ShortType: Represents 2-byte signed integer numbers. The range of numbers is from-32768to32767. IntegerType: Represents 4-byte signed integer numbers. The range of numbers is from-2147483648to2147483647. LongType: Represents 8-byte signed integer numbers. The range of numbers is from-9223372036854...
ShortType: Represents 2-byte signed integer numbers. The range of numbers is from-32768to32767. IntegerType: Represents 4-byte signed integer numbers. The range of numbers is from-2147483648to2147483647. LongType: Represents 8-byte signed integer numbers. The range of numbers is from-9223372036854...
2.12 LongType 长数据类型,即带符号的64位整数,可表示数据范围[-9223372036854775808,9223372036854775807],如果值超出此范围,请使用DecimalType 2.13 ShortType 短数据类型,即带符号的16位整数 2.14 ArrayType(elementType, containsNull=True) 数组数据类型 elementType: DataType数组中每个元素。 containsNull: 布尔值,数...
计算给定列的十六进制值,可以是StringType,BinaryType,IntegerType或LongType >>> sqlContext.createDataFrame([('ABC', 3)], ['a', 'b']).select(hex('a'), hex('b')).collect() [Row(hex(a)=u'414243', hex(b)=u'3')] 36.pyspark.sql.functions.hour(col) 将给定日期的小时数提取为整数。
dataType:该字段的数据类型, nullable: 指示该字段的值是否为空 from pyspark.sql.types import StructType, StructField, LongType, StringType # 导入类型 schema = StructType([ StructField("id", LongType(), True), StructField("name", StringType(), True), StructField("age", LongType(), True)...
sql.types import StructField, MapType, StringType, IntegerType, StructType # 常用的还包括 DateType 等 people_schema= StructType([ StructField('address', MapType(StringType(), StringType()), True), StructField('age', LongType(), True), StructField('name', StringType(), True), ]) df...
from pyspark.sql.types import StructType, StructField, LongType, StringType schema = StructType([ StructField("id", LongType(), True), StructField("name", StringType(), True), StructField("age", LongType(), True), StructField("eyeColor", StringType(), True) ]) 2. 自定义函数的一般...
from pyspark.sql.types import StructField, StringTypedf = spark.createDataFrame([("a", 1)], ["i", "j"])df.show()+---+---+| i| j|+---+---+| a| 1|+---+---+df.schemaStructType([StructField('i', StringType(), True), StructField('j', LongType(), True)])# 设置新...
data.printSchema()root |-- name: string (nullable = true) |-- age: string (nullable = true) |-- id: string (nullable = true) |-- gender: string (nullable = true)# 增加一列使用cast修改类型from pyspark.sql.types import LongTypedata.withColumn('age2',data['age'].cast(LongType())...
使用单个pyspark.sql.types.LongType列名为id,包含从开始到结束(独占)范围内的元素,步长值为step。 参数说明: start:类型[int],开始值 end:类型[int],结束值 step:类型[int],步长 numPartitions:类型[int],DataFrame分区数 代码示例: spark.range(1, 100, 20).collect() ...