In order to calculate percentage and cumulative percentage of column in pyspark we will be using sum() function and partitionBy(). We will explain how to get percentage and cumulative percentage of column by group in Pyspark with an example.
- Calculate Percentage of column in pyspark : Represent the value of the column in terms of percentage
- Calculate cumulative percentage of column in pyspark
- Cumulative percentage of the column by group
We will use the dataframe named df_basket1.
Calculate percentage of column in pyspark
Sum() function and partitionBy() is used to calculate the percentage of column in pyspark
import pyspark.sql.functions as f
from pyspark.sql.window import Window
df_percent = df_basket1.withColumn('price_percent',f.col('Price')/f.sum('Price').over(Window.partitionBy())*100)
df_percent.show()
We use sum function to sum up the price column and partitionBy() none to calculate percentage of column as shown below
Calculate cumulative percentage of column in pyspark
Sum() function and partitionBy() is used to calculate the percentage of column in pyspark
import pyspark.sql.functions as f
import sys
from pyspark.sql.window import Window
df_percent = df_basket1.withColumn('price_percent',f.col('Price')/f.sum('Price').over(Window.partitionBy())*100)
df_cum_percent = df_percent.withColumn('cum_percent', f.sum(df_percent.price_percent).over(Window.partitionBy().orderBy().rowsBetween(-sys.maxsize, 0)))
df_cum_percent.show()
We use sum function to sum up the price column and partitionBy() function to calculate percentage of column as shown below and we name it as price_percent. Then we sum up the price_percent column to calculate the cumulative percentage of column
Calculate cumulative percentage of column by group in spark
Sum() function and partitionBy() the column name, is used to calculate the cumulative percentage of column by group.
import pyspark.sql.functions as f
import sys
from pyspark.sql.window import Window
df_percent = df_basket1.withColumn('price_percent', f.col('Price')/f.sum('Price').over(Window.partitionBy('Item_group'))*100)
df_cum_percent_grp = df_percent.withColumn('cum_percent_grp', f.sum(df_percent.price_percent).over(Window.partitionBy('Item_group').orderBy().rowsBetween(-sys.maxsize, 0)))
df_cum_percent_grp.show()
We use sum function to sum up the price column and partitionBy() function to calculate the cumulative percentage of column as shown below and we name it as price_percent. Then we sum up the price_percent column to calculate the cumulative percentage of column by group.
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