class_docstr: A column in a DataFrame.
  
  .. versionadded:: 1.3.0
  
  .. versionchanged:: 3.4.0
      Supports Spark Connect.
  
  Examples
  --------
  Column instances can be created by
  
  >>> df = spark.createDataFrame(
  ...      [(2, "Alice"), (5, "Bob")], ["age", "name"])
  
  Select a column out of a DataFrame
  >>> df.name
  Column<'name'>
  >>> df["name"]
  Column<'name'>
  
  Create from an expression
  
  >>> df.age + 1
  Column<...>
  >>> 1 / df.age
  Column<...>
class_docstr_tok: ["A","column","in","a","DataFrame",".","..","versionadded",":",":","1.3.0","..","versionchanged",":",":","3.4.0","Supports","Spark","Connect",".","Examples","--","--","--","--","Column","instances","can","be","created","by",">",">",">","df","=","spark.createDataFrame","(","...","[","(","2",",","``","Alice","''",")",",","(","5",",","``","Bob","''",")","]",",","[","``","age","''",",","``","name","''","]",")","Select","a","column","out","of","a","DataFrame",">",">",">","df.name","Column","<","'name","'",">",">",">",">","df","[","``","name","''","]","Column","<","'name","'",">","Create","from","an","expression",">",">",">","df.age","+","1","Column","<","...",">",">",">",">","1","\/","df.age","Column","<","...",">"]
docstr: An expression that drops fields in :class:`StructType` by name.
  This is a no-op if the schema doesn't contain field name(s).
  
  .. versionadded:: 3.1.0
  
  .. versionchanged:: 3.4.0
      Supports Spark Connect.
  
  Parameters
  ----------
  fieldNames : str
      Desired field names (collects all positional arguments passed)
      The result will drop at a location if any field matches in the Column.
  
  Returns
  -------
  :class:`Column`
      Column representing whether each element of Column with field dropped by fieldName.
  
  Examples
  --------
  >>> from pyspark.sql import Row
  >>> from pyspark.sql.functions import col, lit
  >>> df = spark.createDataFrame([
  ...     Row(a=Row(b=1, c=2, d=3, e=Row(f=4, g=5, h=6)))])
  >>> df.withColumn('a', df['a'].dropFields('b')).show()
  +-----------------+
  |                a|
  +-----------------+
  |{2, 3, {4, 5, 6}}|
  +-----------------+
  
  >>> df.withColumn('a', df['a'].dropFields('b', 'c')).show()
  +--------------+
  |             a|
  +--------------+
  |{3, {4, 5, 6}}|
  +--------------+
  
  This method supports dropping multiple nested fields directly e.g.
  
  >>> df.withColumn("a", col("a").dropFields("e.g", "e.h")).show()
  +--------------+
  |             a|
  +--------------+
  |{1, 2, 3, {4}}|
  +--------------+
  
  However, if you are going to add/replace multiple nested fields,
  it is preferred to extract out the nested struct before
  adding/replacing multiple fields e.g.
  
  >>> df.select(col("a").withField(
  ...     "e", col("a.e").dropFields("g", "h")).alias("a")
  ... ).show()
  +--------------+
  |             a|
  +--------------+
  |{1, 2, 3, {4}}|
  +--------------+
docstr_tok: ["An","expression","that","drops","fields","in",":","class",":","`","StructType","`","by","name",".","This","is","a","no-op","if","the","schema","does","n't","contain","field","name","(","s",")",".","..","versionadded",":",":","3.1.0","..","versionchanged",":",":","3.4.0","Supports","Spark","Connect",".","Parameters","--","--","--","--","--","fieldNames",":","str","Desired","field","names","(","collects","all","positional","arguments","passed",")","The","result","will","drop","at","a","location","if","any","field","matches","in","the","Column",".","Returns","--","--","--","-",":","class",":","`","Column","`","Column","representing","whether","each","element","of","Column","with","field","dropped","by","fieldName",".","Examples","--","--","--","--",">",">",">","from","pyspark.sql","import","Row",">",">",">","from","pyspark.sql.functions","import","col",",","lit",">",">",">","df","=","spark.createDataFrame","(","[","...","Row","(","a=Row","(","b=1",",","c=2",",","d=3",",","e=Row","(","f=4",",","g=5",",","h=6",")",")",")","]",")",">",">",">","df.withColumn","(","'","a","'",",","df","[","'","a","'","]",".dropFields","(","'","b","'",")",")",".show","(",")","+","--","--","--","--","--","--","--","--","-+","|","a|","+","--","--","--","--","--","--","--","--","-+","|","{","2",",","3",",","{","4",",","5",",","6","}","}","|","+","--","--","--","--","--","--","--","--","-+",">",">",">","df.withColumn","(","'","a","'",",","df","[","'","a","'","]",".dropFields","(","'","b","'",",","'","c","'",")",")",".show","(",")","+","--","--","--","--","--","--","--","+","|","a|","+","--","--","--","--","--","--","--","+","|","{","3",",","{","4",",","5",",","6","}","}","|","+","--","--","--","--","--","--","--","+","This","method","supports","dropping","multiple","nested","fields","directly","e.g",".",">",">",">","df.withColumn","(","``","a","''",",","col","(","``","a","''",")",".dropFields","(","``","e.g","''",",","``","e.h","''",")",")",".show","(",")","+","--","--","--","--","--","--","--","+","|","a|","+","--","--","--","--","--","--","--","+","|","{","1",",","2",",","3",",","{","4","}","}","|","+","--","--","--","--","--","--","--","+","However",",","if","you","are","going","to","add\/replace","multiple","nested","fields",",","it","is","preferred","to","extract","out","the","nested","struct","before","adding\/replacing","multiple","fields","e.g",".",">",">",">","df.select","(","col","(","``","a","''",")",".withField","(","...","``","e","''",",","col","(","``","a.e","''",")",".dropFields","(","``","g","''",",","``","h","''",")",")",".alias","(","``","a","''",")","...",")",".show","(",")","+","--","--","--","--","--","--","--","+","|","a|","+","--","--","--","--","--","--","--","+","|","{","1",",","2",",","3",",","{","4","}","}","|","+","--","--","--","--","--","--","--","+"]
code: def dropFields(self, *fieldNames: str) -> "Column":
      """
      An expression that drops fields in :class:`StructType` by name.
      This is a no-op if the schema doesn't contain field name(s).
  
      .. versionadded:: 3.1.0
  
      .. versionchanged:: 3.4.0
          Supports Spark Connect.
  
      Parameters
      ----------
      fieldNames : str
          Desired field names (collects all positional arguments passed)
          The result will drop at a location if any field matches in the Column.
  
      Returns
      -------
      :class:`Column`
          Column representing whether each element of Column with field dropped by fieldName.
  
      Examples
      --------
      >>> from pyspark.sql import Row
      >>> from pyspark.sql.functions import col, lit
      >>> df = spark.createDataFrame([
      ...     Row(a=Row(b=1, c=2, d=3, e=Row(f=4, g=5, h=6)))])
      >>> df.withColumn('a', df['a'].dropFields('b')).show()
      +-----------------+
      |                a|
      +-----------------+
      |{2, 3, {4, 5, 6}}|
      +-----------------+
  
      >>> df.withColumn('a', df['a'].dropFields('b', 'c')).show()
      +--------------+
      |             a|
      +--------------+
      |{3, {4, 5, 6}}|
      +--------------+
  
      This method supports dropping multiple nested fields directly e.g.
  
      >>> df.withColumn("a", col("a").dropFields("e.g", "e.h")).show()
      +--------------+
      |             a|
      +--------------+
      |{1, 2, 3, {4}}|
      +--------------+
  
      However, if you are going to add/replace multiple nested fields,
      it is preferred to extract out the nested struct before
      adding/replacing multiple fields e.g.
  
      >>> df.select(col("a").withField(
      ...     "e", col("a.e").dropFields("g", "h")).alias("a")
      ... ).show()
      +--------------+
      |             a|
      +--------------+
      |{1, 2, 3, {4}}|
      +--------------+
  
      """
      sc = get_active_spark_context()
      jc = self._jc.dropFields(_to_seq(sc, fieldNames))
      return Column(jc)
  
imports: import sys
  import json
  import warnings
  from typing import cast, overload, Any, Callable, Iterable, List, Optional, Tuple, TYPE_CHECKING, Union
  from py4j.java_gateway import JavaObject, JVMView
  from pyspark import copy_func
  from pyspark.context import SparkContext
  from pyspark.errors import PySparkAttributeError, PySparkTypeError, PySparkValueError
  from pyspark.sql.types import DataType
  from pyspark.sql.utils import get_active_spark_context
label_desc: Use image node_id 8 for calling the Column obj's underlying member method code with example usage: obj.dropFields() and returns: Column