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December 25, 2020 18:13
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| { | |
| "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": "Requirement already satisfied: pyspark==2.4.5 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (2.4.5)\r\nRequirement already satisfied: py4j==0.10.7 in /opt/conda/envs/Python-3.7-main/lib/python3.7/site-packages (from pyspark==2.4.5) (0.10.7)\r\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": 5, | |
| "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.#YOUR CODE GOES HERE(False,#YOUR CODE GOES HERE)\n\ndef getSample(df,spark):\n x=df.sample(False,0.1)\n return x" | |
| }, | |
| { | |
| "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": 20, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": "# def getListForHistogramAndBoxPlot():\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 #YOUR CODE GOES HERE from washing where temperature is not null\n# \"\"\").rdd.map(lambda row: row.temperature).#YOUR CODE GOES HERE\n# if not type(my_list)==list:\n# raise Exception('return type not a list')\n# return my_list\n\ndef getListForHistogramAndBoxPlot(df,spark):\n result2_df = spark.sql(\"SELECT temperature FROM washing WHERE temperature IS NOT null\")\n result_plt = result2_df.rdd.map(lambda row : row.temperature).collect()\n return result_plt" | |
| }, | |
| { | |
| "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": 14, | |
| "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 #YOUR CODE GOES HERE,#YOUR CODE GOES HERE from washing where temperature is not null order by ts asc\n# # \"\"\"). #YOUR CODE GOES HERE(False,0.1).rdd.map(lambda row : (row.ts,row.temperature))\n# # \n# result_rdd = df.rdd.map(lamda row : (row.ts, row.temprature)).filter(lambda: row[1] is not None).sample(False, 0.1)\n# result_array_ts = result_rdd.map(lambda row: row[0]).collect()\n# result_array_temperature = result_rdd.map(lambda row: row[1]).collect()\n# return (result_array_ts,result_array_temperature)\n\ndef getListsForRunChart(df,spark):\n result_rdd = df.rdd.map(lambda row : (row.ts,row.temperature)).filter(lambda row: row[1] is not None).sample(False, 0.1)\n result_array_ts = result_rdd.map(lambda row: row[0]).collect()\n result_array_temperature = result_rdd.map(lambda row: row[1]).collect()\n return (result_array_ts, result_array_temperature)\n" | |
| }, | |
| { | |
| "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": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "--2020-12-25 18:09:10-- https://github.com/IBM/coursera/blob/master/coursera_ds/washing.parquet?raw=true\nResolving github.com (github.com)... 140.82.112.4\nConnecting to github.com (github.com)|140.82.112.4|: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--2020-12-25 18:09:10-- 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--2020-12-25 18:09:10-- 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--2020-12-25 18:09:10-- https://raw.githubusercontent.com/IBM/skillsnetwork/master/coursera_ds/washing.parquet\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.16.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.16.133|: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 0.004s \n\n2020-12-25 18:09:11 (27.0 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": 16, | |
| "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": 17, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": "%matplotlib inline\nimport matplotlib.pyplot as plt" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "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))\nplt.show()" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 23, | |
| "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))\nplt.show()" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 26, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": "lists = getListsForRunChart(df,spark)" | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 27, | |
| "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": 28, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "--2020-12-25 18:11:28-- https://raw.githubusercontent.com/IBM/coursera/master/rklib.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.16.133\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.16.133|: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\n2020-12-25 18:11:28 (31.1 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": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": "from rklib import submitAll\nimport json\n\nkey = \"S5PNoSHNEeisnA6YLL5C0g\"\nemail = ###_YOUR_CODE_GOES_HERE_###\ntoken = ###_YOUR_CODE_GOES_HERE_### #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": null, | |
| "metadata": {}, | |
| "outputs": [], | |
| "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)" | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "display_name": "Python 3.7", | |
| "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": "3.7.9" | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 1 | |
| } |
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