ADA_week3_lecture2_examples.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/victorbjorsvik/NOVA_Bridging_Python/blob/main/ADA_week3_lecture2_examples.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d9e90bac",
      "metadata": {
        "id": "d9e90bac"
      },
      "source": [
        "# Advanced Data Analysis - week 3, lecture 2\n",
        "\n",
        "In the advanced data analysis course, we assume basic knowledge of Python, as could be acquired by attending the *Introduction to Programming* bridging course.\n",
        "\n",
        "This notebook includes the examples and exercises presented in **Week 3**, lecture 2. There is an additional notebook with the examples and exercises suggested for autonomous study during the week.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install needed software\n",
        "# Run this cell once before running the other cells in the notebook\n",
        "\n",
        "!apt-get install openjdk-17-jdk-headless\n",
        "!pip install pyspark\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_c77ISnQ9O5-",
        "outputId": "050684eb-a81e-4cb9-8ec2-fbd6a2a8d1a7"
      },
      "id": "_c77ISnQ9O5-",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "The following additional packages will be installed:\n",
            "  openjdk-17-jre-headless\n",
            "Suggested packages:\n",
            "  openjdk-17-demo openjdk-17-source libnss-mdns fonts-dejavu-extra fonts-ipafont-gothic\n",
            "  fonts-ipafont-mincho fonts-wqy-microhei | fonts-wqy-zenhei fonts-indic\n",
            "The following NEW packages will be installed:\n",
            "  openjdk-17-jdk-headless openjdk-17-jre-headless\n",
            "0 upgraded, 2 newly installed, 0 to remove and 18 not upgraded.\n",
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            "After this operation, 271 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 openjdk-17-jre-headless amd64 17.0.8.1+1~us1-0ubuntu1~22.04 [48.3 MB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 openjdk-17-jdk-headless amd64 17.0.8.1+1~us1-0ubuntu1~22.04 [71.1 MB]\n",
            "Fetched 119 MB in 4s (30.1 MB/s)\n",
            "Selecting previously unselected package openjdk-17-jre-headless:amd64.\n",
            "(Reading database ... 120876 files and directories currently installed.)\n",
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            "Setting up openjdk-17-jre-headless:amd64 (17.0.8.1+1~us1-0ubuntu1~22.04) ...\n",
            "update-alternatives: using /usr/lib/jvm/java-17-openjdk-amd64/bin/java to provide /usr/bin/java (java) in auto mode\n",
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            "Setting up openjdk-17-jdk-headless:amd64 (17.0.8.1+1~us1-0ubuntu1~22.04) ...\n",
            "update-alternatives: using /usr/lib/jvm/java-17-openjdk-amd64/bin/jar to provide /usr/bin/jar (jar) in auto mode\n",
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            "Collecting pyspark\n",
            "  Downloading pyspark-3.5.0.tar.gz (316.9 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",
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            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.5.0-py2.py3-none-any.whl size=317425344 sha256=22a89c53ce18438858732b302417d66759da7642e442336c473dc281b1af76b6\n",
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            "Successfully installed pyspark-3.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "2af8ecae",
      "metadata": {
        "id": "2af8ecae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a468ccb-4558-4971-ae5c-11909374acc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  inflating: data/VN-covid.csv       \n",
            "  inflating: data/VU-covid.csv       \n",
            "  inflating: data/WS-covid.csv       \n",
            "  inflating: data/XK-covid.csv       \n",
            "  inflating: data/YE-covid.csv       \n",
            "  inflating: data/ZA-covid.csv       \n",
            "  inflating: data/ZM-covid.csv       \n",
            "  inflating: data/ZW-covid.csv       \n",
            "  inflating: data/lec1-example.csv   \n"
          ]
        }
      ],
      "source": [
        "# Install needed data\n",
        "# Run this cell once before running the other cells in the notebook\n",
        "\n",
        "!pip install gdown\n",
        "!gdown 'https://drive.google.com/uc?id=1Suzt37ohetSKLNP0kFUv0Ji1joiXumir'\n",
        "!unzip -o sbe_data_2324.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b6f57801",
      "metadata": {
        "id": "b6f57801"
      },
      "source": [
        "## Programming (with Spark SQL)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d3aac495",
      "metadata": {
        "id": "d3aac495"
      },
      "source": [
        "We now show how to program in Python using [**Spark SQL**](https://spark.apache.org/docs/latest/index.html).\n",
        "\n",
        "For using Spark, we need to import the necessary libraries. In the following cell we also print the version of pyspark."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "69621cc6",
      "metadata": {
        "id": "69621cc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c53e49a4-29f5-43fa-c8ef-6e74a21d5f74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3.5.0\n"
          ]
        }
      ],
      "source": [
        "import pyspark\n",
        "from pyspark.sql import SparkSession\n",
        "from pyspark.sql.types import *\n",
        "from pyspark.sql.functions import *\n",
        "\n",
        "print(pyspark.__version__)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "d3440161",
      "metadata": {
        "id": "d3440161"
      },
      "outputs": [],
      "source": [
        "#Let's import other libraries\n",
        "import os\n",
        "os.environ[\"PYARROW_IGNORE_TIMEZONE\"] = \"1\"\n",
        "\n",
        "# imports standard Pandas as pd\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# imports pandas API for Spark as ps\n",
        "import pyspark.pandas as ps\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "83e60598",
      "metadata": {
        "id": "83e60598"
      },
      "source": [
        "For accessing the Spark system, we need to create a SparkSession.\n",
        "The Spark session accessed is in the local machine - ```master(\"local\")```.\n",
        "\n",
        "The ```.config(\"spark.sql.execution.arrow.pyspark.enabled\", \"true\")``` configuration specifies that the the arrow library can be used - this will allow to convert Spark DataFrames into Pandas DataFrames.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "6ad40951",
      "metadata": {
        "id": "6ad40951"
      },
      "outputs": [],
      "source": [
        "spark = SparkSession.builder \\\n",
        "    .master(\"local\") \\\n",
        "    .appName(\"Simple test\") \\\n",
        "    .config(\"spark.sql.execution.arrow.pyspark.enabled\", \"true\") \\\n",
        "    .getOrCreate()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e95d7e68",
      "metadata": {
        "id": "e95d7e68"
      },
      "source": [
        "### Data model : Table\n",
        "\n",
        "In *Spark SQL*, a table is created from a Spark [**DataFrame**](https://spark.apache.org/docs/latest/sql-programming-guide.html#datasets-and-dataframes). (follow the link for DataFrame documentation)\n",
        "\n",
        "#### Creating Spark DataFrames (1)\n",
        "\n",
        "From a Pandas Dataframe, you can use ```sparkDF = spark.createDataFrame(pandasDF)``` to create a Spark Dataframe."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "population = pd.DataFrame( { \"country\": [\"PT\", \"ES\", \"DE\"] , \\\n",
        "                            \"population\": [10276617, 46937060, 83019213]})\n",
        "\n",
        "populationSDF = spark.createDataFrame(population)\n",
        "\n",
        "populationSDF.printSchema()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yABcoCuI_vDA",
        "outputId": "20befc15-87e8-4525-d500-958a27b9e436"
      },
      "id": "yABcoCuI_vDA",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- country: string (nullable = true)\n",
            " |-- population: long (nullable = true)\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spark DataFrames cannot be directly printed - note that a Spark DataFrame is distributed over multiple machine. For displaying the data, it is possible to use dataframe.show().\n"
      ],
      "metadata": {
        "id": "JQHI7kNoAo6s"
      },
      "id": "JQHI7kNoAo6s"
    },
    {
      "cell_type": "code",
      "source": [
        "populationSDF.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K19xNKFsAnwM",
        "outputId": "0cdef58f-71a6-445b-d58f-9bb742a4f273"
      },
      "id": "K19xNKFsAnwM",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+----------+\n",
            "|country|population|\n",
            "+-------+----------+\n",
            "|     PT|  10276617|\n",
            "|     ES|  46937060|\n",
            "|     DE|  83019213|\n",
            "+-------+----------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Creating Spark DataFrames (2)\n",
        "\n",
        "It is possible to create a Spark DataFrame from a Python dictionary, as follows:\n",
        "\n",
        "The equivalent Pandas code is the following:\n",
        "```\n",
        "population = pd.DataFrame( { \"country\": [\"PT\", \"ES\", \"DE\"] , \\\n",
        "                            \"population\": [10276617, 46937060, 83019213]})\n",
        "\n",
        "print( population)\n",
        "```"
      ],
      "metadata": {
        "id": "QtjoWyEd_vcB"
      },
      "id": "QtjoWyEd_vcB"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "708385d1",
      "metadata": {
        "id": "708385d1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d992340-6975-460c-c681-d2b298fa4113"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+----------+\n",
            "|country|population|\n",
            "+-------+----------+\n",
            "|     PT|  10276617|\n",
            "|     ES|  46937060|\n",
            "|     DE|  83019213|\n",
            "+-------+----------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "populationSDF2 = spark.createDataFrame( [(\"PT\",10276617), (\"ES\",46937060), (\"DE\",83019213)], \\\n",
        "                                    [\"country\",\"population\"])\n",
        "\n",
        "populationSDF2.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "73cda361",
      "metadata": {
        "id": "73cda361"
      },
      "source": [
        "#### Using Spark DataFrames in Python\n",
        "\n",
        "If you want to use the data in a Spark DataFrame with some code written in Python, it is necessary to collect the data (that will be in multiple machines) before using it.\n",
        "\n",
        "This can be done by using the **dataframe.collect()** function, which returns a list of Rows.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "49a94204",
      "metadata": {
        "id": "49a94204",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "035dfe27-cbe8-4325-ee23-61a25e7f42e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Row(country='PT', population=10276617), Row(country='ES', population=46937060), Row(country='DE', population=83019213)]\n"
          ]
        }
      ],
      "source": [
        "populationData = populationSDF.collect() # Collect the DataFrame into a list\n",
        "print(populationData[:5])               # Print the first 5 rows"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c1498c2",
      "metadata": {
        "id": "6c1498c2"
      },
      "source": [
        "Alternatively, it is possible to collect the information to a Pandas DataFrame as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "e977cdc4",
      "metadata": {
        "id": "e977cdc4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "958f78e2-d6dc-4490-ba35-49c8f8ab4519"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  country  population\n",
            "0      PT    10276617\n",
            "1      ES    46937060\n",
            "2      DE    83019213\n"
          ]
        }
      ],
      "source": [
        "populationDF = populationSDF.toPandas() # Collect the DataFrame into a Pandas DataFrame\n",
        "print(populationDF.head(5))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2a985f4a",
      "metadata": {
        "id": "2a985f4a"
      },
      "source": [
        "#### Loading DataFrame from CSV files (1)\n",
        "\n",
        "More often, will want to load the data from files. To create a DataFrame from a CSV file, you can use the Pandas Spark API.\n",
        "\n",
        "We will also rename the column **Educational level** into **EducationalLevel**, as spaces in the names of columns makes some operations impossible in Spark SQL."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "3da4bdbc",
      "metadata": {
        "id": "3da4bdbc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0f7586b-020b-404d-e286-fdd9df67ac83"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- Name: string (nullable = true)\n",
            " |-- Age: integer (nullable = true)\n",
            " |-- EducationalLevel: double (nullable = true)\n",
            " |-- Company: string (nullable = true)\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/utils.py:1016: PandasAPIOnSparkAdviceWarning: If `index_col` is not specified for `read_csv`, the default index is attached which can cause additional overhead.\n",
            "  warnings.warn(message, PandasAPIOnSparkAdviceWarning)\n",
            "/usr/local/lib/python3.10/dist-packages/pyspark/pandas/utils.py:1016: PandasAPIOnSparkAdviceWarning: If `index_col` is not specified for `to_spark`, the existing index is lost when converting to Spark DataFrame.\n",
            "  warnings.warn(message, PandasAPIOnSparkAdviceWarning)\n"
          ]
        }
      ],
      "source": [
        "# Let's create a PATH in a OS independent way\n",
        "# File lec1-example.csv is in directory data\n",
        "fileName = os.path.join( \"data\", \"lec1-example.csv\")\n",
        "\n",
        "# Read a CSV file into a DataFrame\n",
        "dfPS = ps.read_csv(fileName)\n",
        "\n",
        "# Convert Pandas Spark DataFrame into Spark DataFrame\n",
        "dfS = dfPS.to_spark()\n",
        "dfS = dfS.withColumnRenamed(\"Educational level\", \"EducationalLevel\")\n",
        "dfS.printSchema()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYU3iVtoOdtT"
      },
      "source": [
        "#### Loading DataFrame from CSV files (2)\n",
        "\n",
        "An alternative to load a CSV file is to use the Spark native load function ```spark.read.option(...).csv(filename)```.\n",
        "\n",
        "When reading a CSV file, use option **header** to specify whether the file has an header line or not, and **inferSchema** to specify whether Spark should try to infer the types of columns: string, integer, etc.\n"
      ],
      "id": "xYU3iVtoOdtT"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "769c65a7-37fa-441a-cd89-0c007a2d3ae0",
        "id": "tkWstU-sOdte"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "root\n",
            " |-- Name: string (nullable = true)\n",
            " |-- Age: integer (nullable = true)\n",
            " |-- EducationalLevel: double (nullable = true)\n",
            " |-- Company: string (nullable = true)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Let's create a PATH in a OS independent way\n",
        "# File lec1-example.csv is in directory data\n",
        "fileName = os.path.join( \"data\", \"lec1-example.csv\")\n",
        "\n",
        "# Read a CSV file into a DataFrame\n",
        "dfS2 = spark.read.option(\"header\", True).option(\"inferSchema\",True).csv(fileName)\n",
        "dfS2 = dfS2.withColumnRenamed(\"Educational level\", \"EducationalLevel\")\n",
        "\n",
        "dfS2.printSchema()\n"
      ],
      "id": "tkWstU-sOdte"
    },
    {
      "cell_type": "markdown",
      "id": "751c2109",
      "metadata": {
        "id": "751c2109"
      },
      "source": [
        "### From DataFrames to SQL table and back\n",
        "\n",
        "In Spqrk SQL, it is possible to register a DataFrame as a SQL temporaary view, using the function ```dataframe.createOrReplaceTempView(name)```.\n",
        "\n",
        "After this, it is possible to execute SQL operations using (mostly) the standard SQL syntax.\n",
        "\n",
        "The following operation registers the Dataframe with data from ```dfS``` under the name **persons** (and ```dfS2``` under **persons2**)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "f277596d",
      "metadata": {
        "id": "f277596d"
      },
      "outputs": [],
      "source": [
        "dfS.createOrReplaceTempView(\"persons\")\n",
        "dfS2.createOrReplaceTempView(\"persons2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eece8b1d",
      "metadata": {
        "id": "eece8b1d"
      },
      "source": [
        "### Data processing with Spark SQL\n",
        "\n",
        "We now show the transformations necessary to perform the exercises proposed above.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "69e2207f",
      "metadata": {
        "id": "69e2207f"
      },
      "source": [
        "#### SELECT statement\n",
        "\n",
        "For executing SQL statements, Spark SQL provides the operation ```spark.sql( stmt)```. The result of this operation is a DataFrame.\n",
        "\n",
        "The key SQL statement is ```SELECT cols FROM table``` that allows to access data in a SQL table.  \n",
        "The following code select all rows (```*```) from table *persons*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "43614a1c",
      "metadata": {
        "id": "43614a1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5a579ed6-fa7f-4fa3-87f6-ff28af35de9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---+----------------+-------+\n",
            "|    Name|Age|EducationalLevel|Company|\n",
            "+--------+---+----------------+-------+\n",
            "|  Andrew| 55|             1.0|   Good|\n",
            "|Bernhard| 43|             2.0|   Good|\n",
            "|Carolina| 37|             5.0|    Bad|\n",
            "|  Dennis| 82|             3.0|   Good|\n",
            "|     Eve| 23|             3.2|    Bad|\n",
            "+--------+---+----------------+-------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result = spark.sql( \"SELECT * FROM persons\")\n",
        "result.show(5)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a4faa0a8",
      "metadata": {
        "id": "a4faa0a8"
      },
      "source": [
        "We now list **persons2** tables just to check that the alternative functions for creating a table work the same way."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "6332c8ef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6332c8ef",
        "outputId": "3fc3641e-bb42-458f-eb1c-92e0b9ad8d13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---+----------------+-------+\n",
            "|    Name|Age|EducationalLevel|Company|\n",
            "+--------+---+----------------+-------+\n",
            "|  Andrew| 55|             1.0|   Good|\n",
            "|Bernhard| 43|             2.0|   Good|\n",
            "|Carolina| 37|             5.0|    Bad|\n",
            "|  Dennis| 82|             3.0|   Good|\n",
            "|     Eve| 23|             3.2|    Bad|\n",
            "+--------+---+----------------+-------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result = spark.sql( \"SELECT * FROM persons2\")\n",
        "result.show(5)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "37543c40",
      "metadata": {
        "id": "37543c40"
      },
      "source": [
        "And now we select only columns *name* and *age*.\n",
        "\n",
        "The equivalent Spark Pandas API code is the following:\n",
        "\n",
        "```\n",
        "# Select the persons that are good company.\n",
        "persons_age = dfPS[[\"Name\",\"Age\"]]\n",
        "\n",
        "print(persons_age)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "3cd6479b",
      "metadata": {
        "id": "3cd6479b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a971f53-ae97-4975-d873-6accd1048492"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---+\n",
            "|    name|age|\n",
            "+--------+---+\n",
            "|  Andrew| 55|\n",
            "|Bernhard| 43|\n",
            "|Carolina| 37|\n",
            "|  Dennis| 82|\n",
            "|     Eve| 23|\n",
            "+--------+---+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Select the Name and Age.\n",
        "result = spark.sql(\"SELECT name, age FROM persons\")\n",
        "\n",
        "result.show(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4ae30ef2",
      "metadata": {
        "id": "4ae30ef2"
      },
      "source": [
        "#### Selecting rows based on conditions\n",
        "\n",
        "It is possible to select only the rows for which a column has a given value by using ```SELECT cols FROM table WHERE condition``` - in the condition, columns are expressed by their names.\n",
        "\n",
        "The equivalent Spark Pandas API code is the following:\n",
        "\n",
        "```\n",
        "# Select the persons that are good company.\n",
        "goodPS = dfPS[dfPS[\"Company\"] == \"Good\"]\n",
        "```\n",
        "\n",
        "**NOTE:** SQL is not case sensitive - you can use upper or lower case for SQL keywords, table and column names, etc. It is a common practice to use SQL keywords in upper-case and table and column name in lowwer case.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "5e21d039",
      "metadata": {
        "id": "5e21d039",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afdafb61-3b0d-4fdb-85b7-b6b3c10c89f3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---+----------------+-------+\n",
            "|    Name|Age|EducationalLevel|Company|\n",
            "+--------+---+----------------+-------+\n",
            "|  Andrew| 55|             1.0|   Good|\n",
            "|Bernhard| 43|             2.0|   Good|\n",
            "|  Dennis| 82|             3.0|   Good|\n",
            "|    Fred| 46|             5.0|   Good|\n",
            "|   James| 42|             4.1|   Good|\n",
            "|     Lea| 38|             2.5|   Good|\n",
            "|   Nigel| 71|             2.3|   Good|\n",
            "+--------+---+----------------+-------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Select the persons that are good company.\n",
        "good = spark.sql(\"SELECT * FROM persons WHERE company = 'Good'\")\n",
        "\n",
        "good.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "edc64a79",
      "metadata": {
        "id": "edc64a79"
      },
      "source": [
        "The next code uses a more complex condition.\n",
        "\n",
        "**Note:** In Pyhton, it is possible to create a multi-line string using the syntax\n",
        "```\n",
        "\"\"\" line 1\n",
        "...\n",
        "line n\"\"\"\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "a778b139",
      "metadata": {
        "id": "a778b139",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c02da620-c474-4aad-b56c-c36a24b26f8c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+------+---+----------------+-------+\n",
            "|  Name|Age|EducationalLevel|Company|\n",
            "+------+---+----------------+-------+\n",
            "|Dennis| 82|             3.0|   Good|\n",
            "|  Fred| 46|             5.0|   Good|\n",
            "| James| 42|             4.1|   Good|\n",
            "+------+---+----------------+-------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Select the persons that are good company and have educational level larger than 3.\n",
        "goodEd3plus = spark.sql(\"\"\"SELECT * FROM persons\n",
        "                                    WHERE company = 'Good' AND EducationalLevel >= 3.0\n",
        "                        \"\"\")\n",
        "goodEd3plus.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exercise from lecture\n",
        "# Compute the number of persons that are good and bad company for persons that are older than the average age of persons.\n",
        "\n",
        "ans = spark.sql(\"\"\"SELECT  company, COUNT(*) AS over_avg FROM persons\n",
        "                                  WHERE age > (SELECT AVG(age) FROM persons)\n",
        "                                  GROUP BY company\n",
        "                \"\"\")\n",
        "ans.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IIlVxyKMHy3i",
        "outputId": "c700f35a-496a-40bc-83ba-2ad02c274936"
      },
      "id": "IIlVxyKMHy3i",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+--------+\n",
            "|company|over_avg|\n",
            "+-------+--------+\n",
            "|   Good|       4|\n",
            "|    Bad|       1|\n",
            "+-------+--------+\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "26e62b78",
      "metadata": {
        "id": "26e62b78"
      },
      "source": [
        "#### Creating new views in SQL\n",
        "\n",
        "In Spark SQL, besides using ```dataframe.createOrReplaceTempView(name)``` to register a new view, it is possible to register a new view in SQL, using the following statement ```CREATE OR REPLACE TEMPORARY VIEW name AS SELECT ....```.\n",
        "\n",
        "The following code register a view with name *good* by selecting the persons that are good companies.\n",
        "\n",
        "NOTE: A view is a table that is the result of a query.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "b18df7d8",
      "metadata": {
        "id": "b18df7d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09a9fbe0-5382-4a8a-bb8c-9def5827b634"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DataFrame[]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "spark.sql(\"\"\"CREATE OR REPLACE TEMPORARY VIEW good AS\n",
        "                SELECT * FROM persons WHERE company = 'Good'\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9528e095",
      "metadata": {
        "id": "9528e095"
      },
      "source": [
        "Let's check that we can access to good table/view."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "6ba81c80",
      "metadata": {
        "id": "6ba81c80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "527c9990-4d80-4da6-9196-42ad1ea8f3cb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---+----------------+-------+\n",
            "|    Name|Age|EducationalLevel|Company|\n",
            "+--------+---+----------------+-------+\n",
            "|  Andrew| 55|             1.0|   Good|\n",
            "|Bernhard| 43|             2.0|   Good|\n",
            "|  Dennis| 82|             3.0|   Good|\n",
            "|    Fred| 46|             5.0|   Good|\n",
            "|   James| 42|             4.1|   Good|\n",
            "|     Lea| 38|             2.5|   Good|\n",
            "|   Nigel| 71|             2.3|   Good|\n",
            "+--------+---+----------------+-------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "goodAlt = spark.sql(\"SELECT * FROM good\")\n",
        "goodAlt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d19290e",
      "metadata": {
        "id": "1d19290e"
      },
      "source": [
        "#### Deleting view\n",
        "\n",
        "For deleting/unregistering a view from Spark SQL, you can use the ```DROP VIEW name```statement. After this, acessing the table will lead to an **error**!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "de3da681",
      "metadata": {
        "id": "de3da681",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "0ddf8c25-bce2-4941-a824-db22236d168e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------+---+----------------+-------+\n",
            "|    Name|Age|EducationalLevel|Company|\n",
            "+--------+---+----------------+-------+\n",
            "|  Andrew| 55|             1.0|   Good|\n",
            "|Bernhard| 43|             2.0|   Good|\n",
            "|  Dennis| 82|             3.0|   Good|\n",
            "|    Fred| 46|             5.0|   Good|\n",
            "|   James| 42|             4.1|   Good|\n",
            "|     Lea| 38|             2.5|   Good|\n",
            "|   Nigel| 71|             2.3|   Good|\n",
            "+--------+---+----------------+-------+\n",
            "\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AnalysisException",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAnalysisException\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-9db09145e3aa>\u001b[0m in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"DROP VIEW goodalt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# and check that we get an error when accessing the view\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mgoodAlt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT * FROM goodalt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mgoodAlt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyspark/sql/session.py\u001b[0m in \u001b[0;36msql\u001b[0;34m(self, sqlQuery, args, **kwargs)\u001b[0m\n\u001b[1;32m   1629\u001b[0m                     \u001b[0;34m[\u001b[0m\u001b[0m_to_java_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1630\u001b[0m                 )\n\u001b[0;32m-> 1631\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jsparkSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqlQuery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlitArgs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1632\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1633\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m   1320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1321\u001b[0m         \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1322\u001b[0;31m         return_value = get_return_value(\n\u001b[0m\u001b[1;32m   1323\u001b[0m             answer, self.gateway_client, self.target_id, self.name)\n\u001b[1;32m   1324\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyspark/errors/exceptions/captured.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0;31m# Hide where the exception came from that shows a non-Pythonic\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m                 \u001b[0;31m# JVM exception message.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mconverted\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAnalysisException\u001b[0m: [TABLE_OR_VIEW_NOT_FOUND] The table or view `goodalt` cannot be found. Verify the spelling and correctness of the schema and catalog.\nIf you did not qualify the name with a schema, verify the current_schema() output, or qualify the name with the correct schema and catalog.\nTo tolerate the error on drop use DROP VIEW IF EXISTS or DROP TABLE IF EXISTS.; line 1 pos 14;\n'Project [*]\n+- 'UnresolvedRelation [goodalt], [], false\n"
          ]
        }
      ],
      "source": [
        "# Let's register the previous dataframe as goodalt\n",
        "goodAlt.createOrReplaceTempView(\"goodalt\")\n",
        "# and select all rows to check that it exists\n",
        "goodAlt = spark.sql(\"SELECT * FROM goodalt\")\n",
        "goodAlt.show()\n",
        "\n",
        "# Now, we unregister the view\n",
        "spark.sql(\"DROP VIEW goodalt\")\n",
        "# and check that we get an error when accessing the view\n",
        "goodAlt = spark.sql(\"SELECT * FROM goodalt\")\n",
        "goodAlt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90b3d2fc",
      "metadata": {
        "id": "90b3d2fc"
      },
      "source": [
        "#### Applying reduce/aggregation functions\n",
        "\n",
        "Spark SQL allows to compute a function for the values of one or multiple columns using the following syntax ```SELECT fun1(col1),... FROM ...```, where fun1 is an existing Spark function.\n",
        "\n",
        "The following example computes first, the minimum age (```min```function), and then the minimum of both *Age* and *Educational level* at the same time. Spark has multiple useful aggregation functions, including, maximum (```max```), minimum (```min```), mean (```mean```), median (```percentile_approx(col,0.5)```), standard deviation (```std```), etc. - check the  [Spark SQL **built-in** functions documentation](https://spark.apache.org/docs/latest/api/sql/index.html) for the list of available functions.\n",
        "\n",
        "The equivalent Spark Pandas SQL code is the follopwing:\n",
        "\n",
        "```\n",
        "minAgePS = goodPS[\"Age\"].min()\n",
        "print( \"Minimum age is \")\n",
        "print( minAgePS)\n",
        "\n",
        "\n",
        "minsPS = goodPS.agg({\"Age\":\"min\",\"Educational level\":\"min\"})\n",
        "print( \"Minimum information for several columns now\")\n",
        "print(minsPS)\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "865e51fc",
      "metadata": {
        "id": "865e51fc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e32cea2f-22f4-4e66-8563-6b797a7df42f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum age is \n",
            "38\n",
            "Minimum information for several columns now\n",
            "+--------+---------------------+\n",
            "|min(age)|min(EducationalLevel)|\n",
            "+--------+---------------------+\n",
            "|      38|                  1.0|\n",
            "+--------+---------------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "minAge = spark.sql(\"SELECT MIN(age) FROM good\")\n",
        "print( \"Minimum age is \")\n",
        "print( minAge.collect()[0][0])\n",
        "\n",
        "\n",
        "mins = spark.sql(\"SELECT MIN(age), MIN(EducationalLevel) FROM good\")\n",
        "print( \"Minimum information for several columns now\")\n",
        "mins.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f953aedb",
      "metadata": {
        "id": "f953aedb"
      },
      "source": [
        "It is possible to execute different function to different columns.\n",
        "It is also possible to make rename the columns using the keyword ```AS``` for the following syntax: ```SELECT fun(...) AS name, ... FROM ....```.\n",
        "\n",
        "The equivalent Pandas / Spark Pandas code without rename (and returning the result in rows) is the following:\n",
        "```\n",
        "goodPS.agg( {\"Age\":[\"min\",\"max\"],\"Educational level\":\"median\"})\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "b0c8ae1a",
      "metadata": {
        "id": "b0c8ae1a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff3c49cd-dd91-4f32-cea4-36a071a97ccd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+---+------+\n",
            "|min|max|median|\n",
            "+---+---+------+\n",
            "| 38| 82|   2.5|\n",
            "+---+---+------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "minAge = spark.sql(\"\"\"SELECT MIN(age) AS min,\n",
        "                             MAX(age) AS max,\n",
        "                             PERCENTILE(EducationalLevel,0.5) AS median\n",
        "                            FROM good\"\"\")\n",
        "minAge.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "696f1e58",
      "metadata": {
        "id": "696f1e58"
      },
      "source": [
        "Wait, this was not what we wanted in the first place - we want the information about the youngest person that is a good company.\n",
        "\n",
        "There is no function to return the N smallest (or largest elements) in Spark SQL, but it is possible to: (1) order the elements by the value of some columns ```SELECT .... FROM .... ORDER BY col1,...``` or ```SELECT .... FROM .... ORDER BY col1 DESC,...``` for ordering in descending order; and (2) use limit to specify the number of rows returned ```SELECT .... FROM .... LIMIT num```.\n",
        "\n",
        "The equivalent code in Spark Pandas API is the following:\n",
        "\n",
        "```\n",
        "youngestGoodPS = goodPS.sort_values(by=\"Age\")\n",
        "print(youngestGoodPS[:1])\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "857c0251",
      "metadata": {
        "id": "857c0251",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fa00a8c-0230-424e-9a2b-b6061f315973"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----+---+----------------+-------+\n",
            "|Name|Age|EducationalLevel|Company|\n",
            "+----+---+----------------+-------+\n",
            "| Lea| 38|             2.5|   Good|\n",
            "+----+---+----------------+-------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "youngestGood = spark.sql(\"\"\"SELECT *\n",
        "                            FROM good\n",
        "                            ORDER BY age\n",
        "                            LIMIT 1\n",
        "                            \"\"\")\n",
        "youngestGood.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have discussed that if we only want the topmost value, in Pandas, using ```goodPS.nsmallest(1,[\"Age\"])``` is more efficient than sorting.\n",
        "\n",
        "In SQL there is no operation simillar to ```nlargest/nsmallest```.\n",
        "Does it mean that we need to rely on the inneficient sort operation?\n",
        "\n",
        "Let's use ```explain``` to tey to find out. First, we asked Spark to explain the steps to execute the SQL code.\n"
      ],
      "metadata": {
        "id": "lGWUCuPKW2ef"
      },
      "id": "lGWUCuPKW2ef"
    },
    {
      "cell_type": "code",
      "source": [
        "youngestGood.explain()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Or0OujEQW8Gr",
        "outputId": "14c8e137-e46a-416f-a5ef-1fa64de50310"
      },
      "id": "Or0OujEQW8Gr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "== Physical Plan ==\n",
            "TakeOrderedAndProject(limit=1, orderBy=[age#48 ASC NULLS FIRST], output=[Name#47,Age#48,EducationalLevel#386,Company#50])\n",
            "+- *(1) Project [Name#47, Age#48, Educational level#49 AS EducationalLevel#386, Company#50]\n",
            "   +- *(1) Filter (isnotnull(company#50) AND (company#50 = Good))\n",
            "      +- FileScan csv [Name#47,Age#48,Educational level#49,Company#50] Batched: false, DataFilters: [isnotnull(Company#50), (Company#50 = Good)], Format: CSV, Location: InMemoryFileIndex(1 paths)[file:/content/data/lec1-example.csv], PartitionFilters: [], PushedFilters: [IsNotNull(Company), EqualTo(Company,Good)], ReadSchema: struct<Name:string,Age:int,Educational level:double,Company:string>\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And now, we write the Pandas code that, from the original DataFrame, uses ```nsmallest``` to compute the bottom-most element.\n"
      ],
      "metadata": {
        "id": "dNT_txnFaNQ0"
      },
      "id": "dNT_txnFaNQ0"
    },
    {
      "cell_type": "code",
      "source": [
        "goodPS = dfPS[dfPS[\"Company\"] == \"Good\"]\n",
        "youngestGoodPS = goodPS.nsmallest(1,[\"Age\"])\n",
        "youngestGoodPS.to_spark().explain()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLPHkc18aMfW",
        "outputId": "26a542ca-3e8f-4672-a59d-1bac98a8ce9c"
      },
      "id": "dLPHkc18aMfW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "== Physical Plan ==\n",
            "TakeOrderedAndProject(limit=1, orderBy=[Age#48 ASC NULLS LAST,__natural_order__#428L ASC NULLS FIRST], output=[Name#47,Age#48,Educational level#49,Company#50])\n",
            "+- *(1) Project [Name#47, Age#48, Educational level#49, Company#50, monotonically_increasing_id() AS __natural_order__#428L]\n",
            "   +- *(1) Filter CASE WHEN CASE WHEN isnull((Company#50 = Good)) THEN false ELSE isnull((Company#50 = Good)) END THEN false ELSE CASE WHEN isnull((Company#50 = Good)) THEN false ELSE (Company#50 = Good) END END\n",
            "      +- FileScan csv [Name#47,Age#48,Educational level#49,Company#50] Batched: false, DataFilters: [CASE WHEN CASE WHEN isnull((Company#50 = Good)) THEN false ELSE isnull((Company#50 = Good)) END ..., Format: CSV, Location: InMemoryFileIndex(1 paths)[file:/content/data/lec1-example.csv], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<Name:string,Age:int,Educational level:double,Company:string>\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The execution plan is similar - this means that Spark, when executing a SQL statement, is optimizing the execution - it does not sort all data before getting the selected elements."
      ],
      "metadata": {
        "id": "KkuN85uRhlV2"
      },
      "id": "KkuN85uRhlV2"
    },
    {
      "cell_type": "markdown",
      "id": "f1674b54",
      "metadata": {
        "id": "f1674b54"
      },
      "source": [
        "#### Applying reduce/aggregation functions per group\n",
        "\n",
        "```SELECT fun1(col1), ... FROM ... GROUP BY colg1, ...``` allows to group the elements of a table/view before applying aggregation functions to the columns of the groups.\n",
        "\n",
        "The following example computes the lowest age for each value of Company.\n",
        "\n",
        "The equivalent Spark Dataframes code is the following:\n",
        "\n",
        "```\n",
        "youngest = df.select(\"Age\",\"Company\").groupby(\"Company\").agg({\"Age\":\"min\"})\n",
        "youngest.show()\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "17ce4e10",
      "metadata": {
        "id": "17ce4e10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04daf297-c055-409e-a5ef-16fa2b05dff2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+---+\n",
            "|company|age|\n",
            "+-------+---+\n",
            "|   Good| 38|\n",
            "|    Bad| 23|\n",
            "+-------+---+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "youngest = spark.sql(\"\"\"SELECT company, MIN(age) AS age\n",
        "                            FROM persons\n",
        "                            GROUP BY company\n",
        "                            \"\"\")\n",
        "youngest.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "43c03b16",
      "metadata": {
        "id": "43c03b16"
      },
      "source": [
        "In Spark SQL there is no simple way to return the largest/smallest row of a group. It is possible to use Window function or ```JOIN```. We will get back to this when introducing Windows.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "33ea2ed3",
      "metadata": {
        "id": "33ea2ed3"
      },
      "source": [
        "### Exercises\n",
        "\n",
        "What transformations would you use to:\n",
        "\n",
        "* Compute the average age of good and bad companies ?\n",
        "\n",
        "* Know which group has lower average Education level: good or bad companies ?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "ddc16477",
      "metadata": {
        "id": "ddc16477",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a609f91-b70b-4947-9c1b-872649359be6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-------+\n",
            "|company|avg_age|\n",
            "+-------+-------+\n",
            "|   Good|  53.86|\n",
            "|    Bad|  34.71|\n",
            "+-------+-------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Compute the average age of good and bad companies\n",
        "\n",
        "aver = spark.sql(\"\"\"SELECT company, ROUND(AVG(age),2) as avg_age FROM persons\n",
        "                    GROUP BY company\"\"\")\n",
        "aver.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "06b286d8",
      "metadata": {
        "id": "06b286d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc3aeddd-44fd-4e6d-c5aa-cbc84de00e0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+-----------------+\n",
            "|company|     avg_ed_level|\n",
            "+-------+-----------------+\n",
            "|   Good|2.842857142857143|\n",
            "|    Bad|4.314285714285714|\n",
            "+-------+-----------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Know which group has lower average Education level: good or bad companies ?\n",
        "\n",
        "ed_level = spark.sql(\"\"\"SELECT company, AVG(educationallevel) as avg_ed_level\n",
        "                        FROM persons\n",
        "                        GROUP BY company\n",
        "                        ORDER by AVG(educationallevel)\n",
        "                        \"\"\")\n",
        "\n",
        "ed_level.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f8cd37e1",
      "metadata": {
        "id": "f8cd37e1"
      },
      "source": [
        "### Using SELECT results in SELECT statements\n",
        "\n",
        "It is possble to use the result of a SELECT statement as part of another SELECT statement.\n",
        "\n",
        "The following code counts the number of persons older than Fred.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "f967155c",
      "metadata": {
        "id": "f967155c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fa72416-bb48-465f-d2ee-18a8e0bbcc00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---+\n",
            "|cnt|\n",
            "+---+\n",
            "|  4|\n",
            "+---+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "result = spark.sql( \"\"\"SELECT count(*) AS cnt FROM persons\n",
        "                    WHERE age > (SELECT age FROM persons WHERE name = 'Fred')\"\"\")\n",
        "result.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ffa97f15",
      "metadata": {
        "id": "ffa97f15"
      },
      "source": [
        "### Exercise\n",
        "\n",
        "Compute the number of persons that are good and bad company for persons that are older than the average age of persons."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "c1e1b168",
      "metadata": {
        "id": "c1e1b168",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c388f90b-6e28-484a-b6b0-084ca1972727"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------+--------+\n",
            "|company|count(1)|\n",
            "+-------+--------+\n",
            "|   Good|       4|\n",
            "|    Bad|       1|\n",
            "+-------+--------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#TODO: complete\n",
        "\n",
        "spark.sql(\"\"\"SELECT company, COUNT(*)\n",
        "              FROM persons\n",
        "              WHERE age > (SELECT AVG(age) from persons)\n",
        "              GROUP BY company\"\"\").show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0630f83",
      "metadata": {
        "id": "d0630f83"
      },
      "source": [
        "### Plotting data with Spark SQL\n",
        "\n",
        "For plotting, it is necessary to collect the data to print. We can get the data as the result of a SQL statement and use everything we studied before.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "1497d2af",
      "metadata": {
        "id": "1497d2af"
      },
      "outputs": [],
      "source": [
        "pdDF = spark.sql(\"SELECT * FROM persons\").toPandas()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fdf7c84e",
      "metadata": {
        "id": "fdf7c84e"
      },
      "source": [
        "Executing the function ```plot()``` to a DataFrame just plots numeric values, with the X axis being the index."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "c2f77887",
      "metadata": {
        "id": "c2f77887",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "3144f603-d0d6-4248-9b9c-b0469f7dc3db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pdDF.plot()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "70777c14",
      "metadata": {
        "id": "70777c14"
      },
      "source": [
        "You can specify which columns should be used for the X and Y axis, by passing values for ```x```and ```y``` parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "775e9afc",
      "metadata": {
        "id": "775e9afc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "9bb1979f-d82e-4c4c-ef0d-99bdcfde3092"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Name'>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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EbSJnPlJcnPnQtOTK0dHRTf69YMECpKSkYOTIkSgrK8OyZcvw/vvvY8yYMQCAtLQ0dO/eHdu2bcOQIUPOe591dXWoq2vYyy8vL2/pcyA3J59m2z48EGqV1OLbG5SC0zoIISBJLb8PIiJfZzRZkFNizUJ7VOajMaPRiP/973+48847IUkSdu/ejfr6eowdO1a5Trdu3ZCYmIitW7de8H7mz58PvV6vfCUkJLR2SeSm5DNdWrPlAjS02hpNFpRW19ttXUREviS7uApmi0CQVq1klF2l1cHHF198gdLSUkyfPh0AkJeXB61Wi7CwsCbXMxgMyMvLu+D9zJ07F2VlZcpXTo5re4/J/uQBY60pNgUAnUaNiCDrVFRuvRARtU6GrdMlOTrI5RnkFm27NLZs2TJcc801iI+Pb9MCdDoddDrXRmDkWEqnSytmfMhiQnQorjIir6wW3WJD7bU0IiKfobTZRrl2ywVoZeYjKysLP//8M+666y7le7GxsTAajSgtLW1y3fz8fMTGxrZpkeTZ5OCjtdsuQEPdRwHPdyEiahW52NTVMz6AVgYfaWlpiImJwbXXXqt8b8CAAfDz88PatWuV76WnpyM7OxupqaltXyl5LHtkPuR22zxOOSUiapXjbtJmC7Ri28VisSAtLQ3Tpk2DRtNwc71ej5kzZ+KRRx5BREQEQkND8cADDyA1NfWCnS7k/cpq6pUi0dZMN5UZeL4LEVGbKDM+XNxmC7Qi+Pj555+RnZ2NO++885zLFi5cCJVKhcmTJ6Ourg7jxo3D66+/bpeFkmeS22yjgnUI1La6xAgGfUO7LRERtUxptRHFVUYA7rHt0uLfBldffTWEEOe9zN/fH0uWLMGSJUvavDDyDvbYcgEAQwgPlyMiai250yVO79+mPwTthWe7kENl2WZ8tLbNVharZ/BBRNRa7lRsCjD4IAeTt13a0ukCNAwaO1NZB5PZ0uZ1ERH5koZ6D9cXmwIMPsjB7LXtEhmkg1olwSKAM5VGeyyNiMhnMPNBPkWZbtrG4EOtkhATYs1+sN2WiKhllAFjbtBmCzD4IAcymiw4XWY9xKit2y4AEBPKug8iopYyW4Tyh6A7tNkCDD7IgU6V1sAigAA/NaKD2z5CP9ZW91HA4IOIqNlOllTDaLZAp1GhXViAq5cDgMEHOVBWkTXNlxgRaJdDjAycckpE1GLylktyVBBUKtceKCdj8EEOIxebJrax3kNmCOWgMSKilspws2JTgMEHOZDcZtvWGR8yA2s+iIhazN3abAEGH+RAWXbPfPB8FyKilnK3NluAwQc5kJz5aMuBco0pJ9uWMfggImoud2uzBRh8kIMIIRoNGLNPtC232pbXmlBjNNvlPomIvFlFbT0KKqx1csx8kNcrrKxDTb0ZKgl2a+0K9dcgwE8NACioYPaDiOhSjtvqPaKCdQj193Pxahow+CCHyLFlPeL0AdBq7PM2kyRJqfvg1gsR0aU1bLm4T9YDYPBBDmKvsepnUzpeKthuS0R0KXKxaQqDD/IFWXYuNpUpwQczH0REl5Thhm22AIMPcpAcO7fZythuS0TUfNx2IZ8iz/hIirDvG54j1omImsdiETh+Rp7xwcwH+QBHb7sUcMQ6EdFFnS6vRW29BX5qCQnh7nGgnIzBB9ldtdGEM5XW4MDe2y6xernglJkPIqKLkYtNEyMCoVG7169791oNeQV5uFhYoB/0AfbtKzeENEw5FULY9b6JiLyJO042lTH4ILtz1JYLAMTYCk7rTBaU15jsfv9ERN7CHc90kTH4ILtTOl0cEHz4+6kRFmjNprDolIjowuTTbFPcrM0WYPBBDuDIzAfQsPXCdlsiogtz1zZbgMEHOYDSZmvnYlOZQc92WyKii6kxmnGqtAYAaz7IRzRsuzgm2jaEWOs+Chh8EBGdl3ygXFigHyKCtC5ezbkYfJBdmS0CJ0scM91UprTbctYHEdF5ZcrDxaLcb8sFYPBBdpZbWoN6s4BWrUKsbSCYvcVwyikR0UW5c5stwOCD7EzecmkfEQC1SnLIY8QqU04ZfBARnY87t9kCDD7IzrIc2GYrkw+XY+aDiOj8Mt30NFsZgw+yK7nNNsmhwYc181FYUQezhVNOiYgaE0Io2y4pzHyQL1A6XSId94aPCtZBJQEWAeUMGSIisiqsqENlnQkqyXGF/23F4IPsKqvYGm07cttFrZIQbWu35aAxIqKmMmxZj4SIQOg0ahev5vwYfJBdZRc5dsCYTC46ZbstEVFT7t5mCzD4IDsqrTaivNZ62FtCuGODD7bbEhGdn7u32QIMPsiO5GLTmBAdArSOTfWx3ZaI6Pzcvc0WYPBBdpTt4DNdGlPabcsYfBARNebubbYAgw+yo2wHn+nSmLztkl/Bmg8iIlmdyax0Hbprmy3A4IPsKKvI8Z0uMqXglJkPIiJFdlE1LAII1mmUrkB3xOCD7Ma52y5y5oPBBxGRLEMpNg2CJDnmiAt7YPBBdiO32SY4MfNRWl2P2nqzwx+PiMgTeEKbLcDgg+ykzmTGaVvniTMyH6EBGug01rdvAWd9EBEBaGizTXbjYlOAwQfZycmSGggBBGnViAzSOvzxJElCrJ5bL0REjXlCmy3A4IPspPGWi7P2GQ0htkFjLDolIgLQqM2WwQf5ArnTxRlbLrKYUJ7vQkQkK64yorS6HgCQzJoP8gXZxTUAgCQHnmZ7tobzXRh8EBHJWy7xen8EajUuXs3FtTj4OHXqFG677TZERkYiICAAvXv3xq5du5TLhRB4+umnERcXh4CAAIwdOxZHjx6166LJ/WQXN5yi6CwGHi5HRKTwhDNdZC0KPkpKSjBs2DD4+fnh+++/x8GDB/Hyyy8jPDxcuc6LL76IxYsX44033sD27dsRFBSEcePGobaWf516M/lclyRnBh96Zj6IiGSeUu8BAC3Ky7zwwgtISEhAWlqa8r3k5GTlv4UQWLRoEf7xj39g0qRJAICVK1fCYDDgiy++wC233HLOfdbV1aGuruEv1/Ly8hY/CXItIYRTB4zJDCGs+SAikimdLm5e7wG0MPPx1VdfYeDAgbjxxhsRExODfv364e2331YuP378OPLy8jB27Fjle3q9HoMHD8bWrVvPe5/z58+HXq9XvhISElr5VMhVCirqUGeyQK2SEB8W4LTHVVpty+sghHDa4xIRuaOGzIeXbbtkZmZi6dKl6Ny5M9asWYNZs2bhwQcfxLvvvgsAyMvLAwAYDIYmtzMYDMplZ5s7dy7KysqUr5ycnNY8D3IhOesRH+YPP7XzaphjbK22NfVmlNeanPa4RETuxmS2KF2HXrftYrFYMHDgQDz//PMAgH79+uH333/HG2+8gWnTprVqATqdDjqd+x5+Q5fWUO/h3Dd8gFaNUH8NymtNKCivhT7Az6mPT0TkLk6W1KDeLODvp0K83nkZ6NZq0Z+pcXFx6NGjR5Pvde/eHdnZ2QCA2NhYAEB+fn6T6+Tn5yuXkffJLnJ+p4tM3nrJY90HEfkw+UyXDpFBUKnc90A5WYuCj2HDhiE9Pb3J944cOYKkpCQA1uLT2NhYrF27Vrm8vLwc27dvR2pqqh2WS+7IFcWmMrbbEhE1tNmmeEC9B9DCbZeHH34YQ4cOxfPPP4+bbroJO3bswFtvvYW33noLgPW8jTlz5mDevHno3LkzkpOT8dRTTyE+Ph7XXXedI9ZPbiCr2PlttjIDB40RESGj0HPqPYAWBh+DBg3C559/jrlz5+LZZ59FcnIyFi1ahKlTpyrXeeKJJ1BVVYW7774bpaWlGD58OH744Qf4+/vbffHkHhqf6+JsBo5YJyLymAPlZC2evzphwgRMmDDhgpdLkoRnn30Wzz77bJsWRp6hss6EoiojACDRBdsuHLFORNSozTbKM7ZdeLYLtYmc9QgP9EOov/O7TWJC5YJT1nwQkW+qqK1HYYX1M9BTMh8MPqhN5DNdEp14oFxjcs1HATMfROSj5GLT6BAdQlzwR2BrMPigNsl2YbEp0LDtUlBRB7OFU06JyPfIbbaeMFZdxuCD2kQeMJboouAjKlgLlQSYLQJFVdx6ISLf40mn2coYfFCbyJkPVxSbAoBGrUJUsLXjpYB1H0TkgxpmfDDzQT7C1dsuQEPdR14Z6z6IyPdkeFibLcDgg9rAZLbgVEkNANdlPoBGg8YqGHwQkW+xWAROFHlWmy3A4IPaILe0FiaLgFajgiHEdUPklEFjzHwQkY/JLatBbb0FfmoJ7cPd/0A5GYMPajWl3iMi0KUHGfF8FyLyVXK9R1JkEDRqz/mV7jkrJbeTJc/4cGG9B9DQbsuTbYnI1yhj1T2ozRZg8EFtkO3iNltZDM93ISIfpYxV96A2W4DBB7WB0uniwmJTAIjVNwwaIyLyJZkedpqtjMEHtZqrB4zJ5GLX4ioj6kxml66FiMiZ5G0XT5rxATD4oFYSQiDHTTIfYYF+0Gqsb2UOGiMiX1FtNCHX1uXnSW22AIMPaqWS6npU1JkAAO3DXRt8SJLU0G7Lug8i8hHHbfUe4YF+CA/Sung1LcPgg1olyzbUJjbUH/5+ahevpmHrhe22ROQrPPFMFxmDD2oVV5/pcjaDnu22RORblODDw9psAQYf1Eru0mYrkzMfBQw+iMhHZJ6Rz3Rh5oN8RJYbHCjXWKyeNR9E5Fs8tc0W8KHgQwiBdYcL8OW+U65eildwu20XTjklIh8ihPDYNlsA0Lh6Ac7y08F83P3eboQH+uHK7gYE63zmqTuE2227hMrbLiw4JSLvV1BRhyqjGWqVhMQIzws+fCbzMaZbDJKjglBSXY93t5xw9XI8Wm29WckwJEW6x5u+ceZDCOHi1RAROVaGLeuREB6gzDnyJJ634lbSqFV46MrOAIC3NmaiorbexSvyXCdLrFmPEJ0G4YF+Ll6NlTzno9poRqVt/ggRkbfy5DZbwIeCDwCYeFk8UqKDUFZTj7TNJ1y9HI8lj1VPiAiEJEkuXo1VoFaDEH/rVhqLTonI23lymy3gY8GHWiVhztguAIC3f81EWQ2zH60hBx+uHqt+NnnrhYPGiMjbeXKbLeBjwQcAXNs7Dl0MwaioNWHZpuOuXo5HcrdOF1msEnww80FE3s2T22wBHww+VCoJD9uyH8s3HUdptdHFK/I8SvDhJp0ushhb3QfbbYnIm9WZzErtHYMPDzKuZyy6xYagss6Et3/NdPVyPI58rkuSm7V3xbLdloh8QFZRNSzCWvQfHaxz9XJaxSeDD5VKwsNXWbMfaZtPoLiK2Y/mslgEckpqALhvzUdeGTMfROS95OFiHaOD3Kbov6V8MvgAgKt7GNAzPhTVRjPe3Jjh6uV4jPyKWhhNFmhUEuJsh7m5C7ndNr+CwQcRea8MD2+zBXw4+JAkCY/Ysh8rt2ShsIKp+uaQO13ahQdAo3avt4/S7cLMBxF5MU9vswV8OPgArFNPL2uvR029GW9uYPajOdy12BRoNGK9og4WC6ecEpF38vQ2W8DHgw9Jaqj9eG9bFo9jbwZ3O9OlsegQHSQJMFkEitnFREReyHqgnGe32QI+HnwAwMgu0eifGIY6kwVLmf24JDnz4W7FpgDgp1YhMsjWbsutFyLyQsVVRpTV1EOSgGRuu3gua+1HVwDAqu3Z/KV1CVluvO0CALF6a/BRwKJTIvJCmWesWY94fQD8/dQuXk3r+XzwAQDDOkXi8g4RMJoseH39MVcvx61l22Z8uOsRzoYQud2WBcRE5H0at9l6MgYfaFr7sXpHDk6V1rh4Re6pvLYeJdXW83DcbbS6LIYj1onIi8n1HikeXGwKMPhQpKZEIrVjJIxmC5asY/bjfORi08ggLYJ1Ghev5vx4vgsRebMMLyg2BRh8NCFnPz7amYMcW20DNXDXA+UaUwaNMfggIi8kt9l6crEpwOCjicuTI3BF5yiYLAKv/cLsx9mUThc3LTYFAINeznyw5oOIvEu92aJkoD15xgfA4OMcc2wn3n6y56RygBpZZbnxjA+ZXHDKzAcReZuc4mqYLAL+firEhbrX8RYtxeDjLAOSwjGySzTMFoHFa5n9aCy72NbpEum+6T5526WoygijyeLi1RAR2c9xW5ttclQwVCrPPFBOxuDjPOTaj8/3nlTamsi9B4zJIoK08FNbfyg564OIvIk3TDaVMfg4j74JYbiyWwwsAli89qirl+MW6s0W5JZaf5m787aLJEmICWHdBxF5H7nYNMXDi00BBh8XJGc/vtyfi2MFFS5ejeudKqmB2bbXGBOic/VyLipWz7oPIvI+DW22nl1sCjD4uKBe7fS4uocBQgD/Ze1Hk9NsJcm99xrZbktE3shnt12eeeYZSJLU5Ktbt27K5bW1tZg9ezYiIyMRHByMyZMnIz8/3+6Ldha58+Wb33KRnufb2Q93P9OlMUMot12IyLuU19bjTKX1M83TZ3wArch89OzZE6dPn1a+Nm3apFz28MMP4+uvv8bHH3+MDRs2IDc3FzfccINdF+xMPeJD8afesbbsxxFXL8el3P1Ml8YMnHJKRF5GznrEhOgQ4u/n4tW0XYtnZGs0GsTGxp7z/bKyMixbtgzvv/8+xowZAwBIS0tD9+7dsW3bNgwZMuS891dXV4e6uoa/UMvLy1u6JId66Mou+P73PHx3IA8Hc8vRIz7U1UtyiYZtlwAXr+TSuO3i3XKKq/HoR/tx06AE/GVAe1cvh8gpvOVAOVmLMx9Hjx5FfHw8OnbsiKlTpyI7OxsAsHv3btTX12Ps2LHKdbt164bExERs3br1gvc3f/586PV65SshIaEVT8NxusaGYEKfeADAop99N/shDxhLcuMZHzI585HH4MMrvbgmHTtOFGPRz0cghHD1coicItOLik2BFgYfgwcPxooVK/DDDz9g6dKlOH78OK644gpUVFQgLy8PWq0WYWFhTW5jMBiQl5d3wfucO3cuysrKlK+cnJxWPRFHeujKzlBJwI8H83HgZJmrl+N0QgjlrBt3PtdFJgcfBaz58DrpeRX45rdcAMDJkhocK+AcHvINcpttRy+o9wBauO1yzTXXKP/dp08fDB48GElJSfjoo48QENC6dLxOp4NO596tm51igjGpbzt8vvcUFv18BMumD3L1kpyqqMqIKqMZkgS0D/eEbRdr8FFZZ0JlncltT+Cllvvv2iNonOxYl16AzoYQ1y2IyEnkzEeKL2Y+zhYWFoYuXbrg2LFjiI2NhdFoRGlpaZPr5Ofnn7dGxNM8eGVnqFUS1h4uwL6cUlcvx6nkLZe4UH/oNGoXr+bSgnUaJeBg3Yf3OJhbju8O5EGSgCmXJwIA1h0udPGqiBzPYhHKaHWfrflorLKyEhkZGYiLi8OAAQPg5+eHtWvXKpenp6cjOzsbqampbV6oqyVHBeH6fu0AAAt/8q3aD0/acpGx6NT7yDVX1/aOwz0jOgIAdp4oRkVtvSuXReRwp0prUGeyQKtWoX2453wOX0yLgo/HHnsMGzZswIkTJ7BlyxZcf/31UKvVmDJlCvR6PWbOnIlHHnkE69atw+7duzFjxgykpqZesNPF0zw4xpr92HCkELuzil29HKfxhNNsz8Z2W+9y4GQZfjyYD0kC5oztjA5RQegYFQSTRWDT0TOuXh6RQ2Xash5JkYFQe/iBcrIWBR8nT57ElClT0LVrV9x0002IjIzEtm3bEB0dDQBYuHAhJkyYgMmTJ2PEiBGIjY3FZ5995pCFu0JiZCD+0t/a2rfwJ9858yWrWH7je066j4PGvIuc9Zh0WTw6xVhrPEZ1jQFgrfsg8mbe1mYLtLDgdPXq1Re93N/fH0uWLMGSJUvatCh3dv+YTvh0z0lsOnYG2zOLMLhjpKuX5HA5HjTdVKa025Yx8+Hp9uWUYu3hAqgka+2VbEy3GCzffBzr0gshhHD7sf9EreVtbbYAz3ZpsYSIQNw0yDqLZKGPzP3wzG0Xa81HQQWDD08n11hd3699kw/fQcnhCNSqUVhRhz9y3Ws4IZE9eVubLcDgo1Vmj+4ErVqFbZnF2JLh3fvNNUYzCiqsWxdJHlRwGsvMh1fYnVWCDUcKoVZJePDKTk0u02nUGNYpCgCw7jC3Xsh7MfNBAIB2YQG45XJr9mPRT0e9espiTok16xHqr0FYoNbFq2m+GNZ8eAW51uMv/duft+ZoNOs+yMtVG004bfsjKsWLaj4YfLTSfaM6QatRYceJYmw+VuTq5TiMsuXiQVkPAIjV26acVtR6dXDozXYcL8avR89Ao5Jw/5hO573O6G7WYve9OaUorjI6c3lETiFnPSKCtB71B+ClMPhopVi9P6YOtg46euWndK/9BZdlO802yQNOs20sOtha81FvFvyl5KHkWo+bBiUg4QL1RnH6AHSLDYEQwK9HOXCMvI/cZutN9R4Ag482mTUqBf5+KuzJLsWGI975weeJA8YAQKtRITLI+lcCt148z5aMM9iaWQStWoXZo8+f9ZCN7mbdevmFdR/khbyxzRZg8NEmMSH+uH1IEgDrX2nemP3I8sA2WxkHjXkmIQQW2ebo3HJ5AtqFXfw8IbnuY8ORQpgt3vczSL7NG4tNAQYfbXbPyBQE+Kmx/2SZV/7llW2r+UjyyOCDI9Y90eZjRdhxohhajQr3jbp41gMA+ieGIdRfg9Lqep87d4m8nze22QIMPtosKliHaUM7AABe8bLsh9kicLKkBgAuuOfuzuSi0zwGHx5DCIFXfkoHAEwdnKj8P7wYjVqFEV2shafr2fVCXkQIgePMfNCF3D2iI4K0avyRW44fD+a7ejl2k1deC6PZAj+1hPhLpL7dUUwI2209zYYjhdiTXQp/PxVmjUpp9u3krRdvzD6S78ovr0OV0Qy1SvLIre+LYfBhBxFBWswYlgzAWvth8ZJ9Z7nTpX24Zx5mpLTbMvPhEYQQSofL7UOSlOCxOUZ2jYYkAX/klnObjbyGXGyaGBEIrca7fl1717NxobuuSEaIToPDeRX44Y88Vy/HLuROF0/ccgEaaj647eIZfjlcgP0nyxDgp8Y9I5uf9QCs25992ocBADake2fnGfmeDC9tswUYfNhNWKAWM4Y3ZD+8oeo+y4OLTQFuu3gSa62HNetxx9AkRNnmtLTE6K7Wug9OOyVv4a1ttgCDD7uaOTwZIf4aHC2oxLcHTrt6OW2Wbct8eNKZLo3J2y5FVXWoN1tcvJrWqzOZXb0Eh/vxYD7+yC1HkFaNe0a0LOshk+s+fj16BkaT5/7/JpJ5a5stwODDrvQBfvjrFR0BAP/92fOzH9kevu0SEaiFn1qCEEBhhWdmPxZ8fxh9nvkRvxz2nkLms1ksDbUe04d1QERQ60ZI926nR1SwFpV1JuzKKrbnEolcwlvbbAEGH3Y3Y1gH6AP8kFFYha/357p6OW2ibLt4aOZDpZKUrRdPrPsQQuCT3TmoM1nw8If7lRocb7PmjzwczqtAsE6jBO+toVJJGNnFmv1Yz7oP8nC19WZl1AEzH3RJIf5+uHuELfux9ihMHpruL6uuR1lNPQDPnG4qi7EVnXpix0t6fgXOVFrPpSmrqcfs9/d43RaMxSKw0HZy7Z3Dk9t8cJZ80Nw6ttySh8sqqoYQQIi/BlHB3nOgnIzBhwNMG2pNHR8/U4Uv9nlm9kPecokK1iFQq3HxalovNtRzi07l05J7tQtFWKAffjtZhue+PeTiVdnXtwdO40h+JUL8NZhpK9huiys6RUOtknC0oNJrM0XkGxqKTYMhSZ436uBSGHw4QLBOg3ts2Y/Fa496ZLFjVrHtNFsP3XKRyee7eOK2y+ZjZwAAf74sHgtv7gsAWLk1y+O382Rmi8AiW9bjr1d0hD7Ar833qQ/0w4DEcACcdkqeTT7NNsUL6z0ABh8Oc3tqEqKCtcgursZne066ejktpnS6ePCWC9Cw7eJpg6fqzRZsy7RmPoZ1isLorjGYPdraBfK3T39Dhu2vIk/29f5cZBRWQR/ghxnDOtjtfuVTbtex7oM8WIYXt9kCDD4cJlCrwb22QUmL1x7zuNY/+UA5T+10kcV66Mm2+3JKUW00IyJIi+6xoQCAh8d2weDkCFQZzbjvf3tQY/Tc+g+T2YL/rrWeXHv3iI4I8W971kMm131syTiD2nrPfY3It3lzmy3A4MOhbhuShOgQHU6V1uDj3TmuXk6LeHqni8zgoTUf8pZLakokVLbR9hq1Cq9O6YeoYB3S8yvwjy9+99iDDL/Yl4vjZ6oQEaRVDma0l66GEMTp/VFb35A9IvIkQgivHjAGMPhwKH8/NWbbDsd67ZdjHtWp4OkDxmRK8FHmWZkPOfgY3imqyfdjQv3x6pR+UEnAp3tO4uNdnrelV2+2YLEt63HPiI4I1tm3oFmSJIyyDRxj1wt5oqIqI8prTZAkoEMkgw9qhVsuT0RsqD9Ol9Xiw52ekf0wmiw4XWbtL/f0bRf5fJeKOhOq6kwuXk3zVNaZsDe7FMC5wQdgzYY8enVXAMBTX/6Og7nlzlxem3225ySyi6sRFazF7alJDnmMMY3qPjw1O0S+S95yaRcWAH8/tYtX4xgMPhzM30+N2WM6AQCWrDvmEXvQJ0uqYRFAgJ8a0a04Y8OdhPj7IUhr/eEt8JAppzuOF8FkEUiMCLxg8DdrZApGd41GncmC2e/vQUVtvZNX2TpGkwWL1x4DANw7MsVhbdxDUyKhVauQXVytdA0QeYrGbbbeisGHE9w0sD3i9f7IL6/D+9uzXb2cS5K3XBIjAr2iv1xpt/WQrZdNRxu6XC5EpZLwyk190S4sAMfPVOHJT3/ziL/wP96dg1OlNYgO0eG2IY7JegBAkE6DwR0jAHDrhTxPphefZitj8OEEOo0a94/pDAB4fX2G23cpKMGHh9d7yJQppxWeEXxsybDWewzrFHnR64UHafHarf3gp5bw3YE8rNhywgmra706kxmv/WLNesweleLwdLJS98F5H+Rh5MxHipcWmwIMPpzmLwPao314AM5U1uF/27JcvZyLUjpdPLzeQxbrQZmPgopaHM6rAAAMTblw5kPWLzEc//en7gCA5787hL3ZJQ5dX1t8uDMHp8tqERvqj1suT3T4443uam253XG8GJUeUu9DBHh/my3A4MNptBoVHrRlP97YkIFqo/t+GHpb5sOT2m23Zli3XHrGhzb7dNfpQzvg2t5xqDcLzF61ByVVRkcusVVq681Yss6W9Rjt+KwHYP3g7hAZiHqzULqHiNxdvdmifAZ7a5stwODDqa7v3w5JkYEoqjJi5Vb3zX7IA8Y8+UC5xgweNGhs09Hzt9hejCRJWDC5NzpEBiK3rBYPf7QPFot71X98sCMb+eV1iNf746ZBCU57XHnrhaPWyVNkF1fDZBEI1KqVrK03YvDhRH7qhuzHmxsy3DIVLIRoNOPDO6JuTwk+hGj4C/1ixabnE+Lvh9enDoBOo8L69EIs3ZDhiCW2So3RjNfXW9dz/5jO0Gmc1zqojFo/zJZb8gzylktyVJBXFPxfCIMPJ5vUNx4do4JQUl2P/6xJd7sPxMLKOtTUm6GSrD3m3iBWbzvfxc0LTk8UVSO3rBZatQqDOkS0+PY94kPx7KSeAICXf0xXClddbdX2LBRW1KF9eAD+MqC9Ux97cHIEAvzUyCuvxaHTFU59bKLW8IU2W4DBh9Np1Co8Md46IGrFlhOY//1htwpA5C2XOH0AtBrveHvEhDTUfLjTa322TbasR/+kMARoW5cduGlgAib3bw+LAB78YJ/LO3yqjSYstWU9HhzT2envKX8/tdI1xK4X8gRKsakXt9kCDD5cYnyvOOUv1Lc2ZuLf3xxym1+K3jJWvTG51dZosqC02n2HcW1uRb3H2SRJwrzreqGrIQRnKuvw4Ad7YTK77lDDlVuzUFRlRFJkIK7v384la2DdB3mSzDPefaaLjMGHi9yR2gHPXd8LALB883E889UfbhGAZHlZsSlgnbMid47kuWndh9kiGs33aH3wAQABWjVev60/grRqbMssxsKfj9hjiS1WWWfCmxsash5+atd83IyytdzuzipBabX7dQIRNdaQ+eC2CznI1MFJeGFyb0gS8O7WLDz15e8u71LwtjZbWUyIre7DTYOP30+VobzWhBB/DXq307f5/lKig7Fgch8AwJJ1GS6Z8vnulhMoqa5Hx6ggTOob7/THl7UPD0QXQzAsAth41D3qYIjOp6y6HkW2VvlkZj7IkW4elIgXJ/eBJAH/25aNv3/h2gBE2XaJ8K43fqzeWvdR4KazPjbbsh5DOkZCY6cMwcTL4nGH7eC2hz/ah1OlNXa53+Yor63HWxszAQAPje1st+fUWnLXy3ovHrV+sqQaxwoqXb0MagN5y8UQqrP7ac/uhsGHG7hxYAJeuekyqCTrPIS/ffabywIQb9x2AQCDrejUXbdd5BbbttR7nM/fr+2Oy9rrUVpdj9mr9sBock79R9qmEyirqUenmGBM6OO6rIdstFz3caTQ5dlFR6iorcefX9uM8Ys2YuORQlcvh1rJV7ZcAAYfbuP6fu2x8Oa+UEnAR7tO4vFPfoPZyR+SVXUmnKm0Zga8bdvFoHffWR+19WbsPGEdi97Weo+z6TRqvHZrf+gD/LAvpxTPf3fIrvd/PmXV9XhnkzXrMWdsZ6hVrp9VMCApHCE6DYqrjNh/stTVy7G797dno7jKCJNF4L5Ve3DodLmrl0St4CvFpgCDD7cyqW87LJ7SD2qVhE/3nMSjH+1zaqdCTok16xEW6Ad9gJ/THtcZDKHuW/Ox60QJjCYLYkP9HXKQVEJEIF656TIA1vbub387bffHaGzZpkxU1JrQ1RCCP/WKc+hjNZefWoUrulgDu3Xp3pUZqK03451NxwFYa5sq60yYkbbTI84yoqZ84UwXGYMPNzOhTzxem9IPGpWEL/bl4uGP9jstAPHWLRegYdvFHc932dyoy8VREw2v7G7APSM7AgCe/PQ3ZZCRvZVUGbF88wkAwMNXdYbKDbIestFe2nL7+d5TKKyoQ5zeH989dAVSooOQV16LGSt2oqLWfVvL6VwNwQczH+QC1/SOw5Kp/eGnlvD1/lw8tHof6p0QgHjbmS6NyQWn7ljz0TBSPdKhj/P41V1xeYcIVNaZcN+qPaitN9v9Md7+NROVdSb0iAvF1T1i7X7/bTHS1nL728kyFFa4XxDaGmaLUNqZ77qiI6KCdVgx43JEBWtx6HQ5Zr+/1ymfHdR2ZovA8SJr8JHCmg9ylXE9Y7F06gBo1Sp8e+A07n/f8cWCSputFwYf8qCxM5V1Lh26dbbSaiMOnCoDYP96j7Np1Cq8ems/RAVrcTivAv/88g+73n9RZR1WbDkBAHj4qi5ulfUArJNu5TZmb8l+/PB7Hk4UVSMs0A+32A7sS4gIxLJpgxDgp8bGI4V4+svf3WKGEF1cbmkNjCYLtBoV2oV7x9EWF8Pgw42N7WHAm7cPgFajwpo/8nHfqj2oM9n/r1VZlhdON5VFBemgVkkQAjhT6T6DprZmFEEIoHNMsHIAniMZQv3x31v6QZKAD3fl4JPdJ+12329tzES10Yze7fQY2z3GbvdrT6Nt2Y/1XlD3IYTA0g3HAADTUjsgqFFr5mUJYVg8pZ+tgy5HOdiP3FeGbSu0Q2SgWxRpOxqDDzc3ulsM3r5jILQaFX4+lI9Z/3NcAJJtS/kletmMDwBQqSRl0Jg7bb1sauUptm0xrFMUHh7bBQDwjy8O4HBe2zsjCivqsHJrFgDgkau6uO1pnPK8j41HCz1+O2LTsTP4/VQ5AvzUmDa0wzmXX9XDgH9OtB7j8NKadHy575STV0gt4UtttkAbg48FCxZAkiTMmTNH+V5tbS1mz56NyMhIBAcHY/LkycjPz2/rOn3ayC7RWD5tEHQaFX45XIB73ttt9/16s0XgZIl1CJW3tdnK5MyCO3W8bMkoAuDc4AMA7h/dCSO6RKO23oL7Vu1BZZ2pTff35oYM1NSb0TchTBln7o76tA9DRJAWFbUm7MkqcfVy2kQ+sO+WyxOU4wPONm1oB8wcngwAePzj37A9s8hp66OW8aU2W6ANwcfOnTvx5ptvok+fPk2+//DDD+Prr7/Gxx9/jA0bNiA3Nxc33HBDmxfq64Z3jkLa9EHw91NhfXoh/rpyl10DkNzSGpgsAlq1CrFOSP+7gru1254sqcbxM1VQqyQM7hjh1MdWqSQsurkv4vT+yCyswt8+/a3VdQEF5bV4b5s16/GwG2c9AECtkjCyizU4+sWD6z725ZRiS0YRNCoJd13R8aLX/fufumN8z1gYzRbc/d5uTkF1U77UZgu0MviorKzE1KlT8fbbbyM8PFz5fllZGZYtW4ZXXnkFY8aMwYABA5CWloYtW7Zg27Ztdlu0rxraKQorZlyOQK0avx49g5nv7kSN0T4BiFxs2j4iwGv3G90t87HlmPWv0Mva6xHq7/y5KhFBWrx2a39oVBK++e20EkC01OvrM1BnsmBAUjhGdHZuBqc15MzM+sOeW/fxhi3rMalvO7QLu3hxokolYdEtfdEvMQxlNfWYsWKH13T7eBNfarMFWhl8zJ49G9deey3Gjh3b5Pu7d+9GfX19k+9369YNiYmJ2Lp163nvq66uDuXl5U2+6MKGdIzEihmXI0irxuZjRZixYgeqjW1LmQPe3ekik4OPvDL3+ODd5KCR6i0xICkcf7umGwDg398cxP6c0hbd/nRZDd7fkQ3AvWs9GhvZJRoqCUjPr3DqeTf2cqygEmsO5gEA7h158ayHzN9PjXfuGIjEiEDkFNfgrpW77PaHC7VdVZ1JqUXzhTZboBXBx+rVq7Fnzx7Mnz//nMvy8vKg1WoRFhbW5PsGgwF5eXnnvb/58+dDr9crXwkJCS1dks+5PDkCK2dejmCdBtsyizF9+c4279nLA8aSfCD4KKhwfeZDCIEtGc4vNj2fmcOTMa6nAfVm62julhw7//q6DBhNFlyeHIGhKY6dU2IvYYFa9E+0Zmw9seX2rY0ZEMJaUNrZENLs20UG67BixiCEBfphf04pHlq91+lHOND5HT9jzXpEBmmhD/Su6dIX0qLgIycnBw899BBWrVoFf3/71AXMnTsXZWVlyldOTo5d7tfbDUiKwHszL0eIToMdJ4oxffmONk0zzJEzH5Hem/KLdaNtl/T8CpypNCLAT41+ieGXvoEDSZKEF/9yGRIjAnGqtAaPfrS/WYevnSqtweqdnpX1kMldL+s87JTbvLJafL7X2rUya1RKi2/fMTpY6Z778WA+5n170N5LpFaQ22x9ZcsFaGHwsXv3bhQUFKB///7QaDTQaDTYsGEDFi9eDI1GA4PBAKPRiNLS0ia3y8/PR2zs+acd6nQ6hIaGNvmi5umXGI7/3TUYof4a7MoqwR3Ld6C8lQFIVrHcZuvNmQ9bq60bnHmx6ag163F5cgS0Gtd3vOsD/PD61P7QalRYe7gAb27MvORtXvvlGOrNAkNTIjGko2dkPWRy3cfmY0UOmfTqKMs2ZaLeLHB5coSSvWmpQR0i8PKN1rN+0jafwHLbuTDkOr7WZgu0MPi48sorceDAAezbt0/5GjhwIKZOnar8t5+fH9auXavcJj09HdnZ2UhNTbX74sk6TOj9vw6BPsAPe7NLcfs721FW07IARAjRsO3ipW22QMPJtuW1Jpfvd292g3qPs/Vqp8cztrkQ//kx/aJtmTnF1fh4lzVL+fBVXZyyPnvqERcKQ6gONfVm7Dhe7OrlNEtptRHvb7dmmlqT9Whs4mXxDbU+3x7Emj/Ovy1OzpF5xreKTYEWBh8hISHo1atXk6+goCBERkaiV69e0Ov1mDlzJh555BGsW7cOu3fvxowZM5CamoohQ4Y46jn4vF7t9Hj/r4MRHuiH/SfLcNs721u0b19WU4+KWmvNSEK49wYfIToNAvzUAFy79WI0WbDd9gvP1fUeZ5tyeQKu79cOZovAAx/svWBXxKu/HIXJInBF5ygM6uDcNmF7kCRJOWhunYfUfby3NQtVRjO6xYZgVJe2z1K5Z0RH3Do4EUIAD63ei30tLDYm+8lUtl2Y+Wi1hQsXYsKECZg8eTJGjBiB2NhYfPbZZ/Z+GDpLz3g93v/rEEQEaXHgVBlufXs7SqqaF4DIWY+YEB0CtGpHLtOlJElyi1kf+3JKUW00IzJIi26xzS8YdAZJkvDc9b3QOSYYBRV15y1KPHGmCp/usdYdeGLWQzaqq+fUfdQYzUiznZsza1SKXeprJEnCs3/uidFdrcPmZq7YqRwuSc4jhFAKTpn5aIH169dj0aJFyr/9/f2xZMkSFBcXo6qqCp999tkF6z3IvrrHheKDvw5BVLAWB0+XY8rb21BUeem2Um8+0+VsSrutC4MPecslNSXS7Q5fA4BArQZLb+uPQK0aWzKK8N+fjzS5fPEvR2G2CIzuGt3qugN3MLxzFPzUEk4UVSsf/u7qo105KK4yIiEiANf2jrPb/WrUKrx2a3/0jA9FUZUR01fsaFHWlNour7wW1UYzNCrJq2vuzub6Sjeyq66xIVh99xBEh+hwOK8Ct769HWcuEYAonS5eeKbL2ZR223LXzfpwx3qPs3WKCcH8G3oDAF5ddwwbjlgHcmUUVuKLvZ6f9QCAYJ0Glydbt4zcOftRb7bgLVsB8N0jUqBR2/djO0inwfLpgxBvm3Z798rdDj3AkpqSi00TIwLhZ+f/t+7Md56pD+kUYw1AYkJ0SM+vwJS3tl10tkVWkfd3ushi9a5tt62orcde2966u9V7nG1S33aYaqsJmLN6L3JLa7B47VFYBDC2uwF92oe5eolt5gl1H9/8lotTpTWICtbixgHtHfIYhlB/pM1oaN1//OPfmtVuTW2X6YNttgCDD6+VEh2MD+9JRWyoP44WVOKWt7Zd8BeuL3S6yFx9su2O48UwWwSSIgOR4AHB3lMTeqBXu1CUVNdjRtpOfLU/FwAwZ2xnF6/MPuS6j+2Zxahq46A+R7BYhHKA3IxhyfD3c1xNVtfYELxx+wBoVBK+2p+L//yY7rDHogYZPnami4zBhxdLjgrCh/cMUdKpt7y17bwzLuRtF0/4ZdhWcubDVdsum4+55hTb1vL3U+P1WwcgxF+D9PwKCAGM7xmLXu30rl6aXaREByEhIgBGs0U5YdidrEsvwJH8SgTrNLhtSJLDH29YpygsmGw9LPT19RlKa6+n2p9Tijc2ZLh1HYvSZhvFzAd5kaTIIHx4TyrahQXg+Jkq3PzWVuQ2Os+izmTGaVsWwBcyH64uOJXrPYaleEbwAQCJkYH4j20oFQA85CVZD8Da8THGjbde5KzH1CGJ0Ac4Z+z2Xwa0x0NXWv8fP/Xl7x45gn53VgmmLd+BSUs2Y8H3hzFjxU63HSbni222AIMPn5AQEYgP7xmChIgAZBVV4+a3tuJkiTXbkVNcAyGAIK0akUFaF6/U8QwhDTUfrT1CvrUKKmqRnl8BSbJ2uniScT1j8cZtA/DW7QPQPc67phCPso1aX3+4wOnviYvZeaIYu7JKoFWrMHNYslMfe87Yzrihv3Xey+xVe/BHbplTH7+1dhwvxm3vbMfkpVuw4Ugh1CoJgVo19maXYs7qfW53lk1tvVk53JA1H+SV2ocHYvXdqUiKtJ5qefOb25BTXN1ky8WTzuZorRjbnI86k6XFk2Dbaotty6VnfCgiPDDQG98rFlf39L62+dSOkdBpVMgtswaH7kLOekwe0B4xofY5S6u5JEnCghv6YGhKJKqMZty5YmeTjKk7kQ9pvOWtrbjpza3YdOwMNCoJNw9MwC+PjkTa9EHQqlX44Y88PP/dIVcvt4kTRVUQAgj11/jEH3+NMfjwIe3CArD67iFIjgrCqdIa3PzmVvxqO2PEF7ZcAGsNQ5jt1Mh8J9d9bDrmHqfYUlP+fmrlRN51hwtdvBqrQ6fL8cvhAqgk6yRSV9BqVFh62wB0MQQjv7wOd67Y2abDK+1NCIFNR8/g5je34da3t2NbZjH81BJuHZyIdY+Nwgt/6YOkyCAM7hiJl2601rEs23QcKza7z1k2mY2KTX3hj7/GGHz4mDi9NQDpGB2E3LJaLLf9ICZ58Wm2Z3PF6bZCCGzxgPkevmpMN/eq+3hzgzXrcU3vOHRwYSGiPsAPy6cPUuYG3bdqD+rNFpetB7D+LK1PL8DkpVtw27Lt2HGiGFq1CrcPScKGx0fj+et7n1M8P6lvOzw+risA4NlvDuKng/muWPo5fLXNFmDw4ZMMof5YffcQdIppKHDyhU4XWYwLik6Pn6lCblkttGoVBiZ53lko3k5uud2dVeL07biz5RRX4+vfTgMAZo1s2wFy9tA+PBDLpw1CgJ8avx49g79/fsAltTFCCKw9lI/rlmzG9LSd2JNdCp1GhelDO2DjE6Px7+t6IT4s4IK3v29UCqZcngCLAB74YA/2u8FZNnLmI8XHik0BBh8+KybEGoB0NVjPFumfGObaBTlRrK3uo8CJwYfc5TIgKdyrz8/xVAkRgegUEwyzReDXo67dennn10yYbYf2uUtLc+/2erx2az+oJOCjXSfx2i/HnPbYFovAmj/yMOHVTZj57i7sP1kGfz8V7hqejF+fGI1n/txTaaG/GEmS8O9JvTCyi+0sm3d3KjVvrpLho222AIMPnxYVrMPXDwzHr0+MRs949/iQcwZXtNvK9R7DO3PLxV2N7mo9KdaVdR9nKuuwemcOAPfIejR2ZXcD/jWpFwDg5Z+O4PO9Jx36eBaLwHcHTuNPi3/FPe/txh+55QjUqnHPyI7Y9OQY/GNCjxYX4mrUKiyZ2h/d40JxptKI6Wk7UFbtmkyXEMJn22wBBh8+T6tR+dSWC9Cw7eKsglOzRWBrhmcNF/NFo211HxuOFLhstPi7W06gzmTBZe31btmOffuQJKUA9olPflPe1/Zktgh8tT8X4/+7Efet2oPDeRUI1mkwe3QKNj05BnOv6Y6oYF2r7z9Yp0Ha9EGI0/sjo7AKd7+3yyVn2ZypNKKi1gRJ8p2C/8YYfJDPcXbB6e+nylBea0KIvwa93SSNTucamBSBYJ0GZyqN+N0Fcy0q60x4d8sJAMCsUSlu2/3w5PhuuLZ3HOrNAve8twvHCuzTnmwyW/D53pO4auEGPPjBXhzJr0SIvwYPXtkZm54cjcfHdbNbi3qs3h/Lpw9CsE6D7ceL8eQnvzm9jkXOerQPD3Do2Hx3xeCDfI7BVvPhrOBD3nJJ7RgJtco9f6GQNQsodyL94oJTbj/Yno3yWhM6Rgfh6h7uO09FpZLw8k2XYUBSOMprTZi2fOdFD668FJPZgk92n8RVCzfi4Q/3I7OwCqH+Gjw8tgs2PTkGj1zVBWGB9p+B0T0uFEtv6w+NSsIX+3Lx8o9H7P4YF9MwVt33tlwABh/kg+TMR2FFnVMmHm5mvYfHGN3NVveR7ty6jzqTGe9sygQA3DsiBSo3D1L9/dR4+46Bysygu97dhWpjyw7mM5os+HBnNsa8vAGPfbwfx89UISzQD4+P64rNfxuDh8Z2dvhI+Ss6R+P563sDAF5bdwwf7nTeWTa+3GYLMPggHxQZrINaJcEirAV+jlRjNGPXiRIArPfwBHLL7W8nSx3+3mjsi72nkF9eh9hQf0zqF++0x22LiCAt0qYPQkSQFr+dLMODH+xtVjBfZzJj1fYsjP7Pejz56QFkF1cjMkiLv13TDZueHIPZozshxN8559gAwE2DEvDAmE4AgP/7/HdsPOKcwDPTR0+zlTH4IJ+jVkmIDnbO1suurGIYzRbE6f19sp3O0xhC/dEzPhRCwGm/hMwWgTc3WLMed12RDJ3Gc/b/O0QF4e07BkKrUeHnQwV49us/Llg7UVtvxsqtJzDqpfX4++e/41RpDaKCdfjHtd3x65Ojce/IFATrNE5+BlaPXNUF1/eznmVz36o9OJhb7vDHlLddUnz0c4HBB/kkue4jr8yxwcdm23kuQ1Oi3LaAkJoabct+OKvu48c/8pB5pgr6AD/ccnmiUx7TngYkhWPRzX0hScC7W7OwbFPT8eW19WYs33QcI15ch6e//AOny2oRE6LD0xN6YNOTo3HXFR0RqHVN0CGTJAkvTO6DIR0jUFlnwp0rduJ0mePOsjGaLMi2zRhh5oPIhyjtthWOTa031Hu4X9sknZ9c97HxSCFMDh4lLoTAUtso9WmpSS77y7+t/tQ7Dv93TXcAwHPfHcL3B06j2mjCO79mYvgL6/DsNwdRUFGHOL0/np3UExufGI07hye7VZeHVqPCm7cNRKeYYOSV12JGmuPOsskurobZIhCkVSt/CPkaz3ynE7WR0m7rwMxHSVVDy+awFNZ7eIq+CeEIC/RDaXU99uaUYlAHx43D35JRhN9sEzunDe3gsMdxhruuSEZOSTVWbs3CnA/3IVinQVGVEYD1UMv7RqfgLwPau/W2kj7QD2nTB+H617fgcF4FZr+/F8umDYSf2r5/p8vFpsnRQT6bEWXmg3ySM9ptt2YWQQigiyHY6UeiU+upVRJGdpGnnTp262XpemvW45ZBiYhsw+AsdyBJEv45sSfGdo9BncmCoiojEiIC8MLk3lj32ChMHZzk1oGHLCEiEMunD0SAnxobjxTiqS9+t/sMEF9vswUYfJCPMjhh20XecmGXi+dxRt3HbydLsenYGahVEmYOT3bY4ziTWiVh8ZR+eHBMJ7x842X45dFRuHlQIrQaz/pV06d9GF6dYj3LZvXOHLxuCxLtxdfbbAEGH+SjDE7YdlGCD265eJwRXaIhScDhvAqHFR6+Yav1+PNl8V51xEGgVoNHru6KyQPa2327wpnG9jDgnxN7AgBeWpOOL/edstt9+3qbLcDgg3yUfApmfhsmM15MTnE1ThRVQ62SMLij42oGyDEigrTomxAGAFjvgIFjmYWV+P73PADAPSM72v3+yT6mDe2Au2xZqcc//g3bM+1zlk2mD59mK2PwQT7JEGINPkqr61Fbb/9DpbZkWLMefRPCnDowiexnjG3rxRF1H29tzIQQwJXdYtAtNtTu90/2839/6o5resXCaLbg7vd241hBZZvur7TaiGJbIS63XYh8TGiABjrbPnSBA0633XSMp9h6OvmU203Hztj11NP88lp8tseawp81KsVu90uOoVJJWHhzX/RLDENZTT2mp+1AYRtqxTJsWy5xen+XzzdxJQYf5JMkSVK2XvLs3PFisQhsked7MPjwWD3iQhEdokO10Yydx0vsdr/LNx2H0WzBoA7hGOjANl6yH38/Nd65YyCSIgNxsqQGd727EzXG1gWkLDa1YvBBPkveerF3u216fgWKqowI8FMrdQPkeVQqCaPkltt0+2y9lFXX43/bsgAw6+FpIoN1SJs+CGGBfth/sgwPrm7eWTZnY5utFYMP8lkGvWOCD7nLZXDHCI9rMaSmxti2XuwVfPxvexaqjGZ0NYQo7bzkOTpGB+Md21k2Px3Mx7+/Odji+ziudLow80Hkkwwhjhk0tolbLl5jWOcoaFQSMgurkFVU1ab7ks84AaxZD1+dbOnpBnaIwCs3XQYAWLHlxDln2VxK5hl524WZDyKfpLTb2rHg1GiyYHtmMQAWm3qDUH8/DOwQDqDtXS8f78pBUZUR7cMDMKFPnD2WRy4yoU88/nZNNwDAvG8P4gdb2/SlmC0CJ4psB8r5cJstwOCDfJg88tyeBaf7ckpRU29GVLAWXQ0hdrtfch15e2RdG+Z9mMwWvLkxEwBw94iO0Hjw8C2yumdER0wdnAghgIdW78Xe7EsXJZ8qqYHRZIFWo0J8WIATVum++BNAPkvedimwY/Ahb7mkpkRBpWJa3RvIdR9bM4ta3eHw7YHTOFlSg8ggLW4ckGDP5ZGLSJKEf/25J8Z0s55lc9e7uy65NZdh23JJjgyC2sc/Hxh8kM9q3Gprr4OjNiv1HpF2uT9yvU4xwWgXFgCjyaIMj2sJIYRygNyMYR0QoHX/w9WoeTRqFV6d0g8940NRVGXEjLSdKLENEDufTBabKhh8kM+Sz3eprbegvNbU5vurqK3HvpxSAKz38CaSJGF0t9a33K5PL8ThvAoEadW4fUgHO6+OXC1Ip8Hy6YMQr/dH5pkq3P3ergtOTeaMjwYMPshn+fupoQ+wjj63x9bL9sximC0CHSID0T7cew4Ko0Z1H4cLW5wlk7Metw5OhD6Qo/a9kSHUH2kzLkeIToOdJ0rw+Ce/wXKeGSBK5sPHZ3wADD7IxxlCrXUf9ig63WxLyTPr4X2GpkRBq1HhVGlNi8722HWiGDtOFMNPLWHmcB4g5826xobgjdsHQKOS8PX+XLz0Y/o512los2Xmg8EH+TR568Ue7bZyvQeDD+8ToFUjtaO1jueXFrTcvrHBmvW4oV97pcaIvNewTlFYMLkPAGvG6/3t2cpllXUm5XPG12d8AAw+yMc1BB9ty3wUlNfiSH4lJAnKLynyLqO7tqzuIz2vAj8fKoAkAXePZNbDV/xlQHvMGdsZAPDUl78r7xd5smlUsFbZ7vVlDD7Ip8nbLm0NPuQtl17xeoQHadu8LnI/8im3u06UoLy2/pLXf9OW9RjfMxYp/EvXpzx0ZWdM7t8eZovA/av24I/csoYtF9Z7AGDwQT4uVh40VtbG4ONYEQBuuXizpMggdIwOgskisPnoxVtuT5ZU48v9uQCAe0fyADlfI0kS5t/QG0NTIlFlNOPOFTuxyfaeYb2HFYMP8mnylNP8itbXfAghGs33YPDhzeSul0vVfbzz63GYLQLDOkXiMp5s7JO0GhWW3jYAXQzByC+vw8e7TwJg8CFj8EE+Tc58tKXVNvNMFU6X1UKrUSnngJB3koOP9UcKz9tKCQBFlXVYvdNaaDhrZCenrY3cjz7AD2kzLke0bZoywG0XGYMP8mlywWlBRR3MF/hlcily1mNgUjj8/Ti90psNSg5HoFaNwoo6HDxdft7rvLs1C7X1FvRup8cwTrr1ee3CApA2fRACtWqoVRJ6xIe6eklugcEH+bSoYC1UkvW0yaKq1m29yHu5rPfwfjqNWtlaO98pt1V1Jry75QQAYNaoFEiSb5/fQVa92unxzQPDsfruIT5/oJysRcHH0qVL0adPH4SGhiI0NBSpqan4/vvvlctra2sxe/ZsREZGIjg4GJMnT0Z+fr7dF01kLxq1ClHBto6XspYHH2aLwNZMa7Ep6z18g9z18st5Wm4/2JGNspp6JEcFYVzPWGcvjdxYx+hgDOoQ4epluI0WBR/t27fHggULsHv3buzatQtjxozBpEmT8McffwAAHn74YXz99df4+OOPsWHDBuTm5uKGG25wyMKJ7KUtsz4OnCpDRa0Jof4a9Gqnt/fSyA2Nss372JdTiuJGh4gZTRa88+txANbj1n391FKii2lR8DFx4kT86U9/QufOndGlSxc899xzCA4OxrZt21BWVoZly5bhlVdewZgxYzBgwACkpaVhy5Yt2LZtm6PWT9RmcvDRmhHrcr1Hakokf9n4iDh9ALrFhkAIYOORQuX7X+w7hbzyWsSE6HB9/3YuXCGR+2t1zYfZbMbq1atRVVWF1NRU7N69G/X19Rg7dqxynW7duiExMRFbt2694P3U1dWhvLy8yReRM8mDxlrT8SLXe3DLxbeMsW29yNMrLRahjFK/64pk6DQsPCa6mBYHHwcOHEBwcDB0Oh3uvfdefP755+jRowfy8vKg1WoRFhbW5PoGgwF5eXkXvL/58+dDr9crXwkJCS1+EkRtEdvK811qjGbszioBwGJTXyPXfWw4UgizReDHg/nILKxCiL8GUy5PdPHqiNxfi4OPrl27Yt++fdi+fTtmzZqFadOm4eDBg61ewNy5c1FWVqZ85eTktPq+iFqjtdsuu7KKYTRbEK/3R3IUBwf5kn4JYQj116C0uh57s0uw1Jb1uCM1CSH+PLeD6FI0Lb2BVqtFp07WwTkDBgzAzp078d///hc333wzjEYjSktLm2Q/8vPzERt74apvnU4HnU53wcuJHM2gb13B6SZbvcfQTlFsqfQxGrUKI7pE45vfTuPFH9KxP6cUOo0K04cmu3ppRB6hzXM+LBYL6urqMGDAAPj5+WHt2rXKZenp6cjOzkZqampbH4bIYVp7uBxHqvs2ue5jx4liAMBNAxOaTLIkogtrUeZj7ty5uOaaa5CYmIiKigq8//77WL9+PdasWQO9Xo+ZM2fikUceQUREBEJDQ/HAAw8gNTUVQ4YMcdT6idrMEGLNfJRU16POZG5WsWBxlRF/5FqLo4dyiqVPGtElGpIECAGoVRLuHtHR1Usi8hgtCj4KCgpwxx134PTp09Dr9ejTpw/WrFmDq666CgCwcOFCqFQqTJ48GXV1dRg3bhxef/11hyycyF7CAv2g1ahgNFlQUF6HhIjAS95ma0YRhAC6GkIQYwteyLdEBevQp30Y9ueUYkKfuGa9b4jIqkXBx7Jlyy56ub+/P5YsWYIlS5a0aVFEziRJEgyhOuQU1yC/vLZZv0Q2Z3CkOgFPjOuKd7ecwBPju7l6KUQepcUFp0TeKDbU3xZ8NK/dVq734MFhvm1YpygGoEStwIPliADEtKDdNqe4GllF1VCrJAzuyOCDiKilGHwQoWHQWHOmnMpZj34JYQjWMXlIRNRSDD6I0NBu25zMx+YM6ym2TLcTEbUOgw8iNP9kW4tFYIs836Mzgw8iotZg8EGExsHHxQtOD+dVoKjKiECtGpe1D3PCyoiIvA+DDyI0zXwIIS54PbneY3ByBLQa/vgQEbUGPz2J0FDzUW00o7LOdMHrbTrG+R5ERG3F4IMIQKBWgxB/a+fKheo+jCYLdhy3nuPBeg8iotZj8EFkE3uJuo+92SWoqTcjKliLroYQZy6NiMirMPggspHrPvLKzp/5kOs9hqZEQZIkp62LiMjbMPggsomx1X3kV5w/+JDrPYaz3oOIqE0YfBDZKNsu58l8VNTWY//JMgDAMNZ7EBG1CYMPIpuLzfrYnlkMs0UgOSoI7cICnL00IiKvwuCDyEYJPs6z7bKJp9gSEdkNgw8iG3nWx/m2XeRi02Ep3HIhImorBh9ENrF628m2FXWwWBqmnOaX1+JoQSUkCUhNYeaDiKitGHwQ2UQF6yBJgMkiUFRlVL4vZz16t9MjLFDrquUREXkNBh9ENn5qFSKDbFsvjaacbj5WBIAj1YmI7IXBB1EjsfqmwYcQgvUeRER2xuCDqBFDSNN224zCKuSV10KrUWFgh3BXLo2IyGsw+CBqxKCXgw9r5kPOegzqEA5/P7XL1kVE5E0YfBA10pD5aBp8sN6DiMh+GHwQNdK45sNktmBrprXYlOe5EBHZD4MPokZi5JNty+tw4FQZKmpNCPXXoGe83sUrIyLyHgw+iBqRt10KymuVLZehKVFQqyRXLouIyKsw+CBqRJ5yWlRlxLr0QgA8xZaIyN4YfBA1Eh7oB63a+mOxO6sEAOs9iIjsjcEHUSOSJCHGdsAcALQLC0CHyEAXroiIyPsw+CA6i8FWdAoAQ1MiIUms9yAisicGH0RnMTTKfAxnvQcRkd0x+CA6S9PMB4MPIiJ7Y/BBdBY5+OgWG4LoEN0lrk1ERC3F4IPoLON7xiIlOgj3jOzo6qUQEXkljasXQORuOkQFYe2jo1y9DCIir8XMBxERETkVgw8iIiJyKgYfRERE5FQMPoiIiMipGHwQERGRUzH4ICIiIqdi8EFEREROxeCDiIiInIrBBxERETkVgw8iIiJyKgYfRERE5FQMPoiIiMipGHwQERGRUzH4ICIiIqfSuHoBZxNCAADKy8tdvBIiIiJqLvn3tvx7/GLcLvioqKgAACQkJLh4JURERNRSFRUV0Ov1F72OJJoTojiRxWJBbm4uQkJCIEmSXe+7vLwcCQkJyMnJQWhoqF3v21PxNTk/vi7n4mtyLr4m58fX5Vy+8JoIIVBRUYH4+HioVBev6nC7zIdKpUL79u0d+hihoaFe+z+/tfianB9fl3PxNTkXX5Pz4+tyLm9/TS6V8ZCx4JSIiIicisEHEREROZVPBR86nQ7//Oc/odPpXL0Ut8HX5Pz4upyLr8m5+JqcH1+Xc/E1acrtCk6JiIjIu/lU5oOIiIhcj8EHERERORWDDyIiInIqnwg+nnnmGfTt29fVy/AYK1asQFhYmPJvvn7kDNOnT8d1113n6mWQg40aNQpz5sxx9TLIxdwy+Ni6dSvUajWuvfZaVy/FpfLy8vDAAw+gY8eO0Ol0SEhIwMSJE7F27VqnruOxxx5z+mM6wvTp0yFJ0jlf48ePd/XSWi0vLw8PPfQQOnXqBH9/fxgMBgwbNgxLly5FdXW1q5d3XidOnIAkSdi3b5+rlwKAQc/Zzvd6fPLJJ/D398fLL7/c5vv/7LPP8O9//7vN9+Mo8ufEvffee85ls2fPhiRJmD59uvMX5mXcbsIpACxbtgwPPPAAli1bhtzcXMTHxzv08YxGI7RarUMfo6VOnDiBYcOGISwsDC+99BJ69+6N+vp6rFmzBrNnz8bhw4dbfJ9msxmSJF1y7O3ZgoODERwc3OLHc0fjx49HWlpak+95autbZmam8h55/vnn0bt3b+h0Ohw4cABvvfUW2rVrhz//+c+uXqZHc8fPBmd75513MHv2bLzxxhuYMWNGm+8vIiLCDqtyrISEBKxevRoLFy5EQEAAAKC2thbvv/8+EhMT23Tf9fX18PPzs8cyPZrbZT4qKyvx4YcfYtasWbj22muxYsUK5bL169dDkiSsXbsWAwcORGBgIIYOHYr09PQm97FgwQIYDAaEhIRg5syZqK2tbXK5HNk/99xziI+PR9euXQEAOTk5uOmmmxAWFoaIiAhMmjQJJ06cAAD8/vvvUKlUKCwsBAAUFxdDpVLhlltuUe533rx5GD58uF1eh/vuuw+SJGHHjh2YPHkyunTpgp49e+KRRx7Btm3bAACvvPIKevfujaCgICQkJOC+++5DZWWlch/y9slXX32FHj16QKfTITs7GyUlJbjjjjsQHh6OwMBAXHPNNTh69OgF13L2tov8+v3nP/9BXFwcIiMjMXv2bNTX1yvXee+99zBw4ECEhIQgNjYWt956KwoKCuzy2rSFTqdDbGxsk6/w8HDceuutuPnmm5tct76+HlFRUVi5ciUA67lD8+fPR3JyMgICAnDZZZfhk08+ccXTAGB9j2g0GuzatQs33XQTunfvjo4dO2LSpEn49ttvMXHiRDz22GOYMGGCcptFixZBkiT88MMPyvc6deqEd955Bxs3boSfnx/y8vKaPM6cOXNwxRVXAGh4T61Zswbdu3dHcHAwxo8fj9OnTze5zTvvvIPu3bvD398f3bp1w+uvv65clpycDADo168fJEnCqFGjmtz2Yu8rRxs1ahTuv/9+zJkzB1FRURg3bhwA68//Nddcg+DgYBgMBtx+++04c+ZMk9s9+OCDeOKJJxAREYHY2Fg888wzTe67tLQUd911F6KjoxEaGooxY8Zg//79TnturfHiiy/igQcewOrVq5XA48svv0T//v3h7++Pjh074l//+hdMJhMANOvn6Oxtlw4dOuD555/HnXfeiZCQECQmJuKtt95yzhO8gP79+yMhIQGfffaZ8r3PPvsMiYmJ6Nevn/K9H374AcOHD0dYWBgiIyMxYcIEZGRkKJfLWb4PP/wQI0eOhL+/P1atWgUAWL58OXr27AmdToe4uDjcf//9TW7TODNYWloKSZKwfv16AEBJSQmmTp2K6OhoBAQEoHPnzuf8UeX2hJtZtmyZGDhwoBBCiK+//lqkpKQIi8UihBBi3bp1AoAYPHiwWL9+vfjjjz/EFVdcIYYOHarc/sMPPxQ6nU6888474vDhw+Lvf/+7CAkJEZdddplynWnTpong4GBx++23i99//138/vvvwmg0iu7du4s777xT/Pbbb+LgwYPi1ltvFV27dhV1dXXCYrGIqKgo8fHHHwshhPjiiy9EVFSUiI2NVe537Nix4u9//3ubX4OioiIhSZJ4/vnnL3q9hQsXil9++UUcP35crF27VnTt2lXMmjVLuTwtLU34+fmJoUOHis2bN4vDhw+Lqqoq8ec//1l0795dbNy4Uezbt0+MGzdOdOrUSRiNRuV2er1euZ9//vOf57x+oaGh4t577xWHDh0SX3/9tQgMDBRvvfWWcp1ly5aJ7777TmRkZIitW7eK1NRUcc0117T5tWmLadOmiUmTJp33sm+++UYEBASIiooK5Xtff/21CAgIEOXl5UIIIebNmye6desmfvjhB5GRkSHS0tKETqcT69evd8bymzhz5oyQJEnMnz//otf76quvhF6vFyaTSQghxHXXXSeioqLEk08+KYQQ4uTJkwKAOHr0qBBCiC5duogXX3xRub3RaBRRUVFi+fLlQoiG99TYsWPFzp07xe7du0X37t3Frbfeqtzmf//7n4iLixOffvqpyMzMFJ9++qmIiIgQK1asEEIIsWPHDgFA/Pzzz+L06dOiqKhICNG895UjNH5fjBw5UgQHB4vHH39cHD58WBw+fFiUlJSI6OhoMXfuXHHo0CGxZ88ecdVVV4nRo0cr9zFy5EgRGhoqnnnmGXHkyBHx7rvvCkmSxI8//qhcZ+zYsWLixIli586d4siRI+LRRx8VkZGRyvN3F/Lr8cQTT4jg4GDx888/K5dt3LhRhIaGihUrVoiMjAzx448/ig4dOohnnnlGCNG8n6ORI0eKhx56SLk8KSlJREREiCVLloijR4+K+fPnC5VKJQ4fPuycJ3wW+fm/8sor4sorr1S+f+WVV4qFCxeKSZMmiWnTpgkhhPjkk0/Ep59+Ko4ePSr27t0rJk6cKHr37i3MZrMQQojjx48LAKJDhw7Kz0Nubq54/fXXhb+/v1i0aJFIT08XO3bsEAsXLmxym7179yqPXVJSIgCIdevWCSGEmD17tujbt6/YuXOnOH78uPjpp5/EV1995YyXx27cLvgYOnSoWLRokRBCiPr6ehEVFaW84HLw0fiH4dtvvxUARE1NjRBCiNTUVHHfffc1uc/Bgwef88vTYDCIuro65Xvvvfee6Nq1qxLoCCFEXV2dCAgIEGvWrBFCCHHDDTeI2bNnCyGEmDNnjnj88cdFeHi4OHTokDAajSIwMLDJh01rbd++XQAQn332WYtu9/HHH4vIyEjl32lpaQKA2Ldvn/K9I0eOCABi8+bNyvfOnDkjAgICxEcffaTc7lLBR1JSkvILTQghbrzxRnHzzTdfcG07d+4UAJp8KDnbtGnThFqtFkFBQU2+nnvuOeW9tnLlSuX6U6ZMUZ5TbW2tCAwMFFu2bGlynzNnzhRTpkxx6vMQQoht27ad9z0SGRmpPK8nnnhClJSUCJVKJXbu3CksFouIiIgQ8+fPF4MHDxZCWAOFdu3aKbd/4YUXRPfu3ZV/f/rppyI4OFhUVlYKIRreU8eOHVOus2TJEmEwGJR/p6SkiPfff7/Juv7973+L1NRUIcT5P1yFaN37yh7ODj769et3ztqvvvrqJt/LyckRAER6erpyu+HDhze5zqBBg5Qg79dffxWhoaGitra2yXVSUlLEm2++ac+n02bTpk0TWq1WABBr165tctmVV155zh9F7733noiLixNCiEv+HAlx/uDjtttuU/5tsVhETEyMWLp0qT2fVrPJ74eCggKh0+nEiRMnxIkTJ4S/v78oLCxsEnycrbCwUAAQBw4cEEI0vNfl32my+Pj4C/6h2pzgY+LEiWLGjBltfq6u5FbbLunp6dixYwemTJkCANBoNLj55puxbNmyJtfr06eP8t9xcXEAoKT0Dx06hMGDBze5fmpq6jmP1bt37yZ7ufv378exY8cQEhKi1DhERESgtrZWSaONHDlSSXtt2LABY8aMwYgRI7B+/Xrs3LkT9fX1GDZsWBtfBeuxxM3x888/48orr0S7du0QEhKC22+/HUVFRU0KDbVabZPX69ChQ9BoNE1eo8jISHTt2hWHDh1q9hp79uwJtVqt/DsuLq7Jtsru3bsxceJEJCYmIiQkBCNHjgQAZGdnN/sxHGH06NHYt29fk697770XGo0GN910k5ISraqqwpdffompU6cCAI4dO4bq6mpcddVVyvsjODgYK1eubJJmdbUdO3Zg37596NmzJ+rq6hAWFobLLrsM69evx4EDB6DVanH33Xdj7969qKysxIYNG5T/N4B1S+3YsWPK1t6KFStw0003ISgoSLlOYGAgUlJSlH83/n9fVVWFjIwMzJw5s8nrNG/evGa9Tpd6XznDgAEDmvx7//79WLduXZPn061bNwBo8pwa/5wBTde+f/9+VFZWIjIyssn9HD9+3K3eP7I+ffqgQ4cO+Oc//9lkK3f//v149tlnmzyHv/71rzh9+jSqq6sv+XN0sceTSZKE2NhYl2/TRkdHK1v/aWlpuPbaaxEVFdXkOkePHsWUKVPQsWNHhIaGokOHDgDO/ZwbOHCg8t8FBQXIzc3FlVde2eq1zZo1C6tXr0bfvn3xxBNPYMuWLa2+L1dxq4LTZcuWwWQyNSkwFUJAp9PhtddeU77XuFhHkiQA1v34lmj8YQpYa00GDBig/NA0Fh0dDaBhr/Lo0aM4ePAghg8fjsOHD2P9+vUoKSlR6lDaqnPnzpAk6aJFpSdOnMCECRMwa9YsPPfcc4iIiMCmTZswc+ZMGI1GZR0BAQHKa2RPZxdMSZKk/D+oqqrCuHHjMG7cOKxatQrR0dHIzs7GuHHjYDQa7b6WlggKCkKnTp3Oe9nUqVMxcuRIFBQU4KeffkJAQIDSCSN/AH/77bdo165dk9u5omC1U6dOkCTpnHqnjh07AoBSJAdY37fr16+HTqfDyJEjERERge7du2PTpk3YsGEDHn30UeW6MTExmDhxItLS0pCcnIzvv/9eCbhl5/t/LwfM8uv09ttvn/NHQOOg4kIu9r5ylvN9NkycOBEvvPDCOdeV//gBLr72yspKxMXFnfNaAmjS1u4u2rVrh08++QSjR4/G+PHj8f333yMkJASVlZX417/+hRtuuOGc2/j7+wO4+M/RhbjD//fzufPOO5VajCVLlpxz+cSJE5GUlIS3334b8fHxsFgs6NWr1zmfc43fU41/Ns9Hbgho/Efo2XVP11xzDbKysvDdd9/hp59+wpVXXonZs2fjP//5T8ueoAu5TfBhMpmwcuVKvPzyy7j66qubXHbdddfhgw8+UP7auJju3btj+/btuOOOO5TvyX/FXUz//v3x4YcfIiYmBqGhoee9Tu/evREeHo558+ahb9++CA4OxqhRo/DCCy+gpKTknMK51oqIiMC4ceOwZMkSPPjgg+d8GJaWlmL37t2wWCx4+eWXlTfrRx99dMn77t69O0wmE7Zv346hQ4cCAIqKipCeno4ePXrYZf2HDx9GUVERFixYgISEBADArl277HLfjjR06FAkJCTgww8/xPfff48bb7xR+VBsXLDbOFPgKpGRkbjqqqvw2muv4YEHHjjnPdLYyJEjsXz5cmg0GuWXwKhRo/DBBx/gyJEj57xv77rrLkyZMgXt27dHSkpKi7J5BoMB8fHxyMzMvOBfu3LG0Ww2N/t+Xal///749NNP0aFDB2g0rfvI7N+/P/Ly8qDRaJS/jt1dUlISNmzYoAQgP/zwA/r374/09PQLBvDAxX+OPM348eNhNBohSZJSfCyTPzfffvttpSB706ZNl7zPkJAQdOjQAWvXrsXo0aPPuVz+Y/f06dNKcev52tKjo6Mxbdo0TJs2DVdccQUef/xxjwo+3Gbb5ZtvvkFJSQlmzpyJXr16NfmaPHnyOVsvF/LQQw9h+fLlSEtLw5EjR/DPf/4Tf/zxxyVvN3XqVERFRWHSpEn49ddfcfz4caxfvx4PPvggTp48CcAajY8YMQKrVq1SPrD79OmDuro6rF271q6/lJYsWQKz2YzLL78cn376KY4ePYpDhw5h8eLFSE1NRadOnVBfX49XX30VmZmZeO+99/DGG29c8n47d+6MSZMm4a9//Ss2bdqE/fv347bbbkO7du0wadIku6w9MTERWq1WWdtXX33lNn39dXV1yMvLa/LVuGvh1ltvxRtvvIGffvqpyS/PkJAQPPbYY3j44Yfx7rvvIiMjA3v27MGrr76Kd9991xVPBa+//jpMJhMGDhyIDz/8EIcOHUJ6ejr+97//4fDhw0qmYcSIEaioqMA333yjvG9HjRqFVatWIS4uDl26dGlyv+PGjUNoaCjmzZvXqtbKf/3rX5g/fz4WL16MI0eO4MCBA0hLS8Mrr7wCwJpdCQgIwA8//ID8/HyUlZW17YVwsNmzZ6O4uBhTpkzBzp07kZGRgTVr1mDGjBnNDqDGjh2L1NRUXHfddfjxxx9x4sQJbNmyBX//+9/dOjBPSEjA+vXrUVBQgHHjxuGJJ57AypUr8a9//Qt//PEHDh06hNWrV+Mf//hHk9td6OfI06jVahw6dAgHDx48J3MXHh6OyMhIvPXWWzh27Bh++eUXPPLII82632eeeQYvv/wyFi9ejKNHjyqfJYA1MzJkyBAsWLAAhw4dwoYNG855fZ9++ml8+eWXOHbsGP744w9888036N69u32etJO4TfCxbNkyjB07Fnq9/pzLJk+ejF27duG333675P3cfPPNeOqpp/DEE09gwIAByMrKwqxZsy55u8DAQGzcuBGJiYm44YYb0L17d6VNt3EmZOTIkTCbzcqHuEqlwogRIyBJkl3qPWQdO3bEnj17MHr0aDz66KPo1asXrrrqKqxduxZLly7FZZddhldeeQUvvPACevXqhVWrVmH+/PnNuu+0tDQMGDAAEyZMQGpqKoQQ+O677+z210l0dDRWrFiBjz/+GD169MCCBQvcJiL/4YcfEBcX1+SrcXv01KlTcfDgQbRr1+6c/5///ve/8dRTT2H+/Pno3r07xo8fj2+//VZpHXW2lJQU7N27F2PHjsXcuXNx2WWXYeDAgXj11Vfx2GOPKQFfeHg4evfujejoaCV7OGLECFgslvMGzCqVCtOnT4fZbG6SQWyuu+66C++88w7S0tLQu3dvjBw5EitWrFBeJ41Gg8WLF+PNN99EfHy83YJeR4mPj8fmzZthNptx9dVXo3fv3pgzZw7CwsKaPTNHkiR89913GDFiBGbMmIEuXbrglltuQVZWFgwGg4OfQdu0b98e69evx5kzZ7BgwQJ88skn+PHHHzFo0CAMGTIECxcuRFJSUpPbXOznyNOEhoaeNxuuUqmwevVq7N69G7169cLDDz+Ml156qVn3OW3aNCxatAivv/46evbsiQkTJjQZd7B8+XKYTCYMGDAAc+bMwbx585rcXqvVYu7cuejTpw9GjBgBtVqN1atXt+2JOpkkmlvdSEQ+Y+bMmSgsLMRXX33l6qUQkRdym5oPInK9srIyHDhwAO+//z4DDyJyGAYfRKSYNGkSduzYgXvvvRdXXXWVq5dDRF6K2y5ERETkVG5TcEpERES+gcEHERERORWDDyIiInIqBh9ERETkVAw+iIiIyKkYfBAREZFTMfggokuaPn06JEnCggULmnz/iy++cMipyUTk3Rh8EFGz+Pv7Kyc4ExG1BYMPImqWsWPHIjY29oIHGBYVFWHKlClo164dAgMD0bt3b3zwwQdNrjNq1Cg88MADmDNnDsLDw2EwGPD222+jqqoKM2bMQEhICDp16oTvv/++ye1+//13XHPNNQgODobBYMDtt9/e5DRiIvIsDD6IqFnUajWef/55vPrqqzh58uQ5l9fW1mLAgAH49ttv8fvvv+Puu+/G7bffjh07djS53rvvvouoqCjs2LEDDzzwAGbNmoUbb7wRQ4cOxZ49e3D11Vfj9ttvR3V1NQCgtLQUY8aMQb9+/bBr1y788MMPyM/Px0033eSU501E9sfx6kR0SdOnT0dpaSm++OILpKamokePHli2bBm++OILXH/99bjQx8iECRPQrVs3/Oc//wFgzXyYzWb8+uuvAACz2Qy9Xo8bbrgBK1euBADk5eUhLi4OW7duxZAhQzBv3jz8+uuvWLNmjXK/J0+eREJCAtLT09GlSxcHP3sisjceLEdELfLCCy9gzJgxeOyxx5p832w24/nnn8dHH32EU6dOwWg0oq6uDoGBgU2u16dPH+W/1Wo1IiMj0bt3b+V7BoMBAFBQUAAA2L9/P9atW4fg4OBz1pKRkcHgg8gDMfggohYZMWIExo0bh7lz52L69OnK91966SX897//xaJFi9C7d28EBQVhzpw5MBqNTW7v5+fX5N+SJDX5ntw9Y7FYAACVlZWYOHEiXnjhhXPWEhcXZ6+nRUROxOCDiFpswYIF6Nu3L7p27ap8b/PmzZg0aRJuu+02ANbg4ciRI+jRo0ebHqt///749NNP0aFDB2g0/Mgi8gYsOCWiFuvduzemTp2KxYsXK9/r3LkzfvrpJ2zZsgWHDh3CPffcg/z8/DY/1uzZs1FcXIwpU6Zg586dyMjIwJo1azBjxgyYzeY23z8ROR+DDyJqlWeffVbZGgGAf/zjH+jfvz/GjRuHUaNGITY2Ftddd12bHyc+Ph6bN2+G2WzG1Vdfjd69e2POnDkICwuDSsWPMCJPxG4XIiIicir+2UBEREROxeCDiIiInIrBBxERETkVgw8iIiJyKgYfRERE5FQMPoiIiMipGHwQERGRUzH4ICIiIqdi8EFEREROxeCDiIiInIrBBxERETnV/wPqchkVFxshZQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pdDF.plot(x=\"Name\",y=\"Age\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5bbca72e",
      "metadata": {
        "id": "5bbca72e"
      },
      "source": [
        "The following example shows how to plot a histogram for companies, by computing the histogram values.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "14e583ed",
      "metadata": {
        "id": "14e583ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "5208a491-f478-4781-8d83-3d180be5489f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Company'>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "histDF = spark.sql(\"SELECT Company, count(*) AS count FROM persons GROUP BY company\").toPandas()\n",
        "histDF.plot(kind=\"bar\",x=\"Company\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a09480f7",
      "metadata": {
        "id": "a09480f7"
      },
      "source": [
        "## Time-series\n",
        "\n",
        "A time-series is a series of values that have a timestamp.\n",
        "\n",
        "A time-series can be **Univariate** if it has a single feature (e.g. stock index, mortality, birth rate, etc.) , or **Multivariate** if it has multiple features (e.g. Weather, composed by (temperature, pressure, humidity, etc.)).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1272adf7",
      "metadata": {
        "id": "1272adf7"
      },
      "source": [
        "#### Example\n",
        "\n",
        "In the examples, we will use data from COVID cases. The data files are in *data* directory, and consist of tables with 5 columns, *date*, *countrycode*, *population*, *cases*, *deaths*.\n",
        "\n",
        "The name of the data files starts with the Internet code for the country, and has the format *CODE-covid.csv*, where CODE is the country code in uppercase.  \n",
        "\n",
        "![table](images/table.png)\n",
        "\n",
        "Let's read the data for Portugal.\n",
        "<!--Unlike previous examples, we will use the date as the index, by passing ```index_col``` parameter in ```read_csv```function.-->"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "36fd6f58",
      "metadata": {
        "id": "36fd6f58",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2083e220-fa21-4485-b1b9-9a2f7b6289d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+-----------+----------+-----+------+\n",
            "|      date|countrycode|population|cases|deaths|\n",
            "+----------+-----------+----------+-----+------+\n",
            "|2020-03-02|         PT|  10276617|    0|     0|\n",
            "|2020-03-03|         PT|  10276617|    0|     0|\n",
            "|2020-03-04|         PT|  10276617|    3|     0|\n",
            "|2020-03-05|         PT|  10276617|    3|     0|\n",
            "|2020-03-06|         PT|  10276617|    5|     0|\n",
            "+----------+-----------+----------+-----+------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "ptCovidFileName = os.path.join( \"data\", \"PT-covid.csv\")\n",
        "\n",
        "# Read a CSV file into a DataFrame\n",
        "ptCovidDF = spark.read.option(\"header\", True).option(\"inferSchema\",True).csv(ptCovidFileName)\n",
        "\n",
        "# Converts the date into the datetime type\n",
        "ptCovidDF = ptCovidDF.withColumn(\"date\",to_date(col(\"date\"), \"yyyy-MM-dd\"))\n",
        "\n",
        "## Register Dataframe as a temporary view\n",
        "ptCovidDF.createOrReplaceTempView(\"covidPT\")\n",
        "\n",
        "# Print the first 5 lines of data\n",
        "ptCovidDF.show(5)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c7783776",
      "metadata": {
        "id": "c7783776"
      },
      "source": [
        "### Adding columns\n",
        "\n",
        "Until now, the only one-to-* transformations we have seen consisted in filters and selecting a subset of columns. Sometime, it is interesting to add columns based on the values of the row.\n",
        "\n",
        "In the previous example, we want to add columns with the number of cases and deaths per million inhabitants. We can use ```SELECT ... FROM ... ``` to return a DataFrame with as many columns as we want.\n",
        "\n",
        "The equivalente Spark Pandas API code is the following:\n",
        "```\n",
        "ptCovidDF[\"casesPer1M\"] = ptCovidDF[\"cases\"] / ptCovidDF[\"population\"] / 1000000\n",
        "ptCovidDF[\"deathsPer1M\"] = ptCovidDF[\"deaths\"] / ptCovidDF[\"population\"] / 1000000\n",
        "```\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "35a7dc87",
      "metadata": {
        "id": "35a7dc87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4c0e9f2-86ac-4ccc-b98e-95eb7e483e14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+-----------+----------+-----+------+--------------------+-----------+\n",
            "|      date|countrycode|population|cases|deaths|          casesPer1M|deathsPer1M|\n",
            "+----------+-----------+----------+-----+------+--------------------+-----------+\n",
            "|2020-03-02|         PT|  10276617|    0|     0|                 0.0|        0.0|\n",
            "|2020-03-03|         PT|  10276617|    0|     0|                 0.0|        0.0|\n",
            "|2020-03-04|         PT|  10276617|    3|     0|2.919248620436083...|        0.0|\n",
            "|2020-03-05|         PT|  10276617|    3|     0|2.919248620436083...|        0.0|\n",
            "|2020-03-06|         PT|  10276617|    5|     0|4.865414367393472...|        0.0|\n",
            "+----------+-----------+----------+-----+------+--------------------+-----------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "ptCovidDF = spark.sql( \"\"\"SELECT *, cases / population / 1000000 AS casesPer1M,\n",
        "                                    deaths / population / 1000000 AS deathsPer1M\n",
        "                                FROM covidPT\n",
        "                        \"\"\")\n",
        "# Print the first 5 lines of data\n",
        "ptCovidDF.show(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "995f3d0f",
      "metadata": {
        "id": "995f3d0f"
      },
      "source": [
        "It is also possible to register the results as a new table/view."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "feff4fd5",
      "metadata": {
        "id": "feff4fd5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a3a9d26-7749-4138-bb6b-5b18b4fa4293"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+-----------+----------+-----+------+--------------------+-----------+\n",
            "|      date|countrycode|population|cases|deaths|          casesPer1M|deathsPer1M|\n",
            "+----------+-----------+----------+-----+------+--------------------+-----------+\n",
            "|2020-03-02|         PT|  10276617|    0|     0|                 0.0|        0.0|\n",
            "|2020-03-03|         PT|  10276617|    0|     0|                 0.0|        0.0|\n",
            "|2020-03-04|         PT|  10276617|    3|     0|2.919248620436083...|        0.0|\n",
            "|2020-03-05|         PT|  10276617|    3|     0|2.919248620436083...|        0.0|\n",
            "|2020-03-06|         PT|  10276617|    5|     0|4.865414367393472...|        0.0|\n",
            "+----------+-----------+----------+-----+------+--------------------+-----------+\n",
            "only showing top 5 rows\n",
            "\n"
          ]
        }
      ],
      "source": [
        "spark.sql( \"\"\"SELECT *, cases / population / 1000000 AS casesPer1M,\n",
        "                                    deaths / population / 1000000 AS deathsPer1M\n",
        "                                FROM covidPT\n",
        "                        \"\"\").createOrReplaceTempView(\"covidPT2\")\n",
        "\n",
        "ptCovidDF = spark.sql(\"SELECT * FROM covidPT2\")\n",
        "# Print the first 5 lines of data\n",
        "ptCovidDF.show(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b9e2c8cf",
      "metadata": {
        "id": "b9e2c8cf"
      },
      "source": [
        "Let's plot the data, using two Y axis, specifying the columns to be used for the X and Y axis. ```secondary_y```allows to use a secondary index for the second line."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "198aca3b",
      "metadata": {
        "id": "198aca3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "5808c640-4490-47c6-c25a-599016521e5e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ptCovidPdDF = ptCovidDF.toPandas()\n",
        "\n",
        "ptCovidPdDF.plot(x=\"date\",y=[\"cases\",\"deaths\"])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "cdbbdf57",
      "metadata": {
        "id": "cdbbdf57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "7adf2bef-155f-4e06-e049-83274dcb4829"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='date'>"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ptCovidPdDF.plot(x=\"date\",y=[\"cases\",\"deaths\"],secondary_y=[\"deaths\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5cffe6ca",
      "metadata": {
        "id": "5cffe6ca"
      },
      "source": [
        "It is also possible to use subplot to plot multiple lines, one at a time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "47e0e69c",
      "metadata": {
        "id": "47e0e69c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "524a7ee8-9b14-4fd3-e55e-98a5d676ba08"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axis = plt.subplots()\n",
        "ptCovidPdDF.plot(ax=axis,x=\"date\",y=\"casesPer1M\")\n",
        "ptCovidPdDF.plot(ax=axis,x=\"date\",y=\"deathsPer1M\",secondary_y=\"deathsPer1M\")\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1cb1540f",
      "metadata": {
        "id": "1cb1540f"
      },
      "source": [
        "## Exercises"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c32d861",
      "metadata": {
        "id": "1c32d861"
      },
      "source": [
        "### Compute the number of days for which the number of cases is larger than the average number of cases\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "9e5adc83",
      "metadata": {
        "id": "9e5adc83",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93e5cb26-8ec5-4f4f-a226-a8b56170cc31"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-------------+\n",
            "|days_over_avg|\n",
            "+-------------+\n",
            "|          159|\n",
            "+-------------+\n",
            "\n"
          ]
        }
      ],
      "source": [
        "spark.sql(\"\"\"SELECT COUNT(date) as days_over_avg\n",
        "              FROM covidpt\n",
        "              WHERE cases > (SELECT AVG(cases) from covidpt)\n",
        "              \"\"\").show()\n",
        "\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "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": "3.10.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}