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Commitae435ba

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sander-goossusodapop
authored andcommitted
Use date_as_object and timestamp_as_object in pandas conversion pysql client
Use date_as_object and timestamp_as_object options in the to_pandas conversion from Arrow to prevent errors with out-of-bound values. See also:https://issues.apache.org/jira/browse/ARROW-5359Add test cases to TimestampTestsMixin (would be nice if we could replace those with unit tests)
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  • cmdexec/clients/python/src/databricks/sql

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‎cmdexec/clients/python/src/databricks/sql/client.py‎

Lines changed: 2 additions & 8 deletions
Original file line numberDiff line numberDiff line change
@@ -537,14 +537,8 @@ def _convert_arrow_table(self, table):
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# Need to rename columns, as the to_pandas function cannot handle duplicate column names
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table_renamed=table.rename_columns([str(c)forcinrange(table.num_columns)])
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df=table_renamed.to_pandas(types_mapper=dtype_mapping.get)
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for (i,col)inenumerate(df.columns):
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# Check for 0 because .dt doesn't work on empty series
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ifself.description[i][1]=='timestamp'andlen(df)>0:
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# We store the dtype as object so we don't use the pandas datetime dtype but
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# a native datetime.datetime
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df[col]=pandas.Series(df[col].dt.to_pydatetime(),dtype='object')
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df=table_renamed.to_pandas(
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types_mapper=dtype_mapping.get,date_as_object=True,timestamp_as_object=True)
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res=df.to_numpy(na_value=None)
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return [ResultRow(*v)forvinres]

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