Week 4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 4\n",
    "\n",
    "Welcome to Assignment 4. This will be the most fun. Now we will prepare data for plotting.\n",
    "\n",
    "Just 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 Apache Spark runtime. In case you are running it in an IBM Watson Studio standard runtime or outside Watson Studio, we install Apache Spark in local mode for test purposes only. Please don't use it in production."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for a Spark session to start...\n",
      "Spark Initialization Done! ApplicationId = app-20200421014419-0000\n",
      "KERNEL_ID = 31fc1f43-3b7d-478d-85a2-c079856d0cfe\n",
      "Collecting pip\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/54/0c/d01aa759fdc501a58f431eb594a17495f15b88da142ce14b5845662c13f3/pip-20.0.2-py2.py3-none-any.whl (1.4MB)\n",
      "\u001b[K    100% |################################| 1.4MB 1.4MB/s eta 0:00:01\n",
      "\u001b[?25hInstalling collected packages: pip\n",
      "Successfully installed pip-20.0.2\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not ('sc' in locals() or 'sc' in globals()):\n",
    "    print('It seems you are note running in a IBM Watson Studio Apache Spark Notebook. You might be running in a IBM Watson Studio Default Runtime or outside IBM Waston Studio. Therefore installing local Apache Spark environment for you. Please do not use in Production')\n",
    "    \n",
    "    from pip import main\n",
    "    main(['install', 'pyspark==2.4.5'])\n",
    "    \n",
    "    from pyspark import SparkContext, SparkConf\n",
    "    from pyspark.sql import SparkSession\n",
    "\n",
    "    sc = SparkContext.getOrCreate(SparkConf().setMaster(\"local[*]\"))\n",
    "    \n",
    "    spark = 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",
    "\n",
    "Please implement a function which returns a 10% sample of a given data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getSample():\n",
    "    #TODO Please enter your code here, you are not required to use the template code below\n",
    "    #some reference: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame\n",
    "    #https://spark.apache.org/docs/latest/api/sql/\n",
    "    return df.sample(False, fraction = 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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getListForHistogramAndBoxPlot(df,spark):\n",
    "    #TODO Please enter your code here, you are not required to use the template code below\n",
    "    #some reference: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame\n",
    "    #https://spark.apache.org/docs/latest/api/sql/\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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#should return a tuple containing the two lists for timestamp and temperature\n",
    "#please make sure you take only 10% of the data by sampling\n",
    "#please also ensure that you sample in a way that the timestamp samples and temperature samples correspond (=> call sample on an object still containing both dimensions)\n",
    "def getListsForRunChart(df, spark):\n",
    "    #TODO Please enter your code here, you are not required to use the template code below\n",
    "    #some reference: https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrame\n",
    "    #https://spark.apache.org/docs/latest/api/sql/\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": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-04-21 02:00:55--  https://github.com/IBM/coursera/blob/master/coursera_ds/washing.parquet?raw=true\n",
      "Resolving github.com (github.com)... 140.82.112.4\n",
      "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: https://github.com/IBM/skillsnetwork/blob/master/coursera_ds/washing.parquet?raw=true [following]\n",
      "--2020-04-21 02:00:55--  https://github.com/IBM/skillsnetwork/blob/master/coursera_ds/washing.parquet?raw=true\n",
      "Reusing existing connection to github.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://github.com/IBM/skillsnetwork/raw/master/coursera_ds/washing.parquet [following]\n",
      "--2020-04-21 02:00:55--  https://github.com/IBM/skillsnetwork/raw/master/coursera_ds/washing.parquet\n",
      "Reusing existing connection to github.com:443.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: https://raw.githubusercontent.com/IBM/skillsnetwork/master/coursera_ds/washing.parquet [following]\n",
      "--2020-04-21 02:00:55--  https://raw.githubusercontent.com/IBM/skillsnetwork/master/coursera_ds/washing.parquet\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.48.133\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.48.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 112048 (109K) [application/octet-stream]\n",
      "Saving to: 'washing.parquet?raw=true'\n",
      "\n",
      "100%[======================================>] 112,048     --.-K/s   in 0.1s    \n",
      "\n",
      "2020-04-21 02:00:56 (1.08 MB/s) - 'washing.parquet?raw=true' 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": 35,
   "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",
      "+--------------------+--------------------+-----+--------+----------+---------+--------+-----+-----------+-------------+-------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df = spark.read.parquet('washing.parquet')\n",
    "df.createOrReplaceTempView('washing')\n",
    "df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we gonna test the functions you've completed and visualize the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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(df, spark))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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(df, spark))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "lists = getListsForRunChart(df, spark)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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])\n",
    "plt.xlabel(\"time\")\n",
    "plt.ylabel(\"temperature\")\n",
    "plt.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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2020-04-21 02:02:14--  https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.48.133\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.48.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2540 (2.5K) [text/plain]\n",
      "Saving to: 'rklib.py'\n",
      "\n",
      "100%[======================================>] 2,540       --.-K/s   in 0s      \n",
      "\n",
      "2020-04-21 02:02:14 (32.6 MB/s) - 'rklib.py' 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": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rklib import submitAll\n",
    "import json\n",
    "\n",
    "key = \"S5PNoSHNEeisnA6YLL5C0g\"\n",
    "email = \"mengjia.lyu@outlook.com\"\n",
    "token = \"Nhgh72O1wWpIYlAs\" #you can obtain it from the grader page on Coursera (have a look here if you need more information on how to obtain the token https://youtu.be/GcDo0Rwe06U?t=276)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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~PXO3eYN0Eeqjvg5xvDhNDw\",\"courseId\":\"sUpST4RAEeawAApvKZgcCQ\"}],\"paging\":{},\"linked\":{}}\n",
      "-------------------------\n"
     ]
    }
   ],
   "source": [
    "parts_data = {}\n",
    "parts_data[\"iLdHs\"] = json.dumps(str(type(getListForHistogramAndBoxPlot(df,spark))))\n",
    "parts_data[\"xucEM\"] = json.dumps(len(getListForHistogramAndBoxPlot(df,spark)))\n",
    "parts_data[\"IyH7U\"] = json.dumps(str(type(getListsForRunChart(df,spark))))\n",
    "parts_data[\"MsMHO\"] = json.dumps(len(getListsForRunChart(df,spark)[0]))\n",
    "\n",
    "submitAll(email, token, key, parts_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6 with Spark",
   "language": "python3",
   "name": "python36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}