Customers and Orders - Table Joining using Spark RDD Example

scala> val custRDD = sc.textFile("hdfs://localhost:8020/user/data/customers/")

custRDD: org.apache.spark.rdd.RDD[String] = hdfs://localhost:8020/user/data/customers/ MapPartitionsRDD[7] at textFile at <console>:27

scala> custRDD.take(5).foreach(println)

1 Richard Hernandez Brownsville

2 Mary Barrett Littleton

3 Ann Smith Caguas

4 Mary Jones San Marcos

5 Robert Hudson Caguas

scala> custRDD.count()

res4: Long = 12435

scala> val ordersRDD = sc.textFile("hdfs://localhost:8020/user/data/orders/")

ordersRDD: org.apache.spark.rdd.RDD[String] = hdfs://localhost:8020/user/data/orders/ MapPartitionsRDD[9] at textFile at <console>:27

scala> ordersRDD.take(5).foreach(println)

1 2013-07-25 00:00:00.0 11599 CLOSED

2 2013-07-25 00:00:00.0 256 PENDING_PAYMENT

3 2013-07-25 00:00:00.0 12111 COMPLETE

4 2013-07-25 00:00:00.0 8827 CLOSED

5 2013-07-25 00:00:00.0 11318 COMPLETE

scala> ordersRDD.count()

res5: Long = 68893

scala> val orderTuple = ordersRDD.map (line => (line.split("\t")(2),1))

orderTuple: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[10] at map at <console>:29

scala> orderTuple.take(5)

res9: Array[(String, Int)] = Array((11599,1), (256,1), (12111,1), (8827,1), (11318,1))

scala> orderTuple.take(5).foreach(println)

(11599,1)

(256,1)

(12111,1)

(8827,1)

(11318,1)

 

scala> val reducedRDD = orderTuple.reduceByKey(_+_)

reducedRDD: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[11] at reduceByKey at <console>:31

scala> reducedRDD.take(5).foreach(println)

(2828,4)                                                                        

(2350,5)

(8602,8)

(11415,4)

(5302,10)

val custTuple = custRDD.map { line => \

val w = line.split("\t")  

val custid = w(0)  

val cfname = w(1)  

val clname = w(2)  

(custid,cfname + " " + clname) 

}

custTuple.take(5).foreach(println)

scala> custTuple.take(5).foreach(println)

(1,Richard Hernandez)

(2,Mary Barrett)

(3,Ann Smith)

(4,Mary Jones)

(5,Robert Hudson)

scala> val joinedRDD = custTuple.join(reducedRDD)

joinedRDD: org.apache.spark.rdd.RDD[(String, (String, Int))] = MapPartitionsRDD[15] at join at <console>:37

scala> joinedRDD.take(5).foreach(println)

(2828,(Mary Smith,4))

(2350,(Brian Perez,5))

(8602,(Timothy Gilbert,8))

(11415,(George Smith,4))

(5302,(Betty Smith,10))

scala> val sortedRDD = joinedRDD.sortBy(_._2._2,false)

sortedRDD: org.apache.spark.rdd.RDD[(String, (String, Int))] = MapPartitionsRDD[20] at sortBy at <console>:39

scala> sortedRDD.take(5).foreach(println)

(569,(Mary Frye,16))

(5897,(Mary Griffin,16))

(12431,(Mary Rios,16))

(6316,(Kyle Smith,16))

(5654,(Jerry Smith,15))

Find the population of each city using Spark RDD

Populattion of each city

customer.txt:

-------------

1,Richard,Hernandez,XXXXXXXXX,XXXXXXXXX,6303 Heather Plaza,Brownsville,TX,78521

2,Mary,Barrett,XXXXXXXXX,XXXXXXXXX,9526 Noble Embers Ridge,Littleton,CO,80126

3,Ann,Smith,XXXXXXXXX,XXXXXXXXX,3422 Blue Pioneer Bend,Caguas,PR,00725

4,Mary,Jones,XXXXXXXXX,XXXXXXXXX,8324 Little Common,San Marcos,CA,92069

5,Robert,Hudson,XXXXXXXXX,XXXXXXXXX,10 Crystal River Mall ,Caguas,PR,00725

6,Mary,Smith,XXXXXXXXX,XXXXXXXXX,3151 Sleepy Quail Promenade,Passaic,NJ,07055

7,Melissa,Wilcox,XXXXXXXXX,XXXXXXXXX,9453 High Concession,Caguas,PR,00725

8,Megan,Smith,XXXXXXXXX,XXXXXXXXX,3047 Foggy Forest Plaza,Lawrence,MA,01841

9,Mary,Perez,XXXXXXXXX,XXXXXXXXX,3616 Quaking Street,Caguas,PR,00725

10,Melissa,Smith,XXXXXXXXX,XXXXXXXXX,8598 Harvest Beacon Plaza,Stafford,VA,22554

val custRDD = sc.textFile("D:\\Ex\\customer.txt")

val cityRDD = custRDD.map ( x => (x.split(",")(6),1))

val cityCountRDD = cityRDD.reduceByKey(_+_)

val sortedRDD = cityCountRDD.sortBy(x => x._2, false)

sortedRDD.take(10).foreach(println)

scala> val custRDD = sc.textFile("D:\\Ex\\customer.txt")

custRDD: org.apache.spark.rdd.RDD[String] = D:\Ex\customer.txt MapPartitionsRDD[26] at textFile at <console>:24

scala> val cityRDD = custRDD.map ( x => (x.split(",")(6),1))

cityRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[27] at map at <console>:25

scala> val cityCountRDD = cityRDD.reduceByKey(_+_)

cityCountRDD: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[28] at reduceByKey at <console>:25

scala> val sortedRDD = cityCountRDD.sortBy(x => x._2, false)

sortedRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[33] at sortBy at <console>:25

scala> sortedRDD.take(10).foreach(println)

(Caguas,1152)

(Chicago,62)

(Los Angeles,59)

(Brooklyn,47)

(Bronx,28)

(Las Vegas,27)

(Houston,26)

(Phoenix,24)

(Philadelphia,24)

(San Diego,23)

Finding Frequency of word in Text File using Spark RDD

Frequency of word in text file

customer.txt: 

-------------

1,Richard,Hernandez,XXXXXXXXX,XXXXXXXXX,6303 Heather Plaza,Brownsville,TX,78521

2,Mary,Barrett,XXXXXXXXX,XXXXXXXXX,9526 Noble Embers Ridge,Littleton,CO,80126

3,Ann,Smith,XXXXXXXXX,XXXXXXXXX,3422 Blue Pioneer Bend,Caguas,PR,00725

4,Mary,Jones,XXXXXXXXX,XXXXXXXXX,8324 Little Common,San Marcos,CA,92069

5,Robert,Hudson,XXXXXXXXX,XXXXXXXXX,10 Crystal River Mall ,Caguas,PR,00725

6,Mary,Smith,XXXXXXXXX,XXXXXXXXX,3151 Sleepy Quail Promenade,Passaic,NJ,07055

7,Melissa,Wilcox,XXXXXXXXX,XXXXXXXXX,9453 High Concession,Caguas,PR,00725

8,Megan,Smith,XXXXXXXXX,XXXXXXXXX,3047 Foggy Forest Plaza,Lawrence,MA,01841

9,Mary,Perez,XXXXXXXXX,XXXXXXXXX,3616 Quaking Street,Caguas,PR,00725

10,Melissa,Smith,XXXXXXXXX,XXXXXXXXX,8598 Harvest Beacon Plaza,Stafford,VA,22554

val custRDD = sc.textFile("D:\\Ex\\customer.txt")

val words = custRDD.flatMap(x => x.split(",")).filter(x => x != "XXXXXXXXX")

val pairRDD = words.map(x => (x,1))

val reducedRDD = pairRDD.reduceByKey(_+_)

val sortedRDD = reducedRDD.sortBy(x => x._2, false)

sortedRDD.take(10).foreach(println)

scala> val custRDD = sc.textFile("D:\\Ex\\customer.txt")

custRDD: org.apache.spark.rdd.RDD[String] = D:\Ex\customer.txt MapPartitionsRDD[6] at textFile at <console>:24

scala> val words = custRDD.flatMap(x => x.split(",")).filter(x => x != "XXXXXXXXX")

words: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[8] at filter at <console>:25

scala> val pairRDD = words.map(x => (x,1))

pairRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[9] at map at <console>:25

scala> val reducedRDD = pairRDD.reduceByKey(_+_)

reducedRDD: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[10] at reduceByKey at <console>:25

scala> val sortedRDD = reducedRDD.sortBy(x => x._2, false)

sortedRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[15] at sortBy at <console>:25

scala> sortedRDD.take(10).foreach(println)

(Mary,1196)

(PR,1186)

(Smith,1160)

(Caguas,1152)

(00725,1152)

(CA,505)

(TX,183)

(NY,168)

(IL,123)

(FL,73)

Spark RDD programming using Scala - Spark Core Examples

scala> val rdd = sc.parallelize(List(1,2,3,4,5))

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24

scala> val resultRDD = rdd.map ( x => x * x )

resultRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[1] at map at <console>:25

scala> resultRDD.collect()

res0: Array[Int] = Array(1, 4, 9, 16, 25)

scala> resultRDD.foreach(println)

9

4

1

16

25

// We have a file named : employee.txt 

D:\\Ex\\employee.txt

John,33,US

Jim,44,UK

Nav,30,India

// Reading the file content and create RDD

scala> val rdd = sc.textFile("D:\\Ex\\employee.txt")

rdd: org.apache.spark.rdd.RDD[String] = D:\Ex\employee.txt MapPartitionsRDD[3] at textFile at <conso

// find the length of each line

scala> val resultRDD = rdd.map (x => (x, x.length))

resultRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[4] at map at <console>:25

scala> resultRDD.foreach(println)

(John,33,US,10)

(Nav,30,India,12)

(Jim,44,UK,9)

scala> resultRDD.collect()

res3: Array[(String, Int)] = Array((John,33,US,10), (Jim,44,UK,9), (Nav,30,India,12))

scala> val rdd = sc.textFile("D:\\Ex\\employee.txt")

rdd: org.apache.spark.rdd.RDD[String] = D:\Ex\employee.txt MapPartitionsRDD[3] at textFile at <console>:24

scala> rdd.flatMap(x => x.split(",")).collect()

res4: Array[String] = Array(John, 33, US, Jim, 44, UK, Nav, 30, India)

scala> val resultRDD =  rdd.flatMap(x => x.split(","))

resultRDD: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[6] at flatMap at <console>:25

scala> resultRDD.foreach(println)

Nav

30

India

John

33

US

Jim

44

UK

scala> resultRDD.collect()

res6: Array[String] = Array(John, 33, US, Jim, 44, UK, Nav, 30, India)

scala> val rdd = sc.makeRDD (List(1,2,3,4,5))

rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[7] at makeRDD at <console>:24

scala> val resultRDD = rdd.filter( x => x%2 == 0)

resultRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[8] at filter at <console>:25

scala> resultRDD.collect()

res7: Array[Int] = Array(2, 4)

scala> resultRDD.foreach(println)

4

2

scala> val rdd = sc.textFile("D:\\Ex\\employee.txt")

rdd: org.apache.spark.rdd.RDD[String] = D:\Ex\employee.txt MapPartitionsRDD[10] at textFile at <console>:24

scala> val filtered = rdd.filter ( line => line.split(",")(1).toInt < 40)

filtered: org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[12] at filter at <console>:25

scala> filtered.foreach(println)

Nav,30,India

John,33,US

scala> val rdd1 = sc.parallelize(List(1,2,3,4,5))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[13] at parallelize at <console>:24

scala> val rdd2 = sc.parallelize(List(1,2,3,6,7))

rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[14] at parallelize at <console>:24

// intersection

scala> val resultRDD = rdd1.intersection(rdd2)

resultRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[20] at intersection at <console>:27

scala> resultRDD.collect()

res12: Array[Int] = Array(1, 2, 3)

scala> resultRDD.foreach(println)

2

3

1

scala> val rdd1 = sc.parallelize(List(1,2,3,4,5))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[21] at parallelize at <console>:24

scala> val rdd2 = sc.parallelize(List(3,5))

rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[22] at parallelize at <console>:24

// union

scala> val resultRDD = rdd1.union(rdd2)

resultRDD: org.apache.spark.rdd.RDD[Int] = UnionRDD[23] at union at <console>:27

scala> resultRDD.collect()

res14: Array[Int] = Array(1, 2, 3, 4, 5, 3, 5)

// Find the Distinct - Unique Values

scala> resultRDD.distinct().collect()

res21: Array[Int] = Array(1, 2, 3, 4, 5)

// Find the distinct 

scala> val rdd1 = sc.parallelize(List(1,1,2,3,3,2,2,1,4,5,5,4,4,5,5,3,2,1,2,3))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[27] at parallelize at <console>:2

scala> val resultRDD = rdd1.distinct()

resultRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[30] at distinct at <console>:25

scala> resultRDD.collect()

res22: Array[Int] = Array(4, 1, 5, 2, 3)

// Find the union

scala> val rdd1 = sc.makeRDD(List("Raja","Ramesh","Siva"))

rdd1: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[32] at makeRDD at <console>:24

scala> val rdd2 = sc.parallelize(List("Arun","Viswa","Kalai"))

rdd2: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[33] at parallelize at <console>:24

scala> val resultRDD = rdd1.union(rdd2)

resultRDD: org.apache.spark.rdd.RDD[String] = UnionRDD[34] at union at <console>:27

scala> resultRDD.foreach(println)

Raja

Siva

Ramesh

Kalai

Viswa

Arun

birth.txt:

-----------

Dec,Arun

March,Kalai

April,Nila

April,John

Dec,Silva

January,Priya

Dec,Ashwin

January,Ohm

March,Sastha

January,Ashwath

April,Agila

April,Alex

January,Sridevi

Dec,Sridhar

March,Neelaveni

scala> val rdd1 = sc.textFile("D:\\Ex\\birth.txt")

rdd1: org.apache.spark.rdd.RDD[String] = D:\Ex\birth.txt MapPartitionsRDD[36] at textFile at <console>:24

case class birth (month:String, name:String)

val pairRDD = rdd1.map { line => 

              val w = line.split(",")

  val month = w(0)

  val name = w(1)

  (month,name)

}

scala> pairRDD.collect()

res27: Array[(String, String)] = Array((Dec,Arun), (March,Kalai), (April,Nila), (April,John), (Dec,Silva), (

scala> pairRDD.foreach(println)

(Dec,Arun)

(March,Kalai)

(April,Nila)

(April,John)

(January,Ashwath)

(Dec,Silva)

(April,Agila)

(January,Priya)

(April,Alex)

(Dec,Ashwin)

(January,Sridevi)

(January,Ohm)

(Dec,Sridhar)

(March,Sastha)

(March,Neelaveni)

scala> val groupedRDD = pairRDD.groupByKey()

groupedRDD: org.apache.spark.rdd.RDD[(String, Iterable[String])] = ShuffledRDD[38] at groupByKey at <console

scala> groupedRDD.collect()

res29: Array[(String, Iterable[String])] = Array((April,CompactBuffer(Nila, John, Agila, Alex)), (January,Co

scala> groupedRDD.foreach(println)

(March,CompactBuffer(Kalai, Sastha, Neelaveni))

(April,CompactBuffer(Nila, John, Agila, Alex))

(January,CompactBuffer(Priya, Ohm, Ashwath, Sridevi))

(Dec,CompactBuffer(Arun, Silva, Ashwin, Sridhar))

 

// SortByKey Example

val rdd1 = sc.textFile("D:\\Ex\\birth.txt")

 

case class birth (month:String, name:String)

val pairRDD = rdd1.map { line => 

              val w = line.split(",")

  val month = w(0)

  val name = w(1)

  (month,name)

}

val sortedRDD = pairRDD.sortByKey()

 

scala> sortedRDD.collect()

res7: Array[(String, String)] = Array((April,Nila), (April,John), (April,Agila), (April,Alex), (Dec,Arun), (Dec,Silva), (Dec,Ashwin), (Dec,Sridhar), (January,Priya), (January,Ohm)

scala> sortedRDD.foreach(println)

(April,Nila)

(January,Priya)

(January,Ohm)

(April,John)

(January,Ashwath)

(April,Agila)

(January,Sridevi)

(April,Alex)

(March,Kalai)

(Dec,Arun)

(March,Sastha)

(Dec,Silva)

(March,Neelaveni)

(Dec,Ashwin)

(Dec,Sridhar)

scala> groupedRDD.toDebugString

res31: String =

(2) ShuffledRDD[38] at groupByKey at <console>:25 []

 +-(2) MapPartitionsRDD[37] at map at <console>:25 []

    |  D:\Ex\birth.txt MapPartitionsRDD[36] at textFile at <console>:24 []

    |  D:\Ex\birth.txt HadoopRDD[35] at textFile at <console>:24 []

// ReduceByKey Example

 

veggies.txt:

------------

Tomato,10

Brinjal,20

Tomato,20

Brinjal,33

Carrot,23

Potato,20

Carrot,50

Potato,23

Brinjal,22

Potato,20

Carrot,23

scala> val rdd1 = sc.textFile("D:\\Ex\\veggies.txt")

rdd1: org.apache.spark.rdd.RDD[String] = D:\Ex\veggies.txt MapPartitionsRDD[40] at textFile at <console>:24

scala> case class veggies (name:String, price:Int)

defined class veggies

scala> val pairRDD = rdd1.map{ line =>

       val w = line.split(",")

       val name = w(0)

       val price = w(1).toInt

       (name,price)

       }

pairRDD: org.apache.spark.rdd.RDD[(String, Int)] = MapPartitionsRDD[41] at map at <console>:25

scala> val reducedRDD = pairRDD.reduceByKey( (x,y) => (x + y))

reducedRDD: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[42] at reduceByKey at <console>:2

OR

scala> val reducedRDD = pairRDD.reduceByKey(_ + _)

reducedRDD: org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[43] at reduceByKey at <console>:2

scala> reducedRDD.foreach(println)

(Brinjal,75)

(Potato,63)

(Tomato,30)

(Carrot,96)

scala>  reducedRDD.sortByKey().collect().foreach(println)

(Brinjal,75)

(Carrot,96)

(Potato,63)

(Tomato,30)

// Reduce Vs Sum

scala> val rdd1 = sc.parallelize(List(1,2,3,4,5,6,7,8,9,10))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[44] at parallelize at <console>:24

// Scala function - shuffling then sum

scala> val summation = rdd1.sum()

summation: Double = 55.0

// sum then shuffling - data movement between nodes are less

scala> val summ = rdd1.reduce(_+_)

summ: Int = 55

students_roll.txt:

------------------

10,Ravi

11,Rahul

9,Lisa

7,Radha

5,Binal

6,Dev

8,Raj

1,Sri

3,Kalai

2,Arul

4,Priya

scala> val rdd1 = sc.textFile("D:\\Ex\\students_roll.txt")

rdd1: org.apache.spark.rdd.RDD[String] = D:\Ex\students_roll.txt MapPartitionsRDD[38] at textFile at <c

scala> case class student(rollno:Int, name:String)

defined class student

 val pairRDD = rdd1.map { line =>

 val w = line.split(",")

 val rollno = w(0).toInt

 val name = w(1)

 (rollno, name)

 }

 

 

 scala> pairRDD.collect()

res21: Array[(Int, String)] = Array((10,Ravi), (11,Rahul), (9,Lisa), (7,Radha), (5,Binal), (6,Dev), (8,

// Ascending Order

scala> pairRDD.sortByKey(fale).collect().foreach(println)

(1,Sri)

(2,Arul)

(3,Kalai)

(4,Priya)

(5,Binal)

(6,Dev)

(7,Radha)

(8,Raj)

(9,Lisa)

(10,Ravi)

(11,Rahul)

// Descending Order

scala> pairRDD.sortByKey(false).collect().foreach(println)

(11,Rahul)

(10,Ravi)

(9,Lisa)

(8,Raj)

(7,Radha)

(6,Dev)

(5,Binal)

(4,Priya)

(3,Kalai)

(2,Arul)

(1,Sri)

// SortBy Example

scala> pairRDD.sortBy(x => x._2).collect().foreach(println)

(2,Arul)

(5,Binal)

(6,Dev)

(3,Kalai)

(9,Lisa)

(4,Priya)

(7,Radha)

(11,Rahul)

(8,Raj)

(10,Ravi)

(1,Sri)

scala> pairRDD.sortBy(x => x._1).collect().foreach(println)

(1,Sri)

(2,Arul)

(3,Kalai)

(4,Priya)

(5,Binal)

(6,Dev)

(7,Radha)

(8,Raj)

(9,Lisa)

(10,Ravi)

(11,Rahul)

scala> pairRDD.sortBy(x => x._1,false).collect().foreach(println)

(11,Rahul)

(10,Ravi)

(9,Lisa)

(8,Raj)

(7,Radha)

(6,Dev)

(5,Binal)

(4,Priya)

(3,Kalai)

(2,Arul)

(1,Sri)

scala> val rdd1 = sc.parallelize(List(1,2,3,4,5))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[0] at parallelize at <console>:24

scala> rdd1.partitions.size

res0: Int = 4

scala> val rdd2 = sc.parallelize(List(1,2,3,4,5,6),2)

rdd2: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[1] at parallelize at <console>:24

scala> rdd2.partitions.size

res1: Int = 2

scala> rdd1.getNumPartitions

res2: Int = 4

scala> rdd2.getNumPartitions

res3: Int = 2

scala> val rdd1 = sc.textFile("D:\\Ex\\students_roll.txt")

rdd1: org.apache.spark.rdd.RDD[String] = D:\Ex\students_roll.txt MapPartitionsRDD[3] at textFi

scala> rdd1.getNumPartitions

res4: Int = 2

scala> val rdd1 = sc.textFile("D:\\Ex\\students_roll.txt",3)

rdd1: org.apache.spark.rdd.RDD[String] = D:\Ex\students_roll.txt MapPartitionsRDD[7] at textFi

scala> rdd1.getNumPartitions

res6: Int = 3

scala> rdd1.partitions.size

res7: Int = 3

scala> rdd1.partitions.length

res8: Int = 3

scala> val resultRDD = rdd1.mapPartitions(x => List(x.toList).iterator)

resultRDD: org.apache.spark.rdd.RDD[List[String]] = MapPartitionsRDD[8] at mapPartitions at <console>:25

scala> resultRDD.collect

res9: Array[List[String]] = Array(List(10,Ravi, 11,Rahul, 9,Lisa, 7,Radha), List(5,Binal, 6,Dev, 8,Raj, 1,Sri), List(3,Kalai

scala> resultRDD.collect().foreach(println)

List(10,Ravi, 11,Rahul, 9,Lisa, 7,Radha)

List(5,Binal, 6,Dev, 8,Raj, 1,Sri)

List(3,Kalai, 2,Arul, 4,Priya)

Coalese Example:  

// We can reduce the number of partitions using Coalesce. We can't increase the number of partitions

scala> val rdd1 = sc.parallelize(List(1,1,2,2,3,4,5,6,7,8,9,10),3)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[9] at parallelize at <console>:24

scala> rdd1.partitions.size

res11: Int = 3

scala> rdd1.getNumPartitions

res12: Int = 3

scala> val coalescedRDD = rdd1.coalesce(1)

coalescedRDD: org.apache.spark.rdd.RDD[Int] = CoalescedRDD[10] at coalesce at <console>:25

scala> coalescedRDD.getNumPartitions

res13: Int = 1

Repartition Example:

scala> val rdd1 = sc.parallelize(List(1,2,3,4,5,6,7,8,9,10),2)

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[16] at parallelize at <conso

scala> rdd1.getNumPartitions

res17: Int = 2

// Increasing the number of partitions

scala> val rdd2 = rdd1.repartition(5)

rdd2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[24] at repartition at <console>:2

scala> rdd2.getNumPartitions

res20: Int = 5

// Decreasing the number of partitions

scala> val rdd2 = rdd1.repartition(1)

rdd2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[28] at repartition at <console>:2

scala> rdd2.partitions.size

res21: Int = 1

// Not applicable to increase the partitions using Coalesce

scala> val rdd3 = rdd1.coalesce(10)

rdd3: org.apache.spark.rdd.RDD[Int] = CoalescedRDD[29] at coalesce at <console>:25

scala> rdd3.getNumPartitions

res22: Int = 2

val rdd1 = sc.parallelize(Array(("tamil","tamilnenjam@gmail.com"),("vijay","vijaybalajitheboss@gmail.com"),("aish","aishvaryasudha@gmail.com"),("raja","raja@gmail.com")))

rdd1: org.apache.spark.rdd.RDD[(String, String)] = ParallelCollectionRDD[30] at parallelize at <console>:24

val rdd2 = sc.parallelize(Array(("maha","mahalakshmi@gmail.com"),("tamil","tamilnenjam2017@gmail.com"),("vijay","vijaybalaji2001@gmail.com"),("aish","aishvaryasara@gmail.com")))

scala> val joinedRDD = rdd1.join(rdd2)

joinedRDD: org.apache.spark.rdd.RDD[(String, (String, String))] = MapPartitionsRDD[37] at join at <console>:27

scala>  joinedRDD.collect.foreach(println)

(aish,(aishvaryasudha@gmail.com,aishvaryasara@gmail.com))

(tamil,(tamilnenjam@gmail.com,tamilnenjam2017@gmail.com))

(vijay,(vijaybalajitheboss@gmail.com,vijaybalaji2001@gmail.com))

scala> rdd1.leftOuterJoin(rdd2).collect().foreach(println)

(aish,(aishvaryasudha@gmail.com,Some(aishvaryasara@gmail.com)))

(raja,(raja@gmail.com,None))  -- available only in left table 

(tamil,(tamilnenjam@gmail.com,Some(tamilnenjam2017@gmail.com)))

(vijay,(vijaybalajitheboss@gmail.com,Some(vijaybalaji2001@gmail.com)))

scala>  rdd1.rightOuterJoin(rdd2).collect().foreach(println)

(aish,(Some(aishvaryasudha@gmail.com),aishvaryasara@gmail.com))

(maha,(None,mahalakshmi@gmail.com)) -- available only in right table 

(tamil,(Some(tamilnenjam@gmail.com),tamilnenjam2017@gmail.com))

(vijay,(Some(vijaybalajitheboss@gmail.com),vijaybalaji2001@gmail.com))

scala> rdd1.union(rdd2).collect().foreach(println)

(tamil,tamilnenjam@gmail.com)

(vijay,vijaybalajitheboss@gmail.com)

(aish,aishvaryasudha@gmail.com)

(raja,raja@gmail.com)

(maha,mahalakshmi@gmail.com)

(tamil,tamilnenjam2017@gmail.com)

(vijay,vijaybalaji2001@gmail.com)

(aish,aishvaryasara@gmail.com)

scala> val rdd1 = sc.makeRDD(List(1,2,3,4,5))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[45] at makeRDD at <console>:24

scala> val mappedRDD = rdd1.map( x => x*2)

mappedRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[46] at map at <console>:25

scala> mappedRDD.take(2)

res28: Array[Int] = Array(2, 4)

scala> mappedRDD.first

res29: Int = 2

scala> rdd1.count

res37: Long = 4

scala> val rdd1 = sc.parallelize(List(1,2,3,4,5,6,7,8,9,10))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[72] at parallelize at <console>:24

scala> rdd1.top(3)

res39: Array[Int] = Array(10, 9, 8)

scala> rdd1.take(3)

res40: Array[Int] = Array(1, 2, 3)

// countByValue example 

scala> val rdd1 = sc.makeRDD(List(1,1,2,2,2,3,3,4,4,4,4,4,5,5,6,6,6,6,6,6,3,3,2,2,1,1,2,2,3,4,4,5))

rdd1: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[74] at makeRDD at <console>:24

scala> rdd1.countByValue()

res41: scala.collection.Map[Int,Long] = Map(5 -> 3, 1 -> 4, 6 -> 6, 2 -> 7, 3 -> 5, 4 -> 7)

// Reduce Examples

scala> rdd1.reduce(_+_)

res44: Int = 112

scala> rdd1.reduce(_*_)

res45: Int = 2013265920

scala> rdd1.reduce(_-_)

res46: Int = 44

How to read multi sheet excel file using Python?

$ pip3 install pandas  // install pandas

$ pip3 install xlrd // install excel reading package

>>> import pandas as pd

>>> xls = pd.ExcelFile('/home/hadoop/Downloads/multisheets.xlsx')  // read excel file

>>> df1 = pd.read_excel(xls, 'first')  // read particular sheet

>>> df2 = pd.read_excel(xls, 'second')

>>> df3 = pd.read_excel(xls, 'third')

>>> print(df1)  // display the content of python dataframe

    Ravi  22    Chennai

    Rahul  23  Bengaluru

>>> print(df2)

  selvi  32 Chennai

  Usha  38  Singai

>>> print(df3)

  Ayush   5   Bengaluru

  Ram  50  Aranthangi

The content of multi sheets excel file is given below:

Introduction to Pandas - Sample Programs

#Series Example with 0,1,2,3,. are indexes

import pandas as pd

s1 = pd.Series([1,2,3,4,5,6,7,8,9,10])

print(s1)

0     1

1     2

2     3

3     4

4     5

5     6

6     7

7     8

8     9

9    10

dtype: int64

type(s1)

pandas.core.series.Series

#Series example with Custom indexes

import pandas as pd

s1 = pd.Series([86,63,85,81,90],index=["Tamil","English","Maths","Science","Social"])

print(s1)

Tamil      86

English    63

Maths      85

Science    81

Social     90

dtype: int64

#Passing dictionary object to the Series

#Keys of a given Dictionary will become Indexes

import pandas as pd

subjectDict = {"Tamil":85, "English":63, "Maths":85, "Science":81, "Social":90}

s1 = pd.Series(subjectDict)

print(s1)

Tamil      85

English    63

Maths      85

Science    81

Social     90

dtype: int64

# 'b' and 'd' are not there in the given indexes, so, NaN as the value assigned to them

import pandas as pd

s1 = pd.Series({"a":10,"c":30,"e":40},index=["b","c","d","a"])

print(s1)

b     NaN

c    30.0

d     NaN

a    10.0

dtype: float64

s1 = pd.Series([5,7,3,2,88,22,-1,0,33])

print(s1[3])

2

print(s1[:2])

0    5

1    7

dtype: int64

print(s1[-1:])

8    33

dtype: int64

print(s1[:6])

0     5

1     7

2     3

3     2

4    88

5    22

dtype: int64

#Arithmetic operations

s1 = pd.Series([10,20,30,40])

s2 = pd.Series([11,22,33,44])

s3 = s1 + s2

print(s3)

0    21

1    42

2    63

3    84

dtype: int64

s1 = pd.Series([11,66,77,55])

s2 = pd.Series([5,22,22,44])

s3 = s1 - s2

print(s3)

0     6

1    44

2    55

3    11

dtype: int64

print(s1+15)

0    26

1    81

2    92

3    70

dtype: int64

print(s2 ** 1.3)

0      8.103283

1     55.609563

2     55.609563

3    136.926807

dtype: float64

s1 = pd.Series([1,2,3])

s2 = pd.Series([6,7,8])

print(s1,s2)

0    1

1    2

2    3

dtype: int64 0    6

1    7

2    8

dtype: int64

print(s1+s2)

0     7

1     9

2    11

dtype: int64

print(s1*s2)

0     6

1    14

2    24

dtype: int64

print(s1-s2, s2-s1)

0   -5

1   -5

2   -5

dtype: int64 0    5

1    5

2    5

dtype: int64

#DataFrame Example

import pandas as pd

subjectDict = {"Subjects":["Tamil","English","Maths","Science","Social"],"Marks":[86,63,85,81,90]}

df = pd.DataFrame(subjectDict)

print(df)

  Subjects  Marks

0    Tamil     86

1  English     63

2    Maths     85

3  Science     81

4   Social     90

import pandas as pd

subjectDict = {"Names":["Arjun","Ram","Biswa","Kalai","Nila"],"Age":[78,37,88,43,93]}

df = pd.DataFrame(subjectDict)

print(df)

   Names  Age

0  Arjun   78

1    Ram   37

2  Biswa   88

3  Kalai   43

4   Nila   93

df = pd.read_csv("https://gist.githubusercontent.com/netj/8836201/raw/6f9306ad21398ea43cba4f7d537619d0e07d5ae3/iris.csv")

df.head()

df = pd.read_csv("E:\\PyExa\\iris.csv")

df.head()

sepal.length sepal.width petal.length petal.width variety

0 5.1 3.5 1.4 0.2 Setosa

1 4.9 3.0 1.4 0.2 Setosa

2 4.7 3.2 1.3 0.2 Setosa

3 4.6 3.1 1.5 0.2 Setosa

4 5.0 3.6 1.4 0.2 Setosa

df.tail()

sepal.length sepal.width petal.length petal.width variety

145 6.7 3.0 5.2 2.3 Virginica

146 6.3 2.5 5.0 1.9 Virginica

147 6.5 3.0 5.2 2.0 Virginica

148 6.2 3.4 5.4 2.3 Virginica

149 5.9 3.0 5.1 1.8 Virginica

print(df.shape)

(150, 5)  # 150 X 5 ==> 150 Rows X 5 columns

df.describe()

sepal.length sepal.width petal.length petal.width

count 150.000000 150.000000 150.000000 150.000000

mean 5.843333 3.057333 3.758000 1.199333

std 0.828066 0.435866 1.765298 0.762238

min 4.300000 2.000000 1.000000 0.100000

25% 5.100000 2.800000 1.600000 0.300000

50% 5.800000 3.000000 4.350000 1.300000

75% 6.400000 3.300000 5.100000 1.800000

max 7.900000 4.400000 6.900000 2.500000

df.iloc[0:3,0:2]

#1st 3 Rows and 1st 2 Columns

sepal.length sepal.width

0 5.1 3.5

1 4.9 3.0

2 4.7 3.2

df.iloc[0:4,0:4]

#1st 4 Rows and 1st 4 Columns

  sepal.length sepal.width petal.length petal.width

0 5.1 3.5 1.4 0.2

1 4.9 3.0 1.4 0.2

2 4.7 3.2 1.3 0.2

3 4.6 3.1 1.5 0.2

df.loc[0:7,("petal.length","petal.width","variety")]

#1st 7 rows and specified columns

petal.length petal.width variety

0 1.4 0.2 Setosa

1 1.4 0.2 Setosa

2 1.3 0.2 Setosa

3 1.5 0.2 Setosa

4 1.4 0.2 Setosa

5 1.7 0.4 Setosa

6 1.4 0.3 Setosa

7 1.5 0.2 Setosa

#Drop variety column in the dataframe

s1 = df.drop("variety",axis=1)

print (s1.head())

sepal.length  sepal.width  petal.length  petal.width

0           5.1          3.5           1.4          0.2

1           4.9          3.0           1.4          0.2

2           4.7          3.2           1.3          0.2

3           4.6          3.1           1.5          0.2

4           5.0          3.6           1.4          0.2

#Drop  3 rows

s1 = df.drop([1,2,3],axis=0)

print(s1.head())

sepal.length  sepal.width  petal.length  petal.width variety

0           5.1          3.5           1.4          0.2  Setosa

4           5.0          3.6           1.4          0.2  Setosa

5           5.4          3.9           1.7          0.4  Setosa

6           4.6          3.4           1.4          0.3  Setosa

7           5.0          3.4           1.5          0.2  Setosa

df.mean()

sepal.length    5.843333

sepal.width     3.057333

petal.length    3.758000

petal.width     1.199333

dtype: float64

df.median()

sepal.length    5.80

sepal.width     3.00

petal.length    4.35

petal.width     1.30

dtype: float64

df.min()

sepal.length       4.3

sepal.width          2

petal.length         1

petal.width        0.1

variety         Setosa

dtype: object

df.max()

sepal.length          7.9

sepal.width           4.4

petal.length          6.9

petal.width           2.5

variety         Virginica

dtype: object

#applying user defined function

def half(s):

    return s*0.5

s1 = df[["sepal.length","petal.length"]].apply(half) #half is the udf

print(df[["sepal.length","petal.length"]].head())

print(s1.head())

   sepal.length  petal.length

0           5.1           1.4

1           4.9           1.4

2           4.7           1.3

3           4.6           1.5

4           5.0           1.4

   sepal.length  petal.length

0          2.55          0.70

1          2.45          0.70

2          2.35          0.65

3          2.30          0.75

4          2.50          0.70

#user defined function to double the dataframe values

def double_make(s):

    return s*2

print(df[["sepal.width","petal.width"]].head(5))

s1 = df[["sepal.width","petal.width"]].apply(double_make)

print(s1.head())

  sepal.width  petal.width

0          3.5          0.2

1          3.0          0.2

2          3.2          0.2

3          3.1          0.2

4          3.6          0.2

   sepal.width  petal.width

0          7.0          0.4

1          6.0          0.4

2          6.4          0.4

3          6.2          0.4

4          7.2          0.4

#grouping and counting of particular column

s1 = df["variety"].value_counts()

print(s1)

Virginica     50

Setosa        50

Versicolor    50

Name: variety, dtype: int64

#Sort order

s

df.sort_values(by="sepal.length").head()

sepal.length sepal.width petal.length petal.width variety

13 4.3 3.0 1.1 0.1 Setosa

42 4.4 3.2 1.3 0.2 Setosa

38 4.4 3.0 1.3 0.2 Setosa

8 4.4 2.9 1.4 0.2 Setosa

41 4.5 2.3 1.3 0.3 Setosa

Numpy Basics

#1 Dimensional Array

import numpy as np

n1 = np.array([1,2,3,4,5,6])

print(n1)

print(type(n1))

[1 2 3 4 5 6]

<class 'numpy.ndarray'>

#2 Dimensional Array

import numpy as np

n2 = np.array([[5,6,7,8,9,10],[50,51,52,53,54,55]])

print(n2)

print(type(n2))

[[ 5  6  7  8  9 10]

 [50 51 52 53 54 55]]

<class 'numpy.ndarray'>

#Initialize with Zeros - fill the array with zeros

import numpy as np

n3 = np.zeros(6)

print(n3)

[0. 0. 0. 0. 0. 0.]

import numpy as np

n4 = np.zeros((4,4))

print(n4)

[[0. 0. 0. 0.]

 [0. 0. 0. 0.]

 [0. 0. 0. 0.]

 [0. 0. 0. 0.]]

 

 

import numpy as np

n5 = np.zeros((5,3,2))

print(n5)

[[[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]

 [[0. 0.]

  [0. 0.]

  [0. 0.]]]

  

  

#Fill the array with a constant value

n5 = np.full((2,3),5)

print(n5)

[[5 5 5]

 [5 5 5]]

 

n6 = np.full((2,6),3)

print(n6)

[[3 3 3 3 3 3]

 [3 3 3 3 3 3]]

 

 

n7 = np.full((5),6)

print(n7)

[6 6 6 6 6]

#fill with a range (arange) 

n1 = np.arange(10,50,2.5)

print(n1)

[10 11 12 13 14 15 16 17 18 19]

n1 = np.arange(10,50,2.5)

print(n1)

[10.  12.5 15.  17.5 20.  22.5 25.  27.5 30.  32.5 35.  37.5 40.  42.5

 45.  47.5]

 

 

n1 = np.arange(10,50,7)

print(n1)

[10 17 24 31 38 45]

#fill with random integer values

n1 = np.random.randint(1,100,10)

print(n1)

[55 65 10 38 86 97 35 55 11 99]

n1 = np.random.randint(5,55,5)

print(n1)

[37 27 28 11 53]

#shape of the array

n1 = np.array([ [1,2,3],[4,3,2]  ])

print(n1)

(2,3)

n1 = np.array([ [1,2,3],[4,3,2] ,[53,2,3] ])

print(n1.shape)

(3, 3)

#stacking examples - vstack, hstack,column_stack

#vstack

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

np.vstack((n1,n2))

array([[2, 6, 3],

       [6, 3, 8]])

   

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

np.vstack((n1,n2,n3))

array([[ 2,  6,  3],

       [ 6,  3,  8],

       [10, 20, 33]])

#hstack

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

np.hstack((n1,n2))

array([2, 6, 3, 6, 3, 8])

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

np.vstack((n1,n2,n3))

array([[ 2,  6,  3],

       [ 6,  3,  8],

       [10, 20, 33]])

   

#column_stack

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

np.column_stack((n1,n2))

array([[2, 6],

       [6, 3],

       [3, 8]])

   

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

np.column_stack((n1,n2,n3))

array([[ 2,  6, 10],

       [ 6,  3, 20],

       [ 3,  8, 33]])

   

n1 = np.array([2,6,3])

n2 = np.array([6,3,8])

n3 = np.array([10,20,33])

n4 = np.array([55,33,66])

np.column_stack((n1,n2,n3,n4))

array([[ 2,  6, 10, 55],

       [ 6,  3, 20, 33],

       [ 3,  8, 33, 66]])

   

   

#intersection

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.intersect1d(n1,n2)

array([5, 6])

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.intersect1d(n2,n1)

array([5, 6])

#difference

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.setdiff1d(n1,n2)

array([1, 2, 3, 4])

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([5,6,7,8,9])

np.setdiff1d(n2,n1)

array([7, 8, 9])

import numpy as np

n1  = np.array([1,2,3,4,5,6])

n2 = np.array([1,2,3,4,5,6])

np.setdiff1d(n1,n2)

array([], dtype=int32)

#Sum

import numpy as np

n1  = np.array([10,20,30])

n2 = np.array([40,50,60])

np.sum([n1,n2])

210

np.sum([n1,n2],axis=0) #axis 0 : x axis - horizontal

array([50, 70, 90])

np.sum([n1,n2],axis=1) #axis 1 : y axis - vertical

array([ 60, 150])

import numpy as np

n1 = np.array([1,2,3,4,5,6])

n2 = np.array([7,8,9,10,11,12])

n3 = np.array([10,20,30,40,50,60])

n4 = np.array([100,200,300,400,500,600])

np.sum([n1,n2,n3,n4])

2388

np.sum([n1,n2,n3,n4],axis=0)

array([118, 230, 342, 454, 566, 678])

np.sum([n1,n2,n3,n4],axis=1)

array([  21,   57,  210, 2100])

Numpy Array Mathemetics

#Addition

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 + 1

print(n1)

[56 34 45 67 23 12 78]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 + 10

print(n1)

[65 43 54 76 32 21 87]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 - 1

print(n1)

[54 32 43 65 21 10 76]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 - 10

print(n1)

[45 23 34 56 12  1 67]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 * 5

print(n1)

[275 165 220 330 110  55 385]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 * 55

print(n1)

[3025 1815 2420 3630 1210  605 4235]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 / 2.0

print(n1)

[27.5 16.5 22.  33.  11.   5.5 38.5]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 / 5

print(n1)

[11.   6.6  8.8 13.2  4.4  2.2 15.4]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 ** 3

print(n1)

[166375  35937  85184 287496  10648   1331 456533]

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

n1 = n1 / (3 * 3)

print(n1)

[6.11111111 3.66666667 4.88888889 7.33333333 2.44444444 1.22222222 8.55555556]

#statistics functions

import numpy as np

n1 = np.array([55,33,44,66,22,11,77])

np.mean(n1)

np.sum(n1) / len(n1)  # mean is nothing but average

44.0

#middle element 

import numpy as np

n1 = np.array([11,77,33,44,66,22])

np.median(n1)

38.5

#standard deviation

import numpy as np

n1 = np.array([11,77,33,44,66,22])

np.std(n1)

23.262392157490787

#saving and Loading

import numpy as np

n1 = np.array([1,2,3,5,4,6,7,9,8,10,22])

np.save('result',n1)  #saving

 

n2 = np.load('result.npy') #loading

print(n2*10)

[ 10  20  30  50  40  60  70  90  80 100 220]

   

Python OOPs - Class, Parameters, Constructor, Inheritance (Multi Level, Multiple)

#Create the first class in python

class Phone:

    def make_call(self):

        print("Let's make a phone call")

    

    def play_game(self):

        print("Let's play a game")

        

p1 = Phone()

p1.make_call()

p1.play_game()

#Adding parameters to a class method

class Phone:

    def set_color(self,color):

        self.color=color

        

    def set_cost(self,cost):

        self.cost=cost

        

    def show_color(self):

        return self.color

    

    def show_cost(self):

        return self.cost

    

    def play_game(self):

        print("Playing a game")

        

    def make_call(self):

        print("Making a call")

        

p1=Phone()

p1.set_color("Blue")

p1.set_cost(500)

p1.show_cost()

500

p1.show_color()

'Blue'

p1.play_game()

p1.make_call()

Playing a game

Making a call

#creating a class with constructor

class Employee:

    def __init__(self,name,age,gender,city,salary,pin):

        self.name=name

        self.age=age

        self.gender=gender

        self.city=city

        self.salary=salary

        self.pin=pin

        

    def show_details(self):

        print("Name : {}, Age : {}, Gender: {}, City: {}, Salary: {}, Pin: {}".format(self.name,self.age,self.gender,self.city,self.salary,self.pin))

        

        

e1 = Employee("Sankara",43,'M','Bangalore',67000,560093)

e1.show_details()

Name : Sankara, Age : 43, Gender: M, City: Bangalore, Salary: 67000, Pin: 560093

#Inheritance example:

#Multiple Inheritance

class Parent1:

    def assign_str1(self,str1):

        self.str1 = str1

        

    def show_str1(self):

        print(self.str1)

        

class Parent2:

    def assign_str2(self,str2):

        self.str2 = str2

    

    def show_str2(self):

        print(self.str2)

        

class Child(Parent1, Parent2):

    def assign_str3(self,str3):

        self.str3 = str3

        

    def show_str3(self):

        print(self.str3)

c1 = Child()

c1.assign_str1("I")

c1.assign_str2("Love")

c1.assign_str3("India")

c1.show_str1()

I

c1.show_str2()

Love

c1.show_str3()

India

#Multi Level inheritance

class Parent:

    def assign_name(self,name):

        self.name = name

        

    def show_name(self):

        return self.name

    

class Child(Parent):

    def assign_age(self,age):

        self.age=age

        

    def show_age(self):

        return self.age

    

class GrandChild(Child):

    def assign_gender(self,gender):

        self.gender=gender

        

    def show_gender(self):

        return self.gender

g1 = GrandChild()

g1.assign_name("Anbu")

g1.assign_age(5)

g1.assign_gender("F")

name = g1.show_name()

age = g1.show_age()

gender = g1.show_gender()

print("Name : {}, Age : {}, Gender : {}".format(name,age,gender))

Name : Anbu, Age : 5, Gender : F