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Posted to dev@parquet.apache.org by "Wes McKinney (JIRA)" <ji...@apache.org> on 2017/09/11 17:06:00 UTC
[jira] [Moved] (PARQUET-1100) [Python] Segmentation fault after
loading parquet file to pandas dataframe
[ https://issues.apache.org/jira/browse/PARQUET-1100?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Wes McKinney moved ARROW-1440 to PARQUET-1100:
----------------------------------------------
Fix Version/s: (was: 0.7.0)
cpp-1.3.0
Affects Version/s: (was: 0.6.0)
cpp-1.2.0
Environment: (was: ubuntu 16.04.2)
Component/s: (was: Python)
parquet-cpp
Workflow: patch-available, re-open possible (was: jira)
Key: PARQUET-1100 (was: ARROW-1440)
Project: Parquet (was: Apache Arrow)
> [Python] Segmentation fault after loading parquet file to pandas dataframe
> --------------------------------------------------------------------------
>
> Key: PARQUET-1100
> URL: https://issues.apache.org/jira/browse/PARQUET-1100
> Project: Parquet
> Issue Type: Bug
> Components: parquet-cpp
> Affects Versions: cpp-1.2.0
> Reporter: Jarno Seppanen
> Assignee: Wes McKinney
> Fix For: cpp-1.3.0
>
> Attachments: part-00000-6570e34b-b42c-4a39-8adf-21d3a97fb87d.snappy.parquet
>
>
> Reading the attached parquet file into pandas dataframe and then using the dataframe segfaults.
> {noformat}
> Python 3.5.3 |Continuum Analytics, Inc.| (default, Mar 6 2017, 11:58:13)
> [GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux
> Type "help", "copyright", "credits" or "license" for more information.
> >>>
> >>> import pyarrow
> >>> import pyarrow.parquet as pq
> >>> pyarrow.__version__
> '0.6.0'
> >>> import pandas as pd
> >>> pd.__version__
> '0.19.0'
> >>> df = pq.read_table('part-00000-6570e34b-b42c-4a39-8adf-21d3a97fb87d.snappy.parquet') \
> ... .to_pandas()
> >>> len(df)
> 69
> >>> df.info()
> <class 'pandas.core.frame.DataFrame'>
> RangeIndex: 69 entries, 0 to 68
> Data columns (total 6 columns):
> label 69 non-null int32
> account_meta 69 non-null object
> features_type 69 non-null int32
> features_size 69 non-null int32
> features_indices 1 non-null object
> features_values 1 non-null object
> dtypes: int32(3), object(3)
> memory usage: 2.5+ KB
> >>>
> >>> pd.concat([df, df])
> Segmentation fault (core dumped)
> {noformat}
> Actually just print(df) is enough to trigger the segfault
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