# Filter NOT IS IN List values #These show all records with NY (NY is not part of the list) df.filter(~df.state.isin(li)).show() df.filter(df.state.isin(li)==False).show() 2. 11. 12. 13. 14. 15.
from pyspark.sql.functions import isnan, isnull sdf.filter(isnull('Species')) # 填充空值 sdf.na.fill(0) sdf.na.fill({'Species':0,'`Sepal.Length`':'0'}) 列名称重命名 sdf.withColumnRenamed( "id" , "idx" ) 条件筛选 利用when做条件判断 when(condition, value1).otherwise(value2)联合...
Due to the cost of coordinating this value across partitions, the actual watermark used is only guaranteed to be at least delayThreshold behind the actual event time. In some cases we may still process records that arrive more than delayThreshold late. 参数: eventTime –——- the name of th...
filter方法是一个变换算子,不会立即返回结果。 # 找到spark import findspark findspark.init() # 创建spark from pyspark.sql import SparkSession spark = SparkSession.builder.master("local[*]").appName("WordCount").getOrCreate() # 创建SparkContext sc = spark.sparkContext # 创建RDD的方式 rdd = s...
filter(F.col('flag').isNotNull()).select('flag', F.expr(unpivotExpr))\ .withColumn('Bin', when(F.col('value').isNull(), 'Missing').otherwise( when(F.col('value') < f_quantitles_dict[F.col('varname')][0], 'bin_0') .when(F.col('value') < f_quantitles_dict[F.col(...
Row(value='# Apache Spark') 现在,我们可以通过以下方式计算包含单词Spark的行数: lines_with_spark = text_file.filter(text_file.value.contains("Spark")) 在这里,我们使用filter()函数过滤了行,并在filter()函数内部指定了text_file_value.contains包含单词"Spark",然后将这些结果放入了lines_with_spark变量...
1-首先创建SparkContext上下文环境 2-从外部文件数据源读取数据 3-执行flatmap执行扁平化操作 4-执行map转化操作,得到(word,1) 5-reduceByKey将相同Key的Value数据累加操作 6-将结果输出到文件系统或打印 代码: 代码语言:javascript 复制 #-*-coding:utf-8-*-# Programfunction: Spark的第一个程序 ...
df.select(df.age.alias('age_value'),'name') 查询某列为null的行: 代码语言:javascript 复制 from pyspark.sql.functionsimportisnull df=df.filter(isnull("col_a")) 输出list类型,list中每个元素是Row类: 代码语言:javascript 复制 list=df.collect() ...
lazy(***) # 遇到collect才计算 rdda.map().filter()...collect # transformation map/filter/group by/distinct/... actions: return a value to the driver program after running a computation on the dataset # actions count/reduce/collect... # 特点 1) transformation are lazy, nothing actually ...
过滤/filter # 根据key过滤rdd.filter(lambdax:'a'inx).collect()[('a',7),('a',2)] 去重/distinct #对rdd元素去重rdd5.distinct().collect()['a',7,2,'b'] 排序/sortBy # 升序排序(默认)rdd1.sortBy(lambdax:x).collect()[1,2,5,8]# 降序排序rdd1.sortBy(lambdax:x,ascending=False)...