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https://issues.apache.org/jira/browse/ARROW-11473?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17460767#comment-17460767
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jason khadka commented on ARROW-11473:
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[~alenkaf]
Thanks for the solution. Yeah, definitely better than what I came up with. But
still would be better to have an easier way to ignore the missing column,
instead of doing all the gymnastic to go around the error:
df = pd.read_parquet(file_name, columns = read_columns, error = 'ignore')
> [Python] Needs a handling for missing columns while reading parquet file
> -------------------------------------------------------------------------
>
> Key: ARROW-11473
> URL: https://issues.apache.org/jira/browse/ARROW-11473
> Project: Apache Arrow
> Issue Type: New Feature
> Components: Python
> Reporter: jason khadka
> Priority: Major
>
> Currently there is no way to handle the error raised by missing columns in
> parquet file.
> If a column passed is missing, it just raises ArrowInvalid error
> {code:java}
> columns=[item1, item2, item3] #item3 is not there in parquet file
> pd.read_parquet(file_name, columns = columns)
> > ArrowInvalid: Field named 'item3' not found or not unique in the
> > schema.{code}
> There is no way to handle this. The ArrowInvalid also does not carry any
> information that can give out the field name so that in next try this filed
> can be ignored.
> Example :
> {code:java}
> from pyarrow.lib import ArrowInvalid
> read_columns = ['a','b','X']
> df = pd.DataFrame({'a': [1, 2, 3], 'b': ['foo', 'bar', 'jar']})
> file_name = '/tmp/my_df.pq' df.to_parquet(file_name)
> try:
> df = pd.read_parquet(file_name, columns = read_columns)
> except ArrowInvalid as e:
> inval = e
> print(inval.args)
> >("Field named 'X' not found or not unique in the schema.",){code}
>
> You could parse the message above to get 'X', but that is a bit of hectic
> solution. It would be great if the error message contained the field name.
> So, you could do for example :
>
> {code:java}
> inval.field
> > 'X'{code}
> Or a better feature would be to have a error handling in read_table of
> pyarrow, where something like \{{error='ignore'}}could be passed. This would
> then ignore the missing column by checking the schema.
> Example, in case above :
> {code:java}
> df = pd.read_parquet(file_name, columns = read_columns, error =
> 'ignore'){code}
> Would ignore the missing column 'X'
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