复制 spark = SparkSession.builder.appName("Array to String").getOrCreate() 然后,我们可以创建一个包含数组的DataFrame,并使用concat_ws函数将数组转换为字符串: 代码语言:txt 复制 data = [("John", ["apple", "banana", "orange"]), ("Alice", ["grape", "melon"]), ("Bob", ["kiwi", "p...
# 创建SparkSession spark = SparkSession.builder.appName("Array to String").getOrCreate() # 创建示例数据 data = [("Alice", ["apple", "banana", "cherry"]), ("Bob", ["orange", "grape", "melon"]), ("Charlie", ["pear", "kiwi", "mango"])] # 创建DataFrame df = spark.c...
res0: Array[String] = Array(15, 16, 20, 20, 77, 80, 94, 94, 98, 16, 31, 31, 15, 20) scala> idsStr.first res2: String = 15 scala> val idsInt = idsStr.map(_.toInt) idsInt: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[3] at map at <console>:25 // idsInt.col...
(b_matrix.rowsPerBlock) # >> 3 # 把块矩阵转换为局部矩阵 local_mat = b_matrix.toLocalMatrix() # 打印局部矩阵 print(local_mat.toArray()) """ >> array([[1., 2., 1., 0., 0., 0.], [2., 1., 2., 0., 0., 0.], [1., 2., 1., 0., 0., 0.], [0., 0., ...
(s: String): Long = { val arr: Array[String] = s.split("\\.") //判断 if (arr.length != 4) return 0L var decimalIp: Long = 0 for ( i <- 0 to 3 ) decimalIp += (arr(3-i).toLong * Math.pow(256, i).toLong) decimalIp } /** * 封装通过二进制地址查找实际地址的...
|-- tasks: array (nullable = true) | |-- element: string (containsNull = true) +---+---+ |day | tasks | +---+---+ |星期天 |[抽烟, 喝酒, 去烫头] | +---+---+ 接下来获得该数组的大小,对其进行排序,并检查在该数组中是否存在一个指定的值。代码如下: tasks...
object Hi{def main(args:Array[String])=println("Hi!")} 3.编译代码:在工程目录下执行sbt package,则在target目录下的scala-2.10目录生成了.jar文件 4.运行程序:在工程目录下执行spark-submit --class Hi target/scala-2.10/hello_2.10-0.1-SNAPSHOT.jar(hello为工程文件夹的名称) ...
ml.linalg import Vectors, _convert_to_vector, VectorUDT, DenseVector # 数字的可转为vector,但字符串转为vector会报错 to_vec = udf(lambda x: DenseVector([x]), VectorUDT()) # 字符串转为array to_array = udf(lambda x: [x], ArrayType(StringType())) 2、从一个向量或数组列中获取某个...
"LongType", "ShortType", "ArrayType", "MapType", "StructField", "StructType"] 可以看出规律了吧,和sql中的一一对应 :return: a user-defined function. To register a nondeterministic Python function, users need to first build a nondeterministic user-defined function for the Python function and...
df = get_df() func = udf(lambda x: [0]*int(x), ArrayType(IntegerType())) df = df.withColumn('list', func('y')) func = udf(lambda x: {float(y): str(y) for y in range(int(x))}, MapType(FloatType(), StringType())) df = df.withColumn('map', func('y')) df.show...