将按操作的系列分配给数据帧

Question

我正在将一个函数传递给一个groupby操作，该操作返回一个list，该在apply中转换为系列，并最终将系列分配回主数据帧。

df_SOT = pd.DataFrame({'Lane': {26055: 'L2', 26056: 'L2', 26057: 'L2', 26058: 'L2', 26059: 'L2', 25972: 'L1', 25973: 'L1', 25974: 'L1', 25975: 'L1', 25976: 'L1'}, 'Carrier SCAC': {26055: 'JNJR', 26056: 'WOSQ', 26057: 'BGME', 26058: 'ITSB', 26059: 'UCSB', 25972: 'BGME', 25973: 'SCNN', 25974: 'XPOL', 25975: 'SJRG', 25976: 'MTRK'}, 'Annual Volume': {26055: 5604.0, 26056: 5604.0, 26057: 5604.0, 26058: 5604.0, 26059: 5604.0, 25972: 4917.0, 25973: 4917.0, 25974: 4917.0, 25975: 4917.0, 25976: 4917.0}, 'Annual Capacity': {26055: 260.0, 26056: 1300.0, 26057: 2704.0, 26058: 2080.0, 26059: 4368.0, 25972: 5408.0, 25973: 3380.0, 25974: 4940.0, 25975: 156.0, 25976: 4940.0}})

功能

def allocation(df_alloc):
Annual_Volume = df_alloc['Annual Volume']
Annual_Capacity = df_alloc['Annual Capacity']
Allocation = []
Cum_Capacity = 0
for idx in df_alloc.index:
Allocate = (min(0.5*Annual_Volume[idx],Annual_Capacity[idx],Annual_Volume[idx]-Cum_Capacity))
Cum_Capacity+=Allocate
Allocation.append(Allocate)
return Allocation

分组操作：

df_SOT.groupby('Lane').apply(allocation)
>>>Ouput
Lane
L1         [2458.5, 2458.5, 0.0, 0.0, 0.0]
L2    [260.0, 1300.0, 2704.0, 1340.0, 0.0]
dtype: object

df_SOT['Allocation] = df_SOT.groupby('Lane').apply(allocation)

但它返回为 NaN。不知道我在这里做错了什么。

5604.0

	Lane	Carrier SCAC	年量	年度容量	分配
26055	L2	JNJR	5604.0 260.0		NaN
26056	L2	WOSQ	5604.0 1300.0		NaN
26057	L2	BGME	2704.0	NaN
26058	L2	ITSB	5604.0 2080.0		NaN
26059	L2	UCSB	5604.0 4368.0		NaN
25972	L1	BGME	4917.0	5408.0	NaN
25973	L1	SCNN	4917.0	3380.0	NaN
25974	L1	XPOL	4917.0 4940.0		NaN
25975	L1	SJRG	4917.0	156.0	NaN
25976	L1	MTRK	4917.0 4940.0		NaN

一种替代方法是allocation返回具有正确索引的熊猫系列而不是简单的列表，并将group_keys设置为groupby中的False：

def allocation(df_alloc):
Annual_Volume = df_alloc["Annual Volume"]
Annual_Capacity = df_alloc["Annual Capacity"]
Allocation = []
Cum_Capacity = 0
for idx in df_alloc.index:
Allocate = min(
0.5 * Annual_Volume[idx],
Annual_Capacity[idx],
Annual_Volume[idx] - Cum_Capacity,
)
Cum_Capacity += Allocate
Allocation.append(Allocate)
return pd.Series(Allocation, index=df_alloc.index)

df_SOT["Allocation"] = df_SOT.groupby("Lane", group_keys=False).apply(allocation)