[GitHub] [arrow] emkornfield commented on a change in pull request #7604: ARROW-9223: [Python] Propagate timezone information in pandas conversion

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emkornfield commented on a change in pull request #7604:
URL: https://github.com/apache/arrow/pull/7604#discussion_r449096333



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File path: cpp/src/arrow/python/arrow_to_pandas.cc
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@@ -642,24 +641,27 @@ inline Status ConvertStruct(const PandasOptions& options, const ChunkedArray& da
   std::vector<OwnedRef> fields_data(num_fields);
   OwnedRef dict_item;
 
-  // XXX(wesm): In ARROW-7723, we found as a result of ARROW-3789 that second
+  // In ARROW-7723, we found as a result of ARROW-3789 that second
   // through microsecond resolution tz-aware timestamps were being promoted to
   // use the DATETIME_NANO_TZ conversion path, yielding a datetime64[ns] NumPy
   // array in this function. PyArray_GETITEM returns datetime.datetime for
   // units second through microsecond but PyLong for nanosecond (because
-  // datetime.datetime does not support nanoseconds). We inserted this hack to
-  // preserve the <= 0.15.1 behavior until a better solution can be devised
+  // datetime.datetime does not support nanoseconds).
+  // We force the object conversion to preserve the value of the timezone.
   PandasOptions modified_options = options;
-  modified_options.ignore_timezone = true;
   modified_options.coerce_temporal_nanoseconds = false;
 
   for (int c = 0; c < data.num_chunks(); c++) {
     auto arr = checked_cast<const StructArray*>(data.chunk(c).get());
     // Convert the struct arrays first
     for (int32_t i = 0; i < num_fields; i++) {
+      // Se notes above about conversion.
+      std::shared_ptr<Array> field = arr->field(static_cast<int>(i));
+      modified_options.timestamp_as_object =
+          field->type()->id() == Type::TIMESTAMP &&
+          !checked_cast<const TimestampType&>(*field->type()).timezone().empty();

Review comment:
       So on tz-naive timestamps I haven't verified my behavior but my concern with conversion to datetime is thus:
   - timezone naive datetimes reflect system timezone
   - Our timestamp -> datetime conversion assumes UTC as far as I can tell (like I said not sure what pandas does here).
   
   Hence the resulting datetime might be confusing because it represents the datetime assuming UTC and not local time zone.
   




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