zeddit opened a new issue, #45503:
URL: https://github.com/apache/arrow/issues/45503
### Describe the enhancement requested
In my case, my csv has date/datetime fields like `20250210`(pa.date32()),
`2025021106000062`(pa.timestamp('ms')), which cannot be converted smoothly.
Till now, csv.read_csv cannot recognize date32 data type and cannot convert
fractional seconds e.g. miliseconds. what I should do is using pandas like
below:
1. change my schema to pa.string() for those date/datetime fields
2. read_csv the input file
3. manually convert the data type to the one I need, e.g.
`pa.array(pd.to_datetime(table['date'], format='%Y%m%d')).cast(pa.date32(),
safe=True)`, and `pa.array(pd.to_datetime(table['timestamp_ms'],
format='%Y%m%d%H%M%S%f')).cast(pa.timestamp('ms'), safe=True)`
4. assemble the arrow table I need with
pa.Table.from_arrays([converted_arrays, part_of_original_table_arrays],
schema=original_schema)
which would be quite inefficient, is there any other method to boost this
way? thanks
### Component(s)
Python
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