如何转换 pyspark dataframe 列的值？

Question

I have a column in a pyspark dataframe for the age of an electronic device, and these values are given in milliseconds. Is there an easy way to convert that column's values to years? I am not well versed in Spark.

EDIT: I understand that you can convert milliseconds to years pretty easily with basic math, I'm trying to take a column of a pyspark dataframe and iterate through it and convert all column values to a different value. Is there a specific pyspark function that makes this easier or no? I have a column where all values are very large integers with time in milliseconds, and I am trying to filter out values which are too small or large to make sense based on the lifespan of the device.

table.filter(F.col("age")>0).filter(F.col("age")<yearsToSeconds(20))

where yearsToSeconds is a very basic function converting years to seconds. I'd prefer being able to convert the column values to years, but I haven't worked with spark before and I don't know an optimal way to do that.

Answer 1

well, one way is to use withColumn .

here I'm demonstrating adding a new column called "ageinMin" to dataframe and calculate it based on "age" column from dataframe and dividing it by 600 to get equivalent minutes:

df.withColumn("ageinMin",col("age") /600)