docs/examples/China_Flows.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# China Oil Flows during the Covid-19  Outbreak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the beginning of Q2 2020 the global economy finds itself adapting to an unprecedented situation brought on by the Covid-19 pandemic. Oil demand and supply has already been severely impacted across the world. In this notebook, we will see how [Vortexa's Python SDK](https://github.com/VorTECHsa/python-sdk) can help analysts, traders and data scientists identify the earliest flow trends related to **China**, in its capacity as a major crude importer and clean products exporter.<br>\n",
    "\n",
    "For the purposes of this analysis we will take advantage of the SDK's [CargoTimeSeries](https://vortechsa.github.io/python-sdk/endpoints/cargo_timeseries/) endpoint. This endpoint allows SDK users to find aggregate flows between regions and provinces, for various products, vessels or commercial entities such as charterers and vessel owners. To see more details about Endpoints check our [Docs page](https://vortechsa.github.io/python-sdk/endpoints/about-endpoints/). <BR>\n",
    "\n",
    "**PS1**: This notebook was generated on 15 April 2020. Vortexa is constantly improving the quality of our data and models, and consequently some historical data points may change causing future runs of the notebook to yield different results.\n",
    "\n",
    "**PS2**: The following packages were used:\n",
    "* vortexasdk==0.14.0\n",
    "* pandas==0.25.2\n",
    "* matplotlib==3.1.2\n",
    "\n",
    "Please note that `matplotlib` is not part of the default requirements of the SDK and therefore it needs to be installed prior to running the notebook. To install the library simply run `pip install -U matplotlib` or if you are using conda environment `conda install -c conda-forge matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "from vortexasdk import CargoTimeSeries, Products, Geographies\n",
    "\n",
    "pd.options.display.max_columns = None\n",
    "pd.options.display.max_colwidth = 200\n",
    "pd.options.display.max_rows = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# China Flows Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will study China flows starting from January 2018 until the end of March 2020. Note that the SDK provides access to Vortexa data since 2016-01-01 with a maximum date range of 4 years per query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start date: 2018-01-01 00:00:00\n",
      "End date: 2020-03-31 23:59:59\n"
     ]
    }
   ],
   "source": [
    "# Define filter limits, i.e. start and end date for this analysis. We will consider only historical (i.e. closed)\n",
    "# movements. Given that the notebook was populated at mid April, we will then set our max date\n",
    "# to the end of March 2020\n",
    "START_DATE = datetime(2018, 1, 1)\n",
    "\n",
    "# Make sure you include the whole day, i.e. since we want to include movements up to the end of March we should\n",
    "# specify end date either as midnight of March 31st or 1st of April (if we used datetime(2020,3,31)) we would \n",
    "# lose essentially one whole day of movements\n",
    "END_DATE = datetime(2020, 3, 31, 23, 59, 59)\n",
    "\n",
    "# Define cargo unit for the timeseries (will be constant over the analysis)\n",
    "TS_UNIT = 'bpd'\n",
    "\n",
    "# Define the granularity of the timeseries (will be constant over the analysis)\n",
    "TS_FREQ = 'month'\n",
    "\n",
    "print('Start date: {}'.format(START_DATE))\n",
    "print('End date: {}'.format(END_DATE))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## China Crude Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:14:51,901 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['china']}\n",
      "2020-04-16 11:14:51,901 vortexasdk.client — INFO — Creating new VortexaClient\n",
      "2020-04-16 11:14:52,049 vortexasdk.client — INFO — 13 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n",
      "China polygon id: 934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2\n"
     ]
    }
   ],
   "source": [
    "# We want to search the ID that corresponds to the origin `China`.\n",
    "china = [g.id for g in Geographies().search('china').to_list() if 'country' in g.layer]\n",
    "\n",
    "# Check we've only got one ID for China\n",
    "assert len(china) == 1\n",
    "\n",
    "print('China polygon id: {}'.format(china[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point it is worth paying some attention to the `.search` method. Given a *term* argument the query will return all Geographies that match this term. Given the fact that one term can return multiple results we need to make sure we retrieve only the ID(s) we are interested for. In the case above we use the `layer` attribute to keep only the Geographies that are of `layer` country. To understand the different layers in Geographies check the [Geography Entries Docs](https://docs.vortexa.com/reference/intro-geography-entries). We will also illustrate with an example how the results would look if we didn't filter on country layer.\n",
    "\n",
    "PS: As you can see everytime an API query is executed, logs are printed to the console. The SDK uses a `LOG_LEVEL=INFO` as the default option, but users can [configure](https://vortechsa.github.io/python-sdk/config/) the level of detail of logs with the use of environment variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:14:52,411 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['china']}\n",
      "2020-04-16 11:14:52,565 vortexasdk.client — INFO — 13 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>layer</th>\n",
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       "      <td>934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2</td>\n",
       "      <td>China</td>\n",
       "      <td>[country]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>781cacc7033f877caa4b4106d096b74afe006a96391bf5a56a4f55b849359a42</td>\n",
       "      <td>South China</td>\n",
       "      <td>[shipping_region]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a63890260e29d859390fd1a23c690181afd4bd152943a04c00cd6a5ecf3f7d1e</td>\n",
       "      <td>North China</td>\n",
       "      <td>[shipping_region]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674</td>\n",
       "      <td>China (excl. HK &amp; Macau)</td>\n",
       "      <td>[shipping_region]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9a021f43c397b175ddfff7a91d46ee6e6e16d37e9f9d52398ac6895656109d86</td>\n",
       "      <td>China Steel Chemical</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4f5f0b6f433d65718e62b663e802d9e9f0ff9eba2a2b8dd4558ee94dfbf4d8a6</td>\n",
       "      <td>China Energy Services Ningbo</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>f192fa0cb55da8cdc2e658e9102af9ce632e0be358b0519aa75f0cb3b52fb739</td>\n",
       "      <td>East China Sea STS</td>\n",
       "      <td>[sts_zone]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>404fd9f15f7b9bd3e4b2a838307b04dd991813fc9afc9e500d4af03c88fcb7ec</td>\n",
       "      <td>Multipurpose (China Merchants) Terminal</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4988ec0894dd49ddc0e838325906013aa69125c8a93c04262456900a716e3137</td>\n",
       "      <td>China Union, Freeport Of Monrovia</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>46bb424e5c7c951b4a85b38c747769ff8d3f1656ddfd51176f696b7e1941cc50</td>\n",
       "      <td>China Resources Chemical Holding Terminal</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cc637abe27aabcf5665c59ddc7549afedf32702b10fdec9eef2cae45ed736db0</td>\n",
       "      <td>China General Terminal Distribution Corp</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9579af63401ee4a876768a77e5c0d34bd0a7cfe89bacd4b0e8b8b3a1bfe703bf</td>\n",
       "      <td>China Bay(Ioc Terminal) (Ex-Trincomalee)</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>662a45e342d9394ce1e4595195f5fbcf3fbb0b1975be2798c745d9d5e2f8642e</td>\n",
       "      <td>Wenzhou China Petroleum Fuel Bitumen Co., Ltd.</td>\n",
       "      <td>[terminal]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  id  \\\n",
       "0   934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2   \n",
       "1   781cacc7033f877caa4b4106d096b74afe006a96391bf5a56a4f55b849359a42   \n",
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       "\n",
       "                                              name              layer  \n",
       "0                                            China          [country]  \n",
       "1                                      South China  [shipping_region]  \n",
       "2                                      North China  [shipping_region]  \n",
       "3                         China (excl. HK & Macau)  [shipping_region]  \n",
       "4                             China Steel Chemical         [terminal]  \n",
       "5                     China Energy Services Ningbo         [terminal]  \n",
       "6                               East China Sea STS         [sts_zone]  \n",
       "7          Multipurpose (China Merchants) Terminal         [terminal]  \n",
       "8                China Union, Freeport Of Monrovia         [terminal]  \n",
       "9        China Resources Chemical Holding Terminal         [terminal]  \n",
       "10        China General Terminal Distribution Corp         [terminal]  \n",
       "11        China Bay(Ioc Terminal) (Ex-Trincomalee)         [terminal]  \n",
       "12  Wenzhou China Petroleum Fuel Bitumen Co., Ltd.         [terminal]  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is just for illustration purposes\n",
    "china_all = [(g.id, g.name, g.layer) for g in Geographies().search('china').to_list()]\n",
    "china_all_df = pd.DataFrame(data = china_all, columns = ['id', 'name', 'layer'])\n",
    "china_all_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now search for the ID that corresponds to *Crude/Condensates* product. In the same manner as above we will use the product layer to ensure we retrieve the desired ID (although not necessary in this *specific* case since there is only one product with that name). To understand the different layers in Products check the [Product Entries Docs](https://docs.vortexa.com/reference/intro-product-entities)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:14:52,961 vortexasdk.operations — INFO — Searching Products with params: {'term': ['Crude/Condensates'], 'ids': [], 'product_parent': [], 'allowTopLevelProducts': True}\n",
      "Crude id: 54af755a090118dcf9b0724c9a4e9f14745c26165385ffa7f1445bc768f06f11\n"
     ]
    }
   ],
   "source": [
    "# Find Crude/Condensates ID\n",
    "crude = [p.id for p in Products().search('Crude/Condensates').to_list() if p.layer[0] == 'group']\n",
    "\n",
    "# Check we've only got one Crude ID\n",
    "assert len(crude) == 1\n",
    "\n",
    "print('Crude id: {}'.format(crude[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For all the following flow aggregations, **intra-movements** will be **excluded** from analysis. Intra-movements are defined as movements having their origin and destination to the same *geographic area* (e.g. if origin is a country intra-movements are defined as movements starting and finishing at the same country and the same goes for any other layer such as geographic region, trading region etc.). The `.search()` method of `CargoTimeSeries` object accepts an argument called `disable_geographic_exclusion_rules` which is set to None and by default **excludes** all intra-movements. To include intra-movements to aggregations use `disable_geographic_exclusion_rules=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:14:55,447 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'unloading_start', 'filter_activity': 'unloading_start', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['54af755a090118dcf9b0724c9a4e9f14745c26165385ffa7f1445bc768f06f11'], 'filter_vessels': [], 'filter_destinations': ['934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2'], 'filter_origins': [], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n"
     ]
    },
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     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bpd</th>\n",
       "      <th>count</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th>2019-11-01</th>\n",
       "      <td>9.768183e+06</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>9.717057e+06</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>9.739571e+06</td>\n",
       "      <td>368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>8.763636e+06</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>8.909193e+06</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                     bpd  count\n",
       "date                           \n",
       "2019-11-01  9.768183e+06    354\n",
       "2019-12-01  9.717057e+06    384\n",
       "2020-01-01  9.739571e+06    368\n",
       "2020-02-01  8.763636e+06    308\n",
       "2020-03-01  8.909193e+06    335"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Query API\n",
    "df = CargoTimeSeries().search(\n",
    "            # Filter on cargo arrival date (i.e. the date that the unloading operation started) \n",
    "            filter_activity = 'unloading_start',\n",
    "            # We are interested in movements into China \n",
    "            filter_destinations = china,\n",
    "            # Keep only Crude/Condensate movements\n",
    "            filter_products = crude, \n",
    "            # Quantity unit to use\n",
    "            timeseries_unit = TS_UNIT,\n",
    "            # Look on monthly imports\n",
    "            timeseries_frequency = TS_FREQ,\n",
    "            # Uncomment to INCLUDE intra-movements to aggregations\n",
    "            # disable_geographic_exclusion_rules = True,\n",
    "            # Set the date range of analysis\n",
    "            filter_time_min = START_DATE,\n",
    "            filter_time_max = END_DATE).\\\n",
    "        to_df()\n",
    "\n",
    "# Convert key column to datetime and set as index\n",
    "df['key'] = pd.to_datetime(df['key']).dt.date\n",
    "df = df.rename(columns = {'key': 'date', 'value': 'bpd'})\n",
    "df = df.set_index('date')\n",
    "\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Month')"
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "(df['bpd'] / 10**6).plot(kind='bar', figsize = (10,5))\n",
    "plt.xticks(rotation = 60)\n",
    "plt.title('China Crude Imports \\n 2-Year Average Import Volume: {:,}M bpd'.\n",
    "              format(round(df['bpd'][:-3].mean() / 10**6, 2)), fontsize=13)\n",
    "plt.ylabel('Quantity (Millions bpd)')\n",
    "plt.xlabel('Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "March seaborne crude imports into China seem steady at **8.91M bpd**, very close to the **8.76M bpd** of February and to the 2-year historical average (Jan 2018 - Dec 2019) of **8.71M bpd** (note that China also imports crude via pipeline). The data makes sense intuitively, since February/March China arrivals are cargoes that have been bought and loaded at least one or two months before, a period when the coronovirus pandemic hadn't yet escalated at its full extent. The real impact of the pandemic is expected to be seen from April onward.<br><br> Let’s also look at the main crude **suppliers** of China, and how their exports have fared. We will focus on *MEG/AG, West Africa, South America East Coast and Russia Far East* trading regions. Please note that MEG exports include the port of *Ceyhan* due to the *Kirkuk* Iraqi exports and exclude movements loading from *Saudi Arabia Red Sea* region."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:14:56,125 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['MEG']}\n",
      "2020-04-16 11:14:56,240 vortexasdk.client — INFO — 4 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n",
      "2020-04-16 11:14:56,593 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['West Africa']}\n",
      "2020-04-16 11:14:56,728 vortexasdk.client — INFO — 3 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n",
      "2020-04-16 11:14:57,092 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['South America East']}\n",
      "2020-04-16 11:14:57,360 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['Russia Far']}\n"
     ]
    }
   ],
   "source": [
    "# Find region IDs \n",
    "meg = [g.id for g in Geographies().search('MEG').to_list() if 'shipping_region' in g.layer]\n",
    "waf = [g.id for g in Geographies().search('West Africa').to_list() if 'shipping_region' in g.layer]\n",
    "saec = [g.id for g in Geographies().search('South America East').to_list() if 'trading_region' in g.layer]\n",
    "russia = [g.id for g in Geographies().search('Russia Far').to_list() if 'trading_region' in g.layer]\n",
    "suppliers = meg + waf + saec + russia\n",
    "\n",
    "# Ensure we've only got one ID for the desired regions\n",
    "assert len(meg) == 1\n",
    "assert len(waf) == 1\n",
    "assert len(saec) == 1\n",
    "assert len(russia) == 1\n",
    "\n",
    "# Create a dictionary to map region ids to names. This will be useful when we will combine the results from\n",
    "# different queries to a single DataFrame\n",
    "suppliers_dict = {meg[0]: 'MEG/AG',\n",
    "                  waf[0]: 'West Africa',\n",
    "                  saec[0]: 'South America East Coast',\n",
    "                  russia[0]: 'Russia Far East'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now make separate API calls for each origin (i.e. China supplier) and store the results for all suppliers to a single DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading China crude imports from MEG/AG\n",
      "2020-04-16 11:14:57,689 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'unloading_start', 'filter_activity': 'unloading_start', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['54af755a090118dcf9b0724c9a4e9f14745c26165385ffa7f1445bc768f06f11'], 'filter_vessels': [], 'filter_destinations': ['934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2'], 'filter_origins': ['0899599f74faadb7ba7eb65205ee5c20cb434367a6e7203bc274d310cdb54754'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n",
      "Loading China crude imports from West Africa\n",
      "2020-04-16 11:14:58,047 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'unloading_start', 'filter_activity': 'unloading_start', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['54af755a090118dcf9b0724c9a4e9f14745c26165385ffa7f1445bc768f06f11'], 'filter_vessels': [], 'filter_destinations': ['934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2'], 'filter_origins': ['5e0cb0b66780a00576309aecff1b65934fc580d775bab29ac2a79397d8544e04'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n",
      "Loading China crude imports from South America East Coast\n",
      "2020-04-16 11:14:58,358 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'unloading_start', 'filter_activity': 'unloading_start', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['54af755a090118dcf9b0724c9a4e9f14745c26165385ffa7f1445bc768f06f11'], 'filter_vessels': [], 'filter_destinations': ['934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2'], 'filter_origins': ['44bbc77b3eb859ff3135f669b152afb76b56434e7faae835e601d8cc6af110eb'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n",
      "Loading China crude imports from Russia Far East\n",
      "2020-04-16 11:14:58,667 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'unloading_start', 'filter_activity': 'unloading_start', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['54af755a090118dcf9b0724c9a4e9f14745c26165385ffa7f1445bc768f06f11'], 'filter_vessels': [], 'filter_destinations': ['934c47f36c16a58d68ef5e007e62a23f5f036ee3f3d1f5f85a48c572b90ad8b2'], 'filter_origins': ['4bd22474bec28e0d2576ff0ad204966df7f082d85af4f062763c49c4b1cac65d'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>MEG/AG</th>\n",
       "      <th>West Africa</th>\n",
       "      <th>South America East Coast</th>\n",
       "      <th>Russia Far East</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>5020461</td>\n",
       "      <td>1440984</td>\n",
       "      <td>1042541</td>\n",
       "      <td>532488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>4145689</td>\n",
       "      <td>1747375</td>\n",
       "      <td>865083</td>\n",
       "      <td>628027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>4659045</td>\n",
       "      <td>1235428</td>\n",
       "      <td>1212141</td>\n",
       "      <td>756810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>4308542</td>\n",
       "      <td>1297303</td>\n",
       "      <td>1088467</td>\n",
       "      <td>488932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>4355388</td>\n",
       "      <td>1487335</td>\n",
       "      <td>800464</td>\n",
       "      <td>513588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MEG/AG  West Africa  South America East Coast  Russia Far East\n",
       "date                                                                       \n",
       "2019-11-01  5020461      1440984                   1042541           532488\n",
       "2019-12-01  4145689      1747375                    865083           628027\n",
       "2020-01-01  4659045      1235428                   1212141           756810\n",
       "2020-02-01  4308542      1297303                   1088467           488932\n",
       "2020-03-01  4355388      1487335                    800464           513588"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty list. After each API call the resulting DataFrame will be appended to this list. At the end,\n",
    "# the DataFrames of the list will be concatenated to create a single DataFrame\n",
    "df_list = []\n",
    "\n",
    "# Iterate through China crude suppliers\n",
    "for p in suppliers:\n",
    "    print('Loading China crude imports from {}'.format(suppliers_dict[p]))\n",
    "    dfp = CargoTimeSeries().search(\n",
    "                # Filter on cargo arrival date (i.e. the date that the unloading operation started)\n",
    "                filter_activity = 'unloading_start',\n",
    "                # At each iteration use a different origin\n",
    "                filter_origins = p,\n",
    "                # We are only interested in movements into China\n",
    "                filter_destinations = china,\n",
    "                # Keep only Crude/Condensate movements\n",
    "                filter_products = crude, \n",
    "                # Quantity unit to use\n",
    "                timeseries_unit = TS_UNIT,\n",
    "                # Look on monthly imports\n",
    "                timeseries_frequency = TS_FREQ,\n",
    "                # Set the date range of analysis\n",
    "                filter_time_min = START_DATE,\n",
    "                filter_time_max = END_DATE).\\\n",
    "            to_df()\n",
    "    dfp['key'] = pd.to_datetime(dfp['key']).dt.date\n",
    "    dfp = dfp.drop(columns = 'count')\n",
    "    dfp = dfp.rename(columns = {'key': 'date', 'value': '{}'.format(suppliers_dict[p])})\n",
    "    dfp = dfp.set_index('date')\n",
    "    df_list.append(dfp)\n",
    "    print('-----------------------------------------------------------------------------------')\n",
    "\n",
    "# Concatenate DataFrames \n",
    "df_supl = pd.concat(df_list, axis=1)\n",
    "df_supl = round(df_supl).astype(int)\n",
    "df_supl.tail()"
   ]
  },
  {
   "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>avg_bpd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEG/AG</th>\n",
       "      <td>4072638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Africa</th>\n",
       "      <td>1480763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America East Coast</th>\n",
       "      <td>988728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Russia Far East</th>\n",
       "      <td>568111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          avg_bpd\n",
       "MEG/AG                    4072638\n",
       "West Africa               1480763\n",
       "South America East Coast   988728\n",
       "Russia Far East            568111"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate 2-year (Jan 2018 to Dec 2019) historical average exports volume per origin\n",
    "round(df_supl[:-3].mean().to_frame().rename(columns={0: 'avg_bpd'})).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "(df_supl / 10**6).plot(figsize=(10,5))\n",
    "plt.title('China Crude Main Suppliers', fontsize=14)\n",
    "plt.ylabel('Quantity (Millions bpd)')\n",
    "plt.xlabel('Month')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Month')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alternative way to visualize data: Create a stacked barplot\n",
    "(df_supl[list(suppliers_dict.values())] / 10**6).plot.bar(stacked=True, figsize = (14,7))\n",
    "plt.xticks(rotation = 60)\n",
    "plt.title('China Crude Main Suppliers', fontsize=14)\n",
    "plt.ylabel('Quantity (Millions bpd)')\n",
    "plt.xlabel('Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*MEG, West African and Russian Far East* seaborne crude arrivals to China also look steady and close to their historical averages of approximately **4.1M bpd**, **1.48M bpd** and **570k bpd** respectively. On the other hand, arrivals from *South America East Coast* stood at ~**1.1M bpd** on February, a **10%** decrease compared to January numbers, to be followed by a further **26.5%** decrease in March, standing at **800k bpd**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# China Clean Product Exports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's move on now to explore China's exports on clean products. To make the export analysis compatible with the most commonly used benchmark data, instead of using `China` as origin, we will use `China (excl. HK & Macau)`. We will focus our analysis on the following products: *Gasoline/Blending Components, Jet/Kero and Diesel/Gasoil*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:14:59,490 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['China (excl. HK & Macau)']}\n"
     ]
    }
   ],
   "source": [
    "# Find China exl HK & Macau ID\n",
    "china_excl = [g.id for g in Geographies().search('China (excl. HK & Macau)').to_list()]\n",
    "\n",
    "# Check we've only got one ID\n",
    "assert len(china_excl) == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:14:59,775 vortexasdk.operations — INFO — Searching Products with params: {'term': ['Gasoline/Blending Components'], 'ids': [], 'product_parent': [], 'allowTopLevelProducts': True}\n",
      "2020-04-16 11:15:00,076 vortexasdk.operations — INFO — Searching Products with params: {'term': ['Jet/Kero'], 'ids': [], 'product_parent': [], 'allowTopLevelProducts': True}\n",
      "2020-04-16 11:15:00,226 vortexasdk.client — INFO — 3 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n",
      "2020-04-16 11:15:00,776 vortexasdk.operations — INFO — Searching Products with params: {'term': ['Diesel/Gasoil'], 'ids': [], 'product_parent': [], 'allowTopLevelProducts': True}\n",
      "2020-04-16 11:15:01,057 vortexasdk.client — INFO — 2 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n"
     ]
    }
   ],
   "source": [
    "# Find product IDs\n",
    "gasoline = [p.id for p in Products().search('Gasoline/Blending Components').to_list() if p.layer[0] == 'group_product']\n",
    "jet_kero = [p.id for p in Products().search('Jet/Kero').to_list() if p.layer[0] == 'group_product']\n",
    "diesel_gasoil = [p.id for p in Products().search('Diesel/Gasoil').to_list() if p.layer[0] == 'group_product']\n",
    "clean_products = gasoline + jet_kero + diesel_gasoil\n",
    "\n",
    "# Ensure we've only got one ID for the desired clean products\n",
    "assert len(gasoline) == 1\n",
    "assert len(jet_kero) == 1\n",
    "assert len(diesel_gasoil) == 1\n",
    "\n",
    "# Create a dictionary to map product ids to names\n",
    "products_dict = {gasoline[0]: 'Gasoline/Blending Components',\n",
    "                 jet_kero[0]: 'Jet/Kero',\n",
    "                 diesel_gasoil[0]: 'Diesel/Gasoil'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will apply a similar logic and make consecutive API calls, but this time origin and destination will be fixed and product will change at each iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading China Gasoline/Blending Components exports\n",
      "2020-04-16 11:15:01,436 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'loading_end', 'filter_activity': 'loading_end', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['9256907ba7e4ed11ff03aa297a7e62e14484ce5a85c8118c7495b9120ad0e268'], 'filter_vessels': [], 'filter_destinations': [], 'filter_origins': ['b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n",
      "Loading China Jet/Kero exports\n",
      "2020-04-16 11:15:01,745 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'loading_end', 'filter_activity': 'loading_end', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['d72edbab2b5e2e249d8c216909945922a02eb017dd200f90876fee6fa180c743'], 'filter_vessels': [], 'filter_destinations': [], 'filter_origins': ['b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n",
      "Loading China Diesel/Gasoil exports\n",
      "2020-04-16 11:15:02,041 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'loading_end', 'filter_activity': 'loading_end', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['deda35eb9ca56b54e74f0ff370423f9a8c61cf6a3796fcb18eaeeb32a8c290bb'], 'filter_vessels': [], 'filter_destinations': [], 'filter_origins': ['b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\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>Gasoline/Blending Components</th>\n",
       "      <th>Jet/Kero</th>\n",
       "      <th>Diesel/Gasoil</th>\n",
       "      <th>total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>492356</td>\n",
       "      <td>268515</td>\n",
       "      <td>376318</td>\n",
       "      <td>1137189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>435618</td>\n",
       "      <td>332784</td>\n",
       "      <td>412341</td>\n",
       "      <td>1180742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>317788</td>\n",
       "      <td>290461</td>\n",
       "      <td>453342</td>\n",
       "      <td>1061591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>419214</td>\n",
       "      <td>259163</td>\n",
       "      <td>507211</td>\n",
       "      <td>1185588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>391827</td>\n",
       "      <td>292484</td>\n",
       "      <td>513760</td>\n",
       "      <td>1198071</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Gasoline/Blending Components  Jet/Kero  Diesel/Gasoil    total\n",
       "date                                                                      \n",
       "2019-11-01                        492356    268515         376318  1137189\n",
       "2019-12-01                        435618    332784         412341  1180742\n",
       "2020-01-01                        317788    290461         453342  1061591\n",
       "2020-02-01                        419214    259163         507211  1185588\n",
       "2020-03-01                        391827    292484         513760  1198071"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Apply same logic as with Crude Suppliers\n",
    "df_list_2 = []\n",
    "\n",
    "# Iterate through all products\n",
    "for p in clean_products:\n",
    "    print('Loading China {} exports'.format(products_dict[p]))\n",
    "    dfc = CargoTimeSeries().search(\n",
    "                # We look on exports, therefore we will filter on cargo deparure date (i.e. the date where the loading operation was completed)\n",
    "                filter_activity = 'loading_end',\n",
    "                # Will now use China exl HK & Macau instead of China\n",
    "                filter_origins = china_excl,\n",
    "                # At each iteration an API call for a different product will be made\n",
    "                filter_products = p,  \n",
    "                # Keep same unit as before\n",
    "                timeseries_unit = TS_UNIT,\n",
    "                # Look on monthly exports\n",
    "                timeseries_frequency = TS_FREQ,\n",
    "                # Keep same date range\n",
    "                filter_time_min = START_DATE,\n",
    "                filter_time_max = END_DATE).\\\n",
    "            to_df()\n",
    "    dfc['key'] = pd.to_datetime(dfc['key']).dt.date\n",
    "    dfc = dfc.drop(columns = 'count')\n",
    "    dfc = dfc.rename(columns = {'key': 'date', 'value': '{}'.format(products_dict[p])})\n",
    "    dfc = dfc.set_index('date')\n",
    "    df_list_2.append(dfc)\n",
    "    print('-----------------------------------------------------------------------------------')\n",
    "\n",
    "# Concatenate results    \n",
    "df_clean = pd.concat(df_list_2, axis=1)\n",
    "\n",
    "# Calculate total clean exports\n",
    "df_clean['total'] = df_clean.sum(axis=1)\n",
    "\n",
    "# Round results\n",
    "df_clean = round(df_clean).astype(int)\n",
    "\n",
    "df_clean.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>avg_bpd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Gasoline/Blending Components</th>\n",
       "      <td>341510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jet/Kero</th>\n",
       "      <td>200624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Diesel/Gasoil</th>\n",
       "      <td>357895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total</th>\n",
       "      <td>900029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              avg_bpd\n",
       "Gasoline/Blending Components   341510\n",
       "Jet/Kero                       200624\n",
       "Diesel/Gasoil                  357895\n",
       "total                          900029"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate 2-year (Jan 2018 to Dec 2019) historical average exports volume per product\n",
    "round(df_clean[:-3].mean().to_frame().rename(columns={0: 'avg_bpd'})).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "(df_clean / 10**6).plot(figsize=(10,5))\n",
    "plt.title('China Clean Exports', fontsize=14)\n",
    "plt.ylabel('Quantity (Millions bpd)')\n",
    "plt.xlabel('Month')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Month')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Alternative way to visualize data: Create a stacked barplot\n",
    "(df_clean[list(products_dict.values())] / 10**6).plot.bar(stacked=True, figsize = (14,6))\n",
    "plt.xticks(rotation = 60)\n",
    "plt.title('China Clean Exports (Breakdown by Product)', fontsize=14)\n",
    "plt.ylabel('Quantity (Millions bpd)')\n",
    "plt.xlabel('Month')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we see a lag on the coronavirus impact on China's crude imports, the impact on clean product exports is more immediate. **Diesel** exports showed an uptick from Oct 2019 reaching a 2-year high at **~515k bpd** on March, a ~**30%** increase compared to the (2-year) historical average of **358k bpd**. March **Gasoline** and **Jet/Kero** exports held steady at **390k bpd** and **290k bpd** respectively, both matching February levels."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, to finish our analysis, let's look at the historical trend of the main **destinations** for the Chinese *Diesel/Gasoil* exports. This time we won't look at regions, but we will explore results on a *country* level. More specifically, we will focus on exports to the *Phillipines, Australia and Singapore*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-04-16 11:15:02,792 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['Philippines']}\n",
      "2020-04-16 11:15:02,918 vortexasdk.client — INFO — 3 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n",
      "2020-04-16 11:15:03,273 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['Australia']}\n",
      "2020-04-16 11:15:03,387 vortexasdk.client — INFO — 5 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n",
      "2020-04-16 11:15:03,738 vortexasdk.operations — INFO — Searching Geographies with params: {'term': ['Singapore']}\n",
      "2020-04-16 11:15:03,872 vortexasdk.client — INFO — 24 Results to retreive. Sending 1 post requests in parallel using 6 threads.\n"
     ]
    }
   ],
   "source": [
    "# Find country IDs\n",
    "philli = [g.id for g in Geographies().search('Philippines').to_list() if 'country' in g.layer]\n",
    "australia = [g.id for g in Geographies().search('Australia').to_list() if 'country' in g.layer]\n",
    "singapore = [g.id for g in Geographies().search('Singapore').to_list() if 'country' in g.layer]\n",
    "receivers = philli + australia + singapore\n",
    "\n",
    "# Ensure we've only got one ID for the desired countries\n",
    "assert len(philli) == 1\n",
    "assert len(australia) == 1\n",
    "assert len(singapore) == 1\n",
    "\n",
    "# Create a dictionary to map country ids to names. This will be useful when plotting the results\n",
    "receivers_dict = {philli[0]: 'Philippines',\n",
    "                  australia[0]: 'Australia',\n",
    "                  singapore[0]: 'Singapore'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading China diesel/gasoil exports to Philippines\n",
      "2020-04-16 11:15:04,258 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'loading_end', 'filter_activity': 'loading_end', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['deda35eb9ca56b54e74f0ff370423f9a8c61cf6a3796fcb18eaeeb32a8c290bb'], 'filter_vessels': [], 'filter_destinations': ['065aab207e6fe875caf93419bd6cfedcbb0933098c75e52a6702b75bdfe71c53'], 'filter_origins': ['b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n",
      "Loading China diesel/gasoil exports to Australia\n",
      "2020-04-16 11:15:04,806 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'loading_end', 'filter_activity': 'loading_end', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['deda35eb9ca56b54e74f0ff370423f9a8c61cf6a3796fcb18eaeeb32a8c290bb'], 'filter_vessels': [], 'filter_destinations': ['24075b270335ddb25b5f3f0fa8d9657f7a39df829ce9fa262f40a41a8758d21c'], 'filter_origins': ['b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\n",
      "Loading China diesel/gasoil exports to Singapore\n",
      "2020-04-16 11:15:04,988 vortexasdk.operations — INFO — Searching CargoTimeSeries with params: {'timeseries_frequency': 'month', 'timeseries_unit': 'bpd', 'timeseries_activity': 'loading_end', 'filter_activity': 'loading_end', 'filter_time_min': '2018-01-01T00:00:00.000Z', 'filter_time_max': '2020-03-31T23:59:59.000Z', 'size': 500, 'filter_charterers': [], 'filter_owners': [], 'filter_products': ['deda35eb9ca56b54e74f0ff370423f9a8c61cf6a3796fcb18eaeeb32a8c290bb'], 'filter_vessels': [], 'filter_destinations': ['7fed43c640957555ddac588be64822538078409a0acdaf22126623203ef9954a'], 'filter_origins': ['b5fafce6e20de2dc307fb7e0b89978ee91a49a7b6ec6f5461daf2633f3c56674'], 'filter_storage_locations': [], 'filter_ship_to_ship_locations': [], 'filter_waypoints': [], 'disable_geographic_exclusion_rules': None, 'timeseries_activity_time_span_min': None, 'timeseries_activity_time_span_max': None}\n",
      "-----------------------------------------------------------------------------------\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>Philippines</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Singapore</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>62138</td>\n",
       "      <td>74625</td>\n",
       "      <td>46324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>108409</td>\n",
       "      <td>60698</td>\n",
       "      <td>25452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>39039</td>\n",
       "      <td>26275</td>\n",
       "      <td>85208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>45258</td>\n",
       "      <td>54056</td>\n",
       "      <td>101426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>62362</td>\n",
       "      <td>57715</td>\n",
       "      <td>101959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Philippines  Australia  Singapore\n",
       "date                                         \n",
       "2019-11-01        62138      74625      46324\n",
       "2019-12-01       108409      60698      25452\n",
       "2020-01-01        39039      26275      85208\n",
       "2020-02-01        45258      54056     101426\n",
       "2020-03-01        62362      57715     101959"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty list. After each API call the resulting DataFrame will be appended to this list. At the end\n",
    "# the DataFrames of the list will just be concatenated to create a single DataFrame\n",
    "df_list_3 = []\n",
    "\n",
    "# Iterate through all destinations\n",
    "for p in receivers:\n",
    "    print('Loading China diesel/gasoil exports to {}'.format(receivers_dict[p]))\n",
    "    dfp = CargoTimeSeries().search(\n",
    "                # Filter on cargo deparure date\n",
    "                filter_activity = 'loading_end',\n",
    "                # Will again use China exl HK & Macau instead of China\n",
    "                filter_origins = china_excl,\n",
    "                # At each iteration an API call for a different destination will be made\n",
    "                filter_destinations = p,\n",
    "                # Look only at Diesel/F=Gasoil exports\n",
    "                filter_products = diesel_gasoil, \n",
    "                # Keep same quantity as before\n",
    "                timeseries_unit = TS_UNIT,\n",
    "                # Look at monthly exports\n",
    "                timeseries_frequency = TS_FREQ,\n",
    "                # Keep same date range as before\n",
    "                filter_time_min = START_DATE,\n",
    "                filter_time_max = END_DATE).\\\n",
    "            to_df()\n",
    "    dfp['key'] = pd.to_datetime(dfp['key']).dt.date\n",
    "    dfp = dfp.drop(columns = 'count')\n",
    "    dfp = dfp.rename(columns = {'key': 'date', 'value': '{}'.format(receivers_dict[p])})\n",
    "    dfp = dfp.set_index('date')\n",
    "    df_list_3.append(dfp)\n",
    "    print('-----------------------------------------------------------------------------------')\n",
    "\n",
    "# Concatenate DataFrames \n",
    "df_receiv = pd.concat(df_list_3, axis=1)\n",
    "df_receiv = round(df_receiv).astype(int)\n",
    "df_receiv.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>avg_bpd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Philippines</th>\n",
       "      <td>64707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>48511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>29293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             avg_bpd\n",
       "Philippines    64707\n",
       "Australia      48511\n",
       "Singapore      29293"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate 2-year (Jan 2018 to Dec 2019) historical average Diesel/Gasoil exports volume per country destination\n",
    "round(df_receiv[:-3].mean().to_frame().rename(columns={0: 'avg_bpd'})).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data\n",
    "df_receiv.plot(figsize=(10,5))\n",
    "plt.title('China Diesel/Gasoil Exports', fontsize=14)\n",
    "plt.ylabel('Quantity (bpd)')\n",
    "plt.xlabel('Month')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above graph, we see a large increase in China's **Diesel/Gasoil** exports heading towards *Singapore*, cargoes that will be used for *storage* purposes. Exports to Singapore have been steadily rising since January reaching a two-year high of close to **100k bpd** in February and March, more than 3-times higher compared to the historical (2-year) average of **29k bpd** and a **300%** increase compared to the **25k bpd** of December 2019."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That’s a very quick overview of how coronavirus is currently impacting China’s crude and clean products flows. As noted above, there are many important signals already evident in the data that tells us about how the situation could unfold in the coming months. Especially on the crude side, the datasets can additionally be blended with analysis of floating storage data – another leading indicator that will be watched given the current shape of the oil forward curve. Stay tuned and keep an eye on our [Floating Storage](https://github.com/VorTECHsa/python-sdk/blob/master/docs/examples/Crude_Floating_Storage.ipynb) notebook and the upcoming [Predicting Flows, Floating Storage Trends, and Covid-19 Effects with Python SDK](https://zoom.us/webinar/register/3515873726271/WN_AIskOpeNTdCVb1b8irDa7A) webinar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vortexa_sdk",
   "language": "python",
   "name": "vortexa_sdk"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}