[英]Pandas multi-index dataframe cumulative sum
I have a multi-index dataframe below:我在下面有一个多索引 dataframe:
vessels_df.head(10)
eta_date
vessel load_dates
AM OCEAN SILVER 2020-06-05 2020-06-04
2020-06-06
2020-06-07
2020-06-08
2020-06-09
2020-06-10
2020-06-11
APJ ANGAD
2020-06-09 2020-06-08
2020-06-10
2020-06-11
AQUATONKA
2020-06-03 2020-06-02
2020-06-04
2020-06-05
2020-06-06
2020-06-07
2020-06-08
2020-06-09
2020-06-10
2020-06-11
and a dictionary with a list of daily charges that are incurred for every day beyond the FIRST day of the load_dates
和一本字典,其中列出了在
load_dates
的第一天之后的每一天产生的每日费用
demurrage_charges_dict = {
'AM OCEAN SILVER': 11076,
'APJ ANGAD': 21771,
'AQUATONKA': 14312
}
desired output所需 output
I would like to create a column that is the cumulative sum of the daily charge over the period in the index, for example:我想创建一个列,该列是索引中该期间每日费用的累积总和,例如:
eta_date demurrage_charges
vessel load_dates
AM OCEAN SILVER 2020-06-05 2020-06-04 0
2020-06-06 11,076
2020-06-07 22,152
2020-06-08 33,228
2020-06-09 44,304
2020-06-10 55,380
2020-06-11 66,456
I believe I could reset the index of the 'vessels_df'
, convert the demurrage_charges_dict
to df and merge the two then use pd.cumsum()
, but wondered if there is a more elegant way to perform this?我相信我可以重置
'vessels_df'
的索引,将demurrage_charges_dict
转换为 df 并将两者合并,然后使用pd.cumsum()
,但想知道是否有更优雅的方式来执行此操作?
Much appreciated.非常感激。
cumcount
the vessel index level and multiply that by the mapping of the vessel with the dict cumcount
容器索引级别并将其乘以容器与 dict 的映射
idx = df.index.get_level_values('vessel')
df['demurrage_charges'] = (df.groupby(idx).cumcount()
* idx.map(demurrage_charges_dict))
eta_date demurrage_charges
vessel load_dates
AM OCEAN SILVER 2020-06-05 2020-06-04 0
2020-06-06 None 11076
2020-06-07 None 22152
2020-06-08 None 33228
2020-06-09 None 44304
2020-06-10 None 55380
2020-06-11 None 66456
APJ ANGAD 2020-06-09 2020-06-08 0
2020-06-10 None 21771
2020-06-11 None 43542
AQUATONKA 2020-06-03 2020-06-02 0
2020-06-04 None 14312
2020-06-05 None 28624
2020-06-06 None 42936
2020-06-07 None 57248
2020-06-08 None 71560
2020-06-09 None 85872
2020-06-10 None 100184
2020-06-11 None 114496
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