[jira] [Created] (ARROW-6231) [Python] Consider assigning default column names when reading CSV file and header_rows=0

["Wes McKinney (JIRA)" <ji...@apache.org>]

Wes McKinney created ARROW-6231:
-----------------------------------

             Summary: [Python] Consider assigning default column names when reading CSV file and header_rows=0
                 Key: ARROW-6231
                 URL: https://issues.apache.org/jira/browse/ARROW-6231
             Project: Apache Arrow
          Issue Type: Improvement
          Components: Python
            Reporter: Wes McKinney
             Fix For: 0.15.0


This is a slight usability rough edge. Assigning default names (like "f0, f1, ...") would probably be better since then at least you can see how many columns there are and what is in them. 

{code}
In [10]: parse_options = csv.ParseOptions(delimiter='|', header_rows=0)                                                                                         

In [11]: %time table = csv.read_csv('Performance_2016Q4.txt', parse_options=parse_options)                                                                      
---------------------------------------------------------------------------
ArrowInvalid                              Traceback (most recent call last)
<timed exec> in <module>

~/miniconda/envs/pyarrow-14-1/lib/python3.7/site-packages/pyarrow/_csv.pyx in pyarrow._csv.read_csv()

~/miniconda/envs/pyarrow-14-1/lib/python3.7/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()

ArrowInvalid: header_rows == 0 needs explicit column names
{code}

In pandas integers are used, so some kind of default string would have to be defined

{code}
In [18]: df = pd.read_csv('Performance_2016Q4.txt', sep='|', header=None, low_memory=False)                                                                     

In [19]: df.columns                                                                                                                                             
Out[19]: 
Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
           dtype='int64')
{code}



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