Home_Sales.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mt54x3GY98i-",
        "outputId": "7536aacd-bdca-48d0-d5c8-6734e0bd6941"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyspark\n",
            "  Downloading pyspark-3.4.1.tar.gz (310.8 MB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n",
            "Building wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.4.1-py2.py3-none-any.whl size=311285398 sha256=33d8cdbd9e3a0447c5b30312f762f36ddb8a1a87ae116894e3b7694255f47e73\n",
            "  Stored in directory: /root/.cache/pip/wheels/0d/77/a3/ff2f74cc9ab41f8f594dabf0579c2a7c6de920d584206e0834\n",
            "Successfully built pyspark\n",
            "Installing collected packages: pyspark\n",
            "Successfully installed pyspark-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install pyspark"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "9f15c677-e65d-4cfa-977a-ed832f35cb66"
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        {
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          "name": "stdout",
          "text": [
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            "Fetched 14.4 MB in 9s (1,511 kB/s)\n",
            "Reading package lists... Done\n"
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        }
      ],
      "source": [
        "import os\n",
        "# Find the latest version of spark 3.x  from http://www.apache.org/dist/spark/ and enter as the spark version\n",
        "# For example:\n",
        "# spark_version = 'spark-3.4.0'\n",
        "spark_version = 'spark-3.4.0'\n",
        "os.environ['SPARK_VERSION']=spark_version\n",
        "\n",
        "# Install Spark and Java\n",
        "!apt-get update\n",
        "!apt-get install openjdk-11-jdk-headless -qq > /dev/null\n",
        "!wget -q http://www.apache.org/dist/spark/$SPARK_VERSION/$SPARK_VERSION-bin-hadoop3.tgz\n",
        "!tar xf $SPARK_VERSION-bin-hadoop3.tgz\n",
        "!pip install -q findspark\n",
        "\n",
        "# Set Environment Variables\n",
        "os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-11-openjdk-amd64\"\n",
        "os.environ[\"SPARK_HOME\"] = f\"/content/{spark_version}-bin-hadoop3\"\n",
        "\n",
        "# Start a SparkSession\n",
        "import findspark\n",
        "findspark.init()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "2XbWNf1Te5fM"
      },
      "outputs": [],
      "source": [
        "# Import packages\n",
        "from pyspark.sql import SparkSession\n",
        "import time\n",
        "\n",
        "# Create a SparkSession\n",
        "spark = SparkSession.builder.appName(\"SparkSQL\").getOrCreate()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "wOJqxG_RPSwp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ef86403-4797-43ef-e522-92690e764f2c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+----------+----------+------+--------+---------+-----------+--------+------+----------+----+\n",
            "|                  id|      date|date_built| price|bedrooms|bathrooms|sqft_living|sqft_lot|floors|waterfront|view|\n",
            "+--------------------+----------+----------+------+--------+---------+-----------+--------+------+----------+----+\n",
            "|f8a53099-ba1c-47d...|2022-04-08|      2016|936923|       4|        3|       3167|   11733|     2|         1|  76|\n",
            "|7530a2d8-1ae3-451...|2021-06-13|      2013|379628|       2|        2|       2235|   14384|     1|         0|  23|\n",
            "|43de979c-0bf0-4c9...|2019-04-12|      2014|417866|       2|        2|       2127|   10575|     2|         0|   0|\n",
            "|b672c137-b88c-48b...|2019-10-16|      2016|239895|       2|        2|       1631|   11149|     2|         0|   0|\n",
            "|e0726d4d-d595-407...|2022-01-08|      2017|424418|       3|        2|       2249|   13878|     2|         0|   4|\n",
            "|5aa00529-0533-46b...|2019-01-30|      2017|218712|       2|        3|       1965|   14375|     2|         0|   7|\n",
            "|131492a1-72e2-4a8...|2020-02-08|      2017|419199|       2|        3|       2062|    8876|     2|         0|   6|\n",
            "|8d54a71b-c520-44e...|2019-07-21|      2010|323956|       2|        3|       1506|   11816|     1|         0|  25|\n",
            "|e81aacfe-17fe-46b...|2020-06-16|      2016|181925|       3|        3|       2137|   11709|     2|         0|  22|\n",
            "|2ed8d509-7372-46d...|2021-08-06|      2015|258710|       3|        3|       1918|    9666|     1|         0|  25|\n",
            "|f876d86f-3c9f-42b...|2019-02-27|      2011|167864|       3|        3|       2471|   13924|     2|         0|  15|\n",
            "|0a2bd445-8508-4d8...|2021-12-30|      2014|337527|       2|        3|       1926|   12556|     1|         0|  23|\n",
            "|941bad30-eb49-4a7...|2020-05-09|      2015|229896|       3|        3|       2197|    8641|     1|         0|   3|\n",
            "|dd61eb34-6589-4c0...|2021-07-25|      2016|210247|       3|        2|       1672|   11986|     2|         0|  28|\n",
            "|f1e4cef7-d151-439...|2019-02-01|      2011|398667|       2|        3|       2331|   11356|     1|         0|   7|\n",
            "|ea620c7b-c2f7-4c6...|2021-05-31|      2011|437958|       3|        3|       2356|   11052|     1|         0|  26|\n",
            "|f233cb41-6f33-4b0...|2021-07-18|      2016|437375|       4|        3|       1704|   11721|     2|         0|  34|\n",
            "|c797ca12-52cd-4b1...|2019-06-08|      2015|288650|       2|        3|       2100|   10419|     2|         0|   7|\n",
            "|0cfe57f3-28c2-472...|2019-10-04|      2015|308313|       3|        3|       1960|    9453|     2|         0|   2|\n",
            "|4566cd2a-ac6e-435...|2019-07-15|      2016|177541|       3|        3|       2130|   10517|     2|         0|  25|\n",
            "+--------------------+----------+----------+------+--------+---------+-----------+--------+------+----------+----+\n",
            "only showing top 20 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 1. Read in the AWS S3 bucket into a DataFrame.\n",
        "from pyspark import SparkFiles\n",
        "url = \"https://2u-data-curriculum-team.s3.amazonaws.com/dataviz-classroom/v1.2/22-big-data/home_sales_revised.csv\"\n",
        "from pyspark import SparkFiles\n",
        "url = \"https://2u-data-curriculum-team.s3.amazonaws.com/dataviz-classroom/v1.2/22-big-data/home_sales_revised.csv\"\n",
        "spark.sparkContext.addFile(url)\n",
        "df = spark.read.csv(SparkFiles.get(\"home_sales_revised.csv\"), sep=\",\", header=True, ignoreLeadingWhiteSpace=True)\n",
        "df.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "RoljcJ7WPpnm"
      },
      "outputs": [],
      "source": [
        "# 2. Create a temporary view of the DataFrame.\n",
        "df.createOrReplaceTempView('home_sales_data')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "L6fkwOeOmqvq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6aee754b-92d2-480b-c3ce-fb1af433b13c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+----------+\n",
            "|round(avg(price), 2)|year(date)|\n",
            "+--------------------+----------+\n",
            "|           296363.88|      2022|\n",
            "|           301819.44|      2021|\n",
            "|           298353.78|      2020|\n",
            "|            300263.7|      2019|\n",
            "+--------------------+----------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 3. What is the average price for a four bedroom house sold in each year rounded to two decimal places?\n",
        "avg_price_4_bedroom = spark.sql(\"SELECT ROUND(AVG(price),2), YEAR(date) from home_sales_data where bedrooms == 4 group by YEAR(date) ORDER BY YEAR(date) desc\")\n",
        "avg_price_4_bedroom.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "l8p_tUS8h8it",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a1a4955-a8af-4b66-f223-08c8d572f1ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+----------+\n",
            "|round(avg(price), 2)|date_built|\n",
            "+--------------------+----------+\n",
            "|           292676.79|      2017|\n",
            "|           290555.07|      2016|\n",
            "|            288770.3|      2015|\n",
            "|           290852.27|      2014|\n",
            "|           295962.27|      2013|\n",
            "|           293683.19|      2012|\n",
            "|           291117.47|      2011|\n",
            "|           292859.62|      2010|\n",
            "+--------------------+----------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 4. What is the average price of a home for each year the home was built that have 3 bedrooms and 3 bathrooms rounded to two decimal places?\n",
        "avg_price_3_bed_3_bath = spark.sql(\"SELECT ROUND(AVG(price),2), date_built from home_sales_data where bedrooms == 3 AND bathrooms == 3 group by date_built ORDER BY date_built desc\")\n",
        "avg_price_3_bed_3_bath.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "Y-Eytz64liDU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e48a4ba-2425-43f9-e9d4-3826ab18e026"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------------+----------+\n",
            "|round(avg(price), 2)|date_built|\n",
            "+--------------------+----------+\n",
            "|           280317.58|      2017|\n",
            "|            293965.1|      2016|\n",
            "|           297609.97|      2015|\n",
            "|           298264.72|      2014|\n",
            "|           303676.79|      2013|\n",
            "|           307539.97|      2012|\n",
            "|           276553.81|      2011|\n",
            "|           285010.22|      2010|\n",
            "+--------------------+----------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# 5. What is the average price of a home for each year built that have 3 bedrooms, 3 bathrooms, with two floors,\n",
        "# and are greater than or equal to 2,000 square feet rounded to two decimal places?\n",
        "\n",
        "avg_price_3_bed_3_bath_2_floors = spark.sql(\"SELECT ROUND(AVG(price),2), date_built from home_sales_data where bedrooms == 3 AND bathrooms == 3 AND floors == 2 AND sqft_living >= 2000 group by date_built ORDER BY date_built desc\")\n",
        "avg_price_3_bed_3_bath_2_floors.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUrfgOX1pCRd",
        "outputId": "2d1dd009-9b5f-462b-bbb1-dd4af11e2d40"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+--------------------+\n",
            "|view|round(avg(price), 2)|\n",
            "+----+--------------------+\n",
            "|  51|           788128.21|\n",
            "|  54|           798684.82|\n",
            "|  69|           750537.94|\n",
            "|  87|           1072285.2|\n",
            "|  73|           752861.18|\n",
            "|  64|           767036.67|\n",
            "|  59|            791453.0|\n",
            "|  85|          1056336.74|\n",
            "|  52|           733780.26|\n",
            "|  71|            775651.1|\n",
            "|  98|          1053739.33|\n",
            "|  99|          1061201.42|\n",
            "|  96|          1017815.92|\n",
            "| 100|           1026669.5|\n",
            "|  70|           695865.58|\n",
            "|  61|           746877.59|\n",
            "|  75|          1114042.94|\n",
            "|  78|          1080649.37|\n",
            "|  89|          1107839.15|\n",
            "|  77|          1076205.56|\n",
            "+----+--------------------+\n",
            "only showing top 20 rows\n",
            "\n",
            "--- 0.859410285949707 seconds ---\n"
          ]
        }
      ],
      "source": [
        "# 6. What is the \"view\" rating for the average price of a home, rounded to two decimal places, where the homes are greater than\n",
        "# or equal to $350,000? Although this is a small dataset, determine the run time for this query.\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "spark.sql(\"SELECT view, ROUND(AVG(price),2) from home_sales_data group by view having ROUND(AVG(price),2) >= 350000\").show()\n",
        "\n",
        "\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "KAhk3ZD2tFy8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6770357-31cf-43f8-8cdf-c873aaa4e939"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# 7. Cache the the temporary table home_sales.\n",
        "spark.sql(\"cache table home_sales_data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "4opVhbvxtL-i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d97553a-f73d-4ec9-e6d3-ad22b3a73fa6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# 8. Check if the table is cached.\n",
        "spark.catalog.isCached('home_sales_data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GnL46lwTSEk",
        "outputId": "bb945c60-96c1-4265-e0c4-a92eb8ce667a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+--------------------+\n",
            "|view|round(avg(price), 2)|\n",
            "+----+--------------------+\n",
            "|  51|           788128.21|\n",
            "|  54|           798684.82|\n",
            "|  69|           750537.94|\n",
            "|  87|           1072285.2|\n",
            "|  73|           752861.18|\n",
            "|  64|           767036.67|\n",
            "|  59|            791453.0|\n",
            "|  85|          1056336.74|\n",
            "|  52|           733780.26|\n",
            "|  71|            775651.1|\n",
            "|  98|          1053739.33|\n",
            "|  99|          1061201.42|\n",
            "|  96|          1017815.92|\n",
            "| 100|           1026669.5|\n",
            "|  70|           695865.58|\n",
            "|  61|           746877.59|\n",
            "|  75|          1114042.94|\n",
            "|  78|          1080649.37|\n",
            "|  89|          1107839.15|\n",
            "|  77|          1076205.56|\n",
            "+----+--------------------+\n",
            "only showing top 20 rows\n",
            "\n",
            "--- 0.7384788990020752 seconds ---\n"
          ]
        }
      ],
      "source": [
        "# 9. Using the cached data, run the query that filters out the view ratings with average price\n",
        "#  greater than or equal to $350,000. Determine the runtime and compare it to uncached runtime.\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "spark.sql(\"SELECT view, ROUND(AVG(price),2) from home_sales_data group by view having ROUND(AVG(price),2) >= 350000\").show()\n",
        "\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Qm12WN9isHBR"
      },
      "outputs": [],
      "source": [
        "# 10. Partition by the \"date_built\" field on the formatted parquet home sales data\n",
        "df.write.parquet('home_parquet', mode='overwrite')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "AZ7BgY61sRqY"
      },
      "outputs": [],
      "source": [
        "# 11. Read the parquet formatted data.\n",
        "parquet_df = spark.read.parquet('home_parquet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "J6MJkHfvVcvh"
      },
      "outputs": [],
      "source": [
        "# 12. Create a temporary table for the parquet data.\n",
        "parquet_df.createOrReplaceTempView('temp_parquet')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_Vhb52rU1Sn",
        "outputId": "81804f11-90be-43c2-e606-9f320c7cd96e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+--------------------+\n",
            "|view|round(avg(price), 2)|\n",
            "+----+--------------------+\n",
            "|  51|           788128.21|\n",
            "|  54|           798684.82|\n",
            "|  69|           750537.94|\n",
            "|  87|           1072285.2|\n",
            "|  73|           752861.18|\n",
            "|  64|           767036.67|\n",
            "|  59|            791453.0|\n",
            "|  85|          1056336.74|\n",
            "|  52|           733780.26|\n",
            "|  71|            775651.1|\n",
            "|  98|          1053739.33|\n",
            "|  99|          1061201.42|\n",
            "|  96|          1017815.92|\n",
            "| 100|           1026669.5|\n",
            "|  70|           695865.58|\n",
            "|  61|           746877.59|\n",
            "|  75|          1114042.94|\n",
            "|  78|          1080649.37|\n",
            "|  89|          1107839.15|\n",
            "|  77|          1076205.56|\n",
            "+----+--------------------+\n",
            "only showing top 20 rows\n",
            "\n",
            "--- 0.7107806205749512 seconds ---\n"
          ]
        }
      ],
      "source": [
        "# 13. Run the query that filters out the view ratings with average price of greater than or equal to $350,000\n",
        "# with the parquet DataFrame. Round your average to two decimal places.\n",
        "# Determine the runtime and compare it to the cached version.\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "spark.sql(\"SELECT view, ROUND(AVG(price),2) from temp_parquet group by view having ROUND(AVG(price),2) >= 350000\").show()\n",
        "\n",
        "\n",
        "print(\"--- %s seconds ---\" % (time.time() - start_time))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "hjjYzQGjtbq8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "229a6d22-9dee-4985-8a92-4247d06c520d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# 14. Uncache the home_sales temporary table.\n",
        "spark.sql(\"uncache table home_sales_data\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Sy9NBvO7tlmm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07387803-1306-4596-8d8c-c38130270a89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "# 15. Check if the home_sales is no longer cached\n",
        "\n",
        "spark.catalog.isCached('home_sales_data')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Si-BNruRUGK3"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.13"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}