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[SPARK-18069][Doc] improve python API docstrings #15053

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[SPARK-18069][Doc] improve python API docstrings #15053

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31 changes: 22 additions & 9 deletions python/pyspark/sql/column.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,6 +254,7 @@ def substr(self, startPos, length):
:param startPos: start position (int or Column)
:param length: length of the substring (int or Column)

>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob')], ['age', 'name'])
>>> df.select(df.name.substr(1, 3).alias("col")).collect()
[Row(col=u'Ali'), Row(col=u'Bob')]
"""
Expand All @@ -276,6 +277,7 @@ def isin(self, *cols):
A boolean expression that is evaluated to true if the value of this
expression is contained by the evaluated values of the arguments.

>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob')], ['age', 'name'])
>>> df[df.name.isin("Bob", "Mike")].collect()
[Row(age=5, name=u'Bob')]
>>> df[df.age.isin([1, 2, 3])].collect()
Expand Down Expand Up @@ -303,6 +305,7 @@ def alias(self, *alias):
Returns this column aliased with a new name or names (in the case of expressions that
return more than one column, such as explode).

>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob')], ['age', 'name'])
>>> df.select(df.age.alias("age2")).collect()
[Row(age2=2), Row(age2=5)]
"""
Expand All @@ -320,10 +323,13 @@ def alias(self, *alias):
def cast(self, dataType):
""" Convert the column into type ``dataType``.

>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob')], ['age', 'name'])
>>> df.select(df.age.cast("string").alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
[Row(ages='2'), Row(ages='5')]
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@HyukjinKwon HyukjinKwon Oct 24, 2016

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If I remember correctly, this test breaks the tests in Python 2.x. Let's use unicodes here.

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@HyukjinKwon HyukjinKwon Oct 24, 2016 •
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FYI, the unicode string in Python 3 does not have prefix 'u'. So, this is being handled via @ignore_unicode_prefix.


>>> from pyspark.sql.types import StringType
>>> df.select(df.age.cast(StringType()).alias('ages')).collect()
[Row(ages=u'2'), Row(ages=u'5')]
[Row(ages='2'), Row(ages='5')]
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@HyukjinKwon HyukjinKwon Oct 24, 2016

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Here, too. Let us please sweep it back.

"""
if isinstance(dataType, basestring):
jc = self._jc.cast(dataType)
Expand All @@ -344,6 +350,7 @@ def between(self, lowerBound, upperBound):
A boolean expression that is evaluated to true if the value of this
expression is between the given columns.

>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob')], ['age', 'name'])
>>> df.select(df.name, df.age.between(2, 4)).show()
+-----+---------------------------+
| name|((age >= 2) AND (age <= 4))|
Expand All @@ -366,6 +373,7 @@ def when(self, condition, value):
:param value: a literal value, or a :class:`Column` expression.

>>> from pyspark.sql import functions as F
>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob')], ['age', 'name'])
>>> df.select(df.name, F.when(df.age > 4, 1).when(df.age < 3, -1).otherwise(0)).show()
+-----+------------------------------------------------------------+
| name|CASE WHEN (age > 4) THEN 1 WHEN (age < 3) THEN -1 ELSE 0 END|
Expand All @@ -391,6 +399,7 @@ def otherwise(self, value):
:param value: a literal value, or a :class:`Column` expression.

>>> from pyspark.sql import functions as F
>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob')], ['age', 'name'])
>>> df.select(df.name, F.when(df.age > 3, 1).otherwise(0)).show()
+-----+-------------------------------------+
| name|CASE WHEN (age > 3) THEN 1 ELSE 0 END|
Expand All @@ -412,9 +421,17 @@ def over(self, window):
:return: a Column

>>> from pyspark.sql import Window
>>> window = Window.partitionBy("name").orderBy("age").rowsBetween(-1, 1)
>>> from pyspark.sql.functions import rank, min
>>> # df.select(rank().over(window), min('age').over(window))
>>> window = Window.partitionBy("name").orderBy("age")
>>> from pyspark.sql.functions import rank
>>> df = spark.createDataFrame([(2, 'Alice'), (5, 'Bob'), (3, 'Bob')], ['age', 'name'])
>>> df.select('name', 'age', rank().over(window)).show()
+-----+---+-----------------------------------------------------------------+
| name|age|RANK() OVER (PARTITION BY name ORDER BY age ASC UnspecifiedFrame)|
+-----+---+-----------------------------------------------------------------+
| Bob| 3| 1|
| Bob| 5| 2|
|Alice| 2| 1|
+-----+---+-----------------------------------------------------------------+
"""
from pyspark.sql.window import WindowSpec
if not isinstance(window, WindowSpec):
Expand Down Expand Up @@ -442,10 +459,6 @@ def _test():
.getOrCreate()
sc = spark.sparkContext
globs['sc'] = sc
globs['df'] = sc.parallelize([(2, 'Alice'), (5, 'Bob')]) \
.toDF(StructType([StructField('age', IntegerType()),
StructField('name', StringType())]))

(failure_count, test_count) = doctest.testmod(
pyspark.sql.column, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE | doctest.REPORT_NDIFF)
Expand Down
31 changes: 10 additions & 21 deletions python/pyspark/sql/context.py
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Expand Up @@ -338,6 +338,7 @@ def registerDataFrameAsTable(self, df, tableName):

Temporary tables exist only during the lifetime of this instance of :class:`SQLContext`.

>>> df = spark.createDataFrame([(1, 'row1'), (2, 'row2')], ['field1', 'field2'])
>>> sqlContext.registerDataFrameAsTable(df, "table1")
"""
df.createOrReplaceTempView(tableName)
Expand All @@ -346,6 +347,7 @@ def registerDataFrameAsTable(self, df, tableName):
def dropTempTable(self, tableName):
""" Remove the temp table from catalog.

>>> df = spark.createDataFrame([(1, 'row1'), (2, 'row2')], ['field1', 'field2'])
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> sqlContext.dropTempTable("table1")
"""
Expand Down Expand Up @@ -376,10 +378,11 @@ def sql(self, sqlQuery):

:return: :class:`DataFrame`

>>> df = spark.createDataFrame([(1, 'row1'), (2, 'row2')], ['field1', 'field2'])
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> df2 = sqlContext.sql("SELECT field1 AS f1, field2 as f2 from table1")
>>> df2.collect()
[Row(f1=1, f2=u'row1'), Row(f1=2, f2=u'row2'), Row(f1=3, f2=u'row3')]
[Row(f1=1, f2='row1'), Row(f1=2, f2='row2')]
"""
return self.sparkSession.sql(sqlQuery)

Expand All @@ -389,6 +392,7 @@ def table(self, tableName):

:return: :class:`DataFrame`

>>> df = spark.createDataFrame([(1, 'row1'), (2, 'row2')], ['field1', 'field2'])
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> df2 = sqlContext.table("table1")
>>> sorted(df.collect()) == sorted(df2.collect())
Expand All @@ -409,10 +413,11 @@ def tables(self, dbName=None):
:param dbName: string, name of the database to use.
:return: :class:`DataFrame`

>>> df = spark.createDataFrame([(1, 'row1'), (2, 'row2')], ['field1', 'field2'])
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> df2 = sqlContext.tables()
>>> df2.filter("tableName = 'table1'").first()
Row(database=u'', tableName=u'table1', isTemporary=True)
Row(tableName='table1', isTemporary=True)
"""
if dbName is None:
return DataFrame(self._ssql_ctx.tables(), self)
Expand All @@ -426,6 +431,7 @@ def tableNames(self, dbName=None):
:param dbName: string, name of the database to use. Default to the current database.
:return: list of table names, in string

>>> df = spark.createDataFrame([(1, 'row1'), (2, 'row2')], ['field1', 'field2'])
>>> sqlContext.registerDataFrameAsTable(df, "table1")
>>> "table1" in sqlContext.tableNames()
True
Expand Down Expand Up @@ -474,6 +480,7 @@ def readStream(self):

:return: :class:`DataStreamReader`

>>> import tempfile
>>> text_sdf = sqlContext.readStream.text(tempfile.mkdtemp())
>>> text_sdf.isStreaming
True
Expand Down Expand Up @@ -553,34 +560,16 @@ def register(self, name, f, returnType=StringType()):
def _test():
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@HyukjinKwon HyukjinKwon Oct 24, 2016 •
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Let's leave the newline here. Usually the block of imports has a newline before the codes (I believe pep8 will complain).

import os
import doctest
import tempfile
from pyspark.context import SparkContext
from pyspark.sql import Row, SQLContext
import pyspark.sql.context

os.chdir(os.environ["SPARK_HOME"])

globs = pyspark.sql.context.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
globs['tempfile'] = tempfile
globs['os'] = os

globs['sc'] = sc
globs['sqlContext'] = SQLContext(sc)
globs['rdd'] = rdd = sc.parallelize(
[Row(field1=1, field2="row1"),
Row(field1=2, field2="row2"),
Row(field1=3, field2="row3")]
)
globs['df'] = rdd.toDF()
jsonStrings = [
'{"field1": 1, "field2": "row1", "field3":{"field4":11}}',
'{"field1" : 2, "field3":{"field4":22, "field5": [10, 11]},'
'"field6":[{"field7": "row2"}]}',
'{"field1" : null, "field2": "row3", '
'"field3":{"field4":33, "field5": []}}'
]
globs['jsonStrings'] = jsonStrings
globs['json'] = sc.parallelize(jsonStrings)
(failure_count, test_count) = doctest.testmod(
pyspark.sql.context, globs=globs,
optionflags=doctest.ELLIPSIS | doctest.NORMALIZE_WHITESPACE)
Expand Down
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