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Commite6e21af

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author
Pedro Bernardo
committed
Added sparkSql/HousePriceProblem.py and sparkSql/HousePriceSolution.py
1 parent25927ae commite6e21af

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‎sparkSql/HousePriceProblem.py

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if__name__=="__main__":
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'''
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Create a Spark program to read the house data from in/RealEstate.csv,
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group by location, aggregate the average price per SQ Ft and sort by average price per SQ Ft.
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The houses dataset contains a collection of recent real estate listings in San Luis Obispo county and
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around it. 
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The dataset contains the following fields:
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1. MLS: Multiple listing service number for the house (unique ID).
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2. Location: city/town where the house is located. Most locations are in San Luis Obispo county and
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northern Santa Barbara county (Santa Maria­Orcutt, Lompoc, Guadelupe, Los Alamos), but there
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some out of area locations as well.
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3. Price: the most recent listing price of the house (in dollars).
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4. Bedrooms: number of bedrooms.
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5. Bathrooms: number of bathrooms.
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6. Size: size of the house in square feet.
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7. Price/SQ.ft: price of the house per square foot.
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8. Status: type of sale. Thee types are represented in the dataset: Short Sale, Foreclosure and Regular.
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Each field is comma separated.
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Sample output:
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+----------------+-----------------+
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| Location| avg(Price SQ Ft)|
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+----------------+-----------------+
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| Oceano| 95.0|
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| Bradley| 206.0|
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| San Luis Obispo| 359.0|
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| Santa Ynez| 491.4|
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| Cayucos| 887.0|
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|................|.................|
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|................|.................|
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|................|.................|
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'''
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‎sparkSql/HousePriceSolution.py

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frompyspark.sqlimportSparkSession
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PRICE_SQ_FT="Price SQ Ft"
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if__name__=="__main__":
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session=SparkSession.builder.appName("HousePriceSolution").master("local").getOrCreate()
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session.sparkContext.setLogLevel("ERROR")
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realEstate=session.read \
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.option("header","true") \
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.option("inferSchema",value=True) \
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.csv("in/RealEstate.csv")
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realEstate.groupBy("Location") \
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.avg(PRICE_SQ_FT) \
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.orderBy("avg(Price SQ FT)") \
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.show()

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