Efficient way to assign values from another column pandas df

Question

I\'m trying to create a more efficient script that creates a new column based off values in another column. The script below performs this but I can only select

Answer 1

# DataFrame Given
df = pd.DataFrame({
    'Day' : ['Mon','Tues','Mon','Wed','Thurs','Fri','Mon','Sat','Sun','Tues'],                 
    'Location' : ['Home','Home','Away','Home','Home','Home','Home','Home','Home','Away'],                   
     })
Unique_group = ['Mon','Tues','Wed']
df['Group'] = df['Day'].apply(lambda x:1 if x in Unique_group else 2)
df['Assign'] = np.zeros(len(df))
# Assigning the ditionary values for output from numeric
vals = dict([(i,'C'+str(i)) for i in range(len(df))])

Loop to cut the dataframe for each line and checking the previous 'Assign' column info to assign new value

for i in range(1,len(df)+1,1):
    # Slicing the Dataframe line by line
    df1 = df[:i]
    # Incorporating the conditions of Group and Location
    df1 = df1[(df1.Location == df1.Location.loc[i-1]) & (df1.Group == df1.Group.loc[i-1]) ]
    # Writing the 'Assign' value for the first line of sliced df
    if len(df1)==1:
        df.loc[i-1,'Assign'] = df[:i].Assign.max()+1
    # Writing the 'Assign value based on previous values if it has contiuos 2 values of same group
    elif (df1.Assign.value_counts()[df1.Assign.max()] <3):
        df.loc[i-1,'Assign'] = df1.Assign.max()
    # Writing 'Assign' value for new group
    else:
        df.loc[i-1,'Assign'] = df[:i]['Assign'].max()+1
df.Assign = df.Assign.map(vals)

Out:

     Day    Location    Group   Assign
0   Mon Home    1   C1
1   Tues    Home    1   C1
2   Mon Away    1   C2
3   Wed Home    1   C1
4   Thurs   Home    2   C3
5   Fri Home    2   C3
6   Mon Home    1   C4
7   Sat Home    2   C3
8   Sun Home    2   C5
9   Tues    Away    1   C2