de80b1ba4d
Author: Michael Armbrust <michael@databricks.com> Closes #4436 from marmbrus/dfToString and squashes the following commits: 8a3c35f [Michael Armbrust] Merge remote-tracking branch 'origin/master' into dfToString b72a81b [Michael Armbrust] add toString
975 lines
32 KiB
Python
975 lines
32 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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import itertools
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import warnings
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import random
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import os
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from tempfile import NamedTemporaryFile
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from itertools import imap
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from py4j.java_collections import ListConverter, MapConverter
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from pyspark.context import SparkContext
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from pyspark.rdd import RDD, _prepare_for_python_RDD
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from pyspark.serializers import BatchedSerializer, AutoBatchedSerializer, PickleSerializer, \
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UTF8Deserializer
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from pyspark.storagelevel import StorageLevel
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from pyspark.traceback_utils import SCCallSiteSync
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from pyspark.sql.types import *
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from pyspark.sql.types import _create_cls, _parse_datatype_json_string
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__all__ = ["DataFrame", "GroupedData", "Column", "Dsl", "SchemaRDD"]
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class DataFrame(object):
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"""A collection of rows that have the same columns.
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A :class:`DataFrame` is equivalent to a relational table in Spark SQL,
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and can be created using various functions in :class:`SQLContext`::
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people = sqlContext.parquetFile("...")
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Once created, it can be manipulated using the various domain-specific-language
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(DSL) functions defined in: :class:`DataFrame`, :class:`Column`.
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To select a column from the data frame, use the apply method::
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ageCol = people.age
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Note that the :class:`Column` type can also be manipulated
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through its various functions::
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# The following creates a new column that increases everybody's age by 10.
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people.age + 10
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A more concrete example::
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# To create DataFrame using SQLContext
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people = sqlContext.parquetFile("...")
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department = sqlContext.parquetFile("...")
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people.filter(people.age > 30).join(department, people.deptId == department.id)) \
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.groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"})
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"""
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def __init__(self, jdf, sql_ctx):
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self._jdf = jdf
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self.sql_ctx = sql_ctx
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self._sc = sql_ctx and sql_ctx._sc
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self.is_cached = False
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@property
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def rdd(self):
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"""
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Return the content of the :class:`DataFrame` as an :class:`RDD`
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of :class:`Row` s.
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"""
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if not hasattr(self, '_lazy_rdd'):
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jrdd = self._jdf.javaToPython()
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rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
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schema = self.schema()
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def applySchema(it):
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cls = _create_cls(schema)
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return itertools.imap(cls, it)
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self._lazy_rdd = rdd.mapPartitions(applySchema)
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return self._lazy_rdd
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def toJSON(self, use_unicode=False):
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"""Convert a DataFrame into a MappedRDD of JSON documents; one document per row.
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>>> df.toJSON().first()
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'{"age":2,"name":"Alice"}'
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"""
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rdd = self._jdf.toJSON()
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return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode))
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def saveAsParquetFile(self, path):
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"""Save the contents as a Parquet file, preserving the schema.
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Files that are written out using this method can be read back in as
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a DataFrame using the L{SQLContext.parquetFile} method.
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>>> import tempfile, shutil
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>>> parquetFile = tempfile.mkdtemp()
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>>> shutil.rmtree(parquetFile)
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>>> df.saveAsParquetFile(parquetFile)
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>>> df2 = sqlCtx.parquetFile(parquetFile)
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>>> sorted(df2.collect()) == sorted(df.collect())
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True
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"""
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self._jdf.saveAsParquetFile(path)
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def registerTempTable(self, name):
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"""Registers this RDD as a temporary table using the given name.
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The lifetime of this temporary table is tied to the L{SQLContext}
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that was used to create this DataFrame.
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>>> df.registerTempTable("people")
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>>> df2 = sqlCtx.sql("select * from people")
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>>> sorted(df.collect()) == sorted(df2.collect())
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True
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"""
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self._jdf.registerTempTable(name)
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def registerAsTable(self, name):
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"""DEPRECATED: use registerTempTable() instead"""
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warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning)
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self.registerTempTable(name)
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def insertInto(self, tableName, overwrite=False):
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"""Inserts the contents of this DataFrame into the specified table.
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Optionally overwriting any existing data.
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"""
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self._jdf.insertInto(tableName, overwrite)
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def saveAsTable(self, tableName):
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"""Creates a new table with the contents of this DataFrame."""
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self._jdf.saveAsTable(tableName)
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def schema(self):
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"""Returns the schema of this DataFrame (represented by
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a L{StructType}).
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>>> df.schema()
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StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true)))
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"""
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return _parse_datatype_json_string(self._jdf.schema().json())
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def printSchema(self):
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"""Prints out the schema in the tree format.
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>>> df.printSchema()
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root
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|-- age: integer (nullable = true)
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|-- name: string (nullable = true)
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<BLANKLINE>
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"""
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print (self._jdf.schema().treeString())
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def count(self):
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"""Return the number of elements in this RDD.
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Unlike the base RDD implementation of count, this implementation
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leverages the query optimizer to compute the count on the DataFrame,
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which supports features such as filter pushdown.
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>>> df.count()
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2L
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"""
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return self._jdf.count()
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def collect(self):
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"""Return a list that contains all of the rows.
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Each object in the list is a Row, the fields can be accessed as
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attributes.
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>>> df.collect()
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[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
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"""
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with SCCallSiteSync(self._sc) as css:
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bytesInJava = self._jdf.javaToPython().collect().iterator()
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tempFile = NamedTemporaryFile(delete=False, dir=self._sc._temp_dir)
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tempFile.close()
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self._sc._writeToFile(bytesInJava, tempFile.name)
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# Read the data into Python and deserialize it:
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with open(tempFile.name, 'rb') as tempFile:
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rs = list(BatchedSerializer(PickleSerializer()).load_stream(tempFile))
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os.unlink(tempFile.name)
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cls = _create_cls(self.schema())
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return [cls(r) for r in rs]
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def limit(self, num):
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"""Limit the result count to the number specified.
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>>> df.limit(1).collect()
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[Row(age=2, name=u'Alice')]
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>>> df.limit(0).collect()
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[]
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"""
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jdf = self._jdf.limit(num)
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return DataFrame(jdf, self.sql_ctx)
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def take(self, num):
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"""Take the first num rows of the RDD.
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Each object in the list is a Row, the fields can be accessed as
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attributes.
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>>> df.take(2)
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[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
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"""
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return self.limit(num).collect()
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def map(self, f):
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""" Return a new RDD by applying a function to each Row, it's a
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shorthand for df.rdd.map()
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>>> df.map(lambda p: p.name).collect()
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[u'Alice', u'Bob']
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"""
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return self.rdd.map(f)
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def mapPartitions(self, f, preservesPartitioning=False):
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"""
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Return a new RDD by applying a function to each partition.
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>>> rdd = sc.parallelize([1, 2, 3, 4], 4)
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>>> def f(iterator): yield 1
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>>> rdd.mapPartitions(f).sum()
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4
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"""
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return self.rdd.mapPartitions(f, preservesPartitioning)
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def cache(self):
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""" Persist with the default storage level (C{MEMORY_ONLY_SER}).
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"""
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self.is_cached = True
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self._jdf.cache()
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return self
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def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
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""" Set the storage level to persist its values across operations
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after the first time it is computed. This can only be used to assign
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a new storage level if the RDD does not have a storage level set yet.
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If no storage level is specified defaults to (C{MEMORY_ONLY_SER}).
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"""
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self.is_cached = True
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javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
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self._jdf.persist(javaStorageLevel)
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return self
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def unpersist(self, blocking=True):
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""" Mark it as non-persistent, and remove all blocks for it from
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memory and disk.
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"""
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self.is_cached = False
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self._jdf.unpersist(blocking)
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return self
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# def coalesce(self, numPartitions, shuffle=False):
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# rdd = self._jdf.coalesce(numPartitions, shuffle, None)
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# return DataFrame(rdd, self.sql_ctx)
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def repartition(self, numPartitions):
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""" Return a new :class:`DataFrame` that has exactly `numPartitions`
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partitions.
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"""
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rdd = self._jdf.repartition(numPartitions, None)
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return DataFrame(rdd, self.sql_ctx)
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def sample(self, withReplacement, fraction, seed=None):
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"""
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Return a sampled subset of this DataFrame.
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>>> df.sample(False, 0.5, 97).count()
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1L
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"""
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assert fraction >= 0.0, "Negative fraction value: %s" % fraction
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seed = seed if seed is not None else random.randint(0, sys.maxint)
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rdd = self._jdf.sample(withReplacement, fraction, long(seed))
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return DataFrame(rdd, self.sql_ctx)
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# def takeSample(self, withReplacement, num, seed=None):
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# """Return a fixed-size sampled subset of this DataFrame.
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#
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# >>> df = sqlCtx.inferSchema(rdd)
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# >>> df.takeSample(False, 2, 97)
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# [Row(field1=3, field2=u'row3'), Row(field1=1, field2=u'row1')]
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# """
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# seed = seed if seed is not None else random.randint(0, sys.maxint)
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# with SCCallSiteSync(self.context) as css:
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# bytesInJava = self._jdf \
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# .takeSampleToPython(withReplacement, num, long(seed)) \
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# .iterator()
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# cls = _create_cls(self.schema())
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# return map(cls, self._collect_iterator_through_file(bytesInJava))
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@property
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def dtypes(self):
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"""Return all column names and their data types as a list.
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>>> df.dtypes
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[('age', 'integer'), ('name', 'string')]
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"""
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return [(str(f.name), f.dataType.jsonValue()) for f in self.schema().fields]
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@property
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def columns(self):
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""" Return all column names as a list.
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>>> df.columns
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[u'age', u'name']
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"""
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return [f.name for f in self.schema().fields]
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def join(self, other, joinExprs=None, joinType=None):
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"""
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Join with another DataFrame, using the given join expression.
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The following performs a full outer join between `df1` and `df2`::
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:param other: Right side of the join
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:param joinExprs: Join expression
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:param joinType: One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`.
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>>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect()
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[Row(name=None, height=80), Row(name=u'Bob', height=85), Row(name=u'Alice', height=None)]
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"""
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if joinExprs is None:
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jdf = self._jdf.join(other._jdf)
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else:
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assert isinstance(joinExprs, Column), "joinExprs should be Column"
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if joinType is None:
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jdf = self._jdf.join(other._jdf, joinExprs._jc)
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else:
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assert isinstance(joinType, basestring), "joinType should be basestring"
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jdf = self._jdf.join(other._jdf, joinExprs._jc, joinType)
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return DataFrame(jdf, self.sql_ctx)
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def sort(self, *cols):
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""" Return a new :class:`DataFrame` sorted by the specified column.
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:param cols: The columns or expressions used for sorting
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>>> df.sort(df.age.desc()).collect()
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[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
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>>> df.sortBy(df.age.desc()).collect()
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[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
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"""
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if not cols:
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raise ValueError("should sort by at least one column")
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jcols = ListConverter().convert([_to_java_column(c) for c in cols],
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self._sc._gateway._gateway_client)
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jdf = self._jdf.sort(self._sc._jvm.PythonUtils.toSeq(jcols))
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return DataFrame(jdf, self.sql_ctx)
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sortBy = sort
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def head(self, n=None):
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""" Return the first `n` rows or the first row if n is None.
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>>> df.head()
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Row(age=2, name=u'Alice')
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>>> df.head(1)
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[Row(age=2, name=u'Alice')]
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"""
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if n is None:
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rs = self.head(1)
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return rs[0] if rs else None
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return self.take(n)
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def first(self):
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""" Return the first row.
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>>> df.first()
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Row(age=2, name=u'Alice')
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"""
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return self.head()
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def __getitem__(self, item):
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""" Return the column by given name
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>>> df['age'].collect()
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[Row(age=2), Row(age=5)]
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>>> df[ ["name", "age"]].collect()
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[Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)]
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>>> df[ df.age > 3 ].collect()
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[Row(age=5, name=u'Bob')]
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"""
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if isinstance(item, basestring):
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jc = self._jdf.apply(item)
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return Column(jc, self.sql_ctx)
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elif isinstance(item, Column):
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return self.filter(item)
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elif isinstance(item, list):
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return self.select(*item)
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else:
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raise IndexError("unexpected index: %s" % item)
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def __getattr__(self, name):
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""" Return the column by given name
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>>> df.age.collect()
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[Row(age=2), Row(age=5)]
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"""
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if name.startswith("__"):
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raise AttributeError(name)
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jc = self._jdf.apply(name)
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return Column(jc, self.sql_ctx)
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def select(self, *cols):
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""" Selecting a set of expressions.
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>>> df.select().collect()
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[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
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>>> df.select('*').collect()
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[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
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>>> df.select('name', 'age').collect()
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[Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)]
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>>> df.select(df.name, (df.age + 10).alias('age')).collect()
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[Row(name=u'Alice', age=12), Row(name=u'Bob', age=15)]
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"""
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if not cols:
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cols = ["*"]
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jcols = ListConverter().convert([_to_java_column(c) for c in cols],
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self._sc._gateway._gateway_client)
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jdf = self._jdf.select(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols))
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return DataFrame(jdf, self.sql_ctx)
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def selectExpr(self, *expr):
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"""
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Selects a set of SQL expressions. This is a variant of
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`select` that accepts SQL expressions.
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>>> df.selectExpr("age * 2", "abs(age)").collect()
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[Row((age * 2)=4, Abs(age)=2), Row((age * 2)=10, Abs(age)=5)]
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"""
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jexpr = ListConverter().convert(expr, self._sc._gateway._gateway_client)
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jdf = self._jdf.selectExpr(self._sc._jvm.PythonUtils.toSeq(jexpr))
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return DataFrame(jdf, self.sql_ctx)
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def filter(self, condition):
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""" Filtering rows using the given condition, which could be
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Column expression or string of SQL expression.
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where() is an alias for filter().
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>>> df.filter(df.age > 3).collect()
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[Row(age=5, name=u'Bob')]
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>>> df.where(df.age == 2).collect()
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[Row(age=2, name=u'Alice')]
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>>> df.filter("age > 3").collect()
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[Row(age=5, name=u'Bob')]
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>>> df.where("age = 2").collect()
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[Row(age=2, name=u'Alice')]
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"""
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if isinstance(condition, basestring):
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jdf = self._jdf.filter(condition)
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elif isinstance(condition, Column):
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jdf = self._jdf.filter(condition._jc)
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else:
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raise TypeError("condition should be string or Column")
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return DataFrame(jdf, self.sql_ctx)
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where = filter
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def groupBy(self, *cols):
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""" Group the :class:`DataFrame` using the specified columns,
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so we can run aggregation on them. See :class:`GroupedData`
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for all the available aggregate functions.
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>>> df.groupBy().avg().collect()
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[Row(AVG(age#0)=3.5)]
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>>> df.groupBy('name').agg({'age': 'mean'}).collect()
|
|
[Row(name=u'Bob', AVG(age#0)=5.0), Row(name=u'Alice', AVG(age#0)=2.0)]
|
|
>>> df.groupBy(df.name).avg().collect()
|
|
[Row(name=u'Bob', AVG(age#0)=5.0), Row(name=u'Alice', AVG(age#0)=2.0)]
|
|
"""
|
|
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
|
|
self._sc._gateway._gateway_client)
|
|
jdf = self._jdf.groupBy(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols))
|
|
return GroupedData(jdf, self.sql_ctx)
|
|
|
|
def agg(self, *exprs):
|
|
""" Aggregate on the entire :class:`DataFrame` without groups
|
|
(shorthand for df.groupBy.agg()).
|
|
|
|
>>> df.agg({"age": "max"}).collect()
|
|
[Row(MAX(age#0)=5)]
|
|
>>> from pyspark.sql import Dsl
|
|
>>> df.agg(Dsl.min(df.age)).collect()
|
|
[Row(MIN(age#0)=2)]
|
|
"""
|
|
return self.groupBy().agg(*exprs)
|
|
|
|
def unionAll(self, other):
|
|
""" Return a new DataFrame containing union of rows in this
|
|
frame and another frame.
|
|
|
|
This is equivalent to `UNION ALL` in SQL.
|
|
"""
|
|
return DataFrame(self._jdf.unionAll(other._jdf), self.sql_ctx)
|
|
|
|
def intersect(self, other):
|
|
""" Return a new :class:`DataFrame` containing rows only in
|
|
both this frame and another frame.
|
|
|
|
This is equivalent to `INTERSECT` in SQL.
|
|
"""
|
|
return DataFrame(self._jdf.intersect(other._jdf), self.sql_ctx)
|
|
|
|
def subtract(self, other):
|
|
""" Return a new :class:`DataFrame` containing rows in this frame
|
|
but not in another frame.
|
|
|
|
This is equivalent to `EXCEPT` in SQL.
|
|
"""
|
|
return DataFrame(getattr(self._jdf, "except")(other._jdf), self.sql_ctx)
|
|
|
|
def addColumn(self, colName, col):
|
|
""" Return a new :class:`DataFrame` by adding a column.
|
|
|
|
>>> df.addColumn('age2', df.age + 2).collect()
|
|
[Row(age=2, name=u'Alice', age2=4), Row(age=5, name=u'Bob', age2=7)]
|
|
"""
|
|
return self.select('*', col.alias(colName))
|
|
|
|
def to_pandas(self):
|
|
"""
|
|
Collect all the rows and return a `pandas.DataFrame`.
|
|
|
|
>>> df.to_pandas() # doctest: +SKIP
|
|
age name
|
|
0 2 Alice
|
|
1 5 Bob
|
|
"""
|
|
import pandas as pd
|
|
return pd.DataFrame.from_records(self.collect(), columns=self.columns)
|
|
|
|
|
|
# Having SchemaRDD for backward compatibility (for docs)
|
|
class SchemaRDD(DataFrame):
|
|
"""
|
|
SchemaRDD is deprecated, please use DataFrame
|
|
"""
|
|
|
|
|
|
def dfapi(f):
|
|
def _api(self):
|
|
name = f.__name__
|
|
jdf = getattr(self._jdf, name)()
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
_api.__name__ = f.__name__
|
|
_api.__doc__ = f.__doc__
|
|
return _api
|
|
|
|
|
|
class GroupedData(object):
|
|
|
|
"""
|
|
A set of methods for aggregations on a :class:`DataFrame`,
|
|
created by DataFrame.groupBy().
|
|
"""
|
|
|
|
def __init__(self, jdf, sql_ctx):
|
|
self._jdf = jdf
|
|
self.sql_ctx = sql_ctx
|
|
|
|
def agg(self, *exprs):
|
|
""" Compute aggregates by specifying a map from column name
|
|
to aggregate methods.
|
|
|
|
The available aggregate methods are `avg`, `max`, `min`,
|
|
`sum`, `count`.
|
|
|
|
:param exprs: list or aggregate columns or a map from column
|
|
name to aggregate methods.
|
|
|
|
>>> gdf = df.groupBy(df.name)
|
|
>>> gdf.agg({"age": "max"}).collect()
|
|
[Row(name=u'Bob', MAX(age#0)=5), Row(name=u'Alice', MAX(age#0)=2)]
|
|
>>> from pyspark.sql import Dsl
|
|
>>> gdf.agg(Dsl.min(df.age)).collect()
|
|
[Row(MIN(age#0)=5), Row(MIN(age#0)=2)]
|
|
"""
|
|
assert exprs, "exprs should not be empty"
|
|
if len(exprs) == 1 and isinstance(exprs[0], dict):
|
|
jmap = MapConverter().convert(exprs[0],
|
|
self.sql_ctx._sc._gateway._gateway_client)
|
|
jdf = self._jdf.agg(jmap)
|
|
else:
|
|
# Columns
|
|
assert all(isinstance(c, Column) for c in exprs), "all exprs should be Column"
|
|
jcols = ListConverter().convert([c._jc for c in exprs[1:]],
|
|
self.sql_ctx._sc._gateway._gateway_client)
|
|
jdf = self._jdf.agg(exprs[0]._jc, self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols))
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
|
|
@dfapi
|
|
def count(self):
|
|
""" Count the number of rows for each group.
|
|
|
|
>>> df.groupBy(df.age).count().collect()
|
|
[Row(age=2, count=1), Row(age=5, count=1)]
|
|
"""
|
|
|
|
@dfapi
|
|
def mean(self):
|
|
"""Compute the average value for each numeric columns
|
|
for each group. This is an alias for `avg`."""
|
|
|
|
@dfapi
|
|
def avg(self):
|
|
"""Compute the average value for each numeric columns
|
|
for each group."""
|
|
|
|
@dfapi
|
|
def max(self):
|
|
"""Compute the max value for each numeric columns for
|
|
each group. """
|
|
|
|
@dfapi
|
|
def min(self):
|
|
"""Compute the min value for each numeric column for
|
|
each group."""
|
|
|
|
@dfapi
|
|
def sum(self):
|
|
"""Compute the sum for each numeric columns for each
|
|
group."""
|
|
|
|
|
|
def _create_column_from_literal(literal):
|
|
sc = SparkContext._active_spark_context
|
|
return sc._jvm.Dsl.lit(literal)
|
|
|
|
|
|
def _create_column_from_name(name):
|
|
sc = SparkContext._active_spark_context
|
|
return sc._jvm.Dsl.col(name)
|
|
|
|
|
|
def _to_java_column(col):
|
|
if isinstance(col, Column):
|
|
jcol = col._jc
|
|
else:
|
|
jcol = _create_column_from_name(col)
|
|
return jcol
|
|
|
|
|
|
def _unary_op(name, doc="unary operator"):
|
|
""" Create a method for given unary operator """
|
|
def _(self):
|
|
jc = getattr(self._jc, name)()
|
|
return Column(jc, self.sql_ctx)
|
|
_.__doc__ = doc
|
|
return _
|
|
|
|
|
|
def _dsl_op(name, doc=''):
|
|
def _(self):
|
|
jc = getattr(self._sc._jvm.Dsl, name)(self._jc)
|
|
return Column(jc, self.sql_ctx)
|
|
_.__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, self.sql_ctx)
|
|
_.__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, self.sql_ctx)
|
|
_.__doc__ = doc
|
|
return _
|
|
|
|
|
|
class Column(DataFrame):
|
|
|
|
"""
|
|
A column in a DataFrame.
|
|
|
|
`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
|
|
"""
|
|
|
|
def __init__(self, jc, sql_ctx=None):
|
|
self._jc = jc
|
|
super(Column, self).__init__(jc, sql_ctx)
|
|
|
|
# arithmetic operators
|
|
__neg__ = _dsl_op("negate")
|
|
__add__ = _bin_op("plus")
|
|
__sub__ = _bin_op("minus")
|
|
__mul__ = _bin_op("multiply")
|
|
__div__ = _bin_op("divide")
|
|
__mod__ = _bin_op("mod")
|
|
__radd__ = _bin_op("plus")
|
|
__rsub__ = _reverse_op("minus")
|
|
__rmul__ = _bin_op("multiply")
|
|
__rdiv__ = _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__ = _dsl_op('not')
|
|
__rand__ = _bin_op("and")
|
|
__ror__ = _bin_op("or")
|
|
|
|
# container operators
|
|
__contains__ = _bin_op("contains")
|
|
__getitem__ = _bin_op("getItem")
|
|
getField = _bin_op("getField", "An expression that gets a field by name in a StructField.")
|
|
|
|
# string methods
|
|
rlike = _bin_op("rlike")
|
|
like = _bin_op("like")
|
|
startswith = _bin_op("startsWith")
|
|
endswith = _bin_op("endsWith")
|
|
|
|
def substr(self, startPos, length):
|
|
"""
|
|
Return a 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.name.substr(1, 3).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, self.sql_ctx)
|
|
|
|
__getslice__ = substr
|
|
|
|
# order
|
|
asc = _unary_op("asc")
|
|
desc = _unary_op("desc")
|
|
|
|
isNull = _unary_op("isNull", "True if the current expression is null.")
|
|
isNotNull = _unary_op("isNotNull", "True if the current expression is not null.")
|
|
|
|
def alias(self, alias):
|
|
"""Return a alias for this column
|
|
|
|
>>> df.age.alias("age2").collect()
|
|
[Row(age2=2), Row(age2=5)]
|
|
"""
|
|
return Column(getattr(self._jc, "as")(alias), self.sql_ctx)
|
|
|
|
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 self.sql_ctx is None:
|
|
sc = SparkContext._active_spark_context
|
|
ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
|
|
else:
|
|
ssql_ctx = self.sql_ctx._ssql_ctx
|
|
if isinstance(dataType, basestring):
|
|
jc = self._jc.cast(dataType)
|
|
elif isinstance(dataType, DataType):
|
|
jdt = ssql_ctx.parseDataType(dataType.json())
|
|
jc = self._jc.cast(jdt)
|
|
return Column(jc, self.sql_ctx)
|
|
|
|
def to_pandas(self):
|
|
"""
|
|
Return a pandas.Series from the column
|
|
|
|
>>> df.age.to_pandas() # doctest: +SKIP
|
|
0 2
|
|
1 5
|
|
dtype: int64
|
|
"""
|
|
import pandas as pd
|
|
data = [c for c, in self.collect()]
|
|
return pd.Series(data)
|
|
|
|
|
|
def _aggregate_func(name, doc=""):
|
|
""" Create a function for aggregator by name"""
|
|
def _(col):
|
|
sc = SparkContext._active_spark_context
|
|
jc = getattr(sc._jvm.Dsl, name)(_to_java_column(col))
|
|
return Column(jc)
|
|
_.__name__ = name
|
|
_.__doc__ = doc
|
|
return staticmethod(_)
|
|
|
|
|
|
class UserDefinedFunction(object):
|
|
def __init__(self, func, returnType):
|
|
self.func = func
|
|
self.returnType = returnType
|
|
self._broadcast = None
|
|
self._judf = self._create_judf()
|
|
|
|
def _create_judf(self):
|
|
f = self.func # put it in closure `func`
|
|
func = lambda _, it: imap(lambda x: f(*x), it)
|
|
ser = AutoBatchedSerializer(PickleSerializer())
|
|
command = (func, None, ser, ser)
|
|
sc = SparkContext._active_spark_context
|
|
pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command, self)
|
|
ssql_ctx = sc._jvm.SQLContext(sc._jsc.sc())
|
|
jdt = ssql_ctx.parseDataType(self.returnType.json())
|
|
judf = sc._jvm.UserDefinedPythonFunction(f.__name__, bytearray(pickled_command), env,
|
|
includes, sc.pythonExec, broadcast_vars,
|
|
sc._javaAccumulator, jdt)
|
|
return judf
|
|
|
|
def __del__(self):
|
|
if self._broadcast is not None:
|
|
self._broadcast.unpersist()
|
|
self._broadcast = None
|
|
|
|
def __call__(self, *cols):
|
|
sc = SparkContext._active_spark_context
|
|
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
|
|
sc._gateway._gateway_client)
|
|
jc = self._judf.apply(sc._jvm.PythonUtils.toSeq(jcols))
|
|
return Column(jc)
|
|
|
|
|
|
class Dsl(object):
|
|
"""
|
|
A collections of builtin aggregators
|
|
"""
|
|
DSLS = {
|
|
'lit': 'Creates a :class:`Column` of literal value.',
|
|
'col': 'Returns a :class:`Column` based on the given column name.',
|
|
'column': 'Returns a :class:`Column` based on the given column name.',
|
|
'upper': 'Converts a string expression to upper case.',
|
|
'lower': 'Converts a string expression to upper case.',
|
|
'sqrt': 'Computes the square root of the specified float value.',
|
|
'abs': 'Computes the absolutle 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.',
|
|
'first': 'Aggregate function: returns the first value in a group.',
|
|
'last': 'Aggregate function: returns the last value 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.',
|
|
}
|
|
|
|
for _name, _doc in DSLS.items():
|
|
locals()[_name] = _aggregate_func(_name, _doc)
|
|
del _name, _doc
|
|
|
|
@staticmethod
|
|
def countDistinct(col, *cols):
|
|
""" Return a new Column for distinct count of (col, *cols)
|
|
|
|
>>> from pyspark.sql import Dsl
|
|
>>> df.agg(Dsl.countDistinct(df.age, df.name).alias('c')).collect()
|
|
[Row(c=2)]
|
|
|
|
>>> df.agg(Dsl.countDistinct("age", "name").alias('c')).collect()
|
|
[Row(c=2)]
|
|
"""
|
|
sc = SparkContext._active_spark_context
|
|
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
|
|
sc._gateway._gateway_client)
|
|
jc = sc._jvm.Dsl.countDistinct(_to_java_column(col),
|
|
sc._jvm.PythonUtils.toSeq(jcols))
|
|
return Column(jc)
|
|
|
|
@staticmethod
|
|
def approxCountDistinct(col, rsd=None):
|
|
""" Return a new Column for approxiate distinct count of (col, *cols)
|
|
|
|
>>> from pyspark.sql import Dsl
|
|
>>> df.agg(Dsl.approxCountDistinct(df.age).alias('c')).collect()
|
|
[Row(c=2)]
|
|
"""
|
|
sc = SparkContext._active_spark_context
|
|
if rsd is None:
|
|
jc = sc._jvm.Dsl.approxCountDistinct(_to_java_column(col))
|
|
else:
|
|
jc = sc._jvm.Dsl.approxCountDistinct(_to_java_column(col), rsd)
|
|
return Column(jc)
|
|
|
|
@staticmethod
|
|
def udf(f, returnType=StringType()):
|
|
"""Create a user defined function (UDF)
|
|
|
|
>>> slen = Dsl.udf(lambda s: len(s), IntegerType())
|
|
>>> df.select(slen(df.name).alias('slen')).collect()
|
|
[Row(slen=5), Row(slen=3)]
|
|
"""
|
|
return UserDefinedFunction(f, returnType)
|
|
|
|
|
|
def _test():
|
|
import doctest
|
|
from pyspark.context import SparkContext
|
|
from pyspark.sql import Row, SQLContext
|
|
import pyspark.sql.dataframe
|
|
globs = pyspark.sql.dataframe.__dict__.copy()
|
|
sc = SparkContext('local[4]', 'PythonTest')
|
|
globs['sc'] = sc
|
|
globs['sqlCtx'] = sqlCtx = SQLContext(sc)
|
|
rdd2 = sc.parallelize([Row(name='Alice', age=2), Row(name='Bob', age=5)])
|
|
rdd3 = sc.parallelize([Row(name='Tom', height=80), Row(name='Bob', height=85)])
|
|
globs['df'] = sqlCtx.inferSchema(rdd2)
|
|
globs['df2'] = sqlCtx.inferSchema(rdd3)
|
|
(failure_count, test_count) = doctest.testmod(
|
|
pyspark.sql.dataframe, globs=globs, optionflags=doctest.ELLIPSIS)
|
|
globs['sc'].stop()
|
|
if failure_count:
|
|
exit(-1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
_test()
|