PySpark: Convert Python Array/List to Spark Data Frame

In Spark, SparkContext.parallelize function can be used to convert Python list to RDD and then RDD can be converted to DataFrame object. The following sample code is based on Spark 2.x.

In this page, I am going to show you how to convert the following list to a data frame:

data = [('Category A', 100, "This is category A"),
        ('Category B', 120, "This is category B"),
        ('Category C', 150, "This is category C")]

Import types

First, let’s import the data types we need for the data frame.

from pyspark.sql import SparkSession
from pyspark.sql.types import StructField, StructType, StringType, IntegerType

We imported StringType and IntegerType because the sample data have three attributes, two are strings and one is integer.

Create Spark session

Create Spark session using the following code:

from pyspark.sql import SparkSession
from pyspark.sql.types import ArrayType, StructField, StructType, StringType, IntegerType

appName = "PySpark Example - Python Array/List to Spark Data Frame"
master = "local"

# Create Spark session
spark = SparkSession.builder \
    .appName(appName) \
    .master(master) \
    .getOrCreate()

Define the schema

Let’s now define a schema for the data frame based on the structure of the Python list.

# Create a schema for the dataframe
schema = StructType([
    StructField('Category', StringType(), True),
    StructField('Count', IntegerType(), True),
    StructField('Description', StringType(), True)
])

Convert the list to data frame

The list can be converted to RDD through parallelize function:

# Convert list to RDD
rdd = spark.sparkContext.parallelize(data)

# Create data frame
df = spark.createDataFrame(rdd,schema)
print(df.schema)
df.show()

Complete script

from pyspark.sql import SparkSession
from pyspark.sql.types import ArrayType, StructField, StructType, StringType, IntegerType

appName = "PySpark Example - Python Array/List to Spark Data Frame"
master = "local"

# Create Spark session
spark = SparkSession.builder \
    .appName(appName) \
    .master(master) \
    .getOrCreate()

# List
data = [('Category A', 100, "This is category A"),
        ('Category B', 120, "This is category B"),
        ('Category C', 150, "This is category C")]

# Create a schema for the dataframe
schema = StructType([
    StructField('Category', StringType(), True),
    StructField('Count', IntegerType(), True),
    StructField('Description', StringType(), True)
])

# Convert list to RDD
rdd = spark.sparkContext.parallelize(data)

# Create data frame
df = spark.createDataFrame(rdd,schema)
print(df.schema)
df.show()

Sample output

StructType(List(StructField(Category,StringType,true),StructField(Count,IntegerType,true),StructField(Description,StringType,true)))
+----------+-----+------------------+
|  Category|Count|       Description|
+----------+-----+------------------+
|Category A|  100|This is category A|
|Category B|  120|This is category B|
|Category C|  150|This is category C|
+----------+-----+------------------+

Summary

For Python objects, we can convert them to RDD first and then use SparkSession.createDataFrame function to create the data frame based on the RDD.

The following data types are supported for defining the schema:

• NullType
• StringType
• BinaryType
• BooleanType
• DateType
• TimestampType
• DecimalType
• DoubleType
• FloatType
• ByteType
• IntegerType
• LongType
• ShortType
• ArrayType
• MapType

For more information, please refer to the official API documentation pyspark.sql module.