如何用带有逗号分隔符和空格的pandas解析csv？

Question

I currently have the following data.csv which has a comma delimiter:

name,day
Chicken Sandwich,Wednesday
Pesto Pasta,Thursday
Lettuce, Tomato & Onion Sandwich,Friday
Lettuce, Tomato & Onion Pita,Friday
Soup,Saturday

The parser script is:

import pandas as pd


df = pd.read_csv('data.csv', delimiter=',', error_bad_lines=False, index_col=False)
print(df.head(5))

The output is:

Skipping line 4: expected 2 fields, saw 3
Skipping line 5: expected 2 fields, saw 3

               name        day
0  Chicken Sandwich  Wednesday
1       Pesto Pasta   Thursday
2              Soup   Saturday

How do I handle the case Lettuce, Tomato & Onion Sandwich . Each item should be separated by , but it's possible that an item has a comma in it followed by a space. The desired output is:

                               name        day
0                  Chicken Sandwich  Wednesday
1                       Pesto Pasta   Thursday
2  Lettuce, Tomato & Onion Sandwich     Friday
3      Lettuce, Tomato & Onion Pita     Friday
4                              Soup   Saturday

Answer 1

This might help.

import pandas as pd
p = "PATH_TO.csv"
df = pd.read_csv(p, delimiter='(,(?=\S)|:)')
#print(df.head(5))
print "-----"
print df["name"]
print "-----"
print df["day"]

Output:

-----
0                    Chicken Sandwich
1                         Pesto Pasta
2    Lettuce, Tomato & Onion Sandwich
3        Lettuce, Tomato & Onion Pita
4                                Soup
Name: name, dtype: object
-----
0    Wednesday
1     Thursday
2       Friday
3       Friday
4     Saturday
Name: day, dtype: object

Answer 2

An alternative that works in other situations too. OK, it's ugly.

import pandas as pd
from io import StringIO

for_pd = StringIO()
with open('theirry.csv') as input:
    for line in input:
        line = line.rstrip().replace(', ', '|||').replace(',', '```').replace('|||', ', ').replace('```', '|')
        print (line, file=for_pd)
for_pd.seek(0)

df = pd.read_csv(for_pd, sep='|')

print (df)

Result:

                               name        day
0                  Chicken Sandwich  Wednesday
1                       Pesto Pasta   Thursday
2  Lettuce, Tomato & Onion Sandwich     Friday
3      Lettuce, Tomato & Onion Pita     Friday
4                              Soup   Saturday