Dataframe to start with:
df = pd.DataFrame({
# some ways to create random data
'Team Name':np.random.choice( ['Team A','Team B','Team C','Team D','Team E','Team F'], 6,replace=False),
'Product Owner City':np.random.choice( ['New York','Mumbai','Phoenix','Sunrise'], 6),
'Scrum Master City':np.random.choice( ['New York','Mumbai','Phoenix','Sunrise'], 6),
'Developers City':np.random.choice( ['New York','Mumbai','Phoenix','Sunrise'], 6)
})
Is there a function, library to derive the Timezone in any particular way (open to suggestions). So that the output looks like below.
Team name Product Owner City Scrum Master City Developers City
Team A Timezone 1 Timezone 2 Timezone 3
Team B Timezone 1 Timezone 1 Timezone 1
Final outcome I am driving towards is:
- If a team has Product Owner, Scrum Master and Developers in the same city = Team is colocated.
- Teams that have a partial overlap in timezones = Team is partially colocated.
- Teams that have no overlap in timezones = Team is not colocated.
For reference purpose I do have a list of distinct cities and the two digit country codes separately.
City Name | City-Country Code |
---|---|
New York | New York - US |
Mumbai | Mumbai - IN |