lag() & lead() 对一列数据进行平移,实现行数据之间的计算, Lag和Lead函数可以取出字段的前N行的数据(Lag)和后N行的数据(Lead)作为独立的列,结合开窗函数使用,可以便捷计算差值、涨跌幅。 lag(exp_str,offset,defval).over(Window.partitionBy().orderBy()) lead(exp_str,offset,defval).over(Window.part...
window:定义窗口规范。 from pyspark.sql.window import Window from pyspark.sql.functions import row_number, rank, dense_rank, lead, lag # 定义窗口 window_spec = Window.partitionBy("category").orderBy("value") # 行号 df.withColumn("row_num", row_number().over(window_spec)) # 排名 df.wit...
5)]df=spark.createDataFrame(data,["Col1","Col2"])# 创建窗口规范windowSpec=Window.orderBy("Col2")# 使用lead函数获取下一行的值df.withColumn("NextValue",lead("Col1").over(windowSpec)).show()# 使用lag函数获取上一行的值df.withColumn("PreviousValue",lag("Col1").over(windowSpec)).show()...
3.2 lag Window Function This is the same as theLAGfunction in SQL. Thelag()function allows you to access a previous row’s value within the partition based on a specified offset. It retrieves the column value from the previous row, which can be helpful for comparative analysis or calculatin...
pyspark window pyspark window函数不加条件 文章目录 1 Ranking functions 1.1 row_number() 1.2 rank() 1.3 dense_rank() 1.4 percent_rank() 1.5 ntile() 2 Analytic functions 2.1 cume_dist() 2.2 lag() 2.3 lead() 3 Aggregate Functions 参考链接:pyspark-window-functions...
df.withColumn("lag_sales", F.lag("sales", 1).over(Window.orderBy("id"))).show() 这将输出以下结果: 代码语言:txt 复制 +---+---+---+ | id|sales|lag_sales| +---+---+---+ | 1| 100| null| | 2| 200| 100| | 3
from pyspark.sql.functions import lag, col windowSpec = Window.partitionBy().orderBy("column_name") 然后,使用lag函数计算当前行与前一行之间的差值,得到增量值。 代码语言:txt 复制 df = df.withColumn("incremental_value", col("column_name") - lag(col("column_name")).over(windowSpec)) ...
===运用lag与lead,拿到前一个或者后一个数据=== overCategory = Window.partitionBy("depname").orderBy(desc("salary")) df = empsalary.withColumn( "lead",lead("salary",1).over(overCategory)).withColumn( "lag",lag("salary",1).over(overCategory)).select( "depName","empNo","name"...
], ["timestamp","value"])# 创建窗口window_spec = Window.orderBy("timestamp")# 插值逻辑df_interpolated = df_time_series.withColumn("prev_value", lag("value").over(window_spec) ).withColumn("next_value", lead("value").over(window_spec) ...
你提到的操作依赖于你的行的顺序（因为你想得到“前一行”）。因此，你必须将orderBy添加到你的窗口...