access_time 2 years ago languageEnglish

Convert List to Spark Data Frame in Python / Spark

visibility 6,012 comment 0

In Spark, SparkContext.parallelize function can be used to convert list of objects to RDD and then RDD can be converted to DataFrame object through SparkSession.

In PySpark, we can convert a Python list to RDD using SparkContext.parallelize function.

|  Category|Count|       Description|
|Category A|  100|This is category A|
|Category B|  120|This is category B|
|Category C|  150|This is category C|

Code snippet

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

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

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

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

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

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

# Create data frame
df = spark.createDataFrame(rdd,schema)
info Last modified by Raymond 8 months ago copyright This page is subject to Site terms.
Like this article?
Share on

Please log in or register to comment.

account_circle Log in person_add Register

Log in with external accounts