Read multiple column csv file to nested dictionary

Question

I am trying to read from a csv file and store the data in a nested dictionary.

CSV file content

Type, ID, Frequency
Advanced,AAA,30 secs
Advanced,AAA,60 secs
Advanced,BBB,30 secs
Basic,CCC,30 secs
Basic,CCC,60 secs
Basic,DDD,30 secs

Expected output where the 'type' is the higher level key with the values as another dictionary with the ID and frequency as the key/value pair.

{'Advanced': {'AAA':['30 secs', '60 secs'], 'BBB':['30 secs']}, 'Basic': {'CCC':['30 secs', '60 secs'], 'DDD':['30 secs']}}

With two columns I got it to work using a defaultdict container.

symbols = co.defaultdict(list)
with open(filename, 'r') as f:
    lines = csv.DictReader(f)
    for line in lines:
        print(line)
        symbols[line['Type']].append(line['ID'])

blhsing · Accepted Answer · 2019-02-22 22:14:16Z

2

You can use dict.setdefault instead:

symbols = {}
with open(filename, 'r') as f:
    for row in csv.DictReader(f, skipinitialspace=True):
        symbols.setdefault(row['Type'], {}).setdefault(row['ID'], []).append(row['Frequency'])

symbols becomes:

{'Advanced': {'AAA': ['30 secs', '60 secs'], 'BBB': ['30 secs']}, 'Basic': {'CCC': ['30 secs', '60 secs'], 'DDD': ['30 secs']}}

Or if you prefer to use collections.defaultdict, you should make symbols a defaultdict of defaultdict of lists instead:

symbols = defaultdict(lambda: defaultdict(list))
for row in csv.DictReader(f, skipinitialspace=True):
    symbols[row['Type']][row['ID']].append(row['Frequency'])

answered Feb 22, 2019 at 22:14

blhsing

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