Pandas, DataFrame: Splitting one column into multiple columns

df = pd.DataFrame([
        [6, "a: 1, b: 2"],
        [6, "a: 1, b: 2"],
        [6, "a: 1, b: 2"],
        [6, "a: 1, b: 2"],
    ], columns=['ID', 'dictionary'])

def str2dict(s):
    split = s.strip().split(',')
    d = {}
    for pair in split:
        k, v = [_.strip() for _ in pair.split(':')]
        d[k] = v
    return d

df.dictionary.apply(str2dict).apply(pd.Series)

Or:

pd.concat([df, df.dictionary.apply(str2dict).apply(pd.Series)], axis=1)

Here is a function that can convert the string to a dictionary and aggregate values based on the key; After the conversion it will be easy to get the results with the pd.Series method:

def str_to_dict(str1):
    import re
    from collections import defaultdict
    d = defaultdict(int)
    for k, v in zip(re.findall('[A-Z]', str1), re.findall('\d+', str1)):
        d[k] += int(v)
    return d

pd.concat([df, df['dictionary'].apply(str_to_dict).apply(pd.Series).fillna(0).astype(int)], axis=1)