from pyspark.sql.types import LongTypedata.withColumn('age2',data['age'].cast(LongType())).show()+---+---+---+---+---+| name|age| id|gender|age2|+---+---+---+---+---+| ldsx| 12| 1| 男| 12||test1| 20| 1| 女| 20||test2| 26| 1| 男| 26||test3| 19| ...
要将age列的数据类型从 integer 改为 double,我们可以使用 Spark 中的cast方法。我们需要从pyspark.types:导入DoubleType [In]:frompyspark.sql.typesimportStringType,DoubleType [In]: df.withColumn('age_double',df['age'].cast(DoubleType())).show(10,False) [Out]: 因此,上面的命令创建了一个新列(ag...
Column.cast(dataType: Union[pyspark.sql.types.DataType, str]) → pyspark.sql.column.Column 1. 2. 将列强制转换为dataType类型。 sp_df.select(sp_df.linkid.cast("string").alias('linkid_str')).show() 1. 11.contains包含筛选 Column.contains(other: Union[Column, LiteralType, DecimalLiteral,...
拿到pyspark dataframe的字段、类型、是否可以填充空值:df.schema.fields[0].name、df.schema.fields[0].dataType、df.schema.fields[0].nullable columns_type = dict() 统计空缺值: from pyspark.sql.functions import isnan,when,count,col null_dict = dict() for column in df.columns: print(column) ...
In PySpark, a column is a logical abstraction that represents a named attribute or field in a DataFrame. Columns are used to perform various operations such as selecting, filtering, aggregating, and transforming data. Each column has a name and a data type, which allows PySpark to apply functi...
.builder().master("local[2]").getOrCreate().sparkContext test("RDD should be immutable") { //given val data = spark.makeRDD(0to5) 任何命令行输入或输出都以以下方式编写: total_duration/(normal_data.count()) 粗体:表示一个新术语、一个重要词或屏幕上看到的词。例如,菜单或对话框中的词会以...
(df, aggregate_type, column_names): agg_exprs = [] for col in column_names: agg_exprs.append(expr(f"{aggregate_type}({col}) as {col}_{aggregate_type}")) return df.groupBy("id").agg(*agg_exprs) # 使用动态聚合函数 result = dynamic_aggregate(df, "sum", ["value1", "value2"]...
object PythonEvalsextendsStrategy{override defapply(plan:LogicalPlan):Seq[SparkPlan]=plan match{caseArrowEvalPython(udfs,output,child,evalType)=>ArrowEvalPythonExec(udfs,output,planLater(child),evalType)::NilcaseBatchEvalPython(udfs,output,child)=>BatchEvalPythonExec(udfs,output,planLater(child))::...
from pyspark.sql.types import StructType, StructField, StringType, IntegerTypespark = SparkSession.builder.getOrCreate()# 定义结构(模式)schema = StructType([ StructField("name", StringType(), nullable=False), StructField("age", IntegerType(), nullable=True), StructField("city", StringType()...
How to change a dataframe column from String type to Double type in PySpark? 解决方法: # 示例 from pyspark.sql.types import DoubleType changedTypedf = joindf.withColumn("label", joindf["show"].cast(DoubleType())) # or short string ...