首先,我们需要创建一个 PySpark 环境,并初始化一个 SparkSession。 frompyspark.sqlimportSparkSession# 创建 SparkSessionspark=SparkSession.builder \.appName("DataFrame Filtering Example")\.getOrCreate()# 创建一个示例 DataFramedata=[
In PySpark, we can drop one or more columns from a DataFrame using the .drop("column_name") method for a single column or .drop(["column1", "column2", ...]) for multiple columns.
filter: 按给定规则对rdd中的数据进行过滤(和python filter高阶函数用法一致) rdd1 = sc.parallelize([('a',1),('a',1),('b',1),('b',1),('b',1)]) rdd1.filter(lambda x:True if x[0] == 'a' else False).collect() # 输出 ''' [('a', 1), ('a', 1)] ''' # 8. dist...
"dest")# Select the second set of columnstemp=flights.select(flights.origin,flights.dest,flights.carrier)# Define first filterfilterA=flights.origin=="SEA"# Define second filterfilterB=flights.dest=="PDX"# Filter the data, first by filterA then by filterBselected2=temp.filter(filterA).filte...
# VectorAssembler A feature transformer that merges multiple columns into a vector column. # VectorIndexer 之前介绍的StringIndexer是针对单个类别型特征进行转换,倘若所有特征都已经被组织在一个向量中,又想对其中某些单个分量进行处理时,Spark ML 提供了VectorIndexer类来解决向量数据集中的类别性特征转换。 通过为...
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In R’s dplyr package, Hadley Wickham defined the 5 basic verbs — select, filter, mutate, summarize, and arrange. Here are the equivalents of the 5 basic verbs for Spark dataframes. Select I can select a subset of columns. The method select() takes either a list of column names or ...
# Import the necessary classfrom pyspark.ml.feature import VectorAssembler# Create an assembler objectassembler=VectorAssembler(inputCols=['mon','dom','dow','carrier_idx','org_idx','km','depart','duration'],outputCol='features')# Consolidate predictor columnsflights_assembled=assembler.transform(fl...
filter(col("count") > 100) ) # Code snippet result: +---+---+ |cylinders|count| +---+---+ | 4| 204| | 8| 103| +---+---+ Group by multiple columns from pyspark.sql.functions import avg, desc df = ( auto_df.groupBy(["modelyear", "cylinders"]) .agg(avg("horsepower...
people.filter(people.age> 30).join(department, people.deptId == department.id).groupBy(department.name,"gender").agg({"salary":"avg","age":"max"}) New in version 1.3. agg(*exprs) 总计on the entire DataFrame without groups (df.groupBy.agg()的简写). ...