Project_1B_ Project_Template.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Part I. ETL Pipeline for Pre-Processing the Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## PLEASE RUN THE FOLLOWING CODE FOR PRE-PROCESSING THE FILES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Import Python packages "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Import Python packages \n",
    "import pandas as pd\n",
    "import cassandra\n",
    "import re\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import json\n",
    "import csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Creating list of filepaths to process original event csv data files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/workspace\n"
     ]
    }
   ],
   "source": [
    "# checking your current working directory\n",
    "print(os.getcwd())\n",
    "\n",
    "# Get your current folder and subfolder event data\n",
    "filepath = os.getcwd() + '/event_data'\n",
    "\n",
    "#**********************************************************\n",
    "# NOTE: changed glob expression to load all 30 csv files in event_date\n",
    "file_path_list = glob.glob(os.path.join(filepath, '*'))\n",
    "\n",
    "\n",
    "## Create a for loop to create a list of files and collect each filepath\n",
    "#for root, dirs, files in os.walk(filepath):\n",
    "    \n",
    "## join the file path and roots with the subdirectories using glob\n",
    "     #file_path_list = glob.glob(os.path.join(root,'*'))\n",
    "     ##print(file_path_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Processing the files to create the data file csv that will be used for Apache Casssandra tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8056\n"
     ]
    }
   ],
   "source": [
    "# initiating an empty list of rows that will be generated from each file\n",
    "full_data_rows_list = [] \n",
    "    \n",
    "# for every filepath in the file path list \n",
    "for f in file_path_list:\n",
    "\n",
    "# reading csv file \n",
    "    with open(f, 'r', encoding = 'utf8', newline='') as csvfile: \n",
    "        # creating a csv reader object \n",
    "        csvreader = csv.reader(csvfile) \n",
    "        next(csvreader)\n",
    "        \n",
    " # extracting each data row one by one and append it        \n",
    "        for line in csvreader:\n",
    "            #print(line)\n",
    "            full_data_rows_list.append(line) \n",
    "            \n",
    "# uncomment the code below if you would like to get total number of rows \n",
    "print(len(full_data_rows_list))\n",
    "# uncomment the code below if you would like to check to see what the list of event data rows will look like\n",
    "#print(full_data_rows_list)\n",
    "\n",
    "# creating a smaller event data csv file called event_datafile_full csv that will be used to insert data into the \\\n",
    "# Apache Cassandra tables\n",
    "csv.register_dialect('myDialect', quoting=csv.QUOTE_ALL, skipinitialspace=True)\n",
    "\n",
    "with open('event_datafile_new.csv', 'w', encoding = 'utf8', newline='') as f:\n",
    "    writer = csv.writer(f, dialect='myDialect')\n",
    "    writer.writerow(['artist','firstName','gender','itemInSession','lastName','length',\\\n",
    "                'level','location','sessionId','song','userId'])\n",
    "    for row in full_data_rows_list:\n",
    "        if (row[0] == ''):\n",
    "            continue\n",
    "        writer.writerow((row[0], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[12], row[13], row[16]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6821\n"
     ]
    }
   ],
   "source": [
    "# check the number of rows in your csv file\n",
    "with open('event_datafile_new.csv', 'r', encoding = 'utf8') as f:\n",
    "    print(sum(1 for line in f))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "# Part II. Complete the Apache Cassandra coding portion of your project. \n",
    "\n",
    "## Now you are ready to work with the CSV file titled <font color=red>event_datafile_new.csv</font>, located within the Workspace directory.  The event_datafile_new.csv contains the following columns: \n",
    "- artist \n",
    "- firstName of user\n",
    "- gender of user\n",
    "- item number in session\n",
    "- last name of user\n",
    "- length of the song\n",
    "- level (paid or free song)\n",
    "- location of the user\n",
    "- sessionId\n",
    "- song title\n",
    "- userId\n",
    "\n",
    "The image below is a screenshot of what the denormalized data should appear like in the <font color=red>**event_datafile_new.csv**</font> after the code above is run:<br>\n",
    "\n",
    "<img src=\"images/image_event_datafile_new.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Begin writing your Apache Cassandra code in the cells below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Creating a Cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# This should make a connection to a Cassandra instance your local machine \n",
    "# (127.0.0.1)\n",
    "\n",
    "from cassandra.cluster import Cluster\n",
    "try:\n",
    "    cluster = Cluster(['127.0.0.1'])\n",
    "\n",
    "# To establish connection and begin executing queries, need a session\n",
    "    session = cluster.connect()\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Create Keyspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# TO-DO: Create a Keyspace \n",
    "try:\n",
    "    session.execute(\"\"\"\n",
    "    CREATE KEYSPACE IF NOT EXISTS sparkify\n",
    "    WITH REPLICATION =\n",
    "    {'class' : 'SimpleStrategy', 'replication_factor' : 1}\"\"\"\n",
    ")\n",
    "\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Set Keyspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# TO-DO: Set KEYSPACE to the keyspace specified above\n",
    "try:\n",
    "    session.set_keyspace('sparkify')\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Now we need to create tables to run the following queries. Remember, with Apache Cassandra you model the database tables on the queries you want to run."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Create queries to ask the following three questions of the data\n",
    "\n",
    "### 1. Give me the artist, song title and song's length in the music app history that was heard during  sessionId = 338, and itemInSession  = 4\n",
    "\n",
    "\n",
    "### 2. Give me only the following: name of artist, song (sorted by itemInSession) and user (first and last name) for userid = 10, sessionid = 182\n",
    "    \n",
    "\n",
    "### 3. Give me every user name (first and last) in my music app history who listened to the song 'All Hands Against His Own'\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Query 1\n",
    "### Give me the artist, song title and song's length in the music app history that was heard during  sessionId = 338, and itemInSession  = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "## TO-DO: Query 1:  Give me the artist, song title and song's length in the music app history that was heard during \\\n",
    "## sessionId = 338, and itemInSession = 4\n",
    "query = \"CREATE TABLE IF NOT EXISTS music_library\"\n",
    "query = query + \"(artist_name TEXT, song_title TEXT, song_length FLOAT, sessionId INT, itemInSession INT, PRIMARY KEY (sessionId, itemInSession))\"\n",
    "try: \n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "# We have provided part of the code to set up the CSV file. Please complete the Apache Cassandra code below#\n",
    "file = 'event_datafile_new.csv'\n",
    "\n",
    "with open(file, encoding = 'utf8') as f:\n",
    "    csvreader = csv.reader(f)\n",
    "    next(csvreader) # skip header\n",
    "    for line in csvreader:\n",
    "## TO-DO: Assign the INSERT statements into the `query` variable\n",
    "        query = \"INSERT INTO music_library (artist_name, song_title, song_length, sessionId, itemInSession)\"\n",
    "        query = query + \"VALUES (%s, %s, %s, %s, %s)\"\n",
    "        ## TO-DO: Assign which column element should be assigned for each column in the INSERT statement.\n",
    "        ## For e.g., to INSERT artist_name and user first_name, you would change the code below to `line[0], line[1]`\n",
    "        session.execute(query, (line[0], line[9], float(line[5]), int(line[8]), int(line[3])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "#### Do a SELECT to verify that the data have been inserted into each table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Faithless Music Matters (Mark Knight Dub) 495.30731201171875\n"
     ]
    }
   ],
   "source": [
    "## TO-DO: Add in the SELECT statement to verify the data was entered into the table\n",
    "query = \"SELECT artist_name, song_title, song_length FROM music_library WHERE sessionId = 338 AND itemInSession = 4\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "for row in rows:\n",
    "    print (row.artist_name, row.song_title, row.song_length)\n",
    "\n",
    "# OR\n",
    "#try:\n",
    "    #rows = session.execute(query)\n",
    "    #df = rows._current_rows[['artist_name', 'song_title', 'song_length']]\n",
    "#except Exception as e:\n",
    "    #print(e)\n",
    "    \n",
    "#df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Query 2\n",
    "### Give me only the following: name of artist, song (sorted by itemInSession) and user (first and last name) for userid = 10, sessionid = 182\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "## TO-DO: Query 2: Give me only the following: name of artist, song (sorted by itemInSession) and user (first and last name)\\\n",
    "## for userid = 10, sessionid = 182\n",
    "query = \"CREATE TABLE IF NOT EXISTS artist_user_library\"\n",
    "query = query + \"(artist_name TEXT, \\\n",
    "                  song_title TEXT, \\\n",
    "                  itemInSession INT, \\\n",
    "                  user_firstname TEXT,\\\n",
    "                  user_lastname TEXT,\\\n",
    "                  userId INT, \\\n",
    "                  sessionId INT,\\\n",
    "                  PRIMARY KEY((userId, sessionId), itemInSession)) \\\n",
    "                  WITH CLUSTERING ORDER BY (itemInSession DESC)\"\n",
    "try:\n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "file = 'event_datafile_new.csv'\n",
    "\n",
    "with open(file, encoding = 'utf8') as f:\n",
    "    csvreader = csv.reader(f) \n",
    "    next(csvreader) # skip header\n",
    "    for line in csvreader:\n",
    "## TO-DO: Assign the INSERT statements into the 'query' variable\n",
    "        query = \"INSERT INTO artist_user_library (artist_name, song_title, itemInSession, user_firstname, user_lastname, userId, sessionId)\"\n",
    "        query = query + \"VALUES (%s, %s, %s, %s, %s, %s, %s)\"\n",
    "        ## TO-DO: Assign which column element should be assigned for each column in the INSERT statement.\n",
    "        session.execute(query, (line[0], line[9], int(line[3]), line[1], line[4], int(line[10]), int(line[8])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lonnie Gordon Catch You Baby (Steve Pitron & Max Sanna Radio Edit) Sylvie Cruz\n",
      "Sebastien Tellier Kilometer Sylvie Cruz\n",
      "Three Drives Greece 2000 Sylvie Cruz\n",
      "Down To The Bone Keep On Keepin' On Sylvie Cruz\n"
     ]
    }
   ],
   "source": [
    "query = \"SELECT artist_name, song_title, user_firstName, user_lastName FROM artist_user_library WHERE userId = 10 AND sessionId = 182\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "for row in rows:\n",
    "    print (row.artist_name, row.song_title, row.user_firstname, row.user_lastname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "## Query 3\n",
    "### Give me every user name (first and last) in my music app history who listened to the song 'All Hands Against His Own'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "## TO-DO: Query 3: Give me every user name (first and last) in my music app history who listened to the song 'All Hands Against His Own'\n",
    "query = \"CREATE TABLE IF NOT EXISTS user_song\"\n",
    "query = query + \"(song_title TEXT, userId INT, user_firstname TEXT, user_lastname TEXT, PRIMARY KEY (song_title, userId))\"\n",
    "try: \n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "file = 'event_datafile_new.csv'\n",
    "\n",
    "with open(file, encoding = 'utf8') as f:\n",
    "    csvreader = csv.reader(f) \n",
    "    next(csvreader) # skip header\n",
    "    for line in csvreader:\n",
    "## TO-DO: Assign the INSERT statements into the 'query' variable\n",
    "        query = \"INSERT INTO user_song (song_title, userId, user_firstname, user_lastname)\"\n",
    "        query = query + \"VALUES (%s, %s, %s, %s)\"\n",
    "        ## TO-DO: Assign which column element should be assigned for each column in the INSERT statement.\n",
    "        session.execute(query, (line[9], int(line[10]), line[1], line[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jacqueline Lynch\n",
      "Tegan Levine\n",
      "Sara Johnson\n"
     ]
    }
   ],
   "source": [
    "query = \"SELECT user_firstName, user_lastName FROM user_song WHERE song_title = 'All Hands Against His Own'\"\n",
    "try:\n",
    "    rows = session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)\n",
    "\n",
    "for row in rows:\n",
    "    print (row.user_firstname, row.user_lastname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Drop the tables before closing out the sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "## TO-DO: Drop the table before closing out the sessions\n",
    "query = \"DROP TABLE IF EXISTS music_library\"\n",
    "try:\n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "query = \"DROP TABLE IF EXISTS artist_user_library\"\n",
    "try:\n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "query = \"DROP TABLE IF EXISTS user_song\"\n",
    "try:\n",
    "    session.execute(query)\n",
    "except Exception as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true
   },
   "source": [
    "### Close the session and cluster connection¶"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": [
    "session.shutdown()\n",
    "cluster.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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