我要创建2个“运行总计”列，这些列仅根据每个Amount中的TYPE是ANNUAL还是MONTHLY来汇总Deal值
因此它将是DF.groupby(['Deal','Booking Month'])，然后在第一列TYPE==ANNUAL和第二列TYPE==MONTHLY时以某种方式应用求和函数。
如果我的分组DF看起来像+两个所需列，则为
。
Deal TYPE Month Amount Running Total(ANNUAL) Running Total(Monthly)
A ANNUAL April 1000 1000 0
A ANNUAL April 2000 3000 0
A MONTHLY June 1500 3000 1500
B MONTHLY April 11150 0 11150
B ANNUAL July 700 700 11150
B ANNUAL August 303.63 1003.63 11150
C ANNUAL April 25624.59 25624.59 0
D ANNUAL June 5000 5000 0
D ANNUAL July 5000 10000 0
D ANNUAL August 5000 15000 0
E ANNUAL April 10 10 0
E MONTHLY May 1000 10 1000
E ANNUAL May 500 510 1000
E MONTHLY June 500.00 510 1500
E ANNUAL June 600 1110 1500
E MONTHLY July 300 1110 1800
E MONTHLY July 8200 1110 10000
答案 0 :(得分：3)
使用import Foundation
import CoreData
protocol PickerItemProvider: class {
associatedtype PickerType
func itemAt(_ indexPath: IndexPath) -> PickerType?
}
extension PickerItemProvider {
public func refresh(_ sender: Any, completion: (() -> Void)?) {
print("the default refresh implementation")
}
}
public class PickerSectionProvider<ProvidedType: Equatable> : PickerItemProvider {
func itemAt(_ indexPath: IndexPath) -> ProvidedType? {
return nil
}
}
extension PickerItemProvider where PickerType: Equatable & SyncableEntity {
func refresh(_ sender: Any, completion: (() -> Void)?) {
print("we’re trying to have this implementation called instead of the above implementation of refresh")
PickerType.startSync()
completion?()
}
}
protocol SyncableEntity {
associatedtype EntityType
static func startSync()
}
extension SyncableEntity {
static func startSync() {
print("starting sync")
}
}
class ObservationType: Equatable, SyncableEntity {
typealias EntityType = NSManagedObject
}
func ==(lhs: ObservationType, rhs: ObservationType) -> Bool {
return false
}
class GeneralPickerViewController<PickerType: Equatable, ItemProvider: PickerItemProvider> where ItemProvider.PickerType == PickerType {
private var itemProvider: ItemProvider?
private var refresher: ((Any, (() -> ())?) -> ())?
func setup<T: PickerItemProvider>(with itemProvider: T) where T.PickerType == PickerType {
refresher = { sender, completion in
itemProvider.refresh(self, completion: {
completion?()
})
}
self.itemProvider = (itemProvider as! ItemProvider)
}
func setup<T: PickerItemProvider>(with itemProvider: T) where T.PickerType == PickerType, PickerType: SyncableEntity {
refresher = { sender, completion in
itemProvider.refresh(self, completion: {
completion?()
})
}
self.itemProvider = (itemProvider as! ItemProvider)
}
func foo() {
refresher?(self, {
print("finished")
})
}
}
class PopupPickerRow<T: Equatable, ItemProvider: PickerItemProvider> where ItemProvider.PickerType == T {
var pickerController = GeneralPickerViewController<T, ItemProvider>()
}
let pickerSectionProvider = PickerSectionProvider<ObservationType>()
let row = PopupPickerRow<ObservationType, PickerSectionProvider<ObservationType>>()
row.pickerController.setup(with: pickerSectionProvider)
row.pickerController.foo()
和groupby + transform：
ActionMailer::Base.delivery_method答案 1 :(得分：0)
您可以使用.expanding.sum()来执行此操作，这将维护组的multiIndex，您可以将其拆开以获取每种类型的单独列。使用另一个groupby来相应地填充每个组中的缺失值。将其连接回去。
有趣的是，它可以用于多种类型，而无需在任何地方明确定义它们。
import pandas as pd
df2 = (df.groupby(['Deal', 'TYPE'])
.Amount.expanding().sum()
.unstack(level=1)
.groupby(level=0)
.ffill().fillna(0)
.reset_index(level=0, drop=True)
.drop(columns='Deal'))
pd.concat([df, df2], axis=1)
Deal TYPE Month Amount ANNUAL MONTHLY
0 A ANNUAL April 1000.00 1000.00 0.0
1 A ANNUAL April 2000.00 3000.00 0.0
2 A MONTHLY June 1500.00 3000.00 1500.0
3 B MONTHLY April 11150.00 0.00 11150.0
4 B ANNUAL July 700.00 700.00 11150.0
5 B ANNUAL August 303.63 1003.63 11150.0
6 C ANNUAL April 25624.59 25624.59 0.0
7 D ANNUAL June 5000.00 5000.00 0.0
8 D ANNUAL July 5000.00 10000.00 0.0
9 D ANNUAL August 5000.00 15000.00 0.0
10 E ANNUAL April 10.00 10.00 0.0
11 E MONTHLY May 1000.00 10.00 1000.0
12 E ANNUAL May 500.00 510.00 1000.0
13 E MONTHLY June 500.00 510.00 1500.0
14 E ANNUAL June 600.00 1110.00 1500.0
15 E MONTHLY July 300.00 1110.00 1800.0
16 E MONTHLY July 8200.00 1110.00 10000.0