code: #
  # Licensed to the Apache Software Foundation (ASF) under one or more
  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  import sys
  import warnings
  if sys.version >= '3':
      basestring = unicode = str
      xrange = range
  else:
      from itertools import izip as zip
  
  from pyspark import since
  from pyspark.rdd import _load_from_socket
  from pyspark.sql.pandas.serializers import ArrowCollectSerializer
  from pyspark.sql.types import IntegralType
  from pyspark.sql.types import *
  from pyspark.traceback_utils import SCCallSiteSync
  from pyspark.util import _exception_message
  
  
  class PandasConversionMixin(object):
      """
      Min-in for the conversion from Spark to pandas. Currently, only :class:`DataFrame`
      can use this class.
      """
  
      @since(1.3)
      def toPandas(self):
          """
          Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``.
  
          This is only available if Pandas is installed and available.
  
          .. note:: This method should only be used if the resulting Pandas's :class:`DataFrame` is
              expected to be small, as all the data is loaded into the driver's memory.
  
          .. note:: Usage with spark.sql.execution.arrow.pyspark.enabled=True is experimental.
  
          >>> df.toPandas()  # doctest: +SKIP
             age   name
          0    2  Alice
          1    5    Bob
          """
          from pyspark.sql.dataframe import DataFrame
  
          assert isinstance(self, DataFrame)
  
          from pyspark.sql.pandas.utils import require_minimum_pandas_version
          require_minimum_pandas_version()
  
          import numpy as np
          import pandas as pd
  
          timezone = self.sql_ctx._conf.sessionLocalTimeZone()
  
          if self.sql_ctx._conf.arrowPySparkEnabled():
              use_arrow = True
              try:
                  from pyspark.sql.pandas.types import to_arrow_schema
                  from pyspark.sql.pandas.utils import require_minimum_pyarrow_version
  
                  require_minimum_pyarrow_version()
                  to_arrow_schema(self.schema)
              except Exception as e:
  
                  if self.sql_ctx._conf.arrowPySparkFallbackEnabled():
                      msg = (
                          "toPandas attempted Arrow optimization because "
                          "'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, "
                          "failed by the reason below:\n  %s\n"
                          "Attempting non-optimization as "
                          "'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to "
                          "true." % _exception_message(e))
                      warnings.warn(msg)
                      use_arrow = False
                  else:
                      msg = (
                          "toPandas attempted Arrow optimization because "
                          "'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
                          "reached the error below and will not continue because automatic fallback "
                          "with 'spark.sql.execution.arrow.pyspark.fallback.enabled' has been set to "
                          "false.\n  %s" % _exception_message(e))
                      warnings.warn(msg)
                      raise
  
              # Try to use Arrow optimization when the schema is supported and the required version
              # of PyArrow is found, if 'spark.sql.execution.arrow.pyspark.enabled' is enabled.
              if use_arrow:
                  try:
                      from pyspark.sql.pandas.types import _check_dataframe_localize_timestamps
                      import pyarrow
                      batches = self._collect_as_arrow()
                      if len(batches) > 0:
                          table = pyarrow.Table.from_batches(batches)
                          # Pandas DataFrame created from PyArrow uses datetime64[ns] for date type
                          # values, but we should use datetime.date to match the behavior with when
                          # Arrow optimization is disabled.
                          pdf = table.to_pandas(date_as_object=True)
                          return _check_dataframe_localize_timestamps(pdf, timezone)
                      else:
                          return pd.DataFrame.from_records([], columns=self.columns)
                  except Exception as e:
                      # We might have to allow fallback here as well but multiple Spark jobs can
                      # be executed. So, simply fail in this case for now.
                      msg = (
                          "toPandas attempted Arrow optimization because "
                          "'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
                          "reached the error below and can not continue. Note that "
                          "'spark.sql.execution.arrow.pyspark.fallback.enabled' does not have an "
                          "effect on failures in the middle of "
                          "computation.\n  %s" % _exception_message(e))
                      warnings.warn(msg)
                      raise
  
          # Below is toPandas without Arrow optimization.
          pdf = pd.DataFrame.from_records(self.collect(), columns=self.columns)
  
          dtype = {}
          for field in self.schema:
              pandas_type = PandasConversionMixin._to_corrected_pandas_type(field.dataType)
              # SPARK-21766: if an integer field is nullable and has null values, it can be
              # inferred by pandas as float column. Once we convert the column with NaN back
              # to integer type e.g., np.int16, we will hit exception. So we use the inferred
              # float type, not the corrected type from the schema in this case.
              if pandas_type is not None and \
                  not(isinstance(field.dataType, IntegralType) and field.nullable and
                      pdf[field.name].isnull().any()):
                  dtype[field.name] = pandas_type
              # Ensure we fall back to nullable numpy types, even when whole column is null:
              if isinstance(field.dataType, IntegralType) and pdf[field.name].isnull().any():
                  dtype[field.name] = np.float64
              if isinstance(field.dataType, BooleanType) and pdf[field.name].isnull().any():
                  dtype[field.name] = np.object
  
          for f, t in dtype.items():
              pdf[f] = pdf[f].astype(t, copy=False)
  
          if timezone is None:
              return pdf
          else:
              from pyspark.sql.pandas.types import _check_series_convert_timestamps_local_tz
              for field in self.schema:
                  # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
                  if isinstance(field.dataType, TimestampType):
                      pdf[field.name] = \
                          _check_series_convert_timestamps_local_tz(pdf[field.name], timezone)
              return pdf
  
      @staticmethod
      def _to_corrected_pandas_type(dt):
          """
          When converting Spark SQL records to Pandas :class:`DataFrame`, the inferred data type
          may be wrong. This method gets the corrected data type for Pandas if that type may be
          inferred incorrectly.
          """
          import numpy as np
          if type(dt) == ByteType:
              return np.int8
          elif type(dt) == ShortType:
              return np.int16
          elif type(dt) == IntegerType:
              return np.int32
          elif type(dt) == LongType:
              return np.int64
          elif type(dt) == FloatType:
              return np.float32
          elif type(dt) == DoubleType:
              return np.float64
          elif type(dt) == BooleanType:
              return np.bool
          elif type(dt) == TimestampType:
              return np.datetime64
          else:
              return None
  
      def _collect_as_arrow(self):
          """
          Returns all records as a list of ArrowRecordBatches, pyarrow must be installed
          and available on driver and worker Python environments.
  
          .. note:: Experimental.
          """
          from pyspark.sql.dataframe import DataFrame
  
          assert isinstance(self, DataFrame)
  
          with SCCallSiteSync(self._sc):
              port, auth_secret, jsocket_auth_server = self._jdf.collectAsArrowToPython()
  
          # Collect list of un-ordered batches where last element is a list of correct order indices
          try:
              results = list(_load_from_socket((port, auth_secret), ArrowCollectSerializer()))
          finally:
              # Join serving thread and raise any exceptions from collectAsArrowToPython
              jsocket_auth_server.getResult()
  
          # Separate RecordBatches from batch order indices in results
          batches = results[:-1]
          batch_order = results[-1]
  
          # Re-order the batch list using the correct order
          return [batches[i] for i in batch_order]
  
  
  class SparkConversionMixin(object):
      """
      Min-in for the conversion from pandas to Spark. Currently, only :class:`SparkSession`
      can use this class.
      """
      def createDataFrame(self, data, schema=None, samplingRatio=None, verifySchema=True):
          from pyspark.sql import SparkSession
  
          assert isinstance(self, SparkSession)
  
          from pyspark.sql.pandas.utils import require_minimum_pandas_version
          require_minimum_pandas_version()
  
          timezone = self._wrapped._conf.sessionLocalTimeZone()
  
          # If no schema supplied by user then get the names of columns only
          if schema is None:
              schema = [str(x) if not isinstance(x, basestring) else
                        (x.encode('utf-8') if not isinstance(x, str) else x)
                        for x in data.columns]
  
          if self._wrapped._conf.arrowPySparkEnabled() and len(data) > 0:
              try:
                  return self._create_from_pandas_with_arrow(data, schema, timezone)
              except Exception as e:
                  from pyspark.util import _exception_message
  
                  if self._wrapped._conf.arrowPySparkFallbackEnabled():
                      msg = (
                          "createDataFrame attempted Arrow optimization because "
                          "'spark.sql.execution.arrow.pyspark.enabled' is set to true; however, "
                          "failed by the reason below:\n  %s\n"
                          "Attempting non-optimization as "
                          "'spark.sql.execution.arrow.pyspark.fallback.enabled' is set to "
                          "true." % _exception_message(e))
                      warnings.warn(msg)
                  else:
                      msg = (
                          "createDataFrame attempted Arrow optimization because "
                          "'spark.sql.execution.arrow.pyspark.enabled' is set to true, but has "
                          "reached the error below and will not continue because automatic "
                          "fallback with 'spark.sql.execution.arrow.pyspark.fallback.enabled' "
                          "has been set to false.\n  %s" % _exception_message(e))
                      warnings.warn(msg)
                      raise
          data = self._convert_from_pandas(data, schema, timezone)
          return self._create_dataframe(data, schema, samplingRatio, verifySchema)
  
      def _convert_from_pandas(self, pdf, schema, timezone):
          """
           Convert a pandas.DataFrame to list of records that can be used to make a DataFrame
           :return list of records
          """
          from pyspark.sql import SparkSession
  
          assert isinstance(self, SparkSession)
  
          if timezone is not None:
              from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
              copied = False
              if isinstance(schema, StructType):
                  for field in schema:
                      # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
                      if isinstance(field.dataType, TimestampType):
                          s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
                          if s is not pdf[field.name]:
                              if not copied:
                                  # Copy once if the series is modified to prevent the original
                                  # Pandas DataFrame from being updated
                                  pdf = pdf.copy()
                                  copied = True
                              pdf[field.name] = s
              else:
                  for column, series in pdf.iteritems():
                      s = _check_series_convert_timestamps_tz_local(series, timezone)
                      if s is not series:
                          if not copied:
                              # Copy once if the series is modified to prevent the original
                              # Pandas DataFrame from being updated
                              pdf = pdf.copy()
                              copied = True
                          pdf[column] = s
  
          # Convert pandas.DataFrame to list of numpy records
          np_records = pdf.to_records(index=False)
  
          # Check if any columns need to be fixed for Spark to infer properly
          if len(np_records) > 0:
              record_dtype = self._get_numpy_record_dtype(np_records[0])
              if record_dtype is not None:
                  return [r.astype(record_dtype).tolist() for r in np_records]
  
          # Convert list of numpy records to python lists
          return [r.tolist() for r in np_records]
  
      def _get_numpy_record_dtype(self, rec):
          """
          Used when converting a pandas.DataFrame to Spark using to_records(), this will correct
          the dtypes of fields in a record so they can be properly loaded into Spark.
          :param rec: a numpy record to check field dtypes
          :return corrected dtype for a numpy.record or None if no correction needed
          """
          import numpy as np
          cur_dtypes = rec.dtype
          col_names = cur_dtypes.names
          record_type_list = []
          has_rec_fix = False
          for i in xrange(len(cur_dtypes)):
              curr_type = cur_dtypes[i]
              # If type is a datetime64 timestamp, convert to microseconds
              # NOTE: if dtype is datetime[ns] then np.record.tolist() will output values as longs,
              # conversion from [us] or lower will lead to py datetime objects, see SPARK-22417
              if curr_type == np.dtype('datetime64[ns]'):
                  curr_type = 'datetime64[us]'
                  has_rec_fix = True
              record_type_list.append((str(col_names[i]), curr_type))
          return np.dtype(record_type_list) if has_rec_fix else None
  
      def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
          """
          Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
          to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
          data types will be used to coerce the data in Pandas to Arrow conversion.
          """
          from pyspark.sql import SparkSession
          from pyspark.sql.dataframe import DataFrame
  
          assert isinstance(self, SparkSession)
  
          from pyspark.sql.pandas.serializers import ArrowStreamPandasSerializer
          from pyspark.sql.types import TimestampType
          from pyspark.sql.pandas.types import from_arrow_type, to_arrow_type
          from pyspark.sql.pandas.utils import require_minimum_pandas_version, \
              require_minimum_pyarrow_version
  
          require_minimum_pandas_version()
          require_minimum_pyarrow_version()
  
          from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
          import pyarrow as pa
  
          # Create the Spark schema from list of names passed in with Arrow types
          if isinstance(schema, (list, tuple)):
              arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)
              struct = StructType()
              for name, field in zip(schema, arrow_schema):
                  struct.add(name, from_arrow_type(field.type), nullable=field.nullable)
              schema = struct
  
          # Determine arrow types to coerce data when creating batches
          if isinstance(schema, StructType):
              arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
          elif isinstance(schema, DataType):
              raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
          else:
              # Any timestamps must be coerced to be compatible with Spark
              arrow_types = [to_arrow_type(TimestampType())
                             if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
                             for t in pdf.dtypes]
  
          # Slice the DataFrame to be batched
          step = -(-len(pdf) // self.sparkContext.defaultParallelism)  # round int up
          pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))
  
          # Create list of Arrow (columns, type) for serializer dump_stream
          arrow_data = [[(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)]
                        for pdf_slice in pdf_slices]
  
          jsqlContext = self._wrapped._jsqlContext
  
          safecheck = self._wrapped._conf.arrowSafeTypeConversion()
          col_by_name = True  # col by name only applies to StructType columns, can't happen here
          ser = ArrowStreamPandasSerializer(timezone, safecheck, col_by_name)
  
          def reader_func(temp_filename):
              return self._jvm.PythonSQLUtils.readArrowStreamFromFile(jsqlContext, temp_filename)
  
          def create_RDD_server():
              return self._jvm.ArrowRDDServer(jsqlContext)
  
          # Create Spark DataFrame from Arrow stream file, using one batch per partition
          jrdd = self._sc._serialize_to_jvm(arrow_data, ser, reader_func, create_RDD_server)
          jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(), jsqlContext)
          df = DataFrame(jdf, self._wrapped)
          df._schema = schema
          return df
  
  
  def _test():
      import doctest
      from pyspark.sql import SparkSession
      import pyspark.sql.pandas.conversion
      globs = pyspark.sql.pandas.conversion.__dict__.copy()
      spark = SparkSession.builder\
          .master("local[4]")\
          .appName("sql.pandas.conversion tests")\
          .getOrCreate()
      globs['spark'] = spark
      (failure_count, test_count) = doctest.testmod(
          pyspark.sql.pandas.conversion, globs=globs,
          optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
      spark.stop()
      if failure_count:
          sys.exit(-1)
  
  
  if __name__ == "__main__":
      _test()
  
apis: numpy.dtype pandas.DataFrame.from_records pandas.api.types.is_datetime64tz_dtype pandas.api.types.is_datetime64_dtype