CODEWITHSUNDEEP
pythonpandasnumpy
dev
dev

Reputation: 1179

Python unique values per column in csv file row

Crunching on this for a long time. Is there an easy way using Numpy or Pandas or fixing my code to get the unique values for the column in a row separated by "|"

I.e the data:

"id","fname","lname","education","gradyear","attributes"
"1","john","smith","mit|harvard|ft|ft|ft","2003|207|212|212|212","qa|admin,co|master|NULL|NULL"
"2","john","doe","htw","2000","dev"

Output should be:

"id","fname","lname","education","gradyear","attributes"
"1","john","smith","mit|harvard|ft","2003|207|212","qa|admin,co|master|NULL"
"2","john","doe","htw","2000","dev"

My broken code:

import csv
import pprint

your_list = csv.reader(open('out.csv'))
your_list = list(your_list)

#pprint.pprint(your_list)
string = "|"
cols_no=6
for line in your_list:
    i=0
    for col in line:
      if i==cols_no:
        print "\n" 
        i=0
      if string in col:
        values = col.split("|")
        myset = set(values)
        items = list()
        for item in myset:
          items.append(item)
        print items
      else:
        print col+",",
      i=i+1

It outputs:

id, fname, lname, education, gradyear, attributes, 1, john, smith, ['harvard', 'ft', 'mit']
['2003', '212', '207']
['qa', 'admin,co', 'NULL', 'master']
2, john, doe, htw, 2000, dev,

Thanks in advance!

Upvotes: 1

Views: 939

Answers (2)

Julien
Julien

Reputation: 15071

If you don't care about the order when you have many items separated with |, this will work:

lst = ["id","fname","lname","education","gradyear","attributes",
"1","john","smith","mit|harvard|ft|ft|ft","2003|207|212|212|212","qa|admin,co|master|NULL|NULL",
"2","john","doe","htw","2000","dev"]

def no_duplicate(string):
    return "|".join(set(string.split("|")))

result = map(no_duplicate, lst)

print result

result:

['id', 'fname', 'lname', 'education', 'gradyear', 'attributes', '1', 'john', 'smith', 'ft|harvard|mit', '2003|207|212', 'NULL|admin,co|master|qa', '2', 'john', 'doe', 'htw', '2000', 'dev']

Upvotes: 0

Jon Clements
Jon Clements

Reputation: 142116

numpy/pandas is a bit overkill for what you can achieve with csv.DictReader and csv.DictWriter with a collections.OrderedDict, eg:

import csv
from collections import OrderedDict

# If using Python 2.x - use `open('output.csv', 'wb') instead
with open('input.csv') as fin, open('output.csv', 'w') as fout:
    csvin = csv.DictReader(fin)
    csvout = csv.DictWriter(fout, fieldnames=csvin.fieldnames, quoting=csv.QUOTE_ALL)
    csvout.writeheader()
    for row in csvin:
        for k, v in row.items():
            row[k] = '|'.join(OrderedDict.fromkeys(v.split('|')))
        csvout.writerow(row)

Gives you:

"id","fname","lname","education","gradyear","attributes"
"1","john","smith","mit|harvard|ft","2003|207|212","qa|admin,co|master|NULL"
"2","john","doe","htw","2000","dev"

Upvotes: 2

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