Populate a panda's dataframe column based on another column and dictionary value

Question

I have a data frame that contain a column called DIAGNOSES. This DIAGNOSES column contain a list of 1 or multiple strings, starting with a Character.

I want to check the first character of every row in DIAGNOSES and grab its first char to look it up from a dictionary to populate DIAGNOSES_TYPE column with these values.

Minimal Example:

diagnoses = {'A': 'Arbitrary', 'B': 'Brutal', 'C': 'Cluster', 'D': 'Dropped'}

df = pd.DataFrame({'DIAGNOSES': [['A03'], ['A03', 'B23'], ['A30', 'B54', 'D65', 'C60']]})

              DIAGNOSES
0                 [A03]
1            [A03, B23]
2  [A30, B54, D65, C60]

A little visualization to clarify what I want to get, I want to get the df['DIAGNOSES_TYPES'] populated:

I approached it this way:

def map_diagnose(df)
    for col in len(range(df)):
        for d in df['DIAGNOSIS']:
            for diag in d:
                if diag[0] in diagnoses_dict.keys():
                    df['DIAGNOSES_TYPES'] = diagnoses_dict.get(diag[0])
                df['DIAGNOSES_TYPES'] = ''
    return df

Answer 1

use explode , map and groupby :

diagnoses = {'A': 'Arbitrary', 'B': 'Brutal', 'C': 'Cluster', 'D': 'Dropped'}
df1 = df.explode('DIAGNOSES')
df1['SD'] = df1['DIAGNOSES'].str.extract('(\D)')
df1['DIAGNOSES_TYPES'] = df1['SD'].map(diagnoses)
df1.groupby(level=0).agg(list)

output:

    DIAGNOSES                SD             DIAGNOSES_TYPES
0   [A03]                    [A]            [Arbitrary]
1   [A03, B23]               [A, B]         [Arbitrary, Brutal]
2   [A30, B54, D65, C60]     [A, B, D, C]   [Arbitrary, Brutal, Dropped, Cluster]

Column 'SD' there is the first letter of each dagnoses used for mapping; you can drop this column if not needed

Answer 2

You can explode "DIAGNOSES" column, get the first elements of each string using str , map diagnoses dictionary to get types, groupby the index and aggregate to a list:

df['DIAGNOSES_TYPE'] = df['DIAGNOSES'].explode().str[0].map(diagnoses).groupby(level=0).apply(list)

Output:

              DIAGNOSES                         DIAGNOSES_TYPE
0                 [A03]                            [Arbitrary]
1            [A03, B23]                    [Arbitrary, Brutal]
2  [A30, B54, D65, C60]  [Arbitrary, Brutal, Dropped, Cluster]