from pyspark.sql.window import Window from pyspark.sql.functions import col, sum 创建一个SparkSession对象: 代码语言:txt 复制 spark = SparkSession.builder.getOrCreate() 加载数据并创建一个DataFrame: 代码语言:txt 复制 data = [(1, "A
from pyspark.sql.functions import sum # 初始化SparkSession spark = SparkSession.builder.appName("example").getOrCreate() # 创建一个DataFrame data = [("Alice", 100), ("Bob", 200), ("Alice", 150), ("Bob", 50)] columns = ["name", "amount"] df = spark.createDataFrame(dat...
首先,让我们创建一个简单的 DataFrame,以便演示如何按两个字段进行分组: frompyspark.sqlimportSparkSession# 创建 SparkSessionspark=SparkSession.builder \.appName("GroupByExample")\.getOrCreate()# 创建示例数据data=[("Alice","2023-01-01",300),("Bob","2023-01-01",400),("Alice","2023-01-02"...
frompyspark.sql.functionsimportbroadcast# 假设 df 是 DataFrame,key 是小表df=df.join(broadcast(key),df.key==key.id) 1. 2. 3. 4. 使用reduceByKey 代替 groupByKey 在RDD 操作中,使用reduceByKey代替groupByKey。 rdd=sc.parallelize([("key1",1),("key1",2),("key2",3)])result=rdd.reduceB...
今天写pyspark遇到一个问题,要实现同mysql的GROUP_CONCAT函数的功能 数据1: col1 col2 1 a 1 b 1 c 2 d 2 f 想要的结果1: col1 new_col2 1 a,b,c 2 d,f 如果存在多列是否也可行 数据2: col1 col2 col3 1 a 100 1 b 200 1 c 300 2 d 400 2 f 500 想要的结果2: col1 new_col2...
DataFrame(data)) .group_by("a") .agg( nw.col("b").std().alias("std_ddof_1"), nw.col("b").std(ddof=2).alias("std_ddof_2"), ).to_native() ) Raises: ColumnNotFoundError: The following columns were not found: ['std_ddof_1'] Hint: Did you mean one of these columns: ...
df = df.join(df2, ["product_id"])# sort dataframe by product id & start date descdf = df.sort(['product_id','start_date'],ascending=False)# create window to add next start date of the productw = Window.partitionBy("product_id").orderBy(desc("product_id")) ...
inputDf = df_map[prefix]#actual dataframe is created via spark.read.json(s3uris[x]) and then kept under this mapprint("total records",inputDf.count())inputDf.printSchema() glueContext.write_dynamic_frame.from_options(frame=DynamicFrame.fromDF(inputDf, glueContext,"inputDf"), ...
一.问题描述 今天写pyspark遇到一个问题,要实现同mysql的GROUP_CONCAT函数的功能 数据1: col1 col21a1b1c2d2f 想要的结果1: col1 new_col21a,b,c2d,f 如果存在多列是否也可行 数据2: col1 col2 col31a1001b2001c3002d4002f500 想要的结果2:
createDataFrame(data, ["Name", "Class", "Score"]) df.createOrReplaceTempView("student_scores") Python Copy接下来,我们可以使用Spark SQL的窗口函数来为每个班级计算成绩的均值和标准偏差,并将其添加到原始数据集中:from pyspark.sql.window import Window from pyspark.sql import functions as F wind...