3af4c0b4e8
Author: Reynold Xin <rxin@databricks.com>
Closes #6569 from rxin/freqItemsWarning and squashes the following commits:
7eec145 [Reynold Xin] [minor doc] Add exploratory data analysis warning for DataFrame.stat.freqItem API.
(cherry picked from commit 4c868b9943)
Signed-off-by: Reynold Xin <rxin@databricks.com>
1337 lines
47 KiB
Python
1337 lines
47 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 warnings
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import random
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if sys.version >= '3':
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basestring = unicode = str
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long = int
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else:
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from itertools import imap as map
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from pyspark.rdd import RDD, _load_from_socket, ignore_unicode_prefix
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from pyspark.serializers import BatchedSerializer, PickleSerializer, 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 import since
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from pyspark.sql.types import _create_cls, _parse_datatype_json_string
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from pyspark.sql.column import Column, _to_seq, _to_java_column
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from pyspark.sql.readwriter import DataFrameWriter
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from pyspark.sql.types import *
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__all__ = ["DataFrame", "SchemaRDD", "DataFrameNaFunctions", "DataFrameStatFunctions"]
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class DataFrame(object):
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"""A distributed collection of data grouped into named 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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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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.. note:: Experimental
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.. versionadded:: 1.3
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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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self._schema = None # initialized lazily
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self._lazy_rdd = None
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@property
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@since(1.3)
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def rdd(self):
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"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
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"""
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if self._lazy_rdd is None:
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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 map(cls, it)
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self._lazy_rdd = rdd.mapPartitions(applySchema)
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return self._lazy_rdd
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@property
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@since("1.3.1")
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def na(self):
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"""Returns a :class:`DataFrameNaFunctions` for handling missing values.
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"""
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return DataFrameNaFunctions(self)
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@property
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@since(1.4)
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def stat(self):
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"""Returns a :class:`DataFrameStatFunctions` for statistic functions.
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"""
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return DataFrameStatFunctions(self)
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@ignore_unicode_prefix
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@since(1.3)
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def toJSON(self, use_unicode=True):
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"""Converts a :class:`DataFrame` into a :class:`RDD` of string.
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Each row is turned into a JSON document as one element in the returned RDD.
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>>> df.toJSON().first()
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u'{"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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@since(1.3)
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def saveAsParquetFile(self, path):
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"""Saves 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 :class:`DataFrame` using :func:`SQLContext.parquetFile`.
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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 = sqlContext.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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@since(1.3)
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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 :class:`SQLContext`
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that was used to create this :class:`DataFrame`.
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>>> df.registerTempTable("people")
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>>> df2 = sqlContext.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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@since(1.3)
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def registerAsTable(self, name):
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"""DEPRECATED: use :func:`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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@since(1.3)
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def insertInto(self, tableName, overwrite=False):
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"""Inserts the contents of this :class:`DataFrame` into the specified table.
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Optionally overwriting any existing data.
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"""
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self.write.insertInto(tableName, overwrite)
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@since(1.3)
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def saveAsTable(self, tableName, source=None, mode="error", **options):
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"""Saves the contents of this :class:`DataFrame` to a data source as a table.
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The data source is specified by the ``source`` and a set of ``options``.
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If ``source`` is not specified, the default data source configured by
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``spark.sql.sources.default`` will be used.
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Additionally, mode is used to specify the behavior of the saveAsTable operation when
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table already exists in the data source. There are four modes:
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* `append`: Append contents of this :class:`DataFrame` to existing data.
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* `overwrite`: Overwrite existing data.
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* `error`: Throw an exception if data already exists.
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* `ignore`: Silently ignore this operation if data already exists.
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"""
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self.write.saveAsTable(tableName, source, mode, **options)
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@since(1.3)
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def save(self, path=None, source=None, mode="error", **options):
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"""Saves the contents of the :class:`DataFrame` to a data source.
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The data source is specified by the ``source`` and a set of ``options``.
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If ``source`` is not specified, the default data source configured by
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``spark.sql.sources.default`` will be used.
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Additionally, mode is used to specify the behavior of the save operation when
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data already exists in the data source. There are four modes:
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* `append`: Append contents of this :class:`DataFrame` to existing data.
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* `overwrite`: Overwrite existing data.
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* `error`: Throw an exception if data already exists.
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* `ignore`: Silently ignore this operation if data already exists.
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"""
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return self.write.save(path, source, mode, **options)
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@property
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@since(1.4)
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def write(self):
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"""
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Interface for saving the content of the :class:`DataFrame` out
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into external storage.
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:return :class:`DataFrameWriter`
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.. note:: Experimental
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>>> df.write
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<pyspark.sql.readwriter.DataFrameWriter object at ...>
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"""
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return DataFrameWriter(self)
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@property
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@since(1.3)
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def schema(self):
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"""Returns the schema of this :class:`DataFrame` as a :class:`types.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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if self._schema is None:
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self._schema = _parse_datatype_json_string(self._jdf.schema().json())
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return self._schema
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@since(1.3)
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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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@since(1.3)
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def explain(self, extended=False):
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"""Prints the (logical and physical) plans to the console for debugging purpose.
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:param extended: boolean, default ``False``. If ``False``, prints only the physical plan.
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>>> df.explain()
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PhysicalRDD [age#0,name#1], MapPartitionsRDD[...] at applySchemaToPythonRDD at\
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NativeMethodAccessorImpl.java:...
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>>> df.explain(True)
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== Parsed Logical Plan ==
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...
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== Analyzed Logical Plan ==
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...
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== Optimized Logical Plan ==
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...
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== Physical Plan ==
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...
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== RDD ==
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"""
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if extended:
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print(self._jdf.queryExecution().toString())
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else:
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print(self._jdf.queryExecution().executedPlan().toString())
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@since(1.3)
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def isLocal(self):
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"""Returns ``True`` if the :func:`collect` and :func:`take` methods can be run locally
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(without any Spark executors).
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"""
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return self._jdf.isLocal()
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@since(1.3)
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def show(self, n=20):
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"""Prints the first ``n`` rows to the console.
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>>> df
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DataFrame[age: int, name: string]
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>>> df.show()
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+---+-----+
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|age| name|
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+---+-----+
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| 2|Alice|
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| 5| Bob|
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+---+-----+
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"""
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print(self._jdf.showString(n))
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def __repr__(self):
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return "DataFrame[%s]" % (", ".join("%s: %s" % c for c in self.dtypes))
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@since(1.3)
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def count(self):
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"""Returns the number of rows in this :class:`DataFrame`.
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>>> df.count()
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2
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"""
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return int(self._jdf.count())
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@ignore_unicode_prefix
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@since(1.3)
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def collect(self):
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"""Returns all the records as a list of :class:`Row`.
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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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port = self._sc._jvm.PythonRDD.collectAndServe(self._jdf.javaToPython().rdd())
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rs = list(_load_from_socket(port, BatchedSerializer(PickleSerializer())))
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cls = _create_cls(self.schema)
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return [cls(r) for r in rs]
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@ignore_unicode_prefix
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@since(1.3)
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def limit(self, num):
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"""Limits 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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@ignore_unicode_prefix
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@since(1.3)
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def take(self, num):
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"""Returns the first ``num`` rows as a :class:`list` of :class:`Row`.
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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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@ignore_unicode_prefix
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@since(1.3)
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def map(self, f):
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""" Returns a new :class:`RDD` by applying a the ``f`` function to each :class:`Row`.
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This is a 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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@ignore_unicode_prefix
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@since(1.3)
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def flatMap(self, f):
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""" Returns a new :class:`RDD` by first applying the ``f`` function to each :class:`Row`,
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and then flattening the results.
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This is a shorthand for ``df.rdd.flatMap()``.
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>>> df.flatMap(lambda p: p.name).collect()
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[u'A', u'l', u'i', u'c', u'e', u'B', u'o', u'b']
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"""
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return self.rdd.flatMap(f)
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@since(1.3)
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def mapPartitions(self, f, preservesPartitioning=False):
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"""Returns a new :class:`RDD` by applying the ``f`` function to each partition.
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This is a shorthand for ``df.rdd.mapPartitions()``.
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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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@since(1.3)
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def foreach(self, f):
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"""Applies the ``f`` function to all :class:`Row` of this :class:`DataFrame`.
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This is a shorthand for ``df.rdd.foreach()``.
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>>> def f(person):
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... print(person.name)
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>>> df.foreach(f)
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"""
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return self.rdd.foreach(f)
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@since(1.3)
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def foreachPartition(self, f):
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"""Applies the ``f`` function to each partition of this :class:`DataFrame`.
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This a shorthand for ``df.rdd.foreachPartition()``.
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>>> def f(people):
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... for person in people:
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... print(person.name)
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>>> df.foreachPartition(f)
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"""
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return self.rdd.foreachPartition(f)
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@since(1.3)
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def cache(self):
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""" Persists 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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@since(1.3)
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def persist(self, storageLevel=StorageLevel.MEMORY_ONLY_SER):
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"""Sets 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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@since(1.3)
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def unpersist(self, blocking=True):
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"""Marks the :class:`DataFrame` 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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@since(1.4)
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def coalesce(self, numPartitions):
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"""
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Returns a new :class:`DataFrame` that has exactly `numPartitions` partitions.
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Similar to coalesce defined on an :class:`RDD`, this operation results in a
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narrow dependency, e.g. if you go from 1000 partitions to 100 partitions,
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there will not be a shuffle, instead each of the 100 new partitions will
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claim 10 of the current partitions.
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>>> df.coalesce(1).rdd.getNumPartitions()
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1
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"""
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return DataFrame(self._jdf.coalesce(numPartitions), self.sql_ctx)
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@since(1.3)
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def repartition(self, numPartitions):
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"""Returns a new :class:`DataFrame` that has exactly ``numPartitions`` partitions.
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>>> df.repartition(10).rdd.getNumPartitions()
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10
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"""
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return DataFrame(self._jdf.repartition(numPartitions), self.sql_ctx)
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@since(1.3)
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def distinct(self):
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"""Returns a new :class:`DataFrame` containing the distinct rows in this :class:`DataFrame`.
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>>> df.distinct().count()
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2
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"""
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return DataFrame(self._jdf.distinct(), self.sql_ctx)
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@since(1.3)
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def sample(self, withReplacement, fraction, seed=None):
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"""Returns a sampled subset of this :class:`DataFrame`.
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>>> df.sample(False, 0.5, 42).count()
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1
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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.maxsize)
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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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@since(1.4)
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def randomSplit(self, weights, seed=None):
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"""Randomly splits this :class:`DataFrame` with the provided weights.
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:param weights: list of doubles as weights with which to split the DataFrame. Weights will
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be normalized if they don't sum up to 1.0.
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:param seed: The seed for sampling.
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>>> splits = df4.randomSplit([1.0, 2.0], 24)
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>>> splits[0].count()
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1
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>>> splits[1].count()
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3
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"""
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for w in weights:
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if w < 0.0:
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raise ValueError("Weights must be positive. Found weight value: %s" % w)
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seed = seed if seed is not None else random.randint(0, sys.maxsize)
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rdd_array = self._jdf.randomSplit(_to_seq(self.sql_ctx._sc, weights), long(seed))
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return [DataFrame(rdd, self.sql_ctx) for rdd in rdd_array]
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@property
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@since(1.3)
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def dtypes(self):
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"""Returns all column names and their data types as a list.
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>>> df.dtypes
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[('age', 'int'), ('name', 'string')]
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"""
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return [(str(f.name), f.dataType.simpleString()) for f in self.schema.fields]
|
|
|
|
@property
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def columns(self):
|
|
"""Returns all column names as a list.
|
|
|
|
>>> df.columns
|
|
[u'age', u'name']
|
|
"""
|
|
return [f.name for f in self.schema.fields]
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def alias(self, alias):
|
|
"""Returns a new :class:`DataFrame` with an alias set.
|
|
|
|
>>> from pyspark.sql.functions import *
|
|
>>> df_as1 = df.alias("df_as1")
|
|
>>> df_as2 = df.alias("df_as2")
|
|
>>> joined_df = df_as1.join(df_as2, col("df_as1.name") == col("df_as2.name"), 'inner')
|
|
>>> joined_df.select(col("df_as1.name"), col("df_as2.name"), col("df_as2.age")).collect()
|
|
[Row(name=u'Alice', name=u'Alice', age=2), Row(name=u'Bob', name=u'Bob', age=5)]
|
|
"""
|
|
assert isinstance(alias, basestring), "alias should be a string"
|
|
return DataFrame(getattr(self._jdf, "as")(alias), self.sql_ctx)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def join(self, other, joinExprs=None, joinType=None):
|
|
"""Joins 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: a string for join column name, or a join expression (Column).
|
|
If joinExprs is a string indicating the name of the join column,
|
|
the column must exist on both sides, and this performs an inner equi-join.
|
|
:param joinType: str, default 'inner'.
|
|
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'Alice', height=None), Row(name=u'Bob', height=85)]
|
|
|
|
>>> df.join(df2, 'name').select(df.name, df2.height).collect()
|
|
[Row(name=u'Bob', height=85)]
|
|
"""
|
|
|
|
if joinExprs is None:
|
|
jdf = self._jdf.join(other._jdf)
|
|
elif isinstance(joinExprs, basestring):
|
|
jdf = self._jdf.join(other._jdf, joinExprs)
|
|
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)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def sort(self, *cols, **kwargs):
|
|
"""Returns a new :class:`DataFrame` sorted by the specified column(s).
|
|
|
|
:param cols: list of :class:`Column` or column names to sort by.
|
|
:param ascending: boolean or list of boolean (default True).
|
|
Sort ascending vs. descending. Specify list for multiple sort orders.
|
|
If a list is specified, length of the list must equal length of the `cols`.
|
|
|
|
>>> df.sort(df.age.desc()).collect()
|
|
[Row(age=5, name=u'Bob'), Row(age=2, name=u'Alice')]
|
|
>>> df.sort("age", ascending=False).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')]
|
|
>>> df.orderBy(["age", "name"], ascending=[0, 1]).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")
|
|
if len(cols) == 1 and isinstance(cols[0], list):
|
|
cols = cols[0]
|
|
jcols = [_to_java_column(c) for c in cols]
|
|
ascending = kwargs.get('ascending', True)
|
|
if isinstance(ascending, (bool, int)):
|
|
if not ascending:
|
|
jcols = [jc.desc() for jc in jcols]
|
|
elif isinstance(ascending, list):
|
|
jcols = [jc if asc else jc.desc()
|
|
for asc, jc in zip(ascending, jcols)]
|
|
else:
|
|
raise TypeError("ascending can only be boolean or list, but got %s" % type(ascending))
|
|
|
|
jdf = self._jdf.sort(self._jseq(jcols))
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
|
|
orderBy = sort
|
|
|
|
def _jseq(self, cols, converter=None):
|
|
"""Return a JVM Seq of Columns from a list of Column or names"""
|
|
return _to_seq(self.sql_ctx._sc, cols, converter)
|
|
|
|
def _jmap(self, jm):
|
|
"""Return a JVM Scala Map from a dict"""
|
|
return _to_scala_map(self.sql_ctx._sc, jm)
|
|
|
|
def _jcols(self, *cols):
|
|
"""Return a JVM Seq of Columns from a list of Column or column names
|
|
|
|
If `cols` has only one list in it, cols[0] will be used as the list.
|
|
"""
|
|
if len(cols) == 1 and isinstance(cols[0], list):
|
|
cols = cols[0]
|
|
return self._jseq(cols, _to_java_column)
|
|
|
|
@since("1.3.1")
|
|
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|
|
|
+-------+---+
|
|
"""
|
|
jdf = self._jdf.describe(self._jseq(cols))
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def head(self, n=None):
|
|
"""
|
|
Returns the first ``n`` rows as a list of :class:`Row`,
|
|
or the first :class:`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)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def first(self):
|
|
"""Returns the first row as a :class:`Row`.
|
|
|
|
>>> df.first()
|
|
Row(age=2, name=u'Alice')
|
|
"""
|
|
return self.head()
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def __getitem__(self, item):
|
|
"""Returns the column as a :class:`Column`.
|
|
|
|
>>> 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')]
|
|
>>> df[df[0] > 3].collect()
|
|
[Row(age=5, name=u'Bob')]
|
|
"""
|
|
if isinstance(item, basestring):
|
|
if item not in self.columns:
|
|
raise IndexError("no such column: %s" % item)
|
|
jc = self._jdf.apply(item)
|
|
return Column(jc)
|
|
elif isinstance(item, Column):
|
|
return self.filter(item)
|
|
elif isinstance(item, (list, tuple)):
|
|
return self.select(*item)
|
|
elif isinstance(item, int):
|
|
jc = self._jdf.apply(self.columns[item])
|
|
return Column(jc)
|
|
else:
|
|
raise TypeError("unexpected item type: %s" % type(item))
|
|
|
|
@since(1.3)
|
|
def __getattr__(self, name):
|
|
"""Returns the :class:`Column` denoted by ``name``.
|
|
|
|
>>> df.select(df.age).collect()
|
|
[Row(age=2), Row(age=5)]
|
|
"""
|
|
if name not in self.columns:
|
|
raise AttributeError(
|
|
"'%s' object has no attribute '%s'" % (self.__class__.__name__, name))
|
|
jc = self._jdf.apply(name)
|
|
return Column(jc)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def select(self, *cols):
|
|
"""Projects a set of expressions and returns a new :class:`DataFrame`.
|
|
|
|
:param cols: list of column names (string) or expressions (:class:`Column`).
|
|
If one of the column names is '*', that column is expanded to include all columns
|
|
in the current DataFrame.
|
|
|
|
>>> 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)]
|
|
"""
|
|
jdf = self._jdf.select(self._jcols(*cols))
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
|
|
@since(1.3)
|
|
def selectExpr(self, *expr):
|
|
"""Projects a set of SQL expressions and returns a new :class:`DataFrame`.
|
|
|
|
This is a variant of :func:`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)]
|
|
"""
|
|
if len(expr) == 1 and isinstance(expr[0], list):
|
|
expr = expr[0]
|
|
jdf = self._jdf.selectExpr(self._jseq(expr))
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def filter(self, condition):
|
|
"""Filters rows using the given condition.
|
|
|
|
:func:`where` is an alias for :func:`filter`.
|
|
|
|
:param condition: a :class:`Column` of :class:`types.BooleanType`
|
|
or a string of SQL expression.
|
|
|
|
>>> 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
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def groupBy(self, *cols):
|
|
"""Groups the :class:`DataFrame` using the specified columns,
|
|
so we can run aggregation on them. See :class:`GroupedData`
|
|
for all the available aggregate functions.
|
|
|
|
:func:`groupby` is an alias for :func:`groupBy`.
|
|
|
|
:param cols: list of columns to group by.
|
|
Each element should be a column name (string) or an expression (:class:`Column`).
|
|
|
|
>>> df.groupBy().avg().collect()
|
|
[Row(AVG(age)=3.5)]
|
|
>>> df.groupBy('name').agg({'age': 'mean'}).collect()
|
|
[Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)]
|
|
>>> df.groupBy(df.name).avg().collect()
|
|
[Row(name=u'Alice', AVG(age)=2.0), Row(name=u'Bob', AVG(age)=5.0)]
|
|
>>> df.groupBy(['name', df.age]).count().collect()
|
|
[Row(name=u'Bob', age=5, count=1), Row(name=u'Alice', age=2, count=1)]
|
|
"""
|
|
jgd = self._jdf.groupBy(self._jcols(*cols))
|
|
from pyspark.sql.group import GroupedData
|
|
return GroupedData(jgd, self.sql_ctx)
|
|
|
|
@since(1.4)
|
|
def rollup(self, *cols):
|
|
"""
|
|
Create a multi-dimensional rollup for the current :class:`DataFrame` using
|
|
the specified columns, so we can run aggregation on them.
|
|
|
|
>>> df.rollup('name', df.age).count().show()
|
|
+-----+----+-----+
|
|
| name| age|count|
|
|
+-----+----+-----+
|
|
|Alice|null| 1|
|
|
| Bob| 5| 1|
|
|
| Bob|null| 1|
|
|
| null|null| 2|
|
|
|Alice| 2| 1|
|
|
+-----+----+-----+
|
|
"""
|
|
jgd = self._jdf.rollup(self._jcols(*cols))
|
|
from pyspark.sql.group import GroupedData
|
|
return GroupedData(jgd, self.sql_ctx)
|
|
|
|
@since(1.4)
|
|
def cube(self, *cols):
|
|
"""
|
|
Create a multi-dimensional cube for the current :class:`DataFrame` using
|
|
the specified columns, so we can run aggregation on them.
|
|
|
|
>>> df.cube('name', df.age).count().show()
|
|
+-----+----+-----+
|
|
| name| age|count|
|
|
+-----+----+-----+
|
|
| null| 2| 1|
|
|
|Alice|null| 1|
|
|
| Bob| 5| 1|
|
|
| Bob|null| 1|
|
|
| null| 5| 1|
|
|
| null|null| 2|
|
|
|Alice| 2| 1|
|
|
+-----+----+-----+
|
|
"""
|
|
jgd = self._jdf.cube(self._jcols(*cols))
|
|
from pyspark.sql.group import GroupedData
|
|
return GroupedData(jgd, self.sql_ctx)
|
|
|
|
@since(1.3)
|
|
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)
|
|
|
|
@since(1.3)
|
|
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)
|
|
|
|
@since(1.3)
|
|
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)
|
|
|
|
@since(1.3)
|
|
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)
|
|
|
|
@since(1.4)
|
|
def dropDuplicates(self, subset=None):
|
|
"""Return a new :class:`DataFrame` with duplicate rows removed,
|
|
optionally only considering certain columns.
|
|
|
|
>>> from pyspark.sql import Row
|
|
>>> df = sc.parallelize([ \
|
|
Row(name='Alice', age=5, height=80), \
|
|
Row(name='Alice', age=5, height=80), \
|
|
Row(name='Alice', age=10, height=80)]).toDF()
|
|
>>> df.dropDuplicates().show()
|
|
+---+------+-----+
|
|
|age|height| name|
|
|
+---+------+-----+
|
|
| 5| 80|Alice|
|
|
| 10| 80|Alice|
|
|
+---+------+-----+
|
|
|
|
>>> df.dropDuplicates(['name', 'height']).show()
|
|
+---+------+-----+
|
|
|age|height| name|
|
|
+---+------+-----+
|
|
| 5| 80|Alice|
|
|
+---+------+-----+
|
|
"""
|
|
if subset is None:
|
|
jdf = self._jdf.dropDuplicates()
|
|
else:
|
|
jdf = self._jdf.dropDuplicates(self._jseq(subset))
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
|
|
@since("1.3.1")
|
|
def dropna(self, how='any', thresh=None, subset=None):
|
|
"""Returns a new :class:`DataFrame` omitting rows with null values.
|
|
|
|
This is an alias for ``na.drop()``.
|
|
|
|
:param how: 'any' or 'all'.
|
|
If 'any', drop a row if it contains any nulls.
|
|
If 'all', drop a row only if all its values are null.
|
|
:param thresh: int, default None
|
|
If specified, drop rows that have less than `thresh` non-null values.
|
|
This overwrites the `how` parameter.
|
|
:param subset: optional list of column names to consider.
|
|
|
|
>>> df4.dropna().show()
|
|
+---+------+-----+
|
|
|age|height| name|
|
|
+---+------+-----+
|
|
| 10| 80|Alice|
|
|
+---+------+-----+
|
|
|
|
>>> df4.na.drop().show()
|
|
+---+------+-----+
|
|
|age|height| name|
|
|
+---+------+-----+
|
|
| 10| 80|Alice|
|
|
+---+------+-----+
|
|
"""
|
|
if how is not None and how not in ['any', 'all']:
|
|
raise ValueError("how ('" + how + "') should be 'any' or 'all'")
|
|
|
|
if subset is None:
|
|
subset = self.columns
|
|
elif isinstance(subset, basestring):
|
|
subset = [subset]
|
|
elif not isinstance(subset, (list, tuple)):
|
|
raise ValueError("subset should be a list or tuple of column names")
|
|
|
|
if thresh is None:
|
|
thresh = len(subset) if how == 'any' else 1
|
|
|
|
return DataFrame(self._jdf.na().drop(thresh, self._jseq(subset)), self.sql_ctx)
|
|
|
|
@since("1.3.1")
|
|
def fillna(self, value, subset=None):
|
|
"""Replace null values, alias for ``na.fill()``.
|
|
|
|
:param value: int, long, float, string, or dict.
|
|
Value to replace null values with.
|
|
If the value is a dict, then `subset` is ignored and `value` must be a mapping
|
|
from column name (string) to replacement value. The replacement value must be
|
|
an int, long, float, or string.
|
|
:param subset: optional list of column names to consider.
|
|
Columns specified in subset that do not have matching data type are ignored.
|
|
For example, if `value` is a string, and subset contains a non-string column,
|
|
then the non-string column is simply ignored.
|
|
|
|
>>> df4.fillna(50).show()
|
|
+---+------+-----+
|
|
|age|height| name|
|
|
+---+------+-----+
|
|
| 10| 80|Alice|
|
|
| 5| 50| Bob|
|
|
| 50| 50| Tom|
|
|
| 50| 50| null|
|
|
+---+------+-----+
|
|
|
|
>>> df4.fillna({'age': 50, 'name': 'unknown'}).show()
|
|
+---+------+-------+
|
|
|age|height| name|
|
|
+---+------+-------+
|
|
| 10| 80| Alice|
|
|
| 5| null| Bob|
|
|
| 50| null| Tom|
|
|
| 50| null|unknown|
|
|
+---+------+-------+
|
|
|
|
>>> df4.na.fill({'age': 50, 'name': 'unknown'}).show()
|
|
+---+------+-------+
|
|
|age|height| name|
|
|
+---+------+-------+
|
|
| 10| 80| Alice|
|
|
| 5| null| Bob|
|
|
| 50| null| Tom|
|
|
| 50| null|unknown|
|
|
+---+------+-------+
|
|
"""
|
|
if not isinstance(value, (float, int, long, basestring, dict)):
|
|
raise ValueError("value should be a float, int, long, string, or dict")
|
|
|
|
if isinstance(value, (int, long)):
|
|
value = float(value)
|
|
|
|
if isinstance(value, dict):
|
|
return DataFrame(self._jdf.na().fill(value), self.sql_ctx)
|
|
elif subset is None:
|
|
return DataFrame(self._jdf.na().fill(value), self.sql_ctx)
|
|
else:
|
|
if isinstance(subset, basestring):
|
|
subset = [subset]
|
|
elif not isinstance(subset, (list, tuple)):
|
|
raise ValueError("subset should be a list or tuple of column names")
|
|
|
|
return DataFrame(self._jdf.na().fill(value, self._jseq(subset)), self.sql_ctx)
|
|
|
|
@since(1.4)
|
|
def replace(self, to_replace, value, subset=None):
|
|
"""Returns a new :class:`DataFrame` replacing a value with another value.
|
|
|
|
:param to_replace: int, long, float, string, or list.
|
|
Value to be replaced.
|
|
If the value is a dict, then `value` is ignored and `to_replace` must be a
|
|
mapping from column name (string) to replacement value. The value to be
|
|
replaced must be an int, long, float, or string.
|
|
:param value: int, long, float, string, or list.
|
|
Value to use to replace holes.
|
|
The replacement value must be an int, long, float, or string. If `value` is a
|
|
list or tuple, `value` should be of the same length with `to_replace`.
|
|
:param subset: optional list of column names to consider.
|
|
Columns specified in subset that do not have matching data type are ignored.
|
|
For example, if `value` is a string, and subset contains a non-string column,
|
|
then the non-string column is simply ignored.
|
|
|
|
>>> df4.replace(10, 20).show()
|
|
+----+------+-----+
|
|
| age|height| name|
|
|
+----+------+-----+
|
|
| 20| 80|Alice|
|
|
| 5| null| Bob|
|
|
|null| null| Tom|
|
|
|null| null| null|
|
|
+----+------+-----+
|
|
|
|
>>> df4.replace(['Alice', 'Bob'], ['A', 'B'], 'name').show()
|
|
+----+------+----+
|
|
| age|height|name|
|
|
+----+------+----+
|
|
| 10| 80| A|
|
|
| 5| null| B|
|
|
|null| null| Tom|
|
|
|null| null|null|
|
|
+----+------+----+
|
|
"""
|
|
if not isinstance(to_replace, (float, int, long, basestring, list, tuple, dict)):
|
|
raise ValueError(
|
|
"to_replace should be a float, int, long, string, list, tuple, or dict")
|
|
|
|
if not isinstance(value, (float, int, long, basestring, list, tuple)):
|
|
raise ValueError("value should be a float, int, long, string, list, or tuple")
|
|
|
|
rep_dict = dict()
|
|
|
|
if isinstance(to_replace, (float, int, long, basestring)):
|
|
to_replace = [to_replace]
|
|
|
|
if isinstance(to_replace, tuple):
|
|
to_replace = list(to_replace)
|
|
|
|
if isinstance(value, tuple):
|
|
value = list(value)
|
|
|
|
if isinstance(to_replace, list) and isinstance(value, list):
|
|
if len(to_replace) != len(value):
|
|
raise ValueError("to_replace and value lists should be of the same length")
|
|
rep_dict = dict(zip(to_replace, value))
|
|
elif isinstance(to_replace, list) and isinstance(value, (float, int, long, basestring)):
|
|
rep_dict = dict([(tr, value) for tr in to_replace])
|
|
elif isinstance(to_replace, dict):
|
|
rep_dict = to_replace
|
|
|
|
if subset is None:
|
|
return DataFrame(self._jdf.na().replace('*', rep_dict), self.sql_ctx)
|
|
elif isinstance(subset, basestring):
|
|
subset = [subset]
|
|
|
|
if not isinstance(subset, (list, tuple)):
|
|
raise ValueError("subset should be a list or tuple of column names")
|
|
|
|
return DataFrame(
|
|
self._jdf.na().replace(self._jseq(subset), self._jmap(rep_dict)), self.sql_ctx)
|
|
|
|
@since(1.4)
|
|
def corr(self, col1, col2, method=None):
|
|
"""
|
|
Calculates the correlation of two columns of a DataFrame as a double value. Currently only
|
|
supports the Pearson Correlation Coefficient.
|
|
:func:`DataFrame.corr` and :func:`DataFrameStatFunctions.corr` are aliases.
|
|
|
|
:param col1: The name of the first column
|
|
:param col2: The name of the second column
|
|
:param method: The correlation method. Currently only supports "pearson"
|
|
"""
|
|
if not isinstance(col1, str):
|
|
raise ValueError("col1 should be a string.")
|
|
if not isinstance(col2, str):
|
|
raise ValueError("col2 should be a string.")
|
|
if not method:
|
|
method = "pearson"
|
|
if not method == "pearson":
|
|
raise ValueError("Currently only the calculation of the Pearson Correlation " +
|
|
"coefficient is supported.")
|
|
return self._jdf.stat().corr(col1, col2, method)
|
|
|
|
@since(1.4)
|
|
def cov(self, col1, col2):
|
|
"""
|
|
Calculate the sample covariance for the given columns, specified by their names, as a
|
|
double value. :func:`DataFrame.cov` and :func:`DataFrameStatFunctions.cov` are aliases.
|
|
|
|
:param col1: The name of the first column
|
|
:param col2: The name of the second column
|
|
"""
|
|
if not isinstance(col1, str):
|
|
raise ValueError("col1 should be a string.")
|
|
if not isinstance(col2, str):
|
|
raise ValueError("col2 should be a string.")
|
|
return self._jdf.stat().cov(col1, col2)
|
|
|
|
@since(1.4)
|
|
def crosstab(self, col1, col2):
|
|
"""
|
|
Computes a pair-wise frequency table of the given columns. Also known as a contingency
|
|
table. The number of distinct values for each column should be less than 1e4. At most 1e6
|
|
non-zero pair frequencies will be returned.
|
|
The first column of each row will be the distinct values of `col1` and the column names
|
|
will be the distinct values of `col2`. The name of the first column will be `$col1_$col2`.
|
|
Pairs that have no occurrences will have `null` as their counts.
|
|
:func:`DataFrame.crosstab` and :func:`DataFrameStatFunctions.crosstab` are aliases.
|
|
|
|
:param col1: The name of the first column. Distinct items will make the first item of
|
|
each row.
|
|
:param col2: The name of the second column. Distinct items will make the column names
|
|
of the DataFrame.
|
|
"""
|
|
if not isinstance(col1, str):
|
|
raise ValueError("col1 should be a string.")
|
|
if not isinstance(col2, str):
|
|
raise ValueError("col2 should be a string.")
|
|
return DataFrame(self._jdf.stat().crosstab(col1, col2), self.sql_ctx)
|
|
|
|
@since(1.4)
|
|
def freqItems(self, cols, support=None):
|
|
"""
|
|
Finding frequent items for columns, possibly with false positives. Using the
|
|
frequent element count algorithm described in
|
|
"http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou".
|
|
:func:`DataFrame.freqItems` and :func:`DataFrameStatFunctions.freqItems` are aliases.
|
|
|
|
This function is meant for exploratory data analysis, as we make no guarantee about the
|
|
backward compatibility of the schema of the resulting DataFrame.
|
|
|
|
:param cols: Names of the columns to calculate frequent items for as a list or tuple of
|
|
strings.
|
|
:param support: The frequency with which to consider an item 'frequent'. Default is 1%.
|
|
The support must be greater than 1e-4.
|
|
"""
|
|
if isinstance(cols, tuple):
|
|
cols = list(cols)
|
|
if not isinstance(cols, list):
|
|
raise ValueError("cols must be a list or tuple of column names as strings.")
|
|
if not support:
|
|
support = 0.01
|
|
return DataFrame(self._jdf.stat().freqItems(_to_seq(self._sc, cols), support), self.sql_ctx)
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def withColumn(self, colName, col):
|
|
"""Returns a new :class:`DataFrame` by adding a column.
|
|
|
|
:param colName: string, name of the new column.
|
|
:param col: a :class:`Column` expression for the new 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))
|
|
|
|
@ignore_unicode_prefix
|
|
@since(1.3)
|
|
def withColumnRenamed(self, existing, new):
|
|
"""Returns a new :class:`DataFrame` by renaming an existing column.
|
|
|
|
:param existing: string, name of the existing column to rename.
|
|
:param col: string, new name of the column.
|
|
|
|
>>> 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)
|
|
|
|
@since(1.4)
|
|
@ignore_unicode_prefix
|
|
def drop(self, colName):
|
|
"""Returns a new :class:`DataFrame` that drops the specified column.
|
|
|
|
:param colName: string, name of the column to drop.
|
|
|
|
>>> df.drop('age').collect()
|
|
[Row(name=u'Alice'), Row(name=u'Bob')]
|
|
"""
|
|
jdf = self._jdf.drop(colName)
|
|
return DataFrame(jdf, self.sql_ctx)
|
|
|
|
@since(1.3)
|
|
def toPandas(self):
|
|
"""Returns the contents of this :class:`DataFrame` as Pandas ``pandas.DataFrame``.
|
|
|
|
This is only available if Pandas is installed and available.
|
|
|
|
>>> 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)
|
|
|
|
# Pandas compatibility
|
|
groupby = groupBy
|
|
drop_duplicates = dropDuplicates
|
|
|
|
|
|
# Having SchemaRDD for backward compatibility (for docs)
|
|
class SchemaRDD(DataFrame):
|
|
"""SchemaRDD is deprecated, please use :class:`DataFrame`.
|
|
"""
|
|
|
|
|
|
def _to_scala_map(sc, jm):
|
|
"""
|
|
Convert a dict into a JVM Map.
|
|
"""
|
|
return sc._jvm.PythonUtils.toScalaMap(jm)
|
|
|
|
|
|
class DataFrameNaFunctions(object):
|
|
"""Functionality for working with missing data in :class:`DataFrame`.
|
|
"""
|
|
|
|
def __init__(self, df):
|
|
self.df = df
|
|
|
|
def drop(self, how='any', thresh=None, subset=None):
|
|
return self.df.dropna(how=how, thresh=thresh, subset=subset)
|
|
|
|
drop.__doc__ = DataFrame.dropna.__doc__
|
|
|
|
def fill(self, value, subset=None):
|
|
return self.df.fillna(value=value, subset=subset)
|
|
|
|
fill.__doc__ = DataFrame.fillna.__doc__
|
|
|
|
|
|
class DataFrameStatFunctions(object):
|
|
"""Functionality for statistic functions with :class:`DataFrame`.
|
|
"""
|
|
|
|
def __init__(self, df):
|
|
self.df = df
|
|
|
|
def corr(self, col1, col2, method=None):
|
|
return self.df.corr(col1, col2, method)
|
|
|
|
corr.__doc__ = DataFrame.corr.__doc__
|
|
|
|
def cov(self, col1, col2):
|
|
return self.df.cov(col1, col2)
|
|
|
|
cov.__doc__ = DataFrame.cov.__doc__
|
|
|
|
def crosstab(self, col1, col2):
|
|
return self.df.crosstab(col1, col2)
|
|
|
|
crosstab.__doc__ = DataFrame.crosstab.__doc__
|
|
|
|
def freqItems(self, cols, support=None):
|
|
return self.df.freqItems(cols, support)
|
|
|
|
freqItems.__doc__ = DataFrame.freqItems.__doc__
|
|
|
|
|
|
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['sqlContext'] = 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['df4'] = sc.parallelize([Row(name='Alice', age=10, height=80),
|
|
Row(name='Bob', age=5, height=None),
|
|
Row(name='Tom', age=None, height=None),
|
|
Row(name=None, age=None, height=None)]).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()
|