The best answer is to use the CSV library, but there are wrappers around it.
For example, I use a wrapper of my own creation (csv_common.py) @ https://github.com/bschollnick/bas_Utilities <https://github.com/bschollnick/bas_Utilities>. The main benefit is that I can create variant CSV’s by just inheriting and redefining portions of it, very quickly. But, Panda’s and other tools are just as valid as well. But I would really recommend playing with the CSV library first, and understand how it works, before using a wrapper or a totally different tool. Understanding the basics really simplifies the learning process, and gives you a foundation to build upon. - Benjamin > On Mar 16, 2021, at 8:23 AM, Gys <inva...@invalid.com> wrote: > > On 3/12/21 11:28 AM, Johann Klammer wrote: >> Specifically ones with quoted strings. I'll have whitespace in >> there and possibly escaped quotechars. >> maybe newlines too. >> Which means that pyparsing commaSeparatedList.parseString(line) won't work. >> I also like to edit them for visual alignment, so there'll >> be whitespaces outside the strings(more than one) >> ...therefore, csv.DictReader() won't work. >> I'd like them read into a dict or something.. > > Hi Johann Klammer, > I use Pandas for handling *.csv files > > pandas documentation : > > <https://pandas.pydata.org/pandas-docs/stable/index.html> > > Hands on example : > > <https://chrisalbon.com/python/data_wrangling/pandas_dataframe_importing_csv/> > > -hth > Gys > > -- > https://mail.python.org/mailman/listinfo/python-list -- https://mail.python.org/mailman/listinfo/python-list
