All I can do with withColumn in Spark

If you are a spark engineer, you will know to use withColumn usefully and frequently. The `withColumn()` function in Spark is a powerful and flexible tool for manipulating data in a DataFrame. Here are some of the things you can do with it.

Add a new column

You can use `withColumn()` to add a new column to a DataFrame based on an existing column or a computed value.

from pyspark.sql.functions import col

# Add a new column to a DataFrame based on an existing column
df = df.withColumn("newColumn", col("existingColumn"))

# Add a new column to a DataFrame based on a computed value
df = df.withColumn("newColumn", col("existingColumn") * 2)

# use lit to add
df = df.withColumn("newcolumn", lit("USA"))

You can use lit, lit() as a way for us to interact with column literals as Python has no native function for this, you will need to use lit() to tell JVM what we’re talking about (Column literal).

Rename a column

You can use `withColumn()` to rename an existing column in a DataFrame.

# Rename an existing column in a DataFrame
df = df.withColumnRenamed("oldColumnName", "newColumnName")

Replace a column

You can use `withColumn()` to replace an existing column in a DataFrame with a new column based on an existing column or a computed value.

# Replace an existing column in a DataFrame with a new…