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https://issues.apache.org/jira/browse/ARROW-10344?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17217425#comment-17217425
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Gert Hulselmans edited comment on ARROW-10344 at 10/20/20, 8:24 AM:
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Thanks, this looks very helpful.
I was using Feather v2, but had to switch back to v1, due to a metadata bug
when writing Feather v2 files:
https://issues.apache.org/jira/browse/ARROW-10056
Is this pandas metadata very useful to have in my case? My feather files just
contain one string column (row indices) and for the rest I have just columns of
int16, int32, float32 (all other columns have the same type in one feather
file).
was (Author: ghuls):
I was using Feather v2, but had to switch back to v1, due to a metadata bug
when writing Feather v2 files:
https://issues.apache.org/jira/browse/ARROW-10056
Is this pandas metadata very useful to have in my case? My feather files just
contain one string column (row indices) and for the rest I have just columns of
int16, int32, float32 (all other columns have the same type in one feather
file).
> [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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