text: #
  # 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.
  #
  """
  pandas-on-Spark specific features.
  """
  import inspect
  from typing import Any, Optional, Tuple, Union, TYPE_CHECKING, cast
  import types
  
  import numpy as np  # noqa: F401
  import pandas as pd
  from pyspark.sql import functions as F
  from pyspark.sql.functions import pandas_udf, PandasUDFType
  from pyspark.sql.types import StructField, StructType
  
  from pyspark.pandas.internal import (
      InternalFrame,
      SPARK_INDEX_NAME_FORMAT,
      SPARK_DEFAULT_SERIES_NAME,
  )
  from pyspark.pandas.typedef import infer_return_type, DataFrameType, ScalarType, SeriesType
  from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
  from pyspark.pandas.utils import (
      is_name_like_value,
      is_name_like_tuple,
      name_like_string,
      scol_for,
      verify_temp_column_name,
  )
  
  if TYPE_CHECKING:
      from pyspark.pandas.frame import DataFrame  # noqa: F401 (SPARK-34943)
      from pyspark.pandas.series import Series  # noqa: F401 (SPARK-34943)
  
  
  class PandasOnSparkFrameMethods(object):
      """ pandas-on-Spark specific features for DataFrame. """
  
      def __init__(self, frame: "DataFrame"):
          self._psdf = frame
  
      def attach_id_column(self, id_type: str, column: Union[Any, Tuple]) -> "DataFrame":
          """
          Attach a column to be used as identifier of rows similar to the default index.
  
          See also `Default Index type
          <https://koalas.readthedocs.io/en/latest/user_guide/options.html#default-index-type>`_.
  
          Parameters
          ----------
          id_type : string
              The id type.
  
              - 'sequence' : a sequence that increases one by one.
  
                .. note:: this uses Spark's Window without specifying partition specification.
                    This leads to move all data into single partition in single machine and
                    could cause serious performance degradation.
                    Avoid this method against very large dataset.
  
              - 'distributed-sequence' : a sequence that increases one by one,
                by group-by and group-map approach in a distributed manner.
              - 'distributed' : a monotonically increasing sequence simply by using PySpark’s
                monotonically_increasing_id function in a fully distributed manner.
  
          column : string or tuple of string
              The column name.
  
          Returns
          -------
          DataFrame
              The DataFrame attached the column.
  
          Examples
          --------
          >>> df = ps.DataFrame({"x": ['a', 'b', 'c']})
          >>> df.pandas_on_spark.attach_id_column(id_type="sequence", column="id")
             x  id
          0  a   0
          1  b   1
          2  c   2
  
          >>> df.pandas_on_spark.attach_id_column(id_type="distributed-sequence", column=0)
             x  0
          0  a  0
          1  b  1
          2  c  2
  
          >>> df.pandas_on_spark.attach_id_column(id_type="distributed", column=0.0)
          ... # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
             x  0.0
          0  a  ...
          1  b  ...
          2  c  ...
  
          For multi-index columns:
  
          >>> df = ps.DataFrame({("x", "y"): ['a', 'b', 'c']})
          >>> df.pandas_on_spark.attach_id_column(id_type="sequence", column=("id-x", "id-y"))
             x id-x
             y id-y
          0  a    0
          1  b    1
          2  c    2
  
          >>> df.pandas_on_spark.attach_id_column(id_type="distributed-sequence", column=(0, 1.0))
             x   0
             y 1.0
          0  a   0
          1  b   1
          2  c   2
          """
          from pyspark.pandas.frame import DataFrame
  
          if id_type == "sequence":
              attach_func = InternalFrame.attach_sequence_column
          elif id_type == "distributed-sequence":
              attach_func = InternalFrame.attach_distributed_sequence_column
          elif id_type == "distributed":
              attach_func = InternalFrame.attach_distributed_column
          else:
              raise ValueError(
                  "id_type should be one of 'sequence', 'distributed-sequence' and 'distributed'"
              )
  
          assert is_name_like_value(column, allow_none=False), column
          if not is_name_like_tuple(column):
              column = (column,)
  
          internal = self._psdf._internal
  
          if len(column) != internal.column_labels_level:
              raise ValueError(
                  "The given column `{}` must be the same length as the existing columns.".format(
                      column
                  )
              )
          elif column in internal.column_labels:
              raise ValueError(
                  "The given column `{}` already exists.".format(name_like_string(column))
              )
  
          # Make sure the underlying Spark column names are the form of
          # `name_like_string(column_label)`.
          sdf = internal.spark_frame.select(
              [
                  scol.alias(SPARK_INDEX_NAME_FORMAT(i))
                  for i, scol in enumerate(internal.index_spark_columns)
              ]
              + [
                  scol.alias(name_like_string(label))
                  for scol, label in zip(internal.data_spark_columns, internal.column_labels)
              ]
          )
          sdf = attach_func(sdf, name_like_string(column))
  
          return DataFrame(
              InternalFrame(
                  spark_frame=sdf,
                  index_spark_columns=[
                      scol_for(sdf, SPARK_INDEX_NAME_FORMAT(i)) for i in range(internal.index_level)
                  ],
                  index_names=internal.index_names,
                  index_dtypes=internal.index_dtypes,
                  column_labels=internal.column_labels + [column],
                  data_spark_columns=(
                      [scol_for(sdf, name_like_string(label)) for label in internal.column_labels]
                      + [scol_for(sdf, name_like_string(column))]
                  ),
                  data_dtypes=(internal.data_dtypes + [None]),
                  column_label_names=internal.column_label_names,
              ).resolved_copy
          )
  
      def apply_batch(self, func, args=(), **kwds) -> "DataFrame":
          """
          Apply a function that takes pandas DataFrame and outputs pandas DataFrame. The pandas
          DataFrame given to the function is of a batch used internally.
  
          See also `Transform and apply a function
          <https://koalas.readthedocs.io/en/latest/user_guide/transform_apply.html>`_.
  
          .. note:: the `func` is unable to access to the whole input frame. pandas-on-Spark
              internally splits the input series into multiple batches and calls `func` with each
              batch multiple times. Therefore, operations such as global aggregations are impossible.
              See the example below.
  
              >>> # This case does not return the length of whole frame but of the batch internally
              ... # used.
              ... def length(pdf) -> ps.DataFrame[int]:
              ...     return pd.DataFrame([len(pdf)])
              ...
              >>> df = ps.DataFrame({'A': range(1000)})
              >>> df.pandas_on_spark.apply_batch(length)  # doctest: +SKIP
                  c0
              0   83
              1   83
              2   83
              ...
              10  83
              11  83
  
          .. note:: this API executes the function once to infer the type which is
              potentially expensive, for instance, when the dataset is created after
              aggregations or sorting.
  
              To avoid this, specify return type in ``func``, for instance, as below:
  
              >>> def plus_one(x) -> ps.DataFrame[float, float]:
              ...     return x + 1
  
              If the return type is specified, the output column names become
              `c0, c1, c2 ... cn`. These names are positionally mapped to the returned
              DataFrame in ``func``.
  
              To specify the column names, you can assign them in a pandas friendly style as below:
  
              >>> def plus_one(x) -> ps.DataFrame["a": float, "b": float]:
              ...     return x + 1
  
              >>> pdf = pd.DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]})
              >>> def plus_one(x) -> ps.DataFrame[zip(pdf.dtypes, pdf.columns)]:
              ...     return x + 1
  
              When the given function has the return type annotated, the original index of the
              DataFrame will be lost and a default index will be attached to the result DataFrame.
              Please be careful about configuring the default index. See also `Default Index Type
              <https://koalas.readthedocs.io/en/latest/user_guide/options.html#default-index-type>`_.
  
  
          Parameters
          ----------
          func : function
              Function to apply to each pandas frame.
          args : tuple
              Positional arguments to pass to `func` in addition to the
              array/series.
          **kwds
              Additional keyword arguments to pass as keywords arguments to
              `func`.
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          DataFrame.apply: For row/columnwise operations.
          DataFrame.applymap: For elementwise operations.
          DataFrame.aggregate: Only perform aggregating type operations.
          DataFrame.transform: Only perform transforming type operations.
          Series.pandas_on_spark.transform_batch: transform the search as each pandas chunks.
  
          Examples
          --------
          >>> df = ps.DataFrame([(1, 2), (3, 4), (5, 6)], columns=['A', 'B'])
          >>> df
             A  B
          0  1  2
          1  3  4
          2  5  6
  
          >>> def query_func(pdf) -> ps.DataFrame[int, int]:
          ...     return pdf.query('A == 1')
          >>> df.pandas_on_spark.apply_batch(query_func)
             c0  c1
          0   1   2
  
          >>> def query_func(pdf) -> ps.DataFrame["A": int, "B": int]:
          ...     return pdf.query('A == 1')
          >>> df.pandas_on_spark.apply_batch(query_func)
             A  B
          0  1  2
  
          You can also omit the type hints so pandas-on-Spark infers the return schema as below:
  
          >>> df.pandas_on_spark.apply_batch(lambda pdf: pdf.query('A == 1'))
             A  B
          0  1  2
  
          You can also specify extra arguments.
  
          >>> def calculation(pdf, y, z) -> ps.DataFrame[int, int]:
          ...     return pdf ** y + z
          >>> df.pandas_on_spark.apply_batch(calculation, args=(10,), z=20)
                  c0        c1
          0       21      1044
          1    59069   1048596
          2  9765645  60466196
  
          You can also use ``np.ufunc`` and built-in functions as input.
  
          >>> df.pandas_on_spark.apply_batch(np.add, args=(10,))
              A   B
          0  11  12
          1  13  14
          2  15  16
  
          >>> (df * -1).pandas_on_spark.apply_batch(abs)
             A  B
          0  1  2
          1  3  4
          2  5  6
  
          """
          # TODO: codes here partially duplicate `DataFrame.apply`. Can we deduplicate?
  
          from pyspark.pandas.groupby import GroupBy
          from pyspark.pandas.frame import DataFrame
          from pyspark import pandas as ps
  
          if not isinstance(func, types.FunctionType):
              assert callable(func), "the first argument should be a callable function."
              f = func
              func = lambda *args, **kwargs: f(*args, **kwargs)
  
          spec = inspect.getfullargspec(func)
          return_sig = spec.annotations.get("return", None)
          should_infer_schema = return_sig is None
  
          original_func = func
          func = lambda o: original_func(o, *args, **kwds)
  
          self_applied = DataFrame(self._psdf._internal.resolved_copy)  # type: DataFrame
  
          if should_infer_schema:
              # Here we execute with the first 1000 to get the return type.
              # If the records were less than 1000, it uses pandas API directly for a shortcut.
              limit = ps.get_option("compute.shortcut_limit")
              pdf = self_applied.head(limit + 1)._to_internal_pandas()
              applied = func(pdf)
              if not isinstance(applied, pd.DataFrame):
                  raise ValueError(
                      "The given function should return a frame; however, "
                      "the return type was %s." % type(applied)
                  )
              psdf = ps.DataFrame(applied)  # type: DataFrame
              if len(pdf) <= limit:
                  return psdf
  
              return_schema = force_decimal_precision_scale(
                  as_nullable_spark_type(psdf._internal.to_internal_spark_frame.schema)
              )
  
              output_func = GroupBy._make_pandas_df_builder_func(
                  self_applied, func, return_schema, retain_index=True
              )
              sdf = self_applied._internal.to_internal_spark_frame.mapInPandas(
                  lambda iterator: map(output_func, iterator), schema=return_schema
              )
  
              # If schema is inferred, we can restore indexes too.
              internal = psdf._internal.with_new_sdf(sdf)
          else:
              return_type = infer_return_type(original_func)
              is_return_dataframe = isinstance(return_type, DataFrameType)
              if not is_return_dataframe:
                  raise TypeError(
                      "The given function should specify a frame as its type "
                      "hints; however, the return type was %s." % return_sig
                  )
              return_schema = cast(DataFrameType, return_type).spark_type
  
              output_func = GroupBy._make_pandas_df_builder_func(
                  self_applied, func, return_schema, retain_index=False
              )
              sdf = self_applied._internal.to_internal_spark_frame.mapInPandas(
                  lambda iterator: map(output_func, iterator), schema=return_schema
              )
  
              # Otherwise, it loses index.
              internal = InternalFrame(
                  spark_frame=sdf,
                  index_spark_columns=None,
                  data_dtypes=cast(DataFrameType, return_type).dtypes,
              )
  
          return DataFrame(internal)
  
      def transform_batch(self, func, *args, **kwargs) -> Union["DataFrame", "Series"]:
          """
          Transform chunks with a function that takes pandas DataFrame and outputs pandas DataFrame.
          The pandas DataFrame given to the function is of a batch used internally. The length of
          each input and output should be the same.
  
          See also `Transform and apply a function
          <https://koalas.readthedocs.io/en/latest/user_guide/transform_apply.html>`_.
  
          .. note:: the `func` is unable to access to the whole input frame. pandas-on-Spark
              internally splits the input series into multiple batches and calls `func` with each
              batch multiple times. Therefore, operations such as global aggregations are impossible.
              See the example below.
  
              >>> # This case does not return the length of whole frame but of the batch internally
              ... # used.
              ... def length(pdf) -> ps.DataFrame[int]:
              ...     return pd.DataFrame([len(pdf)] * len(pdf))
              ...
              >>> df = ps.DataFrame({'A': range(1000)})
              >>> df.pandas_on_spark.transform_batch(length)  # doctest: +SKIP
                  c0
              0   83
              1   83
              2   83
              ...
  
          .. note:: this API executes the function once to infer the type which is
              potentially expensive, for instance, when the dataset is created after
              aggregations or sorting.
  
              To avoid this, specify return type in ``func``, for instance, as below:
  
              >>> def plus_one(x) -> ps.DataFrame[float, float]:
              ...     return x + 1
  
              If the return type is specified, the output column names become
              `c0, c1, c2 ... cn`. These names are positionally mapped to the returned
              DataFrame in ``func``.
  
              To specify the column names, you can assign them in a pandas friendly style as below:
  
              >>> def plus_one(x) -> ps.DataFrame['a': float, 'b': float]:
              ...     return x + 1
  
              >>> pdf = pd.DataFrame({'a': [1, 2, 3], 'b': [3, 4, 5]})
              >>> def plus_one(x) -> ps.DataFrame[zip(pdf.dtypes, pdf.columns)]:
              ...     return x + 1
  
              When the given function returns DataFrame and has the return type annotated, the
              original index of the DataFrame will be lost and then a default index will be attached
              to the result. Please be careful about configuring the default index. See also
              `Default Index Type
              <https://koalas.readthedocs.io/en/latest/user_guide/options.html#default-index-type>`_.
  
          Parameters
          ----------
          func : function
              Function to transform each pandas frame.
          *args
              Positional arguments to pass to func.
          **kwargs
              Keyword arguments to pass to func.
  
          Returns
          -------
          DataFrame or Series
  
          See Also
          --------
          DataFrame.pandas_on_spark.apply_batch: For row/columnwise operations.
          Series.pandas_on_spark.transform_batch: transform the search as each pandas chunks.
  
          Examples
          --------
          >>> df = ps.DataFrame([(1, 2), (3, 4), (5, 6)], columns=['A', 'B'])
          >>> df
             A  B
          0  1  2
          1  3  4
          2  5  6
  
          >>> def plus_one_func(pdf) -> ps.DataFrame[int, int]:
          ...     return pdf + 1
          >>> df.pandas_on_spark.transform_batch(plus_one_func)
             c0  c1
          0   2   3
          1   4   5
          2   6   7
  
          >>> def plus_one_func(pdf) -> ps.DataFrame['A': int, 'B': int]:
          ...     return pdf + 1
          >>> df.pandas_on_spark.transform_batch(plus_one_func)
             A  B
          0  2  3
          1  4  5
          2  6  7
  
          >>> def plus_one_func(pdf) -> ps.Series[int]:
          ...     return pdf.B + 1
          >>> df.pandas_on_spark.transform_batch(plus_one_func)
          0    3
          1    5
          2    7
          dtype: int64
  
          You can also omit the type hints so pandas-on-Spark infers the return schema as below:
  
          >>> df.pandas_on_spark.transform_batch(lambda pdf: pdf + 1)
             A  B
          0  2  3
          1  4  5
          2  6  7
  
          >>> (df * -1).pandas_on_spark.transform_batch(abs)
             A  B
          0  1  2
          1  3  4
          2  5  6
  
          Note that you should not transform the index. The index information will not change.
  
          >>> df.pandas_on_spark.transform_batch(lambda pdf: pdf.B + 1)
          0    3
          1    5
          2    7
          Name: B, dtype: int64
  
          You can also specify extra arguments as below.
  
          >>> df.pandas_on_spark.transform_batch(lambda pdf, a, b, c: pdf.B + a + b + c, 1, 2, c=3)
          0     8
          1    10
          2    12
          Name: B, dtype: int64
          """
          from pyspark.pandas.groupby import GroupBy
          from pyspark.pandas.frame import DataFrame
          from pyspark.pandas.series import first_series
          from pyspark import pandas as ps
  
          assert callable(func), "the first argument should be a callable function."
          spec = inspect.getfullargspec(func)
          return_sig = spec.annotations.get("return", None)
          should_infer_schema = return_sig is None
          original_func = func
          func = lambda o: original_func(o, *args, **kwargs)
  
          names = self._psdf._internal.to_internal_spark_frame.schema.names
  
          def pandas_concat(series):
              # The input can only be a DataFrame for struct from Spark 3.0.
              # This works around to make the input as a frame. See SPARK-27240
              pdf = pd.concat(series, axis=1)
              pdf.columns = names
              return pdf
  
          def apply_func(pdf):
              return func(pdf).to_frame()
  
          def pandas_extract(pdf, name):
              # This is for output to work around a DataFrame for struct
              # from Spark 3.0.  See SPARK-23836
              return pdf[name]
  
          def pandas_series_func(f):
              ff = f
              return lambda *series: first_series(ff(*series))
  
          def pandas_frame_func(f, field_name):
              ff = f
              return lambda *series: pandas_extract(ff(pandas_concat(series)), field_name)
  
          if should_infer_schema:
              # Here we execute with the first 1000 to get the return type.
              # If the records were less than 1000, it uses pandas API directly for a shortcut.
              limit = ps.get_option("compute.shortcut_limit")
              pdf = self._psdf.head(limit + 1)._to_internal_pandas()
              transformed = func(pdf)
              if not isinstance(transformed, (pd.DataFrame, pd.Series)):
                  raise ValueError(
                      "The given function should return a frame; however, "
                      "the return type was %s." % type(transformed)
                  )
              if len(transformed) != len(pdf):
                  raise ValueError("transform_batch cannot produce aggregated results")
              psdf_or_psser = ps.from_pandas(transformed)
  
              if isinstance(psdf_or_psser, ps.Series):
                  psser = cast(ps.Series, psdf_or_psser)
  
                  spark_return_type = force_decimal_precision_scale(
                      as_nullable_spark_type(psser.spark.data_type)
                  )
                  return_schema = StructType(
                      [StructField(SPARK_DEFAULT_SERIES_NAME, spark_return_type)]
                  )
                  output_func = GroupBy._make_pandas_df_builder_func(
                      self._psdf, apply_func, return_schema, retain_index=False
                  )
  
                  pudf = pandas_udf(returnType=spark_return_type, functionType=PandasUDFType.SCALAR)(
                      pandas_series_func(output_func)
                  )
                  columns = self._psdf._internal.spark_columns
                  # TODO: Index will be lost in this case.
                  internal = self._psdf._internal.copy(
                      column_labels=psser._internal.column_labels,
                      data_spark_columns=[
                          pudf(F.struct(*columns)).alias(psser._internal.data_spark_column_names[0])
                      ],
                      data_dtypes=psser._internal.data_dtypes,
                      column_label_names=psser._internal.column_label_names,
                  )
                  return first_series(DataFrame(internal))
              else:
                  psdf = cast(DataFrame, psdf_or_psser)
                  if len(pdf) <= limit:
                      # only do the short cut when it returns a frame to avoid
                      # operations on different dataframes in case of series.
                      return psdf
  
                  # Force nullability.
                  return_schema = force_decimal_precision_scale(
                      as_nullable_spark_type(psdf._internal.to_internal_spark_frame.schema)
                  )
  
                  self_applied = DataFrame(self._psdf._internal.resolved_copy)  # type: DataFrame
  
                  output_func = GroupBy._make_pandas_df_builder_func(
                      self_applied, func, return_schema, retain_index=True
                  )
                  columns = self_applied._internal.spark_columns
  
                  pudf = pandas_udf(returnType=return_schema, functionType=PandasUDFType.SCALAR)(
                      output_func
                  )
                  temp_struct_column = verify_temp_column_name(
                      self_applied._internal.spark_frame, "__temp_struct__"
                  )
                  applied = pudf(F.struct(*columns)).alias(temp_struct_column)
                  sdf = self_applied._internal.spark_frame.select(applied)
                  sdf = sdf.selectExpr("%s.*" % temp_struct_column)
  
                  return DataFrame(psdf._internal.with_new_sdf(sdf))
          else:
              return_type = infer_return_type(original_func)
              is_return_series = isinstance(return_type, SeriesType)
              is_return_dataframe = isinstance(return_type, DataFrameType)
              if not is_return_dataframe and not is_return_series:
                  raise TypeError(
                      "The given function should specify a frame or series as its type "
                      "hints; however, the return type was %s." % return_sig
                  )
              if is_return_series:
                  spark_return_type = force_decimal_precision_scale(
                      as_nullable_spark_type(cast(SeriesType, return_type).spark_type)
                  )
                  return_schema = StructType(
                      [StructField(SPARK_DEFAULT_SERIES_NAME, spark_return_type)]
                  )
                  output_func = GroupBy._make_pandas_df_builder_func(
                      self._psdf, apply_func, return_schema, retain_index=False
                  )
  
                  pudf = pandas_udf(returnType=spark_return_type, functionType=PandasUDFType.SCALAR)(
                      pandas_series_func(output_func)
                  )
                  columns = self._psdf._internal.spark_columns
                  internal = self._psdf._internal.copy(
                      column_labels=[None],
                      data_spark_columns=[pudf(F.struct(*columns)).alias(SPARK_DEFAULT_SERIES_NAME)],
                      data_dtypes=[cast(SeriesType, return_type).dtype],
                      column_label_names=None,
                  )
                  return first_series(DataFrame(internal))
              else:
                  return_schema = cast(DataFrameType, return_type).spark_type
  
                  self_applied = DataFrame(self._psdf._internal.resolved_copy)
  
                  output_func = GroupBy._make_pandas_df_builder_func(
                      self_applied, func, return_schema, retain_index=False
                  )
                  columns = self_applied._internal.spark_columns
  
                  pudf = pandas_udf(returnType=return_schema, functionType=PandasUDFType.SCALAR)(
                      output_func
                  )
                  temp_struct_column = verify_temp_column_name(
                      self_applied._internal.spark_frame, "__temp_struct__"
                  )
                  applied = pudf(F.struct(*columns)).alias(temp_struct_column)
                  sdf = self_applied._internal.spark_frame.select(applied)
                  sdf = sdf.selectExpr("%s.*" % temp_struct_column)
  
                  internal = InternalFrame(
                      spark_frame=sdf,
                      index_spark_columns=None,
                      data_dtypes=cast(DataFrameType, return_type).dtypes,
                  )
                  return DataFrame(internal)
  
  
  class PandasOnSparkSeriesMethods(object):
      """ pandas-on-Spark specific features for Series. """
  
      def __init__(self, series: "Series"):
          self._psser = series
  
      def transform_batch(self, func, *args, **kwargs) -> "Series":
          """
          Transform the data with the function that takes pandas Series and outputs pandas Series.
          The pandas Series given to the function is of a batch used internally.
  
          See also `Transform and apply a function
          <https://koalas.readthedocs.io/en/latest/user_guide/transform_apply.html>`_.
  
          .. note:: the `func` is unable to access to the whole input series. pandas-on-Spark
              internally splits the input series into multiple batches and calls `func` with each
              batch multiple times. Therefore, operations such as global aggregations are impossible.
              See the example below.
  
              >>> # This case does not return the length of whole frame but of the batch internally
              ... # used.
              ... def length(pser) -> ps.Series[int]:
              ...     return pd.Series([len(pser)] * len(pser))
              ...
              >>> df = ps.DataFrame({'A': range(1000)})
              >>> df.A.pandas_on_spark.transform_batch(length)  # doctest: +SKIP
                  c0
              0   83
              1   83
              2   83
              ...
  
          .. note:: this API executes the function once to infer the type which is
              potentially expensive, for instance, when the dataset is created after
              aggregations or sorting.
  
              To avoid this, specify return type in ``func``, for instance, as below:
  
              >>> def plus_one(x) -> ps.Series[int]:
              ...     return x + 1
  
          Parameters
          ----------
          func : function
              Function to apply to each pandas frame.
          *args
              Positional arguments to pass to func.
          **kwargs
              Keyword arguments to pass to func.
  
          Returns
          -------
          DataFrame
  
          See Also
          --------
          DataFrame.pandas_on_spark.apply_batch : Similar but it takes pandas DataFrame as its
          internal batch.
  
          Examples
          --------
          >>> df = ps.DataFrame([(1, 2), (3, 4), (5, 6)], columns=['A', 'B'])
          >>> df
             A  B
          0  1  2
          1  3  4
          2  5  6
  
          >>> def plus_one_func(pser) -> ps.Series[np.int64]:
          ...     return pser + 1
          >>> df.A.pandas_on_spark.transform_batch(plus_one_func)
          0    2
          1    4
          2    6
          Name: A, dtype: int64
  
          You can also omit the type hints so pandas-on-Spark infers the return schema as below:
  
          >>> df.A.pandas_on_spark.transform_batch(lambda pser: pser + 1)
          0    2
          1    4
          2    6
          Name: A, dtype: int64
  
          You can also specify extra arguments.
  
          >>> def plus_one_func(pser, a, b, c=3) -> ps.Series[np.int64]:
          ...     return pser + a + b + c
          >>> df.A.pandas_on_spark.transform_batch(plus_one_func, 1, b=2)
          0     7
          1     9
          2    11
          Name: A, dtype: int64
  
          You can also use ``np.ufunc`` and built-in functions as input.
  
          >>> df.A.pandas_on_spark.transform_batch(np.add, 10)
          0    11
          1    13
          2    15
          Name: A, dtype: int64
  
          >>> (df * -1).A.pandas_on_spark.transform_batch(abs)
          0    1
          1    3
          2    5
          Name: A, dtype: int64
          """
          assert callable(func), "the first argument should be a callable function."
  
          return_sig = None
          try:
              spec = inspect.getfullargspec(func)
              return_sig = spec.annotations.get("return", None)
          except TypeError:
              # Falls back to schema inference if it fails to get signature.
              pass
  
          return_type = None
          if return_sig is not None:
              # Extract the signature arguments from this function.
              sig_return = infer_return_type(func)
              if not isinstance(sig_return, SeriesType):
                  raise ValueError(
                      "Expected the return type of this function to be of type column,"
                      " but found type {}".format(sig_return)
                  )
              return_type = cast(SeriesType, sig_return)
  
          return self._transform_batch(lambda c: func(c, *args, **kwargs), return_type)
  
      def _transform_batch(self, func, return_type: Optional[Union[SeriesType, ScalarType]]):
          from pyspark.pandas.groupby import GroupBy
          from pyspark.pandas.series import Series, first_series
          from pyspark import pandas as ps
  
          if not isinstance(func, types.FunctionType):
              f = func
              func = lambda *args, **kwargs: f(*args, **kwargs)
  
          if return_type is None:
              # TODO: In this case, it avoids the shortcut for now (but only infers schema)
              #  because it returns a series from a different DataFrame and it has a different
              #  anchor. We should fix this to allow the shortcut or only allow to infer
              #  schema.
              limit = ps.get_option("compute.shortcut_limit")
              pser = self._psser.head(limit + 1)._to_internal_pandas()
              transformed = pser.transform(func)
              psser = Series(transformed)  # type: Series
              spark_return_type = force_decimal_precision_scale(
                  as_nullable_spark_type(psser.spark.data_type)
              )
              dtype = psser.dtype
          else:
              spark_return_type = return_type.spark_type
              dtype = return_type.dtype
  
          psdf = self._psser.to_frame()
          columns = psdf._internal.spark_column_names
  
          def pandas_concat(series):
              # The input can only be a DataFrame for struct from Spark 3.0.
              # This works around to make the input as a frame. See SPARK-27240
              pdf = pd.concat(series, axis=1)
              pdf.columns = columns
              return pdf
  
          def apply_func(pdf):
              return func(first_series(pdf)).to_frame()
  
          return_schema = StructType([StructField(SPARK_DEFAULT_SERIES_NAME, spark_return_type)])
          output_func = GroupBy._make_pandas_df_builder_func(
              psdf, apply_func, return_schema, retain_index=False
          )
  
          pudf = pandas_udf(returnType=spark_return_type, functionType=PandasUDFType.SCALAR)(
              lambda *series: first_series(output_func(pandas_concat(series)))
          )
  
          return self._psser._with_new_scol(
              scol=pudf(*psdf._internal.spark_columns).alias(
                  self._psser._internal.spark_column_names[0]
              ),
              dtype=dtype,
          )
  
  
  def _test():
      import os
      import doctest
      import sys
      from pyspark.sql import SparkSession
      import pyspark.pandas.accessors
  
      os.chdir(os.environ["SPARK_HOME"])
  
      globs = pyspark.pandas.accessors.__dict__.copy()
      globs["ps"] = pyspark.pandas
      spark = (
          SparkSession.builder.master("local[4]")
          .appName("pyspark.pandas.accessors tests")
          .getOrCreate()
      )
      (failure_count, test_count) = doctest.testmod(
          pyspark.pandas.accessors,
          globs=globs,
          optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE,
      )
      spark.stop()
      if failure_count:
          sys.exit(-1)
  
  
  if __name__ == "__main__":
      _test()
  