Python: Iterate over values in a series and replace with dictionary values when key matches series v-CodePudding

I'm trying to iterate over a column in a dataframe and when the value matches a key from my dictionary it should then replace the value in another column with the value of the matching key.

    df = pd.DataFrame({'id': ['123', '456', '789'], 'Full': ['Yes', 'No', 'Yes'], 'Cat':['','','']})
    cats = {'123':'A', '456':'B', '789':'C'}
    for val in df.id:
        for key, cat in cats.items():
            if key == val:
                df.Cat.loc[(df.Full == 'Yes')] = cat
    df
        id  Full Cat
    0   123 Yes  C
    1   456 No  
    2   789 Yes  C

I would expect id 123 to have a Cat of 'A' but instead it only returns 'C'

Can anyone explain to me why the it isn't iterating over the keys in dictionary?

CodePudding user response：

You can use Series.replace and pass the dictionary, and assign the result to Cat column:

>>> df['Cat'] = df.id.replace(cats)
#output:

    id Full Cat
0  123  Yes   A
1  456   No   B
2  789  Yes   C

Or, if you intend to replace in only the rows with Full as Yes one way is to simply apply a function on axis=1 then implement the logic for each rows:

>>> df['Cat'] = df.apply(lambda x: cats.get(x.id, '') if x.Full == 'Yes' else '',
                     axis=1)

    id Full Cat
0  123  Yes   A
1  456   No    
2  789  Yes   C

CodePudding user response：

For filtered values in column use dict.get:

mask = df.Full == 'Yes'
df.loc[mask, 'Cat'] = df.loc[mask, 'id'].apply(lambda x: cats.get(x, ''))
print (df)
    id Full Cat
0  123  Yes   A
1  456   No    
2  789  Yes   C

If no match in dict is possible create Nones use:

cats = {'123':'A', '456':'B', '7890':'C'}

mask = df.Full == 'Yes'
df.loc[mask, 'Cat'] = df.loc[mask, 'id'].apply(cats.get)
print (df)
    id Full   Cat
0  123  Yes     A
1  456   No      
2  789  Yes  None