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https://issues.apache.org/jira/browse/ARROW-2142?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16389417#comment-16389417
]
ASF GitHub Bot commented on ARROW-2142:
---------------------------------------
pitrou commented on a change in pull request #1635: ARROW-2142: [Python] Allow
conversion from Numpy struct array
URL: https://github.com/apache/arrow/pull/1635#discussion_r172811919
##########
File path: cpp/src/arrow/array.cc
##########
@@ -772,6 +773,105 @@ std::shared_ptr<Array> MakeArray(const
std::shared_ptr<ArrayData>& data) {
return out;
}
+// ----------------------------------------------------------------------
+// Misc APIs
+
+namespace internal {
+
+std::vector<ArrayVector> RechunkArraysConsistently(
+ const std::vector<ArrayVector>& groups) {
+ if (groups.size() <= 1) {
+ return groups;
+ }
+ // Adjacent slices defining the desired rechunking
+ std::vector<std::pair<int64_t, int64_t>> slices;
+ // Total number of elements common to all array groups
+ int64_t total_length = -1;
+
+ {
+ // Compute a vector of slices such that each array spans
+ // one or more *entire* slices only
+ // e.g. if group #1 has bounds {0, 2, 4, 5, 10}
+ // and group #2 has bounds {0, 5, 7, 10}
+ // then the computed slices are
+ // {(0, 2), (2, 4), (4, 5), (5, 7), (7, 10)}
+ std::set<int64_t> bounds;
+ for (auto& group : groups) {
+ int64_t cur = 0;
+ bounds.insert(cur);
+ for (auto& array : group) {
+ cur += array->length();
+ bounds.insert(cur);
Review comment:
You're right, rechunking can simply be done on the way. I've now pushed a
change.
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> [Python] Conversion from Numpy struct array unimplemented
> ---------------------------------------------------------
>
> Key: ARROW-2142
> URL: https://issues.apache.org/jira/browse/ARROW-2142
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Affects Versions: 0.8.0
> Reporter: Antoine Pitrou
> Assignee: Antoine Pitrou
> Priority: Major
> Labels: pull-request-available
>
> {code:python}
> >>> arr = np.array([(1.5,)], dtype=np.dtype([('x', np.float32)]))
> >>> arr
> array([(1.5,)], dtype=[('x', '<f4')])
> >>> arr[0]
> (1.5,)
> >>> arr['x']
> array([1.5], dtype=float32)
> >>> arr['x'][0]
> 1.5
> >>> pa.array(arr, type=pa.struct([pa.field('x', pa.float32())]))
> Traceback (most recent call last):
> File "<ipython-input-18-27a52820b7d8>", line 1, in <module>
> pa.array(arr, type=pa.struct([pa.field('x', pa.float32())]))
> File "array.pxi", line 177, in pyarrow.lib.array
> File "error.pxi", line 77, in pyarrow.lib.check_status
> File "error.pxi", line 85, in pyarrow.lib.check_status
> ArrowNotImplementedError:
> /home/antoine/arrow/cpp/src/arrow/python/numpy_to_arrow.cc:1585 code:
> converter.Convert()
> NumPyConverter doesn't implement <struct<x: float>> conversion.
> {code}
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