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https://issues.apache.org/jira/browse/ARROW-1886?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16407177#comment-16407177
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ASF GitHub Bot commented on ARROW-1886:
---------------------------------------
cpcloud commented on a change in pull request #1768: ARROW-1886: [C++/Python]
Flatten struct columns in table
URL: https://github.com/apache/arrow/pull/1768#discussion_r175944797
##########
File path: python/pyarrow/tests/test_table.py
##########
@@ -335,12 +348,34 @@ def test_table_remove_column_empty():
table = pa.Table.from_arrays(data, names=['a'])
t2 = table.remove_column(0)
+ t2._validate()
assert len(t2) == len(table)
t3 = t2.add_column(0, table[0])
+ t3._validate()
assert t3.equals(table)
+def test_table_flatten():
+ ty1 = pa.struct([pa.field('x', pa.int16()),
+ pa.field('y', pa.float32())])
+ ty2 = pa.struct([pa.field('nest', ty1)])
+ a = pa.array([(1, 2.5), (3, 4.5)], type=ty1)
+ b = pa.array([((11, 12.5),), ((13, 14.5),)], type=ty2)
+ c = pa.array([False, True], type=pa.bool_())
+
+ table = pa.Table.from_arrays([a, b, c], names=['a', 'b', 'c'])
+ t2 = table.flatten()
+ t2._validate()
+ expected = pa.Table.from_arrays([
+ pa.array([1, 3], type=pa.int16()),
+ pa.array([2.5, 4.5], type=pa.float32()),
+ pa.array([(11, 12.5), (13, 14.5)], type=ty1),
+ c],
+ names=['a.x', 'a.y', 'b.nest', 'c'])
Review comment:
We should have some tests to acknowledge that we have to deal with
non-variable-name-like-charactesrs at some point.
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> [Python] Add function to "flatten" structs within tables
> --------------------------------------------------------
>
> Key: ARROW-1886
> URL: https://issues.apache.org/jira/browse/ARROW-1886
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Wes McKinney
> Assignee: Antoine Pitrou
> Priority: Major
> Labels: pull-request-available
> Fix For: 0.10.0
>
>
> See discussion in https://issues.apache.org/jira/browse/ARROW-1873
> When a user has a struct column, it may be more efficient to flatten the
> struct into multiple columns of the form {{struct_name.field_name}} for each
> field in the struct. Then when you call {{to_pandas}}, Python dictionaries do
> not have to be created, and the conversion will be much more efficient
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