Pandas dataframe Split One column data into 2 using some condition

Question

I have one dataframe which is below-

             0  
    ____________________________________
0     Country| India  
60        Delhi  
62       Mumbai  
68       Chennai  
75    Country| Italy  
78        Rome  
80       Venice  
85        Milan  
88    Country| Australia  
100      Sydney  
103      Melbourne  
107      Perth  

I want to Split the data in 2 columns so that in one column there will be country and on other there will be city. I have no idea where to start with. I want like below-

             0                    1
    ____________________________________
0     Country| India           Delhi
1     Country| India           Mumbai
2     Country| India           Chennai         
3    Country| Italy           Rome
4    Country| Italy           Venice   
5    Country| Italy           Milan        
6    Country| Australia       Sydney
7   Country| Australia       Melbourne
8   Country| Australia       Perth     

Any Idea how to do this?

Answer 1

Look for rows where | is present and pull into another column, and fill down on the newly created column :

(
    df.rename(columns={"0": "city"})
    # this looks for rows that contain '|' and puts them into a 
    # new column called Country. rows that do not match will be
    # null in the new column.
    .assign(Country=lambda x: x.loc[x.city.str.contains("\|"), "city"])
    # fill down on the Country column, this also has the benefit
    # of linking the Country with the City, 
    .ffill()
    # here we get rid of duplicate Country entries in city and Country
    # this ensures that only Country entries are in the Country column
    # and cities are in the City column
    .query("city != Country")
    # here we reverse the column positions to match your expected output 
    .iloc[:, ::-1]
)


      Country           city
60  Country| India      Delhi
62  Country| India      Mumbai
68  Country| India      Chennai
78  Country| Italy      Rome
80  Country| Italy      Venice
85  Country| Italy      Milan
100 Country| Australia  Sydney
103 Country| Australia  Melbourne
107 Country| Australia  Perth