Converting CSV to Dictionary with one column as keys and one column as its values

Question

Hello I have a CSV file of the format below:

ticket asset
1111   3456
1111   6789
1122   2345
1122   7890

I want to convert it to a Dict like:

{'1111': ['3456', '6789'], '1122':['2345', '7890']}

Basically want to have the ticket as 'key' and all the assets under that ticket as 'values'.

the csv.DictReader() helped a little but I am unable to extract unique ticket number for keys and match all the asset under it for values.

Any help would be great :)

Thanks for getting the CSV > Dict so quick!

If I want to convert a Tuple to Dict, how will that work?

e.g: Tuple: ((1111, 3456), (1111, 6789), (1122, 2345), (1122, 7890))

and I want that to be converted to:

{'1111': ['3456', '6789'], '1122':['2345', '7890']}

https://stackoverflow.com/questions/22219004/grouping-rows-in-list-in-pandas-groupby — Keyur Potdar, May 09 '19 at 15:21

Steven Rumbalski · Accepted Answer · 2019-05-09T15:30:10.497

7

Using collections.defaultdict:

from collections import defaultdict
import csv

d = defaultdict(list)
with open(filename, 'r', newline='') as f:
    reader = csv.reader(f, delimiter='\t')
    next(reader) # toss headers
    for ticket, asset in reader:
        d[ticket].append(asset)

With regular dictionary:

import csv

d = {}
with open(filename, 'r', newline='') as f:
    reader = csv.reader(f, delimiter='\t')
    next(reader) # toss headers
    for ticket, asset in reader:
        d.setdefault(ticket, []).append(asset)

edited May 09 '19 at 15:30

answered May 09 '19 at 15:24

Steven Rumbalski

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1

`for ticket, asset in reader: d.setdefault(ticket, []).append(asset)` is also a possibility. – Tim Pietzcker May 09 '19 at 15:27
@TimPietzcker: Fair enough. Creating and destroying a throwaway list is no worse than the extra in-statement. Plus it's shorter. Updating answer. – Steven Rumbalski May 09 '19 at 15:29
worked like a charm - only thing I had to change was to set delimiter = "," – Karthik May 09 '19 at 17:11

score 0 · Answer 2 · answered May 09 '19 at 15:47

Import your csv into a pandas dataframe. Use applymap to convert the dataframe into string data type. Group by ticket and convert all collected asset as a list. Then convert the dataframe into a dictionary(dict)

import pandas as pd
df=pd.read_csv('file.csv', header=0)
df=df.applymap(str).groupby('ticket')['asset'].apply(list).to_dict()
print(df)

Result:

{'1111': ['3456', '6789'], '1122': ['2345', '7890']}

Converting CSV to Dictionary with one column as keys and one column as its values

2 Answers2

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