NameError: name 'DoubleType' is not defined 问题原因: 由于在Python代码中未引入pyspark.sql.types为DoubleType的数据类型导致 解决方法: from pyspark.sql.types import * 或者 from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType, DoubleType 异常二: TypeError: DoubleType ...
1.在设置Schema字段类型为DoubleType,抛“name 'DoubleType' is not defined”异常; 2.将读取的数据字段转换为DoubleType类型时抛“Double Type can not accept object u'23' in type <type 'unicode'>”异常; 3.将字段定义为StringType类型,SparkSQL也可以对数据进行统计如sum求和,非数值的数据不会被统计。
Here's how to create a DataFrame with one column that's nullable and another column that is not. from pyspark.sql import Row from pyspark.sql.types import * rdd = spark.sparkContext.parallelize([ Row(name='Allie', age=2), Row(name='Sara', age=33), Row(name='Grace', age=31)]) ...
NameError: name 'DoubleType' is not defined 问题原因: 由于在Python代码中未引入pyspark.sql.types为DoubleType的数据类型导致 解决方法: 代码语言:txt 复制 from pyspark.sql.types import * 或者 代码语言:txt 复制 from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType, Doub...
df.select(col("column_name").alias("new_column_name")) 2.字符串操作 concat:连接多个字符串。 substring:从字符串中提取子串。 trim:去除字符串两端的空格。 ltrim:去除字符串左端的空格。 rtrim:去除字符串右端的空格。 upper/lower:将字符串转换为大写/小写。
NameError:name'DoubleType'isnot defined 问题原因: 由于在Python代码中未引入pyspark.sql.types为DoubleType的数据类型导致 解决方法: from pyspark.sql.types import * 或者 from pyspark.sql.types import Row,StructField,StructType,StringType,IntegerType,DoubleType ...
1.问题描述 --- 在使用PySpark的SparkSQL读取HDFS的文本文件创建DataFrame时,在做数据类型转换时会出现一些异常,如下: 1.在设置Schema字段类型为DoubleType...,抛“name 'DoubleType' is not defined”异常; 2.将读取的数据字段转换为Do...
optional binary name (UTF8); required boolean isMan; optional int96 birthday; } , metadata: {org.apache.spark.version=3.0.0, org.apache.spark.sql.parquet.row.metadata={"type":"struct","fields":[{"name":"id1","type":"integer","nullable":false,"metadata":{}},{"name":"id2","typ...
Schemas are defined using the StructType which is made up of StructFields that specify the name, data type and a boolean flag indicating whether they contain a null value or not. You must import data types from pyspark.sql.types.Python Копирај ...
from faker import Faker fake = Faker() # Every value in this `self.options` dictionary is a string. num_rows = int(self.options.get("numRows", 3)) for _ in range(num_rows): row = [] for field in self.schema.fields: value = getattr(fake, field.name)() row.append(value) yiel...