Restart cumulative sum in pandas dataframe - pandas

Question

I am trying to start a cumulative sum in a pandas dataframe, restarting everytime the absolute value is higher than 0.009. Could give you a excerpt of my tries but I assume they would just distract you. Have tried several things with np.where but at a certain point they start to overlap and it takes wrong things out.
Column b is the desired output.
df = pd.DataFrame({'values':(49.925,49.928,49.945,49.928,49.925,49.935,49.938,49.942,49.931,49.952)})
df['a']=df.diff()
values a b
0 49.925 NaN 0.000
1 49.928 0.003 0.003
2 49.945 0.017 0.020 (restart cumsum next row)
3 49.928 -0.017 -0.017 (restart cumsum next row)
4 49.925 -0.003 -0.003
5 49.935 0.010 0.007
6 49.938 0.003 0.010 (restart cumsum next row)
7 49.942 0.004 0.004
8 49.931 -0.011 -0.007
9 49.952 0.021 0.014 (restart cumsum next row)
So the actual objective is for python to understand that I want to restart the cumulative sum when it exceeds the absolute value of 0.009

Answer 1

I couldn't solve this in a vectorized manner, however applying a stateful function appears to work.
import pandas as pd
from pandas.compat import StringIO
print(pd.__version__)
df = pd.DataFrame({'values':(49.925,49.928,49.945,49.928,49.925,49.935,49.938,49.942,49.931,49.952)})
df['a']=df.diff()
accumulator = 0.0
reset = False
def myfunc(x):
global accumulator, reset
if(reset):
accumulator = 0.0
reset = False
accumulator += x
if abs(accumulator) > .009:
reset = True
return accumulator
df['a'].fillna(value=0, inplace=True)
df['b'] = df['a'].apply(myfunc)
print(df)
Produces
0.24.2
values a b
0 49.925 0.000 0.000
1 49.928 0.003 0.003
2 49.945 0.017 0.020
3 49.928 -0.017 -0.017
4 49.925 -0.003 -0.003
5 49.935 0.010 0.007
6 49.938 0.003 0.010
7 49.942 0.004 0.004
8 49.931 -0.011 -0.007
9 49.952 0.021 0.014