How to apply a function to a multi keyed pandas dataframe - pandas

Question

I'm performing a multidimensional lookup to assign a value in a new column.
I have a table that has some historical employee data by month. There are two unique people in this example, and they can have multiple jobs within a month.
I want to create a new column that tells me if each unique person has an eligible job based on the conditions below. The challenge is each row has to be considered by month/year.
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
Eligible Jobs Conditions:
If ( Job # = 1 AND Pay Group = Include ) AND if the prior condition is false, then look for the next largest Job # within the given month/year AND Pay Group = Includes

Answer 1

IIUC:
You want to for each person, within each month/year, you want to grab the smallest job # such that Pay Group == Included.
Filter only those that are included. Sort by Job #. Group by year, month, and name taking the index of the minimum obseravation. Use this to assign a new column.
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