Column renaming is a common action when working with data frames. In this article, I will show you how to change column names in a Spark data frame using Python. The frequently used method is withColumnRenamed.
Construct a dataframe
The following code snippet creates a DataFrame from a Python native dictionary list. PySpark SQL types are used to create the schema and then SparkSession.createDataFrame function is used to convert the dictionary list to a Spark DataFrame.
from pyspark.sql import SparkSession
from pyspark.sql.types import ArrayType, StructField, StructType, StringType, IntegerType, DecimalType
from decimal import Decimal
appName = "Python Example - PySpark Rename DataFrame Column Names"
master = "local"
# Create Spark session
spark = SparkSession.builder \
.appName(appName) \
.master(master) \
.getOrCreate()
# List
data = [{"Category": 'Category A', "ID": 1, "Value": Decimal(12.40)},
{"Category": 'Category B', "ID": 2, "Value": Decimal(30.10)},
{"Category": 'Category C', "ID": 3, "Value": Decimal(100.01)}
]
# Schema
schema = StructType([
StructField('Category', StringType(), False),
StructField('ID', IntegerType(), False),
StructField('Value', DecimalType(scale=2), True)
])
# Create data frame
df = spark.createDataFrame(data, schema)
df.show()
The content looks like the following:
+----------+---+------+
| Category| ID| Value|
+----------+---+------+
|Category A| 1| 12.40|
|Category B| 2| 30.10|
|Category C| 3|100.01|
+----------+---+------+
Print out column names
DataFrame.columns can be used to print out column list of the data frame:
print(df.columns)
Output:
['Category', 'ID', 'Value']
Rename one column
We can use withColumnRenamedfunction to change column names.
df = df.withColumnRenamed('Category', 'category_new')
df.show()
Output:
+------------+---+------+
|category_new| ID| Value|
+------------+---+------+
| Category A| 1| 12.40|
| Category B| 2| 30.10|
| Category C| 3|100.01|
+------------+---+------+
Column Categoryis renamed to category_new.
Rename all columns
Function toDFcan be used to rename all column names. The following code snippet converts all column names to lower case and then append '_new' to each column name.
# Rename columns
new_column_names = [f"{c.lower()}_new" for c in df.columns]
df = df.toDF(*new_column_names)
df.show()
Output:
+------------+------+---------+
|category_new|id_new|value_new|
+------------+------+---------+
| Category A| 1| 12.40|
| Category B| 2| 30.10|
| Category C| 3| 100.01|
+------------+------+---------+
You can use similar approach to remove spaces or special characters from column names.
Use Spark SQL
Of course, you can also use Spark SQL to rename columns like the following code snippet shows:
df.createOrReplaceTempView("df")
spark.sql("select Category as category_new, ID as id_new, Value as value_new from df").show()
The above code snippet first register the dataframe as a temp view. And then Spark SQL is used to change column names.
Output:
+------------+------+---------+
|category_new|id_new|value_new|
+------------+------+---------+
| Category A| 1| 12.40|
| Category B| 2| 30.10|
| Category C| 3| 100.01|
+------------+------+---------+
Run Spark code
You can easily run Spark code on your Windows or UNIX-alike (Linux, MacOS) systems. Follow these articles to setup your Spark environment if you don't have one yet: