frompyspark.sqlimportSparkSession# 创建 Spark 会话spark=SparkSession.builder \.appName("Count Distinct Optimization")\.getOrCreate()# 创建示例数据data=[("Alice",1),("Bob",2),("Alice",3),("Bob",4),("Charlie",1)]columns=["name","id"]df=spark.createDataFrame(data,columns)# 计算近似...
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(...
frompyspark.sql.functionsimportcount# 使用 GROUP BY 和 COUNT 统计多列的独特组合result=df.groupBy("registration_date","country").agg(count("user_id").alias("unique_users"))# 显示结果result.show() 1. 2. 3. 4. 5. 6. 7. 3.1 解释代码: groupBy("registration_date", "country"): 按照regi...
I'm brand new the pyspark (and really python as well). I'm trying to count distinct on each column (not distinct combinations of columns). I want the answer to this SQL statement: sqlStatement = "Select Count(Distinct C1) AS C1, Count(Distinct C2) AS C2, ..., Count(Distinct CN) ...
在PySpark中计算groupby后的sum和countDistinct 、、、 我有一个PySpark数据框架,我想按几列分组,然后计算一些列的总和,并计算另一列的不同值。因为countDistinct不是一个内置的聚合函数,所以我不能使用我在这里尝试过的简单表达式: sum_cols = ['a', 'b']exprs1 = {x: "sum" for x in sum_cols} expr...
import pyspark.sql.functions as F from pyspark.sql.window import Window #example dataset >>> data = sqlContext.createDataFrame([[1,'A'],[2,'B'],[3,'A'],[4,'C'],[5,'C'],[5,'B']],schema=['day','user']) >>> data.show() +---+---+ |day|user| +---+---+ | 1...
本文简要介绍 pyspark.sql.functions.count_distinct 的用法。 用法: pyspark.sql.functions.count_distinct(col, *cols) 为col 或cols 的不同计数返回一个新的 Column。 版本3.2.0 中的新函数。 例子: >>> df.agg(count_distinct(df.age, df.name).alias('c')).collect() [Row(c=2)] >>> df.agg...
# 需要导入模块: from pyspark.sql import functions [as 别名]# 或者: from pyspark.sql.functions importcountDistinct[as 别名]def_nunique(self, dropna=True, approx=False, rsd=0.05):colname = self._internal.data_spark_column_names[0]
In PySpark, you can use distinct().count() of DataFrame or countDistinct() SQL function to get the count distinct. distinct() eliminates duplicate
Thecount()method counts the number of rows in a pyspark dataframe. When we invoke thecount()method on a dataframe, it returns the number of rows in the data frame as shown below. import pyspark.sql as ps spark = ps.SparkSession.builder \ ...