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I am very new to python and pandas working on a pandas dataframe which looks like

Date Time ID Weight
Jul-1 12:00 A 10
Jul-1 12:00 B 20
Jul-1 12:00 C 100
Jul-1 12:10 C 100
Jul-1 12:10 D 30
Jul-1 12:20 C 100
Jul-1 12:20 D 30
Jul-1 12:30 A 10
Jul-1 12:40 E 40
Jul-1 12:50 F 50
Jul-1 1:00 A 40

I am trying to achieve group by date, Time and ids and apply cumulative sum such that if an id is present in the next time-slot the weight is only added once(uniquely). The resulting data frame would look like this

Date Time Weight 
Jul-1 12:00 130 (10+20+100)
Jul-1 12:10 160 (10+20+100+30)
Jul-1 12:20 160 (10+20+100+30)
Jul-1 12:30 160 (10+20+100+30)
Jul-1 12:40 200 (10+20+100+30+40)
Jul-1 12:50 250 (10+20+100+30+40+50)
Jul-1 01:00 250 (10+20+100+30+40+50)

This is what I tried below, however this is still counting the weights multiple times:

df=df.groupby(['date','time','ID'])['Wt'].apply(lambda x: x.unique().sum()).reset_index()
df['cumWt']=df['Wt'].cumsum()

Any help would be really appreciated!

Thanks a lot in advance!!

The code below uses pandas.duplicate(), pandas.merge(), pandas.groupby/sum and pandas.cumsum() to come to the desired output:

# creates a series of weights to be considered and rename it to merge
unique_weights = df['weight'][~df.duplicated(['weight'])]
unique_weights.rename('consider_cum', inplace = True)

# merges the series to the original dataframe and replace the ignored values by 0
df = df.merge(unique_weights.to_frame(), how = 'left', left_index=True, right_index=True)
df.consider_cum = df.consider_cum.fillna(0)

# sums grouping by date and time
df = df.groupby(['date', 'time']).sum().reset_index()

# create the cumulative sum column and present the output
df['weight_cumsum'] = df['consider_cum'].cumsum()
df[['date', 'time', 'weight_cumsum']]

Produces the following output: