docs/examples/academy/20. Cargo Movements Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 4\n",
    "\n",
    "## Video 20: Cargo Movements Example\n",
    "**Python for the Energy Industry**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this lesson, we'll consider another example of querying the SDK, having a look at Cargo Movements. To start with, we will search for all cargos that are currently being loaded. We specify the start and end times using a `datetime` object - this will be discussed in more detail in the next module.\n",
    "\n",
    "Documentation for the Cargo Movements endpoint can be [found here.](https://vortechsa.github.io/python-sdk/endpoints/cargo_movements/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vortexasdk as v\n",
    "from datetime import datetime\n",
    "now = datetime.utcnow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-12-10 15:12:17,839 vortexasdk.client — WARNING — You are using vortexasdk version 0.26.0, however version 0.27.1 is available.\n",
      "You should consider upgrading via the 'pip install vortexasdk --upgrade' command.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading from API: 1000it [00:01, 680.07it/s]             \n"
     ]
    }
   ],
   "source": [
    "cm_query = v.CargoMovements().search(\n",
    "    filter_activity=\"loading_state\",\n",
    "    filter_time_min=now,\n",
    "    filter_time_max=now)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "780"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many cargo movements do we get?\n",
    "len(cm_query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cargo_movement_id': '002fb0cdde927e8758bfdc001d016388002fb0cdde927e8758bfdc001d016388',\n",
       " 'quantity': 26102,\n",
       " 'status': 'unloaded_state',\n",
       " 'vessels': [{'id': 'bd959e52a27d98d327ccdcdbc543ee325aafd6eaa9a1752e1d0e9f4e7b353bd8',\n",
       "   'mmsi': 319498000,\n",
       "   'imo': 9102095,\n",
       "   'name': 'STOLT CREATIVITY',\n",
       "   'dwt': 37271,\n",
       "   'cubic_capacity': 39808,\n",
       "   'vessel_class': 'handysize',\n",
       "   'corporate_entities': [{'id': 'afd995e6928b97fa3799141cec71ed674f739528dd14af15be3791b02f287699',\n",
       "     'label': 'Stolt Tankers',\n",
       "     'layer': 'commercial_owner',\n",
       "     'probability': 1,\n",
       "     'source': 'external'}],\n",
       "   'start_timestamp': '2020-10-30T23:42:06+0000',\n",
       "   'end_timestamp': '2020-11-08T01:47:40+0000',\n",
       "   'fixture_fulfilled': False,\n",
       "   'voyage_id': '9e307b22b8ab351b2a471d168b1ccbda3cc6fe8f6bc41173b8f2794e24efdda3',\n",
       "   'tags': [{'tag': 'vessel_coated_tag'}],\n",
       "   'status': 'vessel_status_laden_known',\n",
       "   'year': 1997,\n",
       "   'scrubber': [],\n",
       "   'flag': [{'tag': 'vessel_flag_tag',\n",
       "     'flag': 'KY',\n",
       "     'flag_country': '9d95bba7023609ee9e3da95b119f27d8f0a7c2412c1c773010f6bd5b8cea0d94'}]}],\n",
       " 'product': [{'id': 'b68cbb746f8b9098c50e2ba36bcad83001a53bd362e9031fb49085d02c36659c',\n",
       "   'layer': 'group',\n",
       "   'probability': 1,\n",
       "   'source': 'model',\n",
       "   'label': 'Clean Petroleum Products'},\n",
       "  {'id': 'cbb5acffa542aad31d26712a8cdfd9884301aa7658ac95c40f86ddb31bc29d9b',\n",
       "   'layer': 'group_product',\n",
       "   'probability': 1,\n",
       "   'source': 'model',\n",
       "   'label': 'Other Clean Products'},\n",
       "  {'id': '903edb3eea47052490c39ce71ce473d6d02ce1f8f1b4076ffbc938d6f5a2afef',\n",
       "   'layer': 'category',\n",
       "   'probability': 1,\n",
       "   'source': 'model',\n",
       "   'label': 'Chemicals'}],\n",
       " 'events': [{'event_type': 'cargo_port_load_event',\n",
       "   'location': [{'id': '3267ef2a83a749052c87e981f1bb12c6396acf590b4b1cd3316cf8f8c5aeb7bc',\n",
       "     'layer': 'country',\n",
       "     'label': 'Malaysia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '7f9850477464b75334a237f353b2ac0ca57a1cc1b87b0d4a8731f126ff873247',\n",
       "     'layer': 'port',\n",
       "     'label': 'Bintulu (Tanjong Kidurong) [MY]',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'e6955a2c59dc90833986fe0894cf6718dddaa7816bb51bc955cdd3eb4470e554',\n",
       "     'layer': 'region',\n",
       "     'label': 'Asia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '07ac020aa082d5d116f829ef28a40e9876c0644aeb4f0480202c1a86bb8adf7f',\n",
       "     'layer': 'shipping_region',\n",
       "     'label': 'Far East',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'a2ca5cb23cfc99e5b355a1b928d16e30cbcdfed1c9ff01ba39b5d4c53ffc1ef3',\n",
       "     'layer': 'shipping_region',\n",
       "     'label': 'Southeast Asia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '956b03529510459d298c4e537d23c6fbb22ffd8414af933b3475310c661f337a',\n",
       "     'layer': 'trading_block',\n",
       "     'label': 'Non-OPEC',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'a2ca5cb23cfc99e5b355a1b928d16e30cbcdfed1c9ff01ba39b5d4c53ffc1ef3',\n",
       "     'layer': 'trading_region',\n",
       "     'label': 'Southeast Asia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '3267ef2a83a749052c87e981f1bb12c6396acf590b4b1cd3316cf8f8c5aeb7bc',\n",
       "     'layer': 'trading_subregion',\n",
       "     'label': 'Malaysia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'ebdb39bc6943ad1b3edac1834c106add337b2cfb69fbcdfc81856a2dca3afd23',\n",
       "     'layer': 'terminal',\n",
       "     'label': 'PST Petronas Shell Terminal, Bintulu',\n",
       "     'source': 'model',\n",
       "     'probability': 1}],\n",
       "   'probability': 1,\n",
       "   'pos': [113.07517683177093, 3.2597980837294065],\n",
       "   'start_timestamp': '2020-10-30T23:42:06+0000',\n",
       "   'end_timestamp': '2020-11-01T05:15:10+0000'},\n",
       "  {'event_type': 'cargo_port_unload_event',\n",
       "   'location': [{'id': 'b15970adfcd316d11d698d13de23e2291a60e1fb05070feab30957c6645e576e',\n",
       "     'layer': 'country',\n",
       "     'label': 'Taiwan',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '4468e532e13fb72058fa338b7507ca6df048d0e5b61f7adb5caf95112d3e693a',\n",
       "     'layer': 'port',\n",
       "     'label': 'Kaohsiung [TW]',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'e6955a2c59dc90833986fe0894cf6718dddaa7816bb51bc955cdd3eb4470e554',\n",
       "     'layer': 'region',\n",
       "     'label': 'Asia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '07ac020aa082d5d116f829ef28a40e9876c0644aeb4f0480202c1a86bb8adf7f',\n",
       "     'layer': 'shipping_region',\n",
       "     'label': 'Far East',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '2b394b41a7ff9e48b40b4ba8e6a6b045340261dfc02133d04af13fe754a71a48',\n",
       "     'layer': 'shipping_region',\n",
       "     'label': 'East Asia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '2b394b41a7ff9e48b40b4ba8e6a6b045340261dfc02133d04af13fe754a71a48',\n",
       "     'layer': 'trading_region',\n",
       "     'label': 'East Asia',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': 'b15970adfcd316d11d698d13de23e2291a60e1fb05070feab30957c6645e576e',\n",
       "     'layer': 'trading_subregion',\n",
       "     'label': 'Taiwan',\n",
       "     'source': 'model',\n",
       "     'probability': 1},\n",
       "    {'id': '1d8064b092ea94ad4b410c430eb56a2b6731667a579f2bf9e3a97143f11769e3',\n",
       "     'layer': 'terminal',\n",
       "     'label': 'Formosa Plastics',\n",
       "     'source': 'model',\n",
       "     'probability': 1}],\n",
       "   'probability': 1,\n",
       "   'pos': [120.29713749728465, 22.597328046059573],\n",
       "   'start_timestamp': '2020-11-07T07:07:51+0000',\n",
       "   'end_timestamp': '2020-11-08T01:47:40+0000'}],\n",
       " 'parent_ids': [{'id': '1d347628f0d562fe3dfb83c99af907781d347628f0d562fe3dfb83c99af90778',\n",
       "   'splinter_timestamp': '2020-11-08T06:52:17+0000'}]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What does a single cargo movement look like?\n",
    "cm_query[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose what bit of information we would like to put in a DataFrame. We'll go with:\n",
    "- Load and unload port locations\n",
    "- Product group label\n",
    "- Vessel name and type\n",
    "- Coordinates\n",
    "\n",
    "Notice how some of this information is 'nested', i.e. the value associated with a key might itself be a dictionary or a list. See below how we specify the column names to access this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm_df = cm_query.to_df(columns=['events.cargo_port_load_event.0.location.port.label',\n",
    "                               'events.cargo_port_unload_event.0.location.port.label',\n",
    "                               'product.group.label',\n",
    "                               'quantity',\n",
    "                               'vessels.0.name',\n",
    "                               'vessels.0.vessel_class',\n",
    "                               'events.cargo_port_load_event.0.pos.0',\n",
    "                               'events.cargo_port_load_event.0.pos.1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These column names are quite long and will make our DataFrame harder to read, so we can use a dictionary to relabel them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "relabel = {'events.cargo_port_load_event.0.location.port.label':'load_port',\n",
    "           'events.cargo_port_unload_event.0.location.port.label':'unload_port',\n",
    "           'product.group.label':'product_group',\n",
    "           'vessels.0.name':'vessel_name',\n",
    "           'vessels.0.vessel_class':'vessel_class',\n",
    "           'events.cargo_port_load_event.0.pos.0':'load_X',\n",
    "           'events.cargo_port_load_event.0.pos.1':'load_Y'}\n",
    "\n",
    "cm_df = cm_df.rename(relabel,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>load_port</th>\n",
       "      <th>unload_port</th>\n",
       "      <th>product_group</th>\n",
       "      <th>quantity</th>\n",
       "      <th>vessel_name</th>\n",
       "      <th>vessel_class</th>\n",
       "      <th>load_X</th>\n",
       "      <th>load_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Bintulu (Tanjong Kidurong) [MY]</td>\n",
       "      <td>Kaohsiung [TW]</td>\n",
       "      <td>Clean Petroleum Products</td>\n",
       "      <td>26102</td>\n",
       "      <td>STOLT CREATIVITY</td>\n",
       "      <td>handysize</td>\n",
       "      <td>113.075177</td>\n",
       "      <td>3.259798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mesaieed [QA]</td>\n",
       "      <td>Singapore [SG]</td>\n",
       "      <td>Clean Petroleum Products</td>\n",
       "      <td>64621</td>\n",
       "      <td>MARITIME ROSEMARY</td>\n",
       "      <td>handymax</td>\n",
       "      <td>51.577603</td>\n",
       "      <td>24.930121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dalian [CN]</td>\n",
       "      <td>Jiangyin, Fuzhou [CN]</td>\n",
       "      <td>Clean Petroleum Products</td>\n",
       "      <td>523896</td>\n",
       "      <td>QI LIN ZUO</td>\n",
       "      <td>panamax</td>\n",
       "      <td>121.280508</td>\n",
       "      <td>39.578469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         load_port            unload_port  \\\n",
       "0  Bintulu (Tanjong Kidurong) [MY]         Kaohsiung [TW]   \n",
       "1                    Mesaieed [QA]         Singapore [SG]   \n",
       "2                      Dalian [CN]  Jiangyin, Fuzhou [CN]   \n",
       "\n",
       "              product_group  quantity        vessel_name vessel_class  \\\n",
       "0  Clean Petroleum Products     26102   STOLT CREATIVITY    handysize   \n",
       "1  Clean Petroleum Products     64621  MARITIME ROSEMARY     handymax   \n",
       "2  Clean Petroleum Products    523896         QI LIN ZUO      panamax   \n",
       "\n",
       "       load_X     load_Y  \n",
       "0  113.075177   3.259798  \n",
       "1   51.577603  24.930121  \n",
       "2  121.280508  39.578469  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a peek at the data\n",
    "cm_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've got our data in a DataFrame, we can visualise it with plots. Histograms are convenient for working with numerical data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'quantity'}>]], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of quantity\n",
    "cm_df.hist(column='quantity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a couple of ways we can try to make this data easier to visualise. Firstly, we note that the number of bins is relatively small, so we can increase it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'quantity'}>]], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_df.hist(column='quantity',bins=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This doesn't help much, as the data is still very skewed. Another thing we can try is to plot the logarithm of quantity instead of quantity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'log quantity'}>]], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "cm_df['log quantity'] = np.log10(cm_df['quantity'])\n",
    "cm_df.hist(column='log quantity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a fair bit easier to interpret - we can see that quantity broadly varies from 10k bbl to 1M bbl, with a mode around 100k bbl. We can see from this plot that the distribution of quantity appears to be approximately log-normal.\n",
    "\n",
    "Let's try a different example - what if we were interested in the composition of vessel classes in these cargo movements?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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>load_port</th>\n",
       "      <th>unload_port</th>\n",
       "      <th>product_group</th>\n",
       "      <th>quantity</th>\n",
       "      <th>vessel_name</th>\n",
       "      <th>load_X</th>\n",
       "      <th>load_Y</th>\n",
       "      <th>log quantity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vessel_class</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aframax</th>\n",
       "      <td>48</td>\n",
       "      <td>44</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>general_purpose</th>\n",
       "      <td>160</td>\n",
       "      <td>157</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>handymax</th>\n",
       "      <td>150</td>\n",
       "      <td>141</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>handysize</th>\n",
       "      <td>101</td>\n",
       "      <td>95</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "      <td>101</td>\n",
       "      <td>101</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lgc</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mgc</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>panamax</th>\n",
       "      <td>27</td>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sgc</th>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suezmax</th>\n",
       "      <td>29</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tiny_tanker</th>\n",
       "      <td>144</td>\n",
       "      <td>135</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlcc_plus</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>36</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vlgc</th>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 load_port  unload_port  product_group  quantity  vessel_name  \\\n",
       "vessel_class                                                                    \n",
       "aframax                 48           44             48        48           48   \n",
       "general_purpose        160          157            161       161          161   \n",
       "handymax               150          141            151       151          151   \n",
       "handysize              101           95            102       102          102   \n",
       "lgc                      5            5              5         5            5   \n",
       "mgc                      4            4              6         6            6   \n",
       "panamax                 27           26             27        27           27   \n",
       "sgc                     47           50             50        50           50   \n",
       "suezmax                 29           27             29        29           29   \n",
       "tiny_tanker            144          135            145       145          145   \n",
       "vlcc_plus               35           35             36        36           36   \n",
       "vlgc                    19           17             20        20           20   \n",
       "\n",
       "                 load_X  load_Y  log quantity  \n",
       "vessel_class                                   \n",
       "aframax              48      48            48  \n",
       "general_purpose     161     161           161  \n",
       "handymax            151     151           151  \n",
       "handysize           101     101           102  \n",
       "lgc                   5       5             5  \n",
       "mgc                   6       6             6  \n",
       "panamax              27      27            27  \n",
       "sgc                  50      50            50  \n",
       "suezmax              29      29            29  \n",
       "tiny_tanker         145     145           145  \n",
       "vlcc_plus            35      35            36  \n",
       "vlgc                 20      20            20  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Start by grouping by vessel class and counting\n",
    "cm_df_classes = cm_df.groupby('vessel_class').count()\n",
    "cm_df_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because we are using `.count()`, columns have the same values except where data is missing. We want to pick one column with no data missing (say, vessel_name) to use for a pie chart.\n",
    "\n",
    "We also note that the number of vessels in classes lgc and mgc are very small, so we shall ignore these."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop vessel classes with fewer than 10 vessels (so just lgc and mgc)\n",
    "cm_df_classes = cm_df_classes[cm_df_classes['vessel_name'] >= 10]\n",
    "\n",
    "# Plot pie chart\n",
    "cm_df_classes.plot.pie(y='vessel_name', legend=False, ylabel='')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Make a scatter plot of the X and Y coordinates. Does this look how you would expect?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}