Setting cumulative values to constant after it rea

Question

I have a pandas time series which contains cumulative monthly values.

If in a month on a certain date, the value becomes a certain number, I need to set rest of the days to 1000.

E.g.

df:

 Date       cummulative_value
1/8/2017    -3
1/9/2017    -6
1/10/2017   -72
1/11/2017   500
1/26/2017   575
2/7/2017    -5
2/14/2017   -6
2/21/2017   -6

My cutoff value is -71 so in above example I need to achieve the following:

 Date       cummulative_value
1/8/2017    -3
1/9/2017    -6
1/10/2017   1000
1/11/2017   1000
1/26/2017   1000
2/7/2017    -5
2/14/2017   -6
2/21/2017   -6

I am trying to leverage groupby in pandas but I am not sure how to go about it. Any other more efficient way will help also.

Answer 1

Here's one approach that involves creating a mask:

df.set_index(pd.to_datetime(df['Date'], format="%m/%d/%Y"), inplace=True)

mask = df['cummulative_value'].lt(-71).groupby(df.index.month).cumsum()

# Date
# 2017-01-08    False
# 2017-01-09    False
# 2017-01-10     True
# 2017-01-11     True
# 2017-01-26     True
# 2017-02-07    False
# 2017-02-14    False
# 2017-02-21    False

df.loc[mask, 'cummulative_value'] = 1000

df.reset_index(drop=True)

#         Date  cummulative_value
# 0   1/8/2017                 -3
# 1   1/9/2017                 -6
# 2  1/10/2017               1000
# 3  1/11/2017               1000
# 4  1/26/2017               1000
# 5   2/7/2017                 -5
# 6  2/14/2017                 -6
# 7  2/21/2017                 -6

Answer 2

Use groupby and cumprod:

df['cummulative_value'] = (df.groupby(df['Date'].dt.strftime('%Y%m'))['cummulative_value']
                            .transform(lambda x: np.where(x.ge(-71).cumprod(),x,1000)))
print(df)

Output:

        Date  cummulative_value
0 2017-01-08                 -3
1 2017-01-09                 -6
2 2017-01-10               1000
3 2017-01-11               1000
4 2017-01-26               1000
5 2017-02-07                 -5
6 2017-02-14                 -6
7 2017-02-21                 -6