dataframe.py

#
# 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 json
import os
import sys
import random
import warnings
from collections.abc import Iterable
from functools import reduce
from typing import (
    Any,
    Callable,
    Dict,
    Iterator,
    List,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
    overload,
    TYPE_CHECKING,
)

from py4j.java_gateway import JavaObject, JVMView

from pyspark import copy_func, _NoValue
from pyspark._globals import _NoValueType
from pyspark.context import SparkContext
from pyspark.errors import PySparkTypeError, PySparkValueError
from pyspark.rdd import (
    RDD,
    _load_from_socket,
    _local_iterator_from_socket,
)
from pyspark.serializers import BatchedSerializer, CPickleSerializer, UTF8Deserializer
from pyspark.storagelevel import StorageLevel
from pyspark.traceback_utils import SCCallSiteSync
from pyspark.sql.column import Column, _to_seq, _to_list, _to_java_column
from pyspark.sql.readwriter import DataFrameWriter, DataFrameWriterV2
from pyspark.sql.streaming import DataStreamWriter
from pyspark.sql.types import (
    StructType,
    Row,
    _parse_datatype_json_string,
)
from pyspark.sql.utils import get_active_spark_context
from pyspark.sql.pandas.conversion import PandasConversionMixin
from pyspark.sql.pandas.map_ops import PandasMapOpsMixin

if TYPE_CHECKING:
    from pyspark._typing import PrimitiveType
    from pyspark.pandas.frame import DataFrame as PandasOnSparkDataFrame
    from pyspark.sql._typing import ColumnOrName, LiteralType, OptionalPrimitiveType
    from pyspark.sql.context import SQLContext
    from pyspark.sql.session import SparkSession
    from pyspark.sql.group import GroupedData
    from pyspark.sql.observation import Observation


__all__ = ["DataFrame", "DataFrameNaFunctions", "DataFrameStatFunctions"]


class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
    """A distributed collection of data grouped into named columns.

    .. versionadded:: 1.3.0

    .. versionchanged:: 3.4.0
        Supports Spark Connect.

    Examples
    --------
    A :class:`DataFrame` is equivalent to a relational table in Spark SQL,
    and can be created using various functions in :class:`SparkSession`:

    >>> people = spark.createDataFrame([
    ...     {"deptId": 1, "age": 40, "name": "Hyukjin Kwon", "gender": "M", "salary": 50},
    ...     {"deptId": 1, "age": 50, "name": "Takuya Ueshin", "gender": "M", "salary": 100},
    ...     {"deptId": 2, "age": 60, "name": "Xinrong Meng", "gender": "F", "salary": 150},
    ...     {"deptId": 3, "age": 20, "name": "Haejoon Lee", "gender": "M", "salary": 200}
    ... ])

    Once created, it can be manipulated using the various domain-specific-language
    (DSL) functions defined in: :class:`DataFrame`, :class:`Column`.

    To select a column from the :class:`DataFrame`, use the apply method:

    >>> age_col = people.age

    A more concrete example:

    >>> # To create DataFrame using SparkSession
    ... department = spark.createDataFrame([
    ...     {"id": 1, "name": "PySpark"},
    ...     {"id": 2, "name": "ML"},
    ...     {"id": 3, "name": "Spark SQL"}
    ... ])

    >>> people.filter(people.age > 30).join(
    ...     department, people.deptId == department.id).groupBy(
    ...     department.name, "gender").agg({"salary": "avg", "age": "max"}).show()
    +-------+------+-----------+--------+
    |   name|gender|avg(salary)|max(age)|
    +-------+------+-----------+--------+
    |     ML|     F|      150.0|      60|
    |PySpark|     M|       75.0|      50|
    +-------+------+-----------+--------+

    Notes
    -----
    A DataFrame should only be created as described above. It should not be directly
    created via using the constructor.
    """

    def __init__(
        self,
        jdf: JavaObject,
        sql_ctx: Union["SQLContext", "SparkSession"],
    ):
        from pyspark.sql.context import SQLContext

        self._sql_ctx: Optional["SQLContext"] = None

        if isinstance(sql_ctx, SQLContext):
            assert not os.environ.get("SPARK_TESTING")  # Sanity check for our internal usage.
            assert isinstance(sql_ctx, SQLContext)
            # We should remove this if-else branch in the future release, and rename
            # sql_ctx to session in the constructor. This is an internal code path but
            # was kept with a warning because it's used intensively by third-party libraries.
            warnings.warn("DataFrame constructor is internal. Do not directly use it.")
            self._sql_ctx = sql_ctx
            session = sql_ctx.sparkSession
        else:
            session = sql_ctx
        self._session: "SparkSession" = session

        self._sc: SparkContext = sql_ctx._sc
        self._jdf: JavaObject = jdf
        self.is_cached = False
        # initialized lazily
        self._schema: Optional[StructType] = None
        self._lazy_rdd: Optional[RDD[Row]] = None
        # Check whether _repr_html is supported or not, we use it to avoid calling _jdf twice
        # by __repr__ and _repr_html_ while eager evaluation opens.
        self._support_repr_html = False

    @property
    def sql_ctx(self) -> "SQLContext":
        from pyspark.sql.context import SQLContext

        warnings.warn(
            "DataFrame.sql_ctx is an internal property, and will be removed "
            "in future releases. Use DataFrame.sparkSession instead."
        )
        if self._sql_ctx is None:
            self._sql_ctx = SQLContext._get_or_create(self._sc)
        return self._sql_ctx

    @property
    def sparkSession(self) -> "SparkSession":
        """Returns Spark session that created this :class:`DataFrame`.

        .. versionadded:: 3.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`SparkSession`

        Examples
        --------
        >>> df = spark.range(1)
        >>> type(df.sparkSession)
        <class '...session.SparkSession'>
        """
        return self._session

    @property
    def rdd(self) -> "RDD[Row]":
        """Returns the content as an :class:`pyspark.RDD` of :class:`Row`.

        .. versionadded:: 1.3.0

        Returns
        -------
        :class:`RDD`

        Examples
        --------
        >>> df = spark.range(1)
        >>> type(df.rdd)
        <class 'pyspark.rdd.RDD'>
        """
        if self._lazy_rdd is None:
            jrdd = self._jdf.javaToPython()
            self._lazy_rdd = RDD(
                jrdd, self.sparkSession._sc, BatchedSerializer(CPickleSerializer())
            )
        return self._lazy_rdd

    @property
    def na(self) -> "DataFrameNaFunctions":
        """Returns a :class:`DataFrameNaFunctions` for handling missing values.

        .. versionadded:: 1.3.1

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`DataFrameNaFunctions`

        Examples
        --------
        >>> df = spark.sql("SELECT 1 AS c1, int(NULL) AS c2")
        >>> type(df.na)
        <class '...dataframe.DataFrameNaFunctions'>

        Replace the missing values as 2.

        >>> df.na.fill(2).show()
        +---+---+
        | c1| c2|
        +---+---+
        |  1|  2|
        +---+---+
        """
        return DataFrameNaFunctions(self)

    @property
    def stat(self) -> "DataFrameStatFunctions":
        """Returns a :class:`DataFrameStatFunctions` for statistic functions.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`DataFrameStatFunctions`

        Examples
        --------
        >>> import pyspark.sql.functions as f
        >>> df = spark.range(3).withColumn("c", f.expr("id + 1"))
        >>> type(df.stat)
        <class '...dataframe.DataFrameStatFunctions'>
        >>> df.stat.corr("id", "c")
        1.0
        """
        return DataFrameStatFunctions(self)

    def toJSON(self, use_unicode: bool = True) -> RDD[str]:
        """Converts a :class:`DataFrame` into a :class:`RDD` of string.

        Each row is turned into a JSON document as one element in the returned RDD.

        .. versionadded:: 1.3.0

        Parameters
        ----------
        use_unicode : bool, optional, default True
            Whether to convert to unicode or not.

        Returns
        -------
        :class:`RDD`

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.toJSON().first()
        '{"age":2,"name":"Alice"}'
        """
        rdd = self._jdf.toJSON()
        return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode))

    def registerTempTable(self, name: str) -> None:
        """Registers this :class:`DataFrame` as a temporary table using the given name.

        The lifetime of this temporary table is tied to the :class:`SparkSession`
        that was used to create this :class:`DataFrame`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        .. deprecated:: 2.0.0
            Use :meth:`DataFrame.createOrReplaceTempView` instead.

        Parameters
        ----------
        name : str
            Name of the temporary table to register.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.registerTempTable("people")
        >>> df2 = spark.sql("SELECT * FROM people")
        >>> sorted(df.collect()) == sorted(df2.collect())
        True
        >>> spark.catalog.dropTempView("people")
        True

        """
        warnings.warn("Deprecated in 2.0, use createOrReplaceTempView instead.", FutureWarning)
        self._jdf.createOrReplaceTempView(name)

    def createTempView(self, name: str) -> None:
        """Creates a local temporary view with this :class:`DataFrame`.

        The lifetime of this temporary table is tied to the :class:`SparkSession`
        that was used to create this :class:`DataFrame`.
        throws :class:`TempTableAlreadyExistsException`, if the view name already exists in the
        catalog.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        name : str
            Name of the view.

        Examples
        --------
        Create a local temporary view.

        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.createTempView("people")
        >>> df2 = spark.sql("SELECT * FROM people")
        >>> sorted(df.collect()) == sorted(df2.collect())
        True

        Throw an exception if the table already exists.

        >>> df.createTempView("people")  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        AnalysisException: "Temporary table 'people' already exists;"
        >>> spark.catalog.dropTempView("people")
        True

        """
        self._jdf.createTempView(name)

    def createOrReplaceTempView(self, name: str) -> None:
        """Creates or replaces a local temporary view with this :class:`DataFrame`.

        The lifetime of this temporary table is tied to the :class:`SparkSession`
        that was used to create this :class:`DataFrame`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        name : str
            Name of the view.

        Examples
        --------
        Create a local temporary view named 'people'.

        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.createOrReplaceTempView("people")

        Replace the local temporary view.

        >>> df2 = df.filter(df.age > 3)
        >>> df2.createOrReplaceTempView("people")
        >>> df3 = spark.sql("SELECT * FROM people")
        >>> sorted(df3.collect()) == sorted(df2.collect())
        True
        >>> spark.catalog.dropTempView("people")
        True

        """
        self._jdf.createOrReplaceTempView(name)

    def createGlobalTempView(self, name: str) -> None:
        """Creates a global temporary view with this :class:`DataFrame`.

        The lifetime of this temporary view is tied to this Spark application.
        throws :class:`TempTableAlreadyExistsException`, if the view name already exists in the
        catalog.

        .. versionadded:: 2.1.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        name : str
            Name of the view.

        Examples
        --------
        Create a global temporary view.

        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.createGlobalTempView("people")
        >>> df2 = spark.sql("SELECT * FROM global_temp.people")
        >>> sorted(df.collect()) == sorted(df2.collect())
        True

        Throws an exception if the global temporary view already exists.

        >>> df.createGlobalTempView("people")  # doctest: +IGNORE_EXCEPTION_DETAIL
        Traceback (most recent call last):
        ...
        AnalysisException: "Temporary table 'people' already exists;"
        >>> spark.catalog.dropGlobalTempView("people")
        True

        """
        self._jdf.createGlobalTempView(name)

    def createOrReplaceGlobalTempView(self, name: str) -> None:
        """Creates or replaces a global temporary view using the given name.

        The lifetime of this temporary view is tied to this Spark application.

        .. versionadded:: 2.2.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        name : str
            Name of the view.

        Examples
        --------
        Create a global temporary view.

        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.createOrReplaceGlobalTempView("people")

        Replace the global temporary view.

        >>> df2 = df.filter(df.age > 3)
        >>> df2.createOrReplaceGlobalTempView("people")
        >>> df3 = spark.sql("SELECT * FROM global_temp.people")
        >>> sorted(df3.collect()) == sorted(df2.collect())
        True
        >>> spark.catalog.dropGlobalTempView("people")
        True

        """
        self._jdf.createOrReplaceGlobalTempView(name)

    @property
    def write(self) -> DataFrameWriter:
        """
        Interface for saving the content of the non-streaming :class:`DataFrame` out into external
        storage.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`DataFrameWriter`

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> type(df.write)
        <class '...readwriter.DataFrameWriter'>

        Write the DataFrame as a table.

        >>> _ = spark.sql("DROP TABLE IF EXISTS tab2")
        >>> df.write.saveAsTable("tab2")
        >>> _ = spark.sql("DROP TABLE tab2")
        """
        return DataFrameWriter(self)

    @property
    def writeStream(self) -> DataStreamWriter:
        """
        Interface for saving the content of the streaming :class:`DataFrame` out into external
        storage.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Returns
        -------
        :class:`DataStreamWriter`

        Examples
        --------
        >>> import tempfile
        >>> df = spark.readStream.format("rate").load()
        >>> type(df.writeStream)
        <class '...streaming.readwriter.DataStreamWriter'>

        >>> with tempfile.TemporaryDirectory() as d:
        ...     # Create a table with Rate source.
        ...     df.writeStream.toTable(
        ...         "my_table", checkpointLocation=d)
        <...streaming.query.StreamingQuery object at 0x...>
        """
        return DataStreamWriter(self)

    @property
    def schema(self) -> StructType:
        """Returns the schema of this :class:`DataFrame` as a :class:`pyspark.sql.types.StructType`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`StructType`

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        Retrieve the schema of the current DataFrame.

        >>> df.schema
        StructType([StructField('age', LongType(), True),
                    StructField('name', StringType(), True)])
        """
        if self._schema is None:
            try:
                self._schema = cast(
                    StructType, _parse_datatype_json_string(self._jdf.schema().json())
                )
            except Exception as e:
                raise PySparkValueError(
                    error_class="CANNOT_PARSE_DATATYPE",
                    message_parameters={"error": str(e)},
                )
        return self._schema

    def printSchema(self, level: Optional[int] = None) -> None:
        """Prints out the schema in the tree format.
        Optionally allows to specify how many levels to print if schema is nested.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        level : int, optional, default None
            How many levels to print for nested schemas.

            .. versionchanged:: 3.5.0
                Added Level parameter.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df.printSchema()
        root
         |-- age: long (nullable = true)
         |-- name: string (nullable = true)

        >>> df = spark.createDataFrame([(1, (2,2))], ["a", "b"])
        >>> df.printSchema(1)
        root
         |-- a: long (nullable = true)
         |-- b: struct (nullable = true)

        >>> df.printSchema(2)
        root
         |-- a: long (nullable = true)
         |-- b: struct (nullable = true)
         |    |-- _1: long (nullable = true)
         |    |-- _2: long (nullable = true)
        """
        if level:
            print(self._jdf.schema().treeString(level))
        else:
            print(self._jdf.schema().treeString())

    def explain(
        self, extended: Optional[Union[bool, str]] = None, mode: Optional[str] = None
    ) -> None:
        """Prints the (logical and physical) plans to the console for debugging purposes.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        extended : bool, optional
            default ``False``. If ``False``, prints only the physical plan.
            When this is a string without specifying the ``mode``, it works as the mode is
            specified.
        mode : str, optional
            specifies the expected output format of plans.

            * ``simple``: Print only a physical plan.
            * ``extended``: Print both logical and physical plans.
            * ``codegen``: Print a physical plan and generated codes if they are available.
            * ``cost``: Print a logical plan and statistics if they are available.
            * ``formatted``: Split explain output into two sections: a physical plan outline \
                and node details.

            .. versionchanged:: 3.0.0
               Added optional argument `mode` to specify the expected output format of plans.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        Print out the physical plan only (default).

        >>> df.explain()  # doctest: +SKIP
        == Physical Plan ==
        *(1) Scan ExistingRDD[age...,name...]

        Print out all of the parsed, analyzed, optimized and physical plans.

        >>> df.explain(True)
        == Parsed Logical Plan ==
        ...
        == Analyzed Logical Plan ==
        ...
        == Optimized Logical Plan ==
        ...
        == Physical Plan ==
        ...

        Print out the plans with two sections: a physical plan outline and node details

        >>> df.explain(mode="formatted")  # doctest: +SKIP
        == Physical Plan ==
        * Scan ExistingRDD (...)
        (1) Scan ExistingRDD [codegen id : ...]
        Output [2]: [age..., name...]
        ...

        Print a logical plan and statistics if they are available.

        >>> df.explain("cost")
        == Optimized Logical Plan ==
        ...Statistics...
        ...
        """

        if extended is not None and mode is not None:
            raise PySparkValueError(
                error_class="CANNOT_SET_TOGETHER",
                message_parameters={"arg_list": "extended and mode"},
            )

        # For the no argument case: df.explain()
        is_no_argument = extended is None and mode is None

        # For the cases below:
        #   explain(True)
        #   explain(extended=False)
        is_extended_case = isinstance(extended, bool) and mode is None

        # For the case when extended is mode:
        #   df.explain("formatted")
        is_extended_as_mode = isinstance(extended, str) and mode is None

        # For the mode specified:
        #   df.explain(mode="formatted")
        is_mode_case = extended is None and isinstance(mode, str)

        if not (is_no_argument or is_extended_case or is_extended_as_mode or is_mode_case):
            if (extended is not None) and (not isinstance(extended, (bool, str))):
                raise PySparkTypeError(
                    error_class="NOT_BOOL_OR_STR",
                    message_parameters={
                        "arg_name": "extended",
                        "arg_type": type(extended).__name__,
                    },
                )
            if (mode is not None) and (not isinstance(mode, str)):
                raise PySparkTypeError(
                    error_class="NOT_STR",
                    message_parameters={"arg_name": "mode", "arg_type": type(mode).__name__},
                )

        # Sets an explain mode depending on a given argument
        if is_no_argument:
            explain_mode = "simple"
        elif is_extended_case:
            explain_mode = "extended" if extended else "simple"
        elif is_mode_case:
            explain_mode = cast(str, mode)
        elif is_extended_as_mode:
            explain_mode = cast(str, extended)
        assert self._sc._jvm is not None
        print(self._sc._jvm.PythonSQLUtils.explainString(self._jdf.queryExecution(), explain_mode))

    def exceptAll(self, other: "DataFrame") -> "DataFrame":
        """Return a new :class:`DataFrame` containing rows in this :class:`DataFrame` but
        not in another :class:`DataFrame` while preserving duplicates.

        This is equivalent to `EXCEPT ALL` in SQL.
        As standard in SQL, this function resolves columns by position (not by name).

        .. versionadded:: 2.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            The other :class:`DataFrame` to compare to.

        Returns
        -------
        :class:`DataFrame`

        Examples
        --------
        >>> df1 = spark.createDataFrame(
        ...         [("a", 1), ("a", 1), ("a", 1), ("a", 2), ("b",  3), ("c", 4)], ["C1", "C2"])
        >>> df2 = spark.createDataFrame([("a", 1), ("b", 3)], ["C1", "C2"])
        >>> df1.exceptAll(df2).show()
        +---+---+
        | C1| C2|
        +---+---+
        |  a|  1|
        |  a|  1|
        |  a|  2|
        |  c|  4|
        +---+---+

        """
        return DataFrame(self._jdf.exceptAll(other._jdf), self.sparkSession)

    def isLocal(self) -> bool:
        """Returns ``True`` if the :func:`collect` and :func:`take` methods can be run locally
        (without any Spark executors).

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        bool

        Examples
        --------
        >>> df = spark.sql("SHOW TABLES")
        >>> df.isLocal()
        True
        """
        return self._jdf.isLocal()

    @property
    def isStreaming(self) -> bool:
        """Returns ``True`` if this :class:`DataFrame` contains one or more sources that
        continuously return data as it arrives. A :class:`DataFrame` that reads data from a
        streaming source must be executed as a :class:`StreamingQuery` using the :func:`start`
        method in :class:`DataStreamWriter`.  Methods that return a single answer, (e.g.,
        :func:`count` or :func:`collect`) will throw an :class:`AnalysisException` when there
        is a streaming source present.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Notes
        -----
        This API is evolving.

        Returns
        -------
        bool
            Whether it's streaming DataFrame or not.

        Examples
        --------
        >>> df = spark.readStream.format("rate").load()
        >>> df.isStreaming
        True
        """
        return self._jdf.isStreaming()

    def isEmpty(self) -> bool:
        """
        Checks if the :class:`DataFrame` is empty and returns a boolean value.

        .. versionadded:: 3.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        bool
            Returns ``True`` if the DataFrame is empty, ``False`` otherwise.

        See Also
        --------
        DataFrame.count : Counts the number of rows in DataFrame.

        Notes
        -----
        - Unlike `count()`, this method does not trigger any computation.
        - An empty DataFrame has no rows. It may have columns, but no data.

        Examples
        --------
        Example 1: Checking if an empty DataFrame is empty

        >>> df_empty = spark.createDataFrame([], 'a STRING')
        >>> df_empty.isEmpty()
        True

        Example 2: Checking if a non-empty DataFrame is empty

        >>> df_non_empty = spark.createDataFrame(["a"], 'STRING')
        >>> df_non_empty.isEmpty()
        False

        Example 3: Checking if a DataFrame with null values is empty

        >>> df_nulls = spark.createDataFrame([(None, None)], 'a STRING, b INT')
        >>> df_nulls.isEmpty()
        False

        Example 4: Checking if a DataFrame with no rows but with columns is empty

        >>> df_no_rows = spark.createDataFrame([], 'id INT, value STRING')
        >>> df_no_rows.isEmpty()
        True
        """
        return self._jdf.isEmpty()

    def show(self, n: int = 20, truncate: Union[bool, int] = True, vertical: bool = False) -> None:
        """Prints the first ``n`` rows to the console.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        n : int, optional
            Number of rows to show.
        truncate : bool or int, optional
            If set to ``True``, truncate strings longer than 20 chars by default.
            If set to a number greater than one, truncates long strings to length ``truncate``
            and align cells right.
        vertical : bool, optional
            If set to ``True``, print output rows vertically (one line
            per column value).

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        Show only top 2 rows.

        >>> df.show(2)
        +---+-----+
        |age| name|
        +---+-----+
        | 14|  Tom|
        | 23|Alice|
        +---+-----+
        only showing top 2 rows

        Show :class:`DataFrame` where the maximum number of characters is 3.

        >>> df.show(truncate=3)
        +---+----+
        |age|name|
        +---+----+
        | 14| Tom|
        | 23| Ali|
        | 16| Bob|
        +---+----+

        Show :class:`DataFrame` vertically.

        >>> df.show(vertical=True)
        -RECORD 0-----
        age  | 14
        name | Tom
        -RECORD 1-----
        age  | 23
        name | Alice
        -RECORD 2-----
        age  | 16
        name | Bob
        """
        print(self._show_string(n, truncate, vertical))

    def _show_string(
        self, n: int = 20, truncate: Union[bool, int] = True, vertical: bool = False
    ) -> str:
        if not isinstance(n, int) or isinstance(n, bool):
            raise PySparkTypeError(
                error_class="NOT_INT",
                message_parameters={"arg_name": "n", "arg_type": type(n).__name__},
            )

        if not isinstance(vertical, bool):
            raise PySparkTypeError(
                error_class="NOT_BOOL",
                message_parameters={"arg_name": "vertical", "arg_type": type(vertical).__name__},
            )

        if isinstance(truncate, bool) and truncate:
            return self._jdf.showString(n, 20, vertical)
        else:
            try:
                int_truncate = int(truncate)
            except ValueError:
                raise PySparkTypeError(
                    error_class="NOT_BOOL",
                    message_parameters={
                        "arg_name": "truncate",
                        "arg_type": type(truncate).__name__,
                    },
                )

            return self._jdf.showString(n, int_truncate, vertical)

    def __repr__(self) -> str:
        if not self._support_repr_html and self.sparkSession._jconf.isReplEagerEvalEnabled():
            vertical = False
            return self._jdf.showString(
                self.sparkSession._jconf.replEagerEvalMaxNumRows(),
                self.sparkSession._jconf.replEagerEvalTruncate(),
                vertical,
            )
        else:
            return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes))

    def _repr_html_(self) -> Optional[str]:
        """Returns a :class:`DataFrame` with html code when you enabled eager evaluation
        by 'spark.sql.repl.eagerEval.enabled', this only called by REPL you are
        using support eager evaluation with HTML.
        """
        if not self._support_repr_html:
            self._support_repr_html = True
        if self.sparkSession._jconf.isReplEagerEvalEnabled():
            return self._jdf.htmlString(
                self.sparkSession._jconf.replEagerEvalMaxNumRows(),
                self.sparkSession._jconf.replEagerEvalTruncate(),
            )
        else:
            return None

    def checkpoint(self, eager: bool = True) -> "DataFrame":
        """Returns a checkpointed version of this :class:`DataFrame`. Checkpointing can be used to
        truncate the logical plan of this :class:`DataFrame`, which is especially useful in
        iterative algorithms where the plan may grow exponentially. It will be saved to files
        inside the checkpoint directory set with :meth:`SparkContext.setCheckpointDir`.

        .. versionadded:: 2.1.0

        Parameters
        ----------
        eager : bool, optional, default True
            Whether to checkpoint this :class:`DataFrame` immediately.

        Returns
        -------
        :class:`DataFrame`
            Checkpointed DataFrame.

        Notes
        -----
        This API is experimental.

        Examples
        --------
        >>> import tempfile
        >>> df = spark.createDataFrame([
        ...     (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> with tempfile.TemporaryDirectory() as d:
        ...     spark.sparkContext.setCheckpointDir("/tmp/bb")
        ...     df.checkpoint(False)
        DataFrame[age: bigint, name: string]
        """
        jdf = self._jdf.checkpoint(eager)
        return DataFrame(jdf, self.sparkSession)

    def localCheckpoint(self, eager: bool = True) -> "DataFrame":
        """Returns a locally checkpointed version of this :class:`DataFrame`. Checkpointing can be
        used to truncate the logical plan of this :class:`DataFrame`, which is especially useful in
        iterative algorithms where the plan may grow exponentially. Local checkpoints are
        stored in the executors using the caching subsystem and therefore they are not reliable.

        .. versionadded:: 2.3.0

        Parameters
        ----------
        eager : bool, optional, default True
            Whether to checkpoint this :class:`DataFrame` immediately.

        Returns
        -------
        :class:`DataFrame`
            Checkpointed DataFrame.

        Notes
        -----
        This API is experimental.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df.localCheckpoint(False)
        DataFrame[age: bigint, name: string]
        """
        jdf = self._jdf.localCheckpoint(eager)
        return DataFrame(jdf, self.sparkSession)

    def withWatermark(self, eventTime: str, delayThreshold: str) -> "DataFrame":
        """Defines an event time watermark for this :class:`DataFrame`. A watermark tracks a point
        in time before which we assume no more late data is going to arrive.

        Spark will use this watermark for several purposes:
          - To know when a given time window aggregation can be finalized and thus can be emitted
            when using output modes that do not allow updates.

          - To minimize the amount of state that we need to keep for on-going aggregations.

        The current watermark is computed by looking at the `MAX(eventTime)` seen across
        all of the partitions in the query minus a user specified `delayThreshold`.  Due to the cost
        of coordinating this value across partitions, the actual watermark used is only guaranteed
        to be at least `delayThreshold` behind the actual event time.  In some cases we may still
        process records that arrive more than `delayThreshold` late.

        .. versionadded:: 2.1.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        eventTime : str
            the name of the column that contains the event time of the row.
        delayThreshold : str
            the minimum delay to wait to data to arrive late, relative to the
            latest record that has been processed in the form of an interval
            (e.g. "1 minute" or "5 hours").

        Returns
        -------
        :class:`DataFrame`
            Watermarked DataFrame

        Notes
        -----
        This is a feature only for Structured Streaming.

        This API is evolving.

        Examples
        --------
        >>> from pyspark.sql import Row
        >>> from pyspark.sql.functions import timestamp_seconds
        >>> df = spark.readStream.format("rate").load().selectExpr(
        ...     "value % 5 AS value", "timestamp")
        >>> df.select("value", df.timestamp.alias("time")).withWatermark("time", '10 minutes')
        DataFrame[value: bigint, time: timestamp]

        Group the data by window and value (0 - 4), and compute the count of each group.

        >>> import time
        >>> from pyspark.sql.functions import window
        >>> query = (df
        ...     .withWatermark("timestamp", "10 minutes")
        ...     .groupBy(
        ...         window(df.timestamp, "10 minutes", "5 minutes"),
        ...         df.value)
        ...     ).count().writeStream.outputMode("complete").format("console").start()
        >>> time.sleep(3)
        >>> query.stop()
        """
        if not eventTime or type(eventTime) is not str:
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "eventTime", "arg_type": type(eventTime).__name__},
            )
        if not delayThreshold or type(delayThreshold) is not str:
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={
                    "arg_name": "delayThreshold",
                    "arg_type": type(delayThreshold).__name__,
                },
            )
        jdf = self._jdf.withWatermark(eventTime, delayThreshold)
        return DataFrame(jdf, self.sparkSession)

    def hint(
        self, name: str, *parameters: Union["PrimitiveType", List["PrimitiveType"]]
    ) -> "DataFrame":
        """Specifies some hint on the current :class:`DataFrame`.

        .. versionadded:: 2.2.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        name : str
            A name of the hint.
        parameters : str, list, float or int
            Optional parameters.

        Returns
        -------
        :class:`DataFrame`
            Hinted DataFrame

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df2 = spark.createDataFrame([Row(height=80, name="Tom"), Row(height=85, name="Bob")])
        >>> df.join(df2, "name").explain()  # doctest: +SKIP
        == Physical Plan ==
        ...
        ... +- SortMergeJoin ...
        ...

        Explicitly trigger the broadcast hashjoin by providing the hint in ``df2``.

        >>> df.join(df2.hint("broadcast"), "name").explain()
        == Physical Plan ==
        ...
        ... +- BroadcastHashJoin ...
        ...
        """
        if len(parameters) == 1 and isinstance(parameters[0], list):
            parameters = parameters[0]  # type: ignore[assignment]

        if not isinstance(name, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "name", "arg_type": type(name).__name__},
            )

        allowed_types = (str, list, float, int)
        for p in parameters:
            if not isinstance(p, allowed_types):
                raise PySparkTypeError(
                    error_class="DISALLOWED_TYPE_FOR_CONTAINER",
                    message_parameters={
                        "arg_name": "parameters",
                        "arg_type": type(parameters).__name__,
                        "allowed_types": ", ".join(map(lambda x: x.__name__, allowed_types)),
                        "return_type": type(p).__name__,
                    },
                )

        jdf = self._jdf.hint(name, self._jseq(parameters))
        return DataFrame(jdf, self.sparkSession)

    def count(self) -> int:
        """Returns the number of rows in this :class:`DataFrame`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        int
            Number of rows.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        Return the number of rows in the :class:`DataFrame`.

        >>> df.count()
        3
        """
        return int(self._jdf.count())

    def collect(self) -> List[Row]:
        """Returns all the records as a list of :class:`Row`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        list
            List of rows.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df.collect()
        [Row(age=14, name='Tom'), Row(age=23, name='Alice'), Row(age=16, name='Bob')]
        """
        with SCCallSiteSync(self._sc):
            sock_info = self._jdf.collectToPython()
        return list(_load_from_socket(sock_info, BatchedSerializer(CPickleSerializer())))

    def toLocalIterator(self, prefetchPartitions: bool = False) -> Iterator[Row]:
        """
        Returns an iterator that contains all of the rows in this :class:`DataFrame`.
        The iterator will consume as much memory as the largest partition in this
        :class:`DataFrame`. With prefetch it may consume up to the memory of the 2 largest
        partitions.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        prefetchPartitions : bool, optional
            If Spark should pre-fetch the next partition before it is needed.

            .. versionchanged:: 3.4.0
                This argument does not take effect for Spark Connect.

        Returns
        -------
        Iterator
            Iterator of rows.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> list(df.toLocalIterator())
        [Row(age=14, name='Tom'), Row(age=23, name='Alice'), Row(age=16, name='Bob')]
        """
        with SCCallSiteSync(self._sc):
            sock_info = self._jdf.toPythonIterator(prefetchPartitions)
        return _local_iterator_from_socket(sock_info, BatchedSerializer(CPickleSerializer()))

    def limit(self, num: int) -> "DataFrame":
        """Limits the result count to the number specified.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        num : int
            Number of records to return. Will return this number of records
            or all records if the DataFrame contains less than this number of records.

        Returns
        -------
        :class:`DataFrame`
            Subset of the records

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df.limit(1).show()
        +---+----+
        |age|name|
        +---+----+
        | 14| Tom|
        +---+----+
        >>> df.limit(0).show()
        +---+----+
        |age|name|
        +---+----+
        +---+----+
        """
        jdf = self._jdf.limit(num)
        return DataFrame(jdf, self.sparkSession)

    def offset(self, num: int) -> "DataFrame":
        """Returns a new :class: `DataFrame` by skipping the first `n` rows.

        .. versionadded:: 3.4.0

        .. versionchanged:: 3.5.0
            Supports vanilla PySpark.

        Parameters
        ----------
        num : int
            Number of records to skip.

        Returns
        -------
        :class:`DataFrame`
            Subset of the records

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df.offset(1).show()
        +---+-----+
        |age| name|
        +---+-----+
        | 23|Alice|
        | 16|  Bob|
        +---+-----+
        >>> df.offset(10).show()
        +---+----+
        |age|name|
        +---+----+
        +---+----+
        """
        jdf = self._jdf.offset(num)
        return DataFrame(jdf, self.sparkSession)

    def take(self, num: int) -> List[Row]:
        """Returns the first ``num`` rows as a :class:`list` of :class:`Row`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        num : int
            Number of records to return. Will return this number of records
            or all records if the DataFrame contains less than this number of records..

        Returns
        -------
        list
            List of rows

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        Return the first 2 rows of the :class:`DataFrame`.

        >>> df.take(2)
        [Row(age=14, name='Tom'), Row(age=23, name='Alice')]
        """
        return self.limit(num).collect()

    def tail(self, num: int) -> List[Row]:
        """
        Returns the last ``num`` rows as a :class:`list` of :class:`Row`.

        Running tail requires moving data into the application's driver process, and doing so with
        a very large ``num`` can crash the driver process with OutOfMemoryError.

        .. versionadded:: 3.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        num : int
            Number of records to return. Will return this number of records
            or all records if the DataFrame contains less than this number of records.

        Returns
        -------
        list
            List of rows

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        >>> df.tail(2)
        [Row(age=23, name='Alice'), Row(age=16, name='Bob')]
        """
        with SCCallSiteSync(self._sc):
            sock_info = self._jdf.tailToPython(num)
        return list(_load_from_socket(sock_info, BatchedSerializer(CPickleSerializer())))

    def foreach(self, f: Callable[[Row], None]) -> None:
        """Applies the ``f`` function to all :class:`Row` of this :class:`DataFrame`.

        This is a shorthand for ``df.rdd.foreach()``.

        .. versionadded:: 1.3.0

        Parameters
        ----------
        f : function
            A function that accepts one parameter which will
            receive each row to process.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> def func(person):
        ...     print(person.name)
        ...
        >>> df.foreach(func)
        """
        self.rdd.foreach(f)

    def foreachPartition(self, f: Callable[[Iterator[Row]], None]) -> None:
        """Applies the ``f`` function to each partition of this :class:`DataFrame`.

        This a shorthand for ``df.rdd.foreachPartition()``.

        .. versionadded:: 1.3.0

        Parameters
        ----------
        f : function
            A function that accepts one parameter which will receive
            each partition to process.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> def func(itr):
        ...     for person in itr:
        ...         print(person.name)
        ...
        >>> df.foreachPartition(func)
        """
        self.rdd.foreachPartition(f)  # type: ignore[arg-type]

    def cache(self) -> "DataFrame":
        """Persists the :class:`DataFrame` with the default storage level (`MEMORY_AND_DISK_DESER`).

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Notes
        -----
        The default storage level has changed to `MEMORY_AND_DISK_DESER` to match Scala in 3.0.

        Returns
        -------
        :class:`DataFrame`
            Cached DataFrame.

        Examples
        --------
        >>> df = spark.range(1)
        >>> df.cache()
        DataFrame[id: bigint]

        >>> df.explain()
        == Physical Plan ==
        AdaptiveSparkPlan isFinalPlan=false
        +- InMemoryTableScan ...
        """
        self.is_cached = True
        self._jdf.cache()
        return self

    def persist(
        self,
        storageLevel: StorageLevel = (StorageLevel.MEMORY_AND_DISK_DESER),
    ) -> "DataFrame":
        """Sets the storage level to persist the contents of the :class:`DataFrame` across
        operations after the first time it is computed. This can only be used to assign
        a new storage level if the :class:`DataFrame` does not have a storage level set yet.
        If no storage level is specified defaults to (`MEMORY_AND_DISK_DESER`)

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Notes
        -----
        The default storage level has changed to `MEMORY_AND_DISK_DESER` to match Scala in 3.0.

        Parameters
        ----------
        storageLevel : :class:`StorageLevel`
            Storage level to set for persistence. Default is MEMORY_AND_DISK_DESER.

        Returns
        -------
        :class:`DataFrame`
            Persisted DataFrame.

        Examples
        --------
        >>> df = spark.range(1)
        >>> df.persist()
        DataFrame[id: bigint]

        >>> df.explain()
        == Physical Plan ==
        AdaptiveSparkPlan isFinalPlan=false
        +- InMemoryTableScan ...

        Persists the data in the disk by specifying the storage level.

        >>> from pyspark.storagelevel import StorageLevel
        >>> df.persist(StorageLevel.DISK_ONLY)
        DataFrame[id: bigint]
        """
        self.is_cached = True
        javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
        self._jdf.persist(javaStorageLevel)
        return self

    @property
    def storageLevel(self) -> StorageLevel:
        """Get the :class:`DataFrame`'s current storage level.

        .. versionadded:: 2.1.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`StorageLevel`
            Currently defined storage level.

        Examples
        --------
        >>> df1 = spark.range(10)
        >>> df1.storageLevel
        StorageLevel(False, False, False, False, 1)
        >>> df1.cache().storageLevel
        StorageLevel(True, True, False, True, 1)

        >>> df2 = spark.range(5)
        >>> df2.persist(StorageLevel.DISK_ONLY_2).storageLevel
        StorageLevel(True, False, False, False, 2)
        """
        java_storage_level = self._jdf.storageLevel()
        storage_level = StorageLevel(
            java_storage_level.useDisk(),
            java_storage_level.useMemory(),
            java_storage_level.useOffHeap(),
            java_storage_level.deserialized(),
            java_storage_level.replication(),
        )
        return storage_level

    def unpersist(self, blocking: bool = False) -> "DataFrame":
        """Marks the :class:`DataFrame` as non-persistent, and remove all blocks for it from
        memory and disk.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Notes
        -----
        `blocking` default has changed to ``False`` to match Scala in 2.0.

        Parameters
        ----------
        blocking : bool
            Whether to block until all blocks are deleted.

        Returns
        -------
        :class:`DataFrame`
            Unpersisted DataFrame.

        Examples
        --------
        >>> df = spark.range(1)
        >>> df.persist()
        DataFrame[id: bigint]
        >>> df.unpersist()
        DataFrame[id: bigint]
        >>> df = spark.range(1)
        >>> df.unpersist(True)
        DataFrame[id: bigint]
        """
        self.is_cached = False
        self._jdf.unpersist(blocking)
        return self

    def coalesce(self, numPartitions: int) -> "DataFrame":
        """
        Returns a new :class:`DataFrame` that has exactly `numPartitions` partitions.

        Similar to coalesce defined on an :class:`RDD`, this operation results in a
        narrow dependency, e.g. if you go from 1000 partitions to 100 partitions,
        there will not be a shuffle, instead each of the 100 new partitions will
        claim 10 of the current partitions. If a larger number of partitions is requested,
        it will stay at the current number of partitions.

        However, if you're doing a drastic coalesce, e.g. to numPartitions = 1,
        this may result in your computation taking place on fewer nodes than
        you like (e.g. one node in the case of numPartitions = 1). To avoid this,
        you can call repartition(). This will add a shuffle step, but means the
        current upstream partitions will be executed in parallel (per whatever
        the current partitioning is).

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        numPartitions : int
            specify the target number of partitions

        Returns
        -------
        :class:`DataFrame`

        Examples
        --------
        >>> df = spark.range(10)
        >>> df.coalesce(1).rdd.getNumPartitions()
        1
        """
        return DataFrame(self._jdf.coalesce(numPartitions), self.sparkSession)

    @overload
    def repartition(self, numPartitions: int, *cols: "ColumnOrName") -> "DataFrame":
        ...

    @overload
    def repartition(self, *cols: "ColumnOrName") -> "DataFrame":
        ...

    def repartition(  # type: ignore[misc]
        self, numPartitions: Union[int, "ColumnOrName"], *cols: "ColumnOrName"
    ) -> "DataFrame":
        """
        Returns a new :class:`DataFrame` partitioned by the given partitioning expressions. The
        resulting :class:`DataFrame` is hash partitioned.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        numPartitions : int
            can be an int to specify the target number of partitions or a Column.
            If it is a Column, it will be used as the first partitioning column. If not specified,
            the default number of partitions is used.
        cols : str or :class:`Column`
            partitioning columns.

            .. versionchanged:: 1.6.0
               Added optional arguments to specify the partitioning columns. Also made numPartitions
               optional if partitioning columns are specified.

        Returns
        -------
        :class:`DataFrame`
            Repartitioned DataFrame.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        Repartition the data into 10 partitions.

        >>> df.repartition(10).rdd.getNumPartitions()
        10

        Repartition the data into 7 partitions by 'age' column.

        >>> df.repartition(7, "age").rdd.getNumPartitions()
        7

        Repartition the data into 7 partitions by 'age' and 'name columns.

        >>> df.repartition(3, "name", "age").rdd.getNumPartitions()
        3
        """
        if isinstance(numPartitions, int):
            if len(cols) == 0:
                return DataFrame(self._jdf.repartition(numPartitions), self.sparkSession)
            else:
                return DataFrame(
                    self._jdf.repartition(numPartitions, self._jcols(*cols)),
                    self.sparkSession,
                )
        elif isinstance(numPartitions, (str, Column)):
            cols = (numPartitions,) + cols
            return DataFrame(self._jdf.repartition(self._jcols(*cols)), self.sparkSession)
        else:
            raise PySparkTypeError(
                error_class="NOT_COLUMN_OR_STR",
                message_parameters={
                    "arg_name": "numPartitions",
                    "arg_type": type(numPartitions).__name__,
                },
            )

    @overload
    def repartitionByRange(self, numPartitions: int, *cols: "ColumnOrName") -> "DataFrame":
        ...

    @overload
    def repartitionByRange(self, *cols: "ColumnOrName") -> "DataFrame":
        ...

    def repartitionByRange(  # type: ignore[misc]
        self, numPartitions: Union[int, "ColumnOrName"], *cols: "ColumnOrName"
    ) -> "DataFrame":
        """
        Returns a new :class:`DataFrame` partitioned by the given partitioning expressions. The
        resulting :class:`DataFrame` is range partitioned.

        .. versionadded:: 2.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        numPartitions : int
            can be an int to specify the target number of partitions or a Column.
            If it is a Column, it will be used as the first partitioning column. If not specified,
            the default number of partitions is used.
        cols : str or :class:`Column`
            partitioning columns.

        Returns
        -------
        :class:`DataFrame`
            Repartitioned DataFrame.

        Notes
        -----
        At least one partition-by expression must be specified.
        When no explicit sort order is specified, "ascending nulls first" is assumed.

        Due to performance reasons this method uses sampling to estimate the ranges.
        Hence, the output may not be consistent, since sampling can return different values.
        The sample size can be controlled by the config
        `spark.sql.execution.rangeExchange.sampleSizePerPartition`.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        Repartition the data into 2 partitions by range in 'age' column.
        For example, the first partition can have ``(14, "Tom")``, and the second
        partition would have ``(16, "Bob")`` and ``(23, "Alice")``.

        >>> df.repartitionByRange(2, "age").rdd.getNumPartitions()
        2
        """
        if isinstance(numPartitions, int):
            if len(cols) == 0:
                raise PySparkValueError(
                    error_class="CANNOT_BE_EMPTY",
                    message_parameters={"item": "partition-by expression"},
                )
            else:
                return DataFrame(
                    self._jdf.repartitionByRange(numPartitions, self._jcols(*cols)),
                    self.sparkSession,
                )
        elif isinstance(numPartitions, (str, Column)):
            cols = (numPartitions,) + cols
            return DataFrame(self._jdf.repartitionByRange(self._jcols(*cols)), self.sparkSession)
        else:
            raise PySparkTypeError(
                error_class="NOT_COLUMN_OR_INT_OR_STR",
                message_parameters={
                    "arg_name": "numPartitions",
                    "arg_type": type(numPartitions).__name__,
                },
            )

    def distinct(self) -> "DataFrame":
        """Returns a new :class:`DataFrame` containing the distinct rows in this :class:`DataFrame`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with distinct records.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (23, "Alice")], ["age", "name"])

        Return the number of distinct rows in the :class:`DataFrame`

        >>> df.distinct().count()
        2
        """
        return DataFrame(self._jdf.distinct(), self.sparkSession)

    @overload
    def sample(self, fraction: float, seed: Optional[int] = ...) -> "DataFrame":
        ...

    @overload
    def sample(
        self,
        withReplacement: Optional[bool],
        fraction: float,
        seed: Optional[int] = ...,
    ) -> "DataFrame":
        ...

    def sample(  # type: ignore[misc]
        self,
        withReplacement: Optional[Union[float, bool]] = None,
        fraction: Optional[Union[int, float]] = None,
        seed: Optional[int] = None,
    ) -> "DataFrame":
        """Returns a sampled subset of this :class:`DataFrame`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        withReplacement : bool, optional
            Sample with replacement or not (default ``False``).
        fraction : float, optional
            Fraction of rows to generate, range [0.0, 1.0].
        seed : int, optional
            Seed for sampling (default a random seed).

        Returns
        -------
        :class:`DataFrame`
            Sampled rows from given DataFrame.

        Notes
        -----
        This is not guaranteed to provide exactly the fraction specified of the total
        count of the given :class:`DataFrame`.

        `fraction` is required and, `withReplacement` and `seed` are optional.

        Examples
        --------
        >>> df = spark.range(10)
        >>> df.sample(0.5, 3).count() # doctest: +SKIP
        7
        >>> df.sample(fraction=0.5, seed=3).count() # doctest: +SKIP
        7
        >>> df.sample(withReplacement=True, fraction=0.5, seed=3).count() # doctest: +SKIP
        1
        >>> df.sample(1.0).count()
        10
        >>> df.sample(fraction=1.0).count()
        10
        >>> df.sample(False, fraction=1.0).count()
        10
        """

        # For the cases below:
        #   sample(True, 0.5 [, seed])
        #   sample(True, fraction=0.5 [, seed])
        #   sample(withReplacement=False, fraction=0.5 [, seed])
        is_withReplacement_set = type(withReplacement) == bool and isinstance(fraction, float)

        # For the case below:
        #   sample(faction=0.5 [, seed])
        is_withReplacement_omitted_kwargs = withReplacement is None and isinstance(fraction, float)

        # For the case below:
        #   sample(0.5 [, seed])
        is_withReplacement_omitted_args = isinstance(withReplacement, float)

        if not (
            is_withReplacement_set
            or is_withReplacement_omitted_kwargs
            or is_withReplacement_omitted_args
        ):
            argtypes = [type(arg).__name__ for arg in [withReplacement, fraction, seed]]
            raise PySparkTypeError(
                error_class="NOT_BOOL_OR_FLOAT_OR_INT",
                message_parameters={
                    "arg_name": "withReplacement (optional), "
                    + "fraction (required) and seed (optional)",
                    "arg_type": ", ".join(argtypes),
                },
            )

        if is_withReplacement_omitted_args:
            if fraction is not None:
                seed = cast(int, fraction)
            fraction = withReplacement
            withReplacement = None

        seed = int(seed) if seed is not None else None
        args = [arg for arg in [withReplacement, fraction, seed] if arg is not None]
        jdf = self._jdf.sample(*args)
        return DataFrame(jdf, self.sparkSession)

    def sampleBy(
        self, col: "ColumnOrName", fractions: Dict[Any, float], seed: Optional[int] = None
    ) -> "DataFrame":
        """
        Returns a stratified sample without replacement based on the
        fraction given on each stratum.

        .. versionadded:: 1.5.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        col : :class:`Column` or str
            column that defines strata

            .. versionchanged:: 3.0.0
               Added sampling by a column of :class:`Column`
        fractions : dict
            sampling fraction for each stratum. If a stratum is not
            specified, we treat its fraction as zero.
        seed : int, optional
            random seed

        Returns
        -------
        a new :class:`DataFrame` that represents the stratified sample

        Examples
        --------
        >>> from pyspark.sql.functions import col
        >>> dataset = spark.range(0, 100).select((col("id") % 3).alias("key"))
        >>> sampled = dataset.sampleBy("key", fractions={0: 0.1, 1: 0.2}, seed=0)
        >>> sampled.groupBy("key").count().orderBy("key").show()
        +---+-----+
        |key|count|
        +---+-----+
        |  0|    3|
        |  1|    6|
        +---+-----+
        >>> dataset.sampleBy(col("key"), fractions={2: 1.0}, seed=0).count()
        33
        """
        if isinstance(col, str):
            col = Column(col)
        elif not isinstance(col, Column):
            raise PySparkTypeError(
                error_class="NOT_COLUMN_OR_STR",
                message_parameters={"arg_name": "col", "arg_type": type(col).__name__},
            )
        if not isinstance(fractions, dict):
            raise PySparkTypeError(
                error_class="NOT_DICT",
                message_parameters={"arg_name": "fractions", "arg_type": type(fractions).__name__},
            )
        for k, v in fractions.items():
            if not isinstance(k, (float, int, str)):
                raise PySparkTypeError(
                    error_class="DISALLOWED_TYPE_FOR_CONTAINER",
                    message_parameters={
                        "arg_name": "fractions",
                        "arg_type": type(fractions).__name__,
                        "allowed_types": "float, int, str",
                        "return_type": type(k).__name__,
                    },
                )
            fractions[k] = float(v)
        col = col._jc
        seed = seed if seed is not None else random.randint(0, sys.maxsize)
        return DataFrame(
            self._jdf.stat().sampleBy(col, self._jmap(fractions), seed), self.sparkSession
        )

    def randomSplit(self, weights: List[float], seed: Optional[int] = None) -> List["DataFrame"]:
        """Randomly splits this :class:`DataFrame` with the provided weights.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        weights : list
            list of doubles as weights with which to split the :class:`DataFrame`.
            Weights will be normalized if they don't sum up to 1.0.
        seed : int, optional
            The seed for sampling.

        Returns
        -------
        list
            List of DataFrames.

        Examples
        --------
        >>> from pyspark.sql import Row
        >>> df = spark.createDataFrame([
        ...     Row(age=10, height=80, name="Alice"),
        ...     Row(age=5, height=None, name="Bob"),
        ...     Row(age=None, height=None, name="Tom"),
        ...     Row(age=None, height=None, name=None),
        ... ])

        >>> splits = df.randomSplit([1.0, 2.0], 24)
        >>> splits[0].count()
        2
        >>> splits[1].count()
        2
        """
        for w in weights:
            if w < 0.0:
                raise PySparkValueError(
                    error_class="VALUE_NOT_POSITIVE",
                    message_parameters={"arg_name": "weights", "arg_value": str(w)},
                )
        seed = seed if seed is not None else random.randint(0, sys.maxsize)
        df_array = self._jdf.randomSplit(
            _to_list(self.sparkSession._sc, cast(List["ColumnOrName"], weights)), int(seed)
        )
        return [DataFrame(df, self.sparkSession) for df in df_array]

    @property
    def dtypes(self) -> List[Tuple[str, str]]:
        """Returns all column names and their data types as a list.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        list
            List of columns as tuple pairs.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df.dtypes
        [('age', 'bigint'), ('name', 'string')]
        """
        return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields]

    @property
    def columns(self) -> List[str]:
        """
        Retrieves the names of all columns in the :class:`DataFrame` as a list.

        The order of the column names in the list reflects their order in the DataFrame.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        list
            List of column names in the DataFrame.

        Examples
        --------
        Example 1: Retrieve column names of a DataFrame

        >>> df = spark.createDataFrame(
        ...     [(14, "Tom", "CA"), (23, "Alice", "NY"), (16, "Bob", "TX")],
        ...     ["age", "name", "state"]
        ... )
        >>> df.columns
        ['age', 'name', 'state']

        Example 2: Using column names to project specific columns

        >>> selected_cols = [col for col in df.columns if col != "age"]
        >>> df.select(selected_cols).show()
        +-----+-----+
        | name|state|
        +-----+-----+
        |  Tom|   CA|
        |Alice|   NY|
        |  Bob|   TX|
        +-----+-----+

        Example 3: Checking if a specific column exists in a DataFrame

        >>> "state" in df.columns
        True
        >>> "salary" in df.columns
        False

        Example 4: Iterating over columns to apply a transformation

        >>> import pyspark.sql.functions as f
        >>> for col_name in df.columns:
        ...     df = df.withColumn(col_name, f.upper(f.col(col_name)))
        >>> df.show()
        +---+-----+-----+
        |age| name|state|
        +---+-----+-----+
        | 14|  TOM|   CA|
        | 23|ALICE|   NY|
        | 16|  BOB|   TX|
        +---+-----+-----+

        Example 5: Renaming columns and checking the updated column names

        >>> df = df.withColumnRenamed("name", "first_name")
        >>> df.columns
        ['age', 'first_name', 'state']

        Example 6: Using the `columns` property to ensure two DataFrames have the
        same columns before a union

        >>> df2 = spark.createDataFrame(
        ...     [(30, "Eve", "FL"), (40, "Sam", "WA")], ["age", "name", "location"])
        >>> df.columns == df2.columns
        False
        """
        return [f.name for f in self.schema.fields]

    def colRegex(self, colName: str) -> Column:
        """
        Selects column based on the column name specified as a regex and returns it
        as :class:`Column`.

        .. versionadded:: 2.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        colName : str
            string, column name specified as a regex.

        Returns
        -------
        :class:`Column`

        Examples
        --------
        >>> df = spark.createDataFrame([("a", 1), ("b", 2), ("c",  3)], ["Col1", "Col2"])
        >>> df.select(df.colRegex("`(Col1)?+.+`")).show()
        +----+
        |Col2|
        +----+
        |   1|
        |   2|
        |   3|
        +----+
        """
        if not isinstance(colName, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "colName", "arg_type": type(colName).__name__},
            )
        jc = self._jdf.colRegex(colName)
        return Column(jc)

    def to(self, schema: StructType) -> "DataFrame":
        """
        Returns a new :class:`DataFrame` where each row is reconciled to match the specified
        schema.

        .. versionadded:: 3.4.0

        Parameters
        ----------
        schema : :class:`StructType`
            Specified schema.

        Returns
        -------
        :class:`DataFrame`
            Reconciled DataFrame.

        Notes
        -----
        * Reorder columns and/or inner fields by name to match the specified schema.

        * Project away columns and/or inner fields that are not needed by the specified schema.
            Missing columns and/or inner fields (present in the specified schema but not input
            DataFrame) lead to failures.

        * Cast the columns and/or inner fields to match the data types in the specified schema,
            if the types are compatible, e.g., numeric to numeric (error if overflows), but
            not string to int.

        * Carry over the metadata from the specified schema, while the columns and/or inner fields
            still keep their own metadata if not overwritten by the specified schema.

        * Fail if the nullability is not compatible. For example, the column and/or inner field
            is nullable but the specified schema requires them to be not nullable.

        Supports Spark Connect.

        Examples
        --------
        >>> from pyspark.sql.types import StructField, StringType
        >>> df = spark.createDataFrame([("a", 1)], ["i", "j"])
        >>> df.schema
        StructType([StructField('i', StringType(), True), StructField('j', LongType(), True)])

        >>> schema = StructType([StructField("j", StringType()), StructField("i", StringType())])
        >>> df2 = df.to(schema)
        >>> df2.schema
        StructType([StructField('j', StringType(), True), StructField('i', StringType(), True)])
        >>> df2.show()
        +---+---+
        |  j|  i|
        +---+---+
        |  1|  a|
        +---+---+
        """
        assert schema is not None
        jschema = self._jdf.sparkSession().parseDataType(schema.json())
        return DataFrame(self._jdf.to(jschema), self.sparkSession)

    def alias(self, alias: str) -> "DataFrame":
        """Returns a new :class:`DataFrame` with an alias set.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        alias : str
            an alias name to be set for the :class:`DataFrame`.

        Returns
        -------
        :class:`DataFrame`
            Aliased DataFrame.

        Examples
        --------
        >>> from pyspark.sql.functions import col, desc
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df_as1 = df.alias("df_as1")
        >>> df_as2 = df.alias("df_as2")
        >>> joined_df = df_as1.join(df_as2, col("df_as1.name") == col("df_as2.name"), 'inner')
        >>> joined_df.select(
        ...     "df_as1.name", "df_as2.name", "df_as2.age").sort(desc("df_as1.name")).show()
        +-----+-----+---+
        | name| name|age|
        +-----+-----+---+
        |  Tom|  Tom| 14|
        |  Bob|  Bob| 16|
        |Alice|Alice| 23|
        +-----+-----+---+
        """
        assert isinstance(alias, str), "alias should be a string"
        return DataFrame(getattr(self._jdf, "as")(alias), self.sparkSession)

    def crossJoin(self, other: "DataFrame") -> "DataFrame":
        """Returns the cartesian product with another :class:`DataFrame`.

        .. versionadded:: 2.1.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Right side of the cartesian product.

        Returns
        -------
        :class:`DataFrame`
            Joined DataFrame.

        Examples
        --------
        >>> from pyspark.sql import Row
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df2 = spark.createDataFrame(
        ...     [Row(height=80, name="Tom"), Row(height=85, name="Bob")])
        >>> df.crossJoin(df2.select("height")).select("age", "name", "height").show()
        +---+-----+------+
        |age| name|height|
        +---+-----+------+
        | 14|  Tom|    80|
        | 14|  Tom|    85|
        | 23|Alice|    80|
        | 23|Alice|    85|
        | 16|  Bob|    80|
        | 16|  Bob|    85|
        +---+-----+------+
        """

        jdf = self._jdf.crossJoin(other._jdf)
        return DataFrame(jdf, self.sparkSession)

    def join(
        self,
        other: "DataFrame",
        on: Optional[Union[str, List[str], Column, List[Column]]] = None,
        how: Optional[str] = None,
    ) -> "DataFrame":
        """Joins with another :class:`DataFrame`, using the given join expression.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Right side of the join
        on : str, list or :class:`Column`, optional
            a string for the join column name, a list of column names,
            a join expression (Column), or a list of Columns.
            If `on` is a string or a list of strings indicating the name of the join column(s),
            the column(s) must exist on both sides, and this performs an equi-join.
        how : str, optional
            default ``inner``. Must be one of: ``inner``, ``cross``, ``outer``,
            ``full``, ``fullouter``, ``full_outer``, ``left``, ``leftouter``, ``left_outer``,
            ``right``, ``rightouter``, ``right_outer``, ``semi``, ``leftsemi``, ``left_semi``,
            ``anti``, ``leftanti`` and ``left_anti``.

        Returns
        -------
        :class:`DataFrame`
            Joined DataFrame.

        Examples
        --------
        The following performs a full outer join between ``df1`` and ``df2``.

        >>> from pyspark.sql import Row
        >>> from pyspark.sql.functions import desc
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")]).toDF("age", "name")
        >>> df2 = spark.createDataFrame([Row(height=80, name="Tom"), Row(height=85, name="Bob")])
        >>> df3 = spark.createDataFrame([Row(age=2, name="Alice"), Row(age=5, name="Bob")])
        >>> df4 = spark.createDataFrame([
        ...     Row(age=10, height=80, name="Alice"),
        ...     Row(age=5, height=None, name="Bob"),
        ...     Row(age=None, height=None, name="Tom"),
        ...     Row(age=None, height=None, name=None),
        ... ])

        Inner join on columns (default)

        >>> df.join(df2, 'name').select(df.name, df2.height).show()
        +----+------+
        |name|height|
        +----+------+
        | Bob|    85|
        +----+------+
        >>> df.join(df4, ['name', 'age']).select(df.name, df.age).show()
        +----+---+
        |name|age|
        +----+---+
        | Bob|  5|
        +----+---+

        Outer join for both DataFrames on the 'name' column.

        >>> df.join(df2, df.name == df2.name, 'outer').select(
        ...     df.name, df2.height).sort(desc("name")).show()
        +-----+------+
        | name|height|
        +-----+------+
        |  Bob|    85|
        |Alice|  NULL|
        | NULL|    80|
        +-----+------+
        >>> df.join(df2, 'name', 'outer').select('name', 'height').sort(desc("name")).show()
        +-----+------+
        | name|height|
        +-----+------+
        |  Tom|    80|
        |  Bob|    85|
        |Alice|  NULL|
        +-----+------+

        Outer join for both DataFrams with multiple columns.

        >>> df.join(
        ...     df3,
        ...     [df.name == df3.name, df.age == df3.age],
        ...     'outer'
        ... ).select(df.name, df3.age).show()
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  2|
        |  Bob|  5|
        +-----+---+
        """

        if on is not None and not isinstance(on, list):
            on = [on]  # type: ignore[assignment]

        if on is not None:
            if isinstance(on[0], str):
                on = self._jseq(cast(List[str], on))
            else:
                assert isinstance(on[0], Column), "on should be Column or list of Column"
                on = reduce(lambda x, y: x.__and__(y), cast(List[Column], on))
                on = on._jc

        if on is None and how is None:
            jdf = self._jdf.join(other._jdf)
        else:
            if how is None:
                how = "inner"
            if on is None:
                on = self._jseq([])
            assert isinstance(how, str), "how should be a string"
            jdf = self._jdf.join(other._jdf, on, how)
        return DataFrame(jdf, self.sparkSession)

    # TODO(SPARK-22947): Fix the DataFrame API.
    def _joinAsOf(
        self,
        other: "DataFrame",
        leftAsOfColumn: Union[str, Column],
        rightAsOfColumn: Union[str, Column],
        on: Optional[Union[str, List[str], Column, List[Column]]] = None,
        how: Optional[str] = None,
        *,
        tolerance: Optional[Column] = None,
        allowExactMatches: bool = True,
        direction: str = "backward",
    ) -> "DataFrame":
        """
        Perform an as-of join.

        This is similar to a left-join except that we match on the nearest
        key rather than equal keys.

        Parameters
        ----------
        other : :class:`DataFrame`
            Right side of the join
        leftAsOfColumn : str or :class:`Column`
            a string for the as-of join column name, or a Column
        rightAsOfColumn : str or :class:`Column`
            a string for the as-of join column name, or a Column
        on : str, list or :class:`Column`, optional
            a string for the join column name, a list of column names,
            a join expression (Column), or a list of Columns.
            If `on` is a string or a list of strings indicating the name of the join column(s),
            the column(s) must exist on both sides, and this performs an equi-join.
        how : str, optional
            default ``inner``. Must be one of: ``inner`` and ``left``.
        tolerance : :class:`Column`, optional
            an asof tolerance within this range; must be compatible
            with the merge index.
        allowExactMatches : bool, optional
            default ``True``.
        direction : str, optional
            default ``backward``. Must be one of: ``backward``, ``forward``, and ``nearest``.

        Examples
        --------
        The following performs an as-of join between ``left`` and ``right``.

        >>> left = spark.createDataFrame([(1, "a"), (5, "b"), (10,  "c")], ["a", "left_val"])
        >>> right = spark.createDataFrame([(1, 1), (2, 2), (3, 3), (6, 6), (7, 7)],
        ...                               ["a", "right_val"])
        >>> left._joinAsOf(
        ...     right, leftAsOfColumn="a", rightAsOfColumn="a"
        ... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
        [Row(a=1, left_val='a', right_val=1),
         Row(a=5, left_val='b', right_val=3),
         Row(a=10, left_val='c', right_val=7)]

        >>> from pyspark.sql import functions as sf
        >>> left._joinAsOf(
        ...     right, leftAsOfColumn="a", rightAsOfColumn="a", tolerance=sf.lit(1)
        ... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
        [Row(a=1, left_val='a', right_val=1)]

        >>> left._joinAsOf(
        ...     right, leftAsOfColumn="a", rightAsOfColumn="a", how="left", tolerance=sf.lit(1)
        ... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
        [Row(a=1, left_val='a', right_val=1),
         Row(a=5, left_val='b', right_val=None),
         Row(a=10, left_val='c', right_val=None)]

        >>> left._joinAsOf(
        ...     right, leftAsOfColumn="a", rightAsOfColumn="a", allowExactMatches=False
        ... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
        [Row(a=5, left_val='b', right_val=3),
         Row(a=10, left_val='c', right_val=7)]

        >>> left._joinAsOf(
        ...     right, leftAsOfColumn="a", rightAsOfColumn="a", direction="forward"
        ... ).select(left.a, 'left_val', 'right_val').sort("a").collect()
        [Row(a=1, left_val='a', right_val=1),
         Row(a=5, left_val='b', right_val=6)]
        """
        if isinstance(leftAsOfColumn, str):
            leftAsOfColumn = self[leftAsOfColumn]
        left_as_of_jcol = leftAsOfColumn._jc
        if isinstance(rightAsOfColumn, str):
            rightAsOfColumn = other[rightAsOfColumn]
        right_as_of_jcol = rightAsOfColumn._jc

        if on is not None and not isinstance(on, list):
            on = [on]  # type: ignore[assignment]

        if on is not None:
            if isinstance(on[0], str):
                on = self._jseq(cast(List[str], on))
            else:
                assert isinstance(on[0], Column), "on should be Column or list of Column"
                on = reduce(lambda x, y: x.__and__(y), cast(List[Column], on))
                on = on._jc

        if how is None:
            how = "inner"
        assert isinstance(how, str), "how should be a string"

        if tolerance is not None:
            assert isinstance(tolerance, Column), "tolerance should be Column"
            tolerance = tolerance._jc

        jdf = self._jdf.joinAsOf(
            other._jdf,
            left_as_of_jcol,
            right_as_of_jcol,
            on,
            how,
            tolerance,
            allowExactMatches,
            direction,
        )
        return DataFrame(jdf, self.sparkSession)

    def sortWithinPartitions(
        self, *cols: Union[str, Column, List[Union[str, Column]]], **kwargs: Any
    ) -> "DataFrame":
        """Returns a new :class:`DataFrame` with each partition sorted by the specified column(s).

        .. versionadded:: 1.6.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : str, list or :class:`Column`, optional
            list of :class:`Column` or column names to sort by.

        Other Parameters
        ----------------
        ascending : bool or list, optional, default True
            boolean or list of boolean.
            Sort ascending vs. descending. Specify list for multiple sort orders.
            If a list is specified, the length of the list must equal the length of the `cols`.

        Returns
        -------
        :class:`DataFrame`
            DataFrame sorted by partitions.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.sortWithinPartitions("age", ascending=False)
        DataFrame[age: bigint, name: string]
        """
        jdf = self._jdf.sortWithinPartitions(self._sort_cols(cols, kwargs))
        return DataFrame(jdf, self.sparkSession)

    def sort(
        self, *cols: Union[str, Column, List[Union[str, Column]]], **kwargs: Any
    ) -> "DataFrame":
        """Returns a new :class:`DataFrame` sorted by the specified column(s).

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : str, list, or :class:`Column`, optional
             list of :class:`Column` or column names to sort by.

        Other Parameters
        ----------------
        ascending : bool or list, optional, default True
            boolean or list of boolean.
            Sort ascending vs. descending. Specify list for multiple sort orders.
            If a list is specified, the length of the list must equal the length of the `cols`.

        Returns
        -------
        :class:`DataFrame`
            Sorted DataFrame.

        Examples
        --------
        >>> from pyspark.sql.functions import desc, asc
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])

        Sort the DataFrame in ascending order.

        >>> df.sort(asc("age")).show()
        +---+-----+
        |age| name|
        +---+-----+
        |  2|Alice|
        |  5|  Bob|
        +---+-----+

        Sort the DataFrame in descending order.

        >>> df.sort(df.age.desc()).show()
        +---+-----+
        |age| name|
        +---+-----+
        |  5|  Bob|
        |  2|Alice|
        +---+-----+
        >>> df.orderBy(df.age.desc()).show()
        +---+-----+
        |age| name|
        +---+-----+
        |  5|  Bob|
        |  2|Alice|
        +---+-----+
        >>> df.sort("age", ascending=False).show()
        +---+-----+
        |age| name|
        +---+-----+
        |  5|  Bob|
        |  2|Alice|
        +---+-----+

        Specify multiple columns

        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (2, "Bob"), (5, "Bob")], schema=["age", "name"])
        >>> df.orderBy(desc("age"), "name").show()
        +---+-----+
        |age| name|
        +---+-----+
        |  5|  Bob|
        |  2|Alice|
        |  2|  Bob|
        +---+-----+

        Specify multiple columns for sorting order at `ascending`.

        >>> df.orderBy(["age", "name"], ascending=[False, False]).show()
        +---+-----+
        |age| name|
        +---+-----+
        |  5|  Bob|
        |  2|  Bob|
        |  2|Alice|
        +---+-----+
        """
        jdf = self._jdf.sort(self._sort_cols(cols, kwargs))
        return DataFrame(jdf, self.sparkSession)

    orderBy = sort

    def _jseq(
        self,
        cols: Sequence,
        converter: Optional[Callable[..., Union["PrimitiveType", JavaObject]]] = None,
    ) -> JavaObject:
        """Return a JVM Seq of Columns from a list of Column or names"""
        return _to_seq(self.sparkSession._sc, cols, converter)

    def _jmap(self, jm: Dict) -> JavaObject:
        """Return a JVM Scala Map from a dict"""
        return _to_scala_map(self.sparkSession._sc, jm)

    def _jcols(self, *cols: "ColumnOrName") -> JavaObject:
        """Return a JVM Seq of Columns from a list of Column or column names

        If `cols` has only one list in it, cols[0] will be used as the list.
        """
        if len(cols) == 1 and isinstance(cols[0], list):
            cols = cols[0]
        return self._jseq(cols, _to_java_column)

    def _sort_cols(
        self, cols: Sequence[Union[str, Column, List[Union[str, Column]]]], kwargs: Dict[str, Any]
    ) -> JavaObject:
        """Return a JVM Seq of Columns that describes the sort order"""
        if not cols:
            raise PySparkValueError(
                error_class="CANNOT_BE_EMPTY",
                message_parameters={"item": "column"},
            )
        if len(cols) == 1 and isinstance(cols[0], list):
            cols = cols[0]
        jcols = [_to_java_column(cast("ColumnOrName", c)) for c in cols]
        ascending = kwargs.get("ascending", True)
        if isinstance(ascending, (bool, int)):
            if not ascending:
                jcols = [jc.desc() for jc in jcols]
        elif isinstance(ascending, list):
            jcols = [jc if asc else jc.desc() for asc, jc in zip(ascending, jcols)]
        else:
            raise PySparkTypeError(
                error_class="NOT_BOOL_OR_LIST",
                message_parameters={"arg_name": "ascending", "arg_type": type(ascending).__name__},
            )
        return self._jseq(jcols)

    def describe(self, *cols: Union[str, List[str]]) -> "DataFrame":
        """Computes basic statistics for numeric and string columns.

        .. versionadded:: 1.3.1

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        This includes count, mean, stddev, min, and max. If no columns are
        given, this function computes statistics for all numerical or string columns.

        Notes
        -----
        This function is meant for exploratory data analysis, as we make no
        guarantee about the backward compatibility of the schema of the resulting
        :class:`DataFrame`.

        Use summary for expanded statistics and control over which statistics to compute.

        Parameters
        ----------
        cols : str, list, optional
             Column name or list of column names to describe by (default All columns).

        Returns
        -------
        :class:`DataFrame`
            A new DataFrame that describes (provides statistics) given DataFrame.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [("Bob", 13, 40.3, 150.5), ("Alice", 12, 37.8, 142.3), ("Tom", 11, 44.1, 142.2)],
        ...     ["name", "age", "weight", "height"],
        ... )
        >>> df.describe(['age']).show()
        +-------+----+
        |summary| age|
        +-------+----+
        |  count|   3|
        |   mean|12.0|
        | stddev| 1.0|
        |    min|  11|
        |    max|  13|
        +-------+----+

        >>> df.describe(['age', 'weight', 'height']).show()
        +-------+----+------------------+-----------------+
        |summary| age|            weight|           height|
        +-------+----+------------------+-----------------+
        |  count|   3|                 3|                3|
        |   mean|12.0| 40.73333333333333|            145.0|
        | stddev| 1.0|3.1722757341273704|4.763402145525822|
        |    min|  11|              37.8|            142.2|
        |    max|  13|              44.1|            150.5|
        +-------+----+------------------+-----------------+

        See Also
        --------
        DataFrame.summary
        """
        if len(cols) == 1 and isinstance(cols[0], list):
            cols = cols[0]  # type: ignore[assignment]
        jdf = self._jdf.describe(self._jseq(cols))
        return DataFrame(jdf, self.sparkSession)

    def summary(self, *statistics: str) -> "DataFrame":
        """Computes specified statistics for numeric and string columns. Available statistics are:
        - count
        - mean
        - stddev
        - min
        - max
        - arbitrary approximate percentiles specified as a percentage (e.g., 75%)

        If no statistics are given, this function computes count, mean, stddev, min,
        approximate quartiles (percentiles at 25%, 50%, and 75%), and max.

        .. versionadded:: 2.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        statistics : str, optional
             Column names to calculate statistics by (default All columns).

        Returns
        -------
        :class:`DataFrame`
            A new DataFrame that provides statistics for the given DataFrame.

        Notes
        -----
        This function is meant for exploratory data analysis, as we make no
        guarantee about the backward compatibility of the schema of the resulting
        :class:`DataFrame`.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [("Bob", 13, 40.3, 150.5), ("Alice", 12, 37.8, 142.3), ("Tom", 11, 44.1, 142.2)],
        ...     ["name", "age", "weight", "height"],
        ... )
        >>> df.select("age", "weight", "height").summary().show()
        +-------+----+------------------+-----------------+
        |summary| age|            weight|           height|
        +-------+----+------------------+-----------------+
        |  count|   3|                 3|                3|
        |   mean|12.0| 40.73333333333333|            145.0|
        | stddev| 1.0|3.1722757341273704|4.763402145525822|
        |    min|  11|              37.8|            142.2|
        |    25%|  11|              37.8|            142.2|
        |    50%|  12|              40.3|            142.3|
        |    75%|  13|              44.1|            150.5|
        |    max|  13|              44.1|            150.5|
        +-------+----+------------------+-----------------+

        >>> df.select("age", "weight", "height").summary("count", "min", "25%", "75%", "max").show()
        +-------+---+------+------+
        |summary|age|weight|height|
        +-------+---+------+------+
        |  count|  3|     3|     3|
        |    min| 11|  37.8| 142.2|
        |    25%| 11|  37.8| 142.2|
        |    75%| 13|  44.1| 150.5|
        |    max| 13|  44.1| 150.5|
        +-------+---+------+------+

        See Also
        --------
        DataFrame.display
        """
        if len(statistics) == 1 and isinstance(statistics[0], list):
            statistics = statistics[0]
        jdf = self._jdf.summary(self._jseq(statistics))
        return DataFrame(jdf, self.sparkSession)

    @overload
    def head(self) -> Optional[Row]:
        ...

    @overload
    def head(self, n: int) -> List[Row]:
        ...

    def head(self, n: Optional[int] = None) -> Union[Optional[Row], List[Row]]:
        """Returns the first ``n`` rows.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Notes
        -----
        This method should only be used if the resulting array is expected
        to be small, as all the data is loaded into the driver's memory.

        Parameters
        ----------
        n : int, optional
            default 1. Number of rows to return.

        Returns
        -------
        If n is greater than 1, return a list of :class:`Row`.
        If n is 1, return a single Row.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.head()
        Row(age=2, name='Alice')
        >>> df.head(1)
        [Row(age=2, name='Alice')]
        """
        if n is None:
            rs = self.head(1)
            return rs[0] if rs else None
        return self.take(n)

    def first(self) -> Optional[Row]:
        """Returns the first row as a :class:`Row`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`Row`
            First row if :class:`DataFrame` is not empty, otherwise ``None``.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.first()
        Row(age=2, name='Alice')
        """
        return self.head()

    @overload
    def __getitem__(self, item: Union[int, str]) -> Column:
        ...

    @overload
    def __getitem__(self, item: Union[Column, List, Tuple]) -> "DataFrame":
        ...

    def __getitem__(self, item: Union[int, str, Column, List, Tuple]) -> Union[Column, "DataFrame"]:
        """Returns the column as a :class:`Column`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        item : int, str, :class:`Column`, list or tuple
            column index, column name, column, or a list or tuple of columns

        Returns
        -------
        :class:`Column` or :class:`DataFrame`
            a specified column, or a filtered or projected dataframe.

            * If the input `item` is an int or str, the output is a :class:`Column`.

            * If the input `item` is a :class:`Column`, the output is a :class:`DataFrame`
                filtered by this given :class:`Column`.

            * If the input `item` is a list or tuple, the output is a :class:`DataFrame`
                projected by this given list or tuple.


        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])

        Retrieve a column instance.

        >>> df.select(df['age']).show()
        +---+
        |age|
        +---+
        |  2|
        |  5|
        +---+

        >>> df.select(df[1]).show()
        +-----+
        | name|
        +-----+
        |Alice|
        |  Bob|
        +-----+

        Select multiple string columns as index.

        >>> df[["name", "age"]].show()
        +-----+---+
        | name|age|
        +-----+---+
        |Alice|  2|
        |  Bob|  5|
        +-----+---+
        >>> df[df.age > 3].show()
        +---+----+
        |age|name|
        +---+----+
        |  5| Bob|
        +---+----+
        >>> df[df[0] > 3].show()
        +---+----+
        |age|name|
        +---+----+
        |  5| Bob|
        +---+----+
        """
        if isinstance(item, str):
            jc = self._jdf.apply(item)
            return Column(jc)
        elif isinstance(item, Column):
            return self.filter(item)
        elif isinstance(item, (list, tuple)):
            return self.select(*item)
        elif isinstance(item, int):
            jc = self._jdf.apply(self.columns[item])
            return Column(jc)
        else:
            raise PySparkTypeError(
                error_class="NOT_COLUMN_OR_FLOAT_OR_INT_OR_LIST_OR_STR",
                message_parameters={"arg_name": "item", "arg_type": type(item).__name__},
            )

    def __getattr__(self, name: str) -> Column:
        """Returns the :class:`Column` denoted by ``name``.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        name : str
            Column name to return as :class:`Column`.

        Returns
        -------
        :class:`Column`
            Requested column.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])

        Retrieve a column instance.

        >>> df.select(df.age).show()
        +---+
        |age|
        +---+
        |  2|
        |  5|
        +---+
        """
        if name not in self.columns:
            raise AttributeError(
                "'%s' object has no attribute '%s'" % (self.__class__.__name__, name)
            )
        jc = self._jdf.apply(name)
        return Column(jc)

    def __dir__(self) -> List[str]:
        """
        Examples
        --------
        >>> from pyspark.sql.functions import lit

        Create a dataframe with a column named 'id'.

        >>> df = spark.range(3)
        >>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Includes column id
        ['id', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty', 'isLocal', 'isStreaming']

        Add a column named 'i_like_pancakes'.

        >>> df = df.withColumn('i_like_pancakes', lit(1))
        >>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Includes columns i_like_pancakes, id
        ['i_like_pancakes', 'id', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty', 'isLocal']

        Try to add an existed column 'inputFiles'.

        >>> df = df.withColumn('inputFiles', lit(2))
        >>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Doesn't duplicate inputFiles
        ['i_like_pancakes', 'id', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty', 'isLocal']

        Try to add a column named 'id2'.

        >>> df = df.withColumn('id2', lit(3))
        >>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # result includes id2 and sorted
        ['i_like_pancakes', 'id', 'id2', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty']

        Don't include columns that are not valid python identifiers.

        >>> df = df.withColumn('1', lit(4))
        >>> df = df.withColumn('name 1', lit(5))
        >>> [attr for attr in dir(df) if attr[0] == 'i'][:7] # Doesn't include 1 or name 1
        ['i_like_pancakes', 'id', 'id2', 'inputFiles', 'intersect', 'intersectAll', 'isEmpty']
        """
        attrs = set(super().__dir__())
        attrs.update(filter(lambda s: s.isidentifier(), self.columns))
        return sorted(attrs)

    @overload
    def select(self, *cols: "ColumnOrName") -> "DataFrame":
        ...

    @overload
    def select(self, __cols: Union[List[Column], List[str]]) -> "DataFrame":
        ...

    def select(self, *cols: "ColumnOrName") -> "DataFrame":  # type: ignore[misc]
        """Projects a set of expressions and returns a new :class:`DataFrame`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : str, :class:`Column`, or list
            column names (string) or expressions (:class:`Column`).
            If one of the column names is '*', that column is expanded to include all columns
            in the current :class:`DataFrame`.

        Returns
        -------
        :class:`DataFrame`
            A DataFrame with subset (or all) of columns.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])

        Select all columns in the DataFrame.

        >>> df.select('*').show()
        +---+-----+
        |age| name|
        +---+-----+
        |  2|Alice|
        |  5|  Bob|
        +---+-----+

        Select a column with other expressions in the DataFrame.

        >>> df.select(df.name, (df.age + 10).alias('age')).show()
        +-----+---+
        | name|age|
        +-----+---+
        |Alice| 12|
        |  Bob| 15|
        +-----+---+
        """
        jdf = self._jdf.select(self._jcols(*cols))
        return DataFrame(jdf, self.sparkSession)

    @overload
    def selectExpr(self, *expr: str) -> "DataFrame":
        ...

    @overload
    def selectExpr(self, *expr: List[str]) -> "DataFrame":
        ...

    def selectExpr(self, *expr: Union[str, List[str]]) -> "DataFrame":
        """Projects a set of SQL expressions and returns a new :class:`DataFrame`.

        This is a variant of :func:`select` that accepts SQL expressions.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        :class:`DataFrame`
            A DataFrame with new/old columns transformed by expressions.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.selectExpr("age * 2", "abs(age)").show()
        +---------+--------+
        |(age * 2)|abs(age)|
        +---------+--------+
        |        4|       2|
        |       10|       5|
        +---------+--------+
        """
        if len(expr) == 1 and isinstance(expr[0], list):
            expr = expr[0]  # type: ignore[assignment]
        jdf = self._jdf.selectExpr(self._jseq(expr))
        return DataFrame(jdf, self.sparkSession)

    def filter(self, condition: "ColumnOrName") -> "DataFrame":
        """Filters rows using the given condition.

        :func:`where` is an alias for :func:`filter`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        condition : :class:`Column` or str
            a :class:`Column` of :class:`types.BooleanType`
            or a string of SQL expressions.

        Returns
        -------
        :class:`DataFrame`
            Filtered DataFrame.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (5, "Bob")], schema=["age", "name"])

        Filter by :class:`Column` instances.

        >>> df.filter(df.age > 3).show()
        +---+----+
        |age|name|
        +---+----+
        |  5| Bob|
        +---+----+
        >>> df.where(df.age == 2).show()
        +---+-----+
        |age| name|
        +---+-----+
        |  2|Alice|
        +---+-----+

        Filter by SQL expression in a string.

        >>> df.filter("age > 3").show()
        +---+----+
        |age|name|
        +---+----+
        |  5| Bob|
        +---+----+
        >>> df.where("age = 2").show()
        +---+-----+
        |age| name|
        +---+-----+
        |  2|Alice|
        +---+-----+
        """
        if isinstance(condition, str):
            jdf = self._jdf.filter(condition)
        elif isinstance(condition, Column):
            jdf = self._jdf.filter(condition._jc)
        else:
            raise PySparkTypeError(
                error_class="NOT_COLUMN_OR_STR",
                message_parameters={"arg_name": "condition", "arg_type": type(condition).__name__},
            )
        return DataFrame(jdf, self.sparkSession)

    @overload
    def groupBy(self, *cols: "ColumnOrName") -> "GroupedData":
        ...

    @overload
    def groupBy(self, __cols: Union[List[Column], List[str]]) -> "GroupedData":
        ...

    def groupBy(self, *cols: "ColumnOrName") -> "GroupedData":  # type: ignore[misc]
        """Groups the :class:`DataFrame` using the specified columns,
        so we can run aggregation on them. See :class:`GroupedData`
        for all the available aggregate functions.

        :func:`groupby` is an alias for :func:`groupBy`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : list, str or :class:`Column`
            columns to group by.
            Each element should be a column name (string) or an expression (:class:`Column`)
            or list of them.

        Returns
        -------
        :class:`GroupedData`
            Grouped data by given columns.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (2, "Alice"), (2, "Bob"), (2, "Bob"), (5, "Bob")], schema=["age", "name"])

        Empty grouping columns triggers a global aggregation.

        >>> df.groupBy().avg().show()
        +--------+
        |avg(age)|
        +--------+
        |    2.75|
        +--------+

        Group-by 'name', and specify a dictionary to calculate the summation of 'age'.

        >>> df.groupBy("name").agg({"age": "sum"}).sort("name").show()
        +-----+--------+
        | name|sum(age)|
        +-----+--------+
        |Alice|       2|
        |  Bob|       9|
        +-----+--------+

        Group-by 'name', and calculate maximum values.

        >>> df.groupBy(df.name).max().sort("name").show()
        +-----+--------+
        | name|max(age)|
        +-----+--------+
        |Alice|       2|
        |  Bob|       5|
        +-----+--------+

        Group-by 'name' and 'age', and calculate the number of rows in each group.

        >>> df.groupBy(["name", df.age]).count().sort("name", "age").show()
        +-----+---+-----+
        | name|age|count|
        +-----+---+-----+
        |Alice|  2|    1|
        |  Bob|  2|    2|
        |  Bob|  5|    1|
        +-----+---+-----+
        """
        jgd = self._jdf.groupBy(self._jcols(*cols))
        from pyspark.sql.group import GroupedData

        return GroupedData(jgd, self)

    @overload
    def rollup(self, *cols: "ColumnOrName") -> "GroupedData":
        ...

    @overload
    def rollup(self, __cols: Union[List[Column], List[str]]) -> "GroupedData":
        ...

    def rollup(self, *cols: "ColumnOrName") -> "GroupedData":  # type: ignore[misc]
        """
        Create a multi-dimensional rollup for the current :class:`DataFrame` using
        the specified columns, so we can run aggregation on them.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : list, str or :class:`Column`
            Columns to roll-up by.
            Each element should be a column name (string) or an expression (:class:`Column`)
            or list of them.

        Returns
        -------
        :class:`GroupedData`
            Rolled-up data by given columns.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.rollup("name", df.age).count().orderBy("name", "age").show()
        +-----+----+-----+
        | name| age|count|
        +-----+----+-----+
        | NULL|NULL|    2|
        |Alice|NULL|    1|
        |Alice|   2|    1|
        |  Bob|NULL|    1|
        |  Bob|   5|    1|
        +-----+----+-----+
        """
        jgd = self._jdf.rollup(self._jcols(*cols))
        from pyspark.sql.group import GroupedData

        return GroupedData(jgd, self)

    @overload
    def cube(self, *cols: "ColumnOrName") -> "GroupedData":
        ...

    @overload
    def cube(self, __cols: Union[List[Column], List[str]]) -> "GroupedData":
        ...

    def cube(self, *cols: "ColumnOrName") -> "GroupedData":  # type: ignore[misc]
        """
        Create a multi-dimensional cube for the current :class:`DataFrame` using
        the specified columns, so we can run aggregations on them.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : list, str or :class:`Column`
            columns to create cube by.
            Each element should be a column name (string) or an expression (:class:`Column`)
            or list of them.

        Returns
        -------
        :class:`GroupedData`
            Cube of the data by given columns.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.cube("name", df.age).count().orderBy("name", "age").show()
        +-----+----+-----+
        | name| age|count|
        +-----+----+-----+
        | NULL|NULL|    2|
        | NULL|   2|    1|
        | NULL|   5|    1|
        |Alice|NULL|    1|
        |Alice|   2|    1|
        |  Bob|NULL|    1|
        |  Bob|   5|    1|
        +-----+----+-----+
        """
        jgd = self._jdf.cube(self._jcols(*cols))
        from pyspark.sql.group import GroupedData

        return GroupedData(jgd, self)

    def unpivot(
        self,
        ids: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]],
        values: Optional[Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]]],
        variableColumnName: str,
        valueColumnName: str,
    ) -> "DataFrame":
        """
        Unpivot a DataFrame from wide format to long format, optionally leaving
        identifier columns set. This is the reverse to `groupBy(...).pivot(...).agg(...)`,
        except for the aggregation, which cannot be reversed.

        This function is useful to massage a DataFrame into a format where some
        columns are identifier columns ("ids"), while all other columns ("values")
        are "unpivoted" to the rows, leaving just two non-id columns, named as given
        by `variableColumnName` and `valueColumnName`.

        When no "id" columns are given, the unpivoted DataFrame consists of only the
        "variable" and "value" columns.

        The `values` columns must not be empty so at least one value must be given to be unpivoted.
        When `values` is `None`, all non-id columns will be unpivoted.

        All "value" columns must share a least common data type. Unless they are the same data type,
        all "value" columns are cast to the nearest common data type. For instance, types
        `IntegerType` and `LongType` are cast to `LongType`, while `IntegerType` and `StringType`
        do not have a common data type and `unpivot` fails.

        .. versionadded:: 3.4.0

        Parameters
        ----------
        ids : str, Column, tuple, list
            Column(s) to use as identifiers. Can be a single column or column name,
            or a list or tuple for multiple columns.
        values : str, Column, tuple, list, optional
            Column(s) to unpivot. Can be a single column or column name, or a list or tuple
            for multiple columns. If specified, must not be empty. If not specified, uses all
            columns that are not set as `ids`.
        variableColumnName : str
            Name of the variable column.
        valueColumnName : str
            Name of the value column.

        Returns
        -------
        :class:`DataFrame`
            Unpivoted DataFrame.

        Notes
        -----
        Supports Spark Connect.

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(1, 11, 1.1), (2, 12, 1.2)],
        ...     ["id", "int", "double"],
        ... )
        >>> df.show()
        +---+---+------+
        | id|int|double|
        +---+---+------+
        |  1| 11|   1.1|
        |  2| 12|   1.2|
        +---+---+------+

        >>> df.unpivot("id", ["int", "double"], "var", "val").show()
        +---+------+----+
        | id|   var| val|
        +---+------+----+
        |  1|   int|11.0|
        |  1|double| 1.1|
        |  2|   int|12.0|
        |  2|double| 1.2|
        +---+------+----+

        See Also
        --------
        DataFrame.melt
        """
        assert ids is not None, "ids must not be None"

        def to_jcols(
            cols: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]]
        ) -> JavaObject:
            if isinstance(cols, list):
                return self._jcols(*cols)
            if isinstance(cols, tuple):
                return self._jcols(*list(cols))
            return self._jcols(cols)

        jids = to_jcols(ids)
        if values is None:
            jdf = self._jdf.unpivotWithSeq(jids, variableColumnName, valueColumnName)
        else:
            jvals = to_jcols(values)
            jdf = self._jdf.unpivotWithSeq(jids, jvals, variableColumnName, valueColumnName)

        return DataFrame(jdf, self.sparkSession)

    def melt(
        self,
        ids: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]],
        values: Optional[Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName", ...]]],
        variableColumnName: str,
        valueColumnName: str,
    ) -> "DataFrame":
        """
        Unpivot a DataFrame from wide format to long format, optionally leaving
        identifier columns set. This is the reverse to `groupBy(...).pivot(...).agg(...)`,
        except for the aggregation, which cannot be reversed.

        :func:`melt` is an alias for :func:`unpivot`.

        .. versionadded:: 3.4.0

        Parameters
        ----------
        ids : str, Column, tuple, list, optional
            Column(s) to use as identifiers. Can be a single column or column name,
            or a list or tuple for multiple columns.
        values : str, Column, tuple, list, optional
            Column(s) to unpivot. Can be a single column or column name, or a list or tuple
            for multiple columns. If not specified or empty, use all columns that
            are not set as `ids`.
        variableColumnName : str
            Name of the variable column.
        valueColumnName : str
            Name of the value column.

        Returns
        -------
        :class:`DataFrame`
            Unpivoted DataFrame.

        See Also
        --------
        DataFrame.unpivot

        Notes
        -----
        Supports Spark Connect.
        """
        return self.unpivot(ids, values, variableColumnName, valueColumnName)

    def agg(self, *exprs: Union[Column, Dict[str, str]]) -> "DataFrame":
        """Aggregate on the entire :class:`DataFrame` without groups
        (shorthand for ``df.groupBy().agg()``).

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        exprs : :class:`Column` or dict of key and value strings
            Columns or expressions to aggregate DataFrame by.

        Returns
        -------
        :class:`DataFrame`
            Aggregated DataFrame.

        Examples
        --------
        >>> from pyspark.sql import functions as sf
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.agg({"age": "max"}).show()
        +--------+
        |max(age)|
        +--------+
        |       5|
        +--------+
        >>> df.agg(sf.min(df.age)).show()
        +--------+
        |min(age)|
        +--------+
        |       2|
        +--------+
        """
        return self.groupBy().agg(*exprs)  # type: ignore[arg-type]

    def observe(
        self,
        observation: Union["Observation", str],
        *exprs: Column,
    ) -> "DataFrame":
        """Define (named) metrics to observe on the DataFrame. This method returns an 'observed'
        DataFrame that returns the same result as the input, with the following guarantees:

        * It will compute the defined aggregates (metrics) on all the data that is flowing through
            the Dataset at that point.

        * It will report the value of the defined aggregate columns as soon as we reach a completion
            point. A completion point is either the end of a query (batch mode) or the end of a
            streaming epoch. The value of the aggregates only reflects the data processed since
            the previous completion point.

        The metrics columns must either contain a literal (e.g. lit(42)), or should contain one or
        more aggregate functions (e.g. sum(a) or sum(a + b) + avg(c) - lit(1)). Expressions that
        contain references to the input Dataset's columns must always be wrapped in an aggregate
        function.

        A user can observe these metrics by adding
        Python's :class:`~pyspark.sql.streaming.StreamingQueryListener`,
        Scala/Java's ``org.apache.spark.sql.streaming.StreamingQueryListener`` or Scala/Java's
        ``org.apache.spark.sql.util.QueryExecutionListener`` to the spark session.

        .. versionadded:: 3.3.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Parameters
        ----------
        observation : :class:`Observation` or str
            `str` to specify the name, or an :class:`Observation` instance to obtain the metric.

            .. versionchanged:: 3.4.0
               Added support for `str` in this parameter.
        exprs : :class:`Column`
            column expressions (:class:`Column`).

        Returns
        -------
        :class:`DataFrame`
            the observed :class:`DataFrame`.

        Notes
        -----
        When ``observation`` is :class:`Observation`, this method only supports batch queries.
        When ``observation`` is a string, this method works for both batch and streaming queries.
        Continuous execution is currently not supported yet.

        Examples
        --------
        When ``observation`` is :class:`Observation`, only batch queries work as below.

        >>> from pyspark.sql.functions import col, count, lit, max
        >>> from pyspark.sql import Observation
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> observation = Observation("my metrics")
        >>> observed_df = df.observe(observation, count(lit(1)).alias("count"), max(col("age")))
        >>> observed_df.count()
        2
        >>> observation.get
        {'count': 2, 'max(age)': 5}

        When ``observation`` is a string, streaming queries also work as below.

        >>> from pyspark.sql.streaming import StreamingQueryListener
        >>> class MyErrorListener(StreamingQueryListener):
        ...    def onQueryStarted(self, event):
        ...        pass
        ...
        ...    def onQueryProgress(self, event):
        ...        row = event.progress.observedMetrics.get("my_event")
        ...        # Trigger if the number of errors exceeds 5 percent
        ...        num_rows = row.rc
        ...        num_error_rows = row.erc
        ...        ratio = num_error_rows / num_rows
        ...        if ratio > 0.05:
        ...            # Trigger alert
        ...            pass
        ...
        ...    def onQueryIdle(self, event):
        ...        pass
        ...
        ...    def onQueryTerminated(self, event):
        ...        pass
        ...
        >>> spark.streams.addListener(MyErrorListener())
        >>> # Observe row count (rc) and error row count (erc) in the streaming Dataset
        ... observed_ds = df.observe(
        ...     "my_event",
        ...     count(lit(1)).alias("rc"),
        ...     count(col("error")).alias("erc"))  # doctest: +SKIP
        >>> observed_ds.writeStream.format("console").start()  # doctest: +SKIP
        """
        from pyspark.sql import Observation

        if len(exprs) == 0:
            raise PySparkValueError(
                error_class="CANNOT_BE_EMPTY",
                message_parameters={"item": "exprs"},
            )
        if not all(isinstance(c, Column) for c in exprs):
            raise PySparkTypeError(
                error_class="NOT_LIST_OF_COLUMN",
                message_parameters={"arg_name": "exprs"},
            )

        if isinstance(observation, Observation):
            return observation._on(self, *exprs)
        elif isinstance(observation, str):
            return DataFrame(
                self._jdf.observe(
                    observation, exprs[0]._jc, _to_seq(self._sc, [c._jc for c in exprs[1:]])
                ),
                self.sparkSession,
            )
        else:
            raise PySparkTypeError(
                error_class="NOT_LIST_OF_COLUMN",
                message_parameters={
                    "arg_name": "observation",
                    "arg_type": type(observation).__name__,
                },
            )

    def union(self, other: "DataFrame") -> "DataFrame":
        """Return a new :class:`DataFrame` containing the union of rows in this and another
        :class:`DataFrame`.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Another :class:`DataFrame` that needs to be unioned.

        Returns
        -------
        :class:`DataFrame`
            A new :class:`DataFrame` containing the combined rows with corresponding columns.

        See Also
        --------
        DataFrame.unionAll

        Notes
        -----
        This method performs a SQL-style set union of the rows from both `DataFrame` objects,
        with no automatic deduplication of elements.

        Use the `distinct()` method to perform deduplication of rows.

        The method resolves columns by position (not by name), following the standard behavior
        in SQL.

        Examples
        --------
        Example 1: Combining two DataFrames with the same schema

        >>> df1 = spark.createDataFrame([(1, 'A'), (2, 'B')], ['id', 'value'])
        >>> df2 = spark.createDataFrame([(3, 'C'), (4, 'D')], ['id', 'value'])
        >>> df3 = df1.union(df2)
        >>> df3.show()
        +---+-----+
        | id|value|
        +---+-----+
        |  1|    A|
        |  2|    B|
        |  3|    C|
        |  4|    D|
        +---+-----+

        Example 2: Combining two DataFrames with different schemas

        >>> from pyspark.sql.functions import lit
        >>> df1 = spark.createDataFrame([("Alice", 1), ("Bob", 2)], ["name", "id"])
        >>> df2 = spark.createDataFrame([(3, "Charlie"), (4, "Dave")], ["id", "name"])
        >>> df1 = df1.withColumn("age", lit(30))
        >>> df2 = df2.withColumn("age", lit(40))
        >>> df3 = df1.union(df2)
        >>> df3.show()
        +-----+-------+---+
        | name|     id|age|
        +-----+-------+---+
        |Alice|      1| 30|
        |  Bob|      2| 30|
        |    3|Charlie| 40|
        |    4|   Dave| 40|
        +-----+-------+---+

        Example 3: Combining two DataFrames with mismatched columns

        >>> df1 = spark.createDataFrame([(1, 2)], ["A", "B"])
        >>> df2 = spark.createDataFrame([(3, 4)], ["C", "D"])
        >>> df3 = df1.union(df2)
        >>> df3.show()
        +---+---+
        |  A|  B|
        +---+---+
        |  1|  2|
        |  3|  4|
        +---+---+

        Example 4: Combining duplicate rows from two different DataFrames

        >>> df1 = spark.createDataFrame([(1, 'A'), (2, 'B'), (3, 'C')], ['id', 'value'])
        >>> df2 = spark.createDataFrame([(3, 'C'), (4, 'D')], ['id', 'value'])
        >>> df3 = df1.union(df2).distinct().sort("id")
        >>> df3.show()
        +---+-----+
        | id|value|
        +---+-----+
        |  1|    A|
        |  2|    B|
        |  3|    C|
        |  4|    D|
        +---+-----+
        """
        return DataFrame(self._jdf.union(other._jdf), self.sparkSession)

    def unionAll(self, other: "DataFrame") -> "DataFrame":
        """Return a new :class:`DataFrame` containing the union of rows in this and another
        :class:`DataFrame`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Another :class:`DataFrame` that needs to be combined

        Returns
        -------
        :class:`DataFrame`
            A new :class:`DataFrame` containing combined rows from both dataframes.

        Notes
        -----
        This method combines all rows from both `DataFrame` objects with no automatic
        deduplication of elements.

        Use the `distinct()` method to perform deduplication of rows.

        :func:`unionAll` is an alias to :func:`union`

        See Also
        --------
        DataFrame.union
        """
        return self.union(other)

    def unionByName(self, other: "DataFrame", allowMissingColumns: bool = False) -> "DataFrame":
        """Returns a new :class:`DataFrame` containing union of rows in this and another
        :class:`DataFrame`.

        This method performs a union operation on both input DataFrames, resolving columns by
        name (rather than position). When `allowMissingColumns` is True, missing columns will
        be filled with null.

        .. versionadded:: 2.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Another :class:`DataFrame` that needs to be combined.
        allowMissingColumns : bool, optional, default False
           Specify whether to allow missing columns.

           .. versionadded:: 3.1.0

        Returns
        -------
        :class:`DataFrame`
            A new :class:`DataFrame` containing the combined rows with corresponding
            columns of the two given DataFrames.

        Examples
        --------
        Example 1: Union of two DataFrames with same columns in different order.

        >>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
        >>> df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col0"])
        >>> df1.unionByName(df2).show()
        +----+----+----+
        |col0|col1|col2|
        +----+----+----+
        |   1|   2|   3|
        |   6|   4|   5|
        +----+----+----+

        Example 2: Union with missing columns and setting `allowMissingColumns=True`.

        >>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
        >>> df2 = spark.createDataFrame([[4, 5, 6]], ["col1", "col2", "col3"])
        >>> df1.unionByName(df2, allowMissingColumns=True).show()
        +----+----+----+----+
        |col0|col1|col2|col3|
        +----+----+----+----+
        |   1|   2|   3|NULL|
        |NULL|   4|   5|   6|
        +----+----+----+----+

        Example 3: Union of two DataFrames with few common columns.

        >>> df1 = spark.createDataFrame([[1, 2, 3]], ["col0", "col1", "col2"])
        >>> df2 = spark.createDataFrame([[4, 5, 6, 7]], ["col1", "col2", "col3", "col4"])
        >>> df1.unionByName(df2, allowMissingColumns=True).show()
        +----+----+----+----+----+
        |col0|col1|col2|col3|col4|
        +----+----+----+----+----+
        |   1|   2|   3|NULL|NULL|
        |NULL|   4|   5|   6|   7|
        +----+----+----+----+----+

        Example 4: Union of two DataFrames with completely different columns.

        >>> df1 = spark.createDataFrame([[0, 1, 2]], ["col0", "col1", "col2"])
        >>> df2 = spark.createDataFrame([[3, 4, 5]], ["col3", "col4", "col5"])
        >>> df1.unionByName(df2, allowMissingColumns=True).show()
        +----+----+----+----+----+----+
        |col0|col1|col2|col3|col4|col5|
        +----+----+----+----+----+----+
        |   0|   1|   2|NULL|NULL|NULL|
        |NULL|NULL|NULL|   3|   4|   5|
        +----+----+----+----+----+----+
        """
        return DataFrame(self._jdf.unionByName(other._jdf, allowMissingColumns), self.sparkSession)

    def intersect(self, other: "DataFrame") -> "DataFrame":
        """Return a new :class:`DataFrame` containing rows only in
        both this :class:`DataFrame` and another :class:`DataFrame`.
        Note that any duplicates are removed. To preserve duplicates
        use :func:`intersectAll`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Another :class:`DataFrame` that needs to be combined.

        Returns
        -------
        :class:`DataFrame`
            Combined DataFrame.

        Notes
        -----
        This is equivalent to `INTERSECT` in SQL.

        Examples
        --------
        >>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
        >>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
        >>> df1.intersect(df2).sort(df1.C1.desc()).show()
        +---+---+
        | C1| C2|
        +---+---+
        |  b|  3|
        |  a|  1|
        +---+---+
        """
        return DataFrame(self._jdf.intersect(other._jdf), self.sparkSession)

    def intersectAll(self, other: "DataFrame") -> "DataFrame":
        """Return a new :class:`DataFrame` containing rows in both this :class:`DataFrame`
        and another :class:`DataFrame` while preserving duplicates.

        This is equivalent to `INTERSECT ALL` in SQL. As standard in SQL, this function
        resolves columns by position (not by name).

        .. versionadded:: 2.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Another :class:`DataFrame` that needs to be combined.

        Returns
        -------
        :class:`DataFrame`
            Combined DataFrame.

        Examples
        --------
        >>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
        >>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
        >>> df1.intersectAll(df2).sort("C1", "C2").show()
        +---+---+
        | C1| C2|
        +---+---+
        |  a|  1|
        |  a|  1|
        |  b|  3|
        +---+---+
        """
        return DataFrame(self._jdf.intersectAll(other._jdf), self.sparkSession)

    def subtract(self, other: "DataFrame") -> "DataFrame":
        """Return a new :class:`DataFrame` containing rows in this :class:`DataFrame`
        but not in another :class:`DataFrame`.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        other : :class:`DataFrame`
            Another :class:`DataFrame` that needs to be subtracted.

        Returns
        -------
        :class:`DataFrame`
            Subtracted DataFrame.

        Notes
        -----
        This is equivalent to `EXCEPT DISTINCT` in SQL.

        Examples
        --------
        >>> df1 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3), ("c", 4)], ["C1", "C2"])
        >>> df2 = spark.createDataFrame([("a", 1), ("a", 1), ("b", 3)], ["C1", "C2"])
        >>> df1.subtract(df2).show()
        +---+---+
        | C1| C2|
        +---+---+
        |  c|  4|
        +---+---+
        """
        return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sparkSession)

    def dropDuplicates(self, subset: Optional[List[str]] = None) -> "DataFrame":
        """Return a new :class:`DataFrame` with duplicate rows removed,
        optionally only considering certain columns.

        For a static batch :class:`DataFrame`, it just drops duplicate rows. For a streaming
        :class:`DataFrame`, it will keep all data across triggers as intermediate state to drop
        duplicates rows. You can use :func:`withWatermark` to limit how late the duplicate data can
        be and the system will accordingly limit the state. In addition, data older than
        watermark will be dropped to avoid any possibility of duplicates.

        :func:`drop_duplicates` is an alias for :func:`dropDuplicates`.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        subset : List of column names, optional
            List of columns to use for duplicate comparison (default All columns).

        Returns
        -------
        :class:`DataFrame`
            DataFrame without duplicates.

        Examples
        --------
        >>> from pyspark.sql import Row
        >>> df = spark.createDataFrame([
        ...     Row(name='Alice', age=5, height=80),
        ...     Row(name='Alice', age=5, height=80),
        ...     Row(name='Alice', age=10, height=80)
        ... ])

        Deduplicate the same rows.

        >>> df.dropDuplicates().show()
        +-----+---+------+
        | name|age|height|
        +-----+---+------+
        |Alice|  5|    80|
        |Alice| 10|    80|
        +-----+---+------+

        Deduplicate values on 'name' and 'height' columns.

        >>> df.dropDuplicates(['name', 'height']).show()
        +-----+---+------+
        | name|age|height|
        +-----+---+------+
        |Alice|  5|    80|
        +-----+---+------+
        """
        if subset is not None and (not isinstance(subset, Iterable) or isinstance(subset, str)):
            raise PySparkTypeError(
                error_class="NOT_LIST_OR_TUPLE",
                message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
            )

        if subset is None:
            jdf = self._jdf.dropDuplicates()
        else:
            jdf = self._jdf.dropDuplicates(self._jseq(subset))
        return DataFrame(jdf, self.sparkSession)

    def dropDuplicatesWithinWatermark(self, subset: Optional[List[str]] = None) -> "DataFrame":
        """Return a new :class:`DataFrame` with duplicate rows removed,
         optionally only considering certain columns, within watermark.

         This only works with streaming :class:`DataFrame`, and watermark for the input
         :class:`DataFrame` must be set via :func:`withWatermark`.

        For a streaming :class:`DataFrame`, this will keep all data across triggers as intermediate
        state to drop duplicated rows. The state will be kept to guarantee the semantic, "Events
        are deduplicated as long as the time distance of earliest and latest events are smaller
        than the delay threshold of watermark." Users are encouraged to set the delay threshold of
        watermark longer than max timestamp differences among duplicated events.

        Note: too late data older than watermark will be dropped.

         .. versionadded:: 3.5.0

         Parameters
         ----------
         subset : List of column names, optional
             List of columns to use for duplicate comparison (default All columns).

         Returns
         -------
         :class:`DataFrame`
             DataFrame without duplicates.

         Notes
         -----
         Supports Spark Connect.

         Examples
         --------
         >>> from pyspark.sql import Row
         >>> from pyspark.sql.functions import timestamp_seconds
         >>> df = spark.readStream.format("rate").load().selectExpr(
         ...     "value % 5 AS value", "timestamp")
         >>> df.select("value", df.timestamp.alias("time")).withWatermark("time", '10 minutes')
         DataFrame[value: bigint, time: timestamp]

         Deduplicate the same rows.

         >>> df.dropDuplicatesWithinWatermark() # doctest: +SKIP

         Deduplicate values on 'value' columns.

         >>> df.dropDuplicatesWithinWatermark(['value'])  # doctest: +SKIP
        """
        if subset is not None and (not isinstance(subset, Iterable) or isinstance(subset, str)):
            raise PySparkTypeError(
                error_class="NOT_LIST_OR_TUPLE",
                message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
            )

        if subset is None:
            jdf = self._jdf.dropDuplicatesWithinWatermark()
        else:
            jdf = self._jdf.dropDuplicatesWithinWatermark(self._jseq(subset))
        return DataFrame(jdf, self.sparkSession)

    def dropna(
        self,
        how: str = "any",
        thresh: Optional[int] = None,
        subset: Optional[Union[str, Tuple[str, ...], List[str]]] = None,
    ) -> "DataFrame":
        """Returns a new :class:`DataFrame` omitting rows with null values.
        :func:`DataFrame.dropna` and :func:`DataFrameNaFunctions.drop` are aliases of each other.

        .. versionadded:: 1.3.1

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        how : str, optional
            'any' or 'all'.
            If 'any', drop a row if it contains any nulls.
            If 'all', drop a row only if all its values are null.
        thresh: int, optional
            default None
            If specified, drop rows that have less than `thresh` non-null values.
            This overwrites the `how` parameter.
        subset : str, tuple or list, optional
            optional list of column names to consider.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with null only rows excluded.

        Examples
        --------
        >>> from pyspark.sql import Row
        >>> df = spark.createDataFrame([
        ...     Row(age=10, height=80, name="Alice"),
        ...     Row(age=5, height=None, name="Bob"),
        ...     Row(age=None, height=None, name="Tom"),
        ...     Row(age=None, height=None, name=None),
        ... ])
        >>> df.na.drop().show()
        +---+------+-----+
        |age|height| name|
        +---+------+-----+
        | 10|    80|Alice|
        +---+------+-----+
        """
        if how is not None and how not in ["any", "all"]:
            raise PySparkValueError(
                error_class="VALUE_NOT_ANY_OR_ALL",
                message_parameters={"arg_name": "how", "arg_type": how},
            )

        if subset is None:
            subset = self.columns
        elif isinstance(subset, str):
            subset = [subset]
        elif not isinstance(subset, (list, tuple)):
            raise PySparkTypeError(
                error_class="NOT_LIST_OR_STR_OR_TUPLE",
                message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
            )

        if thresh is None:
            thresh = len(subset) if how == "any" else 1

        return DataFrame(self._jdf.na().drop(thresh, self._jseq(subset)), self.sparkSession)

    @overload
    def fillna(
        self,
        value: "LiteralType",
        subset: Optional[Union[str, Tuple[str, ...], List[str]]] = ...,
    ) -> "DataFrame":
        ...

    @overload
    def fillna(self, value: Dict[str, "LiteralType"]) -> "DataFrame":
        ...

    def fillna(
        self,
        value: Union["LiteralType", Dict[str, "LiteralType"]],
        subset: Optional[Union[str, Tuple[str, ...], List[str]]] = None,
    ) -> "DataFrame":
        """Replace null values, alias for ``na.fill()``.
        :func:`DataFrame.fillna` and :func:`DataFrameNaFunctions.fill` are aliases of each other.

        .. versionadded:: 1.3.1

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        value : int, float, string, bool or dict
            Value to replace null values with.
            If the value is a dict, then `subset` is ignored and `value` must be a mapping
            from column name (string) to replacement value. The replacement value must be
            an int, float, boolean, or string.
        subset : str, tuple or list, optional
            optional list of column names to consider.
            Columns specified in subset that do not have matching data types are ignored.
            For example, if `value` is a string, and subset contains a non-string column,
            then the non-string column is simply ignored.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with replaced null values.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (10, 80.5, "Alice", None),
        ...     (5, None, "Bob", None),
        ... 	(None, None, "Tom", None),
        ...     (None, None, None, True)],
        ...     schema=["age", "height", "name", "bool"])

        Fill all null values with 50 for numeric columns.

        >>> df.na.fill(50).show()
        +---+------+-----+----+
        |age|height| name|bool|
        +---+------+-----+----+
        | 10|  80.5|Alice|NULL|
        |  5|  50.0|  Bob|NULL|
        | 50|  50.0|  Tom|NULL|
        | 50|  50.0| NULL|true|
        +---+------+-----+----+

        Fill all null values with ``False`` for boolean columns.

        >>> df.na.fill(False).show()
        +----+------+-----+-----+
        | age|height| name| bool|
        +----+------+-----+-----+
        |  10|  80.5|Alice|false|
        |   5|  NULL|  Bob|false|
        |NULL|  NULL|  Tom|false|
        |NULL|  NULL| NULL| true|
        +----+------+-----+-----+

        Fill all null values with to 50 and "unknown" for 'age' and 'name' column respectively.

        >>> df.na.fill({'age': 50, 'name': 'unknown'}).show()
        +---+------+-------+----+
        |age|height|   name|bool|
        +---+------+-------+----+
        | 10|  80.5|  Alice|NULL|
        |  5|  NULL|    Bob|NULL|
        | 50|  NULL|    Tom|NULL|
        | 50|  NULL|unknown|true|
        +---+------+-------+----+
        """
        if not isinstance(value, (float, int, str, bool, dict)):
            raise PySparkTypeError(
                error_class="NOT_BOOL_OR_DICT_OR_FLOAT_OR_INT_OR_STR",
                message_parameters={"arg_name": "value", "arg_type": type(value).__name__},
            )

        # Note that bool validates isinstance(int), but we don't want to
        # convert bools to floats

        if not isinstance(value, bool) and isinstance(value, int):
            value = float(value)

        if isinstance(value, dict):
            return DataFrame(self._jdf.na().fill(value), self.sparkSession)
        elif subset is None:
            return DataFrame(self._jdf.na().fill(value), self.sparkSession)
        else:
            if isinstance(subset, str):
                subset = [subset]
            elif not isinstance(subset, (list, tuple)):
                raise PySparkTypeError(
                    error_class="NOT_LIST_OR_TUPLE",
                    message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
                )

            return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sparkSession)

    @overload
    def replace(
        self,
        to_replace: "LiteralType",
        value: "OptionalPrimitiveType",
        subset: Optional[List[str]] = ...,
    ) -> "DataFrame":
        ...

    @overload
    def replace(
        self,
        to_replace: List["LiteralType"],
        value: List["OptionalPrimitiveType"],
        subset: Optional[List[str]] = ...,
    ) -> "DataFrame":
        ...

    @overload
    def replace(
        self,
        to_replace: Dict["LiteralType", "OptionalPrimitiveType"],
        subset: Optional[List[str]] = ...,
    ) -> "DataFrame":
        ...

    @overload
    def replace(
        self,
        to_replace: List["LiteralType"],
        value: "OptionalPrimitiveType",
        subset: Optional[List[str]] = ...,
    ) -> "DataFrame":
        ...

    def replace(  # type: ignore[misc]
        self,
        to_replace: Union[
            "LiteralType", List["LiteralType"], Dict["LiteralType", "OptionalPrimitiveType"]
        ],
        value: Optional[
            Union["OptionalPrimitiveType", List["OptionalPrimitiveType"], _NoValueType]
        ] = _NoValue,
        subset: Optional[List[str]] = None,
    ) -> "DataFrame":
        """Returns a new :class:`DataFrame` replacing a value with another value.
        :func:`DataFrame.replace` and :func:`DataFrameNaFunctions.replace` are
        aliases of each other.
        Values to_replace and value must have the same type and can only be numerics, booleans,
        or strings. Value can have None. When replacing, the new value will be cast
        to the type of the existing column.
        For numeric replacements all values to be replaced should have unique
        floating point representation. In case of conflicts (for example with `{42: -1, 42.0: 1}`)
        and arbitrary replacement will be used.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        to_replace : bool, int, float, string, list or dict
            Value to be replaced.
            If the value is a dict, then `value` is ignored or can be omitted, and `to_replace`
            must be a mapping between a value and a replacement.
        value : bool, int, float, string or None, optional
            The replacement value must be a bool, int, float, string or None. If `value` is a
            list, `value` should be of the same length and type as `to_replace`.
            If `value` is a scalar and `to_replace` is a sequence, then `value` is
            used as a replacement for each item in `to_replace`.
        subset : list, optional
            optional list of column names to consider.
            Columns specified in subset that do not have matching data types are ignored.
            For example, if `value` is a string, and subset contains a non-string column,
            then the non-string column is simply ignored.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with replaced values.

        Examples
        --------
        >>> df = spark.createDataFrame([
        ...     (10, 80, "Alice"),
        ...     (5, None, "Bob"),
        ...     (None, 10, "Tom"),
        ...     (None, None, None)],
        ...     schema=["age", "height", "name"])

        Replace 10 to 20 in all columns.

        >>> df.na.replace(10, 20).show()
        +----+------+-----+
        | age|height| name|
        +----+------+-----+
        |  20|    80|Alice|
        |   5|  NULL|  Bob|
        |NULL|    20|  Tom|
        |NULL|  NULL| NULL|
        +----+------+-----+

        Replace 'Alice' to null in all columns.

        >>> df.na.replace('Alice', None).show()
        +----+------+----+
        | age|height|name|
        +----+------+----+
        |  10|    80|NULL|
        |   5|  NULL| Bob|
        |NULL|    10| Tom|
        |NULL|  NULL|NULL|
        +----+------+----+

        Replace 'Alice' to 'A', and 'Bob' to 'B' in the 'name' column.

        >>> df.na.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
        +----+------+----+
        | age|height|name|
        +----+------+----+
        |  10|    80|   A|
        |   5|  NULL|   B|
        |NULL|    10| Tom|
        |NULL|  NULL|NULL|
        +----+------+----+
        """
        if value is _NoValue:
            if isinstance(to_replace, dict):
                value = None
            else:
                raise PySparkTypeError(
                    error_class="ARGUMENT_REQUIRED",
                    message_parameters={"arg_name": "value", "condition": "`to_replace` is dict"},
                )

        # Helper functions
        def all_of(types: Union[Type, Tuple[Type, ...]]) -> Callable[[Iterable], bool]:
            """Given a type or tuple of types and a sequence of xs
            check if each x is instance of type(s)

            >>> all_of(bool)([True, False])
            True
            >>> all_of(str)(["a", 1])
            False
            """

            def all_of_(xs: Iterable) -> bool:
                return all(isinstance(x, types) for x in xs)

            return all_of_

        all_of_bool = all_of(bool)
        all_of_str = all_of(str)
        all_of_numeric = all_of((float, int))

        # Validate input types
        valid_types = (bool, float, int, str, list, tuple)
        if not isinstance(to_replace, valid_types + (dict,)):
            raise PySparkTypeError(
                error_class="NOT_BOOL_OR_DICT_OR_FLOAT_OR_INT_OR_LIST_OR_STR_OR_TUPLE",
                message_parameters={
                    "arg_name": "to_replace",
                    "arg_type": type(to_replace).__name__,
                },
            )

        if (
            not isinstance(value, valid_types)
            and value is not None
            and not isinstance(to_replace, dict)
        ):
            raise PySparkTypeError(
                error_class="NOT_BOOL_OR_FLOAT_OR_INT_OR_LIST_OR_NONE_OR_STR_OR_TUPLE",
                message_parameters={
                    "arg_name": "value",
                    "arg_type": type(value).__name__,
                },
            )

        if isinstance(to_replace, (list, tuple)) and isinstance(value, (list, tuple)):
            if len(to_replace) != len(value):
                raise PySparkValueError(
                    error_class="LENGTH_SHOULD_BE_THE_SAME",
                    message_parameters={
                        "arg1": "to_replace",
                        "arg2": "value",
                        "arg1_length": str(len(to_replace)),
                        "arg2_length": str(len(value)),
                    },
                )

        if not (subset is None or isinstance(subset, (list, tuple, str))):
            raise PySparkTypeError(
                error_class="NOT_LIST_OR_STR_OR_TUPLE",
                message_parameters={"arg_name": "subset", "arg_type": type(subset).__name__},
            )

        # Reshape input arguments if necessary
        if isinstance(to_replace, (float, int, str)):
            to_replace = [to_replace]

        if isinstance(to_replace, dict):
            rep_dict = to_replace
            if value is not None:
                warnings.warn("to_replace is a dict and value is not None. value will be ignored.")
        else:
            if isinstance(value, (float, int, str)) or value is None:
                value = [value for _ in range(len(to_replace))]
            rep_dict = dict(zip(to_replace, cast("Iterable[Optional[Union[float, str]]]", value)))

        if isinstance(subset, str):
            subset = [subset]

        # Verify we were not passed in mixed type generics.
        if not any(
            all_of_type(rep_dict.keys())
            and all_of_type(x for x in rep_dict.values() if x is not None)
            for all_of_type in [all_of_bool, all_of_str, all_of_numeric]
        ):
            raise PySparkValueError(
                error_class="MIXED_TYPE_REPLACEMENT",
                message_parameters={},
            )

        if subset is None:
            return DataFrame(self._jdf.na().replace("*", rep_dict), self.sparkSession)
        else:
            return DataFrame(
                self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)),
                self.sparkSession,
            )

    @overload
    def approxQuantile(
        self,
        col: str,
        probabilities: Union[List[float], Tuple[float]],
        relativeError: float,
    ) -> List[float]:
        ...

    @overload
    def approxQuantile(
        self,
        col: Union[List[str], Tuple[str]],
        probabilities: Union[List[float], Tuple[float]],
        relativeError: float,
    ) -> List[List[float]]:
        ...

    def approxQuantile(
        self,
        col: Union[str, List[str], Tuple[str]],
        probabilities: Union[List[float], Tuple[float]],
        relativeError: float,
    ) -> Union[List[float], List[List[float]]]:
        """
        Calculates the approximate quantiles of numerical columns of a
        :class:`DataFrame`.

        The result of this algorithm has the following deterministic bound:
        If the :class:`DataFrame` has N elements and if we request the quantile at
        probability `p` up to error `err`, then the algorithm will return
        a sample `x` from the :class:`DataFrame` so that the *exact* rank of `x` is
        close to (p * N). More precisely,

          floor((p - err) * N) <= rank(x) <= ceil((p + err) * N).

        This method implements a variation of the Greenwald-Khanna
        algorithm (with some speed optimizations). The algorithm was first
        present in [[https://doi.org/10.1145/375663.375670
        Space-efficient Online Computation of Quantile Summaries]]
        by Greenwald and Khanna.

        .. versionadded:: 2.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        col: str, tuple or list
            Can be a single column name, or a list of names for multiple columns.

            .. versionchanged:: 2.2.0
               Added support for multiple columns.
        probabilities : list or tuple
            a list of quantile probabilities
            Each number must belong to [0, 1].
            For example 0 is the minimum, 0.5 is the median, 1 is the maximum.
        relativeError : float
            The relative target precision to achieve
            (>= 0). If set to zero, the exact quantiles are computed, which
            could be very expensive. Note that values greater than 1 are
            accepted but gives the same result as 1.

        Returns
        -------
        list
            the approximate quantiles at the given probabilities.

            * If the input `col` is a string, the output is a list of floats.

            * If the input `col` is a list or tuple of strings, the output is also a
                list, but each element in it is a list of floats, i.e., the output
                is a list of list of floats.

        Notes
        -----
        Null values will be ignored in numerical columns before calculation.
        For columns only containing null values, an empty list is returned.
        """

        if not isinstance(col, (str, list, tuple)):
            raise PySparkTypeError(
                error_class="NOT_LIST_OR_STR_OR_TUPLE",
                message_parameters={"arg_name": "col", "arg_type": type(col).__name__},
            )

        isStr = isinstance(col, str)

        if isinstance(col, tuple):
            col = list(col)
        elif isStr:
            col = [cast(str, col)]

        for c in col:
            if not isinstance(c, str):
                raise PySparkTypeError(
                    error_class="DISALLOWED_TYPE_FOR_CONTAINER",
                    message_parameters={
                        "arg_name": "col",
                        "arg_type": type(col).__name__,
                        "allowed_types": "str",
                        "return_type": type(c).__name__,
                    },
                )
        col = _to_list(self._sc, cast(List["ColumnOrName"], col))

        if not isinstance(probabilities, (list, tuple)):
            raise PySparkTypeError(
                error_class="NOT_LIST_OR_TUPLE",
                message_parameters={
                    "arg_name": "probabilities",
                    "arg_type": type(probabilities).__name__,
                },
            )
        if isinstance(probabilities, tuple):
            probabilities = list(probabilities)
        for p in probabilities:
            if not isinstance(p, (float, int)) or p < 0 or p > 1:
                raise PySparkTypeError(
                    error_class="NOT_LIST_OF_FLOAT_OR_INT",
                    message_parameters={
                        "arg_name": "probabilities",
                        "arg_type": type(p).__name__,
                    },
                )
        probabilities = _to_list(self._sc, cast(List["ColumnOrName"], probabilities))

        if not isinstance(relativeError, (float, int)):
            raise PySparkTypeError(
                error_class="NOT_FLOAT_OR_INT",
                message_parameters={
                    "arg_name": "relativeError",
                    "arg_type": type(relativeError).__name__,
                },
            )
        if relativeError < 0:
            raise PySparkValueError(
                error_class="NEGATIVE_VALUE",
                message_parameters={
                    "arg_name": "relativeError",
                    "arg_value": str(relativeError),
                },
            )
        relativeError = float(relativeError)

        jaq = self._jdf.stat().approxQuantile(col, probabilities, relativeError)
        jaq_list = [list(j) for j in jaq]
        return jaq_list[0] if isStr else jaq_list

    def corr(self, col1: str, col2: str, method: Optional[str] = None) -> float:
        """
        Calculates the correlation of two columns of a :class:`DataFrame` as a double value.
        Currently only supports the Pearson Correlation Coefficient.
        :func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases of each other.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        col1 : str
            The name of the first column
        col2 : str
            The name of the second column
        method : str, optional
            The correlation method. Currently only supports "pearson"

        Returns
        -------
        float
            Pearson Correlation Coefficient of two columns.

        Examples
        --------
        >>> df = spark.createDataFrame([(1, 12), (10, 1), (19, 8)], ["c1", "c2"])
        >>> df.corr("c1", "c2")
        -0.3592106040535498
        >>> df = spark.createDataFrame([(11, 12), (10, 11), (9, 10)], ["small", "bigger"])
        >>> df.corr("small", "bigger")
        1.0

        """
        if not isinstance(col1, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "col1", "arg_type": type(col1).__name__},
            )
        if not isinstance(col2, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "col2", "arg_type": type(col2).__name__},
            )
        if not method:
            method = "pearson"
        if not method == "pearson":
            raise PySparkValueError(
                error_class="VALUE_NOT_PEARSON",
                message_parameters={"arg_name": "method", "arg_value": method},
            )
        return self._jdf.stat().corr(col1, col2, method)

    def cov(self, col1: str, col2: str) -> float:
        """
        Calculate the sample covariance for the given columns, specified by their names, as a
        double value. :func:`DataFrame.cov` and :func:`DataFrameStatFunctions.cov` are aliases.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        col1 : str
            The name of the first column
        col2 : str
            The name of the second column

        Returns
        -------
        float
            Covariance of two columns.

        Examples
        --------
        >>> df = spark.createDataFrame([(1, 12), (10, 1), (19, 8)], ["c1", "c2"])
        >>> df.cov("c1", "c2")
        -18.0
        >>> df = spark.createDataFrame([(11, 12), (10, 11), (9, 10)], ["small", "bigger"])
        >>> df.cov("small", "bigger")
        1.0

        """
        if not isinstance(col1, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "col1", "arg_type": type(col1).__name__},
            )
        if not isinstance(col2, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "col2", "arg_type": type(col2).__name__},
            )
        return self._jdf.stat().cov(col1, col2)

    def crosstab(self, col1: str, col2: str) -> "DataFrame":
        """
        Computes a pair-wise frequency table of the given columns. Also known as a contingency
        table.
        The first column of each row will be the distinct values of `col1` and the column names
        will be the distinct values of `col2`. The name of the first column will be `$col1_$col2`.
        Pairs that have no occurrences will have zero as their counts.
        :func:`DataFrame.crosstab` and :func:`DataFrameStatFunctions.crosstab` are aliases.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        col1 : str
            The name of the first column. Distinct items will make the first item of
            each row.
        col2 : str
            The name of the second column. Distinct items will make the column names
            of the :class:`DataFrame`.

        Returns
        -------
        :class:`DataFrame`
            Frequency matrix of two columns.

        Examples
        --------
        >>> df = spark.createDataFrame([(1, 11), (1, 11), (3, 10), (4, 8), (4, 8)], ["c1", "c2"])
        >>> df.crosstab("c1", "c2").sort("c1_c2").show()
        +-----+---+---+---+
        |c1_c2| 10| 11|  8|
        +-----+---+---+---+
        |    1|  0|  2|  0|
        |    3|  1|  0|  0|
        |    4|  0|  0|  2|
        +-----+---+---+---+

        """
        if not isinstance(col1, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "col1", "arg_type": type(col1).__name__},
            )
        if not isinstance(col2, str):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "col2", "arg_type": type(col2).__name__},
            )
        return DataFrame(self._jdf.stat().crosstab(col1, col2), self.sparkSession)

    def freqItems(
        self, cols: Union[List[str], Tuple[str]], support: Optional[float] = None
    ) -> "DataFrame":
        """
        Finding frequent items for columns, possibly with false positives. Using the
        frequent element count algorithm described in
        "https://doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou".
        :func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases.

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols : list or tuple
            Names of the columns to calculate frequent items for as a list or tuple of
            strings.
        support : float, optional
            The frequency with which to consider an item 'frequent'. Default is 1%.
            The support must be greater than 1e-4.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with frequent items.

        Notes
        -----
        This function is meant for exploratory data analysis, as we make no
        guarantee about the backward compatibility of the schema of the resulting
        :class:`DataFrame`.

        Examples
        --------
        >>> df = spark.createDataFrame([(1, 11), (1, 11), (3, 10), (4, 8), (4, 8)], ["c1", "c2"])
        >>> df.freqItems(["c1", "c2"]).show()  # doctest: +SKIP
        +------------+------------+
        |c1_freqItems|c2_freqItems|
        +------------+------------+
        |   [4, 1, 3]| [8, 11, 10]|
        +------------+------------+
        """
        if isinstance(cols, tuple):
            cols = list(cols)
        if not isinstance(cols, list):
            raise PySparkTypeError(
                error_class="NOT_LIST_OR_TUPLE",
                message_parameters={"arg_name": "cols", "arg_type": type(cols).__name__},
            )
        if not support:
            support = 0.01
        return DataFrame(
            self._jdf.stat().freqItems(_to_seq(self._sc, cols), support), self.sparkSession
        )

    def _ipython_key_completions_(self) -> List[str]:
        """Returns the names of columns in this :class:`DataFrame`.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age", "name"])
        >>> df._ipython_key_completions_()
        ['age', 'name']

        Would return illegal identifiers.
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], ["age 1", "name?1"])
        >>> df._ipython_key_completions_()
        ['age 1', 'name?1']
        """
        return self.columns

    def withColumns(self, *colsMap: Dict[str, Column]) -> "DataFrame":
        """
        Returns a new :class:`DataFrame` by adding multiple columns or replacing the
        existing columns that have the same names.

        The colsMap is a map of column name and column, the column must only refer to attributes
        supplied by this Dataset. It is an error to add columns that refer to some other Dataset.

        .. versionadded:: 3.3.0
           Added support for multiple columns adding

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        colsMap : dict
            a dict of column name and :class:`Column`. Currently, only a single map is supported.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with new or replaced columns.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.withColumns({'age2': df.age + 2, 'age3': df.age + 3}).show()
        +---+-----+----+----+
        |age| name|age2|age3|
        +---+-----+----+----+
        |  2|Alice|   4|   5|
        |  5|  Bob|   7|   8|
        +---+-----+----+----+
        """
        # Below code is to help enable kwargs in future.
        assert len(colsMap) == 1
        colsMap = colsMap[0]  # type: ignore[assignment]

        if not isinstance(colsMap, dict):
            raise PySparkTypeError(
                error_class="NOT_DICT",
                message_parameters={"arg_name": "colsMap", "arg_type": type(colsMap).__name__},
            )

        col_names = list(colsMap.keys())
        cols = list(colsMap.values())

        return DataFrame(
            self._jdf.withColumns(_to_seq(self._sc, col_names), self._jcols(*cols)),
            self.sparkSession,
        )

    def withColumn(self, colName: str, col: Column) -> "DataFrame":
        """
        Returns a new :class:`DataFrame` by adding a column or replacing the
        existing column that has the same name.

        The column expression must be an expression over this :class:`DataFrame`; attempting to add
        a column from some other :class:`DataFrame` will raise an error.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        colName : str
            string, name of the new column.
        col : :class:`Column`
            a :class:`Column` expression for the new column.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with new or replaced column.

        Notes
        -----
        This method introduces a projection internally. Therefore, calling it multiple
        times, for instance, via loops in order to add multiple columns can generate big
        plans which can cause performance issues and even `StackOverflowException`.
        To avoid this, use :func:`select` with multiple columns at once.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.withColumn('age2', df.age + 2).show()
        +---+-----+----+
        |age| name|age2|
        +---+-----+----+
        |  2|Alice|   4|
        |  5|  Bob|   7|
        +---+-----+----+
        """
        if not isinstance(col, Column):
            raise PySparkTypeError(
                error_class="NOT_COLUMN",
                message_parameters={"arg_name": "col", "arg_type": type(col).__name__},
            )
        return DataFrame(self._jdf.withColumn(colName, col._jc), self.sparkSession)

    def withColumnRenamed(self, existing: str, new: str) -> "DataFrame":
        """Returns a new :class:`DataFrame` by renaming an existing column.
        This is a no-op if the schema doesn't contain the given column name.

        .. versionadded:: 1.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        existing : str
            string, name of the existing column to rename.
        new : str
            string, new name of the column.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with renamed column.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df.withColumnRenamed('age', 'age2').show()
        +----+-----+
        |age2| name|
        +----+-----+
        |   2|Alice|
        |   5|  Bob|
        +----+-----+
        """
        return DataFrame(self._jdf.withColumnRenamed(existing, new), self.sparkSession)

    def withColumnsRenamed(self, colsMap: Dict[str, str]) -> "DataFrame":
        """
        Returns a new :class:`DataFrame` by renaming multiple columns.
        This is a no-op if the schema doesn't contain the given column names.

        .. versionadded:: 3.4.0
           Added support for multiple columns renaming

        Parameters
        ----------
        colsMap : dict
            a dict of existing column names and corresponding desired column names.
            Currently, only a single map is supported.

        Returns
        -------
        :class:`DataFrame`
            DataFrame with renamed columns.

        See Also
        --------
        :meth:`withColumnRenamed`

        Notes
        -----
        Support Spark Connect

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df = df.withColumns({'age2': df.age + 2, 'age3': df.age + 3})
        >>> df.withColumnsRenamed({'age2': 'age4', 'age3': 'age5'}).show()
        +---+-----+----+----+
        |age| name|age4|age5|
        +---+-----+----+----+
        |  2|Alice|   4|   5|
        |  5|  Bob|   7|   8|
        +---+-----+----+----+
        """
        if not isinstance(colsMap, dict):
            raise PySparkTypeError(
                error_class="NOT_DICT",
                message_parameters={"arg_name": "colsMap", "arg_type": type(colsMap).__name__},
            )

        return DataFrame(self._jdf.withColumnsRenamed(colsMap), self.sparkSession)

    def withMetadata(self, columnName: str, metadata: Dict[str, Any]) -> "DataFrame":
        """Returns a new :class:`DataFrame` by updating an existing column with metadata.

        .. versionadded:: 3.3.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        columnName : str
            string, name of the existing column to update the metadata.
        metadata : dict
            dict, new metadata to be assigned to df.schema[columnName].metadata

        Returns
        -------
        :class:`DataFrame`
            DataFrame with updated metadata column.

        Examples
        --------
        >>> df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
        >>> df_meta = df.withMetadata('age', {'foo': 'bar'})
        >>> df_meta.schema['age'].metadata
        {'foo': 'bar'}
        """
        if not isinstance(metadata, dict):
            raise PySparkTypeError(
                error_class="NOT_DICT",
                message_parameters={"arg_name": "metadata", "arg_type": type(metadata).__name__},
            )
        sc = get_active_spark_context()
        jmeta = cast(JVMView, sc._jvm).org.apache.spark.sql.types.Metadata.fromJson(
            json.dumps(metadata)
        )
        return DataFrame(self._jdf.withMetadata(columnName, jmeta), self.sparkSession)

    @overload
    def drop(self, cols: "ColumnOrName") -> "DataFrame":
        ...

    @overload
    def drop(self, *cols: str) -> "DataFrame":
        ...

    def drop(self, *cols: "ColumnOrName") -> "DataFrame":  # type: ignore[misc]
        """Returns a new :class:`DataFrame` without specified columns.
        This is a no-op if the schema doesn't contain the given column name(s).

        .. versionadded:: 1.4.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        cols: str or :class:`Column`
            a name of the column, or the :class:`Column` to drop

        Returns
        -------
        :class:`DataFrame`
            DataFrame without given columns.

        Notes
        -----
        When an input is a column name, it is treated literally without further interpretation.
        Otherwise, will try to match the equivalent expression.
        So that dropping column by its name `drop(colName)` has different semantic with directly
        dropping the column `drop(col(colName))`.

        Examples
        --------
        >>> from pyspark.sql import Row
        >>> from pyspark.sql.functions import col, lit
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df2 = spark.createDataFrame([Row(height=80, name="Tom"), Row(height=85, name="Bob")])

        >>> df.drop('age').show()
        +-----+
        | name|
        +-----+
        |  Tom|
        |Alice|
        |  Bob|
        +-----+
        >>> df.drop(df.age).show()
        +-----+
        | name|
        +-----+
        |  Tom|
        |Alice|
        |  Bob|
        +-----+

        Drop the column that joined both DataFrames on.

        >>> df.join(df2, df.name == df2.name, 'inner').drop('name').sort('age').show()
        +---+------+
        |age|height|
        +---+------+
        | 14|    80|
        | 16|    85|
        +---+------+

        >>> df3 = df.join(df2)
        >>> df3.show()
        +---+-----+------+----+
        |age| name|height|name|
        +---+-----+------+----+
        | 14|  Tom|    80| Tom|
        | 14|  Tom|    85| Bob|
        | 23|Alice|    80| Tom|
        | 23|Alice|    85| Bob|
        | 16|  Bob|    80| Tom|
        | 16|  Bob|    85| Bob|
        +---+-----+------+----+

        Drop two column by the same name.

        >>> df3.drop("name").show()
        +---+------+
        |age|height|
        +---+------+
        | 14|    80|
        | 14|    85|
        | 23|    80|
        | 23|    85|
        | 16|    80|
        | 16|    85|
        +---+------+

        Can not drop col('name') due to ambiguous reference.

        >>> df3.drop(col("name")).show()
        Traceback (most recent call last):
        ...
        pyspark.errors.exceptions.captured.AnalysisException: [AMBIGUOUS_REFERENCE] Reference...

        >>> df4 = df.withColumn("a.b.c", lit(1))
        >>> df4.show()
        +---+-----+-----+
        |age| name|a.b.c|
        +---+-----+-----+
        | 14|  Tom|    1|
        | 23|Alice|    1|
        | 16|  Bob|    1|
        +---+-----+-----+

        >>> df4.drop("a.b.c").show()
        +---+-----+
        |age| name|
        +---+-----+
        | 14|  Tom|
        | 23|Alice|
        | 16|  Bob|
        +---+-----+

        Can not find a column matching the expression "a.b.c".

        >>> df4.drop(col("a.b.c")).show()
        +---+-----+-----+
        |age| name|a.b.c|
        +---+-----+-----+
        | 14|  Tom|    1|
        | 23|Alice|    1|
        | 16|  Bob|    1|
        +---+-----+-----+
        """
        column_names: List[str] = []
        java_columns: List[JavaObject] = []

        for c in cols:
            if isinstance(c, str):
                column_names.append(c)
            elif isinstance(c, Column):
                java_columns.append(c._jc)
            else:
                raise PySparkTypeError(
                    error_class="NOT_COLUMN_OR_STR",
                    message_parameters={"arg_name": "col", "arg_type": type(c).__name__},
                )

        jdf = self._jdf
        if len(java_columns) > 0:
            first_column, *remaining_columns = java_columns
            jdf = jdf.drop(first_column, self._jseq(remaining_columns))
        if len(column_names) > 0:
            jdf = jdf.drop(self._jseq(column_names))

        return DataFrame(jdf, self.sparkSession)

    def toDF(self, *cols: str) -> "DataFrame":
        """Returns a new :class:`DataFrame` that with new specified column names

        .. versionadded:: 1.6.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        *cols : tuple
            a tuple of string new column name. The length of the
            list needs to be the same as the number of columns in the initial
            :class:`DataFrame`

        Returns
        -------
        :class:`DataFrame`
            DataFrame with new column names.

        Examples
        --------
        >>> df = spark.createDataFrame([(14, "Tom"), (23, "Alice"),
        ...     (16, "Bob")], ["age", "name"])
        >>> df.toDF('f1', 'f2').show()
        +---+-----+
        | f1|   f2|
        +---+-----+
        | 14|  Tom|
        | 23|Alice|
        | 16|  Bob|
        +---+-----+
        """
        for col in cols:
            if not isinstance(col, str):
                raise PySparkTypeError(
                    error_class="NOT_LIST_OF_STR",
                    message_parameters={"arg_name": "cols", "arg_type": type(col).__name__},
                )
        jdf = self._jdf.toDF(self._jseq(cols))
        return DataFrame(jdf, self.sparkSession)

    def transform(self, func: Callable[..., "DataFrame"], *args: Any, **kwargs: Any) -> "DataFrame":
        """Returns a new :class:`DataFrame`. Concise syntax for chaining custom transformations.

        .. versionadded:: 3.0.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        func : function
            a function that takes and returns a :class:`DataFrame`.
        *args
            Positional arguments to pass to func.

            .. versionadded:: 3.3.0
        **kwargs
            Keyword arguments to pass to func.

            .. versionadded:: 3.3.0

        Returns
        -------
        :class:`DataFrame`
            Transformed DataFrame.

        Examples
        --------
        >>> from pyspark.sql.functions import col
        >>> df = spark.createDataFrame([(1, 1.0), (2, 2.0)], ["int", "float"])
        >>> def cast_all_to_int(input_df):
        ...     return input_df.select([col(col_name).cast("int") for col_name in input_df.columns])
        ...
        >>> def sort_columns_asc(input_df):
        ...     return input_df.select(*sorted(input_df.columns))
        ...
        >>> df.transform(cast_all_to_int).transform(sort_columns_asc).show()
        +-----+---+
        |float|int|
        +-----+---+
        |    1|  1|
        |    2|  2|
        +-----+---+

        >>> def add_n(input_df, n):
        ...     return input_df.select([(col(col_name) + n).alias(col_name)
        ...                             for col_name in input_df.columns])
        >>> df.transform(add_n, 1).transform(add_n, n=10).show()
        +---+-----+
        |int|float|
        +---+-----+
        | 12| 12.0|
        | 13| 13.0|
        +---+-----+
        """
        result = func(self, *args, **kwargs)
        assert isinstance(
            result, DataFrame
        ), "Func returned an instance of type [%s], " "should have been DataFrame." % type(result)
        return result

    def sameSemantics(self, other: "DataFrame") -> bool:
        """
        Returns `True` when the logical query plans inside both :class:`DataFrame`\\s are equal and
        therefore return the same results.

        .. versionadded:: 3.1.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        The equality comparison here is simplified by tolerating the cosmetic differences
        such as attribute names.

        This API can compare both :class:`DataFrame`\\s very fast but can still return
        `False` on the :class:`DataFrame` that return the same results, for instance, from
        different plans. Such false negative semantic can be useful when caching as an example.

        This API is a developer API.

        Parameters
        ----------
        other : :class:`DataFrame`
            The other DataFrame to compare against.

        Returns
        -------
        bool
            Whether these two DataFrames are similar.

        Examples
        --------
        >>> df1 = spark.range(10)
        >>> df2 = spark.range(10)
        >>> df1.withColumn("col1", df1.id * 2).sameSemantics(df2.withColumn("col1", df2.id * 2))
        True
        >>> df1.withColumn("col1", df1.id * 2).sameSemantics(df2.withColumn("col1", df2.id + 2))
        False
        >>> df1.withColumn("col1", df1.id * 2).sameSemantics(df2.withColumn("col0", df2.id * 2))
        True
        """
        if not isinstance(other, DataFrame):
            raise PySparkTypeError(
                error_class="NOT_STR",
                message_parameters={"arg_name": "other", "arg_type": type(other).__name__},
            )
        return self._jdf.sameSemantics(other._jdf)

    def semanticHash(self) -> int:
        """
        Returns a hash code of the logical query plan against this :class:`DataFrame`.

        .. versionadded:: 3.1.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        Notes
        -----
        Unlike the standard hash code, the hash is calculated against the query plan
        simplified by tolerating the cosmetic differences such as attribute names.

        This API is a developer API.

        Returns
        -------
        int
            Hash value.

        Examples
        --------
        >>> spark.range(10).selectExpr("id as col0").semanticHash()  # doctest: +SKIP
        1855039936
        >>> spark.range(10).selectExpr("id as col1").semanticHash()  # doctest: +SKIP
        1855039936
        """
        return self._jdf.semanticHash()

    def inputFiles(self) -> List[str]:
        """
        Returns a best-effort snapshot of the files that compose this :class:`DataFrame`.
        This method simply asks each constituent BaseRelation for its respective files and
        takes the union of all results. Depending on the source relations, this may not find
        all input files. Duplicates are removed.

        .. versionadded:: 3.1.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Returns
        -------
        list
            List of file paths.

        Examples
        --------
        >>> import tempfile
        >>> with tempfile.TemporaryDirectory() as d:
        ...     # Write a single-row DataFrame into a JSON file
        ...     spark.createDataFrame(
        ...         [{"age": 100, "name": "Hyukjin Kwon"}]
        ...     ).repartition(1).write.json(d, mode="overwrite")
        ...
        ...     # Read the JSON file as a DataFrame.
        ...     df = spark.read.format("json").load(d)
        ...
        ...     # Returns the number of input files.
        ...     len(df.inputFiles())
        1
        """
        return list(self._jdf.inputFiles())

    where = copy_func(filter, sinceversion=1.3, doc=":func:`where` is an alias for :func:`filter`.")

    # Two aliases below were added for pandas compatibility many years ago.
    # There are too many differences compared to pandas and we cannot just
    # make it "compatible" by adding aliases. Therefore, we stop adding such
    # aliases as of Spark 3.0. Two methods below remain just
    # for legacy users currently.
    groupby = copy_func(
        groupBy, sinceversion=1.4, doc=":func:`groupby` is an alias for :func:`groupBy`."
    )

    drop_duplicates = copy_func(
        dropDuplicates,
        sinceversion=1.4,
        doc=":func:`drop_duplicates` is an alias for :func:`dropDuplicates`.",
    )

    def writeTo(self, table: str) -> DataFrameWriterV2:
        """
        Create a write configuration builder for v2 sources.

        This builder is used to configure and execute write operations.

        For example, to append or create or replace existing tables.

        .. versionadded:: 3.1.0

        .. versionchanged:: 3.4.0
            Supports Spark Connect.

        Parameters
        ----------
        table : str
            Target table name to write to.

        Returns
        -------
        :class:`DataFrameWriterV2`
            DataFrameWriterV2 to use further to specify how to save the data

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])
        >>> df.writeTo("catalog.db.table").append()  # doctest: +SKIP
        >>> df.writeTo(                              # doctest: +SKIP
        ...     "catalog.db.table"
        ... ).partitionedBy("col").createOrReplace()
        """
        return DataFrameWriterV2(self, table)

    # Keep to_pandas_on_spark for backward compatibility for now.
    def to_pandas_on_spark(
        self, index_col: Optional[Union[str, List[str]]] = None
    ) -> "PandasOnSparkDataFrame":
        warnings.warn(
            "DataFrame.to_pandas_on_spark is deprecated. Use DataFrame.pandas_api instead.",
            FutureWarning,
        )
        return self.pandas_api(index_col)

    def pandas_api(
        self, index_col: Optional[Union[str, List[str]]] = None
    ) -> "PandasOnSparkDataFrame":
        """
        Converts the existing DataFrame into a pandas-on-Spark DataFrame.

        .. versionadded:: 3.2.0

        .. versionchanged:: 3.5.0
            Supports Spark Connect.

        If a pandas-on-Spark DataFrame is converted to a Spark DataFrame and then back
        to pandas-on-Spark, it will lose the index information and the original index
        will be turned into a normal column.

        This is only available if Pandas is installed and available.

        Parameters
        ----------
        index_col: str or list of str, optional, default: None
            Index column of table in Spark.

        Returns
        -------
        :class:`PandasOnSparkDataFrame`

        See Also
        --------
        pyspark.pandas.frame.DataFrame.to_spark

        Examples
        --------
        >>> df = spark.createDataFrame(
        ...     [(14, "Tom"), (23, "Alice"), (16, "Bob")], ["age", "name"])

        >>> df.pandas_api()  # doctest: +SKIP
           age   name
        0   14    Tom
        1   23  Alice
        2   16    Bob

        We can specify the index columns.

        >>> df.pandas_api(index_col="age")  # doctest: +SKIP
              name
        age
        14     Tom
        23   Alice
        16     Bob
        """
        from pyspark.pandas.namespace import _get_index_map
        from pyspark.pandas.frame import DataFrame as PandasOnSparkDataFrame
        from pyspark.pandas.internal import InternalFrame

        index_spark_columns, index_names = _get_index_map(self, index_col)
        internal = InternalFrame(
            spark_frame=self,
            index_spark_columns=index_spark_columns,
            index_names=index_names,  # type: ignore[arg-type]
        )
        return PandasOnSparkDataFrame(internal)

    # Keep to_koalas for backward compatibility for now.
    def to_koalas(
        self, index_col: Optional[Union[str, List[str]]] = None
    ) -> "PandasOnSparkDataFrame":
        return self.pandas_api(index_col)


def _to_scala_map(sc: SparkContext, jm: Dict) -> JavaObject:
    """
    Convert a dict into a JVM Map.
    """
    assert sc._jvm is not None
    return sc._jvm.PythonUtils.toScalaMap(jm)


class DataFrameNaFunctions:
    """Functionality for working with missing data in :class:`DataFrame`.

    .. versionadded:: 1.4.0

    .. versionchanged:: 3.4.0
        Supports Spark Connect.
    """

    def __init__(self, df: DataFrame):
        self.df = df

    def drop(
        self,
        how: str = "any",
        thresh: Optional[int] = None,
        subset: Optional[Union[str, Tuple[str, ...], List[str]]] = None,
    ) -> DataFrame:
        return self.df.dropna(how=how, thresh=thresh, subset=subset)

    drop.__doc__ = DataFrame.dropna.__doc__

    @overload
    def fill(self, value: "LiteralType", subset: Optional[List[str]] = ...) -> DataFrame:
        ...

    @overload
    def fill(self, value: Dict[str, "LiteralType"]) -> DataFrame:
        ...

    def fill(
        self,
        value: Union["LiteralType", Dict[str, "LiteralType"]],
        subset: Optional[List[str]] = None,
    ) -> DataFrame:
        return self.df.fillna(value=value, subset=subset)  # type: ignore[arg-type]

    fill.__doc__ = DataFrame.fillna.__doc__

    @overload
    def replace(
        self,
        to_replace: List["LiteralType"],
        value: List["OptionalPrimitiveType"],
        subset: Optional[List[str]] = ...,
    ) -> DataFrame:
        ...

    @overload
    def replace(
        self,
        to_replace: Dict["LiteralType", "OptionalPrimitiveType"],
        subset: Optional[List[str]] = ...,
    ) -> DataFrame:
        ...

    @overload
    def replace(
        self,
        to_replace: List["LiteralType"],
        value: "OptionalPrimitiveType",
        subset: Optional[List[str]] = ...,
    ) -> DataFrame:
        ...

    def replace(  # type: ignore[misc]
        self,
        to_replace: Union[List["LiteralType"], Dict["LiteralType", "OptionalPrimitiveType"]],
        value: Optional[
            Union["OptionalPrimitiveType", List["OptionalPrimitiveType"], _NoValueType]
        ] = _NoValue,
        subset: Optional[List[str]] = None,
    ) -> DataFrame:
        return self.df.replace(to_replace, value, subset)  # type: ignore[arg-type]

    replace.__doc__ = DataFrame.replace.__doc__


class DataFrameStatFunctions:
    """Functionality for statistic functions with :class:`DataFrame`.

    .. versionadded:: 1.4.0

    .. versionchanged:: 3.4.0
        Supports Spark Connect.
    """

    def __init__(self, df: DataFrame):
        self.df = df

    @overload
    def approxQuantile(
        self,
        col: str,
        probabilities: Union[List[float], Tuple[float]],
        relativeError: float,
    ) -> List[float]:
        ...

    @overload
    def approxQuantile(
        self,
        col: Union[List[str], Tuple[str]],
        probabilities: Union[List[float], Tuple[float]],
        relativeError: float,
    ) -> List[List[float]]:
        ...

    def approxQuantile(
        self,
        col: Union[str, List[str], Tuple[str]],
        probabilities: Union[List[float], Tuple[float]],
        relativeError: float,
    ) -> Union[List[float], List[List[float]]]:
        return self.df.approxQuantile(col, probabilities, relativeError)

    approxQuantile.__doc__ = DataFrame.approxQuantile.__doc__

    def corr(self, col1: str, col2: str, method: Optional[str] = None) -> float:
        return self.df.corr(col1, col2, method)

    corr.__doc__ = DataFrame.corr.__doc__

    def cov(self, col1: str, col2: str) -> float:
        return self.df.cov(col1, col2)

    cov.__doc__ = DataFrame.cov.__doc__

    def crosstab(self, col1: str, col2: str) -> DataFrame:
        return self.df.crosstab(col1, col2)

    crosstab.__doc__ = DataFrame.crosstab.__doc__

    def freqItems(self, cols: List[str], support: Optional[float] = None) -> DataFrame:
        return self.df.freqItems(cols, support)

    freqItems.__doc__ = DataFrame.freqItems.__doc__

    def sampleBy(
        self, col: str, fractions: Dict[Any, float], seed: Optional[int] = None
    ) -> DataFrame:
        return self.df.sampleBy(col, fractions, seed)

    sampleBy.__doc__ = DataFrame.sampleBy.__doc__


def _test() -> None:
    import doctest
    from pyspark.sql import SparkSession
    import pyspark.sql.dataframe

    globs = pyspark.sql.dataframe.__dict__.copy()
    spark = SparkSession.builder.master("local[4]").appName("sql.dataframe tests").getOrCreate()
    globs["spark"] = spark
    (failure_count, test_count) = doctest.testmod(
        pyspark.sql.dataframe,
        globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF,
    )
    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()