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https://issues.apache.org/jira/browse/ARROW-1632?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16369657#comment-16369657
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ASF GitHub Bot commented on ARROW-1632:
---------------------------------------

cpcloud commented on a change in pull request #1620: ARROW-1632: [Python] 
Permit categorical conversions in Table.to_pandas on a per-column basis
URL: https://github.com/apache/arrow/pull/1620#discussion_r169205821
 
 

 ##########
 File path: cpp/src/arrow/python/arrow_to_pandas.cc
 ##########
 @@ -1771,7 +1790,33 @@ Status ConvertColumnToPandas(PandasOptions options, 
const std::shared_ptr<Column
 
 Status ConvertTableToPandas(PandasOptions options, const 
std::shared_ptr<Table>& table,
                             int nthreads, MemoryPool* pool, PyObject** out) {
-  DataFrameBlockCreator helper(options, table, pool);
+  return ConvertTableToPandas(options, std::unordered_set<std::string>(), 
table, nthreads,
+                              pool, out);
+}
+
+Status ConvertTableToPandas(PandasOptions options,
+                            const std::unordered_set<std::string>& 
categorical_columns,
+                            const std::shared_ptr<Table>& table, int nthreads,
+                            MemoryPool* pool, PyObject** out) {
+  std::shared_ptr<Table> current_table = table;
+  if (categorical_columns.size() > 0) {
 
 Review comment:
   Any reason not to use `!categorical_columns.empty()`?

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> [Python] Permit categorical conversions in Table.to_pandas on a per-column 
> basis
> --------------------------------------------------------------------------------
>
>                 Key: ARROW-1632
>                 URL: https://issues.apache.org/jira/browse/ARROW-1632
>             Project: Apache Arrow
>          Issue Type: Improvement
>          Components: Python
>            Reporter: Wes McKinney
>            Assignee: Uwe L. Korn
>            Priority: Major
>              Labels: pull-request-available
>             Fix For: 0.9.0
>
>
> Currently this is all or nothing



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