[jira] [Commented] (ARROW-4032) [Python] New pyarrow.Table.from_pydict() function

2018-12-14 Thread David Lee (JIRA)


[ 
https://issues.apache.org/jira/browse/ARROW-4032?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16721745#comment-16721745
 ] 

David Lee commented on ARROW-4032:
--

Updated the sample code to include Schema and Safe options..

Passing in a schema will allow conversions from microseconds to milliseconds.

> [Python] New pyarrow.Table.from_pydict() function
> -
>
> Key: ARROW-4032
> URL: https://issues.apache.org/jira/browse/ARROW-4032
> Project: Apache Arrow
>  Issue Type: Task
>  Components: Python
>Reporter: David Lee
>Priority: Minor
>
> Here's a proposal to create a pyarrow.Table.from_pydict() function.
> Right now only pyarrow.Table.from_pandas() exist and there are inherit 
> problems using Pandas with NULL support for Int(s) and Boolean(s)
> [http://pandas.pydata.org/pandas-docs/version/0.23.4/gotchas.html]
> {{NaN}}, Integer {{NA}} values and {{NA}} type promotions:
> Sample python code on how this would work.
>  
> {code:java}
> import pyarrow as pa
> from datetime import datetime
> # convert microseconds to milliseconds. More support for MS in parquet.
> today = datetime.now()
> today = datetime(today.year, today.month, today.day, today.hour, 
> today.minute, today.second, today.microsecond - today.microsecond % 1000)
> pylist = [
> {"name": "Tom", "age": 10},
> {"name": "Mark", "age": 5, "city": "San Francisco"},
> {"name": "Pam", "age": 7, "birthday": today}
> ]
> def from_pydict(pylist, schema=None, columns=None, safe=True):
> arrow_columns = list()
> if schema:
> columns = schema.names
> if not columns:
> return
> for column in columns:
> arrow_columns.append(pa.array([v[column] if column in v else None for v in 
> pylist]))
> arrow_table = pa.Table.from_arrays(arrow_columns, columns)
> if schema:
> arrow_table = arrow_table.cast(schema, safe=safe)
> return arrow_table
> test = from_pydict(pylist, columns=['name' , 'age', 'city', 'birthday', 
> 'dummy'])
> test_schema = pa.schema([
> pa.field('name', pa.string()),
> pa.field('age', pa.int16()),
> pa.field('city', pa.string()),
> pa.field('birthday', pa.timestamp('ms'))
> ])
> test2 = from_pydict(pylist, schema=test_schema)
> {code}



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[jira] [Commented] (ARROW-4032) [Python] New pyarrow.Table.from_pydict() function

2018-12-14 Thread Wes McKinney (JIRA)


[ 
https://issues.apache.org/jira/browse/ARROW-4032?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=16721727#comment-16721727
 ] 

Wes McKinney commented on ARROW-4032:
-

You can do {{pa.array(pylist)}} already. So if we had a function to convert 
StructArray to Table then this would mostly do what you're describing. This was 
partly the intent of ARROW-40

> [Python] New pyarrow.Table.from_pydict() function
> -
>
> Key: ARROW-4032
> URL: https://issues.apache.org/jira/browse/ARROW-4032
> Project: Apache Arrow
>  Issue Type: Task
>  Components: Python
>Reporter: David Lee
>Priority: Minor
>
> Here's a proposal to create a pyarrow.Table.from_pydict() function.
> Right now only pyarrow.Table.from_pandas() exist and there are inherit 
> problems using Pandas with NULL support for Int(s) and Boolean(s)
> [http://pandas.pydata.org/pandas-docs/version/0.23.4/gotchas.html]
> {{NaN}}, Integer {{NA}} values and {{NA}} type promotions:
> Sample python code on how this would work.
>  
> {code:java}
> import pyarrow as pa
> from datetime import datetime
> pylist = [
> {"name": "Tom", "age": 10},
> {"name": "Mark", "age": 5, "city": "San Francisco"},
> {"name": "Pam", "age": 7, "birthday": datetime.now()}
> ]
> def from_pydict(pylist, columns):
> arrow_columns = list()
> for column in columns:
> arrow_columns.append(pa.array([v[column] if column in v else None for 
> v in pylist]))
> arrow_table = pa.Table.from_arrays(arrow_columns, columns)
> return arrow_table
> test = from_pydict(pylist, ['name' , 'age', 'city', 'birthday', 'dummy'])
> {code}
>  



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