We also saw the internal working and the advantages of FLATMAP in PySpark Data Frame and its usage for various programming purpose. Also, the syntax and examples helped us to understand much precisely the function. Recommended Articles This is a guide to PySpark withColumnRenamed. Here we discuss...
When you have nested columns on PySpark DatFrame and if you want to rename it, usewithColumnon a data frame object to create a new column from an existing and we will need to drop the existing column. Below example creates a “fname” column from “name.firstname” and drops the “nam...
The "withColumn" function in PySpark allows you to add, replace, or update columns in a DataFrame. it returns a new DataFrame with the specified changes, without altering the original DataFrame
DF中所有剩余列上的withColumn 我想匿名化或替换pyspark数据框中几乎所有的列,除了少数列。 我知道这样做是可能的: anonymized_df = employee_df.withColumn("EMPLOYEENUMBER", col("EMPLOYEENUMBER"))\ .withColumn("NAME1", lit(""))\ .withColumn("TELEPHONE", lit(""))\ .withColumn("ELECTRONICMAILADDRESS"...