Reading csv containing a list in Pandas

One option is to use ast.literal_eval as converter:

>>> import ast
>>> df = pd.read_clipboard(header=None, quotechar='"', sep=',', 
...                   converters={1:ast.literal_eval})
>>> df
    0                                             1
0  HK  [5328.1, 5329.3, 2013-12-27 13:58:57.973614]
1  HK  [5328.1, 5329.3, 2013-12-27 13:58:59.237387]
2  HK  [5328.1, 5329.3, 2013-12-27 13:59:00.346325]

And convert those lists to a DataFrame if needed, for example with:

>>> df = pd.DataFrame.from_records(df[1].tolist(), index=df[0],
...                           columns=list('ABC')).reset_index()
>>> df['C'] = pd.to_datetime(df['C'])
>>> df
    0       A       B                          C
0  HK  5328.1  5329.3 2013-12-27 13:58:57.973614
1  HK  5328.1  5329.3 2013-12-27 13:58:59.237387
2  HK  5328.1  5329.3 2013-12-27 13:59:00.346325

df['new_column'] = df['column'].apply(lambda x: ast.literal_eval(x))

Just run the above code on the column containing list as string.

Based alko's answer, you can use the df.apply() function for the first part to read the actual data in the list string:

 >>> df = pd.read_clipboard(header=None,sep=',')
 >>> df
     0                                                  1
  0  HK  [u'5328.1', u'5329.3', '2013-12-27 13:58:57.97...
  1  HK  [u'5328.1', u'5329.3', '2013-12-27 13:58:59.23...
  2  HK  [u'5328.1', u'5329.3', '2013-12-27 13:59:00.34...
 >>> df[1] = df[1].apply(eval)
 >>> df
     0                                             1
  0  HK  [5328.1, 5329.3, 2013-12-27 13:58:57.973614]
  1  HK  [5328.1, 5329.3, 2013-12-27 13:58:59.237387]
  2  HK  [5328.1, 5329.3, 2013-12-27 13:59:00.346325]

use .strip() in python.

with open(csvfile, 'r')as infile:
    reader = csv.reader(infile)
    for row in reader:
        col1 = row[0]
        col2 = row[1:].strip("[]")