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Returns a new DataFrame by renaming an existing column. This is a no-op if the schema doesn't contain the given column name.
Syntax
withColumnRenamed(existing: str, new: str)
Parameters
| Parameter | Type | Description |
|---|---|---|
existing |
str | The name of the existing column to be renamed. |
new |
str | The new name to be assigned to the column. |
Returns
DataFrame: A new DataFrame with renamed column.
Examples
df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
df.withColumnRenamed("age", "age2").show()
# +----+-----+
# |age2| name|
# +----+-----+
# | 2|Alice|
# | 5| Bob|
# +----+-----+
df.withColumnRenamed("non_existing", "new_name").show()
# +---+-----+
# |age| name|
# +---+-----+
# | 2|Alice|
# | 5| Bob|
# +---+-----+
df.withColumnRenamed("age", "age2").withColumnRenamed("name", "name2").show()
# +----+-----+
# |age2|name2|
# +----+-----+
# | 2|Alice|
# | 5| Bob|
# +----+-----+