#
# 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.
#

"""
A collections of builtin functions
"""
import sys
import functools
import warnings

if sys.version < "3":
    from itertools import imap as map

if sys.version >= '3':
    basestring = str

from pyspark import since, SparkContext
from pyspark.rdd import ignore_unicode_prefix, PythonEvalType
from pyspark.sql.column import Column, _to_java_column, _to_seq, _create_column_from_literal, \
    _create_column_from_name
from pyspark.sql.dataframe import DataFrame
from pyspark.sql.types import StringType, DataType
# Keep UserDefinedFunction import for backwards compatible import; moved in SPARK-22409
from pyspark.sql.udf import UserDefinedFunction, _create_udf
from pyspark.sql.utils import to_str

# Note to developers: all of PySpark functions here take string as column names whenever possible.
# Namely, if columns are referred as arguments, they can be always both Column or string,
# even though there might be few exceptions for legacy or inevitable reasons.
# If you are fixing other language APIs together, also please note that Scala side is not the case
# since it requires to make every single overridden definition.


def _create_function(name, doc=""):
    """Create a PySpark function by its name"""
    def _(col):
        sc = SparkContext._active_spark_context
        jc = getattr(sc._jvm.functions, name)(col._jc if isinstance(col, Column) else col)
        return Column(jc)
    _.__name__ = name
    _.__doc__ = doc
    return _


def _create_function_over_column(name, doc=""):
    """Similar with `_create_function` but creates a PySpark function that takes a column
    (as string as well). This is mainly for PySpark functions to take strings as
    column names.
    """
    def _(col):
        sc = SparkContext._active_spark_context
        jc = getattr(sc._jvm.functions, name)(_to_java_column(col))
        return Column(jc)
    _.__name__ = name
    _.__doc__ = doc
    return _


def _wrap_deprecated_function(func, message):
    """ Wrap the deprecated function to print out deprecation warnings"""
    def _(col):
        warnings.warn(message, DeprecationWarning)
        return func(col)
    return functools.wraps(func)(_)


def _create_binary_mathfunction(name, doc=""):
    """ Create a binary mathfunction by name"""
    def _(col1, col2):
        sc = SparkContext._active_spark_context
        # For legacy reasons, the arguments here can be implicitly converted into floats,
        # if they are not columns or strings.
        if isinstance(col1, Column):
            arg1 = col1._jc
        elif isinstance(col1, basestring):
            arg1 = _create_column_from_name(col1)
        else:
            arg1 = float(col1)

        if isinstance(col2, Column):
            arg2 = col2._jc
        elif isinstance(col2, basestring):
            arg2 = _create_column_from_name(col2)
        else:
            arg2 = float(col2)

        jc = getattr(sc._jvm.functions, name)(arg1, arg2)
        return Column(jc)
    _.__name__ = name
    _.__doc__ = doc
    return _


def _create_window_function(name, doc=''):
    """ Create a window function by name """
    def _():
        sc = SparkContext._active_spark_context
        jc = getattr(sc._jvm.functions, name)()
        return Column(jc)
    _.__name__ = name
    _.__doc__ = 'Window function: ' + doc
    return _


def _options_to_str(options):
    return {key: to_str(value) for (key, value) in options.items()}

_lit_doc = """
    Creates a :class:`Column` of literal value.

    >>> df.select(lit(5).alias('height')).withColumn('spark_user', lit(True)).take(1)
    [Row(height=5, spark_user=True)]
    """
_functions = {
    'lit': _lit_doc,
    'col': 'Returns a :class:`Column` based on the given column name.',
    'column': 'Returns a :class:`Column` based on the given column name.',
    'asc': 'Returns a sort expression based on the ascending order of the given column name.',
    'desc': 'Returns a sort expression based on the descending order of the given column name.',
}

_functions_over_column = {
    'sqrt': 'Computes the square root of the specified float value.',
    'abs': 'Computes the absolute value.',

    'max': 'Aggregate function: returns the maximum value of the expression in a group.',
    'min': 'Aggregate function: returns the minimum value of the expression in a group.',
    'count': 'Aggregate function: returns the number of items in a group.',
    'sum': 'Aggregate function: returns the sum of all values in the expression.',
    'avg': 'Aggregate function: returns the average of the values in a group.',
    'mean': 'Aggregate function: returns the average of the values in a group.',
    'sumDistinct': 'Aggregate function: returns the sum of distinct values in the expression.',
}

_functions_1_4_over_column = {
    # unary math functions
    'acos': ':return: inverse cosine of `col`, as if computed by `java.lang.Math.acos()`',
    'asin': ':return: inverse sine of `col`, as if computed by `java.lang.Math.asin()`',
    'atan': ':return: inverse tangent of `col`, as if computed by `java.lang.Math.atan()`',
    'cbrt': 'Computes the cube-root of the given value.',
    'ceil': 'Computes the ceiling of the given value.',
    'cos': """:param col: angle in radians
           :return: cosine of the angle, as if computed by `java.lang.Math.cos()`.""",
    'cosh': """:param col: hyperbolic angle
           :return: hyperbolic cosine of the angle, as if computed by `java.lang.Math.cosh()`""",
    'exp': 'Computes the exponential of the given value.',
    'expm1': 'Computes the exponential of the given value minus one.',
    'floor': 'Computes the floor of the given value.',
    'log': 'Computes the natural logarithm of the given value.',
    'log10': 'Computes the logarithm of the given value in Base 10.',
    'log1p': 'Computes the natural logarithm of the given value plus one.',
    'rint': 'Returns the double value that is closest in value to the argument and' +
            ' is equal to a mathematical integer.',
    'signum': 'Computes the signum of the given value.',
    'sin': """:param col: angle in radians
           :return: sine of the angle, as if computed by `java.lang.Math.sin()`""",
    'sinh': """:param col: hyperbolic angle
           :return: hyperbolic sine of the given value,
                    as if computed by `java.lang.Math.sinh()`""",
    'tan': """:param col: angle in radians
           :return: tangent of the given value, as if computed by `java.lang.Math.tan()`""",
    'tanh': """:param col: hyperbolic angle
            :return: hyperbolic tangent of the given value,
                     as if computed by `java.lang.Math.tanh()`""",
    'toDegrees': '.. note:: Deprecated in 2.1, use :func:`degrees` instead.',
    'toRadians': '.. note:: Deprecated in 2.1, use :func:`radians` instead.',
    'bitwiseNOT': 'Computes bitwise not.',
}

_functions_2_4 = {
    'asc_nulls_first': 'Returns a sort expression based on the ascending order of the given' +
                       ' column name, and null values return before non-null values.',
    'asc_nulls_last': 'Returns a sort expression based on the ascending order of the given' +
                      ' column name, and null values appear after non-null values.',
    'desc_nulls_first': 'Returns a sort expression based on the descending order of the given' +
                        ' column name, and null values appear before non-null values.',
    'desc_nulls_last': 'Returns a sort expression based on the descending order of the given' +
                       ' column name, and null values appear after non-null values',
}

_collect_list_doc = """
    Aggregate function: returns a list of objects with duplicates.

    .. note:: The function is non-deterministic because the order of collected results depends
        on order of rows which may be non-deterministic after a shuffle.

    >>> df2 = spark.createDataFrame([(2,), (5,), (5,)], ('age',))
    >>> df2.agg(collect_list('age')).collect()
    [Row(collect_list(age)=[2, 5, 5])]
    """
_collect_set_doc = """
    Aggregate function: returns a set of objects with duplicate elements eliminated.

    .. note:: The function is non-deterministic because the order of collected results depends
        on order of rows which may be non-deterministic after a shuffle.

    >>> df2 = spark.createDataFrame([(2,), (5,), (5,)], ('age',))
    >>> df2.agg(collect_set('age')).collect()
    [Row(collect_set(age)=[5, 2])]
    """
_functions_1_6_over_column = {
    # unary math functions
    'stddev': 'Aggregate function: alias for stddev_samp.',
    'stddev_samp': 'Aggregate function: returns the unbiased sample standard deviation of' +
                   ' the expression in a group.',
    'stddev_pop': 'Aggregate function: returns population standard deviation of' +
                  ' the expression in a group.',
    'variance': 'Aggregate function: alias for var_samp.',
    'var_samp': 'Aggregate function: returns the unbiased sample variance of' +
                ' the values in a group.',
    'var_pop':  'Aggregate function: returns the population variance of the values in a group.',
    'skewness': 'Aggregate function: returns the skewness of the values in a group.',
    'kurtosis': 'Aggregate function: returns the kurtosis of the values in a group.',
    'collect_list': _collect_list_doc,
    'collect_set': _collect_set_doc
}

_functions_2_1_over_column = {
    # unary math functions
    'degrees': """
               Converts an angle measured in radians to an approximately equivalent angle
               measured in degrees.
               :param col: angle in radians
               :return: angle in degrees, as if computed by `java.lang.Math.toDegrees()`
               """,
    'radians': """
               Converts an angle measured in degrees to an approximately equivalent angle
               measured in radians.
               :param col: angle in degrees
               :return: angle in radians, as if computed by `java.lang.Math.toRadians()`
               """,
}

# math functions that take two arguments as input
_binary_mathfunctions = {
    'atan2': """
             :param col1: coordinate on y-axis
             :param col2: coordinate on x-axis
             :return: the `theta` component of the point
                (`r`, `theta`)
                in polar coordinates that corresponds to the point
                (`x`, `y`) in Cartesian coordinates,
                as if computed by `java.lang.Math.atan2()`
             """,
    'hypot': 'Computes ``sqrt(a^2 + b^2)`` without intermediate overflow or underflow.',
    'pow': 'Returns the value of the first argument raised to the power of the second argument.',
}

_window_functions = {
    'row_number':
        """returns a sequential number starting at 1 within a window partition.""",
    'dense_rank':
        """returns the rank of rows within a window partition, without any gaps.

        The difference between rank and dense_rank is that dense_rank leaves no gaps in ranking
        sequence when there are ties. That is, if you were ranking a competition using dense_rank
        and had three people tie for second place, you would say that all three were in second
        place and that the next person came in third. Rank would give me sequential numbers, making
        the person that came in third place (after the ties) would register as coming in fifth.

        This is equivalent to the DENSE_RANK function in SQL.""",
    'rank':
        """returns the rank of rows within a window partition.

        The difference between rank and dense_rank is that dense_rank leaves no gaps in ranking
        sequence when there are ties. That is, if you were ranking a competition using dense_rank
        and had three people tie for second place, you would say that all three were in second
        place and that the next person came in third. Rank would give me sequential numbers, making
        the person that came in third place (after the ties) would register as coming in fifth.

        This is equivalent to the RANK function in SQL.""",
    'cume_dist':
        """returns the cumulative distribution of values within a window partition,
        i.e. the fraction of rows that are below the current row.""",
    'percent_rank':
        """returns the relative rank (i.e. percentile) of rows within a window partition.""",
}

# Wraps deprecated functions (keys) with the messages (values).
_functions_deprecated = {
}

for _name, _doc in _functions.items():
    globals()[_name] = since(1.3)(_create_function(_name, _doc))
for _name, _doc in _functions_over_column.items():
    globals()[_name] = since(1.3)(_create_function_over_column(_name, _doc))
for _name, _doc in _functions_1_4_over_column.items():
    globals()[_name] = since(1.4)(_create_function_over_column(_name, _doc))
for _name, _doc in _binary_mathfunctions.items():
    globals()[_name] = since(1.4)(_create_binary_mathfunction(_name, _doc))
for _name, _doc in _window_functions.items():
    globals()[_name] = since(1.6)(_create_window_function(_name, _doc))
for _name, _doc in _functions_1_6_over_column.items():
    globals()[_name] = since(1.6)(_create_function_over_column(_name, _doc))
for _name, _doc in _functions_2_1_over_column.items():
    globals()[_name] = since(2.1)(_create_function_over_column(_name, _doc))
for _name, _message in _functions_deprecated.items():
    globals()[_name] = _wrap_deprecated_function(globals()[_name], _message)
for _name, _doc in _functions_2_4.items():
    globals()[_name] = since(2.4)(_create_function(_name, _doc))
del _name, _doc


@since(2.1)
def approx_count_distinct(col, rsd=None):
    """Aggregate function: returns a new :class:`Column` for approximate distinct count of
    column `col`.

    :param rsd: maximum estimation error allowed (default = 0.05). For rsd < 0.01, it is more
        efficient to use :func:`countDistinct`

    >>> df.agg(approx_count_distinct(df.age).alias('distinct_ages')).collect()
    [Row(distinct_ages=2)]
    """
    sc = SparkContext._active_spark_context
    if rsd is None:
        jc = sc._jvm.functions.approx_count_distinct(_to_java_column(col))
    else:
        jc = sc._jvm.functions.approx_count_distinct(_to_java_column(col), rsd)
    return Column(jc)


@since(1.6)
def broadcast(df):
    """Marks a DataFrame as small enough for use in broadcast joins."""

    sc = SparkContext._active_spark_context
    return DataFrame(sc._jvm.functions.broadcast(df._jdf), df.sql_ctx)


@since(1.4)
def coalesce(*cols):
    """Returns the first column that is not null.

    >>> cDf = spark.createDataFrame([(None, None), (1, None), (None, 2)], ("a", "b"))
    >>> cDf.show()
    +----+----+
    |   a|   b|
    +----+----+
    |null|null|
    |   1|null|
    |null|   2|
    +----+----+

    >>> cDf.select(coalesce(cDf["a"], cDf["b"])).show()
    +--------------+
    |coalesce(a, b)|
    +--------------+
    |          null|
    |             1|
    |             2|
    +--------------+

    >>> cDf.select('*', coalesce(cDf["a"], lit(0.0))).show()
    +----+----+----------------+
    |   a|   b|coalesce(a, 0.0)|
    +----+----+----------------+
    |null|null|             0.0|
    |   1|null|             1.0|
    |null|   2|             0.0|
    +----+----+----------------+
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.coalesce(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(1.6)
def corr(col1, col2):
    """Returns a new :class:`Column` for the Pearson Correlation Coefficient for ``col1``
    and ``col2``.

    >>> a = range(20)
    >>> b = [2 * x for x in range(20)]
    >>> df = spark.createDataFrame(zip(a, b), ["a", "b"])
    >>> df.agg(corr("a", "b").alias('c')).collect()
    [Row(c=1.0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.corr(_to_java_column(col1), _to_java_column(col2)))


@since(2.0)
def covar_pop(col1, col2):
    """Returns a new :class:`Column` for the population covariance of ``col1`` and ``col2``.

    >>> a = [1] * 10
    >>> b = [1] * 10
    >>> df = spark.createDataFrame(zip(a, b), ["a", "b"])
    >>> df.agg(covar_pop("a", "b").alias('c')).collect()
    [Row(c=0.0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.covar_pop(_to_java_column(col1), _to_java_column(col2)))


@since(2.0)
def covar_samp(col1, col2):
    """Returns a new :class:`Column` for the sample covariance of ``col1`` and ``col2``.

    >>> a = [1] * 10
    >>> b = [1] * 10
    >>> df = spark.createDataFrame(zip(a, b), ["a", "b"])
    >>> df.agg(covar_samp("a", "b").alias('c')).collect()
    [Row(c=0.0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.covar_samp(_to_java_column(col1), _to_java_column(col2)))


@since(1.3)
def countDistinct(col, *cols):
    """Returns a new :class:`Column` for distinct count of ``col`` or ``cols``.

    >>> df.agg(countDistinct(df.age, df.name).alias('c')).collect()
    [Row(c=2)]

    >>> df.agg(countDistinct("age", "name").alias('c')).collect()
    [Row(c=2)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.countDistinct(_to_java_column(col), _to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(1.3)
def first(col, ignorenulls=False):
    """Aggregate function: returns the first value in a group.

    The function by default returns the first values it sees. It will return the first non-null
    value it sees when ignoreNulls is set to true. If all values are null, then null is returned.

    .. note:: The function is non-deterministic because its results depends on order of rows which
        may be non-deterministic after a shuffle.
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.first(_to_java_column(col), ignorenulls)
    return Column(jc)


@since(2.0)
def grouping(col):
    """
    Aggregate function: indicates whether a specified column in a GROUP BY list is aggregated
    or not, returns 1 for aggregated or 0 for not aggregated in the result set.

    >>> df.cube("name").agg(grouping("name"), sum("age")).orderBy("name").show()
    +-----+--------------+--------+
    | name|grouping(name)|sum(age)|
    +-----+--------------+--------+
    | null|             1|       7|
    |Alice|             0|       2|
    |  Bob|             0|       5|
    +-----+--------------+--------+
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.grouping(_to_java_column(col))
    return Column(jc)


@since(2.0)
def grouping_id(*cols):
    """
    Aggregate function: returns the level of grouping, equals to

       (grouping(c1) << (n-1)) + (grouping(c2) << (n-2)) + ... + grouping(cn)

    .. note:: The list of columns should match with grouping columns exactly, or empty (means all
        the grouping columns).

    >>> df.cube("name").agg(grouping_id(), sum("age")).orderBy("name").show()
    +-----+-------------+--------+
    | name|grouping_id()|sum(age)|
    +-----+-------------+--------+
    | null|            1|       7|
    |Alice|            0|       2|
    |  Bob|            0|       5|
    +-----+-------------+--------+
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.grouping_id(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(1.6)
def input_file_name():
    """Creates a string column for the file name of the current Spark task.
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.input_file_name())


@since(1.6)
def isnan(col):
    """An expression that returns true iff the column is NaN.

    >>> df = spark.createDataFrame([(1.0, float('nan')), (float('nan'), 2.0)], ("a", "b"))
    >>> df.select(isnan("a").alias("r1"), isnan(df.a).alias("r2")).collect()
    [Row(r1=False, r2=False), Row(r1=True, r2=True)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.isnan(_to_java_column(col)))


@since(1.6)
def isnull(col):
    """An expression that returns true iff the column is null.

    >>> df = spark.createDataFrame([(1, None), (None, 2)], ("a", "b"))
    >>> df.select(isnull("a").alias("r1"), isnull(df.a).alias("r2")).collect()
    [Row(r1=False, r2=False), Row(r1=True, r2=True)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.isnull(_to_java_column(col)))


@since(1.3)
def last(col, ignorenulls=False):
    """Aggregate function: returns the last value in a group.

    The function by default returns the last values it sees. It will return the last non-null
    value it sees when ignoreNulls is set to true. If all values are null, then null is returned.

    .. note:: The function is non-deterministic because its results depends on order of rows
        which may be non-deterministic after a shuffle.
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.last(_to_java_column(col), ignorenulls)
    return Column(jc)


@since(1.6)
def monotonically_increasing_id():
    """A column that generates monotonically increasing 64-bit integers.

    The generated ID is guaranteed to be monotonically increasing and unique, but not consecutive.
    The current implementation puts the partition ID in the upper 31 bits, and the record number
    within each partition in the lower 33 bits. The assumption is that the data frame has
    less than 1 billion partitions, and each partition has less than 8 billion records.

    .. note:: The function is non-deterministic because its result depends on partition IDs.

    As an example, consider a :class:`DataFrame` with two partitions, each with 3 records.
    This expression would return the following IDs:
    0, 1, 2, 8589934592 (1L << 33), 8589934593, 8589934594.

    >>> df0 = sc.parallelize(range(2), 2).mapPartitions(lambda x: [(1,), (2,), (3,)]).toDF(['col1'])
    >>> df0.select(monotonically_increasing_id().alias('id')).collect()
    [Row(id=0), Row(id=1), Row(id=2), Row(id=8589934592), Row(id=8589934593), Row(id=8589934594)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.monotonically_increasing_id())


@since(1.6)
def nanvl(col1, col2):
    """Returns col1 if it is not NaN, or col2 if col1 is NaN.

    Both inputs should be floating point columns (:class:`DoubleType` or :class:`FloatType`).

    >>> df = spark.createDataFrame([(1.0, float('nan')), (float('nan'), 2.0)], ("a", "b"))
    >>> df.select(nanvl("a", "b").alias("r1"), nanvl(df.a, df.b).alias("r2")).collect()
    [Row(r1=1.0, r2=1.0), Row(r1=2.0, r2=2.0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.nanvl(_to_java_column(col1), _to_java_column(col2)))


@ignore_unicode_prefix
@since(1.4)
def rand(seed=None):
    """Generates a random column with independent and identically distributed (i.i.d.) samples
    from U[0.0, 1.0].

    .. note:: The function is non-deterministic in general case.

    >>> df.withColumn('rand', rand(seed=42) * 3).collect()
    [Row(age=2, name=u'Alice', rand=2.4052597283576684),
     Row(age=5, name=u'Bob', rand=2.3913904055683974)]
    """
    sc = SparkContext._active_spark_context
    if seed is not None:
        jc = sc._jvm.functions.rand(seed)
    else:
        jc = sc._jvm.functions.rand()
    return Column(jc)


@ignore_unicode_prefix
@since(1.4)
def randn(seed=None):
    """Generates a column with independent and identically distributed (i.i.d.) samples from
    the standard normal distribution.

    .. note:: The function is non-deterministic in general case.

    >>> df.withColumn('randn', randn(seed=42)).collect()
    [Row(age=2, name=u'Alice', randn=1.1027054481455365),
    Row(age=5, name=u'Bob', randn=0.7400395449950132)]
    """
    sc = SparkContext._active_spark_context
    if seed is not None:
        jc = sc._jvm.functions.randn(seed)
    else:
        jc = sc._jvm.functions.randn()
    return Column(jc)


@since(1.5)
def round(col, scale=0):
    """
    Round the given value to `scale` decimal places using HALF_UP rounding mode if `scale` >= 0
    or at integral part when `scale` < 0.

    >>> spark.createDataFrame([(2.5,)], ['a']).select(round('a', 0).alias('r')).collect()
    [Row(r=3.0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.round(_to_java_column(col), scale))


@since(2.0)
def bround(col, scale=0):
    """
    Round the given value to `scale` decimal places using HALF_EVEN rounding mode if `scale` >= 0
    or at integral part when `scale` < 0.

    >>> spark.createDataFrame([(2.5,)], ['a']).select(bround('a', 0).alias('r')).collect()
    [Row(r=2.0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.bround(_to_java_column(col), scale))


@since(1.5)
def shiftLeft(col, numBits):
    """Shift the given value numBits left.

    >>> spark.createDataFrame([(21,)], ['a']).select(shiftLeft('a', 1).alias('r')).collect()
    [Row(r=42)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.shiftLeft(_to_java_column(col), numBits))


@since(1.5)
def shiftRight(col, numBits):
    """(Signed) shift the given value numBits right.

    >>> spark.createDataFrame([(42,)], ['a']).select(shiftRight('a', 1).alias('r')).collect()
    [Row(r=21)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.shiftRight(_to_java_column(col), numBits)
    return Column(jc)


@since(1.5)
def shiftRightUnsigned(col, numBits):
    """Unsigned shift the given value numBits right.

    >>> df = spark.createDataFrame([(-42,)], ['a'])
    >>> df.select(shiftRightUnsigned('a', 1).alias('r')).collect()
    [Row(r=9223372036854775787)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.shiftRightUnsigned(_to_java_column(col), numBits)
    return Column(jc)


@since(1.6)
def spark_partition_id():
    """A column for partition ID.

    .. note:: This is indeterministic because it depends on data partitioning and task scheduling.

    >>> df.repartition(1).select(spark_partition_id().alias("pid")).collect()
    [Row(pid=0), Row(pid=0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.spark_partition_id())


@since(1.5)
def expr(str):
    """Parses the expression string into the column that it represents

    >>> df.select(expr("length(name)")).collect()
    [Row(length(name)=5), Row(length(name)=3)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.expr(str))


@ignore_unicode_prefix
@since(1.4)
def struct(*cols):
    """Creates a new struct column.

    :param cols: list of column names (string) or list of :class:`Column` expressions

    >>> df.select(struct('age', 'name').alias("struct")).collect()
    [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))]
    >>> df.select(struct([df.age, df.name]).alias("struct")).collect()
    [Row(struct=Row(age=2, name=u'Alice')), Row(struct=Row(age=5, name=u'Bob'))]
    """
    sc = SparkContext._active_spark_context
    if len(cols) == 1 and isinstance(cols[0], (list, set)):
        cols = cols[0]
    jc = sc._jvm.functions.struct(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(1.5)
def greatest(*cols):
    """
    Returns the greatest value of the list of column names, skipping null values.
    This function takes at least 2 parameters. It will return null iff all parameters are null.

    >>> df = spark.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
    >>> df.select(greatest(df.a, df.b, df.c).alias("greatest")).collect()
    [Row(greatest=4)]
    """
    if len(cols) < 2:
        raise ValueError("greatest should take at least two columns")
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.greatest(_to_seq(sc, cols, _to_java_column)))


@since(1.5)
def least(*cols):
    """
    Returns the least value of the list of column names, skipping null values.
    This function takes at least 2 parameters. It will return null iff all parameters are null.

    >>> df = spark.createDataFrame([(1, 4, 3)], ['a', 'b', 'c'])
    >>> df.select(least(df.a, df.b, df.c).alias("least")).collect()
    [Row(least=1)]
    """
    if len(cols) < 2:
        raise ValueError("least should take at least two columns")
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.least(_to_seq(sc, cols, _to_java_column)))


@since(1.4)
def when(condition, value):
    """Evaluates a list of conditions and returns one of multiple possible result expressions.
    If :func:`Column.otherwise` is not invoked, None is returned for unmatched conditions.

    :param condition: a boolean :class:`Column` expression.
    :param value: a literal value, or a :class:`Column` expression.

    >>> df.select(when(df['age'] == 2, 3).otherwise(4).alias("age")).collect()
    [Row(age=3), Row(age=4)]

    >>> df.select(when(df.age == 2, df.age + 1).alias("age")).collect()
    [Row(age=3), Row(age=None)]
    """
    sc = SparkContext._active_spark_context
    if not isinstance(condition, Column):
        raise TypeError("condition should be a Column")
    v = value._jc if isinstance(value, Column) else value
    jc = sc._jvm.functions.when(condition._jc, v)
    return Column(jc)


@since(1.5)
def log(arg1, arg2=None):
    """Returns the first argument-based logarithm of the second argument.

    If there is only one argument, then this takes the natural logarithm of the argument.

    >>> df.select(log(10.0, df.age).alias('ten')).rdd.map(lambda l: str(l.ten)[:7]).collect()
    ['0.30102', '0.69897']

    >>> df.select(log(df.age).alias('e')).rdd.map(lambda l: str(l.e)[:7]).collect()
    ['0.69314', '1.60943']
    """
    sc = SparkContext._active_spark_context
    if arg2 is None:
        jc = sc._jvm.functions.log(_to_java_column(arg1))
    else:
        jc = sc._jvm.functions.log(arg1, _to_java_column(arg2))
    return Column(jc)


@since(1.5)
def log2(col):
    """Returns the base-2 logarithm of the argument.

    >>> spark.createDataFrame([(4,)], ['a']).select(log2('a').alias('log2')).collect()
    [Row(log2=2.0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.log2(_to_java_column(col)))


@since(1.5)
@ignore_unicode_prefix
def conv(col, fromBase, toBase):
    """
    Convert a number in a string column from one base to another.

    >>> df = spark.createDataFrame([("010101",)], ['n'])
    >>> df.select(conv(df.n, 2, 16).alias('hex')).collect()
    [Row(hex=u'15')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.conv(_to_java_column(col), fromBase, toBase))


@since(1.5)
def factorial(col):
    """
    Computes the factorial of the given value.

    >>> df = spark.createDataFrame([(5,)], ['n'])
    >>> df.select(factorial(df.n).alias('f')).collect()
    [Row(f=120)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.factorial(_to_java_column(col)))


# ---------------  Window functions ------------------------

@since(1.4)
def lag(col, offset=1, default=None):
    """
    Window function: returns the value that is `offset` rows before the current row, and
    `defaultValue` if there is less than `offset` rows before the current row. For example,
    an `offset` of one will return the previous row at any given point in the window partition.

    This is equivalent to the LAG function in SQL.

    :param col: name of column or expression
    :param offset: number of row to extend
    :param default: default value
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.lag(_to_java_column(col), offset, default))


@since(1.4)
def lead(col, offset=1, default=None):
    """
    Window function: returns the value that is `offset` rows after the current row, and
    `defaultValue` if there is less than `offset` rows after the current row. For example,
    an `offset` of one will return the next row at any given point in the window partition.

    This is equivalent to the LEAD function in SQL.

    :param col: name of column or expression
    :param offset: number of row to extend
    :param default: default value
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.lead(_to_java_column(col), offset, default))


@since(1.4)
def ntile(n):
    """
    Window function: returns the ntile group id (from 1 to `n` inclusive)
    in an ordered window partition. For example, if `n` is 4, the first
    quarter of the rows will get value 1, the second quarter will get 2,
    the third quarter will get 3, and the last quarter will get 4.

    This is equivalent to the NTILE function in SQL.

    :param n: an integer
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.ntile(int(n)))


# ---------------------- Date/Timestamp functions ------------------------------

@since(1.5)
def current_date():
    """
    Returns the current date as a :class:`DateType` column.
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.current_date())


def current_timestamp():
    """
    Returns the current timestamp as a :class:`TimestampType` column.
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.current_timestamp())


@ignore_unicode_prefix
@since(1.5)
def date_format(date, format):
    """
    Converts a date/timestamp/string to a value of string in the format specified by the date
    format given by the second argument.

    A pattern could be for instance `dd.MM.yyyy` and could return a string like '18.03.1993'. All
    pattern letters of the Java class `java.time.format.DateTimeFormatter` can be used.

    .. note:: Use when ever possible specialized functions like `year`. These benefit from a
        specialized implementation.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(date_format('dt', 'MM/dd/yyy').alias('date')).collect()
    [Row(date=u'04/08/2015')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.date_format(_to_java_column(date), format))


@since(1.5)
def year(col):
    """
    Extract the year of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(year('dt').alias('year')).collect()
    [Row(year=2015)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.year(_to_java_column(col)))


@since(1.5)
def quarter(col):
    """
    Extract the quarter of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(quarter('dt').alias('quarter')).collect()
    [Row(quarter=2)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.quarter(_to_java_column(col)))


@since(1.5)
def month(col):
    """
    Extract the month of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(month('dt').alias('month')).collect()
    [Row(month=4)]
   """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.month(_to_java_column(col)))


@since(2.3)
def dayofweek(col):
    """
    Extract the day of the week of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(dayofweek('dt').alias('day')).collect()
    [Row(day=4)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.dayofweek(_to_java_column(col)))


@since(1.5)
def dayofmonth(col):
    """
    Extract the day of the month of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(dayofmonth('dt').alias('day')).collect()
    [Row(day=8)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.dayofmonth(_to_java_column(col)))


@since(1.5)
def dayofyear(col):
    """
    Extract the day of the year of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(dayofyear('dt').alias('day')).collect()
    [Row(day=98)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.dayofyear(_to_java_column(col)))


@since(1.5)
def hour(col):
    """
    Extract the hours of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08 13:08:15',)], ['ts'])
    >>> df.select(hour('ts').alias('hour')).collect()
    [Row(hour=13)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.hour(_to_java_column(col)))


@since(1.5)
def minute(col):
    """
    Extract the minutes of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08 13:08:15',)], ['ts'])
    >>> df.select(minute('ts').alias('minute')).collect()
    [Row(minute=8)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.minute(_to_java_column(col)))


@since(1.5)
def second(col):
    """
    Extract the seconds of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08 13:08:15',)], ['ts'])
    >>> df.select(second('ts').alias('second')).collect()
    [Row(second=15)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.second(_to_java_column(col)))


@since(1.5)
def weekofyear(col):
    """
    Extract the week number of a given date as integer.

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(weekofyear(df.dt).alias('week')).collect()
    [Row(week=15)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.weekofyear(_to_java_column(col)))


@since(1.5)
def date_add(start, days):
    """
    Returns the date that is `days` days after `start`

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(date_add(df.dt, 1).alias('next_date')).collect()
    [Row(next_date=datetime.date(2015, 4, 9))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.date_add(_to_java_column(start), days))


@since(1.5)
def date_sub(start, days):
    """
    Returns the date that is `days` days before `start`

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(date_sub(df.dt, 1).alias('prev_date')).collect()
    [Row(prev_date=datetime.date(2015, 4, 7))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.date_sub(_to_java_column(start), days))


@since(1.5)
def datediff(end, start):
    """
    Returns the number of days from `start` to `end`.

    >>> df = spark.createDataFrame([('2015-04-08','2015-05-10')], ['d1', 'd2'])
    >>> df.select(datediff(df.d2, df.d1).alias('diff')).collect()
    [Row(diff=32)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.datediff(_to_java_column(end), _to_java_column(start)))


@since(1.5)
def add_months(start, months):
    """
    Returns the date that is `months` months after `start`

    >>> df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> df.select(add_months(df.dt, 1).alias('next_month')).collect()
    [Row(next_month=datetime.date(2015, 5, 8))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.add_months(_to_java_column(start), months))


@since(1.5)
def months_between(date1, date2, roundOff=True):
    """
    Returns number of months between dates date1 and date2.
    If date1 is later than date2, then the result is positive.
    If date1 and date2 are on the same day of month, or both are the last day of month,
    returns an integer (time of day will be ignored).
    The result is rounded off to 8 digits unless `roundOff` is set to `False`.

    >>> df = spark.createDataFrame([('1997-02-28 10:30:00', '1996-10-30')], ['date1', 'date2'])
    >>> df.select(months_between(df.date1, df.date2).alias('months')).collect()
    [Row(months=3.94959677)]
    >>> df.select(months_between(df.date1, df.date2, False).alias('months')).collect()
    [Row(months=3.9495967741935485)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.months_between(
        _to_java_column(date1), _to_java_column(date2), roundOff))


@since(2.2)
def to_date(col, format=None):
    """Converts a :class:`Column` of :class:`pyspark.sql.types.StringType` or
    :class:`pyspark.sql.types.TimestampType` into :class:`pyspark.sql.types.DateType`
    using the optionally specified format. Specify formats according to
    `DateTimeFormatter <https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html>`_. # noqa
    By default, it follows casting rules to :class:`pyspark.sql.types.DateType` if the format
    is omitted (equivalent to ``col.cast("date")``).

    >>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
    >>> df.select(to_date(df.t).alias('date')).collect()
    [Row(date=datetime.date(1997, 2, 28))]

    >>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
    >>> df.select(to_date(df.t, 'yyyy-MM-dd HH:mm:ss').alias('date')).collect()
    [Row(date=datetime.date(1997, 2, 28))]
    """
    sc = SparkContext._active_spark_context
    if format is None:
        jc = sc._jvm.functions.to_date(_to_java_column(col))
    else:
        jc = sc._jvm.functions.to_date(_to_java_column(col), format)
    return Column(jc)


@since(2.2)
def to_timestamp(col, format=None):
    """Converts a :class:`Column` of :class:`pyspark.sql.types.StringType` or
    :class:`pyspark.sql.types.TimestampType` into :class:`pyspark.sql.types.DateType`
    using the optionally specified format. Specify formats according to
    `DateTimeFormatter <https://docs.oracle.com/javase/8/docs/api/java/time/format/DateTimeFormatter.html>`_. # noqa
    By default, it follows casting rules to :class:`pyspark.sql.types.TimestampType` if the format
    is omitted (equivalent to ``col.cast("timestamp")``).

    >>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
    >>> df.select(to_timestamp(df.t).alias('dt')).collect()
    [Row(dt=datetime.datetime(1997, 2, 28, 10, 30))]

    >>> df = spark.createDataFrame([('1997-02-28 10:30:00',)], ['t'])
    >>> df.select(to_timestamp(df.t, 'yyyy-MM-dd HH:mm:ss').alias('dt')).collect()
    [Row(dt=datetime.datetime(1997, 2, 28, 10, 30))]
    """
    sc = SparkContext._active_spark_context
    if format is None:
        jc = sc._jvm.functions.to_timestamp(_to_java_column(col))
    else:
        jc = sc._jvm.functions.to_timestamp(_to_java_column(col), format)
    return Column(jc)


@since(1.5)
def trunc(date, format):
    """
    Returns date truncated to the unit specified by the format.

    :param format: 'year', 'yyyy', 'yy' or 'month', 'mon', 'mm'

    >>> df = spark.createDataFrame([('1997-02-28',)], ['d'])
    >>> df.select(trunc(df.d, 'year').alias('year')).collect()
    [Row(year=datetime.date(1997, 1, 1))]
    >>> df.select(trunc(df.d, 'mon').alias('month')).collect()
    [Row(month=datetime.date(1997, 2, 1))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.trunc(_to_java_column(date), format))


@since(2.3)
def date_trunc(format, timestamp):
    """
    Returns timestamp truncated to the unit specified by the format.

    :param format: 'year', 'yyyy', 'yy', 'month', 'mon', 'mm',
        'day', 'dd', 'hour', 'minute', 'second', 'week', 'quarter'

    >>> df = spark.createDataFrame([('1997-02-28 05:02:11',)], ['t'])
    >>> df.select(date_trunc('year', df.t).alias('year')).collect()
    [Row(year=datetime.datetime(1997, 1, 1, 0, 0))]
    >>> df.select(date_trunc('mon', df.t).alias('month')).collect()
    [Row(month=datetime.datetime(1997, 2, 1, 0, 0))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.date_trunc(format, _to_java_column(timestamp)))


@since(1.5)
def next_day(date, dayOfWeek):
    """
    Returns the first date which is later than the value of the date column.

    Day of the week parameter is case insensitive, and accepts:
        "Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun".

    >>> df = spark.createDataFrame([('2015-07-27',)], ['d'])
    >>> df.select(next_day(df.d, 'Sun').alias('date')).collect()
    [Row(date=datetime.date(2015, 8, 2))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.next_day(_to_java_column(date), dayOfWeek))


@since(1.5)
def last_day(date):
    """
    Returns the last day of the month which the given date belongs to.

    >>> df = spark.createDataFrame([('1997-02-10',)], ['d'])
    >>> df.select(last_day(df.d).alias('date')).collect()
    [Row(date=datetime.date(1997, 2, 28))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.last_day(_to_java_column(date)))


@ignore_unicode_prefix
@since(1.5)
def from_unixtime(timestamp, format="uuuu-MM-dd HH:mm:ss"):
    """
    Converts the number of seconds from unix epoch (1970-01-01 00:00:00 UTC) to a string
    representing the timestamp of that moment in the current system time zone in the given
    format.

    >>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
    >>> time_df = spark.createDataFrame([(1428476400,)], ['unix_time'])
    >>> time_df.select(from_unixtime('unix_time').alias('ts')).collect()
    [Row(ts=u'2015-04-08 00:00:00')]
    >>> spark.conf.unset("spark.sql.session.timeZone")
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.from_unixtime(_to_java_column(timestamp), format))


@since(1.5)
def unix_timestamp(timestamp=None, format='uuuu-MM-dd HH:mm:ss'):
    """
    Convert time string with given pattern ('uuuu-MM-dd HH:mm:ss', by default)
    to Unix time stamp (in seconds), using the default timezone and the default
    locale, return null if fail.

    if `timestamp` is None, then it returns current timestamp.

    >>> spark.conf.set("spark.sql.session.timeZone", "America/Los_Angeles")
    >>> time_df = spark.createDataFrame([('2015-04-08',)], ['dt'])
    >>> time_df.select(unix_timestamp('dt', 'yyyy-MM-dd').alias('unix_time')).collect()
    [Row(unix_time=1428476400)]
    >>> spark.conf.unset("spark.sql.session.timeZone")
    """
    sc = SparkContext._active_spark_context
    if timestamp is None:
        return Column(sc._jvm.functions.unix_timestamp())
    return Column(sc._jvm.functions.unix_timestamp(_to_java_column(timestamp), format))


@since(1.5)
def from_utc_timestamp(timestamp, tz):
    """
    This is a common function for databases supporting TIMESTAMP WITHOUT TIMEZONE. This function
    takes a timestamp which is timezone-agnostic, and interprets it as a timestamp in UTC, and
    renders that timestamp as a timestamp in the given time zone.

    However, timestamp in Spark represents number of microseconds from the Unix epoch, which is not
    timezone-agnostic. So in Spark this function just shift the timestamp value from UTC timezone to
    the given timezone.

    This function may return confusing result if the input is a string with timezone, e.g.
    '2018-03-13T06:18:23+00:00'. The reason is that, Spark firstly cast the string to timestamp
    according to the timezone in the string, and finally display the result by converting the
    timestamp to string according to the session local timezone.

    :param timestamp: the column that contains timestamps
    :param tz: a string that has the ID of timezone, e.g. "GMT", "America/Los_Angeles", etc

    .. versionchanged:: 2.4
       `tz` can take a :class:`Column` containing timezone ID strings.

    >>> df = spark.createDataFrame([('1997-02-28 10:30:00', 'JST')], ['ts', 'tz'])
    >>> df.select(from_utc_timestamp(df.ts, "PST").alias('local_time')).collect()
    [Row(local_time=datetime.datetime(1997, 2, 28, 2, 30))]
    >>> df.select(from_utc_timestamp(df.ts, df.tz).alias('local_time')).collect()
    [Row(local_time=datetime.datetime(1997, 2, 28, 19, 30))]

    .. note:: Deprecated in 3.0. See SPARK-25496
    """
    warnings.warn("Deprecated in 3.0. See SPARK-25496", DeprecationWarning)
    sc = SparkContext._active_spark_context
    if isinstance(tz, Column):
        tz = _to_java_column(tz)
    return Column(sc._jvm.functions.from_utc_timestamp(_to_java_column(timestamp), tz))


@since(1.5)
def to_utc_timestamp(timestamp, tz):
    """
    This is a common function for databases supporting TIMESTAMP WITHOUT TIMEZONE. This function
    takes a timestamp which is timezone-agnostic, and interprets it as a timestamp in the given
    timezone, and renders that timestamp as a timestamp in UTC.

    However, timestamp in Spark represents number of microseconds from the Unix epoch, which is not
    timezone-agnostic. So in Spark this function just shift the timestamp value from the given
    timezone to UTC timezone.

    This function may return confusing result if the input is a string with timezone, e.g.
    '2018-03-13T06:18:23+00:00'. The reason is that, Spark firstly cast the string to timestamp
    according to the timezone in the string, and finally display the result by converting the
    timestamp to string according to the session local timezone.

    :param timestamp: the column that contains timestamps
    :param tz: a string that has the ID of timezone, e.g. "GMT", "America/Los_Angeles", etc

    .. versionchanged:: 2.4
       `tz` can take a :class:`Column` containing timezone ID strings.

    >>> df = spark.createDataFrame([('1997-02-28 10:30:00', 'JST')], ['ts', 'tz'])
    >>> df.select(to_utc_timestamp(df.ts, "PST").alias('utc_time')).collect()
    [Row(utc_time=datetime.datetime(1997, 2, 28, 18, 30))]
    >>> df.select(to_utc_timestamp(df.ts, df.tz).alias('utc_time')).collect()
    [Row(utc_time=datetime.datetime(1997, 2, 28, 1, 30))]

    .. note:: Deprecated in 3.0. See SPARK-25496
    """
    warnings.warn("Deprecated in 3.0. See SPARK-25496", DeprecationWarning)
    sc = SparkContext._active_spark_context
    if isinstance(tz, Column):
        tz = _to_java_column(tz)
    return Column(sc._jvm.functions.to_utc_timestamp(_to_java_column(timestamp), tz))


@since(2.0)
@ignore_unicode_prefix
def window(timeColumn, windowDuration, slideDuration=None, startTime=None):
    """Bucketize rows into one or more time windows given a timestamp specifying column. Window
    starts are inclusive but the window ends are exclusive, e.g. 12:05 will be in the window
    [12:05,12:10) but not in [12:00,12:05). Windows can support microsecond precision. Windows in
    the order of months are not supported.

    The time column must be of :class:`pyspark.sql.types.TimestampType`.

    Durations are provided as strings, e.g. '1 second', '1 day 12 hours', '2 minutes'. Valid
    interval strings are 'week', 'day', 'hour', 'minute', 'second', 'millisecond', 'microsecond'.
    If the ``slideDuration`` is not provided, the windows will be tumbling windows.

    The startTime is the offset with respect to 1970-01-01 00:00:00 UTC with which to start
    window intervals. For example, in order to have hourly tumbling windows that start 15 minutes
    past the hour, e.g. 12:15-13:15, 13:15-14:15... provide `startTime` as `15 minutes`.

    The output column will be a struct called 'window' by default with the nested columns 'start'
    and 'end', where 'start' and 'end' will be of :class:`pyspark.sql.types.TimestampType`.

    >>> df = spark.createDataFrame([("2016-03-11 09:00:07", 1)]).toDF("date", "val")
    >>> w = df.groupBy(window("date", "5 seconds")).agg(sum("val").alias("sum"))
    >>> w.select(w.window.start.cast("string").alias("start"),
    ...          w.window.end.cast("string").alias("end"), "sum").collect()
    [Row(start=u'2016-03-11 09:00:05', end=u'2016-03-11 09:00:10', sum=1)]
    """
    def check_string_field(field, fieldName):
        if not field or type(field) is not str:
            raise TypeError("%s should be provided as a string" % fieldName)

    sc = SparkContext._active_spark_context
    time_col = _to_java_column(timeColumn)
    check_string_field(windowDuration, "windowDuration")
    if slideDuration and startTime:
        check_string_field(slideDuration, "slideDuration")
        check_string_field(startTime, "startTime")
        res = sc._jvm.functions.window(time_col, windowDuration, slideDuration, startTime)
    elif slideDuration:
        check_string_field(slideDuration, "slideDuration")
        res = sc._jvm.functions.window(time_col, windowDuration, slideDuration)
    elif startTime:
        check_string_field(startTime, "startTime")
        res = sc._jvm.functions.window(time_col, windowDuration, windowDuration, startTime)
    else:
        res = sc._jvm.functions.window(time_col, windowDuration)
    return Column(res)


# ---------------------------- misc functions ----------------------------------

@since(1.5)
@ignore_unicode_prefix
def crc32(col):
    """
    Calculates the cyclic redundancy check value  (CRC32) of a binary column and
    returns the value as a bigint.

    >>> spark.createDataFrame([('ABC',)], ['a']).select(crc32('a').alias('crc32')).collect()
    [Row(crc32=2743272264)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.crc32(_to_java_column(col)))


@ignore_unicode_prefix
@since(1.5)
def md5(col):
    """Calculates the MD5 digest and returns the value as a 32 character hex string.

    >>> spark.createDataFrame([('ABC',)], ['a']).select(md5('a').alias('hash')).collect()
    [Row(hash=u'902fbdd2b1df0c4f70b4a5d23525e932')]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.md5(_to_java_column(col))
    return Column(jc)


@ignore_unicode_prefix
@since(1.5)
def sha1(col):
    """Returns the hex string result of SHA-1.

    >>> spark.createDataFrame([('ABC',)], ['a']).select(sha1('a').alias('hash')).collect()
    [Row(hash=u'3c01bdbb26f358bab27f267924aa2c9a03fcfdb8')]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.sha1(_to_java_column(col))
    return Column(jc)


@ignore_unicode_prefix
@since(1.5)
def sha2(col, numBits):
    """Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384,
    and SHA-512). The numBits indicates the desired bit length of the result, which must have a
    value of 224, 256, 384, 512, or 0 (which is equivalent to 256).

    >>> digests = df.select(sha2(df.name, 256).alias('s')).collect()
    >>> digests[0]
    Row(s=u'3bc51062973c458d5a6f2d8d64a023246354ad7e064b1e4e009ec8a0699a3043')
    >>> digests[1]
    Row(s=u'cd9fb1e148ccd8442e5aa74904cc73bf6fb54d1d54d333bd596aa9bb4bb4e961')
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.sha2(_to_java_column(col), numBits)
    return Column(jc)


@since(2.0)
def hash(*cols):
    """Calculates the hash code of given columns, and returns the result as an int column.

    >>> spark.createDataFrame([('ABC',)], ['a']).select(hash('a').alias('hash')).collect()
    [Row(hash=-757602832)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.hash(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(3.0)
def xxhash64(*cols):
    """Calculates the hash code of given columns using the 64-bit variant of the xxHash algorithm,
    and returns the result as a long column.

    >>> spark.createDataFrame([('ABC',)], ['a']).select(xxhash64('a').alias('hash')).collect()
    [Row(hash=4105715581806190027)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.xxhash64(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


# ---------------------- String/Binary functions ------------------------------

_string_functions = {
    'upper': 'Converts a string expression to upper case.',
    'lower': 'Converts a string expression to lower case.',
    'ascii': 'Computes the numeric value of the first character of the string column.',
    'base64': 'Computes the BASE64 encoding of a binary column and returns it as a string column.',
    'unbase64': 'Decodes a BASE64 encoded string column and returns it as a binary column.',
    'ltrim': 'Trim the spaces from left end for the specified string value.',
    'rtrim': 'Trim the spaces from right end for the specified string value.',
    'trim': 'Trim the spaces from both ends for the specified string column.',
}


for _name, _doc in _string_functions.items():
    globals()[_name] = since(1.5)(_create_function_over_column(_name, _doc))
del _name, _doc


@since(1.5)
@ignore_unicode_prefix
def concat_ws(sep, *cols):
    """
    Concatenates multiple input string columns together into a single string column,
    using the given separator.

    >>> df = spark.createDataFrame([('abcd','123')], ['s', 'd'])
    >>> df.select(concat_ws('-', df.s, df.d).alias('s')).collect()
    [Row(s=u'abcd-123')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.concat_ws(sep, _to_seq(sc, cols, _to_java_column)))


@since(1.5)
def decode(col, charset):
    """
    Computes the first argument into a string from a binary using the provided character set
    (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.decode(_to_java_column(col), charset))


@since(1.5)
def encode(col, charset):
    """
    Computes the first argument into a binary from a string using the provided character set
    (one of 'US-ASCII', 'ISO-8859-1', 'UTF-8', 'UTF-16BE', 'UTF-16LE', 'UTF-16').
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.encode(_to_java_column(col), charset))


@ignore_unicode_prefix
@since(1.5)
def format_number(col, d):
    """
    Formats the number X to a format like '#,--#,--#.--', rounded to d decimal places
    with HALF_EVEN round mode, and returns the result as a string.

    :param col: the column name of the numeric value to be formatted
    :param d: the N decimal places

    >>> spark.createDataFrame([(5,)], ['a']).select(format_number('a', 4).alias('v')).collect()
    [Row(v=u'5.0000')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.format_number(_to_java_column(col), d))


@ignore_unicode_prefix
@since(1.5)
def format_string(format, *cols):
    """
    Formats the arguments in printf-style and returns the result as a string column.

    :param format: string that can contain embedded format tags and used as result column's value
    :param cols: list of column names (string) or list of :class:`Column` expressions to
        be used in formatting

    >>> df = spark.createDataFrame([(5, "hello")], ['a', 'b'])
    >>> df.select(format_string('%d %s', df.a, df.b).alias('v')).collect()
    [Row(v=u'5 hello')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.format_string(format, _to_seq(sc, cols, _to_java_column)))


@since(1.5)
def instr(str, substr):
    """
    Locate the position of the first occurrence of substr column in the given string.
    Returns null if either of the arguments are null.

    .. note:: The position is not zero based, but 1 based index. Returns 0 if substr
        could not be found in str.

    >>> df = spark.createDataFrame([('abcd',)], ['s',])
    >>> df.select(instr(df.s, 'b').alias('s')).collect()
    [Row(s=2)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.instr(_to_java_column(str), substr))


@since(1.5)
@ignore_unicode_prefix
def substring(str, pos, len):
    """
    Substring starts at `pos` and is of length `len` when str is String type or
    returns the slice of byte array that starts at `pos` in byte and is of length `len`
    when str is Binary type.

    .. note:: The position is not zero based, but 1 based index.

    >>> df = spark.createDataFrame([('abcd',)], ['s',])
    >>> df.select(substring(df.s, 1, 2).alias('s')).collect()
    [Row(s=u'ab')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.substring(_to_java_column(str), pos, len))


@since(1.5)
@ignore_unicode_prefix
def substring_index(str, delim, count):
    """
    Returns the substring from string str before count occurrences of the delimiter delim.
    If count is positive, everything the left of the final delimiter (counting from left) is
    returned. If count is negative, every to the right of the final delimiter (counting from the
    right) is returned. substring_index performs a case-sensitive match when searching for delim.

    >>> df = spark.createDataFrame([('a.b.c.d',)], ['s'])
    >>> df.select(substring_index(df.s, '.', 2).alias('s')).collect()
    [Row(s=u'a.b')]
    >>> df.select(substring_index(df.s, '.', -3).alias('s')).collect()
    [Row(s=u'b.c.d')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.substring_index(_to_java_column(str), delim, count))


@ignore_unicode_prefix
@since(1.5)
def levenshtein(left, right):
    """Computes the Levenshtein distance of the two given strings.

    >>> df0 = spark.createDataFrame([('kitten', 'sitting',)], ['l', 'r'])
    >>> df0.select(levenshtein('l', 'r').alias('d')).collect()
    [Row(d=3)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.levenshtein(_to_java_column(left), _to_java_column(right))
    return Column(jc)


@since(1.5)
def locate(substr, str, pos=1):
    """
    Locate the position of the first occurrence of substr in a string column, after position pos.

    .. note:: The position is not zero based, but 1 based index. Returns 0 if substr
        could not be found in str.

    :param substr: a string
    :param str: a Column of :class:`pyspark.sql.types.StringType`
    :param pos: start position (zero based)

    >>> df = spark.createDataFrame([('abcd',)], ['s',])
    >>> df.select(locate('b', df.s, 1).alias('s')).collect()
    [Row(s=2)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.locate(substr, _to_java_column(str), pos))


@since(1.5)
@ignore_unicode_prefix
def lpad(col, len, pad):
    """
    Left-pad the string column to width `len` with `pad`.

    >>> df = spark.createDataFrame([('abcd',)], ['s',])
    >>> df.select(lpad(df.s, 6, '#').alias('s')).collect()
    [Row(s=u'##abcd')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.lpad(_to_java_column(col), len, pad))


@since(1.5)
@ignore_unicode_prefix
def rpad(col, len, pad):
    """
    Right-pad the string column to width `len` with `pad`.

    >>> df = spark.createDataFrame([('abcd',)], ['s',])
    >>> df.select(rpad(df.s, 6, '#').alias('s')).collect()
    [Row(s=u'abcd##')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.rpad(_to_java_column(col), len, pad))


@since(1.5)
@ignore_unicode_prefix
def repeat(col, n):
    """
    Repeats a string column n times, and returns it as a new string column.

    >>> df = spark.createDataFrame([('ab',)], ['s',])
    >>> df.select(repeat(df.s, 3).alias('s')).collect()
    [Row(s=u'ababab')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.repeat(_to_java_column(col), n))


@since(1.5)
@ignore_unicode_prefix
def split(str, pattern, limit=-1):
    """
    Splits str around matches of the given pattern.

    :param str: a string expression to split
    :param pattern: a string representing a regular expression. The regex string should be
        a Java regular expression.
    :param limit: an integer which controls the number of times `pattern` is applied.

        * ``limit > 0``: The resulting array's length will not be more than `limit`, and the
                         resulting array's last entry will contain all input beyond the last
                         matched pattern.
        * ``limit <= 0``: `pattern` will be applied as many times as possible, and the resulting
                          array can be of any size.

    .. versionchanged:: 3.0
       `split` now takes an optional `limit` field. If not provided, default limit value is -1.

    >>> df = spark.createDataFrame([('oneAtwoBthreeC',)], ['s',])
    >>> df.select(split(df.s, '[ABC]', 2).alias('s')).collect()
    [Row(s=[u'one', u'twoBthreeC'])]
    >>> df.select(split(df.s, '[ABC]', -1).alias('s')).collect()
    [Row(s=[u'one', u'two', u'three', u''])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.split(_to_java_column(str), pattern, limit))


@ignore_unicode_prefix
@since(1.5)
def regexp_extract(str, pattern, idx):
    r"""Extract a specific group matched by a Java regex, from the specified string column.
    If the regex did not match, or the specified group did not match, an empty string is returned.

    >>> df = spark.createDataFrame([('100-200',)], ['str'])
    >>> df.select(regexp_extract('str', r'(\d+)-(\d+)', 1).alias('d')).collect()
    [Row(d=u'100')]
    >>> df = spark.createDataFrame([('foo',)], ['str'])
    >>> df.select(regexp_extract('str', r'(\d+)', 1).alias('d')).collect()
    [Row(d=u'')]
    >>> df = spark.createDataFrame([('aaaac',)], ['str'])
    >>> df.select(regexp_extract('str', '(a+)(b)?(c)', 2).alias('d')).collect()
    [Row(d=u'')]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.regexp_extract(_to_java_column(str), pattern, idx)
    return Column(jc)


@ignore_unicode_prefix
@since(1.5)
def regexp_replace(str, pattern, replacement):
    r"""Replace all substrings of the specified string value that match regexp with rep.

    >>> df = spark.createDataFrame([('100-200',)], ['str'])
    >>> df.select(regexp_replace('str', r'(\d+)', '--').alias('d')).collect()
    [Row(d=u'-----')]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.regexp_replace(_to_java_column(str), pattern, replacement)
    return Column(jc)


@ignore_unicode_prefix
@since(1.5)
def initcap(col):
    """Translate the first letter of each word to upper case in the sentence.

    >>> spark.createDataFrame([('ab cd',)], ['a']).select(initcap("a").alias('v')).collect()
    [Row(v=u'Ab Cd')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.initcap(_to_java_column(col)))


@since(1.5)
@ignore_unicode_prefix
def soundex(col):
    """
    Returns the SoundEx encoding for a string

    >>> df = spark.createDataFrame([("Peters",),("Uhrbach",)], ['name'])
    >>> df.select(soundex(df.name).alias("soundex")).collect()
    [Row(soundex=u'P362'), Row(soundex=u'U612')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.soundex(_to_java_column(col)))


@ignore_unicode_prefix
@since(1.5)
def bin(col):
    """Returns the string representation of the binary value of the given column.

    >>> df.select(bin(df.age).alias('c')).collect()
    [Row(c=u'10'), Row(c=u'101')]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.bin(_to_java_column(col))
    return Column(jc)


@ignore_unicode_prefix
@since(1.5)
def hex(col):
    """Computes hex value of the given column, which could be :class:`pyspark.sql.types.StringType`,
    :class:`pyspark.sql.types.BinaryType`, :class:`pyspark.sql.types.IntegerType` or
    :class:`pyspark.sql.types.LongType`.

    >>> spark.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect()
    [Row(hex(a)=u'414243', hex(b)=u'3')]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.hex(_to_java_column(col))
    return Column(jc)


@ignore_unicode_prefix
@since(1.5)
def unhex(col):
    """Inverse of hex. Interprets each pair of characters as a hexadecimal number
    and converts to the byte representation of number.

    >>> spark.createDataFrame([('414243',)], ['a']).select(unhex('a')).collect()
    [Row(unhex(a)=bytearray(b'ABC'))]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.unhex(_to_java_column(col)))


@ignore_unicode_prefix
@since(1.5)
def length(col):
    """Computes the character length of string data or number of bytes of binary data.
    The length of character data includes the trailing spaces. The length of binary data
    includes binary zeros.

    >>> spark.createDataFrame([('ABC ',)], ['a']).select(length('a').alias('length')).collect()
    [Row(length=4)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.length(_to_java_column(col)))


@ignore_unicode_prefix
@since(1.5)
def translate(srcCol, matching, replace):
    """A function translate any character in the `srcCol` by a character in `matching`.
    The characters in `replace` is corresponding to the characters in `matching`.
    The translate will happen when any character in the string matching with the character
    in the `matching`.

    >>> spark.createDataFrame([('translate',)], ['a']).select(translate('a', "rnlt", "123") \\
    ...     .alias('r')).collect()
    [Row(r=u'1a2s3ae')]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.translate(_to_java_column(srcCol), matching, replace))


# ---------------------- Collection functions ------------------------------

@ignore_unicode_prefix
@since(2.0)
def create_map(*cols):
    """Creates a new map column.

    :param cols: list of column names (string) or list of :class:`Column` expressions that are
        grouped as key-value pairs, e.g. (key1, value1, key2, value2, ...).

    >>> df.select(create_map('name', 'age').alias("map")).collect()
    [Row(map={u'Alice': 2}), Row(map={u'Bob': 5})]
    >>> df.select(create_map([df.name, df.age]).alias("map")).collect()
    [Row(map={u'Alice': 2}), Row(map={u'Bob': 5})]
    """
    sc = SparkContext._active_spark_context
    if len(cols) == 1 and isinstance(cols[0], (list, set)):
        cols = cols[0]
    jc = sc._jvm.functions.map(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(2.4)
def map_from_arrays(col1, col2):
    """Creates a new map from two arrays.

    :param col1: name of column containing a set of keys. All elements should not be null
    :param col2: name of column containing a set of values

    >>> df = spark.createDataFrame([([2, 5], ['a', 'b'])], ['k', 'v'])
    >>> df.select(map_from_arrays(df.k, df.v).alias("map")).show()
    +----------------+
    |             map|
    +----------------+
    |[2 -> a, 5 -> b]|
    +----------------+
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.map_from_arrays(_to_java_column(col1), _to_java_column(col2)))


@since(1.4)
def array(*cols):
    """Creates a new array column.

    :param cols: list of column names (string) or list of :class:`Column` expressions that have
        the same data type.

    >>> df.select(array('age', 'age').alias("arr")).collect()
    [Row(arr=[2, 2]), Row(arr=[5, 5])]
    >>> df.select(array([df.age, df.age]).alias("arr")).collect()
    [Row(arr=[2, 2]), Row(arr=[5, 5])]
    """
    sc = SparkContext._active_spark_context
    if len(cols) == 1 and isinstance(cols[0], (list, set)):
        cols = cols[0]
    jc = sc._jvm.functions.array(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(1.5)
def array_contains(col, value):
    """
    Collection function: returns null if the array is null, true if the array contains the
    given value, and false otherwise.

    :param col: name of column containing array
    :param value: value or column to check for in array

    >>> df = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
    >>> df.select(array_contains(df.data, "a")).collect()
    [Row(array_contains(data, a)=True), Row(array_contains(data, a)=False)]
    >>> df.select(array_contains(df.data, lit("a"))).collect()
    [Row(array_contains(data, a)=True), Row(array_contains(data, a)=False)]
    """
    sc = SparkContext._active_spark_context
    value = value._jc if isinstance(value, Column) else value
    return Column(sc._jvm.functions.array_contains(_to_java_column(col), value))


@since(2.4)
def arrays_overlap(a1, a2):
    """
    Collection function: returns true if the arrays contain any common non-null element; if not,
    returns null if both the arrays are non-empty and any of them contains a null element; returns
    false otherwise.

    >>> df = spark.createDataFrame([(["a", "b"], ["b", "c"]), (["a"], ["b", "c"])], ['x', 'y'])
    >>> df.select(arrays_overlap(df.x, df.y).alias("overlap")).collect()
    [Row(overlap=True), Row(overlap=False)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.arrays_overlap(_to_java_column(a1), _to_java_column(a2)))


@since(2.4)
def slice(x, start, length):
    """
    Collection function: returns an array containing  all the elements in `x` from index `start`
    (array indices start at 1, or from the end if `start` is negative) with the specified `length`.

    :param x: the array to be sliced
    :param start: the starting index
    :param length: the length of the slice

    >>> df = spark.createDataFrame([([1, 2, 3],), ([4, 5],)], ['x'])
    >>> df.select(slice(df.x, 2, 2).alias("sliced")).collect()
    [Row(sliced=[2, 3]), Row(sliced=[5])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.slice(_to_java_column(x), start, length))


@ignore_unicode_prefix
@since(2.4)
def array_join(col, delimiter, null_replacement=None):
    """
    Concatenates the elements of `column` using the `delimiter`. Null values are replaced with
    `null_replacement` if set, otherwise they are ignored.

    >>> df = spark.createDataFrame([(["a", "b", "c"],), (["a", None],)], ['data'])
    >>> df.select(array_join(df.data, ",").alias("joined")).collect()
    [Row(joined=u'a,b,c'), Row(joined=u'a')]
    >>> df.select(array_join(df.data, ",", "NULL").alias("joined")).collect()
    [Row(joined=u'a,b,c'), Row(joined=u'a,NULL')]
    """
    sc = SparkContext._active_spark_context
    if null_replacement is None:
        return Column(sc._jvm.functions.array_join(_to_java_column(col), delimiter))
    else:
        return Column(sc._jvm.functions.array_join(
            _to_java_column(col), delimiter, null_replacement))


@since(1.5)
@ignore_unicode_prefix
def concat(*cols):
    """
    Concatenates multiple input columns together into a single column.
    The function works with strings, binary and compatible array columns.

    >>> df = spark.createDataFrame([('abcd','123')], ['s', 'd'])
    >>> df.select(concat(df.s, df.d).alias('s')).collect()
    [Row(s=u'abcd123')]

    >>> df = spark.createDataFrame([([1, 2], [3, 4], [5]), ([1, 2], None, [3])], ['a', 'b', 'c'])
    >>> df.select(concat(df.a, df.b, df.c).alias("arr")).collect()
    [Row(arr=[1, 2, 3, 4, 5]), Row(arr=None)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.concat(_to_seq(sc, cols, _to_java_column)))


@since(2.4)
def array_position(col, value):
    """
    Collection function: Locates the position of the first occurrence of the given value
    in the given array. Returns null if either of the arguments are null.

    .. note:: The position is not zero based, but 1 based index. Returns 0 if the given
        value could not be found in the array.

    >>> df = spark.createDataFrame([(["c", "b", "a"],), ([],)], ['data'])
    >>> df.select(array_position(df.data, "a")).collect()
    [Row(array_position(data, a)=3), Row(array_position(data, a)=0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_position(_to_java_column(col), value))


@ignore_unicode_prefix
@since(2.4)
def element_at(col, extraction):
    """
    Collection function: Returns element of array at given index in extraction if col is array.
    Returns value for the given key in extraction if col is map.

    :param col: name of column containing array or map
    :param extraction: index to check for in array or key to check for in map

    .. note:: The position is not zero based, but 1 based index.

    >>> df = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
    >>> df.select(element_at(df.data, 1)).collect()
    [Row(element_at(data, 1)=u'a'), Row(element_at(data, 1)=None)]

    >>> df = spark.createDataFrame([({"a": 1.0, "b": 2.0},), ({},)], ['data'])
    >>> df.select(element_at(df.data, lit("a"))).collect()
    [Row(element_at(data, a)=1.0), Row(element_at(data, a)=None)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.element_at(
        _to_java_column(col), lit(extraction)._jc))  # noqa: F821 'lit' is dynamically defined.


@since(2.4)
def array_remove(col, element):
    """
    Collection function: Remove all elements that equal to element from the given array.

    :param col: name of column containing array
    :param element: element to be removed from the array

    >>> df = spark.createDataFrame([([1, 2, 3, 1, 1],), ([],)], ['data'])
    >>> df.select(array_remove(df.data, 1)).collect()
    [Row(array_remove(data, 1)=[2, 3]), Row(array_remove(data, 1)=[])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_remove(_to_java_column(col), element))


@since(2.4)
def array_distinct(col):
    """
    Collection function: removes duplicate values from the array.
    :param col: name of column or expression

    >>> df = spark.createDataFrame([([1, 2, 3, 2],), ([4, 5, 5, 4],)], ['data'])
    >>> df.select(array_distinct(df.data)).collect()
    [Row(array_distinct(data)=[1, 2, 3]), Row(array_distinct(data)=[4, 5])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_distinct(_to_java_column(col)))


@ignore_unicode_prefix
@since(2.4)
def array_intersect(col1, col2):
    """
    Collection function: returns an array of the elements in the intersection of col1 and col2,
    without duplicates.

    :param col1: name of column containing array
    :param col2: name of column containing array

    >>> from pyspark.sql import Row
    >>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a", "f"])])
    >>> df.select(array_intersect(df.c1, df.c2)).collect()
    [Row(array_intersect(c1, c2)=[u'a', u'c'])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_intersect(_to_java_column(col1), _to_java_column(col2)))


@ignore_unicode_prefix
@since(2.4)
def array_union(col1, col2):
    """
    Collection function: returns an array of the elements in the union of col1 and col2,
    without duplicates.

    :param col1: name of column containing array
    :param col2: name of column containing array

    >>> from pyspark.sql import Row
    >>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a", "f"])])
    >>> df.select(array_union(df.c1, df.c2)).collect()
    [Row(array_union(c1, c2)=[u'b', u'a', u'c', u'd', u'f'])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_union(_to_java_column(col1), _to_java_column(col2)))


@ignore_unicode_prefix
@since(2.4)
def array_except(col1, col2):
    """
    Collection function: returns an array of the elements in col1 but not in col2,
    without duplicates.

    :param col1: name of column containing array
    :param col2: name of column containing array

    >>> from pyspark.sql import Row
    >>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a", "f"])])
    >>> df.select(array_except(df.c1, df.c2)).collect()
    [Row(array_except(c1, c2)=[u'b'])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_except(_to_java_column(col1), _to_java_column(col2)))


@since(1.4)
def explode(col):
    """
    Returns a new row for each element in the given array or map.
    Uses the default column name `col` for elements in the array and
    `key` and `value` for elements in the map unless specified otherwise.

    >>> from pyspark.sql import Row
    >>> eDF = spark.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
    >>> eDF.select(explode(eDF.intlist).alias("anInt")).collect()
    [Row(anInt=1), Row(anInt=2), Row(anInt=3)]

    >>> eDF.select(explode(eDF.mapfield).alias("key", "value")).show()
    +---+-----+
    |key|value|
    +---+-----+
    |  a|    b|
    +---+-----+
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.explode(_to_java_column(col))
    return Column(jc)


@since(2.1)
def posexplode(col):
    """
    Returns a new row for each element with position in the given array or map.
    Uses the default column name `pos` for position, and `col` for elements in the
    array and `key` and `value` for elements in the map unless specified otherwise.

    >>> from pyspark.sql import Row
    >>> eDF = spark.createDataFrame([Row(a=1, intlist=[1,2,3], mapfield={"a": "b"})])
    >>> eDF.select(posexplode(eDF.intlist)).collect()
    [Row(pos=0, col=1), Row(pos=1, col=2), Row(pos=2, col=3)]

    >>> eDF.select(posexplode(eDF.mapfield)).show()
    +---+---+-----+
    |pos|key|value|
    +---+---+-----+
    |  0|  a|    b|
    +---+---+-----+
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.posexplode(_to_java_column(col))
    return Column(jc)


@since(2.3)
def explode_outer(col):
    """
    Returns a new row for each element in the given array or map.
    Unlike explode, if the array/map is null or empty then null is produced.
    Uses the default column name `col` for elements in the array and
    `key` and `value` for elements in the map unless specified otherwise.

    >>> df = spark.createDataFrame(
    ...     [(1, ["foo", "bar"], {"x": 1.0}), (2, [], {}), (3, None, None)],
    ...     ("id", "an_array", "a_map")
    ... )
    >>> df.select("id", "an_array", explode_outer("a_map")).show()
    +---+----------+----+-----+
    | id|  an_array| key|value|
    +---+----------+----+-----+
    |  1|[foo, bar]|   x|  1.0|
    |  2|        []|null| null|
    |  3|      null|null| null|
    +---+----------+----+-----+

    >>> df.select("id", "a_map", explode_outer("an_array")).show()
    +---+----------+----+
    | id|     a_map| col|
    +---+----------+----+
    |  1|[x -> 1.0]| foo|
    |  1|[x -> 1.0]| bar|
    |  2|        []|null|
    |  3|      null|null|
    +---+----------+----+
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.explode_outer(_to_java_column(col))
    return Column(jc)


@since(2.3)
def posexplode_outer(col):
    """
    Returns a new row for each element with position in the given array or map.
    Unlike posexplode, if the array/map is null or empty then the row (null, null) is produced.
    Uses the default column name `pos` for position, and `col` for elements in the
    array and `key` and `value` for elements in the map unless specified otherwise.

    >>> df = spark.createDataFrame(
    ...     [(1, ["foo", "bar"], {"x": 1.0}), (2, [], {}), (3, None, None)],
    ...     ("id", "an_array", "a_map")
    ... )
    >>> df.select("id", "an_array", posexplode_outer("a_map")).show()
    +---+----------+----+----+-----+
    | id|  an_array| pos| key|value|
    +---+----------+----+----+-----+
    |  1|[foo, bar]|   0|   x|  1.0|
    |  2|        []|null|null| null|
    |  3|      null|null|null| null|
    +---+----------+----+----+-----+
    >>> df.select("id", "a_map", posexplode_outer("an_array")).show()
    +---+----------+----+----+
    | id|     a_map| pos| col|
    +---+----------+----+----+
    |  1|[x -> 1.0]|   0| foo|
    |  1|[x -> 1.0]|   1| bar|
    |  2|        []|null|null|
    |  3|      null|null|null|
    +---+----------+----+----+
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.posexplode_outer(_to_java_column(col))
    return Column(jc)


@ignore_unicode_prefix
@since(1.6)
def get_json_object(col, path):
    """
    Extracts json object from a json string based on json path specified, and returns json string
    of the extracted json object. It will return null if the input json string is invalid.

    :param col: string column in json format
    :param path: path to the json object to extract

    >>> data = [("1", '''{"f1": "value1", "f2": "value2"}'''), ("2", '''{"f1": "value12"}''')]
    >>> df = spark.createDataFrame(data, ("key", "jstring"))
    >>> df.select(df.key, get_json_object(df.jstring, '$.f1').alias("c0"), \\
    ...                   get_json_object(df.jstring, '$.f2').alias("c1") ).collect()
    [Row(key=u'1', c0=u'value1', c1=u'value2'), Row(key=u'2', c0=u'value12', c1=None)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.get_json_object(_to_java_column(col), path)
    return Column(jc)


@ignore_unicode_prefix
@since(1.6)
def json_tuple(col, *fields):
    """Creates a new row for a json column according to the given field names.

    :param col: string column in json format
    :param fields: list of fields to extract

    >>> data = [("1", '''{"f1": "value1", "f2": "value2"}'''), ("2", '''{"f1": "value12"}''')]
    >>> df = spark.createDataFrame(data, ("key", "jstring"))
    >>> df.select(df.key, json_tuple(df.jstring, 'f1', 'f2')).collect()
    [Row(key=u'1', c0=u'value1', c1=u'value2'), Row(key=u'2', c0=u'value12', c1=None)]
    """
    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.json_tuple(_to_java_column(col), _to_seq(sc, fields))
    return Column(jc)


@ignore_unicode_prefix
@since(2.1)
def from_json(col, schema, options={}):
    """
    Parses a column containing a JSON string into a :class:`MapType` with :class:`StringType`
    as keys type, :class:`StructType` or :class:`ArrayType` with
    the specified schema. Returns `null`, in the case of an unparseable string.

    :param col: string column in json format
    :param schema: a StructType or ArrayType of StructType to use when parsing the json column.
    :param options: options to control parsing. accepts the same options as the json datasource

    .. note:: Since Spark 2.3, the DDL-formatted string or a JSON format string is also
              supported for ``schema``.

    >>> from pyspark.sql.types import *
    >>> data = [(1, '''{"a": 1}''')]
    >>> schema = StructType([StructField("a", IntegerType())])
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(from_json(df.value, schema).alias("json")).collect()
    [Row(json=Row(a=1))]
    >>> df.select(from_json(df.value, "a INT").alias("json")).collect()
    [Row(json=Row(a=1))]
    >>> df.select(from_json(df.value, "MAP<STRING,INT>").alias("json")).collect()
    [Row(json={u'a': 1})]
    >>> data = [(1, '''[{"a": 1}]''')]
    >>> schema = ArrayType(StructType([StructField("a", IntegerType())]))
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(from_json(df.value, schema).alias("json")).collect()
    [Row(json=[Row(a=1)])]
    >>> schema = schema_of_json(lit('''{"a": 0}'''))
    >>> df.select(from_json(df.value, schema).alias("json")).collect()
    [Row(json=Row(a=None))]
    >>> data = [(1, '''[1, 2, 3]''')]
    >>> schema = ArrayType(IntegerType())
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(from_json(df.value, schema).alias("json")).collect()
    [Row(json=[1, 2, 3])]
    """

    sc = SparkContext._active_spark_context
    if isinstance(schema, DataType):
        schema = schema.json()
    elif isinstance(schema, Column):
        schema = _to_java_column(schema)
    jc = sc._jvm.functions.from_json(_to_java_column(col), schema, _options_to_str(options))
    return Column(jc)


@ignore_unicode_prefix
@since(2.1)
def to_json(col, options={}):
    """
    Converts a column containing a :class:`StructType`, :class:`ArrayType` or a :class:`MapType`
    into a JSON string. Throws an exception, in the case of an unsupported type.

    :param col: name of column containing a struct, an array or a map.
    :param options: options to control converting. accepts the same options as the JSON datasource.
                    Additionally the function supports the `pretty` option which enables
                    pretty JSON generation.

    >>> from pyspark.sql import Row
    >>> from pyspark.sql.types import *
    >>> data = [(1, Row(name='Alice', age=2))]
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(to_json(df.value).alias("json")).collect()
    [Row(json=u'{"age":2,"name":"Alice"}')]
    >>> data = [(1, [Row(name='Alice', age=2), Row(name='Bob', age=3)])]
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(to_json(df.value).alias("json")).collect()
    [Row(json=u'[{"age":2,"name":"Alice"},{"age":3,"name":"Bob"}]')]
    >>> data = [(1, {"name": "Alice"})]
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(to_json(df.value).alias("json")).collect()
    [Row(json=u'{"name":"Alice"}')]
    >>> data = [(1, [{"name": "Alice"}, {"name": "Bob"}])]
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(to_json(df.value).alias("json")).collect()
    [Row(json=u'[{"name":"Alice"},{"name":"Bob"}]')]
    >>> data = [(1, ["Alice", "Bob"])]
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(to_json(df.value).alias("json")).collect()
    [Row(json=u'["Alice","Bob"]')]
    """

    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.to_json(_to_java_column(col), _options_to_str(options))
    return Column(jc)


@ignore_unicode_prefix
@since(2.4)
def schema_of_json(json, options={}):
    """
    Parses a JSON string and infers its schema in DDL format.

    :param json: a JSON string or a string literal containing a JSON string.
    :param options: options to control parsing. accepts the same options as the JSON datasource

    .. versionchanged:: 3.0
       It accepts `options` parameter to control schema inferring.

    >>> df = spark.range(1)
    >>> df.select(schema_of_json(lit('{"a": 0}')).alias("json")).collect()
    [Row(json=u'struct<a:bigint>')]
    >>> schema = schema_of_json('{a: 1}', {'allowUnquotedFieldNames':'true'})
    >>> df.select(schema.alias("json")).collect()
    [Row(json=u'struct<a:bigint>')]
    """
    if isinstance(json, basestring):
        col = _create_column_from_literal(json)
    elif isinstance(json, Column):
        col = _to_java_column(json)
    else:
        raise TypeError("schema argument should be a column or string")

    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.schema_of_json(col, _options_to_str(options))
    return Column(jc)


@ignore_unicode_prefix
@since(3.0)
def schema_of_csv(csv, options={}):
    """
    Parses a CSV string and infers its schema in DDL format.

    :param col: a CSV string or a string literal containing a CSV string.
    :param options: options to control parsing. accepts the same options as the CSV datasource

    >>> df = spark.range(1)
    >>> df.select(schema_of_csv(lit('1|a'), {'sep':'|'}).alias("csv")).collect()
    [Row(csv=u'struct<_c0:int,_c1:string>')]
    >>> df.select(schema_of_csv('1|a', {'sep':'|'}).alias("csv")).collect()
    [Row(csv=u'struct<_c0:int,_c1:string>')]
    """
    if isinstance(csv, basestring):
        col = _create_column_from_literal(csv)
    elif isinstance(csv, Column):
        col = _to_java_column(csv)
    else:
        raise TypeError("schema argument should be a column or string")

    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.schema_of_csv(col, _options_to_str(options))
    return Column(jc)


@ignore_unicode_prefix
@since(3.0)
def to_csv(col, options={}):
    """
    Converts a column containing a :class:`StructType` into a CSV string.
    Throws an exception, in the case of an unsupported type.

    :param col: name of column containing a struct.
    :param options: options to control converting. accepts the same options as the CSV datasource.

    >>> from pyspark.sql import Row
    >>> data = [(1, Row(name='Alice', age=2))]
    >>> df = spark.createDataFrame(data, ("key", "value"))
    >>> df.select(to_csv(df.value).alias("csv")).collect()
    [Row(csv=u'2,Alice')]
    """

    sc = SparkContext._active_spark_context
    jc = sc._jvm.functions.to_csv(_to_java_column(col), _options_to_str(options))
    return Column(jc)


@since(1.5)
def size(col):
    """
    Collection function: returns the length of the array or map stored in the column.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([([1, 2, 3],),([1],),([],)], ['data'])
    >>> df.select(size(df.data)).collect()
    [Row(size(data)=3), Row(size(data)=1), Row(size(data)=0)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.size(_to_java_column(col)))


@since(2.4)
def array_min(col):
    """
    Collection function: returns the minimum value of the array.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([([2, 1, 3],), ([None, 10, -1],)], ['data'])
    >>> df.select(array_min(df.data).alias('min')).collect()
    [Row(min=1), Row(min=-1)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_min(_to_java_column(col)))


@since(2.4)
def array_max(col):
    """
    Collection function: returns the maximum value of the array.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([([2, 1, 3],), ([None, 10, -1],)], ['data'])
    >>> df.select(array_max(df.data).alias('max')).collect()
    [Row(max=3), Row(max=10)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_max(_to_java_column(col)))


@since(1.5)
def sort_array(col, asc=True):
    """
    Collection function: sorts the input array in ascending or descending order according
    to the natural ordering of the array elements. Null elements will be placed at the beginning
    of the returned array in ascending order or at the end of the returned array in descending
    order.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([([2, 1, None, 3],),([1],),([],)], ['data'])
    >>> df.select(sort_array(df.data).alias('r')).collect()
    [Row(r=[None, 1, 2, 3]), Row(r=[1]), Row(r=[])]
    >>> df.select(sort_array(df.data, asc=False).alias('r')).collect()
    [Row(r=[3, 2, 1, None]), Row(r=[1]), Row(r=[])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.sort_array(_to_java_column(col), asc))


@since(2.4)
def array_sort(col):
    """
    Collection function: sorts the input array in ascending order. The elements of the input array
    must be orderable. Null elements will be placed at the end of the returned array.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([([2, 1, None, 3],),([1],),([],)], ['data'])
    >>> df.select(array_sort(df.data).alias('r')).collect()
    [Row(r=[1, 2, 3, None]), Row(r=[1]), Row(r=[])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_sort(_to_java_column(col)))


@since(2.4)
def shuffle(col):
    """
    Collection function: Generates a random permutation of the given array.

    .. note:: The function is non-deterministic.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([([1, 20, 3, 5],), ([1, 20, None, 3],)], ['data'])
    >>> df.select(shuffle(df.data).alias('s')).collect()  # doctest: +SKIP
    [Row(s=[3, 1, 5, 20]), Row(s=[20, None, 3, 1])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.shuffle(_to_java_column(col)))


@since(1.5)
@ignore_unicode_prefix
def reverse(col):
    """
    Collection function: returns a reversed string or an array with reverse order of elements.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([('Spark SQL',)], ['data'])
    >>> df.select(reverse(df.data).alias('s')).collect()
    [Row(s=u'LQS krapS')]
    >>> df = spark.createDataFrame([([2, 1, 3],) ,([1],) ,([],)], ['data'])
    >>> df.select(reverse(df.data).alias('r')).collect()
    [Row(r=[3, 1, 2]), Row(r=[1]), Row(r=[])]
     """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.reverse(_to_java_column(col)))


@since(2.4)
def flatten(col):
    """
    Collection function: creates a single array from an array of arrays.
    If a structure of nested arrays is deeper than two levels,
    only one level of nesting is removed.

    :param col: name of column or expression

    >>> df = spark.createDataFrame([([[1, 2, 3], [4, 5], [6]],), ([None, [4, 5]],)], ['data'])
    >>> df.select(flatten(df.data).alias('r')).collect()
    [Row(r=[1, 2, 3, 4, 5, 6]), Row(r=None)]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.flatten(_to_java_column(col)))


@since(2.3)
def map_keys(col):
    """
    Collection function: Returns an unordered array containing the keys of the map.

    :param col: name of column or expression

    >>> from pyspark.sql.functions import map_keys
    >>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
    >>> df.select(map_keys("data").alias("keys")).show()
    +------+
    |  keys|
    +------+
    |[1, 2]|
    +------+
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.map_keys(_to_java_column(col)))


@since(2.3)
def map_values(col):
    """
    Collection function: Returns an unordered array containing the values of the map.

    :param col: name of column or expression

    >>> from pyspark.sql.functions import map_values
    >>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
    >>> df.select(map_values("data").alias("values")).show()
    +------+
    |values|
    +------+
    |[a, b]|
    +------+
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.map_values(_to_java_column(col)))


@since(3.0)
def map_entries(col):
    """
    Collection function: Returns an unordered array of all entries in the given map.

    :param col: name of column or expression

    >>> from pyspark.sql.functions import map_entries
    >>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as data")
    >>> df.select(map_entries("data").alias("entries")).show()
    +----------------+
    |         entries|
    +----------------+
    |[[1, a], [2, b]]|
    +----------------+
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.map_entries(_to_java_column(col)))


@since(2.4)
def map_from_entries(col):
    """
    Collection function: Returns a map created from the given array of entries.

    :param col: name of column or expression

    >>> from pyspark.sql.functions import map_from_entries
    >>> df = spark.sql("SELECT array(struct(1, 'a'), struct(2, 'b')) as data")
    >>> df.select(map_from_entries("data").alias("map")).show()
    +----------------+
    |             map|
    +----------------+
    |[1 -> a, 2 -> b]|
    +----------------+
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.map_from_entries(_to_java_column(col)))


@ignore_unicode_prefix
@since(2.4)
def array_repeat(col, count):
    """
    Collection function: creates an array containing a column repeated count times.

    >>> df = spark.createDataFrame([('ab',)], ['data'])
    >>> df.select(array_repeat(df.data, 3).alias('r')).collect()
    [Row(r=[u'ab', u'ab', u'ab'])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.array_repeat(
        _to_java_column(col),
        _to_java_column(count) if isinstance(count, Column) else count
    ))


@since(2.4)
def arrays_zip(*cols):
    """
    Collection function: Returns a merged array of structs in which the N-th struct contains all
    N-th values of input arrays.

    :param cols: columns of arrays to be merged.

    >>> from pyspark.sql.functions import arrays_zip
    >>> df = spark.createDataFrame([(([1, 2, 3], [2, 3, 4]))], ['vals1', 'vals2'])
    >>> df.select(arrays_zip(df.vals1, df.vals2).alias('zipped')).collect()
    [Row(zipped=[Row(vals1=1, vals2=2), Row(vals1=2, vals2=3), Row(vals1=3, vals2=4)])]
    """
    sc = SparkContext._active_spark_context
    return Column(sc._jvm.functions.arrays_zip(_to_seq(sc, cols, _to_java_column)))


@since(2.4)
def map_concat(*cols):
    """Returns the union of all the given maps.

    :param cols: list of column names (string) or list of :class:`Column` expressions

    >>> from pyspark.sql.functions import map_concat
    >>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(3, 'c', 1, 'd') as map2")
    >>> df.select(map_concat("map1", "map2").alias("map3")).show(truncate=False)
    +------------------------+
    |map3                    |
    +------------------------+
    |[1 -> d, 2 -> b, 3 -> c]|
    +------------------------+
    """
    sc = SparkContext._active_spark_context
    if len(cols) == 1 and isinstance(cols[0], (list, set)):
        cols = cols[0]
    jc = sc._jvm.functions.map_concat(_to_seq(sc, cols, _to_java_column))
    return Column(jc)


@since(2.4)
def sequence(start, stop, step=None):
    """
    Generate a sequence of integers from `start` to `stop`, incrementing by `step`.
    If `step` is not set, incrementing by 1 if `start` is less than or equal to `stop`,
    otherwise -1.

    >>> df1 = spark.createDataFrame([(-2, 2)], ('C1', 'C2'))
    >>> df1.select(sequence('C1', 'C2').alias('r')).collect()
    [Row(r=[-2, -1, 0, 1, 2])]
    >>> df2 = spark.createDataFrame([(4, -4, -2)], ('C1', 'C2', 'C3'))
    >>> df2.select(sequence('C1', 'C2', 'C3').alias('r')).collect()
    [Row(r=[4, 2, 0, -2, -4])]
    """
    sc = SparkContext._active_spark_context
    if step is None:
        return Column(sc._jvm.functions.sequence(_to_java_column(start), _to_java_column(stop)))
    else:
        return Column(sc._jvm.functions.sequence(
            _to_java_column(start), _to_java_column(stop), _to_java_column(step)))


@ignore_unicode_prefix
@since(3.0)
def from_csv(col, schema, options={}):
    """
    Parses a column containing a CSV string to a row with the specified schema.
    Returns `null`, in the case of an unparseable string.

    :param col: string column in CSV format
    :param schema: a string with schema in DDL format to use when parsing the CSV column.
    :param options: options to control parsing. accepts the same options as the CSV datasource

    >>> data = [("1,2,3",)]
    >>> df = spark.createDataFrame(data, ("value",))
    >>> df.select(from_csv(df.value, "a INT, b INT, c INT").alias("csv")).collect()
    [Row(csv=Row(a=1, b=2, c=3))]
    >>> value = data[0][0]
    >>> df.select(from_csv(df.value, schema_of_csv(value)).alias("csv")).collect()
    [Row(csv=Row(_c0=1, _c1=2, _c2=3))]
    >>> data = [("   abc",)]
    >>> df = spark.createDataFrame(data, ("value",))
    >>> options = {'ignoreLeadingWhiteSpace': True}
    >>> df.select(from_csv(df.value, "s string", options).alias("csv")).collect()
    [Row(csv=Row(s=u'abc'))]
    """

    sc = SparkContext._active_spark_context
    if isinstance(schema, basestring):
        schema = _create_column_from_literal(schema)
    elif isinstance(schema, Column):
        schema = _to_java_column(schema)
    else:
        raise TypeError("schema argument should be a column or string")

    jc = sc._jvm.functions.from_csv(_to_java_column(col), schema, _options_to_str(options))
    return Column(jc)


# ---------------------------- User Defined Function ----------------------------------

class PandasUDFType(object):
    """Pandas UDF Types. See :meth:`pyspark.sql.functions.pandas_udf`.
    """
    SCALAR = PythonEvalType.SQL_SCALAR_PANDAS_UDF

    SCALAR_ITER = PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF

    GROUPED_MAP = PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF

    COGROUPED_MAP = PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF

    GROUPED_AGG = PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF

    MAP_ITER = PythonEvalType.SQL_MAP_PANDAS_ITER_UDF


@since(1.3)
def udf(f=None, returnType=StringType()):
    """Creates a user defined function (UDF).

    .. note:: The user-defined functions are considered deterministic by default. Due to
        optimization, duplicate invocations may be eliminated or the function may even be invoked
        more times than it is present in the query. If your function is not deterministic, call
        `asNondeterministic` on the user defined function. E.g.:

    >>> from pyspark.sql.types import IntegerType
    >>> import random
    >>> random_udf = udf(lambda: int(random.random() * 100), IntegerType()).asNondeterministic()

    .. note:: The user-defined functions do not support conditional expressions or short circuiting
        in boolean expressions and it ends up with being executed all internally. If the functions
        can fail on special rows, the workaround is to incorporate the condition into the functions.

    .. note:: The user-defined functions do not take keyword arguments on the calling side.

    :param f: python function if used as a standalone function
    :param returnType: the return type of the user-defined function. The value can be either a
        :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.

    >>> from pyspark.sql.types import IntegerType
    >>> slen = udf(lambda s: len(s), IntegerType())
    >>> @udf
    ... def to_upper(s):
    ...     if s is not None:
    ...         return s.upper()
    ...
    >>> @udf(returnType=IntegerType())
    ... def add_one(x):
    ...     if x is not None:
    ...         return x + 1
    ...
    >>> df = spark.createDataFrame([(1, "John Doe", 21)], ("id", "name", "age"))
    >>> df.select(slen("name").alias("slen(name)"), to_upper("name"), add_one("age")).show()
    +----------+--------------+------------+
    |slen(name)|to_upper(name)|add_one(age)|
    +----------+--------------+------------+
    |         8|      JOHN DOE|          22|
    +----------+--------------+------------+
    """

    # The following table shows most of Python data and SQL type conversions in normal UDFs that
    # are not yet visible to the user. Some of behaviors are buggy and might be changed in the near
    # future. The table might have to be eventually documented externally.
    # Please see SPARK-28131's PR to see the codes in order to generate the table below.
    #
    # +-----------------------------+--------------+----------+------+---------------+--------------------+-----------------------------+----------+----------------------+---------+--------------------+----------------------------+------------+--------------+------------------+----------------------+  # noqa
    # |SQL Type \ Python Value(Type)|None(NoneType)|True(bool)|1(int)|         a(str)|    1970-01-01(date)|1970-01-01 00:00:00(datetime)|1.0(float)|array('i', [1])(array)|[1](list)|         (1,)(tuple)|bytearray(b'ABC')(bytearray)|  1(Decimal)|{'a': 1}(dict)|Row(kwargs=1)(Row)|Row(namedtuple=1)(Row)|  # noqa
    # +-----------------------------+--------------+----------+------+---------------+--------------------+-----------------------------+----------+----------------------+---------+--------------------+----------------------------+------------+--------------+------------------+----------------------+  # noqa
    # |                      boolean|          None|      True|  None|           None|                None|                         None|      None|                  None|     None|                None|                        None|        None|          None|                 X|                     X|  # noqa
    # |                      tinyint|          None|      None|     1|           None|                None|                         None|      None|                  None|     None|                None|                        None|        None|          None|                 X|                     X|  # noqa
    # |                     smallint|          None|      None|     1|           None|                None|                         None|      None|                  None|     None|                None|                        None|        None|          None|                 X|                     X|  # noqa
    # |                          int|          None|      None|     1|           None|                None|                         None|      None|                  None|     None|                None|                        None|        None|          None|                 X|                     X|  # noqa
    # |                       bigint|          None|      None|     1|           None|                None|                         None|      None|                  None|     None|                None|                        None|        None|          None|                 X|                     X|  # noqa
    # |                       string|          None|    'true'|   '1'|            'a'|'java.util.Gregor...|         'java.util.Gregor...|     '1.0'|         '[I@66cbb73a'|    '[1]'|'[Ljava.lang.Obje...|               '[B@5a51eb1a'|         '1'|       '{a=1}'|                 X|                     X|  # noqa
    # |                         date|          None|         X|     X|              X|datetime.date(197...|         datetime.date(197...|         X|                     X|        X|                   X|                           X|           X|             X|                 X|                     X|  # noqa
    # |                    timestamp|          None|         X|     X|              X|                   X|         datetime.datetime...|         X|                     X|        X|                   X|                           X|           X|             X|                 X|                     X|  # noqa
    # |                        float|          None|      None|  None|           None|                None|                         None|       1.0|                  None|     None|                None|                        None|        None|          None|                 X|                     X|  # noqa
    # |                       double|          None|      None|  None|           None|                None|                         None|       1.0|                  None|     None|                None|                        None|        None|          None|                 X|                     X|  # noqa
    # |                   array<int>|          None|      None|  None|           None|                None|                         None|      None|                   [1]|      [1]|                 [1]|                [65, 66, 67]|        None|          None|                 X|                     X|  # noqa
    # |                       binary|          None|      None|  None|bytearray(b'a')|                None|                         None|      None|                  None|     None|                None|           bytearray(b'ABC')|        None|          None|                 X|                     X|  # noqa
    # |                decimal(10,0)|          None|      None|  None|           None|                None|                         None|      None|                  None|     None|                None|                        None|Decimal('1')|          None|                 X|                     X|  # noqa
    # |              map<string,int>|          None|      None|  None|           None|                None|                         None|      None|                  None|     None|                None|                        None|        None|      {'a': 1}|                 X|                     X|  # noqa
    # |               struct<_1:int>|          None|         X|     X|              X|                   X|                            X|         X|                     X|Row(_1=1)|           Row(_1=1)|                           X|           X|  Row(_1=None)|         Row(_1=1)|             Row(_1=1)|  # noqa
    # +-----------------------------+--------------+----------+------+---------------+--------------------+-----------------------------+----------+----------------------+---------+--------------------+----------------------------+------------+--------------+------------------+----------------------+  # noqa
    #
    # Note: DDL formatted string is used for 'SQL Type' for simplicity. This string can be
    #       used in `returnType`.
    # Note: The values inside of the table are generated by `repr`.
    # Note: 'X' means it throws an exception during the conversion.
    # Note: Python 3.7.3 is used.

    # decorator @udf, @udf(), @udf(dataType())
    if f is None or isinstance(f, (str, DataType)):
        # If DataType has been passed as a positional argument
        # for decorator use it as a returnType
        return_type = f or returnType
        return functools.partial(_create_udf, returnType=return_type,
                                 evalType=PythonEvalType.SQL_BATCHED_UDF)
    else:
        return _create_udf(f=f, returnType=returnType,
                           evalType=PythonEvalType.SQL_BATCHED_UDF)


@since(2.3)
def pandas_udf(f=None, returnType=None, functionType=None):
    """
    Creates a vectorized user defined function (UDF).

    :param f: user-defined function. A python function if used as a standalone function
    :param returnType: the return type of the user-defined function. The value can be either a
        :class:`pyspark.sql.types.DataType` object or a DDL-formatted type string.
    :param functionType: an enum value in :class:`pyspark.sql.functions.PandasUDFType`.
                         Default: SCALAR.

    The function type of the UDF can be one of the following:

    1. SCALAR

       A scalar UDF defines a transformation: One or more `pandas.Series` -> A `pandas.Series`.
       The length of the returned `pandas.Series` must be of the same as the input `pandas.Series`.
       If the return type is :class:`StructType`, the returned value should be a `pandas.DataFrame`.

       :class:`MapType`, nested :class:`StructType` are currently not supported as output types.

       Scalar UDFs can be used with :meth:`pyspark.sql.DataFrame.withColumn` and
       :meth:`pyspark.sql.DataFrame.select`.

       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
       >>> from pyspark.sql.types import IntegerType, StringType
       >>> slen = pandas_udf(lambda s: s.str.len(), IntegerType())  # doctest: +SKIP
       >>> @pandas_udf(StringType())  # doctest: +SKIP
       ... def to_upper(s):
       ...     return s.str.upper()
       ...
       >>> @pandas_udf("integer", PandasUDFType.SCALAR)  # doctest: +SKIP
       ... def add_one(x):
       ...     return x + 1
       ...
       >>> df = spark.createDataFrame([(1, "John Doe", 21)],
       ...                            ("id", "name", "age"))  # doctest: +SKIP
       >>> df.select(slen("name").alias("slen(name)"), to_upper("name"), add_one("age")) \\
       ...     .show()  # doctest: +SKIP
       +----------+--------------+------------+
       |slen(name)|to_upper(name)|add_one(age)|
       +----------+--------------+------------+
       |         8|      JOHN DOE|          22|
       +----------+--------------+------------+
       >>> @pandas_udf("first string, last string")  # doctest: +SKIP
       ... def split_expand(n):
       ...     return n.str.split(expand=True)
       >>> df.select(split_expand("name")).show()  # doctest: +SKIP
       +------------------+
       |split_expand(name)|
       +------------------+
       |       [John, Doe]|
       +------------------+

       .. note:: The length of `pandas.Series` within a scalar UDF is not that of the whole input
           column, but is the length of an internal batch used for each call to the function.
           Therefore, this can be used, for example, to ensure the length of each returned
           `pandas.Series`, and can not be used as the column length.

    2. SCALAR_ITER

       A scalar iterator UDF is semantically the same as the scalar Pandas UDF above except that the
       wrapped Python function takes an iterator of batches as input instead of a single batch and,
       instead of returning a single output batch, it yields output batches or explicitly returns an
       generator or an iterator of output batches.
       It is useful when the UDF execution requires initializing some state, e.g., loading a machine
       learning model file to apply inference to every input batch.

       .. note:: It is not guaranteed that one invocation of a scalar iterator UDF will process all
           batches from one partition, although it is currently implemented this way.
           Your code shall not rely on this behavior because it might change in the future for
           further optimization, e.g., one invocation processes multiple partitions.

       Scalar iterator UDFs are used with :meth:`pyspark.sql.DataFrame.withColumn` and
       :meth:`pyspark.sql.DataFrame.select`.

       >>> import pandas as pd  # doctest: +SKIP
       >>> from pyspark.sql.functions import col, pandas_udf, struct, PandasUDFType
       >>> pdf = pd.DataFrame([1, 2, 3], columns=["x"])  # doctest: +SKIP
       >>> df = spark.createDataFrame(pdf)  # doctest: +SKIP

       When the UDF is called with a single column that is not `StructType`, the input to the
       underlying function is an iterator of `pd.Series`.

       >>> @pandas_udf("long", PandasUDFType.SCALAR_ITER)  # doctest: +SKIP
       ... def plus_one(batch_iter):
       ...     for x in batch_iter:
       ...         yield x + 1
       ...
       >>> df.select(plus_one(col("x"))).show()  # doctest: +SKIP
       +-----------+
       |plus_one(x)|
       +-----------+
       |          2|
       |          3|
       |          4|
       +-----------+

       When the UDF is called with more than one columns, the input to the underlying function is an
       iterator of `pd.Series` tuple.

       >>> @pandas_udf("long", PandasUDFType.SCALAR_ITER)  # doctest: +SKIP
       ... def multiply_two_cols(batch_iter):
       ...     for a, b in batch_iter:
       ...         yield a * b
       ...
       >>> df.select(multiply_two_cols(col("x"), col("x"))).show()  # doctest: +SKIP
       +-----------------------+
       |multiply_two_cols(x, x)|
       +-----------------------+
       |                      1|
       |                      4|
       |                      9|
       +-----------------------+

       When the UDF is called with a single column that is `StructType`, the input to the underlying
       function is an iterator of `pd.DataFrame`.

       >>> @pandas_udf("long", PandasUDFType.SCALAR_ITER)  # doctest: +SKIP
       ... def multiply_two_nested_cols(pdf_iter):
       ...    for pdf in pdf_iter:
       ...        yield pdf["a"] * pdf["b"]
       ...
       >>> df.select(
       ...     multiply_two_nested_cols(
       ...         struct(col("x").alias("a"), col("x").alias("b"))
       ...     ).alias("y")
       ... ).show()  # doctest: +SKIP
       +---+
       |  y|
       +---+
       |  1|
       |  4|
       |  9|
       +---+

       In the UDF, you can initialize some states before processing batches, wrap your code with
       `try ... finally ...` or use context managers to ensure the release of resources at the end
       or in case of early termination.

       >>> y_bc = spark.sparkContext.broadcast(1)  # doctest: +SKIP
       >>> @pandas_udf("long", PandasUDFType.SCALAR_ITER)  # doctest: +SKIP
       ... def plus_y(batch_iter):
       ...     y = y_bc.value  # initialize some state
       ...     try:
       ...         for x in batch_iter:
       ...             yield x + y
       ...     finally:
       ...         pass  # release resources here, if any
       ...
       >>> df.select(plus_y(col("x"))).show()  # doctest: +SKIP
       +---------+
       |plus_y(x)|
       +---------+
       |        2|
       |        3|
       |        4|
       +---------+

    3. GROUPED_MAP

       A grouped map UDF defines transformation: A `pandas.DataFrame` -> A `pandas.DataFrame`
       The returnType should be a :class:`StructType` describing the schema of the returned
       `pandas.DataFrame`. The column labels of the returned `pandas.DataFrame` must either match
       the field names in the defined returnType schema if specified as strings, or match the
       field data types by position if not strings, e.g. integer indices.
       The length of the returned `pandas.DataFrame` can be arbitrary.

       Grouped map UDFs are used with :meth:`pyspark.sql.GroupedData.apply`.

       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
       >>> df = spark.createDataFrame(
       ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
       ...     ("id", "v"))  # doctest: +SKIP
       >>> @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)  # doctest: +SKIP
       ... def normalize(pdf):
       ...     v = pdf.v
       ...     return pdf.assign(v=(v - v.mean()) / v.std())
       >>> df.groupby("id").apply(normalize).show()  # doctest: +SKIP
       +---+-------------------+
       | id|                  v|
       +---+-------------------+
       |  1|-0.7071067811865475|
       |  1| 0.7071067811865475|
       |  2|-0.8320502943378437|
       |  2|-0.2773500981126146|
       |  2| 1.1094003924504583|
       +---+-------------------+

       Alternatively, the user can define a function that takes two arguments.
       In this case, the grouping key(s) will be passed as the first argument and the data will
       be passed as the second argument. The grouping key(s) will be passed as a tuple of numpy
       data types, e.g., `numpy.int32` and `numpy.float64`. The data will still be passed in
       as a `pandas.DataFrame` containing all columns from the original Spark DataFrame.
       This is useful when the user does not want to hardcode grouping key(s) in the function.

       >>> import pandas as pd  # doctest: +SKIP
       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
       >>> df = spark.createDataFrame(
       ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
       ...     ("id", "v"))  # doctest: +SKIP
       >>> @pandas_udf("id long, v double", PandasUDFType.GROUPED_MAP)  # doctest: +SKIP
       ... def mean_udf(key, pdf):
       ...     # key is a tuple of one numpy.int64, which is the value
       ...     # of 'id' for the current group
       ...     return pd.DataFrame([key + (pdf.v.mean(),)])
       >>> df.groupby('id').apply(mean_udf).show()  # doctest: +SKIP
       +---+---+
       | id|  v|
       +---+---+
       |  1|1.5|
       |  2|6.0|
       +---+---+
       >>> @pandas_udf(
       ...    "id long, `ceil(v / 2)` long, v double",
       ...    PandasUDFType.GROUPED_MAP)  # doctest: +SKIP
       >>> def sum_udf(key, pdf):
       ...     # key is a tuple of two numpy.int64s, which is the values
       ...     # of 'id' and 'ceil(df.v / 2)' for the current group
       ...     return pd.DataFrame([key + (pdf.v.sum(),)])
       >>> df.groupby(df.id, ceil(df.v / 2)).apply(sum_udf).show()  # doctest: +SKIP
       +---+-----------+----+
       | id|ceil(v / 2)|   v|
       +---+-----------+----+
       |  2|          5|10.0|
       |  1|          1| 3.0|
       |  2|          3| 5.0|
       |  2|          2| 3.0|
       +---+-----------+----+

       .. note:: If returning a new `pandas.DataFrame` constructed with a dictionary, it is
           recommended to explicitly index the columns by name to ensure the positions are correct,
           or alternatively use an `OrderedDict`.
           For example, `pd.DataFrame({'id': ids, 'a': data}, columns=['id', 'a'])` or
           `pd.DataFrame(OrderedDict([('id', ids), ('a', data)]))`.

       .. seealso:: :meth:`pyspark.sql.GroupedData.apply`

    4. GROUPED_AGG

       A grouped aggregate UDF defines a transformation: One or more `pandas.Series` -> A scalar
       The `returnType` should be a primitive data type, e.g., :class:`DoubleType`.
       The returned scalar can be either a python primitive type, e.g., `int` or `float`
       or a numpy data type, e.g., `numpy.int64` or `numpy.float64`.

       :class:`MapType` and :class:`StructType` are currently not supported as output types.

       Group aggregate UDFs are used with :meth:`pyspark.sql.GroupedData.agg` and
       :class:`pyspark.sql.Window`

       This example shows using grouped aggregated UDFs with groupby:

       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
       >>> df = spark.createDataFrame(
       ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
       ...     ("id", "v"))
       >>> @pandas_udf("double", PandasUDFType.GROUPED_AGG)  # doctest: +SKIP
       ... def mean_udf(v):
       ...     return v.mean()
       >>> df.groupby("id").agg(mean_udf(df['v'])).show()  # doctest: +SKIP
       +---+-----------+
       | id|mean_udf(v)|
       +---+-----------+
       |  1|        1.5|
       |  2|        6.0|
       +---+-----------+

       This example shows using grouped aggregated UDFs as window functions.

       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
       >>> from pyspark.sql import Window
       >>> df = spark.createDataFrame(
       ...     [(1, 1.0), (1, 2.0), (2, 3.0), (2, 5.0), (2, 10.0)],
       ...     ("id", "v"))
       >>> @pandas_udf("double", PandasUDFType.GROUPED_AGG)  # doctest: +SKIP
       ... def mean_udf(v):
       ...     return v.mean()
       >>> w = (Window.partitionBy('id')
       ...            .orderBy('v')
       ...            .rowsBetween(-1, 0))
       >>> df.withColumn('mean_v', mean_udf(df['v']).over(w)).show()  # doctest: +SKIP
       +---+----+------+
       | id|   v|mean_v|
       +---+----+------+
       |  1| 1.0|   1.0|
       |  1| 2.0|   1.5|
       |  2| 3.0|   3.0|
       |  2| 5.0|   4.0|
       |  2|10.0|   7.5|
       +---+----+------+

       .. note:: For performance reasons, the input series to window functions are not copied.
            Therefore, mutating the input series is not allowed and will cause incorrect results.
            For the same reason, users should also not rely on the index of the input series.

       .. seealso:: :meth:`pyspark.sql.GroupedData.agg` and :class:`pyspark.sql.Window`

    5. MAP_ITER

       A map iterator Pandas UDFs are used to transform data with an iterator of batches.
       It can be used with :meth:`pyspark.sql.DataFrame.mapInPandas`.

       It can return the output of arbitrary length in contrast to the scalar Pandas UDF.
       It maps an iterator of batches in the current :class:`DataFrame` using a Pandas user-defined
       function and returns the result as a :class:`DataFrame`.

       The user-defined function should take an iterator of `pandas.DataFrame`\\s and return another
       iterator of `pandas.DataFrame`\\s. All columns are passed together as an
       iterator of `pandas.DataFrame`\\s to the user-defined function and the returned iterator of
       `pandas.DataFrame`\\s are combined as a :class:`DataFrame`.

       >>> df = spark.createDataFrame([(1, 21), (2, 30)],
       ...                            ("id", "age"))  # doctest: +SKIP
       >>> @pandas_udf(df.schema, PandasUDFType.MAP_ITER)  # doctest: +SKIP
       ... def filter_func(batch_iter):
       ...     for pdf in batch_iter:
       ...         yield pdf[pdf.id == 1]
       >>> df.mapInPandas(filter_func).show()  # doctest: +SKIP
       +---+---+
       | id|age|
       +---+---+
       |  1| 21|
       +---+---+

    6. COGROUPED_MAP

       A cogrouped map UDF defines transformation: (`pandas.DataFrame`, `pandas.DataFrame`) ->
       `pandas.DataFrame`. The `returnType` should be a :class:`StructType` describing the schema
       of the returned `pandas.DataFrame`. The column labels of the returned `pandas.DataFrame`
       must either match the field names in the defined `returnType` schema if specified as strings,
       or match the field data types by position if not strings, e.g. integer indices. The length
       of the returned `pandas.DataFrame` can be arbitrary.

       CoGrouped map UDFs are used with :meth:`pyspark.sql.CoGroupedData.apply`.

       >>> from pyspark.sql.functions import pandas_udf, PandasUDFType
       >>> df1 = spark.createDataFrame(
       ...     [(20000101, 1, 1.0), (20000101, 2, 2.0), (20000102, 1, 3.0), (20000102, 2, 4.0)],
       ...     ("time", "id", "v1"))
       >>> df2 = spark.createDataFrame(
       ...     [(20000101, 1, "x"), (20000101, 2, "y")],
       ...     ("time", "id", "v2"))
       >>> @pandas_udf("time int, id int, v1 double, v2 string",
       ...             PandasUDFType.COGROUPED_MAP)  # doctest: +SKIP
       ... def asof_join(l, r):
       ...     return pd.merge_asof(l, r, on="time", by="id")
       >>> df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show()  # doctest: +SKIP
       +---------+---+---+---+
       |     time| id| v1| v2|
       +---------+---+---+---+
       | 20000101|  1|1.0|  x|
       | 20000102|  1|3.0|  x|
       | 20000101|  2|2.0|  y|
       | 20000102|  2|4.0|  y|
       +---------+---+---+---+

       Alternatively, the user can define a function that takes three arguments.  In this case,
       the grouping key(s) will be passed as the first argument and the data will be passed as the
       second and third arguments.  The grouping key(s) will be passed as a tuple of numpy data
       types, e.g., `numpy.int32` and `numpy.float64`. The data will still be passed in as two
       `pandas.DataFrame` containing all columns from the original Spark DataFrames.
       >>> @pandas_udf("time int, id int, v1 double, v2 string",
       ...             PandasUDFType.COGROUPED_MAP)  # doctest: +SKIP
       ... def asof_join(k, l, r):
       ...     if k == (1,):
       ...         return pd.merge_asof(l, r, on="time", by="id")
       ...     else:
       ...         return pd.DataFrame(columns=['time', 'id', 'v1', 'v2'])
       >>> df1.groupby("id").cogroup(df2.groupby("id")).apply(asof_join).show()  # doctest: +SKIP
       +---------+---+---+---+
       |     time| id| v1| v2|
       +---------+---+---+---+
       | 20000101|  1|1.0|  x|
       | 20000102|  1|3.0|  x|
       +---------+---+---+---+

    .. note:: The user-defined functions are considered deterministic by default. Due to
        optimization, duplicate invocations may be eliminated or the function may even be invoked
        more times than it is present in the query. If your function is not deterministic, call
        `asNondeterministic` on the user defined function. E.g.:

    >>> @pandas_udf('double', PandasUDFType.SCALAR)  # doctest: +SKIP
    ... def random(v):
    ...     import numpy as np
    ...     import pandas as pd
    ...     return pd.Series(np.random.randn(len(v))
    >>> random = random.asNondeterministic()  # doctest: +SKIP

    .. note:: The user-defined functions do not support conditional expressions or short circuiting
        in boolean expressions and it ends up with being executed all internally. If the functions
        can fail on special rows, the workaround is to incorporate the condition into the functions.

    .. note:: The user-defined functions do not take keyword arguments on the calling side.

    .. note:: The data type of returned `pandas.Series` from the user-defined functions should be
        matched with defined returnType (see :meth:`types.to_arrow_type` and
        :meth:`types.from_arrow_type`). When there is mismatch between them, Spark might do
        conversion on returned data. The conversion is not guaranteed to be correct and results
        should be checked for accuracy by users.
    """

    # The following table shows most of Pandas data and SQL type conversions in Pandas UDFs that
    # are not yet visible to the user. Some of behaviors are buggy and might be changed in the near
    # future. The table might have to be eventually documented externally.
    # Please see SPARK-28132's PR to see the codes in order to generate the table below.
    #
    # +-----------------------------+----------------------+------------------+------------------+------------------+--------------------+--------------------+------------------+------------------+------------------+------------------+--------------+--------------+--------------+-----------------------------------+-----------------------------------------------------+-----------------+--------------------+-----------------------------+--------------+-----------------+------------------+-----------+--------------------------------+  # noqa
    # |SQL Type \ Pandas Value(Type)|None(object(NoneType))|        True(bool)|           1(int8)|          1(int16)|            1(int32)|            1(int64)|          1(uint8)|         1(uint16)|         1(uint32)|         1(uint64)|  1.0(float16)|  1.0(float32)|  1.0(float64)|1970-01-01 00:00:00(datetime64[ns])|1970-01-01 00:00:00-05:00(datetime64[ns, US/Eastern])|a(object(string))|  1(object(Decimal))|[1 2 3](object(array[int32]))| 1.0(float128)|(1+0j)(complex64)|(1+0j)(complex128)|A(category)|1 days 00:00:00(timedelta64[ns])|  # noqa
    # +-----------------------------+----------------------+------------------+------------------+------------------+--------------------+--------------------+------------------+------------------+------------------+------------------+--------------+--------------+--------------+-----------------------------------+-----------------------------------------------------+-----------------+--------------------+-----------------------------+--------------+-----------------+------------------+-----------+--------------------------------+  # noqa
    # |                      boolean|                  None|              True|              True|              True|                True|                True|              True|              True|              True|              True|          True|          True|          True|                                  X|                                                    X|                X|                   X|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                      tinyint|                  None|                 1|                 1|                 1|                   1|                   1|                 1|                 1|                 1|                 1|             1|             1|             1|                                  X|                                                    X|                X|                   1|                            X|             X|                X|                 X|          0|                               X|  # noqa
    # |                     smallint|                  None|                 1|                 1|                 1|                   1|                   1|                 1|                 1|                 1|                 1|             1|             1|             1|                                  X|                                                    X|                X|                   1|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                          int|                  None|                 1|                 1|                 1|                   1|                   1|                 1|                 1|                 1|                 1|             1|             1|             1|                                  X|                                                    X|                X|                   1|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                       bigint|                  None|                 1|                 1|                 1|                   1|                   1|                 1|                 1|                 1|                 1|             1|             1|             1|                                  0|                                       18000000000000|                X|                   1|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                        float|                  None|               1.0|               1.0|               1.0|                 1.0|                 1.0|               1.0|               1.0|               1.0|               1.0|           1.0|           1.0|           1.0|                                  X|                                                    X|                X|                   X|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                       double|                  None|               1.0|               1.0|               1.0|                 1.0|                 1.0|               1.0|               1.0|               1.0|               1.0|           1.0|           1.0|           1.0|                                  X|                                                    X|                X|                   X|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                         date|                  None|                 X|                 X|                 X|datetime.date(197...|                   X|                 X|                 X|                 X|                 X|             X|             X|             X|               datetime.date(197...|                                 datetime.date(197...|                X|datetime.date(197...|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                    timestamp|                  None|                 X|                 X|                 X|                   X|datetime.datetime...|                 X|                 X|                 X|                 X|             X|             X|             X|               datetime.datetime...|                                 datetime.datetime...|                X|datetime.datetime...|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                       string|                  None|                ''|                ''|                ''|              '\x01'|              '\x01'|                ''|                ''|            '\x01'|            '\x01'|            ''|            ''|            ''|                                  X|                                                    X|              'a'|                   X|                            X|            ''|                X|                ''|          X|                               X|  # noqa
    # |                decimal(10,0)|                  None|                 X|                 X|                 X|                   X|                   X|                 X|                 X|                 X|                 X|             X|             X|             X|                                  X|                                                    X|                X|        Decimal('1')|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                   array<int>|                  None|                 X|                 X|                 X|                   X|                   X|                 X|                 X|                 X|                 X|             X|             X|             X|                                  X|                                                    X|                X|                   X|                    [1, 2, 3]|             X|                X|                 X|          X|                               X|  # noqa
    # |              map<string,int>|                     X|                 X|                 X|                 X|                   X|                   X|                 X|                 X|                 X|                 X|             X|             X|             X|                                  X|                                                    X|                X|                   X|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |               struct<_1:int>|                     X|                 X|                 X|                 X|                   X|                   X|                 X|                 X|                 X|                 X|             X|             X|             X|                                  X|                                                    X|                X|                   X|                            X|             X|                X|                 X|          X|                               X|  # noqa
    # |                       binary|                  None|bytearray(b'\x01')|bytearray(b'\x01')|bytearray(b'\x01')|  bytearray(b'\x01')|  bytearray(b'\x01')|bytearray(b'\x01')|bytearray(b'\x01')|bytearray(b'\x01')|bytearray(b'\x01')|bytearray(b'')|bytearray(b'')|bytearray(b'')|                     bytearray(b'')|                                       bytearray(b'')|  bytearray(b'a')|                   X|                            X|bytearray(b'')|   bytearray(b'')|    bytearray(b'')|          X|                  bytearray(b'')|  # noqa
    # +-----------------------------+----------------------+------------------+------------------+------------------+--------------------+--------------------+------------------+------------------+------------------+------------------+--------------+--------------+--------------+-----------------------------------+-----------------------------------------------------+-----------------+--------------------+-----------------------------+--------------+-----------------+------------------+-----------+--------------------------------+  # noqa
    #
    # Note: DDL formatted string is used for 'SQL Type' for simplicity. This string can be
    #       used in `returnType`.
    # Note: The values inside of the table are generated by `repr`.
    # Note: Python 3.7.3, Pandas 0.24.2 and PyArrow 0.13.0 are used.
    # Note: Timezone is KST.
    # Note: 'X' means it throws an exception during the conversion.

    # decorator @pandas_udf(returnType, functionType)
    is_decorator = f is None or isinstance(f, (str, DataType))

    if is_decorator:
        # If DataType has been passed as a positional argument
        # for decorator use it as a returnType
        return_type = f or returnType

        if functionType is not None:
            # @pandas_udf(dataType, functionType=functionType)
            # @pandas_udf(returnType=dataType, functionType=functionType)
            eval_type = functionType
        elif returnType is not None and isinstance(returnType, int):
            # @pandas_udf(dataType, functionType)
            eval_type = returnType
        else:
            # @pandas_udf(dataType) or @pandas_udf(returnType=dataType)
            eval_type = PythonEvalType.SQL_SCALAR_PANDAS_UDF
    else:
        return_type = returnType

        if functionType is not None:
            eval_type = functionType
        else:
            eval_type = PythonEvalType.SQL_SCALAR_PANDAS_UDF

    if return_type is None:
        raise ValueError("Invalid returnType: returnType can not be None")

    if eval_type not in [PythonEvalType.SQL_SCALAR_PANDAS_UDF,
                         PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
                         PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF,
                         PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
                         PythonEvalType.SQL_MAP_PANDAS_ITER_UDF,
                         PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF]:
        raise ValueError("Invalid functionType: "
                         "functionType must be one the values from PandasUDFType")

    if is_decorator:
        return functools.partial(_create_udf, returnType=return_type, evalType=eval_type)
    else:
        return _create_udf(f=f, returnType=return_type, evalType=eval_type)


blacklist = ['map', 'since', 'ignore_unicode_prefix']
__all__ = [k for k, v in globals().items()
           if not k.startswith('_') and k[0].islower() and callable(v) and k not in blacklist]
__all__ += ["PandasUDFType"]
__all__.sort()


def _test():
    import doctest
    from pyspark.sql import Row, SparkSession
    import pyspark.sql.functions
    globs = pyspark.sql.functions.__dict__.copy()
    spark = SparkSession.builder\
        .master("local[4]")\
        .appName("sql.functions tests")\
        .getOrCreate()
    sc = spark.sparkContext
    globs['sc'] = sc
    globs['spark'] = spark
    globs['df'] = spark.createDataFrame([Row(name='Alice', age=2), Row(name='Bob', age=5)])

    spark.conf.set("spark.sql.legacy.utcTimestampFunc.enabled", "true")
    (failure_count, test_count) = doctest.testmod(
        pyspark.sql.functions, globs=globs,
        optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
    spark.conf.unset("spark.sql.legacy.utcTimestampFunc.enabled")

    spark.stop()
    if failure_count:
        sys.exit(-1)


if __name__ == "__main__":
    _test()