Iterate through rows in a dataframe and change value of a column based on other column

Question

Assuming I have a dataframe called df which looks like the one shown below:

Id      Place        
1        NY        
2       Berlin          
3       Paris        
4       Paris         
5       Berlin       

And a dictionary, which has IDs as keys and places as values as shown below:

id_to_place = { 1 : "Berlin", 2: "Berlin", 3: "NY"}

I want to iterate through every row of the dataframe and see if the ID is contained in the id_to_place dictionary. If so, then I wanna replace the column Place of that row with the dictionary value. For instance after runninh the code I want the output to be:

Id      Place        
1       Berlin       
2       Berlin          
3       NY        
4       Paris         
5       Berlin       

So far I have tried this code:

id_to_place = { 1 : "Berlin", 2: "Berlin", 3: "NY"}

for index,row in df.iterrows():
    id = row['id']
    place = row['place']
    for item in id_to_place:
        if item == str(id):
          df.loc[df.id =id,'place'] = id_to_place[item]

print(df)

But when I run the code the dataframe stays unchangable. Does anyone have an idea as to why this happens? I appreciate any help!

Answer 1

Use Series.map for replace matched values, then replace NaNs by original column by Series.fillna:

df['Place'] = df['Id'].map(id_to_place).fillna(df['Place'])
print (df)
   Id   Place
0   1  Berlin
1   2  Berlin
2   3      NY
3   4   Paris
4   5  Berlin

Answer 2

Your current method isn't working because your items in your dictionaries are integers and you're checking them against str(id) which always returns False. If you remove the str and just check item against id then it works.

id_to_place = { 1 : "Berlin", 2: "Berlin", 3: "NY"}

for index,row in df.iterrows():
    id = row['id']
    place = row['place']
    for item in id_to_place:
        if item == id: # this line changed
          df.loc[df.id =id,'place'] = id_to_place[item]

print(df)