from pyspark.sql.functions import first, collect_list, mean In: df.groupBy("ID").agg(mean("P"), first("index"), first("xinf"), first("xup"), first("yinf"), first("ysup"), collect_list("M")) from pyspark.sql import SparkSession from pyspark.sql import functions as f spark ...
Gradient Tree Boosting or Gradient Boosted Regression Trees (GBRT) is a generalization of boosting to arbitrary differentiable loss functions. GBRT is an accurate and effective off-the-shelf procedure that can be used for both regression and classification problems. Gradient Tree Boosting models are us...
您可以先执行内部聚合,然后再次聚合结果: from pyspark.sql import functions as F df = ... df1=df.groupBy("transaction_id", "transaction_date", "partition_key") \ .agg(F.sum("amount").alias("record_amount_sum"), F.collect_list(F.struct("record_id", "amount", "record_in_date")).a...
...以下是一个示例代码,展示了如何在 PySpark 中使用groupBy()和agg()进行数据聚合操作:from pyspark.sql import SparkSessionfrom pyspark.sql.functions...按某一列进行分组:使用 groupBy("column_name1") 方法按 column_name1 列对数据进行分组。进行聚合计算:使用 agg() 方法对分组后的数据进行聚合计算。.....
from pyspark.sql import SparkSession from pyspark.sql.functions import col spark = SparkSession.builder.getOrCreate() df = spark.createDataFrame([(1, 1.0), (2, 2.0)], ["int", "float"]) def cast_all_to_int(df): return df.select([col(col_name).cast('int') for col_name in df....
您正在使用的built-in函数“count”需要一个可迭代的对象,而不是列名。 您需要显式导入具有相同名称的“count”函数pyspark.sql.functions frompyspark.sql.functionsimportcountas_count old_table.groupby('name').agg(countDistinct('age'), _count('age')) ...
pyspark dataframe agg **pyspark dataframe agg** ## 简介 在PySpark中,DataFrame是一种表示分布式数据集的数据结构,它可以进行各种操作和转换。聚合(agg)操作是DataFrame中一个非常常用且强大的操作,它可以对数据进行分组并计算各种汇总统计。 本文将介绍PySpark DataFrame的agg操作,并通过代码示例演示其用法和功能。