#
# 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
if sys.version >= '3':
basestring = str
long = int
from pyspark.context import SparkContext
from pyspark.rdd import ignore_unicode_prefix
from pyspark.sql import since
from pyspark.sql.types import *
__all__ = ["DataFrame", "Column", "SchemaRDD", "DataFrameNaFunctions",
"DataFrameStatFunctions"]
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
else:
jcol = _create_column_from_name(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 _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_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 _
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
.. note:: Experimental
.. 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")
# 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")
# `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
__contains__ = _bin_op("contains")
__getitem__ = _bin_op("apply")
# bitwise operators
bitwiseOR = _bin_op("bitwiseOR")
bitwiseAND = _bin_op("bitwiseAND")
bitwiseXOR = _bin_op("bitwiseXOR")
@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 = sc.parallelize([([1, 2], {"key": "value"})]).toDF(["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]
@since(1.3)
def getField(self, name):
"""
An expression that gets a field by name in a StructField.
>>> from pyspark.sql import Row
>>> df = sc.parallelize([Row(r=Row(a=1, b="b"))]).toDF()
>>> 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)
# string methods
rlike = _bin_op("rlike")
like = _bin_op("like")
startswith = _bin_op("startsWith")
endswith = _bin_op("endsWith")
@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("Can not mix the type")
if isinstance(startPos, (int, long)):
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)
__getslice__ = substr
@ignore_unicode_prefix
@since(1.3)
def inSet(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.inSet("Bob", "Mike")].collect()
[Row(age=5, name=u'Bob')]
>>> df[df.age.inSet([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, "in")(_to_seq(sc, cols))
return Column(jc)
# order
asc = _unary_op("asc", "Returns a sort expression based on the"
" ascending order of the given column name.")
desc = _unary_op("desc", "Returns a sort expression based on the"
" descending order of the given column name.")
isNull = _unary_op("isNull", "True if the current expression is null.")
isNotNull = _unary_op("isNotNull", "True if the current expression is not null.")
@since(1.3)
def alias(self, *alias):
"""
Returns this column aliased with a new name or names (in the case of expressions that
return more than one column, such as explode).
>>> df.select(df.age.alias("age2")).collect()
[Row(age2=2), Row(age2=5)]
"""
if len(alias) == 1:
return Column(getattr(self._jc, "as")(alias[0]))
else:
sc = SparkContext._active_spark_context
return Column(getattr(self._jc, "as")(_to_seq(sc, list(alias))))
@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):
sc = SparkContext._active_spark_context
ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
jdt = ssql_ctx.parseDataType(dataType.json())
jc = self._jc.cast(jdt)
else:
raise TypeError("unexpected type: %s" % type(dataType))
return Column(jc)
astype = cast
@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) && (age <= 4))|
+-----+--------------------------+
|Alice| true|
| Bob| false|
+-----+--------------------------+
"""
return (self >= lowerBound) & (self <= upperBound)
@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|
+-----+--------------------------------------------------------+
|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)
@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|
+-----+---------------------------------+
|Alice| 0|
| Bob| 1|
+-----+---------------------------------+
"""
v = value._jc if isinstance(value, Column) else value
jc = self._jc.otherwise(v)
return Column(jc)
@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))
.. note:: Window functions is only supported with HiveContext in 1.4
"""
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.context import SparkContext
from pyspark.sql import SQLContext
import pyspark.sql.column
globs = pyspark.sql.column.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
globs['sc'] = sc
globs['sqlContext'] = SQLContext(sc)
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)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
