Create nested dictionary from file column and row headers

Question

I have a csv file where I want to query results based on the first row and first column. Given a csv that looks like:

subj_id, Sample1, Sample2, Sample3
XXX, 1, 2, 3
YYY, 4, 5, 6
ZZZ, 7, 8, 9

I would like to use the subj_id and SampleN to query the value. For example:

dct['XXX']['Sample1'] = 1, dct['YYY']['Sample2'] = 5

I have tried using csv.DictReader, but without success. How can I create a dictionary like the above? (Note: I am using python 3.6, as I see that DictReader is slightly different depending on the python version.)

My attempt was based on this SO question, where the start was:

import csv
from collections import defaultdict

dct = defaultdict(dict)

with open('toy_out_test_results.csv', 'r') as csv_file:
    for row in csv.DictReader(csv_file, fieldnames=['subj_id','Sample1','Sample2','Sample3']):
        dct[row['subj_id']][row['???']] =

but I ran into a roadblock here.

Sorry about that. I added my minimal example, which I couldn't even flesh out. — Adam_G
– Adam_G, Commented Sep 11, 2020 at 15:16
Thanks, that's perfect. Is the whitespace in your CSV intentional or do you want to trim all the fields? — ggorlen
– ggorlen, Commented Sep 11, 2020 at 15:24
Does this answer your question? Creating a dictionary from a CSV file — ggorlen
– ggorlen, Commented Sep 11, 2020 at 15:40

ggorlen · Accepted Answer · 2020-09-11 15:41:44Z

1

You can assign the row dictionary to the dct dict based on the row['subj_id'] value which you can pop out of the row:

import csv

dct = {}

with open('toy_out_test_results.csv', 'r') as csv_file:
    for row in csv.DictReader(csv_file):
        dct[row.pop('subj_id')] = row

print(dct['XXX']['Sample1']) # => '1'

This assumes a CSV without extra whitespace:

subj_id,Sample1,Sample2,Sample3
XXX,1,2,3
YYY,4,5,6
ZZZ,7,8,9

Beware that duplicate subj_ids will be overwritten with the last seen.

edited Sep 11, 2020 at 15:41

answered Sep 11, 2020 at 15:28

ggorlen

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1 Comment

Adam_G Over a year ago

Ah, now I see what I was missing. Thank you!

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