#
# 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 itertools
import warnings
import random
from py4j.java_collections import ListConverter, MapConverter
from pyspark.context import SparkContext
from pyspark.rdd import RDD, _load_from_socket
from pyspark.serializers import BatchedSerializer, PickleSerializer, UTF8Deserializer
from pyspark.storagelevel import StorageLevel
from pyspark.traceback_utils import SCCallSiteSync
from pyspark.sql.types import *
from pyspark.sql.types import _create_cls, _parse_datatype_json_string
__all__ = ["DataFrame", "GroupedData", "Column", "SchemaRDD"]
class DataFrame(object):
"""A collection of rows that have the same columns.
A :class:`DataFrame` is equivalent to a relational table in Spark SQL,
and can be created using various functions in :class:`SQLContext`::
people = sqlContext.parquetFile("...")
Once created, it can be manipulated using the various domain-specific-language
(DSL) functions defined in: :class:`DataFrame`, :class:`Column`.
To select a column from the data frame, use the apply method::
ageCol = people.age
A more concrete example::
# To create DataFrame using SQLContext
people = sqlContext.parquetFile("...")
department = sqlContext.parquetFile("...")
people.filter(people.age > 30).join(department, people.deptId == department.id)) \
.groupBy(department.name, "gender").agg({"salary": "avg", "age": "max"})
"""
def __init__(self, jdf, sql_ctx):
self._jdf = jdf
self.sql_ctx = sql_ctx
self._sc = sql_ctx and sql_ctx._sc
self.is_cached = False
self._schema = None # initialized lazily
@property
def rdd(self):
"""
Return the content of the :class:`DataFrame` as an :class:`pyspark.RDD`
of :class:`Row` s.
"""
if not hasattr(self, '_lazy_rdd'):
jrdd = self._jdf.javaToPython()
rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
schema = self.schema
def applySchema(it):
cls = _create_cls(schema)
return itertools.imap(cls, it)
self._lazy_rdd = rdd.mapPartitions(applySchema)
return self._lazy_rdd
def toJSON(self, use_unicode=False):
"""Convert a :class:`DataFrame` into a MappedRDD of JSON documents; one document per row.
>>> df.toJSON().first()
'{"age":2,"name":"Alice"}'
"""
rdd = self._jdf.toJSON()
return RDD(rdd.toJavaRDD(), self._sc, UTF8Deserializer(use_unicode))
def saveAsParquetFile(self, path):
"""Save the contents as a Parquet file, preserving the schema.
Files that are written out using this method can be read back in as
a :class:`DataFrame` using the L{SQLContext.parquetFile} method.
>>> import tempfile, shutil
>>> parquetFile = tempfile.mkdtemp()
>>> shutil.rmtree(parquetFile)
>>> df.saveAsParquetFile(parquetFile)
>>> df2 = sqlCtx.parquetFile(parquetFile)
>>> sorted(df2.collect()) == sorted(df.collect())
True
"""
self._jdf.saveAsParquetFile(path)
def registerTempTable(self, name):
"""Registers this RDD as a temporary table using the given name.
The lifetime of this temporary table is tied to the L{SQLContext}
that was used to create this DataFrame.
>>> df.registerTempTable("people")
>>> df2 = sqlCtx.sql("select * from people")
>>> sorted(df.collect()) == sorted(df2.collect())
True
"""
self._jdf.registerTempTable(name)
def registerAsTable(self, name):
"""DEPRECATED: use registerTempTable() instead"""
warnings.warn("Use registerTempTable instead of registerAsTable.", DeprecationWarning)
self.registerTempTable(name)
def insertInto(self, tableName, overwrite=False):
"""Inserts the contents of this :class:`DataFrame` into the specified table.
Optionally overwriting any existing data.
"""
self._jdf.insertInto(tableName, overwrite)
def _java_save_mode(self, mode):
"""Returns the Java save mode based on the Python save mode represented by a string.
"""
jSaveMode = self._sc._jvm.org.apache.spark.sql.SaveMode
jmode = jSaveMode.ErrorIfExists
mode = mode.lower()
if mode == "append":
jmode = jSaveMode.Append
elif mode == "overwrite":
jmode = jSaveMode.Overwrite
elif mode == "ignore":
jmode = jSaveMode.Ignore
elif mode == "error":
pass
else:
raise ValueError(
"Only 'append', 'overwrite', 'ignore', and 'error' are acceptable save mode.")
return jmode
def saveAsTable(self, tableName, source=None, mode="error", **options):
"""Saves the contents of the :class:`DataFrame` to a data source as a table.
The data source is specified by the `source` and a set of `options`.
If `source` is not specified, the default data source configured by
spark.sql.sources.default will be used.
Additionally, mode is used to specify the behavior of the saveAsTable operation when
table already exists in the data source. There are four modes:
* append: Contents of this :class:`DataFrame` are expected to be appended \
to existing table.
* overwrite: Data in the existing table is expected to be overwritten by \
the contents of this DataFrame.
* error: An exception is expected to be thrown.
* ignore: The save operation is expected to not save the contents of the \
:class:`DataFrame` and to not change the existing table.
"""
if source is None:
source = self.sql_ctx.getConf("spark.sql.sources.default",
"org.apache.spark.sql.parquet")
jmode = self._java_save_mode(mode)
joptions = MapConverter().convert(options,
self.sql_ctx._sc._gateway._gateway_client)
self._jdf.saveAsTable(tableName, source, jmode, joptions)
def save(self, path=None, source=None, mode="error", **options):
"""Saves the contents of the :class:`DataFrame` to a data source.
The data source is specified by the `source` and a set of `options`.
If `source` is not specified, the default data source configured by
spark.sql.sources.default will be used.
Additionally, mode is used to specify the behavior of the save operation when
data already exists in the data source. There are four modes:
* append: Contents of this :class:`DataFrame` are expected to be appended to existing data.
* overwrite: Existing data is expected to be overwritten by the contents of this DataFrame.
* error: An exception is expected to be thrown.
* ignore: The save operation is expected to not save the contents of \
the :class:`DataFrame` and to not change the existing data.
"""
if path is not None:
options["path"] = path
if source is None:
source = self.sql_ctx.getConf("spark.sql.sources.default",
"org.apache.spark.sql.parquet")
jmode = self._java_save_mode(mode)
joptions = MapConverter().convert(options,
self._sc._gateway._gateway_client)
self._jdf.save(source, jmode, joptions)
@property
def schema(self):
"""Returns the schema of this :class:`DataFrame` (represented by
a L{StructType}).
>>> df.schema
StructType(List(StructField(age,IntegerType,true),StructField(name,StringType,true)))
"""
if self._schema is None:
self._schema = _parse_datatype_json_string(self._jdf.schema().json())
return self._schema
def printSchema(self):
"""Prints out the schema in the tree format.
>>> df.printSchema()
root
|-- age: integer (nullable = true)
|-- name: string (nullable = true)
<BLANKLINE>
"""
print (self._jdf.schema().treeString())
def explain(self, extended=False):
"""
Prints the plans (logical and physical) to the console for
debugging purpose.
If extended is False, only prints the physical plan.
>>> df.explain()
PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at mapPartitions at SQLContext.scala:...
>>> df.explain(True)
== Parsed Logical Plan ==
...
== Analyzed Logical Plan ==
...
== Optimized Logical Plan ==
...
== Physical Plan ==
...
== RDD ==
"""
if extended:
print self._jdf.queryExecution().toString()
else:
print self._jdf.queryExecution().executedPlan().toString()
def isLocal(self):
"""
Returns True if the `collect` and `take` methods can be run locally
(without any Spark executors).
"""
return self._jdf.isLocal()
def show(self, n=20):
"""
Print the first n rows.
>>> df
DataFrame[age: int, name: string]
>>> df.show()
age name
2 Alice
5 Bob
"""
print self._jdf.showString(n).encode('utf8', 'ignore')
def __repr__(self):
return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes))
def count(self):
"""Return the number of elements in this RDD.
Unlike the base RDD implementation of count, this implementation
leverages the query optimizer to compute the count on the DataFrame,
which supports features such as filter pushdown.
>>> df.count()
2L
"""
return self._jdf.count()
def collect(self):
"""Return a list that contains all of the rows.
Each object in the list is a Row, the fields can be accessed as
attributes.
>>> df.collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
"""
with SCCallSiteSync(self._sc) as css:
port = self._sc._jvm.PythonRDD.collectAndServe(self._jdf.javaToPython().rdd())
rs = list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
cls = _create_cls(self.schema)
return [cls(r) for r in rs]
def limit(self, num):
"""Limit the result count to the number specified.
>>> df.limit(1).collect()
[Row(age=2, name=u'Alice')]
>>> df.limit(0).collect()
[]
"""
jdf = self._jdf.limit(num)
return DataFrame(jdf, self.sql_ctx)
def take(self, num):
"""Take the first num rows of the RDD.
Each object in the list is a Row, the fields can be accessed as
attributes.
>>> df.take(2)
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
"""
return self.limit(num).collect()
def map(self, f):
""" Return a new RDD by applying a function to each Row
It's a shorthand for df.rdd.map()
>>> df.map(lambda p: p.name).collect()
[u'Alice', u'Bob']
"""
return self.rdd.map(f)
def flatMap(self, f):
""" Return a new RDD by first applying a function to all elements of this,
and then flattening the results.
It's a shorthand for df.rdd.flatMap()
>>> df.flatMap(lambda p: p.name).collect()
[u'A', u'l', u'i', u'c', u'e', u'B', u'o', u'b']
"""
return self.rdd.flatMap(f)
def mapPartitions(self, f, preservesPartitioning=False):
"""
Return a new RDD by applying a function to each partition.
It's a shorthand for df.rdd.mapPartitions()
>>> rdd = sc.parallelize([1, 2, 3, 4], 4)
>>> def f(iterator): yield 1
>>> rdd.mapPartitions(f).sum()
4
"""
return self.rdd.mapPartitions(f, preservesPartitioning)
def foreach(self, f):
"""
Applies a function to all rows of this DataFrame.
It's a shorthand for df.rdd.foreach()
>>> def f(person):
... print person.name
>>> df.foreach(f)
"""
return self.rdd.foreach(f)
def foreachPartition(self, f):
"""
Applies a function to each partition of this DataFrame.
It's a shorthand for df.rdd.foreachPartition()
>>> def f(people):
... for person in people:
... print person.name
>>> df.foreachPartition(f)
"""
return self.rdd.foreachPartition(f)
def cache(self):
""" Persist with the default storage level (C{MEMORY_ONLY_SER}).
"""
self.is_cached = True
self._jdf.cache()
return self
def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
""" Set the storage level to persist its values across operations
after the first time it is computed. This can only be used to assign
a new storage level if the RDD does not have a storage level set yet.
If no storage level is specified defaults to (C{MEMORY_ONLY_SER}).
"""
self.is_cached = True
javaStorageLevel = self._sc._getJavaStorageLevel(storageLevel)
self._jdf.persist(javaStorageLevel)
return self
def unpersist(self, blocking=True):
""" Mark it as non-persistent, and remove all blocks for it from
memory and disk.
"""
self.is_cached = False
self._jdf.unpersist(blocking)
return self
# def coalesce(self, numPartitions, shuffle=False):
# rdd = self._jdf.coalesce(numPartitions, shuffle, None)
# return DataFrame(rdd, self.sql_ctx)
def repartition(self, numPartitions):
""" Return a new :class:`DataFrame` that has exactly `numPartitions`
partitions.
>>> df.repartition(10).rdd.getNumPartitions()
10
"""
return DataFrame(self._jdf.repartition(numPartitions), self.sql_ctx)
def distinct(self):
"""
Return a new :class:`DataFrame` containing the distinct rows in this DataFrame.
>>> df.distinct().count()
2L
"""
return DataFrame(self._jdf.distinct(), self.sql_ctx)
def sample(self, withReplacement, fraction, seed=None):
"""
Return a sampled subset of this DataFrame.
>>> df.sample(False, 0.5, 97).count()
1L
"""
assert fraction >= 0.0, "Negative fraction value: %s" % fraction
seed = seed if seed is not None else random.randint(0, sys.maxint)
rdd = self._jdf.sample(withReplacement, fraction, long(seed))
return DataFrame(rdd, self.sql_ctx)
@property
def dtypes(self):
"""Return all column names and their data types as a list.
>>> df.dtypes
[('age', 'int'), ('name', 'string')]
"""
return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields]
@property
def columns(self):
""" Return all column names as a list.
>>> df.columns
[u'age', u'name']
"""
return [f.name for f in self.schema.fields]
def join(self, other, joinExprs=None, joinType=None):
"""
Join with another :class:`DataFrame`, using the given join expression.
The following performs a full outer join between `df1` and `df2`.
:param other: Right side of the join
:param joinExprs: Join expression
:param joinType: One of `inner`, `outer`, `left_outer`, `right_outer`, `semijoin`.
>>> df.join(df2, df.name == df2.name, 'outer').select(df.name, df2.height).collect()
[Row(name=None, height=80), Row(name=u'Bob', height=85), Row(name=u'Alice', height=None)]
"""
if joinExprs is None:
jdf = self._jdf.join(other._jdf)
else:
assert isinstance(joinExprs, Column), "joinExprs should be Column"
if joinType is None:
jdf = self._jdf.join(other._jdf, joinExprs._jc)
else:
assert isinstance(joinType, basestring), "joinType should be basestring"
jdf = self._jdf.join(other._jdf, joinExprs._jc, joinType)
return DataFrame(jdf, self.sql_ctx)
def sort(self, *cols):
""" Return a new :class:`DataFrame` sorted by the specified column(s).
:param cols: The columns or expressions used for sorting
>>> df.sort(df.age.desc()).collect()
[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
>>> df.orderBy(df.age.desc()).collect()
[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
>>> from pyspark.sql.functions import *
>>> df.sort(asc("age")).collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
>>> df.orderBy(desc("age"), "name").collect()
[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
"""
if not cols:
raise ValueError("should sort by at least one column")
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
self._sc._gateway._gateway_client)
jdf = self._jdf.sort(self._sc._jvm.PythonUtils.toSeq(jcols))
return DataFrame(jdf, self.sql_ctx)
orderBy = sort
def describe(self, *cols):
"""Computes statistics for numeric columns.
This include count, mean, stddev, min, and max. If no columns are
given, this function computes statistics for all numerical columns.
>>> df.describe().show()
summary age
count 2
mean 3.5
stddev 1.5
min 2
max 5
"""
cols = ListConverter().convert(cols,
self.sql_ctx._sc._gateway._gateway_client)
jdf = self._jdf.describe(self.sql_ctx._sc._jvm.PythonUtils.toSeq(cols))
return DataFrame(jdf, self.sql_ctx)
def head(self, n=None):
""" Return the first `n` rows or the first row if n is None.
>>> df.head()
Row(age=2, name=u'Alice')
>>> df.head(1)
[Row(age=2, name=u'Alice')]
"""
if n is None:
rs = self.head(1)
return rs[0] if rs else None
return self.take(n)
def first(self):
""" Return the first row.
>>> df.first()
Row(age=2, name=u'Alice')
"""
return self.head()
def __getitem__(self, item):
""" Return the column by given name
>>> df.select(df['age']).collect()
[Row(age=2), Row(age=5)]
>>> df[ ["name", "age"]].collect()
[Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)]
>>> df[ df.age > 3 ].collect()
[Row(age=5, name=u'Bob')]
"""
if isinstance(item, basestring):
jc = self._jdf.apply(item)
return Column(jc)
elif isinstance(item, Column):
return self.filter(item)
elif isinstance(item, list):
return self.select(*item)
else:
raise IndexError("unexpected index: %s" % item)
def __getattr__(self, name):
""" Return the column by given name
>>> df.select(df.age).collect()
[Row(age=2), Row(age=5)]
"""
if name.startswith("__"):
raise AttributeError(name)
jc = self._jdf.apply(name)
return Column(jc)
def select(self, *cols):
""" Selecting a set of expressions.
>>> df.select('*').collect()
[Row(age=2, name=u'Alice'), Row(age=5, name=u'Bob')]
>>> df.select('name', 'age').collect()
[Row(name=u'Alice', age=2), Row(name=u'Bob', age=5)]
>>> df.select(df.name, (df.age + 10).alias('age')).collect()
[Row(name=u'Alice', age=12), Row(name=u'Bob', age=15)]
"""
jcols = ListConverter().convert([_to_java_column(c) for c in cols],
self._sc._gateway._gateway_client)
jdf = self._jdf.select(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jcols))
return DataFrame(jdf, self.sql_ctx)
def selectExpr(self, *expr):
"""
Selects a set of SQL expressions. This is a variant of
`select` that accepts SQL expressions.
>>> df.selectExpr("age * 2", "abs(age)").collect()
[Row((age * 2)=4, Abs(age)=2), Row((age * 2)=10, Abs(age)=5)]
"""
jexpr = ListConverter().convert(expr, self._sc._gateway._gateway_client)
jdf = self._jdf.selectExpr(self._sc._jvm.PythonUtils.toSeq(jexpr))
return DataFrame(jdf, self.sql_ctx)
def filter(self, condition):
""" Filtering rows using the given condition, which could be
:class:`Column` expression or string of SQL expression.
where() is an alias for filter().
>>> df.filter(df.age > 3).collect()
[Row(age=5, name=u'Bob')]
>>> df.where(df.age == 2).collect()
[Row(age=2, name=u'Alice')]
>>> df.filter("age > 3").collect()
[Row(age=5, name=u'Bob')]
>>> df.where("age = 2").collect()
[Row(age=2, name=u'Alice')]
"""
if isinstance(condition, basestring):
jdf = self._jdf.filter(condition)
elif isinstance(condition, Column):
jdf = self._jdf.filter(condition._jc)
else:
raise TypeError("condition should be string or Column")
return DataFrame(jdf, self.sql_ctx)
where = filter
def groupBy(self, *cols):
""" Group the :class:`DataFrame` using the specified columns,
so we can run aggregation on them. See :class:`GroupedData`
for all the available aggregate functions.
>>> df.groupBy().avg().collect()
[Row(AVG(age)=3.5)]
>>> df.groupBy('name').agg({'age': 'mean'}).collect()
[Row(name=u'Bob', AVG(age)=5.0), Row(name=u'Alice', AVG(age)=2.0)]
>>> df.groupBy(df.name).avg().collect()
[Row(name=u'Bob', AVG(age)=5.0), Row(name=u'Alice', AVG(age)=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)=5)]
>>> from pyspark.sql import functions as F
>>> df.agg(F.min(df.age)).collect()
[Row(MIN(age)=2)]
"""
return self.groupBy().agg(*exprs)
def unionAll(self, other):
""" Return a new :class:`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 withColumn(self, colName, col):
""" Return a new :class:`DataFrame` by adding a column.
>>> df.withColumn('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 withColumnRenamed(self, existing, new):
""" Rename an existing column to a new name
>>> df.withColumnRenamed('age', 'age2').collect()
[Row(age2=2, name=u'Alice'), Row(age2=5, name=u'Bob')]
"""
cols = [Column(_to_java_column(c)).alias(new)
if c == existing else c
for c in self.columns]
return self.select(*cols)
def toPandas(self):
"""
Collect all the rows and return a `pandas.DataFrame`.
>>> df.toPandas() # 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
def df_varargs_api(f):
def _api(self, *args):
jargs = ListConverter().convert(args,
self.sql_ctx._sc._gateway._gateway_client)
name = f.__name__
jdf = getattr(self._jdf, name)(self.sql_ctx._sc._jvm.PythonUtils.toSeq(jargs))
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({"*": "count"}).collect()
[Row(name=u'Bob', COUNT(1)=1), Row(name=u'Alice', COUNT(1)=1)]
>>> from pyspark.sql import functions as F
>>> gdf.agg(F.min(df.age)).collect()
[Row(MIN(age)=5), Row(MIN(age)=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)]
"""
@df_varargs_api
def mean(self, *cols):
"""Compute the average value for each numeric columns
for each group. This is an alias for `avg`.
>>> df.groupBy().mean('age').collect()
[Row(AVG(age)=3.5)]
>>> df3.groupBy().mean('age', 'height').collect()
[Row(AVG(age)=3.5, AVG(height)=82.5)]
"""
@df_varargs_api
def avg(self, *cols):
"""Compute the average value for each numeric columns
for each group.
>>> df.groupBy().avg('age').collect()
[Row(AVG(age)=3.5)]
>>> df3.groupBy().avg('age', 'height').collect()
[Row(AVG(age)=3.5, AVG(height)=82.5)]
"""
@df_varargs_api
def max(self, *cols):
"""Compute the max value for each numeric columns for
each group.
>>> df.groupBy().max('age').collect()
[Row(MAX(age)=5)]
>>> df3.groupBy().max('age', 'height').collect()
[Row(MAX(age)=5, MAX(height)=85)]
"""
@df_varargs_api
def min(self, *cols):
"""Compute the min value for each numeric column for
each group.
>>> df.groupBy().min('age').collect()
[Row(MIN(age)=2)]
>>> df3.groupBy().min('age', 'height').collect()
[Row(MIN(age)=2, MIN(height)=80)]
"""
@df_varargs_api
def sum(self, *cols):
"""Compute the sum for each numeric columns for each
group.
>>> df.groupBy().sum('age').collect()
[Row(SUM(age)=7)]
>>> df3.groupBy().sum('age', 'height').collect()
[Row(SUM(age)=7, SUM(height)=165)]
"""
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 _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
"""
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")
__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__ = _func_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 :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
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
jcols = ListConverter().convert(cols, sc._gateway._gateway_client)
jc = getattr(self._jc, "in")(sc._jvm.PythonUtils.toSeq(jcols))
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.")
def alias(self, alias):
"""Return a alias for this column
>>> df.select(df.age.alias("age2")).collect()
[Row(age2=2), Row(age2=5)]
"""
return Column(getattr(self._jc, "as")(alias))
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)
def __repr__(self):
return 'Column<%s>' % self._jc.toString().encode('utf8')
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'] = SQLContext(sc)
globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')])\
.toDF(StructType([StructField('age', IntegerType()),
StructField('name', StringType())]))
globs['df2'] = sc.parallelize([Row(name='Tom', height=80), Row(name='Bob', height=85)]).toDF()
globs['df3'] = sc.parallelize([Row(name='Alice', age=2, height=80),
Row(name='Bob', age=5, height=85)]).toDF()
(failure_count, test_count) = doctest.testmod(
pyspark.sql.dataframe, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()