Hi, I am using pandas to parse a file with the following structure:
Name fileset type KB quota limit in_doubt grace | files quota limit in_doubt grace shortname sharedhome USR 14097664 524288000 545259520 0 none | 107110 0 0 0 none gracedays sharedhome USR 774858944 524288000 775946240 0 5 days | 1115717 0 0 0 none nametoolong sharedhome USR 27418496 524288000 545259520 0 none | 11581 0 0 0 none I was initially able to use df = pandas.read_csv(file_name, delimiter=r"\s+") because all the values for 'grace' were 'none'. Now, however, non-"none" values have appeared and this fails. I can't use pandas.read_fwf even with an explicit colspec, because the names in the first column which are too long for the column will displace the rest of the data to the right. The report which produces the file could in fact also generate a properly delimited CSV file, but I have a lot of historical data in the readable but poorly parsable format above that I need to deal with. If I were doing something similar in the shell, I would just pipe the file through sed or something to replace '5 days' with, say '5_days'. How could I achieve a similar sort of preprocessing in Python, ideally without having to generate a lot of temporary files? Cheers, Loris -- This signature is currently under constuction. -- https://mail.python.org/mailman/listinfo/python-list
