Setup

We'll use the following imports for all code extracts:

# Imports
import pandas as pd
import lib.SAAT as SAAT
import lib.utils as utils

First we read in the data:

# Read & Clean
data = pd.read_csv("data/example_data.csv")

Then we extract each persons department from their email:

# Get department from email
data["Department"] = data["Email"].map(lambda email: utils.email_to_department(email))

(this is only appropriate if your organisation follows the person@department.organisation.com email format, otherwise you'll probably already have a department field)

And select only those who are going to discuss 'Topic1':

data = data[data['Topic'] == 'Topic1']
data.head()

Name	Email	Years_In_Org	Grade	Setup	Topic	1-2pm	...	5-6pm3	Department
Person1	Person1@DepartmentG.com	<1 year	Level1	If needed	Topic1	0	...	1	departmentg
Person10	Person10@DepartmentF.com	1-3 years	Level2	If needed	Topic1	1	...	0	departmentf
Person12	Person12@DepartmentH.com	5-10 years	Level2	Yes	Topic1	0	...	0	departmenth
Person16	Person16@DepartmentL.com	1-3 years	Level2	If needed	Topic1	0	...	0	departmentl
Person17	Person17@DepartmentG.com	>10 years	Level2	No	Topic1	1	...	0	departmentg

Note, your setup is likely to look somewhat different to this depending on your data format.