class_docstr: Spark related features. Usually, the features here are missing in pandas
  but Spark has it.
class_docstr_tok: ["Spark","related","features",".","Usually",",","the","features","here","are","missing","in","pandas","but","Spark","has","it","."]
docstr: Returns a new DataFrame with the analyzed Spark DataFrame.
  
  After multiple operations, the underlying Spark plan could grow huge
  and make the Spark planner take a long time to finish the planning.
  
  This function is for the workaround to avoid it.
  
  .. note:: After analysis, operations between the analyzed DataFrame and the original one
      will **NOT** work without setting a config `compute.ops_on_diff_frames` to `True`.
  
  Returns
  -------
  DataFrame
  
  Examples
  --------
  >>> df = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, columns=["a", "b"])
  >>> df
     a  b
  0  1  4
  1  2  5
  2  3  6
  
  The analyzed one should return the same value.
  
  >>> df.spark.analyzed
     a  b
  0  1  4
  1  2  5
  2  3  6
  
  However, it won't work with the same anchor Series.
  
  >>> df + df.spark.analyzed
  Traceback (most recent call last):
  ...
  ValueError: ... enable 'compute.ops_on_diff_frames' option.
  
  >>> with ps.option_context('compute.ops_on_diff_frames', True):
  ...     (df + df.spark.analyzed).sort_index()
     a   b
  0  2   8
  1  4  10
  2  6  12
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code: def analyzed(self) -> "ps.DataFrame":
      """
      Returns a new DataFrame with the analyzed Spark DataFrame.
  
      After multiple operations, the underlying Spark plan could grow huge
      and make the Spark planner take a long time to finish the planning.
  
      This function is for the workaround to avoid it.
  
      .. note:: After analysis, operations between the analyzed DataFrame and the original one
          will **NOT** work without setting a config `compute.ops_on_diff_frames` to `True`.
  
      Returns
      -------
      DataFrame
  
      Examples
      --------
      >>> df = ps.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}, columns=["a", "b"])
      >>> df
         a  b
      0  1  4
      1  2  5
      2  3  6
  
      The analyzed one should return the same value.
  
      >>> df.spark.analyzed
         a  b
      0  1  4
      1  2  5
      2  3  6
  
      However, it won't work with the same anchor Series.
  
      >>> df + df.spark.analyzed
      Traceback (most recent call last):
      ...
      ValueError: ... enable 'compute.ops_on_diff_frames' option.
  
      >>> with ps.option_context('compute.ops_on_diff_frames', True):
      ...     (df + df.spark.analyzed).sort_index()
         a   b
      0  2   8
      1  4  10
      2  6  12
      """
      from pyspark.pandas.frame import DataFrame
  
      return DataFrame(self._psdf._internal.resolved_copy)
  
imports: from abc import ABCMeta, abstractmethod
  from typing import TYPE_CHECKING, Callable, Generic, List, Optional, Union
  from pyspark import StorageLevel
  from pyspark.sql import Column, DataFrame
  from pyspark.sql.types import DataType, StructType
  from pyspark.pandas._typing import IndexOpsLike
  from pyspark.pandas.internal import InternalField
  from pyspark.sql.utils import get_column_class, get_dataframe_class
label_desc: Use image node_id 16 for calling the SparkFrameMethods obj's underlying member method code with example usage: obj.analyzed() and returns: DataFrame