5. kpmg pyspark interview question & answer | databricks scenario based interview question & answer

#Databricks #PysparkInterviewQuestions #deltalake
Azure Databricks #spark #pyspark #azuredatabricks #azure
In this video, I discussed KPMG PySpark scenario based interview questions and answers.

PySpark advanced interview questions answers?
databricks interview questions and answers?
kpmg pyspark interview questions and answers?

Create dataframe:
======================================================
#Employees Salary info
data1=[(100,"Raj",None,1,"01-04-23",50000),
(200,"Joanne",100,1,"01-04-23",4000),(200,"Joanne",100,1,"13-04-23",4500),(200,"Joanne",100,1,"14-04-23",4020)]
schema1=["EmpId","EmpName","Mgrid","deptid","salarydt","salary"]
df_salary=spark.createDataFrame(data1,schema1)
display(df_salary)
#department dataframe
data2=[(1,"IT"),
(2,"HR")]
schema2=["deptid","deptname"]
df_dept=spark.createDataFrame(data2,schema2)
display(df_dept)

-----------------------------------------------------------------------------------------------------------------------
df=df_salary.withColumn('Newsaldt',to_date('salarydt','dd-MM-yy'))
display(df)
---------------------------------------------------------------------------------------------------------------------
from pyspark.sql.functions import col
df1=df.join(df_dept,['deptid'])
#display(df1)
df2=df1.alias('a').join(df1.alias('b'),col('a.Mgrid')==col('b.EmpId'),'left').select(
col('a.deptname'),
col('b.EmpName').alias('ManagerName'),
col('a.EmpName'),
col('a.Newsaldt'),
col('a.salary')
)
display(df2)
-------------------------------------------------------------------------------------------------------------------
from pyspark.sql.functions import year,month
df3=df2.groupBy('deptname','ManagerName','EMpName',year('Newsaldt').alias('Year'),date_format('Newsaldt','MMMM').alias('Month')).sum('salary')

display(df3)
============================================================

Learn PySpark, an interface for Apache Spark in Python. PySpark is often used for large-scale data processing and machine learning.

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