{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 6\n",
    "\n",
    "## Video 29: Working with Aggregated Cargo Movements Data\n",
    "**Python for the Energy Industry**\n",
    "\n",
    "In this lesson, we will be working with the data from the previous lesson. We will practice visualising this data. \n",
    "\n",
    "[Cargo Movements documentation](https://vortechsa.github.io/python-sdk/endpoints/cargo_movements/)\n",
    "\n",
    "To start we follow the steps to get our Cargo Movements DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:04.714478Z",
     "iopub.status.busy": "2021-07-13T11:16:04.713647Z",
     "iopub.status.idle": "2021-07-13T11:16:05.152149Z",
     "shell.execute_reply": "2021-07-13T11:16:05.152498Z"
    }
   },
   "outputs": [],
   "source": [
    "# initial imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import vortexasdk as v\n",
    "\n",
    "# datetimes to access last 7 weeks of data\n",
    "now = datetime.utcnow()\n",
    "seven_weeks_ago = now - relativedelta(weeks=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:05.159096Z",
     "iopub.status.busy": "2021-07-13T11:16:05.158490Z",
     "iopub.status.idle": "2021-07-13T11:16:08.410098Z",
     "shell.execute_reply": "2021-07-13T11:16:08.409572Z"
    }
   },
   "outputs": [],
   "source": [
    "# Find US ID\n",
    "us = [g.id for g in v.Geographies().search('united states').to_list() if 'country' in g.layer]\n",
    "assert len(us) == 1\n",
    "\n",
    "# Find crude ID\n",
    "crude = [p.id for p in v.Products().search('crude').to_list() if p.name=='Crude']\n",
    "assert len(crude) == 1\n",
    "\n",
    "# Columns to pull out, and shortened names\n",
    "required_columns = [\"vessels.0.name\",\"vessels.0.vessel_class\",\"product.group.label\",\"product.category.label\",\"quantity\",\n",
    "    \"status\",\"events.cargo_port_load_event.0.location.port.label\",\"events.cargo_port_load_event.0.end_timestamp\",\n",
    "    \"events.cargo_port_unload_event.0.location.port.label\",\"events.cargo_port_unload_event.0.location.country.label\",\n",
    "    \"events.cargo_port_unload_event.0.end_timestamp\"]\n",
    "\n",
    "new_labels = [\"vessel_name\",\"vessel_class\",\"product_group\",\"product_category\",\"quantity\",\"status\",\n",
    "    \"loading_port\",\"loading_finish\",\"unloading_port\",\"unloading_country\",\"unloading_finish\"]\n",
    "\n",
    "relabel = dict(zip(required_columns,new_labels))\n",
    "\n",
    "cms = v.CargoMovements().search(\n",
    "        filter_activity = 'loading_end',\n",
    "        filter_origins = us,\n",
    "        exclude_destinations = us,\n",
    "        filter_products = crude,\n",
    "        filter_time_min = seven_weeks_ago,\n",
    "        filter_time_max = now,\n",
    "        cm_unit = 'b'\n",
    "    ).to_df(columns=required_columns).rename(relabel,axis=1)\n",
    "\n",
    "cms['loading_week'] = cms['loading_finish'].dt.isocalendar().week"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start by making a bar chart of weekly exports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:08.415901Z",
     "iopub.status.busy": "2021-07-13T11:16:08.415286Z",
     "iopub.status.idle": "2021-07-13T11:16:08.827495Z",
     "shell.execute_reply": "2021-07-13T11:16:08.827930Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'US exports (bbl)')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly_quantity = cms.groupby('loading_week').sum()\n",
    "\n",
    "ax = weekly_quantity.plot.bar(y='quantity',legend=False,figsize=(8,6))\n",
    "ax.set_xlabel('Week')\n",
    "ax.set_ylabel('US exports (bbl)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By assigning the plot to the variable `ax` we can make some further tweaks, like setting the x and y axis labels.\n",
    "\n",
    "What if we wanted to represent the breakdown by product category? These can be plotted with bars side-by-side:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:08.837222Z",
     "iopub.status.busy": "2021-07-13T11:16:08.834014Z",
     "iopub.status.idle": "2021-07-13T11:16:09.053691Z",
     "shell.execute_reply": "2021-07-13T11:16:09.054035Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'US exports (bbl)')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantity_by_category = cms.groupby(by = ['loading_week','product_category']).sum().reset_index()\n",
    "quantity_by_category = quantity_by_category.pivot(index = 'loading_week',columns = 'product_category',values = 'quantity')\n",
    "quantity_by_category = quantity_by_category.fillna(0)\n",
    "\n",
    "ax = quantity_by_category.plot.bar(figsize=(8,6))\n",
    "ax.set_xlabel('Week')\n",
    "ax.set_ylabel('US exports (bbl)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are many products with zero exports, this leaves a lot of holes in the plot. A better way to represent this is 'stacked':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:09.099596Z",
     "iopub.status.busy": "2021-07-13T11:16:09.058773Z",
     "iopub.status.idle": "2021-07-13T11:16:09.259272Z",
     "shell.execute_reply": "2021-07-13T11:16:09.259608Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'US exports (bbl)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = quantity_by_category.plot.bar(stacked=True,figsize=(8,6))\n",
    "ax.set_xlabel('Week')\n",
    "ax.set_ylabel('US exports (bbl)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about visualising the share of exports to destination countries? A pie chart would be suitable for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:09.298962Z",
     "iopub.status.busy": "2021-07-13T11:16:09.298239Z",
     "iopub.status.idle": "2021-07-13T11:16:09.409585Z",
     "shell.execute_reply": "2021-07-13T11:16:09.410361Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='%'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "quantity_by_destination = cms.groupby('unloading_country').sum()[['quantity']]\n",
    "quantity_by_destination.sort_values(by='quantity',ascending = False, inplace=True)\n",
    "\n",
    "top_destination_countries = quantity_by_destination.head(10)\n",
    "rest = pd.DataFrame(index = ['Other'], columns = ['quantity'])\n",
    "rest.loc['Other'] = quantity_by_destination[10:].sum().values\n",
    "top_destination_countries = pd.concat([top_destination_countries, rest]).astype(int)\n",
    "top_destination_countries['%'] = round(top_destination_countries['quantity']*100 / top_destination_countries['quantity'].sum(),2)\n",
    "\n",
    "top_destination_countries.plot.pie(y='%',figsize=(6,6),legend=False,autopct='%.0f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another type of plot we can make is the histogram, which shows the distribution of values in a column. Here's the distribution of quantity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:09.418213Z",
     "iopub.status.busy": "2021-07-13T11:16:09.416125Z",
     "iopub.status.idle": "2021-07-13T11:16:09.571662Z",
     "shell.execute_reply": "2021-07-13T11:16:09.572007Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms.plot.hist(y='quantity',bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do we think this distribution is different for different products? We can test by pivoting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-07-13T11:16:09.576789Z",
     "iopub.status.busy": "2021-07-13T11:16:09.576304Z",
     "iopub.status.idle": "2021-07-13T11:16:09.850237Z",
     "shell.execute_reply": "2021-07-13T11:16:09.850648Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cms_product = cms.pivot(columns = 'product_category',values = 'quantity')[['Heavy-Sour','Light-Sweet','Medium-Sour']]\n",
    "cms_product.plot.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise\n",
    "\n",
    "Instead of US crude exports, pick a different dataset to examine. Say, Saudi Arabian exports, or Chinese imports. Follow the steps of the last 2 lessons to aggregate and visualise different aspects of this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d0a480ee27f8d5367d468be4cf8e618aff62fff37975ed624adc4f23b43958cc"
  },
  "kernelspec": {
   "display_name": "Python 3.7.6 64-bit ('base': conda)",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}