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Returns a new DataFrame by adding a column or replacing the existing column that has the same name.
Syntax
withColumn(colName: str, col: Column)
Parameters
| Parameter | Type | Description |
|---|---|---|
colName |
str | string, name of the new column. |
col |
Column | a Column expression for the new column. |
Returns
DataFrame: DataFrame with new or replaced column.
Notes
This method introduces a projection internally. Therefore, calling it multiple times, for instance, via loops in order to add multiple columns can generate big plans which can cause performance issues and even StackOverflowException. To avoid this, use select with multiple columns at once.
Examples
df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
df.withColumn('age2', df.age + 2).show()
# +---+-----+----+
# |age| name|age2|
# +---+-----+----+
# | 2|Alice| 4|
# | 5| Bob| 7|
# +---+-----+----+