Pandas Grouper "Cumulative" sum()

user_13002051 0
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I'm trying to calculate the cumulative total for the next 4 weeks.

Here is an example of my data frame

d = {'account': [10, 10, 10, 10, 10, 10, 10, 10],
     'volume': [25, 60, 40, 100, 50, 100, 40, 50]}
df = pd.DataFrame(d)
df['week_starting'] = pd.date_range('05/02/2021',
                                    periods=8,
                                    freq='W')

df['volume_next_4_weeks'] = [225, 250, 290, 290, 240, 190, 90, 50]
df['volume_next_4_weeks_cumulative'] = ['(25+60+40+100)', '(60+40+100+50)', '(40+100+50+100)', '(100+50+100+40)', '(50+100+40+50)', '(100+40+50)', '(40+50)', '(50)']
df.head(10)

dataframe_table_view

I would to find a way to calculate the cumulative amount by pd.Grouper freq = 4W.

cumsum pandas-groupby pandas numpy python
2 Answers

This should work:

df['volume_next_4_weeks']  = [sum(df['volume'][i:i+4]) for i in range(len(df))]

For the other column showing the addition as string, I have stored the values in a list using the same logic above but not applying sum and then joining the list elements as string:

df['volume_next_4_weeks_cumulative'] = [df['volume'][i:i+4].to_list() for i in range(len(df))]
df['volume_next_4_weeks_cumulative'] = df['volume_next_4_weeks_cumulative'].apply(lambda row: ' + '.join(str(x) for x in row))

Now as you mentioned you have different multiple accounts and you want to do it separately for all of them, create a custom function and then use groupby and apply to create the columns:

def create_mov_cols(df):
    df['volume_next_4_weeks']  = [sum(df['volume'][i:i+4]) for i in range(len(df))]
    df['volume_next_4_weeks_cumulative'] = [df['volume'][i:i+4].to_list() for i in range(len(df))]
    df['volume_next_4_weeks_cumulative'] = df['volume_next_4_weeks_cumulative'].apply(lambda row: ' + '.join(str(x) for x in row))
    return df

Apply the function to the DataFrame:

df = df.groupby(['account']).apply(create_mov_cols)
user_156044840 
df['volume_next_4_weeks'] = df[['week_starting', 'volume']][::-1].rolling(window='28D', on='week_starting').sum()[::-1]['volume']

28D is used instead of 4W since latter is not a fixed frequency.

user_85165130
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