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https://issues.apache.org/jira/browse/ARROW-7661?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Krisztian Szucs resolved ARROW-7661.
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Resolution: Fixed
Issue resolved by pull request 6305
[https://github.com/apache/arrow/pull/6305]
> [Python] Non-optimal CSV chunking when no newline at end
> --------------------------------------------------------
>
> Key: ARROW-7661
> URL: https://issues.apache.org/jira/browse/ARROW-7661
> Project: Apache Arrow
> Issue Type: Bug
> Components: C++, Python
> Affects Versions: 0.14.1, 0.15.0, 0.15.1
> Reporter: Sascha Hofmann
> Assignee: Antoine Pitrou
> Priority: Major
> Labels: pull-request-available
> Fix For: 0.16.0
>
> Time Spent: 0.5h
> Remaining Estimate: 0h
>
> We are reading a very simple csv (see below).
> The file is only 245 bytes so way below the default _block_size_ in the
> _ReadOptions_. Thus we would expect the resulting table to have only one
> batch. At least, if I understand correctly that a _block_ refers to the
> number of lines of certain byte size?
> The docs state: _This will determine multi-threading granularity as well as
> the size of individual chunks in the Table._ For me, that means also the size
> of individual batches?
> Previously, we thought by fixing the block_size to the total file size, we
> would ensure that even for files larger than 1MB we get a pa.Table with only
> one batch. This mini file seems to prove us wrong?
> Additionally, if I convert back and forth to pandas we get only one batch.
>
> To reproduce:
> {code:java}
> import os
> from pyarrow import csv as pc
> import pyarrow as pa
> path = "test.csv"
> read_options = pc.ReadOptions(block_size=os.stat(path).st_size)
> df = pc.read_csv(path, read_options=read_options)
> print(len(df.to_batches()))
> # returns 2
> print(pa.Table.from_batches([df.to_batches()[1]]).to_pandas())
> # returns the last line of the file
> pdf = df.to_pandas()
> ndf = pa.Table.from_pandas(pdf)
> print(len(ndf.to_batches()))
> # returns 1{code}
> test.csv:
> {code:java}
> "Name","Month","Change in %"
> "Surrey Quays","Sep 18","1.01"
> "Surrey Quays","Oct 18","0.38"
> "Surrey Quays","Nov 18","0.97"
> "Surrey Quays","Dec 18","1.28"
> "Surrey Quays","Jan 19","2.43"
> "Surrey Quays","Feb 19","2.49"
> "Surrey Quays","Mar 19","0.81"
> {code}
>
>
>
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