在pandas库中实现Excel的数据透视表效果通常用的是df['a'].value_counts()这个函数，表示统计数据框(...
from pyspark.sql import functions as func df = spark.createDataFrame([(1, 2, 3) if i % 2 == 0 else (i, 2 * i, i % 4) for i in range(10)], ["a", "b", "c"]) # 注意agg函数的使用 df.agg(func.countDistinct('a')).show() 1. 2. 3. 4. 5. 6. 13. 聚合函数 g...
Pyspark是一个基于Python的开源分布式计算框架,用于处理大规模数据集。它结合了Python的简洁性和Spark的高性能,可以在分布式环境中进行数据处理和分析。 在Pyspark中,可以使用group by和count函数对数据进行分组和计数。同时,还可以添加条件来筛选数据。 下面是一个完善且全面的答案: Pyspark中的group by和count...
SELECT SUM(income) AS income FROM test_youhua.test_avg_medium_freq GROUP BY name ) AS a''').show()#2.sum/人数sc.sql('''SELECT SUM(income)/COUNT(DISTINCT name) AS avg_income FROM test_youhua.test_avg_medium_freq''').show() +---+ |avg(income)| +---+ | 55000.0| +---+ ...
Spark SQL DENSE_RANK() Window function as a Count Distinct Alternative TheSpark SQL rank analytic functionis used to get a rank of the rows in column or within a group. In the result set, the rows with equal or similar values receive the same rank with next rank value skipped. ...
import pyspark from pyspark.sql import SparkSession sc=SparkSession.builder.master("local")\ .appName('first_name1')\ .config('spark.executor.memory','2g')\ .config('spark.driver.memory','2g')\ .enableHiveSupport()\ .getOrCreate()sc.sql(''' drop table test_youhua.test_avg_medium_...
PYSPARK_DRIVER_PYTHON_OPTS=notebook ./bin/pyspark 1. 在使用bin/pyspark命令打开Spark交互式环境后,默认情况下,Spark 已经创建了名为 sc 的 SparkContext 变量,因此创建新的环境变量将不起作用。 但是,在提交的独立spark 应用程序中或者常规的python环境,需要自行创建SparkContext 对象连接集群。
By using countDistinct() PySpark SQL function you can get the count distinct of the DataFrame that resulted from PySpark groupBy(). countDistinct() is used to get the count of unique values of the specified column. AdvertisementsWhen you perform group by, the data having the same key are ...
性能不佳的主要原因是groupby通常会导致执行者之间的数据混乱。您可以使用内置的spark函数countDistinct以...
In PySpark, you can use distinct().count() of DataFrame or countDistinct() SQL function to get the count distinct. distinct() eliminates duplicate