Programming Assignment Week 4.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Assignment 4\n\nWelcome to Assignment 4. This will be the most fun. Now we will prepare data for plotting.\n\nJust make sure you hit the play button on each cell from top to down. There are three functions you have to implement. Please also make sure than on each change on a function you hit the play button again on the corresponding cell to make it available to the rest of this notebook.\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "This notebook is designed to run in a IBM Watson Studio default runtime (NOT the Watson Studio Apache Spark Runtime as the default runtime with 1 vCPU is free of charge). Therefore, we install Apache Spark in local mode for test purposes only. Please don't use it in production.\n\nIn case you are facing issues, please read the following two documents first:\n\nhttps://github.com/IBM/skillsnetwork/wiki/Environment-Setup\n\nhttps://github.com/IBM/skillsnetwork/wiki/FAQ\n\nThen, please feel free to ask:\n\nhttps://coursera.org/learn/machine-learning-big-data-apache-spark/discussions/all\n\nPlease make sure to follow the guidelines before asking a question:\n\nhttps://github.com/IBM/skillsnetwork/wiki/FAQ#im-feeling-lost-and-confused-please-help-me\n\n\nIf running outside Watson Studio, this should work as well. In case you are running in an Apache Spark context outside Watson Studio, please remove the Apache Spark setup in the first notebook cells."
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "from IPython.display import Markdown, display\ndef printmd(string):\n    display(Markdown('# <span style=\"color:red\">'+string+'</span>'))\n\n\nif ('sc' in locals() or 'sc' in globals()):\n    printmd('<<<<<!!!!! It seems that you are running in a IBM Watson Studio Apache Spark Notebook. Please run it in an IBM Watson Studio Default Runtime (without Apache Spark) !!!!!>>>>>')\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Collecting pyspark==2.4.5\n  Downloading pyspark-2.4.5.tar.gz (217.8 MB)\n\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 217.8 MB 9.7 kB/s eta 0:00:013MB 9.6 MB/s eta 0:00:17     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b                  | 92.8 MB 5.1 MB/s eta 0:00:25     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f                | 103.4 MB 5.1 MB/s eta 0:00:23     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588               | 116.5 MB 9.6 MB/s eta 0:00:11\ufffd\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f          | 144.1 MB 5.5 MB/s eta 0:00:14\u2588\u2588\u2588\u2588\u2588\u258e        | 158.8 MB 5.5 MB/s eta 0:00:11     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d     | 179.9 MB 11.5 MB/s eta 0:00:04\ufffd\ufffd\u2588\u2588\u2589     | 182.6 MB 11.5 MB/s eta 0:00:04     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 206.0 MB 5.4 MB/s eta 0:00:03\n\u001b[?25hCollecting py4j==0.10.7\n  Downloading py4j-0.10.7-py2.py3-none-any.whl (197 kB)\n\u001b[K     |\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 197 kB 36.3 MB/s eta 0:00:01\n\u001b[?25hBuilding wheels for collected packages: pyspark\n  Building wheel for pyspark (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for pyspark: filename=pyspark-2.4.5-py2.py3-none-any.whl size=218257927 sha256=18ad0684f86cfe39397fc60bede59b2de390ebf89e25e336260665447a386ff8\n  Stored in directory: /tmp/wsuser/.cache/pip/wheels/01/c0/03/1c241c9c482b647d4d99412a98a5c7f87472728ad41ae55e1e\nSuccessfully built pyspark\nInstalling collected packages: py4j, pyspark\nSuccessfully installed py4j-0.10.7 pyspark-2.4.5\n"
                }
            ],
            "source": "!pip install pyspark==2.4.5"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [],
            "source": "try:\n    from pyspark import SparkContext, SparkConf\n    from pyspark.sql import SparkSession\nexcept ImportError as e:\n    printmd('<<<<<!!!!! Please restart your kernel after installing Apache Spark !!!!!>>>>>')"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": "sc = SparkContext.getOrCreate(SparkConf().setMaster(\"local[*]\"))\n\nspark = SparkSession \\\n    .builder \\\n    .getOrCreate()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Sampling is one of the most important things when it comes to visualization because often the data set gets so huge that you simply\n\n- can't copy all data to a local Spark driver (Watson Studio is using a \"local\" Spark driver)\n- can't throw all data at the plotting library\n\nPlease implement a function which returns a 10% sample of a given data frame:"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": "def getSample():\n    result = spark.sql(\"select temperature from washing where temperature is not null\")\n    result_array = result.rdd.map(lambda row : row.temperature).sample(False,0.1).collect()\n    result_array[:10]\n\n    return df.sample(False,0.1)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now we want to create a histogram and boxplot. Please ignore the sampling for now and return a python list containing all temperature values from the data set"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [],
            "source": "def getListForHistogramAndBoxPlot():\n    my_list = spark.sql(\"select temperature from washing where temperature is not null\")\n    my_list_array = my_list.rdd.map(lambda row : row.temperature).sample(False,0.1).collect()\n    my_list_array[:10]\n    \n    my_list = spark.sql(\"\"\"\n        SELECT temperature from washing where temperature is not null\n    \"\"\").rdd.map(lambda row: row.temperature).collect()\n    if not type(my_list)==list:\n        raise Exception('return type not a list')\n    return my_list"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Finally we want to create a run chart. Please return two lists (encapsulated in a python tuple object) containing temperature and timestamp (ts) ordered by timestamp. Please refer to the following link to learn more about tuples in python: https://www.tutorialspoint.com/python/python_tuples.htm"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [],
            "source": "def getListsForRunChart():\n    double_tuple_rdd = spark.sql(\"\"\"\n        select ts, temperature from washing where temperature is not null order by ts asc\n    \"\"\").sample(False,0.1).rdd.map(lambda row : (row.ts,row.temperature))\n    result_array_ts = double_tuple_rdd.map(lambda ts_temperature: ts_temperature[0]).collect()\n    result_array_temperature = double_tuple_rdd.map(lambda ts_temperature: ts_temperature[1]).collect()\n    return (result_array_ts,result_array_temperature)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now it is time to grab a PARQUET file and create a dataframe out of it. Using SparkSQL you can handle it like a database. "
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2020-12-08 17:31:53--  https://github.com/IBM/coursera/blob/master/coursera_ds/washing.parquet?raw=true\nResolving github.com (github.com)... 140.82.114.3\nConnecting to github.com (github.com)|140.82.114.3|:443... connected.\nHTTP request sent, awaiting response... 301 Moved Permanently\nLocation: https://github.com/IBM/skillsnetwork/blob/master/coursera_ds/washing.parquet?raw=true [following]\n--2020-12-08 17:31:54--  https://github.com/IBM/skillsnetwork/blob/master/coursera_ds/washing.parquet?raw=true\nReusing existing connection to github.com:443.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://github.com/IBM/skillsnetwork/raw/master/coursera_ds/washing.parquet [following]\n--2020-12-08 17:31:54--  https://github.com/IBM/skillsnetwork/raw/master/coursera_ds/washing.parquet\nReusing existing connection to github.com:443.\nHTTP request sent, awaiting response... 302 Found\nLocation: https://raw.githubusercontent.com/IBM/skillsnetwork/master/coursera_ds/washing.parquet [following]\n--2020-12-08 17:31:54--  https://raw.githubusercontent.com/IBM/skillsnetwork/master/coursera_ds/washing.parquet\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.48.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.48.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 112048 (109K) [application/octet-stream]\nSaving to: \u2018washing.parquet?raw=true\u2019\n\nwashing.parquet?raw 100%[===================>] 109.42K  --.-KB/s    in 0.007s  \n\n2020-12-08 17:31:54 (15.8 MB/s) - \u2018washing.parquet?raw=true\u2019 saved [112048/112048]\n\n"
                }
            ],
            "source": "!wget https://github.com/IBM/coursera/blob/master/coursera_ds/washing.parquet?raw=true\n!mv washing.parquet?raw=true washing.parquet"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "+--------------------+--------------------+-----+--------+----------+---------+--------+-----+-----------+-------------+-------+\n|                 _id|                _rev|count|flowrate|fluidlevel|frequency|hardness|speed|temperature|           ts|voltage|\n+--------------------+--------------------+-----+--------+----------+---------+--------+-----+-----------+-------------+-------+\n|0d86485d0f88d1f9d...|1-57940679fb8a713...|    4|      11|acceptable|     null|      77| null|        100|1547808723923|   null|\n|0d86485d0f88d1f9d...|1-15ff3a0b304d789...|    2|    null|      null|     null|    null| 1046|       null|1547808729917|   null|\n|0d86485d0f88d1f9d...|1-97c2742b68c7b07...|    4|    null|      null|       71|    null| null|       null|1547808731918|    236|\n|0d86485d0f88d1f9d...|1-eefb903dbe45746...|   19|      11|acceptable|     null|      75| null|         86|1547808738999|   null|\n|0d86485d0f88d1f9d...|1-5f68b4c72813c25...|    7|    null|      null|       75|    null| null|       null|1547808740927|    235|\n|0d86485d0f88d1f9d...|1-cd4b6c57ddbe77e...|    5|    null|      null|     null|    null| 1014|       null|1547808744923|   null|\n|0d86485d0f88d1f9d...|1-a35b25b5bf43aaf...|   32|      11|acceptable|     null|      73| null|         84|1547808752028|   null|\n|0d86485d0f88d1f9d...|1-b717f7289a8476d...|   48|      11|acceptable|     null|      79| null|         84|1547808768065|   null|\n|0d86485d0f88d1f9d...|1-c2f1f8fcf178b2f...|   18|    null|      null|       73|    null| null|       null|1547808773944|    228|\n|0d86485d0f88d1f9d...|1-15033dd9eebb4a8...|   59|      11|acceptable|     null|      72| null|         96|1547808779093|   null|\n|0d86485d0f88d1f9d...|1-753dae825f9a6c2...|   62|      11|acceptable|     null|      73| null|         88|1547808782113|   null|\n|0d86485d0f88d1f9d...|1-b168089f44f03f0...|   13|    null|      null|     null|    null| 1097|       null|1547808784940|   null|\n|0d86485d0f88d1f9d...|1-403b687c6be0dea...|   23|    null|      null|       80|    null| null|       null|1547808788955|    236|\n|0d86485d0f88d1f9d...|1-195551e0455a24b...|   72|      11|acceptable|     null|      77| null|         87|1547808792134|   null|\n|0d86485d0f88d1f9d...|1-060a39fc6c2ddee...|   26|    null|      null|       62|    null| null|       null|1547808797959|    233|\n|0d86485d0f88d1f9d...|1-2234514bffee465...|   27|    null|      null|       61|    null| null|       null|1547808800960|    226|\n|0d86485d0f88d1f9d...|1-4265898bb401db0...|   82|      11|acceptable|     null|      79| null|         96|1547808802154|   null|\n|0d86485d0f88d1f9d...|1-2fbf7ca9a0425a0...|   94|      11|acceptable|     null|      73| null|         90|1547808814186|   null|\n|0d86485d0f88d1f9d...|1-203c0ee6d7fbd21...|   97|      11|acceptable|     null|      77| null|         88|1547808817190|   null|\n|0d86485d0f88d1f9d...|1-47e1965db94fcab...|  104|      11|acceptable|     null|      75| null|         80|1547808824198|   null|\n+--------------------+--------------------+-----+--------+----------+---------+--------+-----+-----------+-------------+-------+\nonly showing top 20 rows\n\n"
                }
            ],
            "source": "df = spark.read.parquet('washing.parquet')\ndf.createOrReplaceTempView('washing')\ndf.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now we gonna test the functions you've completed and visualize the data."
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [],
            "source": "%matplotlib inline\nimport matplotlib.pyplot as plt"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.hist(getListForHistogramAndBoxPlot())\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.boxplot(getListForHistogramAndBoxPlot())\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [],
            "source": "lists = getListsForRunChart()"
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.plot(lists[0],lists[1])\nplt.xlabel(\"time\")\nplt.ylabel(\"temperature\")\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Congratulations, you are done! The following code submits your solution to the grader. Again, please update your token from the grader's submission page on Coursera"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2020-12-08 17:33:41--  https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 199.232.8.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|199.232.8.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 2540 (2.5K) [text/plain]\nSaving to: \u2018rklib.py\u2019\n\nrklib.py            100%[===================>]   2.48K  --.-KB/s    in 0s      \n\n2020-12-08 17:33:41 (30.9 MB/s) - \u2018rklib.py\u2019 saved [2540/2540]\n\n"
                }
            ],
            "source": "!rm -f rklib.py\n!wget https://raw.githubusercontent.com/IBM/coursera/master/rklib.py"
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [],
            "source": "from rklib import submitAll\nimport json\n\nkey = \"S5PNoSHNEeisnA6YLL5C0g\"\nemail = \"gerald.suleh@strathmore.edu\"\ntoken = \"idzrBsBfZ8saVF2S\""
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Submission successful, please check on the coursera grader page for the status\n-------------------------\n{\"elements\":[{\"itemId\":\"95cS6\",\"id\":\"sUpST4RAEeawAApvKZgcCQ~95cS6~xVD1rzl7EeukCRLXVv4gFQ\",\"courseId\":\"sUpST4RAEeawAApvKZgcCQ\"}],\"paging\":{},\"linked\":{}}\n-------------------------\n"
                }
            ],
            "source": "parts_data = {}\nparts_data[\"iLdHs\"] = json.dumps(str(type(getListForHistogramAndBoxPlot())))\nparts_data[\"xucEM\"] = json.dumps(len(getListForHistogramAndBoxPlot()))\nparts_data[\"IyH7U\"] = json.dumps(str(type(getListsForRunChart())))\nparts_data[\"MsMHO\"] = json.dumps(len(getListsForRunChart()[0]))\n\nsubmitAll(email, token, key, parts_data)"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.7",
            "language": "python",
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