Aggregations can be performed on columns in PySpark using functions such asgroupBy,agg, andsum. These functions allow you to group data based on certain columns and compute aggregate statistics. Here is an example of how to calculate the sum of ages for each name: df.groupBy("name").agg({...
你也可以结合其他聚合函数一起使用,如 sum、avg 等,来完成更复杂的聚合操作。总之,collect_list 函数在 PySpark 中用于将指定列的值收集到一个列表中,并适用于对数据进行分组和聚合的场景。Structstruct 函数在 PySpark 中的作用是将多个列组合成一个复杂类型(StructType)的单列。它可以用于创建结构化的数据,方便...
array_max (col) #计算指定列的最大值 pyspark.sql.functions.stddev(col) # 返回组中表达式的无偏样本标准差 pyspark.sql.functions.sumDistinct(col) #返回表达式中不同值的总和 pyspark.sql.functions.trim(col) #去除空格 pyspark.sql.functions. greatest (col1,col2) #求行的最大值,可以计算一行中多列的...
sample=result.sample(False,0.5,0)# randomly select50%oflines — 1.2 列元素操作 — 获取Row元素的所有列名: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 r=Row(age=11,name='Alice')print r.columns #['age','name'] 选择一列或多列:select 代码语言:javascript 代码运行次数:0 运行 AI代码...
from pyspark.sql.functions import count, sum, avg, mean, min, max, collect_list, collect_set # 计数 df.agg(count("*").alias("total_count")) # 求和 df.agg(sum("value").alias("total_sum")) # 平均值 df.agg(avg("value").alias("average_value")) ...
note:: The list of columns should match with grouping columns exactly, or empty (means all the grouping columns). df.cube("name").agg(grouping_id(), sum("age")).orderBy("name").show() # +---+---+---+ # | name|grouping_id()|...
data.select('columns').distinct().show() 随机抽样有两种方式,一种是在HIVE里面查数随机;另一种是在pyspark之中 1 2 3 4 5 #HIVE里面查数随机 sql="select * from data order by rand() limit 2000" #pyspark之中 sample=result.sample(False,0.5,0)# randomly select 50% of lines ...
# schema只需要给出列名即可columns=["firstname","middlename","lastname","dob","gender","salary"]# 增加df=spark.createDataFrame(data=data,schema=columns)df.show()# 增加or修改列df2=df.withColumn("salary",col("salary").cast("Integer"))df2.show()df3=df.withColumn("salary",col("salary"...
tuple: Spark dataframe and dictionary of converted columns and their data types """ conv_cols = dict() selects = list() for field in df.schema: if is_complex_dtype(field.dataType): conv_cols[field.name] = field.dataType selects.append(to_json(field.name).alias(field.name)) ...
# Select the first set of columnsselected1=flights.select("tailnum","origin","dest")# Select the second set of columnstemp=flights.select(flights.origin,flights.dest,flights.carrier)#这个列名的选择很像R里面的# Define first filterfilterA=flights.origin=="SEA"# Define second filterfilterB=fligh...