Adding support for Date type for pandas integrations

awswrangler/athena.py

@@ -31,25 +31,30 @@ def _type_athena2pandas(dtype):
             return "bool"
         elif dtype in ["string", "char", "varchar", "array", "row", "map"]:
             return "object"
-        elif dtype in ["timestamp", "date"]:
+        elif dtype == "timestamp":
             return "datetime64"
+        elif dtype == "date":
+            return "date"
         else:
             raise UnsupportedType(f"Unsupported Athena type: {dtype}")
 
     def get_query_dtype(self, query_execution_id):
         cols_metadata = self.get_query_columns_metadata(
             query_execution_id=query_execution_id)
         dtype = {}
+        parse_timestamps = []
         parse_dates = []
         for col_name, col_type in cols_metadata.items():
             ptype = Athena._type_athena2pandas(dtype=col_type)
-            if ptype == "datetime64":
-                parse_dates.append(col_name)
+            if ptype in ["datetime64", "date"]:
+                parse_timestamps.append(col_name)
+                if ptype == "date":
+                    parse_dates.append(col_name)
             else:
                 dtype[col_name] = ptype
         logger.debug(f"dtype: {dtype}")
         logger.debug(f"parse_dates: {parse_dates}")
-        return dtype, parse_dates
+        return dtype, parse_timestamps, parse_dates
 
     def create_athena_bucket(self):
         """

awswrangler/glue.py

@@ -3,6 +3,8 @@
 import logging
 from datetime import datetime, date
 
+import pyarrow
+
 from awswrangler.exceptions import UnsupportedType, UnsupportedFileFormat
 
 logger = logging.getLogger(__name__)
@@ -43,6 +45,28 @@ def get_table_python_types(self, database, table):
         dtypes = self.get_table_athena_types(database=database, table=table)
         return {k: Glue.type_athena2python(v) for k, v in dtypes.items()}
 
+    @staticmethod
+    def type_pyarrow2athena(dtype):
+        dtype = str(dtype).lower()
+        if dtype == "int32":
+            return "int"
+        elif dtype == "int64":
+            return "bigint"
+        elif dtype == "float":
+            return "float"
+        elif dtype == "double":
+            return "double"
+        elif dtype == "bool":
+            return "boolean"
+        elif dtype == "string":
+            return "string"
+        elif dtype.startswith("timestamp"):
+            return "timestamp"
+        elif dtype.startswith("date"):
+            return "date"
+        else:
+            raise UnsupportedType(f"Unsupported Pyarrow type: {dtype}")
+
     @staticmethod
     def type_pandas2athena(dtype):
         dtype = dtype.lower()
@@ -58,7 +82,7 @@ def type_pandas2athena(dtype):
             return "boolean"
         elif dtype == "object":
             return "string"
-        elif dtype[:10] == "datetime64":
+        elif dtype.startswith("datetime64"):
             return "timestamp"
         else:
             raise UnsupportedType(f"Unsupported Pandas type: {dtype}")
@@ -113,8 +137,7 @@ def metadata_to_glue(self,
                          extra_args=None):
         schema = Glue._build_schema(dataframe=dataframe,
                                     partition_cols=partition_cols,
-                                    preserve_index=preserve_index,
-                                    cast_columns=cast_columns)
+                                    preserve_index=preserve_index)
         table = table if table else Glue._parse_table_name(path)
         table = table.lower().replace(".", "_")
         if mode == "overwrite":
@@ -198,31 +221,38 @@ def get_connection_details(self, name):
             Name=name, HidePassword=False)["Connection"]
 
     @staticmethod
-    def _build_schema(dataframe,
-                      partition_cols,
-                      preserve_index,
-                      cast_columns=None):
+    def _extract_pyarrow_schema(dataframe, preserve_index):
+        cols = []
+        schema = []
+        for name, dtype in dataframe.dtypes.to_dict().items():
+            dtype = str(dtype)
+            if str(dtype) == "Int64":
+                schema.append((name, "int64"))
+            else:
+                cols.append(name)
+
+        # Convert pyarrow.Schema to list of tuples (e.g. [(name1, type1), (name2, type2)...])
+        schema += [(str(x.name), str(x.type))
+                   for x in pyarrow.Schema.from_pandas(
+                       df=dataframe[cols], preserve_index=preserve_index)]
+        logger.debug(f"schema: {schema}")
+        return schema
+
+    @staticmethod
+    def _build_schema(dataframe, partition_cols, preserve_index):
         logger.debug(f"dataframe.dtypes:\n{dataframe.dtypes}")
         if not partition_cols:
             partition_cols = []
+
+        pyarrow_schema = Glue._extract_pyarrow_schema(
+            dataframe=dataframe, preserve_index=preserve_index)
+
         schema_built = []
-        if preserve_index:
-            name = str(
-                dataframe.index.name) if dataframe.index.name else "index"
-            dataframe.index.name = "index"
-            dtype = str(dataframe.index.dtype)
-            if name not in partition_cols:
-                athena_type = Glue.type_pandas2athena(dtype)
-                schema_built.append((name, athena_type))
-        for col in dataframe.columns:
-            name = str(col)
-            if cast_columns and name in cast_columns:
-                dtype = cast_columns[name]
-            else:
-                dtype = str(dataframe[name].dtype)
+        for name, dtype in pyarrow_schema:
             if name not in partition_cols:
-                athena_type = Glue.type_pandas2athena(dtype)
+                athena_type = Glue.type_pyarrow2athena(dtype)
                 schema_built.append((name, athena_type))
+
         logger.debug(f"schema_built:\n{schema_built}")
         return schema_built
 

awswrangler/pandas.py

@@ -419,14 +419,16 @@ def read_sql_athena(self,
             message_error = f"Query error: {reason}"
             raise AthenaQueryError(message_error)
         else:
-            dtype, parse_dates = self._session.athena.get_query_dtype(
+            dtype, parse_timestamps, parse_dates = self._session.athena.get_query_dtype(
                 query_execution_id=query_execution_id)
             path = f"{s3_output}{query_execution_id}.csv"
             ret = self.read_csv(path=path,
                                 dtype=dtype,
-                                parse_dates=parse_dates,
+                                parse_dates=parse_timestamps,
                                 quoting=csv.QUOTE_ALL,
                                 max_result_size=max_result_size)
+            for col in parse_dates:
+                ret[col] = ret[col].dt.date
         return ret
 
     def to_csv(

testing/test_awswrangler/test_pandas.py

@@ -489,3 +489,56 @@ def test_to_csv_with_sep(
     assert len(list(dataframe.columns)) == len(list(dataframe2.columns))
     assert dataframe[dataframe["id"] == 0].iloc[0]["name"] == dataframe2[
         dataframe2["id"] == 0].iloc[0]["name"]
+
+
+@pytest.mark.parametrize("index", [None, "default", "my_date", "my_timestamp"])
+def test_to_parquet_types(session, bucket, database, index):
+    dataframe = pandas.read_csv("data_samples/complex.csv",
+                                dtype={"my_int_with_null": "Int64"},
+                                parse_dates=["my_timestamp", "my_date"])
+    dataframe["my_date"] = dataframe["my_date"].dt.date
+    dataframe["my_bool"] = True
+
+    preserve_index = True
+    if not index:
+        preserve_index = False
+    elif index != "default":
+        dataframe["new_index"] = dataframe[index]
+        dataframe = dataframe.set_index("new_index")
+
+    session.pandas.to_parquet(dataframe=dataframe,
+                              database=database,
+                              path=f"s3://{bucket}/test/",
+                              preserve_index=preserve_index,
+                              mode="overwrite",
+                              procs_cpu_bound=1)
+    sleep(1)
+    dataframe2 = session.pandas.read_sql_athena(sql="select * from test",
+                                                database=database)
+    for row in dataframe2.itertuples():
+        if index:
+            if index == "default":
+                assert isinstance(row[8], numpy.int64)
+            elif index == "my_date":
+                assert isinstance(row.new_index, date)
+            elif index == "my_timestamp":
+                assert isinstance(row.new_index, datetime)
+        assert isinstance(row.my_timestamp, datetime)
+        assert type(row.my_date) == date
+        assert isinstance(row.my_float, float)
+        assert isinstance(row.my_int, numpy.int64)
+        assert isinstance(row.my_string, str)
+        assert isinstance(row.my_bool, bool)
+        assert str(row.my_timestamp) == "2018-01-01 04:03:02.001000"
+        assert str(row.my_date) == "2019-02-02"
+        assert str(row.my_float) == "12345.6789"
+        assert str(row.my_int) == "123456789"
+        assert str(row.my_bool) == "True"
+        assert str(
+            row.my_string
+        ) == "foo\nboo\nbar\nFOO\nBOO\nBAR\nxxxxx\nÁÃÀÂÇ\n汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå汉字汉字汉字汉字汉字汉字汉字æøåæøåæøåæøåæøåæøåæøåæøåæøåæøå"
+    assert len(dataframe.index) == len(dataframe2.index)
+    if index:
+        assert (len(list(dataframe.columns)) + 1) == len(list(dataframe2.columns))
+    else:
+        assert len(list(dataframe.columns)) == len(list(dataframe2.columns))