Problems reading CSV file with commas and characters in pandas

Question

I am trying to read a csv file using pandas and the file has a column called Tags which consist of user provided tags and has tags like - , "", '',1950's, 16th-century. Since these are user provided, there are many special characters which are entered by mistake as well. The issue is that I cannot open the csv file using pandas read_csv. It shows error:Cparser, error tokenizing data. Can someone help me with reading the csv file into pandas?

Answer 1

Okay. Starting from a badly formatted CSV we can't read:

>>> !cat unquoted.csv
1950's,xyz.nl/user_003,bad, 123
17th,red,flower,xyz.nl/user_001,good,203
"",xyz.nl/user_239,not very,345
>>> pd.read_csv("unquoted.csv", header=None)
Traceback (most recent call last):
  File "<ipython-input-40-7d9aadb2fad5>", line 1, in <module>
    pd.read_csv("unquoted.csv", header=None)
[...]
  File "parser.pyx", line 1572, in pandas._parser.raise_parser_error (pandas/src/parser.c:17041)
CParserError: Error tokenizing data. C error: Expected 4 fields in line 2, saw 6

We can make a nicer version, taking advantage of the fact the last three columns are well-behaved:

import csv

with open("unquoted.csv", "rb") as infile, open("quoted.csv", "wb") as outfile:
    reader = csv.reader(infile)
    writer = csv.writer(outfile)
    for line in reader:
        newline = [','.join(line[:-3])] + line[-3:]
        writer.writerow(newline)

which produces

>>> !cat quoted.csv
1950's,xyz.nl/user_003,bad, 123
"17th,red,flower",xyz.nl/user_001,good,203
,xyz.nl/user_239,not very,345

and then we can read it:

>>> pd.read_csv("quoted.csv", header=None)
                 0                1         2    3
0           1950's  xyz.nl/user_003       bad  123
1  17th,red,flower  xyz.nl/user_001      good  203
2              NaN  xyz.nl/user_239  not very  345

I'd look into fixing this problem at source and getting data in a tolerable format, though. Tricks like this shouldn't be necessary, and it would have been very easy for it to be impossible to repair.