如何将函数应用于多键熊猫数据框

Question

我正在执行多维查找以在新列中分配一个值。

我有一个按月包含一些历史员工数据的表。此示例中有两个独特的人，他们可以在一个月内拥有多个工作。

我想创建一个新列，根据以下条件告诉我每个独特的人是否都有符合条件的工作。挑战在于必须按月/年考虑每一行。

import pandas as pd
import numpy as np

data = {'Month': ["January", "January", "January", "February", "February", "February", "March", "March", "March", "March"],
        'Year': [2015,2015,2015,2015,2015,2015,2016,2016,2016,2016],
        'Job #': [1,1,2,1,2,1,1,1,2,3],
        'Pay Group': ["Excluded","Included","Excluded","Excluded","Included","Included","Excluded","Exclcuded","Excluded","Included"],
        'Name': ["John","Bill","Bill","John","John","Bill","John","Bill","Bill","Bill"]}
df = pd.DataFrame(data, columns=['Month', 'Year', 'Job #', 'Pay Group', 'Name'])

df

符合条件的工作条件：

• 如果 (Job # = 1 AND Pay Group = Include ) AND 如果先验条件为 false，则在给定的 month/year AND Pay Group = Includes 中查找下一个最大的 Job #

Answer 1

IIUC：

你想为每个人，在每个月/年中，你想抓住最小的工作#比如Pay Group == Included。

仅过滤包含的那些。按作业编号排序。按年、月和名称分组，采用最低观测指数。使用它来分配一个新列。

dfi = df[df['Pay Group'] == 'Included'].sort_values('Job #')
gc = ['Year', 'Month', 'Name'] 
idx = dfi.groupby(gc, as_index=False)['Job #'].idxmin()
df['Eligible Job'] = 'Not Eligible'
df.ix[idx] = 'Eligible'

df