[jira] [Created] (ARROW-5977) Method for read_csv to limit which columns are read?
Jordan Samuels created ARROW-5977: - Summary: Method for read_csv to limit which columns are read? Key: ARROW-5977 URL: https://issues.apache.org/jira/browse/ARROW-5977 Project: Apache Arrow Issue Type: Improvement Components: Python Affects Versions: 0.14.0 Reporter: Jordan Samuels In pandas there is pd.read_csv(usecols=...) but I can't see a way to do this in pyarrow. -- This message was sent by Atlassian JIRA (v7.6.14#76016)
[jira] [Created] (ARROW-5974) read_csv returns truncated read for some valid gzip files
Jordan Samuels created ARROW-5974:
-
Summary: read_csv returns truncated read for some valid gzip files
Key: ARROW-5974
URL: https://issues.apache.org/jira/browse/ARROW-5974
Project: Apache Arrow
Issue Type: Bug
Components: Python
Affects Versions: 0.14.0
Reporter: Jordan Samuels
If two gzipped files are concatenated together, the result is a valid gzip
file. However, it appears that pyarrow.csv.read_csv will only read the portion
related to the first file.
If the repro script
[here|https://gist.github.com/jordansamuels/d69f1c22c58418f5dfa0785b9ecd211e]
is run, the output is:
{{$ python repro.py}}
{{pyarrow.csv only reads one row:}}
{{ x}}
{{0 1}}
{{pandas reads two rows:}}
{{ x}}
{{0 1}}
{{1 2}}
{{pyarrow version: 0.14.0}}
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[jira] [Created] (ARROW-1957) Handle nanosecond timestamps in parquet serialization
Jordan Samuels created ARROW-1957:
-
Summary: Handle nanosecond timestamps in parquet serialization
Key: ARROW-1957
URL: https://issues.apache.org/jira/browse/ARROW-1957
Project: Apache Arrow
Issue Type: Improvement
Affects Versions: 0.8.0
Environment: Python 3.6.4, Mac OSX
Reporter: Jordan Samuels
Priority: Minor
The following code
{code:python}
import pyarrow as pa
import pyarrow.parquet as pq
import pandas as pd
n=3
df = pd.DataFrame({'x': range(n)}, index=pd.DatetimeIndex(start='2017-01-01',
freq='1n', periods=n))
pq.write_table(pa.Table.from_pandas(df), '/tmp/t.parquet'){code}
results in:
{{ArrowInvalid: Casting from timestamp[ns] to timestamp[us] would lose data:
14832288001}}
The desired effect is that we can save nanosecond resolution without losing
precision (e.g. conversion to ms). Note that if {{freq='1u'}} is used, the
code runs properly.
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