Create a new column in Pandas dataframe by arbitrary function over rows - pandas

Question

I have a Pandas dataframe. I want to add a column to the dataframe, where the value in the new column is dependent on other values in the row.
What is an efficient way to go about this?
Example
Begin
Start with this dataframe (let's call it df), and a dictionary of people's roles.
first last
--------------------------
0 Jon McSmith
1 Jennifer Foobar
2 Dan Raizman
3 Alden Lowe
role_dict = {
"Raizman": "sales",
"McSmith": "analyst",
"Foobar": "analyst",
"Lowe": "designer"
}
End
We have a dataframe where we've 'iterated' over each row, and used the last_name to lookup values from our role_dict and add that value to each row as role.
first last role
--------------------------------------
0 Jon McSmith analyst
1 Jennifer Foobar analyst
2 Dan Raizman sales
3 Alden Lowe designer

Answer 1

One solution is using series map function since the role is a dictionary
df['role'] = df.loc[:, 'last'].map(role_dict)

Answer 2

try this using merge
import pandas as pd
df = pd.DataFrame([["Jon","McSmith"],
["Jennifer","Foobar"],
["Dan","Raizman"],
["Alden","Lowe"]],columns=["first","last"])
role_dict = {
"Raizman": "sales",
"McSmith": "analyst",
"Foobar": "analyst",
"Lowe": "designer"
}
df_2 = pd.DataFrame(role_dict.items(),columns=["last","role"])
result = pd.merge(df,df_2,on=["last"],how="left")
output
first last role
0 Jon McSmith analyst
1 Jennifer Foobar analyst
2 Dan Raizman sales
3 Alden Lowe designer