[英]How to get cumulative sum of unique IDs with group by?
I am very new to python and pandas working on a pandas dataframe which looks like我对 python 和 Pandas 非常陌生,它在 Pandas 数据框上工作,看起来像
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).我正在尝试按日期、时间和 id 实现分组并应用累积总和,这样如果下一个时间段中存在 id,则权重仅添加一次(唯一)。 The resulting data frame would look like this
结果数据框看起来像这样
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:这是我在下面尝试过的,但是这仍然多次计算重量:
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!任何帮助将非常感激!
Thanks a lot in advance!!非常感谢提前!
The code below uses pandas.duplicate() , pandas.merge() , pandas.groupby/sum and pandas.cumsum() to come to the desired output:下面的代码使用pandas.duplicate() 、 pandas.merge() 、 pandas.groupby/sum和pandas.cumsum()来获得所需的输出:
# 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:产生以下输出:
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