Conditional cumulative sum in Python/Pandas

Question

Consider my dataframe, df:

data  data_binary  sum_data
  2       1            1
  5       0            0
  1       1            1
  4       1            2
  3       1            3
  10      0            0
  7       0            0
  3       1            1

How can I calculate the cumulative sum of data_binary within groups of contiguous 1 values?

The first group of 1's had a single 1 and sum_data has only a 1. However, the second group of 1's has 3 1's and sum_data is [1, 2, 3].

I've tried using np.where(df['data_binary'] == 1, df['data_binary'].cumsum(), 0), but that returns

array([1, 0, 2, 3, 4, 0, 0, 5])

Which is not what I want.

Answer 1

You want to take the cumulative sum of data_binary and subtract the most recent cumulative sum where data_binary was zero.

b = df.data_binary
c = b.cumsum()
c.sub(c.mask(b != 0).ffill(), fill_value=0).astype(int)

Output

0    1
1    0
2    1
3    2
4    3
5    0
6    0
7    1
Name: data_binary, dtype: int64

---

Explanation

Let's start by looking at each step side by side

cols = ['data_binary', 'cumulative_sum', 'nan_non_zero', 'forward_fill', 'final_result']
print(pd.concat([
        b, c,
        c.mask(b != 0),
        c.mask(b != 0).ffill(),
        c.sub(c.mask(b != 0).ffill(), fill_value=0).astype(int)
    ], axis=1, keys=cols))

Output

data_binary  cumulative_sum  nan_non_zero  forward_fill  final_result
0            1               1             NaN           NaN             1
1            0               1             1.0           1.0             0
2            1               2             NaN           1.0             1
3            1               3             NaN           1.0             2
4            1               4             NaN           1.0             3
5            0               4             4.0           4.0             0
6            0               4             4.0           4.0             0
7            1               5             NaN           4.0             1

The problem with cumulative_sum is that the rows where data_binary is zero, do not reset the sum. And that is the motivation for this solution. How do we "reset" the sum when data_binary is zero? Easy! I slice the cumulative sum where data_binary is zero and forward fill the values. When I take the difference between this and the cumulative sum, I've effectively reset the sum.

Answer 2

I think you can groupby with DataFrameGroupBy.cumsum by Series, where first compare the next value by the shifted column if not equal (!=) and then create groups by cumsum. Last, replace 0 by column data_binary with mask:

print (df.data_binary.ne(df.data_binary.shift()).cumsum())
0    1
1    2
2    3
3    3
4    3
5    4
6    4
7    5
Name: data_binary, dtype: int32

df['sum_data1'] = df.data_binary.groupby(df.data_binary.ne(df.data_binary.shift()).cumsum())
                                .cumsum()
df['sum_data1'] = df['sum_data1'].mask(df.data_binary == 0, 0)
print (df)
   data  data_binary  sum_data  sum_data1
0     2            1         1          1
1     5            0         0          0
2     1            1         1          1
3     4            1         2          2
4     3            1         3          3
5    10            0         0          0
6     7            0         0          0
7     3            1         1          1

Answer 3

If you want the excellent piRSquared's answer in just one single command:

df['sum_data'] = df[['data_binary']].apply(
    lambda x: x.cumsum().sub(x.cumsum().mask(x != 0).ffill(), fill_value=0).astype(int), 
    axis=0)

Note that the double squared bracket on the right hand side is necessary to make a one-column DataFrame instead of a Series in order to use apply with the axis argument (which is not available when apply is used on Series).