Pandas: Fill rows if 2 column strings are the same - python-3.x

Question

I have a data set with ton of columns, I just want to back fill the rows that are missing with existing row values. I am trying to back fill with this logic like: if 'school' and 'country' are the same string then replace 'state' value into the empty 'state' column.
Here is an example. Problem with this is that its combining the other rows I am trying not split the rows. Is there a way? Thanks!
Sample Data:
import pandas as pd
school = ['Univ of CT','Univ of CT','Oxford','Oxford','ABC Univ']
name = ['John','Matt','John','Ashley','John']
country = ['US','US','UK','UK','']
state = ['CT','','','ENG','']
df = pd.DataFrame({'school':school,'country':country,'state':state,'name':name})
df['school'] = df['school'].str.upper()
Above data gives preview like:
school country state name
UNIV OF CT US CT John
UNIV OF CT US Matt
OXFORD UK John
OXFORD UK ENG Ashley
ABC UNIV John
I am looking for output like this:
school country state name
UNIV OF CT US CT John
UNIV OF CT US CT Matt
OXFORD UK ENG John
OXFORD UK ENG Ashley
ABC UNIV John
Code I tried:
df = df.fillna('')
df = df.reset_index().groupby(['school','country']).agg(';'.join)
df = pd.DataFrame(df).reset_index()
len(df)

Answer 1

You can write a small function to basically look up the state if it is blank based on the school and country.
def find_state(school, country, state):
if len(state) > 0:
return state
found_state = df['state'][(df['school'] == school) & (df['country'] == country)]
return max(found_state)
So the full example would be as follows:
import pandas as pd
school = ['Univ of CT','Univ of CT','Oxford','Oxford','ABC Univ']
name = ['John','Matt','John','Ashley','John']
country = ['US','US','UK','UK','']
state = ['CT','','','ENG','']
df = pd.DataFrame({'school':school,'country':country,'state':state,'name':name})
df['school'] = df['school'].str.upper()
def find_state(school, country, state):
if len(state) > 0:
return state
found_state = df['state'][(df['school'] == school) & (df['country'] == country)]
return max(found_state)
df['state_new'] = [find_state(school, country, state) for school, country, state in
df[['school','country','state']].values]
print(df)
school country state name state_new
0 UNIV OF CT US CT John CT
1 UNIV OF CT US Matt CT
2 OXFORD UK John ENG
3 OXFORD UK ENG Ashley ENG
4 ABC UNIV John

Answer 2

try this
first try to convert empty space to nan and then simply use ffill() and bfill()
df = pd.DataFrame({'school':school,'country':country,'state':state,'name':name})
df['school'] = df['school'].str.upper()
df['state'] = df['state'].astype(str).replace('',np.nan)
df['state'] = df.groupby(['school', 'country'])['state'].transform(lambda x: x.ffill()).transform(lambda y: y.bfill())
print(df)
school country state name
UNIV OF CT US CT John
UNIV OF CT US CT Matt
OXFORD UK ENG John
OXFORD UK ENG Ashley
ABC UNIV NaN John