本文整理匯總了Python中pyspark.sql.Row類的典型用法代碼示例。如果您正苦於以下問題：Python Row類的具體用法？Python Row怎麽用？Python Row使用的例子？那麽, 這裏精選的類代碼示例或許可以為您提供幫助。
在下文中一共展示了Row類的4個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_convert_row_to_dict
def test_convert_row_to_dict(self):
row = Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})
self.assertEqual(1, row.asDict()['l'][0].a)
df = self.sc.parallelize([row]).toDF()
df.registerTempTable("test")
row = self.sqlCtx.sql("select l, d from test").head()
self.assertEqual(1, row.asDict()["l"][0].a)
self.assertEqual(1.0, row.asDict()['d']['key'].c)示例2: test_convert_row_to_dict
def test_convert_row_to_dict(self):
row = Row(l=[Row(a=1, b='s')], d={"key": Row(c=1.0, d="2")})
self.assertEqual(1, row.asDict()['l'][0].a)
df = self.sc.parallelize([row]).toDF()
with self.tempView("test"):
df.createOrReplaceTempView("test")
row = self.spark.sql("select l, d from test").head()
self.assertEqual(1, row.asDict()["l"][0].a)
self.assertEqual(1.0, row.asDict()['d']['key'].c)示例3: _create_row
def _create_row(fields, values):
row = Row(*values)
row.__fields__ = fields
return row示例4: StructField
StructField("pix6",DoubleType(),True),
StructField("pix7",DoubleType(),True),
StructField("pix8",DoubleType(),True),
StructField("pix9",DoubleType(),True),
StructField("pix10",DoubleType(),True),
StructField("pix11",DoubleType(),True),
StructField("pix12",DoubleType(),True),
StructField("pix13",DoubleType(),True),
StructField("pix14",DoubleType(),True),
StructField("pix15",DoubleType(),True),
StructField("pix16",DoubleType(),True),
StructField("label",DoubleType(),True)
])
pen_raw = sc.textFile("first-edition/ch08/penbased.dat", 4).map(lambda x: x.split(", ")).map(lambda row: [float(x) for x in row])
dfpen = sqlContext.createDataFrame(pen_raw.map(Row.fromSeq(_)), penschema)
def parseRow(row):
d = {("pix"+str(i)): row[i-1] for i in range(1,17)}
d.update({"label": row[16]})
return d
dfpen = sqlContext.createDataFrame(pen_raw.map(parseRow), penschema)
va = VectorAssembler(outputCol="features", inputCols=dfpen.columns[0:-1])
penlpoints = va.transform(dfpen).select("features", "label")
pensets = penlpoints.randomSplit([0.8, 0.2])
pentrain = pensets[0].cache()
penvalid = pensets[1].cache()
penlr = LogisticRegression(regParam=0.01)