Row对象记录一行数据 Column对象记录一列数据并包含列的信息 2.DataFrame之DSL """ 1. agg: 它是GroupedData对象的API, 作用是 在里面可以写多个聚合 2. alias: 它是Column对象的API, 可以针对一个列 进行改名 3. withColumnRenamed: 它是DataFrame的API, 可以对DF中的列进行改名, 一次改一个列, 改多个列 ...
To count the values in a column of a pyspark dataframe, we will first select the particular column using theselect()method by passing the column name as input to theselect()method. Next, we will use thecount()method to count the number of values in the selected column as shown in the ...
forkeyincolumnsToPivot:ifkey[1] !='': df = df.withColumn(key[0], F.lit(key[1])) It happens that I just wrote all rows with the same value when I want to fill the RAW_INFO where the table matcher the values mapped with the same 'PROCESS', 'SUBPROCESS' AND ...
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) ...
里的功能函数, 返回值多数都是Column对象. 例: 5.SparkSQL Shuffle 分区数目 在SparkSQL中当Job中产生产生Shuffle时,默认的分区数(spark.sql.shuffle.partitions)为200,在实际项目中要合理的设置。可以设置在: 6.SparkSQL 数据清洗API 1.去重方法 dropDuplicates ...
("data.csv", header=True, inferSchema=True) # 使用when函数根据条件设置值1 result = data.withColumn("new_column", when((data["ID"] == "unique_id") & (data["column_condition"] == "condition"), 1).otherwise(data["column_name"])) # 显示结果 result.show() # 停止SparkSession ...
去重set操作,跟py中的set一样,可以distinct()一下去重,同时也可以.count()计算剩余个数 1 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" ...
方法1 : 使用groupBy()和 distinct()。count()方法groupBy(): 用于根据列名对数据进行分组语法:data frame = data frame . group by(' column _ name 1 ')。sum('列名 2 ') 截然不同的()。count(): 用于计数和显示数据框中的不同行语法: dataframe.distinct()。计数() ...
# 类似python中的values_count df.agg( fn.count('id').alias('id_count'), fn.countDistinct('id').alias('id_ditinctcount'), fn.count('label').alias('label_count'), fn.countDistinct('label').alias('label_distinctcount'), ).show() ...
df.select('id').distinct().rdd.map(lambdar:r[0]).collect() show显示 #show和head函数显示数据帧的前N行df.show(5)df.head(5) 统计分析 (1)频繁项目 # 查找每列出现次数占总的30%以上频繁项目df.stat.freqItems(["id","gender"],0.3).show()+---+---+|id_freqItems|gender_freqItems|+-...