withColumnsRenamed
Returns a new DataFrame by renaming multiple columns. This is a no-op if the schema doesn't contain the given column names.
Syntax
withColumnsRenamed(colsMap: Dict[str, str])
Parameters
Parameter | Type | Description |
|---|---|---|
| dict | A dict of existing column names and corresponding desired column names. Currently, only a single map is supported. |
Returns
DataFrame: DataFrame with renamed columns.
Examples
Python
df = spark.createDataFrame([(2, "Alice"), (5, "Bob")], schema=["age", "name"])
df.withColumnsRenamed({"age": "age2"}).show()
# +----+-----+
# |age2| name|
# +----+-----+
# | 2|Alice|
# | 5| Bob|
# +----+-----+
df.withColumnsRenamed({"age": "age2", "name": "name2"}).show()
# +----+-----+
# |age2|name2|
# +----+-----+
# | 2|Alice|
# | 5| Bob|
# +----+-----+
df.withColumnsRenamed({"non_existing": "new_name"}).show()
# +---+-----+
# |age| name|
# +---+-----+
# | 2|Alice|
# | 5| Bob|
# +---+-----+