consecutive False - then cumulative sum value is not changed:

a = df.groupby((df.col2 != df.col2.shift()).cumsum())['col1'].transform('sum')
df['d'] = a.where(df.col2).ffill().mask(df.col2).fillna(0).astype(int)
print (df)
   col1   col2  d
0     1   True  0
1     3   True  0
2     3   True  0
3     1  False  7
4     2   True  0
5     3   True  0
6     2  False  5
7     2   True  0

---

#added 2 last rows with False in col2
print (df)
   col1   col2
0     1   True
1     3   True
2     3   True
3     1  False
4     2   True
5     3   True
6     2  False
7     2   True
8     4  False
9     4  False
a = df.groupby((df.col2 != df.col2.shift()).cumsum())['col1'].transform('sum')
df['d'] = a.where(df.col2).ffill().mask(df.col2).fillna(0).astype(int)
print (df)
   col1   col2  d
0     1   True  0
1     3   True  0
2     3   True  0
3     1  False  7
4     2   True  0
5     3   True  0
6     2  False  5
7     2   True  0
8     4  False  2
9     4  False  2