#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import sys
import json
if sys.version >= '3':
basestring = str
long = int
from pyspark import copy_func, since
from pyspark.context import SparkContext
from pyspark.rdd import ignore_unicode_prefix
from pyspark.sql.types import *
__all__ = ["Column"]
def _create_column_from_literal(literal):
sc = SparkContext._active_spark_context
return sc._jvm.functions.lit(literal)
def _create_column_from_name(name):
sc = SparkContext._active_spark_context
return sc._jvm.functions.col(name)
def _to_java_column(col):
if isinstance(col, Column):
jcol = col._jc
elif isinstance(col, basestring):
jcol = _create_column_from_name(col)
else:
raise TypeError(
"Invalid argument, not a string or column: "
"{0} of type {1}. "
"For column literals, use 'lit', 'array', 'struct' or 'create_map' "
"function.".format(col, type(col)))
return jcol
def _to_seq(sc, cols, converter=None):
"""
Convert a list of Column (or names) into a JVM Seq of Column.
An optional `converter` could be used to convert items in `cols`
into JVM Column objects.
"""
if converter:
cols = [converter(c) for c in cols]
return sc._jvm.PythonUtils.toSeq(cols)
def _to_list(sc, cols, converter=None):
"""
Convert a list of Column (or names) into a JVM (Scala) List of Column.
An optional `converter` could be used to convert items in `cols`
into JVM Column objects.
"""
if converter:
cols = [converter(c) for c in cols]
return sc._jvm.PythonUtils.toList(cols)
def _unary_op(name, doc="unary operator"):
""" Create a method for given unary operator """
def _(self):
jc = getattr(self._jc, name)()
return Column(jc)
_.__doc__ = doc
return _
def _func_op(name, doc=''):
def _(self):
sc = SparkContext._active_spark_context
jc = getattr(sc._jvm.functions, name)(self._jc)
return Column(jc)
_.__doc__ = doc
return _
def _bin_func_op(name, reverse=False, doc="binary function"):
def _(self, other):
sc = SparkContext._active_spark_context
fn = getattr(sc._jvm.functions, name)
jc = other._jc if isinstance(other, Column) else _create_column_from_literal(other)
njc = fn(self._jc, jc) if not reverse else fn(jc, self._jc)
return Column(njc)
_.__doc__ = doc
return _
def _bin_op(name, doc="binary operator"):
""" Create a method for given binary operator
"""
def _(self, other):
jc = other._jc if isinstance(other, Column) else other
njc = getattr(self._jc, name)(jc)
return Column(njc)
_.__doc__ = doc
return _
def _reverse_op(name, doc="binary operator"):
""" Create a method for binary operator (this object is on right side)
"""
def _(self, other):
jother = _create_column_from_literal(other)
jc = getattr(jother, name)(self._jc)
return Column(jc)
_.__doc__ = doc
return _
[docs]class Column(object):
"""
A column in a DataFrame.
:class:`Column` instances can be created by::
# 1. Select a column out of a DataFrame
df.colName
df["colName"]
# 2. Create from an expression
df.colName + 1
1 / df.colName
.. versionadded:: 1.3
"""
def __init__(self, jc):
self._jc = jc
# arithmetic operators
__neg__ = _func_op("negate")
__add__ = _bin_op("plus")
__sub__ = _bin_op("minus")
__mul__ = _bin_op("multiply")
__div__ = _bin_op("divide")
__truediv__ = _bin_op("divide")
__mod__ = _bin_op("mod")
__radd__ = _bin_op("plus")
__rsub__ = _reverse_op("minus")
__rmul__ = _bin_op("multiply")
__rdiv__ = _reverse_op("divide")
__rtruediv__ = _reverse_op("divide")
__rmod__ = _reverse_op("mod")
__pow__ = _bin_func_op("pow")
__rpow__ = _bin_func_op("pow", reverse=True)
# logistic operators
__eq__ = _bin_op("equalTo")
__ne__ = _bin_op("notEqual")
__lt__ = _bin_op("lt")
__le__ = _bin_op("leq")
__ge__ = _bin_op("geq")
__gt__ = _bin_op("gt")
_eqNullSafe_doc = """
Equality test that is safe for null values.
:param other: a value or :class:`Column`
>>> from pyspark.sql import Row
>>> df1 = spark.createDataFrame([
... Row(id=1, value='foo'),
... Row(id=2, value=None)
... ])
>>> df1.select(
... df1['value'] == 'foo',
... df1['value'].eqNullSafe('foo'),
... df1['value'].eqNullSafe(None)
... ).show()
+-------------+---------------+----------------+
|(value = foo)|(value <=> foo)|(value <=> NULL)|
+-------------+---------------+----------------+
| true| true| false|
| null| false| true|
+-------------+---------------+----------------+
>>> df2 = spark.createDataFrame([
... Row(value = 'bar'),
... Row(value = None)
... ])
>>> df1.join(df2, df1["value"] == df2["value"]).count()
0
>>> df1.join(df2, df1["value"].eqNullSafe(df2["value"])).count()
1
>>> df2 = spark.createDataFrame([
... Row(id=1, value=float('NaN')),
... Row(id=2, value=42.0),
... Row(id=3, value=None)
... ])
>>> df2.select(
... df2['value'].eqNullSafe(None),
... df2['value'].eqNullSafe(float('NaN')),
... df2['value'].eqNullSafe(42.0)
... ).show()
+----------------+---------------+----------------+
|(value <=> NULL)|(value <=> NaN)|(value <=> 42.0)|
+----------------+---------------+----------------+
| false| true| false|
| false| false| true|
| true| false| false|
+----------------+---------------+----------------+
.. note:: Unlike Pandas, PySpark doesn't consider NaN values to be NULL.
See the `NaN Semantics`_ for details.
.. _NaN Semantics:
https://spark.apache.org/docs/latest/sql-programming-guide.html#nan-semantics
.. versionadded:: 2.3.0
"""
eqNullSafe = _bin_op("eqNullSafe", _eqNullSafe_doc)
# `and`, `or`, `not` cannot be overloaded in Python,
# so use bitwise operators as boolean operators
__and__ = _bin_op('and')
__or__ = _bin_op('or')
__invert__ = _func_op('not')
__rand__ = _bin_op("and")
__ror__ = _bin_op("or")
# container operators
def __contains__(self, item):
raise ValueError("Cannot apply 'in' operator against a column: please use 'contains' "
"in a string column or 'array_contains' function for an array column.")
# bitwise operators
_bitwiseOR_doc = """
Compute bitwise OR of this expression with another expression.
:param other: a value or :class:`Column` to calculate bitwise or(|) against
this :class:`Column`.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(a=170, b=75)])
>>> df.select(df.a.bitwiseOR(df.b)).collect()
[Row((a | b)=235)]
"""
_bitwiseAND_doc = """
Compute bitwise AND of this expression with another expression.
:param other: a value or :class:`Column` to calculate bitwise and(&) against
this :class:`Column`.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(a=170, b=75)])
>>> df.select(df.a.bitwiseAND(df.b)).collect()
[Row((a & b)=10)]
"""
_bitwiseXOR_doc = """
Compute bitwise XOR of this expression with another expression.
:param other: a value or :class:`Column` to calculate bitwise xor(^) against
this :class:`Column`.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(a=170, b=75)])
>>> df.select(df.a.bitwiseXOR(df.b)).collect()
[Row((a ^ b)=225)]
"""
bitwiseOR = _bin_op("bitwiseOR", _bitwiseOR_doc)
bitwiseAND = _bin_op("bitwiseAND", _bitwiseAND_doc)
bitwiseXOR = _bin_op("bitwiseXOR", _bitwiseXOR_doc)
[docs] @since(1.3)
def getItem(self, key):
"""
An expression that gets an item at position ``ordinal`` out of a list,
or gets an item by key out of a dict.
>>> df = spark.createDataFrame([([1, 2], {"key": "value"})], ["l", "d"])
>>> df.select(df.l.getItem(0), df.d.getItem("key")).show()
+----+------+
|l[0]|d[key]|
+----+------+
| 1| value|
+----+------+
>>> df.select(df.l[0], df.d["key"]).show()
+----+------+
|l[0]|d[key]|
+----+------+
| 1| value|
+----+------+
"""
return self[key]
[docs] @since(1.3)
def getField(self, name):
"""
An expression that gets a field by name in a StructField.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(r=Row(a=1, b="b"))])
>>> df.select(df.r.getField("b")).show()
+---+
|r.b|
+---+
| b|
+---+
>>> df.select(df.r.a).show()
+---+
|r.a|
+---+
| 1|
+---+
"""
return self[name]
def __getattr__(self, item):
if item.startswith("__"):
raise AttributeError(item)
return self.getField(item)
def __getitem__(self, k):
if isinstance(k, slice):
if k.step is not None:
raise ValueError("slice with step is not supported.")
return self.substr(k.start, k.stop)
else:
return _bin_op("apply")(self, k)
def __iter__(self):
raise TypeError("Column is not iterable")
# string methods
_contains_doc = """
Contains the other element. Returns a boolean :class:`Column` based on a string match.
:param other: string in line
>>> df.filter(df.name.contains('o')).collect()
[Row(age=5, name=u'Bob')]
"""
_rlike_doc = """
SQL RLIKE expression (LIKE with Regex). Returns a boolean :class:`Column` based on a regex
match.
:param other: an extended regex expression
>>> df.filter(df.name.rlike('ice$')).collect()
[Row(age=2, name=u'Alice')]
"""
_like_doc = """
SQL like expression. Returns a boolean :class:`Column` based on a SQL LIKE match.
:param other: a SQL LIKE pattern
See :func:`rlike` for a regex version
>>> df.filter(df.name.like('Al%')).collect()
[Row(age=2, name=u'Alice')]
"""
_startswith_doc = """
String starts with. Returns a boolean :class:`Column` based on a string match.
:param other: string at start of line (do not use a regex `^`)
>>> df.filter(df.name.startswith('Al')).collect()
[Row(age=2, name=u'Alice')]
>>> df.filter(df.name.startswith('^Al')).collect()
[]
"""
_endswith_doc = """
String ends with. Returns a boolean :class:`Column` based on a string match.
:param other: string at end of line (do not use a regex `$`)
>>> df.filter(df.name.endswith('ice')).collect()
[Row(age=2, name=u'Alice')]
>>> df.filter(df.name.endswith('ice$')).collect()
[]
"""
contains = ignore_unicode_prefix(_bin_op("contains", _contains_doc))
rlike = ignore_unicode_prefix(_bin_op("rlike", _rlike_doc))
like = ignore_unicode_prefix(_bin_op("like", _like_doc))
startswith = ignore_unicode_prefix(_bin_op("startsWith", _startswith_doc))
endswith = ignore_unicode_prefix(_bin_op("endsWith", _endswith_doc))
[docs] @ignore_unicode_prefix
@since(1.3)
def substr(self, startPos, length):
"""
Return a :class:`Column` which is a substring of the column.
:param startPos: start position (int or Column)
:param length: length of the substring (int or Column)
>>> df.select(df.name.substr(1, 3).alias("col")).collect()
[Row(col=u'Ali'), Row(col=u'Bob')]
"""
if type(startPos) != type(length):
raise TypeError(
"startPos and length must be the same type. "
"Got {startPos_t} and {length_t}, respectively."
.format(
startPos_t=type(startPos),
length_t=type(length),
))
if isinstance(startPos, int):
jc = self._jc.substr(startPos, length)
elif isinstance(startPos, Column):
jc = self._jc.substr(startPos._jc, length._jc)
else:
raise TypeError("Unexpected type: %s" % type(startPos))
return Column(jc)
[docs] @ignore_unicode_prefix
@since(1.5)
def isin(self, *cols):
"""
A boolean expression that is evaluated to true if the value of this
expression is contained by the evaluated values of the arguments.
>>> df[df.name.isin("Bob", "Mike")].collect()
[Row(age=5, name=u'Bob')]
>>> df[df.age.isin([1, 2, 3])].collect()
[Row(age=2, name=u'Alice')]
"""
if len(cols) == 1 and isinstance(cols[0], (list, set)):
cols = cols[0]
cols = [c._jc if isinstance(c, Column) else _create_column_from_literal(c) for c in cols]
sc = SparkContext._active_spark_context
jc = getattr(self._jc, "isin")(_to_seq(sc, cols))
return Column(jc)
# order
_asc_doc = """
Returns a sort expression based on ascending order of the column.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.asc()).collect()
[Row(name=u'Alice'), Row(name=u'Tom')]
"""
_asc_nulls_first_doc = """
Returns a sort expression based on ascending order of the column, and null values
return before non-null values.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.asc_nulls_first()).collect()
[Row(name=None), Row(name=u'Alice'), Row(name=u'Tom')]
.. versionadded:: 2.4
"""
_asc_nulls_last_doc = """
Returns a sort expression based on ascending order of the column, and null values
appear after non-null values.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.asc_nulls_last()).collect()
[Row(name=u'Alice'), Row(name=u'Tom'), Row(name=None)]
.. versionadded:: 2.4
"""
_desc_doc = """
Returns a sort expression based on the descending order of the column.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.desc()).collect()
[Row(name=u'Tom'), Row(name=u'Alice')]
"""
_desc_nulls_first_doc = """
Returns a sort expression based on the descending order of the column, and null values
appear before non-null values.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.desc_nulls_first()).collect()
[Row(name=None), Row(name=u'Tom'), Row(name=u'Alice')]
.. versionadded:: 2.4
"""
_desc_nulls_last_doc = """
Returns a sort expression based on the descending order of the column, and null values
appear after non-null values.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([('Tom', 80), (None, 60), ('Alice', None)], ["name", "height"])
>>> df.select(df.name).orderBy(df.name.desc_nulls_last()).collect()
[Row(name=u'Tom'), Row(name=u'Alice'), Row(name=None)]
.. versionadded:: 2.4
"""
asc = ignore_unicode_prefix(_unary_op("asc", _asc_doc))
asc_nulls_first = ignore_unicode_prefix(_unary_op("asc_nulls_first", _asc_nulls_first_doc))
asc_nulls_last = ignore_unicode_prefix(_unary_op("asc_nulls_last", _asc_nulls_last_doc))
desc = ignore_unicode_prefix(_unary_op("desc", _desc_doc))
desc_nulls_first = ignore_unicode_prefix(_unary_op("desc_nulls_first", _desc_nulls_first_doc))
desc_nulls_last = ignore_unicode_prefix(_unary_op("desc_nulls_last", _desc_nulls_last_doc))
_isNull_doc = """
True if the current expression is null.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(name=u'Tom', height=80), Row(name=u'Alice', height=None)])
>>> df.filter(df.height.isNull()).collect()
[Row(height=None, name=u'Alice')]
"""
_isNotNull_doc = """
True if the current expression is NOT null.
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(name=u'Tom', height=80), Row(name=u'Alice', height=None)])
>>> df.filter(df.height.isNotNull()).collect()
[Row(height=80, name=u'Tom')]
"""
isNull = ignore_unicode_prefix(_unary_op("isNull", _isNull_doc))
isNotNull = ignore_unicode_prefix(_unary_op("isNotNull", _isNotNull_doc))
[docs] @since(1.3)
def alias(self, *alias, **kwargs):
"""
Returns this column aliased with a new name or names (in the case of expressions that
return more than one column, such as explode).
:param alias: strings of desired column names (collects all positional arguments passed)
:param metadata: a dict of information to be stored in ``metadata`` attribute of the
corresponding :class: `StructField` (optional, keyword only argument)
.. versionchanged:: 2.2
Added optional ``metadata`` argument.
>>> df.select(df.age.alias("age2")).collect()
[Row(age2=2), Row(age2=5)]
>>> df.select(df.age.alias("age3", metadata={'max': 99})).schema['age3'].metadata['max']
99
"""
metadata = kwargs.pop('metadata', None)
assert not kwargs, 'Unexpected kwargs where passed: %s' % kwargs
sc = SparkContext._active_spark_context
if len(alias) == 1:
if metadata:
jmeta = sc._jvm.org.apache.spark.sql.types.Metadata.fromJson(
json.dumps(metadata))
return Column(getattr(self._jc, "as")(alias[0], jmeta))
else:
return Column(getattr(self._jc, "as")(alias[0]))
else:
if metadata:
raise ValueError('metadata can only be provided for a single column')
return Column(getattr(self._jc, "as")(_to_seq(sc, list(alias))))
name = copy_func(alias, sinceversion=2.0, doc=":func:`name` is an alias for :func:`alias`.")
[docs] @ignore_unicode_prefix
@since(1.3)
def cast(self, dataType):
""" Convert the column into type ``dataType``.
>>> df.select(df.age.cast("string").alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
>>> df.select(df.age.cast(StringType()).alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
"""
if isinstance(dataType, basestring):
jc = self._jc.cast(dataType)
elif isinstance(dataType, DataType):
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
jdt = spark._jsparkSession.parseDataType(dataType.json())
jc = self._jc.cast(jdt)
else:
raise TypeError("unexpected type: %s" % type(dataType))
return Column(jc)
astype = copy_func(cast, sinceversion=1.4, doc=":func:`astype` is an alias for :func:`cast`.")
[docs] @since(1.3)
def between(self, lowerBound, upperBound):
"""
A boolean expression that is evaluated to true if the value of this
expression is between the given columns.
>>> df.select(df.name, df.age.between(2, 4)).show()
+-----+---------------------------+
| name|((age >= 2) AND (age <= 4))|
+-----+---------------------------+
|Alice| true|
| Bob| false|
+-----+---------------------------+
"""
return (self >= lowerBound) & (self <= upperBound)
[docs] @since(1.4)
def when(self, 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.
See :func:`pyspark.sql.functions.when` for example usage.
:param condition: a boolean :class:`Column` expression.
:param value: a literal value, or a :class:`Column` expression.
>>> from pyspark.sql import functions as F
>>> df.select(df.name, F.when(df.age > 4, 1).when(df.age < 3, -1).otherwise(0)).show()
+-----+------------------------------------------------------------+
| name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0 END|
+-----+------------------------------------------------------------+
|Alice| -1|
| Bob| 1|
+-----+------------------------------------------------------------+
"""
if not isinstance(condition, Column):
raise TypeError("condition should be a Column")
v = value._jc if isinstance(value, Column) else value
jc = self._jc.when(condition._jc, v)
return Column(jc)
[docs] @since(1.4)
def otherwise(self, 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.
See :func:`pyspark.sql.functions.when` for example usage.
:param value: a literal value, or a :class:`Column` expression.
>>> from pyspark.sql import functions as F
>>> df.select(df.name, F.when(df.age > 3, 1).otherwise(0)).show()
+-----+-------------------------------------+
| name|CASE WHEN (age > 3) THEN 1 ELSE 0 END|
+-----+-------------------------------------+
|Alice| 0|
| Bob| 1|
+-----+-------------------------------------+
"""
v = value._jc if isinstance(value, Column) else value
jc = self._jc.otherwise(v)
return Column(jc)
[docs] @since(1.4)
def over(self, window):
"""
Define a windowing column.
:param window: a :class:`WindowSpec`
:return: a Column
>>> from pyspark.sql import Window
>>> window = Window.partitionBy("name").orderBy("age").rowsBetween(-1, 1)
>>> from pyspark.sql.functions import rank, min
>>> # df.select(rank().over(window), min('age').over(window))
"""
from pyspark.sql.window import WindowSpec
if not isinstance(window, WindowSpec):
raise TypeError("window should be WindowSpec")
jc = self._jc.over(window._jspec)
return Column(jc)
def __nonzero__(self):
raise ValueError("Cannot convert column into bool: please use '&' for 'and', '|' for 'or', "
"'~' for 'not' when building DataFrame boolean expressions.")
__bool__ = __nonzero__
def __repr__(self):
return 'Column<%s>' % self._jc.toString().encode('utf8')
def _test():
import doctest
from pyspark.sql import SparkSession
import pyspark.sql.column
globs = pyspark.sql.column.__dict__.copy()
spark = SparkSession.builder\
.master("local[4]")\
.appName("sql.column tests")\
.getOrCreate()
sc = spark.sparkContext
globs['spark'] = spark
globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \
.toDF(StructType([StructField('age', IntegerType()),
StructField('name', StringType())]))
(failure_count, test_count) = doctest.testmod(
pyspark.sql.column, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
spark.stop()
if failure_count:
sys.exit(-1)
if __name__ == "__main__":
_test()
