[
https://issues.apache.org/jira/browse/ARROW-4032?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16738614#comment-16738614
]
David Lee commented on ARROW-4032:
----------------------------------
Tests: With and Without safe=False
{code:python}
my_list = [
{'a':'one', 'b': 1},
{'a':'two', 'b': 2},
{'a':'three', 'b': 3},
{'a':'missing', 'b': None}
]
schema = pa.schema([
pa.field('a', pa.string()),
pa.field('b', pa.int16())
])
arrow_table = from_pylist(my_list, schema=schema)
arrow_table2 = pa.Table.from_pandas(pd.DataFrame(my_list), preserve_index=False)
arrow_table3 = pa.Table.from_pandas(pd.DataFrame(my_list), schema = schema,
preserve_index=False, safe=False)
>>> arrow_table.schema
a: string
b: int16
>>> arrow_table2.schema
a: string
b: double
metadata
--------
OrderedDict([(b'pandas',
b'{"index_columns": [], "column_indexes": [], "columns": [{"na'
b'me": "a", "field_name": "a", "pandas_type": "unicode", "nump'
b'y_type": "object", "metadata": null}, {"name": "b", "field_n'
b'ame": "b", "pandas_type": "float64", "numpy_type": "float64"'
b', "metadata": null}], "pandas_version": "0.23.4"}')])
>>> arrow_table3.schema
a: string
b: int16
metadata
--------
OrderedDict([(b'pandas',
b'{"index_columns": [], "column_indexes": [], "columns": [{"na'
b'me": "a", "field_name": "a", "pandas_type": "unicode", "nump'
b'y_type": "object", "metadata": null}, {"name": "b", "field_n'
b'ame": "b", "pandas_type": "int16", "numpy_type": "float64", '
b'"metadata": null}], "pandas_version": "0.23.4"}')])
{code}
> [Python] New pyarrow.Table functions: from_pydict(), from_pylist() and
> to_pylist()
> ----------------------------------------------------------------------------------
>
> 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)
> test_list = [
> {"name": "Tom", "age": 10},
> {"name": "Mark", "age": 5, "city": "San Francisco"},
> {"name": "Pam", "age": 7, "birthday": today}
> ]
> def from_pylist(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], safe=safe))
> arrow_table = pa.Table.from_arrays(arrow_columns, columns)
> if schema:
> arrow_table = arrow_table.cast(schema, safe=safe)
> return arrow_table
> test = from_pylist(test_list, 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_pylist(test_list, schema=test_schema)
> {code}
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