GP-PostGIS-Demo.ipynb

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   "source": [
    "# PostGIS on Greenplum Database\n",
    "## 1 System preparation\n",
    "### 1.1 Set system variables, connection string, Greenplum Database auto stats, etc."
   ]
  },
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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
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     "data": {
      "text/plain": [
       "'Connected: gpadmin@gpadmin'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os, re\n",
    "from IPython.display import display_html\n",
    "\n",
    "import pygments.lexers\n",
    "from pygments import highlight\n",
    "from pygments.formatters import HtmlFormatter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "CONNECTION_STRING = os.getenv('AWSGPDBCONN')\n",
    "\n",
    "cs = re.match('^postgresql:\\/\\/(\\S+):(\\S+)@(\\S+):(\\S+)\\/(\\S+)$', CONNECTION_STRING)\n",
    "\n",
    "DB_USER   = cs.group(1)\n",
    "DB_PWD    = cs.group(2)\n",
    "DB_SERVER = cs.group(3)\n",
    "DB_PORT   = cs.group(4)\n",
    "DB_NAME   = cs.group(5)\n",
    "\n",
    "%reload_ext sql\n",
    "%sql $CONNECTION_STRING"
   ]
  },
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   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 rows affected.\n",
      "Done.\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>gp_autostats_mode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>ON_NO_STATS</td>\n",
       "    </tr>\n",
       "</table>"
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       "[('ON_NO_STATS',)]"
      ]
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     "execution_count": 2,
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   "source": [
    "%%sql $DB_USER@$DB_SERVER\n",
    "SHOW gp_autostats_mode;\n",
    "ALTER DATABASE gpadmin SET gp_autostats_mode TO 'NONE';\n",
    "SHOW gp_autostats_mode;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Prepare AWS System and setup awscli library via pip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"ch\">#!/bin/bash</span>\n",
       "\n",
       "<span class=\"nb\">cd</span> <span class=\"nv\">$HOME</span>\n",
       "\n",
       "curl -O https://bootstrap.pypa.io/get-pip.py\n",
       "python <span class=\"nv\">$HOME</span>/get-pip.py --force-reinstall\n",
       "rm <span class=\"nv\">$HOME</span>/get-pip.py\n",
       "\n",
       "mv <span class=\"nv\">$HOME</span>/.bash_profile <span class=\"nv\">$HOME</span>/.bash_profile.old\n",
       "\n",
       "cat &gt;&gt; <span class=\"nv\">$HOME</span>/.bash_profile <span class=\"s\">&lt;&lt; &#39;EOF&#39;</span>\n",
       "<span class=\"s\"># .bash_profile</span>\n",
       "\n",
       "<span class=\"s\"># Get the aliases and functions</span>\n",
       "<span class=\"s\">if [ -f ~/.bashrc ]; then</span>\n",
       "<span class=\"s\">\t. ~/.bashrc</span>\n",
       "<span class=\"s\">fi</span>\n",
       "\n",
       "<span class=\"s\"># User specific environment and startup programs</span>\n",
       "\n",
       "<span class=\"s\">export PATH=$HOME/.local/bin:$HOME/bin:$PATH</span>\n",
       "\n",
       "<span class=\"s\">EOF</span>\n",
       "\n",
       "chmod <span class=\"m\">644</span> <span class=\"nv\">$HOME</span>/.bash_profile\n",
       "<span class=\"nb\">source</span> <span class=\"nv\">$HOME</span>/.bash_profile\n",
       "\n",
       "pip install --upgrade --force-reinstall pip\n",
       "pip install --upgrade --force-reinstall boto3\n",
       "pip install --upgrade --force-reinstall awscli\n",
       "\n",
       "<span class=\"nb\">echo</span> <span class=\"s2\">&quot;***********************&quot;</span>\n",
       "<span class=\"nb\">echo</span> <span class=\"s2\">&quot;* Get AWS CLI version *&quot;</span>\n",
       "<span class=\"nb\">echo</span> <span class=\"s2\">&quot;***********************&quot;</span>\n",
       "aws --version\n",
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      "# ec2-35-178-123-46.eu-west-2.compute.amazonaws.com:22 SSH-2.0-OpenSSH_7.4\n",
      "# ec2-35-178-123-46.eu-west-2.compute.amazonaws.com:22 SSH-2.0-OpenSSH_7.4\n",
      "Warning: Permanently added the ECDSA host key for IP address '35.178.123.46' to the list of known hosts.\n",
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      "  Building wheel for PyYAML (setup.py): started\n",
      "  Building wheel for PyYAML (setup.py): finished with status 'done'\n",
      "  Created wheel for PyYAML: filename=PyYAML-5.1.2-cp27-cp27mu-linux_x86_64.whl size=44890 sha256=175b9e7213d72516ecb4029d6b08a60bc538f69857cffe708d86af2ed0e9dc82\n",
      "  Stored in directory: /root/.cache/pip/wheels/d9/45/dd/65f0b38450c47cf7e5312883deb97d065e030c5cca0a365030\n",
      "Successfully built PyYAML\n",
      "Installing collected packages: docutils, PyYAML, pyasn1, rsa, colorama, futures, jmespath, six, python-dateutil, urllib3, botocore, s3transfer, awscli\n",
      "  Found existing installation: docutils 0.15.2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Uninstalling docutils-0.15.2:\n",
      "      Successfully uninstalled docutils-0.15.2\n",
      "  Found existing installation: PyYAML 3.10\n",
      "ERROR: Cannot uninstall 'PyYAML'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.\n",
      "***********************\n",
      "* Get AWS CLI version *\n",
      "***********************\n",
      "aws-cli/1.15.30 Python/2.7.5 Linux/3.10.0-957.21.3.el7.x86_64 botocore/1.10.30\n"
     ]
    }
   ],
   "source": [
    "!ssh-keygen -R $DB_SERVER\n",
    "!ssh-keyscan $DB_SERVER >> ~/.ssh/known_hosts\n",
    "!scp -i ~/.ssh/aws-gp.pem script/1-1-system-prepare.sh $DB_USER@$DB_SERVER:system-prepare.sh\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'chmod +x ./system-prepare.sh'\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'sudo ./system-prepare.sh'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Provide AWS Access Key ID & Secret Access Key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "\n",
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       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"ch\">#!/bin/bash</span>\n",
       "\n",
       "<span class=\"k\">if</span> <span class=\"o\">[</span> -d <span class=\"nv\">$HOME</span>/.aws <span class=\"o\">]</span><span class=\"p\">;</span> <span class=\"k\">then</span> rm -rf <span class=\"nv\">$HOME</span>/.aws<span class=\"p\">;</span> <span class=\"k\">fi</span>\n",
       "\n",
       "mkdir <span class=\"nv\">$HOME</span>/.aws\n",
       "\n",
       "<span class=\"nb\">echo</span> <span class=\"s2\">&quot;[default]</span>\n",
       "<span class=\"s2\">aws_access_key_id=&quot;</span><span class=\"nv\">$1</span><span class=\"s2\">&quot;</span>\n",
       "<span class=\"s2\">aws_secret_access_key=&quot;</span><span class=\"nv\">$2</span><span class=\"s2\">&quot;&quot;</span> &gt; <span class=\"nv\">$HOME</span>/.aws/credentials\n",
       "\n",
       "<span class=\"nb\">echo</span> <span class=\"s2\">&quot;[default]</span>\n",
       "<span class=\"s2\">region=eu-west-2</span>\n",
       "<span class=\"s2\">output=json&quot;</span> &gt; <span class=\"nv\">$HOME</span>/.aws/config\n",
       "\n",
       "<span class=\"c1\">#test aws config</span>\n",
       "<span class=\"k\">if</span> <span class=\"o\">[</span> <span class=\"s2\">&quot;</span><span class=\"k\">$(</span>/usr/local/bin/aws s3 ls s3://amazon-reviews-pds/tsv/ <span class=\"p\">|</span> awk <span class=\"s1\">&#39;{print $4}&#39;</span> <span class=\"p\">|</span> awk <span class=\"s1\">&#39;END {print NR}&#39;</span><span class=\"k\">)</span><span class=\"s2\">&quot;</span> -eq <span class=\"s2\">&quot;0&quot;</span> <span class=\"o\">]</span><span class=\"p\">;</span> \n",
       "  <span class=\"k\">then</span> <span class=\"nb\">echo</span> <span class=\"s2\">&quot;Error on AWS S3 Configuration setup&quot;</span><span class=\"p\">;</span> \n",
       "  <span class=\"k\">else</span> <span class=\"nb\">echo</span> <span class=\"s2\">&quot;AWS S3 Configuration setup correctly&quot;</span><span class=\"p\">;</span> \n",
       "<span class=\"k\">fi</span>\n",
       "</pre></div>\n",
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   ],
   "source": [
    "shfilecode = !pygmentize -f html -O full,style=friendly -l bash script/1-2-aws-configure.sh\n",
    "display_html('\\n'.join(shfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1-2-aws-configure.sh                          100%  484    66.4KB/s   00:00    \n",
      "AWS Access Key ID [None]:········\n",
      "AWS Secret Access Key [None]:········\n",
      "AWS S3 Configuration setup correctly\n"
     ]
    }
   ],
   "source": [
    "import getpass\n",
    "\n",
    "!scp -i ~/.ssh/aws-gp.pem script/1-2-aws-configure.sh $DB_USER@$DB_SERVER:aws-configure.sh\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'chmod +x ./aws-configure.sh'\n",
    "\n",
    "cmd = 'sudo ./aws-configure.sh ' \n",
    "cmd = cmd + getpass.getpass(\"AWS Access Key ID [None]:\") \n",
    "cmd = cmd + ' ' + getpass.getpass(\"AWS Secret Access Key [None]:\")\n",
    "\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER $cmd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Create Greenplum Database Schema and Tables for Demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">DROP</span> <span class=\"k\">SCHEMA</span> <span class=\"k\">IF</span> <span class=\"k\">EXISTS</span> <span class=\"n\">demo</span> <span class=\"k\">CASCADE</span><span class=\"p\">;</span>\n",
       "\n",
       "<span class=\"k\">VACUUM</span> <span class=\"k\">FULL</span> <span class=\"k\">VERBOSE</span><span class=\"p\">;</span>\n",
       "\n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">SCHEMA</span> <span class=\"n\">demo</span><span class=\"p\">;</span>\n",
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    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-0-db-maintenance.sql\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
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  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Done.\n",
      "Done.\n"
     ]
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    {
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       "[]"
      ]
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     "execution_count": 33,
     "metadata": {},
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   "source": [
    "query = !cat script/8-0-db-maintenance.sql\n",
    "%sql $DB_USER@$DB_SERVER {''.join(query)}"
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  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
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       "body .ge { font-style: italic } /* Generic.Emph */\n",
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       "body .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       "body .gi { color: #00A000 } /* Generic.Inserted */\n",
       "body .go { color: #888888 } /* Generic.Output */\n",
       "body .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */\n",
       "body .gs { font-weight: bold } /* Generic.Strong */\n",
       "body .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       "body .gt { color: #0044DD } /* Generic.Traceback */\n",
       "body .kc { color: #007020; font-weight: bold } /* Keyword.Constant */\n",
       "body .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */\n",
       "body .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */\n",
       "body .kp { color: #007020 } /* Keyword.Pseudo */\n",
       "body .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */\n",
       "body .kt { color: #902000 } /* Keyword.Type */\n",
       "body .m { color: #40a070 } /* Literal.Number */\n",
       "body .s { color: #4070a0 } /* Literal.String */\n",
       "body .na { color: #4070a0 } /* Name.Attribute */\n",
       "body .nb { color: #007020 } /* Name.Builtin */\n",
       "body .nc { color: #0e84b5; font-weight: bold } /* Name.Class */\n",
       "body .no { color: #60add5 } /* Name.Constant */\n",
       "body .nd { color: #555555; font-weight: bold } /* Name.Decorator */\n",
       "body .ni { color: #d55537; font-weight: bold } /* Name.Entity */\n",
       "body .ne { color: #007020 } /* Name.Exception */\n",
       "body .nf { color: #06287e } /* Name.Function */\n",
       "body .nl { color: #002070; font-weight: bold } /* Name.Label */\n",
       "body .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */\n",
       "body .nt { color: #062873; font-weight: bold } /* Name.Tag */\n",
       "body .nv { color: #bb60d5 } /* Name.Variable */\n",
       "body .ow { color: #007020; font-weight: bold } /* Operator.Word */\n",
       "body .w { color: #bbbbbb } /* Text.Whitespace */\n",
       "body .mb { color: #40a070 } /* Literal.Number.Bin */\n",
       "body .mf { color: #40a070 } /* Literal.Number.Float */\n",
       "body .mh { color: #40a070 } /* Literal.Number.Hex */\n",
       "body .mi { color: #40a070 } /* Literal.Number.Integer */\n",
       "body .mo { color: #40a070 } /* Literal.Number.Oct */\n",
       "body .sa { color: #4070a0 } /* Literal.String.Affix */\n",
       "body .sb { color: #4070a0 } /* Literal.String.Backtick */\n",
       "body .sc { color: #4070a0 } /* Literal.String.Char */\n",
       "body .dl { color: #4070a0 } /* Literal.String.Delimiter */\n",
       "body .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */\n",
       "body .s2 { color: #4070a0 } /* Literal.String.Double */\n",
       "body .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */\n",
       "body .sh { color: #4070a0 } /* Literal.String.Heredoc */\n",
       "body .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */\n",
       "body .sx { color: #c65d09 } /* Literal.String.Other */\n",
       "body .sr { color: #235388 } /* Literal.String.Regex */\n",
       "body .s1 { color: #4070a0 } /* Literal.String.Single */\n",
       "body .ss { color: #517918 } /* Literal.String.Symbol */\n",
       "body .bp { color: #007020 } /* Name.Builtin.Pseudo */\n",
       "body .fm { color: #06287e } /* Name.Function.Magic */\n",
       "body .vc { color: #bb60d5 } /* Name.Variable.Class */\n",
       "body .vg { color: #bb60d5 } /* Name.Variable.Global */\n",
       "body .vi { color: #bb60d5 } /* Name.Variable.Instance */\n",
       "body .vm { color: #bb60d5 } /* Name.Variable.Magic */\n",
       "body .il { color: #40a070 } /* Literal.Number.Integer.Long */\n",
       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">uber_mov_ldn</span> <span class=\"p\">(</span>\n",
       "    <span class=\"n\">qoy</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">sourceid</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">dstid</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hod</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">mean_travel_time</span> <span class=\"nb\">NUMERIC</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">standard_deviation_travel_time</span> <span class=\"nb\">NUMERIC</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">geometric_mean_travel_time</span> <span class=\"nb\">NUMERIC</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">geometric_standard_deviation_travel_time</span> <span class=\"nb\">NUMERIC</span>\n",
       "<span class=\"p\">)</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">)</span> \n",
       "<span class=\"k\">PARTITION</span> <span class=\"k\">BY</span> <span class=\"n\">LIST</span> <span class=\"p\">(</span><span class=\"n\">qoy</span><span class=\"p\">)</span> \n",
       "<span class=\"n\">SUBPARTITION</span> <span class=\"k\">BY</span> <span class=\"k\">RANGE</span> <span class=\"p\">(</span><span class=\"n\">hod</span><span class=\"p\">)</span> \n",
       "<span class=\"n\">SUBPARTITION</span> <span class=\"k\">TEMPLATE</span> <span class=\"p\">(</span>\n",
       "    <span class=\"k\">START</span> <span class=\"p\">(</span><span class=\"mf\">0</span><span class=\"p\">)</span> <span class=\"k\">END</span> <span class=\"p\">(</span><span class=\"mf\">23</span><span class=\"p\">)</span> <span class=\"n\">EVERY</span> <span class=\"p\">(</span><span class=\"mf\">1</span><span class=\"p\">),</span> <span class=\"k\">DEFAULT</span> <span class=\"n\">SUBPARTITION</span> <span class=\"n\">OTHERHOD</span><span class=\"p\">)</span> \n",
       "<span class=\"p\">(</span><span class=\"k\">VALUES</span> <span class=\"p\">(</span><span class=\"mf\">201601</span><span class=\"p\">,</span> <span class=\"mf\">201602</span><span class=\"p\">,</span> <span class=\"mf\">201603</span><span class=\"p\">,</span> <span class=\"mf\">201604</span><span class=\"p\">,</span> <span class=\"mf\">201701</span><span class=\"p\">,</span> <span class=\"mf\">201702</span><span class=\"p\">,</span> <span class=\"mf\">201703</span><span class=\"p\">,</span> <span class=\"mf\">201704</span><span class=\"p\">,</span> <span class=\"mf\">201801</span><span class=\"p\">),</span> <span class=\"k\">DEFAULT</span> <span class=\"k\">PARTITION</span> <span class=\"n\">OTHERQOY</span><span class=\"p\">);</span>\n",
       "\n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">postcode_lookup</span> <span class=\"p\">(</span>\n",
       "    <span class=\"n\">pcd7</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">pcd8</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">pcds</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">dointr</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">doterm</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">usertype</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">oa11cd</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">lsoa11cd</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">msoa11cd</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">ladcd</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">lsoa11nm</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">msoa11nm</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">ladnm</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">ladnmw</span> <span class=\"nb\">TEXT</span><span class=\"p\">)</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"n\">RANDOMLY</span><span class=\"p\">;</span>\n",
       "\n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">postcodelatlng</span> <span class=\"p\">(</span>\n",
       "    <span class=\"n\">id</span> <span class=\"nb\">INT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">postcode</span> <span class=\"nb\">VARCHAR</span><span class=\"p\">(</span><span class=\"mf\">8</span><span class=\"p\">),</span> \n",
       "    <span class=\"n\">latitude</span> <span class=\"nb\">DECIMAL</span><span class=\"p\">(</span><span class=\"mf\">18</span><span class=\"p\">,</span><span class=\"mf\">15</span><span class=\"p\">),</span> \n",
       "    <span class=\"n\">longitude</span> <span class=\"nb\">DECIMAL</span><span class=\"p\">(</span><span class=\"mf\">18</span><span class=\"p\">,</span><span class=\"mf\">15</span><span class=\"p\">))</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">id</span><span class=\"p\">);</span>\n",
       "                      \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">dft_traffic_counts_raw</span> <span class=\"p\">(</span>\n",
       "    <span class=\"n\">count_point_id</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">direction_of_travel</span> <span class=\"nb\">CHAR</span><span class=\"p\">(</span><span class=\"mf\">1</span><span class=\"p\">),</span> \n",
       "    <span class=\"k\">YEAR</span> <span class=\"nb\">SMALLINT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">count_date</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"k\">HOUR</span> <span class=\"nb\">SMALLINT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">region_id</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">region_name</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">region_ons_code</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">local_authority_id</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">local_authority_name</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">local_authoirty_ons_code</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">road_name</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">road_category</span> <span class=\"nb\">VARCHAR</span><span class=\"p\">(</span><span class=\"mf\">3</span><span class=\"p\">),</span> \n",
       "    <span class=\"n\">road_type</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">start_junction_road_name</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">end_junction_road_name</span> <span class=\"nb\">TEXT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">easting</span> <span class=\"nb\">BIGINT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">northing</span> <span class=\"nb\">BIGINT</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">latitude</span> <span class=\"nb\">NUMERIC</span><span class=\"p\">(</span><span class=\"mf\">18</span><span class=\"p\">,</span><span class=\"mf\">15</span><span class=\"p\">),</span> \n",
       "    <span class=\"n\">longitude</span> <span class=\"nb\">NUMERIC</span><span class=\"p\">(</span><span class=\"mf\">18</span><span class=\"p\">,</span><span class=\"mf\">15</span><span class=\"p\">),</span> \n",
       "    <span class=\"n\">link_length_km</span> <span class=\"nb\">NUMERIC</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">link_length_miles</span> <span class=\"nb\">NUMERIC</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">pedal_cycles</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">two_wheeled_motor_vehicles</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">cars_and_taxis</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">buses_and_coaches</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">lgvs</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hgvs_2_rigid_axle</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hgvs_3_rigid_axle</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hgvs_4_or_more_rigid_axle</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hgvs_3_or_4_articulated_axle</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hgvs_5_articulated_axle</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hgvs_6_articulated_axle</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">all_hgvs</span> <span class=\"nb\">INTEGER</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">all_motor_vehicles</span> <span class=\"nb\">INTEGER</span><span class=\"p\">)</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">count_point_id</span><span class=\"p\">);</span>\n",
       "                      \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_lsoa</span> <span class=\"p\">(</span> \n",
       "    <span class=\"n\">dat_type</span> <span class=\"nb\">text</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">geometry</span> <span class=\"n\">geometry</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">msoa_code</span> <span class=\"nb\">text</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">msoa_name</span> <span class=\"nb\">text</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">la_code</span> <span class=\"nb\">text</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">la_name</span> <span class=\"nb\">text</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">geoeast</span> <span class=\"nb\">int4</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">geonorth</span> <span class=\"nb\">int4</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">popeast</span> <span class=\"nb\">int4</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">popnorth</span> <span class=\"nb\">int4</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">area_km2</span> <span class=\"nb\">numeric</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">movement_id</span> <span class=\"nb\">int4</span> <span class=\"k\">NULL</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">display_name</span> <span class=\"nb\">text</span> <span class=\"k\">NULL</span> \n",
       "<span class=\"p\">)</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">movement_id</span><span class=\"p\">);</span> \n",
       "  \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_lsoa_txt</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">input</span> <span class=\"nb\">TEXT</span><span class=\"p\">)</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"n\">RANDOMLY</span><span class=\"p\">;</span> \n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-1-create-schema-tables.sql\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = !cat script/8-1-create-schema-tables.sql\n",
    "%sql $DB_USER@$DB_SERVER {''.join(query)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Copy source files from AWS S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<!DOCTYPE html PUBLIC \"-//W3C//DTD HTML 4.01//EN\"\n",
       "   \"http://www.w3.org/TR/html4/strict.dtd\">\n",
       "<!--\n",
       "generated by Pygments <http://pygments.org>\n",
       "Copyright 2006-2019 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "-->\n",
       "<html>\n",
       "<head>\n",
       "  <title></title>\n",
       "  <meta http-equiv=\"content-type\" content=\"text/html; charset=UTF-8\">\n",
       "  <style type=\"text/css\">\n",
       "/*\n",
       "generated by Pygments <http://pygments.org>\n",
       "Copyright 2006-2019 by the Pygments team.\n",
       "Licensed under the BSD license, see LICENSE for details.\n",
       "*/\n",
       "td.linenos { background-color: #f0f0f0; padding-right: 10px; }\n",
       "span.lineno { background-color: #f0f0f0; padding: 0 5px 0 5px; }\n",
       "pre { line-height: 125%; }\n",
       "body .hll { background-color: #ffffcc }\n",
       "body  { background: #f0f0f0; }\n",
       "body .c { color: #60a0b0; font-style: italic } /* Comment */\n",
       "body .err { border: 1px solid #FF0000 } /* Error */\n",
       "body .k { color: #007020; font-weight: bold } /* Keyword */\n",
       "body .o { color: #666666 } /* Operator */\n",
       "body .ch { color: #60a0b0; font-style: italic } /* Comment.Hashbang */\n",
       "body .cm { color: #60a0b0; font-style: italic } /* Comment.Multiline */\n",
       "body .cp { color: #007020 } /* Comment.Preproc */\n",
       "body .cpf { color: #60a0b0; font-style: italic } /* Comment.PreprocFile */\n",
       "body .c1 { color: #60a0b0; font-style: italic } /* Comment.Single */\n",
       "body .cs { color: #60a0b0; background-color: #fff0f0 } /* Comment.Special */\n",
       "body .gd { color: #A00000 } /* Generic.Deleted */\n",
       "body .ge { font-style: italic } /* Generic.Emph */\n",
       "body .gr { color: #FF0000 } /* Generic.Error */\n",
       "body .gh { color: #000080; font-weight: bold } /* Generic.Heading */\n",
       "body .gi { color: #00A000 } /* Generic.Inserted */\n",
       "body .go { color: #888888 } /* Generic.Output */\n",
       "body .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */\n",
       "body .gs { font-weight: bold } /* Generic.Strong */\n",
       "body .gu { color: #800080; font-weight: bold } /* Generic.Subheading */\n",
       "body .gt { color: #0044DD } /* Generic.Traceback */\n",
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       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"ch\">#!/bin/bash</span>\n",
       "\n",
       "<span class=\"nb\">export</span> <span class=\"nv\">S3_DATA_DIR</span><span class=\"o\">=</span>/var/tmp_s3_data\n",
       "\n",
       "<span class=\"k\">if</span> <span class=\"o\">[</span> -d <span class=\"nv\">$S3_DATA_DIR</span> <span class=\"o\">]</span><span class=\"p\">;</span> <span class=\"k\">then</span> rm -rf <span class=\"nv\">$S3_DATA_DIR</span><span class=\"p\">;</span> <span class=\"k\">fi</span>\n",
       "\n",
       "mkdir <span class=\"nv\">$S3_DATA_DIR</span>\n",
       "sudo chown gpadmin <span class=\"nv\">$S3_DATA_DIR</span>\n",
       "<span class=\"nb\">cd</span> <span class=\"nv\">$S3_DATA_DIR</span>\n",
       "\n",
       "ls -laF <span class=\"nv\">$S3_DATA_DIR</span>\n",
       "\n",
       "/usr/local/bin/aws s3 cp s3://uber-movement-ldn/PCD_OA_LSOA_MSOA_LAD_AUG19_UK_LU.csv PCD_OA_LSOA_MSOA_LAD_AUG19_UK_LU.csv\n",
       "/usr/local/bin/aws s3 cp s3://uber-movement-ldn/uber-movement-ldn.tar.gz uber-movement-ldn.tar.gz\n",
       "/usr/local/bin/aws s3 cp s3://uber-movement-ldn/ukpostcodes.csv ukpostcodes.csv\n",
       "/usr/local/bin/aws s3 cp s3://uber-movement-ldn/2018-MRDB-minimal.shp <span class=\"m\">2018</span>-MRDB-minimal.shp\n",
       "/usr/local/bin/aws s3 cp s3://uber-movement-ldn/2018-MRDB-minimal.shx <span class=\"m\">2018</span>-MRDB-minimal.shx\n",
       "/usr/local/bin/aws s3 cp s3://uber-movement-ldn/2018-MRDB-minimal.dbf <span class=\"m\">2018</span>-MRDB-minimal.dbf\n",
       "/usr/local/bin/aws s3 cp s3://uber-movement-ldn/dft_traffic_counts_raw_counts.tar.gz dft_traffic_counts_raw_counts.tar.gz\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shfilecode = !pygmentize -f html -O full,style=friendly -l bash script/8-3-copy-data-from-s3.sh\n",
    "display_html('\\n'.join(shfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8-3-copy-data-from-s3.sh                      100%  892   128.2KB/s   00:00    \n",
      "total 4\n",
      "drwxr-xr-x   2 gpadmin root    6 Oct 25 09:36 ./\n",
      "drwxr-xr-x. 21 root    root 4096 Oct 25 09:36 ../\n",
      "download: s3://uber-movement-ldn/PCD_OA_LSOA_MSOA_LAD_AUG19_UK_LU.csv to ./PCD_OA_LSOA_MSOA_LAD_AUG19_UK_LU.csv\n",
      "download: s3://uber-movement-ldn/uber-movement-ldn.tar.gz to ./uber-movement-ldn.tar.gz\n",
      "download: s3://uber-movement-ldn/ukpostcodes.csv to ./ukpostcodes.csv\n",
      "download: s3://uber-movement-ldn/2018-MRDB-minimal.shp to ./2018-MRDB-minimal.shp\n",
      "download: s3://uber-movement-ldn/2018-MRDB-minimal.shx to ./2018-MRDB-minimal.shx\n",
      "download: s3://uber-movement-ldn/2018-MRDB-minimal.dbf to ./2018-MRDB-minimal.dbf\n",
      "download: s3://uber-movement-ldn/dft_traffic_counts_raw_counts.tar.gz to ./dft_traffic_counts_raw_counts.tar.gz\n"
     ]
    }
   ],
   "source": [
    "!scp -i ~/.ssh/aws-gp.pem script/8-3-copy-data-from-s3.sh $DB_USER@$DB_SERVER:copy-data-from-s3.sh\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'chmod +x ./copy-data-from-s3.sh'\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER \"sudo ./copy-data-from-s3.sh\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.6 Load data\n",
    "- **Load Uber Movement (London) data from `uber-movement-ldn.tar.gz` file into `demo.uber_mov_ldn` table using GPLoad**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"nn\">---</span>\n",
       "<span class=\"nt\">VERSION</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">1.0.0.1</span>\n",
       "<span class=\"nt\">GPLOAD</span><span class=\"p\">:</span>\n",
       "   <span class=\"nt\">INPUT</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">SOURCE</span><span class=\"p\">:</span>\n",
       "         <span class=\"nt\">FILE</span><span class=\"p\">:</span>\n",
       "           <span class=\"p p-Indicator\">-</span> <span class=\"l l-Scalar l-Scalar-Plain\">/var/tmp_s3_data/uber-movement-ldn.tar.gz</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">FORMAT</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">csv</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">HEADER</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">LOG_ERRORS</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">MAX_LINE_LENGTH</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">1000000</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">ERROR_LIMIT</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">50000</span>\n",
       "   <span class=\"nt\">OUTPUT</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">TABLE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">demo.uber_mov_ldn</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">MODE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">insert</span>\n",
       "   <span class=\"nt\">PRELOAD</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">TRUNCATE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">REUSE_TABLES</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "</pre></div>\n",
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   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l yaml script/8-4-load-uber-movement-ldn.yaml\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8-4-load-uber-movement-ldn.yaml               100%  357    52.4KB/s   00:00    \n",
      "2019-10-25 09:36:41|INFO|gpload session started 2019-10-25 09:36:41\n",
      "2019-10-25 09:36:41|INFO|no host supplied, defaulting to localhost\n",
      "2019-10-25 09:36:41|INFO|started gpfdist -p 8000 -P 9000 -f \"/var/tmp_s3_data/uber-movement-ldn.tar.gz\" -t 30 -m 1000000\n",
      "2019-10-25 09:36:46|INFO|did not find an external table to reuse. creating ext_gpload_reusable_918e3642_f702_11e9_b376_06a90659ec6c\n",
      "2019-10-25 09:37:49|WARN|24 bad rows\n",
      "2019-10-25 09:37:49|WARN|Please use following query to access the detailed error\n",
      "2019-10-25 09:37:49|WARN|select * from gp_read_error_log('ext_gpload_reusable_918e3642_f702_11e9_b376_06a90659ec6c') where cmdtime > to_timestamp('1571992601.55')\n",
      "2019-10-25 09:37:49|INFO|running time: 67.94 seconds\n",
      "2019-10-25 09:37:49|INFO|rows Inserted          = 68939823\n",
      "2019-10-25 09:37:49|INFO|rows Updated           = 0\n",
      "2019-10-25 09:37:49|INFO|data formatting errors = 24\n",
      "2019-10-25 09:37:49|INFO|gpload succeeded with warnings\n"
     ]
    }
   ],
   "source": [
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'if [ -f ./load-uber-movement-ldn.yaml ]; then rm ./load-uber-movement-ldn.yaml; fi'\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'if [ -f ./load-uber-movement-ldn.log ]; then rm ./load-uber-movement-ldn.log; fi'\n",
    "\n",
    "!scp -i ~/.ssh/aws-gp.pem script/8-4-load-uber-movement-ldn.yaml $DB_USER@$DB_SERVER:load-uber-movement-ldn.yaml\n",
    "cmd = \"gpload -d {0} -f ./load-uber-movement-ldn.yaml -l ./load-uber-movement-ldn.log 2>&1\".format(DB_USER) \n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER $cmd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Load UK Postcodes/Lower-/Middle Layer Super Output Area Information from `PCD_OA_LSOA_MSOA_LAD_AUG19_UK_LU.csv` file into `demo.postcode_lookup` table using GPLoad**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
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       "<span class=\"nt\">VERSION</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">1.0.0.1</span>\n",
       "<span class=\"nt\">GPLOAD</span><span class=\"p\">:</span>\n",
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       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">LOG_ERRORS</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
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       "   <span class=\"nt\">OUTPUT</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">TABLE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">demo.postcode_lookup</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">MODE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">insert</span>\n",
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       "   <span class=\"nt\">SQL</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">AFTER</span><span class=\"p\">:</span> <span class=\"s\">&quot;ALTER</span><span class=\"nv\"> </span><span class=\"s\">TABLE</span><span class=\"nv\"> </span><span class=\"s\">demo.postcode_lookup</span><span class=\"nv\"> </span><span class=\"s\">ALTER</span><span class=\"nv\"> </span><span class=\"s\">COLUMN</span><span class=\"nv\"> </span><span class=\"s\">dointr</span><span class=\"nv\"> </span><span class=\"s\">TYPE</span><span class=\"nv\"> </span><span class=\"s\">INTEGER</span><span class=\"nv\"> </span><span class=\"s\">USING</span><span class=\"nv\"> </span><span class=\"s\">COALESCE(NULLIF(dointr,</span><span class=\"nv\"> </span><span class=\"s\">&#39;&#39;),</span><span class=\"nv\"> </span><span class=\"s\">&#39;0&#39;)::INT;</span><span class=\"nv\"> </span><span class=\"s\">ALTER</span><span class=\"nv\"> </span><span class=\"s\">TABLE</span><span class=\"nv\"> </span><span class=\"s\">demo.postcode_lookup</span><span class=\"nv\"> </span><span class=\"s\">ALTER</span><span class=\"nv\"> </span><span class=\"s\">COLUMN</span><span class=\"nv\"> </span><span class=\"s\">doterm</span><span class=\"nv\"> </span><span class=\"s\">TYPE</span><span class=\"nv\"> </span><span class=\"s\">INTEGER</span><span class=\"nv\"> </span><span class=\"s\">USING</span><span class=\"nv\"> </span><span class=\"s\">COALESCE(NULLIF(doterm,</span><span class=\"nv\"> </span><span class=\"s\">&#39;&#39;),</span><span class=\"nv\"> </span><span class=\"s\">&#39;0&#39;)::INT;</span><span class=\"nv\"> </span><span class=\"s\">ALTER</span><span class=\"nv\"> </span><span class=\"s\">TABLE</span><span class=\"nv\"> </span><span class=\"s\">demo.postcode_lookup</span><span class=\"nv\"> </span><span class=\"s\">ALTER</span><span class=\"nv\"> </span><span class=\"s\">COLUMN</span><span class=\"nv\"> </span><span class=\"s\">usertype</span><span class=\"nv\"> </span><span class=\"s\">TYPE</span><span class=\"nv\"> </span><span class=\"s\">SMALLINT</span><span class=\"nv\"> </span><span class=\"s\">USING</span><span class=\"nv\"> </span><span class=\"s\">COALESCE(NULLIF(usertype,</span><span class=\"nv\"> </span><span class=\"s\">&#39;&#39;),</span><span class=\"nv\"> </span><span class=\"s\">&#39;0&#39;)::SMALLINT;&quot;</span>\n",
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    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l yaml script/8-4-load-postcodes.yaml\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
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  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8-4-load-postcodes.yaml                       100%  742    81.6KB/s   00:00    \n",
      "2019-10-25 09:37:51|INFO|gpload session started 2019-10-25 09:37:51\n",
      "2019-10-25 09:37:51|INFO|no host supplied, defaulting to localhost\n",
      "2019-10-25 09:37:51|INFO|started gpfdist -p 8000 -P 9000 -f \"/var/tmp_s3_data/PCD_OA_LSOA_MSOA_LAD_AUG19_UK_LU.csv\" -t 30 -m 1000000\n",
      "2019-10-25 09:37:51|INFO|did not find an external table to reuse. creating ext_gpload_reusable_bafde0cc_f702_11e9_85c5_06a90659ec6c\n",
      "2019-10-25 09:38:11|INFO|running time: 20.10 seconds\n",
      "2019-10-25 09:38:11|INFO|rows Inserted          = 2632804\n",
      "2019-10-25 09:38:11|INFO|rows Updated           = 0\n",
      "2019-10-25 09:38:11|INFO|data formatting errors = 0\n",
      "2019-10-25 09:38:11|INFO|gpload succeeded\n"
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   ],
   "source": [
    "!scp -i ~/.ssh/aws-gp.pem script/8-4-load-postcodes.yaml $DB_USER@$DB_SERVER:load-postcodes.yaml\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'if [ -f ./load-postcodes.log ]; then rm ./load-postcodes.log; fi'\n",
    "\n",
    "cmd = \"gpload -d {0} -f ./load-postcodes.yaml -l ./load-postcodes.log 2>&1\".format(DB_USER) \n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER $cmd"
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   "source": [
    "- **Load UK Postcodes Geogaphical Information `ukpostcodes.csv` file into `demo.postcodelatlng` table using GPLoad**"
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  {
   "cell_type": "code",
   "execution_count": 42,
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       "<span class=\"nt\">VERSION</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">1.0.0.1</span>\n",
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   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l yaml script/8-4-load-postcodes-coord.yaml\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
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  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "text": [
      "8-4-load-postcodes-coord.yaml                 100%  351    27.1KB/s   00:00    \n",
      "2019-10-25 09:38:12|INFO|gpload session started 2019-10-25 09:38:12\n",
      "2019-10-25 09:38:12|INFO|no host supplied, defaulting to localhost\n",
      "2019-10-25 09:38:12|INFO|started gpfdist -p 8000 -P 9000 -f \"/var/tmp_s3_data/ukpostcodes.csv\" -t 30 -m 1000000\n",
      "2019-10-25 09:38:12|INFO|did not find an external table to reuse. creating ext_gpload_reusable_c7f5a882_f702_11e9_87d0_06a90659ec6c\n",
      "2019-10-25 09:38:13|INFO|running time: 0.69 seconds\n",
      "2019-10-25 09:38:13|INFO|rows Inserted          = 1762397\n",
      "2019-10-25 09:38:13|INFO|rows Updated           = 0\n",
      "2019-10-25 09:38:13|INFO|data formatting errors = 0\n",
      "2019-10-25 09:38:13|INFO|gpload succeeded\n"
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    }
   ],
   "source": [
    "!scp -i ~/.ssh/aws-gp.pem script/8-4-load-postcodes-coord.yaml $DB_USER@$DB_SERVER:load-postcodes-coord.yaml\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'if [ -f ./load-postcodes-coord.log ]; then rm ./load-postcodes-coord.log; fi'\n",
    "\n",
    "cmd = \"gpload -d {0} -f ./load-postcodes-coord.yaml -l ./load-postcodes-coord.log 2>&1\".format(DB_USER) \n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER $cmd"
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   "metadata": {},
   "source": [
    "- **Load Lower Layer Super Output Area information from `london-lsoa.json` file into `demo.london_LSOA` table**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">DELETE</span> <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_LSOA_TXT</span><span class=\"p\">;</span> \n",
       "<span class=\"k\">INSERT</span> <span class=\"k\">INTO</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_LSOA_TXT</span> <span class=\"p\">(</span><span class=\"k\">input</span><span class=\"p\">)</span> <span class=\"k\">VALUES</span><span class=\"p\">(</span><span class=\"s1\">&#39;{0}&#39;</span><span class=\"p\">);</span> \n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-4-load-london-lsoa-txt.sql\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 rows affected.\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import boto3\n",
    "import json\n",
    "\n",
    "s3 = boto3.resource('s3')\n",
    "\n",
    "content_object = s3.Object('uber-movement-ldn', 'london-lsoa.json')\n",
    "file_content = content_object.get()['Body'].read().decode('utf-8')\n",
    "json_content = json.loads(file_content)\n",
    "\n",
    "data = json.dumps(json_content)\n",
    "\n",
    "lnd_lsoa_txt_cmd = !cat script/8-4-load-london-lsoa-txt.sql\n",
    "lnd_lsoa_txt_cmd = ''.join(lnd_lsoa_txt_cmd).format(str(data))\n",
    "\n",
    "%sql $DB_USER@$DB_SERVER {lnd_lsoa_txt_cmd}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">INSERT</span> <span class=\"k\">INTO</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_LSOA</span> \n",
       "<span class=\"k\">SELECT</span> \n",
       "    <span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;type&#39;</span> <span class=\"k\">AS</span> <span class=\"n\">dat_type</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">ST_GeomFromGeoJSON</span><span class=\"p\">(</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;geometry&#39;</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">geometry</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;msoa_code&#39;</span> <span class=\"k\">AS</span> <span class=\"n\">msoa_code</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;msoa_name&#39;</span> <span class=\"k\">AS</span> <span class=\"n\">msoa_name</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;la_code&#39;</span> <span class=\"k\">AS</span> <span class=\"n\">la_code</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;la_name&#39;</span> <span class=\"k\">AS</span> <span class=\"n\">la_name</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">(((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;geoeast&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">INT</span> <span class=\"k\">AS</span> <span class=\"n\">geoeast</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">(((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;geonorth&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">INT</span> <span class=\"k\">AS</span> <span class=\"n\">geonorth</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">(((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;popeast&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">INT</span> <span class=\"k\">AS</span> <span class=\"n\">popeast</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">(((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;popnorth&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">INT</span> <span class=\"k\">AS</span> <span class=\"n\">popnorth</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">(((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;area_km2&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">NUMERIC</span> <span class=\"k\">AS</span> <span class=\"n\">area_km2</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">(((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;MOVEMENT_ID&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">INT</span> <span class=\"k\">AS</span> <span class=\"n\">movement_id</span><span class=\"p\">,</span> \n",
       "    <span class=\"p\">((</span><span class=\"n\">dat_features</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;properties&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span><span class=\"p\">)</span><span class=\"o\">-&gt;&gt;</span><span class=\"s1\">&#39;DISPLAY_NAME&#39;</span> <span class=\"k\">AS</span> <span class=\"n\">display_name</span> \n",
       "<span class=\"k\">FROM</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">JSON_ARRAY_ELEMENTS</span><span class=\"p\">(</span><span class=\"n\">dat</span><span class=\"o\">-&gt;</span><span class=\"s1\">&#39;features&#39;</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">json</span> <span class=\"k\">AS</span> <span class=\"n\">dat_features</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"p\">(</span> \n",
       "        <span class=\"k\">SELECT</span> <span class=\"k\">input</span><span class=\"o\">::</span><span class=\"nb\">JSON</span> <span class=\"k\">AS</span> <span class=\"n\">dat</span> \n",
       "        <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_LSOA_TXT</span> \n",
       "    <span class=\"p\">)</span> <span class=\"n\">A</span> \n",
       "<span class=\"p\">)</span> <span class=\"n\">foo</span><span class=\"p\">;</span> \n",
       "\n",
       "<span class=\"k\">SELECT</span> <span class=\"n\">COUNT</span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)</span> <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_lsoa</span><span class=\"p\">;</span> \n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-4-load-london-lsoa.sql\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "983 rows affected.\n",
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
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       "<table>\n",
       "    <tr>\n",
       "        <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>983</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(983,)]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lnd_lsoa_cmd = !cat script/8-4-load-london-lsoa.sql\n",
    "\n",
    "%sql $DB_USER@$DB_SERVER {''.join(lnd_lsoa_cmd)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Load Shape file of the major road network Information from `2018-MRDB-minimal.shp` file into `demo.major_roads_network` table using SHP2PGSQL and psql utilities**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "<div class=\"highlight\"><pre><span></span>/usr/local/greenplum-db/bin/shp2pgsql -d -D -i /var/tmp_s3_data/2018-MRDB-minimal.shp demo.major_roads_network &gt; /var/tmp_s3_data/2018-MRDB-minimal.sql\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "shp2pgsqlcode = !pygmentize -f html -O full,style=friendly -l shell script/8-5-major-road-network-shp2pgsql.sh\n",
    "display_html('\\n'.join(shp2pgsqlcode), raw=True)"
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  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapefile type: Arc\r\n",
      "Postgis type: MULTILINESTRING[2]\r\n"
     ]
    }
   ],
   "source": [
    "shp2pgsqlcmd = !cat script/8-5-major-road-network-shp2pgsql.sh\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER \"{''.join(shp2pgsqlcmd)}\""
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  {
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   "execution_count": 50,
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       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"n\">psql</span> <span class=\"o\">-</span><span class=\"n\">d</span> <span class=\"p\">{</span><span class=\"mf\">0</span><span class=\"p\">}</span> <span class=\"o\">-</span><span class=\"n\">f</span> <span class=\"s1\">&#39;/var/tmp_s3_data/2018-MRDB-minimal.sql&#39;</span>\n",
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    "psqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-5-major-road-network-psql.sh\n",
    "display_html('\\n'.join(psqlfilecode), raw=True)"
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  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SET\n",
      "SET\n",
      "BEGIN\n",
      "psql:/var/tmp_s3_data/2018-MRDB-minimal.sql:3: ERROR:  column not found in geometry_columns table\n",
      "CONTEXT:  SQL statement \"SELECT DropGeometryColumn('', $1 , $2 , $3 )\"\n",
      "PL/pgSQL function \"dropgeometrycolumn\" line 4 at SQL statement\n",
      "psql:/var/tmp_s3_data/2018-MRDB-minimal.sql:4: ERROR:  table \"major_roads_network\" does not exist\n",
      "psql:/var/tmp_s3_data/2018-MRDB-minimal.sql:8: NOTICE:  CREATE TABLE will create implicit sequence \"major_roads_network_gid_seq\" for serial column \"major_roads_network.gid\"\n",
      "psql:/var/tmp_s3_data/2018-MRDB-minimal.sql:8: NOTICE:  Table doesn't have 'DISTRIBUTED BY' clause -- Using column named 'gid' as the Greenplum Database data distribution key for this table.\n",
      "HINT:  The 'DISTRIBUTED BY' clause determines the distribution of data. Make sure column(s) chosen are the optimal data distribution key to minimize skew.\n",
      "CREATE TABLE\n",
      "psql:/var/tmp_s3_data/2018-MRDB-minimal.sql:9: NOTICE:  ALTER TABLE / ADD PRIMARY KEY will create implicit index \"major_roads_network_pkey\" for table \"major_roads_network\"\n",
      "ALTER TABLE\n",
      "                         addgeometrycolumn                         \n",
      "-------------------------------------------------------------------\n",
      " demo.major_roads_network.geom SRID:0 TYPE:MULTILINESTRING DIMS:2 \n",
      "(1 row)\n",
      "\n",
      "COMMIT\n"
     ]
    }
   ],
   "source": [
    "psql_cmd = !cat script/8-5-major-road-network-psql.sh\n",
    "psql_cmd = ''.join(psql_cmd).format(DB_USER)\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER \"{psql_cmd}\""
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       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">SELECT</span> <span class=\"n\">COUNT</span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"p\">)</span> <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">major_roads_network</span><span class=\"p\">;</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-5-major-road-network-check.sql\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>18193</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(18193,)]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = !cat script/8-5-major-road-network-check.sql\n",
    "%sql $DB_USER@$DB_SERVER {''.join(query)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- **Load Vehicle counts recorded on major and minor roads (2000 - 2018) from `dft_traffic_counts_raw_counts.zip` file into `demo.dft_traffic_counts_raw` table using GPLoad**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"nn\">---</span>\n",
       "<span class=\"nt\">VERSION</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">1.0.0.1</span>\n",
       "<span class=\"nt\">GPLOAD</span><span class=\"p\">:</span>\n",
       "   <span class=\"nt\">INPUT</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">SOURCE</span><span class=\"p\">:</span>\n",
       "         <span class=\"nt\">FILE</span><span class=\"p\">:</span>\n",
       "           <span class=\"p p-Indicator\">-</span> <span class=\"l l-Scalar l-Scalar-Plain\">/var/tmp_s3_data/dft_traffic_counts_raw_counts.tar.gz</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">FORMAT</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">csv</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">HEADER</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">NULL_AS</span><span class=\"p\">:</span> <span class=\"s\">&#39;NULL&#39;</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">LOG_ERRORS</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">MAX_LINE_LENGTH</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">1000000</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">ERROR_LIMIT</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">50000</span>\n",
       "   <span class=\"nt\">OUTPUT</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">TABLE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">demo.dft_traffic_counts_raw</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">MODE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">insert</span>\n",
       "   <span class=\"nt\">PRELOAD</span><span class=\"p\">:</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">TRUNCATE</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "    <span class=\"p p-Indicator\">-</span> <span class=\"nt\">REUSE_TABLES</span><span class=\"p\">:</span> <span class=\"l l-Scalar l-Scalar-Plain\">true</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l yaml script/8-6-load-raw-traffic-counts.yaml\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8-6-load-raw-traffic-counts.yaml              100%  401    52.3KB/s   00:00    \n",
      "2019-10-25 09:38:23|INFO|gpload session started 2019-10-25 09:38:23\n",
      "2019-10-25 09:38:23|INFO|no host supplied, defaulting to localhost\n",
      "2019-10-25 09:38:23|INFO|started gpfdist -p 8000 -P 9000 -f \"/var/tmp_s3_data/dft_traffic_counts_raw_counts.tar.gz\" -t 30 -m 1000000\n",
      "2019-10-25 09:38:23|INFO|did not find an external table to reuse. creating ext_gpload_reusable_ce34dc90_f702_11e9_ac18_06a90659ec6c\n",
      "2019-10-25 09:38:26|WARN|1 bad row\n",
      "2019-10-25 09:38:26|WARN|Please use following query to access the detailed error\n",
      "2019-10-25 09:38:26|WARN|select * from gp_read_error_log('ext_gpload_reusable_ce34dc90_f702_11e9_ac18_06a90659ec6c') where cmdtime > to_timestamp('1571992703.31')\n",
      "2019-10-25 09:38:26|INFO|running time: 3.38 seconds\n",
      "2019-10-25 09:38:26|INFO|rows Inserted          = 3919788\n",
      "2019-10-25 09:38:26|INFO|rows Updated           = 0\n",
      "2019-10-25 09:38:26|INFO|data formatting errors = 1\n",
      "2019-10-25 09:38:26|INFO|gpload succeeded with warnings\n"
     ]
    }
   ],
   "source": [
    "!scp -i ~/.ssh/aws-gp.pem script/8-6-load-raw-traffic-counts.yaml $DB_USER@$DB_SERVER:load-raw-traffic-counts.yaml\n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER 'if [ -f ./load-raw-traffic-counts.log ]; then rm ./load-raw-traffic-counts.log; fi'\n",
    "\n",
    "cmd = \"gpload -d {0} -f ./load-raw-traffic-counts.yaml -l ./load-raw-traffic-counts.log 2>&1\".format(DB_USER) \n",
    "!ssh -i ~/.ssh/aws-gp.pem $DB_USER@$DB_SERVER $cmd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.7 Collect statictics over the imported datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
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       "body .bp { color: #007020 } /* Name.Builtin.Pseudo */\n",
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       "body .vc { color: #bb60d5 } /* Name.Variable.Class */\n",
       "body .vg { color: #bb60d5 } /* Name.Variable.Global */\n",
       "body .vi { color: #bb60d5 } /* Name.Variable.Instance */\n",
       "body .vm { color: #bb60d5 } /* Name.Variable.Magic */\n",
       "body .il { color: #40a070 } /* Literal.Number.Integer.Long */\n",
       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">ANALYZE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">uber_mov_ldn</span> <span class=\"p\">(</span><span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">);</span>\n",
       "<span class=\"k\">ANALYZE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_lsoa</span> <span class=\"p\">(</span><span class=\"n\">movement_id</span><span class=\"p\">);</span>\n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-7-collect-table-statistics.sql\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = !cat script/8-7-collect-table-statistics.sql\n",
    "%sql $DB_USER@$DB_SERVER {''.join(query)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.8 Prepare data structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "body .bp { color: #007020 } /* Name.Builtin.Pseudo */\n",
       "body .fm { color: #06287e } /* Name.Function.Magic */\n",
       "body .vc { color: #bb60d5 } /* Name.Variable.Class */\n",
       "body .vg { color: #bb60d5 } /* Name.Variable.Global */\n",
       "body .vi { color: #bb60d5 } /* Name.Variable.Instance */\n",
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       "body .il { color: #40a070 } /* Literal.Number.Integer.Long */\n",
       "\n",
       "  </style>\n",
       "</head>\n",
       "<body>\n",
       "<h2></h2>\n",
       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">DROP</span> <span class=\"k\">VIEW</span> <span class=\"k\">IF</span> <span class=\"k\">EXISTS</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span><span class=\"p\">;</span> \n",
       " \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">VIEW</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> <span class=\"k\">AS</span> \n",
       "<span class=\"k\">SELECT</span> \n",
       "    <span class=\"n\">qoy</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">hod</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">sourceid</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">dstid</span><span class=\"p\">,</span>  \n",
       "    <span class=\"n\">uml</span><span class=\"mf\">.</span><span class=\"n\">mean_travel_time</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">ROUND</span><span class=\"p\">(((</span><span class=\"mf\">10</span> <span class=\"o\">*</span> <span class=\"n\">ST_Distance</span><span class=\"p\">(</span><span class=\"n\">ST_Centroid</span><span class=\"p\">(</span><span class=\"n\">ll1</span><span class=\"mf\">.</span><span class=\"n\">geometry</span><span class=\"p\">),</span> <span class=\"n\">ST_Centroid</span><span class=\"p\">(</span><span class=\"n\">ll2</span><span class=\"mf\">.</span><span class=\"n\">geometry</span><span class=\"p\">))</span> <span class=\"o\">*</span> <span class=\"mf\">111257</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mf\">36</span> <span class=\"o\">*</span> <span class=\"n\">uml</span><span class=\"mf\">.</span><span class=\"n\">mean_travel_time</span><span class=\"p\">))</span><span class=\"o\">::</span><span class=\"nb\">NUMERIC</span><span class=\"p\">,</span> <span class=\"mf\">5</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">ST_Kmph</span><span class=\"p\">,</span> \n",
       "    <span class=\"n\">ROUND</span><span class=\"p\">((</span><span class=\"mf\">10</span> <span class=\"o\">*</span> <span class=\"n\">ST_Distance</span><span class=\"p\">(</span><span class=\"n\">ST_Centroid</span><span class=\"p\">(</span><span class=\"n\">ll1</span><span class=\"mf\">.</span><span class=\"n\">geometry</span><span class=\"p\">),</span> <span class=\"n\">ST_Centroid</span><span class=\"p\">(</span><span class=\"n\">ll2</span><span class=\"mf\">.</span><span class=\"n\">geometry</span><span class=\"p\">))</span> <span class=\"o\">*</span> <span class=\"mf\">111257</span><span class=\"p\">)</span><span class=\"o\">::</span><span class=\"nb\">NUMERIC</span><span class=\"p\">,</span> <span class=\"mf\">5</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">ST_DistanceKm</span> \n",
       "<span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">uber_mov_ldn</span> <span class=\"k\">AS</span> <span class=\"n\">uml</span> \n",
       "<span class=\"k\">INNER</span> <span class=\"k\">JOIN</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_lsoa</span> <span class=\"k\">AS</span> <span class=\"n\">ll1</span> \n",
       "    <span class=\"k\">ON</span> <span class=\"n\">uml</span><span class=\"mf\">.</span><span class=\"n\">sourceid</span> <span class=\"o\">=</span> <span class=\"n\">ll1</span><span class=\"mf\">.</span><span class=\"n\">movement_id</span> \n",
       "<span class=\"k\">INNER</span> <span class=\"k\">JOIN</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">london_lsoa</span> <span class=\"k\">AS</span> <span class=\"n\">ll2</span> \n",
       "    <span class=\"k\">ON</span> <span class=\"n\">uml</span><span class=\"mf\">.</span><span class=\"n\">dstid</span> <span class=\"o\">=</span> <span class=\"n\">ll2</span><span class=\"mf\">.</span><span class=\"n\">movement_id</span><span class=\"p\">;</span> \n",
       " \n",
       "<span class=\"k\">DROP</span> <span class=\"k\">TABLE</span> <span class=\"k\">IF</span> <span class=\"k\">EXISTS</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_max_kmph_per_ts_src_dst</span><span class=\"p\">;</span> \n",
       "     \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_max_kmph_per_ts_src_dst</span> <span class=\"k\">AS</span> \n",
       "<span class=\"k\">SELECT</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"o\">*</span> \n",
       "<span class=\"k\">FROM</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">,</span> <span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"n\">ST_Kmph</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">max_kmph</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">)</span> <span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"n\">ST_Kmph</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">max_kmph</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">)</span> <span class=\"n\">B</span> \n",
       "<span class=\"k\">WHERE</span> \n",
       "    <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">hod</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">hod</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">max_kmph</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">max_kmph</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">);</span> \n",
       "     \n",
       "<span class=\"k\">DROP</span> <span class=\"k\">TABLE</span> <span class=\"k\">IF</span> <span class=\"k\">EXISTS</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_max_distancekm_per_ts_src_dst</span><span class=\"p\">;</span> \n",
       "     \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_max_distancekm_per_ts_src_dst</span> <span class=\"k\">AS</span> \n",
       "<span class=\"k\">SELECT</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"o\">*</span> \n",
       "<span class=\"k\">FROM</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">,</span> <span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"n\">ST_DistanceKm</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">max_distancekm</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">)</span> <span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">MAX</span><span class=\"p\">(</span><span class=\"n\">ST_DistanceKm</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">max_distancekm</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">)</span> <span class=\"n\">B</span> \n",
       "<span class=\"k\">WHERE</span> \n",
       "    <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">hod</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">hod</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">max_distancekm</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">max_distancekm</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">);</span> \n",
       " \n",
       "<span class=\"k\">DROP</span> <span class=\"k\">TABLE</span> <span class=\"k\">IF</span> <span class=\"k\">EXISTS</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_min_kmph_per_ts_src_dst</span><span class=\"p\">;</span> \n",
       "     \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_min_kmph_per_ts_src_dst</span> <span class=\"k\">AS</span> \n",
       "<span class=\"k\">SELECT</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"o\">*</span> \n",
       "<span class=\"k\">FROM</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">,</span> <span class=\"n\">MIN</span><span class=\"p\">(</span><span class=\"n\">ST_Kmph</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">min_kmph</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">)</span> <span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">MIN</span><span class=\"p\">(</span><span class=\"n\">ST_Kmph</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">min_kmph</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">)</span> <span class=\"n\">B</span> \n",
       "<span class=\"k\">WHERE</span> \n",
       "    <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">hod</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">hod</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">min_kmph</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">min_kmph</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">);</span> \n",
       "     \n",
       "<span class=\"k\">DROP</span> <span class=\"k\">TABLE</span> <span class=\"k\">IF</span> <span class=\"k\">EXISTS</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_min_distancekm_per_ts_src_dst</span><span class=\"p\">;</span> \n",
       "     \n",
       "<span class=\"k\">CREATE</span> <span class=\"k\">TABLE</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">ldn_min_distancekm_per_ts_src_dst</span> <span class=\"k\">AS</span> \n",
       "<span class=\"k\">SELECT</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"o\">*</span> \n",
       "<span class=\"k\">FROM</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">,</span> <span class=\"n\">MIN</span><span class=\"p\">(</span><span class=\"n\">ST_DistanceKm</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">min_distancekm</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">)</span> <span class=\"n\">A</span><span class=\"p\">,</span> <span class=\"p\">(</span> \n",
       "    <span class=\"k\">SELECT</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">,</span> <span class=\"n\">MIN</span><span class=\"p\">(</span><span class=\"n\">ST_DistanceKm</span><span class=\"p\">)</span> <span class=\"k\">AS</span> <span class=\"n\">min_distancekm</span> \n",
       "    <span class=\"k\">FROM</span> <span class=\"n\">demo</span><span class=\"mf\">.</span><span class=\"n\">travel_speed</span> \n",
       "    <span class=\"k\">GROUP</span> <span class=\"k\">BY</span> <span class=\"n\">qoy</span><span class=\"p\">,</span> <span class=\"n\">hod</span><span class=\"p\">)</span> <span class=\"n\">B</span> \n",
       "<span class=\"k\">WHERE</span> \n",
       "    <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">qoy</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">hod</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">hod</span> \n",
       "    <span class=\"k\">AND</span> <span class=\"n\">A</span><span class=\"mf\">.</span><span class=\"n\">min_distancekm</span> <span class=\"o\">=</span> <span class=\"n\">B</span><span class=\"mf\">.</span><span class=\"n\">min_distancekm</span> \n",
       "<span class=\"n\">DISTRIBUTED</span> <span class=\"k\">BY</span> <span class=\"p\">(</span><span class=\"n\">sourceid</span><span class=\"p\">,</span> <span class=\"n\">dstid</span><span class=\"p\">);</span> \n",
       "</pre></div>\n",
       "</body>\n",
       "</html>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sqlfilecode = !pygmentize -f html -O full,style=friendly -l postgres script/8-8-postgis-support-data-structures.sql\n",
    "display_html('\\n'.join(sqlfilecode), raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n",
      "Done.\n",
      "Done.\n",
      "216 rows affected.\n",
      "Done.\n",
      "263 rows affected.\n",
      "Done.\n",
      "8319 rows affected.\n",
      "Done.\n",
      "8517 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = !cat script/8-8-postgis-support-data-structures.sql\n",
    "%sql $DB_USER@$DB_SERVER {''.join(query)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 PostGIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 rows affected.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hod</th>\n",
       "      <th>max_kmph_avg_qoy_201601</th>\n",
       "      <th>max_kmph_avg_qoy_201602</th>\n",
       "      <th>max_kmph_avg_qoy_201603</th>\n",
       "      <th>max_kmph_avg_qoy_201604</th>\n",
       "      <th>max_kmph_avg_qoy_201701</th>\n",
       "      <th>max_kmph_avg_qoy_201702</th>\n",
       "      <th>max_kmph_avg_qoy_201703</th>\n",
       "      <th>max_kmph_avg_qoy_201704</th>\n",
       "      <th>max_kmph_avg_qoy_201801</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.8942200000000000</td>\n",
       "      <td>18.8006700000000000</td>\n",
       "      <td>20.7449300000000000</td>\n",
       "      <td>20.6964700000000000</td>\n",
       "      <td>20.9865300000000000</td>\n",
       "      <td>20.8466200000000000</td>\n",
       "      <td>21.0488600000000000</td>\n",
       "      <td>21.4264000000000000</td>\n",
       "      <td>21.6180900000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21.5611000000000000</td>\n",
       "      <td>22.3874000000000000</td>\n",
       "      <td>23.3716800000000000</td>\n",
       "      <td>23.7316500000000000</td>\n",
       "      <td>23.3460200000000000</td>\n",
       "      <td>21.7998300000000000</td>\n",
       "      <td>22.4963600000000000</td>\n",
       "      <td>22.4157200000000000</td>\n",
       "      <td>22.8100800000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22.4536000000000000</td>\n",
       "      <td>22.6111800000000000</td>\n",
       "      <td>24.3457200000000000</td>\n",
       "      <td>23.0574200000000000</td>\n",
       "      <td>21.3919100000000000</td>\n",
       "      <td>22.1080800000000000</td>\n",
       "      <td>22.9727300000000000</td>\n",
       "      <td>22.6497200000000000</td>\n",
       "      <td>21.7063600000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>20.3556900000000000</td>\n",
       "      <td>21.2338300000000000</td>\n",
       "      <td>23.3306500000000000</td>\n",
       "      <td>20.6602700000000000</td>\n",
       "      <td>20.1550300000000000</td>\n",
       "      <td>22.0988900000000000</td>\n",
       "      <td>20.4221600000000000</td>\n",
       "      <td>21.2465600000000000</td>\n",
       "      <td>20.9575700000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>20.8425400000000000</td>\n",
       "      <td>20.6442300000000000</td>\n",
       "      <td>22.1541500000000000</td>\n",
       "      <td>20.0221800000000000</td>\n",
       "      <td>19.9246200000000000</td>\n",
       "      <td>20.5723200000000000</td>\n",
       "      <td>20.0902800000000000</td>\n",
       "      <td>21.4436800000000000</td>\n",
       "      <td>19.3445700000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>20.8777800000000000</td>\n",
       "      <td>21.8420100000000000</td>\n",
       "      <td>23.9757000000000000</td>\n",
       "      <td>20.4778400000000000</td>\n",
       "      <td>20.7154800000000000</td>\n",
       "      <td>21.8222000000000000</td>\n",
       "      <td>20.7733000000000000</td>\n",
       "      <td>20.6984900000000000</td>\n",
       "      <td>20.9534400000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>18.7278100000000000</td>\n",
       "      <td>20.0297200000000000</td>\n",
       "      <td>18.6228400000000000</td>\n",
       "      <td>18.8406500000000000</td>\n",
       "      <td>18.8039900000000000</td>\n",
       "      <td>21.1872800000000000</td>\n",
       "      <td>18.8607000000000000</td>\n",
       "      <td>19.3094400000000000</td>\n",
       "      <td>19.1184600000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>15.5648400000000000</td>\n",
       "      <td>16.2441200000000000</td>\n",
       "      <td>15.7737600000000000</td>\n",
       "      <td>15.6741500000000000</td>\n",
       "      <td>15.3971600000000000</td>\n",
       "      <td>15.4964200000000000</td>\n",
       "      <td>16.0455400000000000</td>\n",
       "      <td>15.2969400000000000</td>\n",
       "      <td>15.1914800000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>15.4376100000000000</td>\n",
       "      <td>14.8014300000000000</td>\n",
       "      <td>15.1046900000000000</td>\n",
       "      <td>15.4083700000000000</td>\n",
       "      <td>15.0616700000000000</td>\n",
       "      <td>14.5759500000000000</td>\n",
       "      <td>15.1241300000000000</td>\n",
       "      <td>14.3895300000000000</td>\n",
       "      <td>14.9678800000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>17.0636900000000000</td>\n",
       "      <td>15.1533800000000000</td>\n",
       "      <td>15.6107800000000000</td>\n",
       "      <td>15.3882000000000000</td>\n",
       "      <td>15.7761100000000000</td>\n",
       "      <td>15.1068500000000000</td>\n",
       "      <td>15.5191600000000000</td>\n",
       "      <td>15.4896100000000000</td>\n",
       "      <td>15.5305500000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>15.8114900000000000</td>\n",
       "      <td>14.4742100000000000</td>\n",
       "      <td>15.3837200000000000</td>\n",
       "      <td>15.2947300000000000</td>\n",
       "      <td>15.4760100000000000</td>\n",
       "      <td>15.2616300000000000</td>\n",
       "      <td>15.3949200000000000</td>\n",
       "      <td>15.3546800000000000</td>\n",
       "      <td>15.4782800000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>15.7410800000000000</td>\n",
       "      <td>14.2059200000000000</td>\n",
       "      <td>15.2858900000000000</td>\n",
       "      <td>15.1306200000000000</td>\n",
       "      <td>15.3770100000000000</td>\n",
       "      <td>15.1284600000000000</td>\n",
       "      <td>15.1025400000000000</td>\n",
       "      <td>16.2580400000000000</td>\n",
       "      <td>15.2111400000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>15.3970300000000000</td>\n",
       "      <td>14.4091400000000000</td>\n",
       "      <td>15.1958400000000000</td>\n",
       "      <td>15.2969400000000000</td>\n",
       "      <td>15.0231600000000000</td>\n",
       "      <td>14.9046000000000000</td>\n",
       "      <td>14.4643100000000000</td>\n",
       "      <td>15.0082400000000000</td>\n",
       "      <td>15.0402500000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>16.7845600000000000</td>\n",
       "      <td>14.6466000000000000</td>\n",
       "      <td>14.2441800000000000</td>\n",
       "      <td>14.7199800000000000</td>\n",
       "      <td>14.7982500000000000</td>\n",
       "      <td>14.7528300000000000</td>\n",
       "      <td>14.5860000000000000</td>\n",
       "      <td>14.8272900000000000</td>\n",
       "      <td>14.8044600000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>15.2067700000000000</td>\n",
       "      <td>14.8460200000000000</td>\n",
       "      <td>14.0736000000000000</td>\n",
       "      <td>14.8127500000000000</td>\n",
       "      <td>14.7487200000000000</td>\n",
       "      <td>14.9718800000000000</td>\n",
       "      <td>15.0572600000000000</td>\n",
       "      <td>14.8815300000000000</td>\n",
       "      <td>14.6709800000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15.5359000000000000</td>\n",
       "      <td>14.0232100000000000</td>\n",
       "      <td>14.8397700000000000</td>\n",
       "      <td>14.8127500000000000</td>\n",
       "      <td>14.5598900000000000</td>\n",
       "      <td>14.5418800000000000</td>\n",
       "      <td>14.4228900000000000</td>\n",
       "      <td>14.3817100000000000</td>\n",
       "      <td>14.2422700000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>14.3623000000000000</td>\n",
       "      <td>14.4327300000000000</td>\n",
       "      <td>14.2981000000000000</td>\n",
       "      <td>14.0287900000000000</td>\n",
       "      <td>14.0474300000000000</td>\n",
       "      <td>14.0979800000000000</td>\n",
       "      <td>13.8017800000000000</td>\n",
       "      <td>13.8505800000000000</td>\n",
       "      <td>13.6099700000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>13.6697800000000000</td>\n",
       "      <td>13.0830100000000000</td>\n",
       "      <td>13.7069800000000000</td>\n",
       "      <td>12.6389900000000000</td>\n",
       "      <td>13.3407000000000000</td>\n",
       "      <td>13.4541500000000000</td>\n",
       "      <td>13.1546100000000000</td>\n",
       "      <td>12.6465500000000000</td>\n",
       "      <td>12.9121100000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>12.6814200000000000</td>\n",
       "      <td>12.8353200000000000</td>\n",
       "      <td>13.5316100000000000</td>\n",
       "      <td>12.2241200000000000</td>\n",
       "      <td>13.2705100000000000</td>\n",
       "      <td>13.4473100000000000</td>\n",
       "      <td>13.1726300000000000</td>\n",
       "      <td>12.6753400000000000</td>\n",
       "      <td>12.6374800000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>13.0234000000000000</td>\n",
       "      <td>13.5437400000000000</td>\n",
       "      <td>13.6082200000000000</td>\n",
       "      <td>12.7717700000000000</td>\n",
       "      <td>13.6765600000000000</td>\n",
       "      <td>13.7927500000000000</td>\n",
       "      <td>14.3076000000000000</td>\n",
       "      <td>13.0147800000000000</td>\n",
       "      <td>12.8729600000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>16.1381000000000000</td>\n",
       "      <td>15.2777400000000000</td>\n",
       "      <td>16.4731600000000000</td>\n",
       "      <td>15.1941300000000000</td>\n",
       "      <td>16.4833800000000000</td>\n",
       "      <td>16.4419600000000000</td>\n",
       "      <td>16.1283000000000000</td>\n",
       "      <td>15.2208700000000000</td>\n",
       "      <td>15.2514400000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>18.0474900000000000</td>\n",
       "      <td>16.3792600000000000</td>\n",
       "      <td>16.9080400000000000</td>\n",
       "      <td>18.0628200000000000</td>\n",
       "      <td>18.5287200000000000</td>\n",
       "      <td>17.2031300000000000</td>\n",
       "      <td>17.5732100000000000</td>\n",
       "      <td>17.3608400000000000</td>\n",
       "      <td>19.2429500000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>17.4177200000000000</td>\n",
       "      <td>16.7958600000000000</td>\n",
       "      <td>17.2314100000000000</td>\n",
       "      <td>17.0983900000000000</td>\n",
       "      <td>19.4899700000000000</td>\n",
       "      <td>16.5707300000000000</td>\n",
       "      <td>17.3551700000000000</td>\n",
       "      <td>16.2764500000000000</td>\n",
       "      <td>18.0444300000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>14.4062000000000000</td>\n",
       "      <td>14.9490700000000000</td>\n",
       "      <td>15.2317700000000000</td>\n",
       "      <td>16.3918900000000000</td>\n",
       "      <td>17.2930700000000000</td>\n",
       "      <td>14.6121200000000000</td>\n",
       "      <td>16.3314600000000000</td>\n",
       "      <td>16.3239300000000000</td>\n",
       "      <td>16.8946100000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hod max_kmph_avg_qoy_201601 max_kmph_avg_qoy_201602  \\\n",
       "0     0     18.8942200000000000     18.8006700000000000   \n",
       "1     1     21.5611000000000000     22.3874000000000000   \n",
       "2     2     22.4536000000000000     22.6111800000000000   \n",
       "3     3     20.3556900000000000     21.2338300000000000   \n",
       "4     4     20.8425400000000000     20.6442300000000000   \n",
       "5     5     20.8777800000000000     21.8420100000000000   \n",
       "6     6     18.7278100000000000     20.0297200000000000   \n",
       "7     7     15.5648400000000000     16.2441200000000000   \n",
       "8     8     15.4376100000000000     14.8014300000000000   \n",
       "9     9     17.0636900000000000     15.1533800000000000   \n",
       "10   10     15.8114900000000000     14.4742100000000000   \n",
       "11   11     15.7410800000000000     14.2059200000000000   \n",
       "12   12     15.3970300000000000     14.4091400000000000   \n",
       "13   13     16.7845600000000000     14.6466000000000000   \n",
       "14   14     15.2067700000000000     14.8460200000000000   \n",
       "15   15     15.5359000000000000     14.0232100000000000   \n",
       "16   16     14.3623000000000000     14.4327300000000000   \n",
       "17   17     13.6697800000000000     13.0830100000000000   \n",
       "18   18     12.6814200000000000     12.8353200000000000   \n",
       "19   19     13.0234000000000000     13.5437400000000000   \n",
       "20   20     16.1381000000000000     15.2777400000000000   \n",
       "21   21     18.0474900000000000     16.3792600000000000   \n",
       "22   22     17.4177200000000000     16.7958600000000000   \n",
       "23   23     14.4062000000000000     14.9490700000000000   \n",
       "\n",
       "   max_kmph_avg_qoy_201603 max_kmph_avg_qoy_201604 max_kmph_avg_qoy_201701  \\\n",
       "0      20.7449300000000000     20.6964700000000000     20.9865300000000000   \n",
       "1      23.3716800000000000     23.7316500000000000     23.3460200000000000   \n",
       "2      24.3457200000000000     23.0574200000000000     21.3919100000000000   \n",
       "3      23.3306500000000000     20.6602700000000000     20.1550300000000000   \n",
       "4      22.1541500000000000     20.0221800000000000     19.9246200000000000   \n",
       "5      23.9757000000000000     20.4778400000000000     20.7154800000000000   \n",
       "6      18.6228400000000000     18.8406500000000000     18.8039900000000000   \n",
       "7      15.7737600000000000     15.6741500000000000     15.3971600000000000   \n",
       "8      15.1046900000000000     15.4083700000000000     15.0616700000000000   \n",
       "9      15.6107800000000000     15.3882000000000000     15.7761100000000000   \n",
       "10     15.3837200000000000     15.2947300000000000     15.4760100000000000   \n",
       "11     15.2858900000000000     15.1306200000000000     15.3770100000000000   \n",
       "12     15.1958400000000000     15.2969400000000000     15.0231600000000000   \n",
       "13     14.2441800000000000     14.7199800000000000     14.7982500000000000   \n",
       "14     14.0736000000000000     14.8127500000000000     14.7487200000000000   \n",
       "15     14.8397700000000000     14.8127500000000000     14.5598900000000000   \n",
       "16     14.2981000000000000     14.0287900000000000     14.0474300000000000   \n",
       "17     13.7069800000000000     12.6389900000000000     13.3407000000000000   \n",
       "18     13.5316100000000000     12.2241200000000000     13.2705100000000000   \n",
       "19     13.6082200000000000     12.7717700000000000     13.6765600000000000   \n",
       "20     16.4731600000000000     15.1941300000000000     16.4833800000000000   \n",
       "21     16.9080400000000000     18.0628200000000000     18.5287200000000000   \n",
       "22     17.2314100000000000     17.0983900000000000     19.4899700000000000   \n",
       "23     15.2317700000000000     16.3918900000000000     17.2930700000000000   \n",
       "\n",
       "   max_kmph_avg_qoy_201702 max_kmph_avg_qoy_201703 max_kmph_avg_qoy_201704  \\\n",
       "0      20.8466200000000000     21.0488600000000000     21.4264000000000000   \n",
       "1      21.7998300000000000     22.4963600000000000     22.4157200000000000   \n",
       "2      22.1080800000000000     22.9727300000000000     22.6497200000000000   \n",
       "3      22.0988900000000000     20.4221600000000000     21.2465600000000000   \n",
       "4      20.5723200000000000     20.0902800000000000     21.4436800000000000   \n",
       "5      21.8222000000000000     20.7733000000000000     20.6984900000000000   \n",
       "6      21.1872800000000000     18.8607000000000000     19.3094400000000000   \n",
       "7      15.4964200000000000     16.0455400000000000     15.2969400000000000   \n",
       "8      14.5759500000000000     15.1241300000000000     14.3895300000000000   \n",
       "9      15.1068500000000000     15.5191600000000000     15.4896100000000000   \n",
       "10     15.2616300000000000     15.3949200000000000     15.3546800000000000   \n",
       "11     15.1284600000000000     15.1025400000000000     16.2580400000000000   \n",
       "12     14.9046000000000000     14.4643100000000000     15.0082400000000000   \n",
       "13     14.7528300000000000     14.5860000000000000     14.8272900000000000   \n",
       "14     14.9718800000000000     15.0572600000000000     14.8815300000000000   \n",
       "15     14.5418800000000000     14.4228900000000000     14.3817100000000000   \n",
       "16     14.0979800000000000     13.8017800000000000     13.8505800000000000   \n",
       "17     13.4541500000000000     13.1546100000000000     12.6465500000000000   \n",
       "18     13.4473100000000000     13.1726300000000000     12.6753400000000000   \n",
       "19     13.7927500000000000     14.3076000000000000     13.0147800000000000   \n",
       "20     16.4419600000000000     16.1283000000000000     15.2208700000000000   \n",
       "21     17.2031300000000000     17.5732100000000000     17.3608400000000000   \n",
       "22     16.5707300000000000     17.3551700000000000     16.2764500000000000   \n",
       "23     14.6121200000000000     16.3314600000000000     16.3239300000000000   \n",
       "\n",
       "   max_kmph_avg_qoy_201801  \n",
       "0      21.6180900000000000  \n",
       "1      22.8100800000000000  \n",
       "2      21.7063600000000000  \n",
       "3      20.9575700000000000  \n",
       "4      19.3445700000000000  \n",
       "5      20.9534400000000000  \n",
       "6      19.1184600000000000  \n",
       "7      15.1914800000000000  \n",
       "8      14.9678800000000000  \n",
       "9      15.5305500000000000  \n",
       "10     15.4782800000000000  \n",
       "11     15.2111400000000000  \n",
       "12     15.0402500000000000  \n",
       "13     14.8044600000000000  \n",
       "14     14.6709800000000000  \n",
       "15     14.2422700000000000  \n",
       "16     13.6099700000000000  \n",
       "17     12.9121100000000000  \n",
       "18     12.6374800000000000  \n",
       "19     12.8729600000000000  \n",
       "20     15.2514400000000000  \n",
       "21     19.2429500000000000  \n",
       "22     18.0444300000000000  \n",
       "23     16.8946100000000000  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    " \n",
    "# Make a data frame\n",
    "result = %sql $DB_USER@$DB_SERVER SELECT * FROM demo.ldn_pivot_1 ORDER BY 1\n",
    "df = result.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scratchpad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['12 AM',\n",
       " '01 AM',\n",
       " '02 AM',\n",
       " '03 AM',\n",
       " '04 AM',\n",
       " '05 AM',\n",
       " '06 AM',\n",
       " '07 AM',\n",
       " '08 AM',\n",
       " '09 AM',\n",
       " '10 AM',\n",
       " '11 AM',\n",
       " '12 PM',\n",
       " '01 PM',\n",
       " '02 PM',\n",
       " '03 PM',\n",
       " '04 PM',\n",
       " '05 PM',\n",
       " '06 PM',\n",
       " '07 PM',\n",
       " '08 PM',\n",
       " '09 PM',\n",
       " '10 PM',\n",
       " '11 PM']"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime as dt\n",
    "hours = [dt.time(i).strftime('%I %p') for i in range(24)]\n",
    "hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Speed(km/h)')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### style\n",
    "plt.style.use('seaborn-darkgrid')\n",
    "plt.figure(figsize=(24,8))\n",
    "\n",
    "plt.xticks(np.arange(24, step=1), hours[0:25])\n",
    " \n",
    "# create a color palette\n",
    "palette = plt.get_cmap('Set3')\n",
    " \n",
    "# multiple line plot\n",
    "num=0\n",
    "for column in df.drop('hod', axis=1):\n",
    "    num+=1\n",
    "    plt.plot(df['hod'], df[column], marker='', color=palette(num), linewidth=2, alpha=0.9, label=column[-6:])\n",
    " \n",
    "# Add legend\n",
    "plt.legend(loc='best', ncol=3)\n",
    " \n",
    "# Add titles\n",
    "plt.title(\"Average Speed per Time of Day (Source: Uber Movement, London dataset)\", \n",
    "          loc='left', fontsize=12, fontweight=2)\n",
    "plt.xlabel(\"Time of Day\")\n",
    "plt.ylabel(\"Speed(km/h)\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Raw Cell Format",
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}