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https://issues.apache.org/jira/browse/ARROW-10344?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17218187#comment-17218187
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Gert Hulselmans commented on ARROW-10344:
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We need the final data to be readable from Python and R, so Feather looked like
a good choice.
To create the dataset the data is generated:
- 30k columns with 1M entries (data is generated separately for each of the
30k columns):
- This first part I had split up before so each feather file (of 10
files) had 3k columns (I can transpose those so I can used the dataset API).
- transpose data ==> 1M columns with 30k entries
Transposed data needs to be usable from python and R: around 20k columns (all
30k values) from 1M are extracted in each analysis.
> [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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