Creating a PySpark DataFrame from a Pandas DataFrame

spark_pandas_dataframes.py

import pandas as pd
from pyspark.sql.types import *

#Create Pandas DataFrame
pd_person = pd.DataFrame({'PERSONID':'0','LASTNAME':'Doe','FIRSTNAME':'John','ADDRESS':'Museumplein','CITY':'Amsterdam'}, index=[0])

#Create PySpark DataFrame Schema
p_schema = StructType([StructField('ADDRESS',StringType(),True),StructField('CITY',StringType(),True),StructField('FIRSTNAME',StringType(),True),StructField('LASTNAME',StringType(),True),StructField('PERSONID',DecimalType(),True)])

#Create Spark DataFrame from Pandas
df_person = sqlContext.createDataFrame(pd_person, p_schema)
#Important to order columns in the same order as the target database
df_person  = df_person.select("PERSONID", "LASTNAME", "FIRSTNAME", "CITY", "ADDRESS")

#Writing Spark DataFrame to local Oracle Expression Edition 11.2.0.2
#This uses the relatively older Spark jdbc DataFrameWriter api
df_person.write.jdbc(url='jdbc:oracle:thin:@127.0.0.1:1521:XE', table='HR.PERSONS', mode='append', properties={'driver':'oracle.jdbc.driver.OracleDriver', 'user' : 'SYSTEM', 'password' : 'password'})

Added Spark DataFrame Schema
Order columns to have the same order as target database