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https://issues.apache.org/jira/browse/ARROW-10344?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17217594#comment-17217594
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Joris Van den Bossche commented on ARROW-10344:
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bq. For filtering the data, is there an easy way to create a filter for
thousands of columns? e.g.: only return rows for which at least one column has
a value < 5000?
I don't think we provide a direct way, but with some python utilities, you
could construct such a filter. Eg with:
{code}
In [13]: import pyarrow.dataset as ds
In [14]: import operator
In [15]: import functools
In [16]: expr = functools.reduce(operator.or_, [ds.field(f"col{i}") < 5000 for
i in range(5000)])
{code}
But I have _no_ idea how that will perform if you use this as a filter (I don't
think we really considered such usecase for filtering up to now, so not sure
the expressions/filtering code are optimized for a filter with that many
columns)
> [Python] Get all columns names (or schema) from Feather file, before loading
> whole Feather file
> ------------------------------------------------------------------------------------------------
>
> Key: ARROW-10344
> URL: https://issues.apache.org/jira/browse/ARROW-10344
> Project: Apache Arrow
> Issue Type: New Feature
> Components: Python
> Affects Versions: 1.0.1
> Reporter: Gert Hulselmans
> Priority: Major
>
> Is there a way to get all column names (or schema) from a Feather file before
> loading the full Feather file?
> My Feather files are big (like 100GB) and the names of the columns are
> different per analysis and can't be hard coded.
> {code:python}
> import pyarrow.feather as feather
> # Code here to check which columns are in the feather file.
> ...
> my_columns = ...
> # Result is pandas.DataFrame
> read_df = feather.read_feather('/path/to/file', columns=my_columns)
> # Result is pyarrow.Table
> read_arrow = feather.read_table('/path/to/file', columns=my_columns)
> {code}
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