根据不同的列值分配唯一值

Question

我知道问题名称有点含糊不清.

我的目标是在我的数据框中根据2列+唯一值分配全局键列.

例如

CountryCode | Accident
   AFG          Car
   AFG          Bike
   AFG          Car
   AFG          Plane
   USA          Car
   USA          Bike
   UK           Car

设Car = 01,Bike = 02,Plane = 03

我的愿望全局密钥格式是[事故] [CountryCode] [UniqueValue]

唯一值是类似[Accident] [CountryCode]的计数

因此,如果Accident = Car和CountryCode = AFG并且它是第一次出现,则全局密钥将为01AFG01

所需的数据框如下所示:

CountryCode | Accident | GlobalKey
   AFG          Car        01AFG01
   AFG          Bike       02AFG01
   AFG          Car        01AFG02
   AFG          Plane      01AFG03
   USA          Car        01USA01
   USA          Bike       01USA02
   UK           Car        01UK01

我尝试运行for循环将Accident Number和CountryCode一起添加

例如:

globalKey = []

for x in range(0,6):
    string = df.iloc[x, 1]
    string2 = df.iloc[x, 2]
    if string2 == 'Car':
        number = '01'
    elif string2 == 'Bike':
        number = '02'
    elif string2 == 'Plane':
        number = '03'
    #Concat the number of accident and Country Code
    subKey = number + string
    #Append to the list
    globalKey.append(subKey)

此代码将提供给我类似01AFG,02AFG基于我赋值.但我想通过计算何时CountryCode和Accident类似的发生来分配一个唯一的值.

我坚持上面的代码.我认为在Pandas中使用map函数应该有更好的方法.

谢谢你的帮助!非常感谢!

Answer 1

您可以尝试通过cumcount多个步骤实现此目的,如下所示:

In [1]: df = pd.DataFrame({'Country':['AFG','AFG','AFG','AFG','USA','USA','UK'], 'Accident':['Car','Bike','Car','Plane','Car','Bike','Car']})

In [2]: df
Out[2]: 
  Accident Country
0      Car     AFG
1     Bike     AFG
2      Car     AFG
3    Plane     AFG
4      Car     USA
5     Bike     USA
6      Car      UK

## Create a column to keep incremental values for `Country`
In [3]: df['cumcount'] = df.groupby('Country').cumcount()

In [4]: df
Out[4]: 
  Accident Country  cumcount
0      Car     AFG         0
1     Bike     AFG         1
2      Car     AFG         2
3    Plane     AFG         3
4      Car     USA         0
5     Bike     USA         1
6      Car      UK         0

## Create a column to keep incremental values for combination of `Country`,`Accident`
In [5]: df['cumcount_type'] = df.groupby(['Country','Accident']).cumcount()

In [6]: df
Out[6]: 
  Accident Country  cumcount  cumcount_type
0      Car     AFG         0              0
1     Bike     AFG         1              0
2      Car     AFG         2              1
3    Plane     AFG         3              0
4      Car     USA         0              0
5     Bike     USA         1              0
6      Car      UK         0              0

从那时起,你可以连接你的价值观cumcount,cumcount_type并Country实现你所追求的目标.

也许您想要添加1到不同计数下的每个值,具体取决于您是否要从0或1开始计数.

我希望这有帮助.