Crude_oil_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nigeria Crude Oil Analysis\n",
    "## By Goodrich Okoro\n",
    "\n",
    "# Nigeria Export Crude Oil Production and Price\n",
    "According to [Wikipedia](https://en.wikipedia.org/wiki/Petroleum_industry_in_Nigeria#:~:text=Nigeria%20is%20the%20largest%20oil,paraffinic%20and%20low%20in%20sulfur.), Nigeria is the largest oil and gas producer in Africa. [Crude oil](https://en.wikipedia.org/wiki/Petroleum) from the [Niger delta basin](https://en.wikipedia.org/wiki/Niger_Delta) comes in two types: [light](https://en.wikipedia.org/wiki/Light_crude_oil), and comparatively [heavy](https://en.wikipedia.org/wiki/Heavy_crude_oil) – the lighter has around 36 gravity while the heavier has 20–25 gravity. Both types are [paraffinic](https://en.wikipedia.org/wiki/Alkane) and low in [sulfur](https://en.wikipedia.org/wiki/Sulfur).Nigeria's economy and budget have been largely supported from income and revenues generated from the petroleum industry since 1960. Statistics as at February 2021 shows that the Nigerian oil sector contributes to about 9% of the entire [GDP](https://en.wikipedia.org/wiki/Gross_domestic_product) of the nation. Nigeria is the largest oil and gas producer in Africa, a major exporter of crude oil and petroleum products to the United States of America. In 2010, Nigeria exported over one million barrels per day to the United States of America, representing 9% of the U.S. total crude oil and petroleum products imports and over 40% of Nigeria exports.\n",
    "\n",
    "<p><center><img src=\"https://cdn08.allafrica.com/download/pic/main/main/csiid/00410084:4f39f9cb5c0f3b30f3087c8a62f45009:arc614x376:w735:us1.jpg\" alt></center></p>\n",
    "\n",
    "This project involves the use of [**SQL**](https://www.w3schools.com/sql/) and [**Python**](https://www.python.org/) programming language. SQL will be used to answer the given question while Python will be used to present the answers as a dataframe and visualize it where necessary.\n",
    "\n",
    "The data used for this analysis can be found on [Central Bank of Nigeria (CBN) Statistic section](https://www.cbn.gov.ng/rates/crudeoil.asp). It contain 5 fields with 201 records.\n",
    "\n",
    "<h1 id=\"crude_oil\"><code>CrudeOilPrice</code></h1>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>column</th>\n",
    "<th>type</th>\n",
    "<th>description</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody>\n",
    "<tr>\n",
    "<td><code>year</code></td>\n",
    "<td><code>varchar</code></td>\n",
    "<td>Year of production</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td><code>month</code></td>\n",
    "<td><code>smallint</code></td>\n",
    "<td>Month in each year</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td><code>crude_oil_price</code></td>\n",
    "<td><code>decimal</code></td>\n",
    "<td>Price of crude oil (US$/Barrel)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td><code>production</code></td>\n",
    "<td><code>decimal</code></td>\n",
    "<td>Crude oil produced in millions barrels per day (mbd)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td><code>crude_oil_export</code></td>\n",
    "<td><code>decimal</code></td>\n",
    "<td>Crude oil exported in millions barrels per day (mbd)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "## Questions \n",
    "* Which year was the largest production ever made? Narrow it down to the month.\n",
    "* Which year has the highest average production of crude oil for our record?\n",
    "* Which year has the highest average price?\n",
    "* Is there a month where all barrels of oil produced was exported totally?\n",
    "* How many times was crude oil exported less than 70% of the production total?\n",
    "* How has production and price change with three years gap?\n",
    "\n",
    "Other insights. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.14.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Importing required libraries\n",
    "import sqlite3 as sq\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as po\n",
    "po.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating Database\n",
    "conn = sq.connect(\"crude_oil.db\")\n",
    "\n",
    "# Create a table called crudeOilPrice\n",
    "query = \"\"\"\n",
    "CREATE TABLE crudeOilPrice(\n",
    "    year VARCHAR(4),\n",
    "    month SMALLINT,\n",
    "    crude_oil_price DECIMAL(5,2),\n",
    "    production DECIMAL(3,2),\n",
    "    crude_oil_export DECIMAL(3,2)\n",
    "    )\"\"\"\n",
    "\n",
    "with conn:\n",
    "    cur = conn.cursor()\n",
    "    cur.execute(\"DROP TABLE IF EXISTS crudeOilPrice\")\n",
    "    cur.execute(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>crudeOilPrice</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            name\n",
       "0  crudeOilPrice"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking our sqlite master for confirmation\n",
    "query = \"SELECT name FROM sqlite_master WHERE type='table'\"\n",
    "import pandas as pd\n",
    "sqlDF = pd.read_sql_query(query, conn)\n",
    "sqlDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let fill in values from the [csv file](https://www.cbn.gov.ng/Functions/export.asp?tablename=CrudeOilProdPrice) gotten from CBN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Crude Oil Price</th>\n",
       "      <th>Production</th>\n",
       "      <th>Crude Oil Export</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>63.85</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>61.33</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>65.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>4</td>\n",
       "      <td>72.09</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>5</td>\n",
       "      <td>71.18</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Crude Oil Price  Production  Crude Oil Export\n",
       "0  2006      1            63.85        2.59              2.14\n",
       "1  2006      2            61.33        2.47              2.02\n",
       "2  2006      3            65.00        2.25              1.80\n",
       "3  2006      4            72.09        2.32              1.87\n",
       "4  2006      5            71.18        2.28              1.83"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading CSV file\n",
    "csv = pd.read_csv('CrudeOilProdPrice25102022.csv', index_col=False)\n",
    "csv.head()"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>crude_oil_price</th>\n",
       "      <th>production</th>\n",
       "      <th>crude_oil_export</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>1</td>\n",
       "      <td>63.85</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>2</td>\n",
       "      <td>61.33</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>3</td>\n",
       "      <td>65.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>4</td>\n",
       "      <td>72.09</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>5</td>\n",
       "      <td>71.18</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "      <td>116.72</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "      <td>130.10</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2022</td>\n",
       "      <td>7</td>\n",
       "      <td>120.54</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2022</td>\n",
       "      <td>8</td>\n",
       "      <td>106.34</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2022</td>\n",
       "      <td>9</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month  crude_oil_price  production  crude_oil_export\n",
       "0    2006      1            63.85        2.59              2.14\n",
       "1    2006      2            61.33        2.47              2.02\n",
       "2    2006      3            65.00        2.25              1.80\n",
       "3    2006      4            72.09        2.32              1.87\n",
       "4    2006      5            71.18        2.28              1.83\n",
       "..    ...    ...              ...         ...               ...\n",
       "196  2022      5           116.72        1.02              0.57\n",
       "197  2022      6           130.10        1.16              0.71\n",
       "198  2022      7           120.54        1.08              0.63\n",
       "199  2022      8           106.34        0.97              0.52\n",
       "200  2022      9            93.25        0.94              0.49\n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating a tuple of values in each row\n",
    "csvTuple = tuple(csv.itertuples(index=False, name=None))\n",
    "csvTuple[0]\n",
    "\n",
    "# inserting into table crudeOilPrice\n",
    "insertQuery = \"INSERT INTO crudeOilPrice values(?,?,?,?,?)\"\n",
    "cur.executemany(insertQuery, csvTuple)\n",
    "\n",
    "# Reading table as a pandas dataframe\n",
    "query = \"SELECT * FROM crudeOilPrice\"\n",
    "pd.read_sql_query(query, conn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data Cleaning\n",
    "In the data, moths were represented with numbers. This will be replaced with the actual month name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>crude_oil_price</th>\n",
       "      <th>production</th>\n",
       "      <th>crude_oil_export</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>January</td>\n",
       "      <td>63.85</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2006</td>\n",
       "      <td>February</td>\n",
       "      <td>61.33</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>March</td>\n",
       "      <td>65.00</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2006</td>\n",
       "      <td>April</td>\n",
       "      <td>72.09</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2006</td>\n",
       "      <td>May</td>\n",
       "      <td>71.18</td>\n",
       "      <td>2.28</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2022</td>\n",
       "      <td>May</td>\n",
       "      <td>116.72</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2022</td>\n",
       "      <td>June</td>\n",
       "      <td>130.10</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2022</td>\n",
       "      <td>July</td>\n",
       "      <td>120.54</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2022</td>\n",
       "      <td>August</td>\n",
       "      <td>106.34</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2022</td>\n",
       "      <td>September</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year      month  crude_oil_price  production  crude_oil_export\n",
       "0    2006    January            63.85        2.59              2.14\n",
       "1    2006   February            61.33        2.47              2.02\n",
       "2    2006      March            65.00        2.25              1.80\n",
       "3    2006      April            72.09        2.32              1.87\n",
       "4    2006        May            71.18        2.28              1.83\n",
       "..    ...        ...              ...         ...               ...\n",
       "196  2022        May           116.72        1.02              0.57\n",
       "197  2022       June           130.10        1.16              0.71\n",
       "198  2022       July           120.54        1.08              0.63\n",
       "199  2022     August           106.34        0.97              0.52\n",
       "200  2022  September            93.25        0.94              0.49\n",
       "\n",
       "[201 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing month numbers with actual name\n",
    "query = \"\"\"\n",
    "UPDATE\n",
    "    crudeOilPrice\n",
    "SET \n",
    "    month = \n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(\n",
    "        REPLACE(month, 12, 'December'), \n",
    "        2, 'February'), \n",
    "        3, 'March'),\n",
    "        4, 'April'),\n",
    "        5, 'May'), \n",
    "        6, 'June'),\n",
    "        7, 'July'),\n",
    "        8, 'August'),\n",
    "        9, 'September'),\n",
    "        10, 'October'),\n",
    "        11, 'November'),\n",
    "        1, 'January')\n",
    "\"\"\"\n",
    "# Previewing for confirmation\n",
    "cur.execute(query)\n",
    "query = \"SELECT * FROM crudeOilPrice\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Records</th>\n",
       "      <th>Years_record</th>\n",
       "      <th>Distinct_Month</th>\n",
       "      <th>Start_year</th>\n",
       "      <th>End_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>2006</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Records  Years_record  Distinct_Month Start_year End_year\n",
       "0      201            17              12       2006     2022"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overview of record\n",
    "query = \"\"\"\n",
    "SELECT  COUNT(*) Records, \n",
    "        COUNT(DISTINCT year) Years_record,\n",
    "        COUNT(DISTINCT month) Distinct_Month,\n",
    "        MIN(year) AS Start_year,\n",
    "        MAX(year) AS End_year\n",
    "  FROM  crudeOilPrice\n",
    "\"\"\"\n",
    "sqlDF = pd.read_sql_query(query, conn)\n",
    "sqlDF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Which year was the largest production ever made? Narrow it down to the month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>Max_Prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>October</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year    month  Max_Prod\n",
       "0  2010  October      2.88"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT  year, month, max(production) AS Max_Prod\n",
    "  FROM  crudeOilPrice\n",
    "\"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The largest production was made in October 2010\n",
    "### Which year has the highest average production of crude oil for our record?"
   ]
  },
  {
   "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>Year</th>\n",
       "      <th>Max_Avg_Prod</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>2.4675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Max_Avg_Prod\n",
       "0  2010        2.4675"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT sub.Year, max(sub.Avg_Prod) AS Max_Avg_Prod\n",
    "  FROM (\n",
    "          SELECT year AS Year, avg(production) AS Avg_Prod\n",
    "            FROM crudeOilPrice\n",
    "        GROUP BY Year) AS sub\n",
    "\"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " The highest average production was made in 2010\n",
    " ### Which year has the highest average price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Year</th>\n",
       "      <th>Max_Avg_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>113.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Max_Avg_Price\n",
       "0  2011         113.76"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT sub.Year, max(Avg_Price) AS Max_Avg_Price\n",
    "  FROM (\n",
    "          SELECT year AS Year, avg(crude_oil_price) AS Avg_Price\n",
    "            FROM crudeOilPrice\n",
    "        GROUP BY Year) AS sub\n",
    "\"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011 has the highest average price\n",
    "### Is there a month where all barrels of oil produced was exported totally?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   count\n",
       "0      0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT COUNT(*) AS count\n",
    "  FROM crudeOilPrice\n",
    " WHERE production = crude_oil_export\n",
    "\"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nigeria has never exported all the crude oil it produce.\n",
    "### How many times was crude oil exported less than 70% of the total production?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count\n",
       "0     22"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "SELECT  COUNT(*) AS Count\n",
    "  FROM  (\n",
    "        SELECT production, year, crude_oil_export, 0.7 * production AS seventy_perc_prod\n",
    "  FROM  crudeOilPrice)\n",
    " WHERE  crude_oil_export < seventy_perc_prod\n",
    "\"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 22 count, let's narrow it down to year with the highest count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Year</th>\n",
       "      <th>Export_Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Export_Count\n",
       "0  2020             1\n",
       "1  2021            12\n",
       "2  2022             9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "  SELECT year AS Year, COUNT(*) AS Export_Count\n",
    "    FROM (\n",
    "           SELECT production, year, crude_oil_export, 0.7 * production AS fifty_perc_prod\n",
    "             FROM crudeOilPrice)\n",
    "   WHERE crude_oil_export < fifty_perc_prod\n",
    "GROUP BY Year\n",
    "\"\"\"\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have seen years that exported oil was below 70% of production. \n",
    "\n",
    "Let's look at when exported oil was above 80%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Export_Count\n",
      "Year              \n",
      "2006            12\n",
      "2007             5\n",
      "2008             1\n",
      "2009             1\n",
      "2010            11\n",
      "2011            12\n",
      "2012             8\n",
      "2013             2\n",
      "2014             3\n"
     ]
    },
    {
     "data": {
      "text/html": [
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oil export above 80% of production\"},\"width\":700,\"yaxis\":{\"showticklabels\":false}},                        {\"responsive\": true}                    ).then(function(){\n",
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   ],
   "source": [
    "# Extracting data\n",
    "query = \"\"\"\n",
    "  SELECT year AS Year, COUNT(*) AS Export_Count\n",
    "    FROM (\n",
    "          SELECT production, year, crude_oil_export, 0.8 * production AS fifty_perc_prod\n",
    "          FROM crudeOilPrice)\n",
    "   WHERE crude_oil_export >= fifty_perc_prod\n",
    "GROUP BY Year\n",
    "\"\"\"\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='Year')\n",
    "print(sqlDF)\n",
    "sqlDF\n",
    "\n",
    "# Plotting viz\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF.Export_Count,\n",
    "                            mode= 'lines+markers+text',\n",
    "                            text= sqlDF.Export_Count, textposition='top center',\n",
    "                            line=dict(color='forestgreen', \n",
    "                                        width=3, dash='dot'),\n",
    "                            textfont=dict(size=13)))\n",
    "fig.update_layout(title='Crude oil export above 80% of production', width=700)\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "fig.show(\"notebook_connected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that the last time oil was exported above 80% of production was in 2014. \n",
    "\n",
    "And its only in 2006 and 2011 that 80% of produced oil was exported in all months.\n",
    "\n",
    "### How has production and price change with three years gap?\n",
    "**Price Difference**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price2006</th>\n",
       "      <th>Price2009</th>\n",
       "      <th>Price2012</th>\n",
       "      <th>Price2015</th>\n",
       "      <th>Price2018</th>\n",
       "      <th>Price2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>January</th>\n",
       "      <td>63.85</td>\n",
       "      <td>44.95</td>\n",
       "      <td>113.81</td>\n",
       "      <td>48.81</td>\n",
       "      <td>69.68</td>\n",
       "      <td>54.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>61.33</td>\n",
       "      <td>46.52</td>\n",
       "      <td>121.87</td>\n",
       "      <td>58.09</td>\n",
       "      <td>66.67</td>\n",
       "      <td>62.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>65.00</td>\n",
       "      <td>49.70</td>\n",
       "      <td>128.00</td>\n",
       "      <td>56.69</td>\n",
       "      <td>74.72</td>\n",
       "      <td>65.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>72.09</td>\n",
       "      <td>51.16</td>\n",
       "      <td>122.62</td>\n",
       "      <td>57.45</td>\n",
       "      <td>72.37</td>\n",
       "      <td>64.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>71.18</td>\n",
       "      <td>60.02</td>\n",
       "      <td>113.08</td>\n",
       "      <td>65.08</td>\n",
       "      <td>77.64</td>\n",
       "      <td>67.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>69.32</td>\n",
       "      <td>72.24</td>\n",
       "      <td>98.06</td>\n",
       "      <td>62.06</td>\n",
       "      <td>75.38</td>\n",
       "      <td>73.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>75.13</td>\n",
       "      <td>66.52</td>\n",
       "      <td>104.62</td>\n",
       "      <td>57.01</td>\n",
       "      <td>74.72</td>\n",
       "      <td>75.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>75.15</td>\n",
       "      <td>74.00</td>\n",
       "      <td>113.76</td>\n",
       "      <td>47.09</td>\n",
       "      <td>73.35</td>\n",
       "      <td>70.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>62.97</td>\n",
       "      <td>70.22</td>\n",
       "      <td>114.36</td>\n",
       "      <td>48.08</td>\n",
       "      <td>79.59</td>\n",
       "      <td>74.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>59.49</td>\n",
       "      <td>78.25</td>\n",
       "      <td>108.92</td>\n",
       "      <td>48.86</td>\n",
       "      <td>79.18</td>\n",
       "      <td>84.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>59.81</td>\n",
       "      <td>78.11</td>\n",
       "      <td>111.05</td>\n",
       "      <td>44.82</td>\n",
       "      <td>66.59</td>\n",
       "      <td>82.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>64.70</td>\n",
       "      <td>75.11</td>\n",
       "      <td>114.49</td>\n",
       "      <td>37.80</td>\n",
       "      <td>62.00</td>\n",
       "      <td>65.41</td>\n",
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      "text/plain": [
       "           Price2006  Price2009  Price2012  Price2015  Price2018  Price2021\n",
       "month                                                                      \n",
       "January        63.85      44.95     113.81      48.81      69.68      54.87\n",
       "February       61.33      46.52     121.87      58.09      66.67      62.48\n",
       "March          65.00      49.70     128.00      56.69      74.72      65.62\n",
       "April          72.09      51.16     122.62      57.45      72.37      64.30\n",
       "May            71.18      60.02     113.08      65.08      77.64      67.83\n",
       "June           69.32      72.24      98.06      62.06      75.38      73.46\n",
       "July           75.13      66.52     104.62      57.01      74.72      75.93\n",
       "August         75.15      74.00     113.76      47.09      73.35      70.72\n",
       "September      62.97      70.22     114.36      48.08      79.59      74.55\n",
       "October        59.49      78.25     108.92      48.86      79.18      84.11\n",
       "November       59.81      78.11     111.05      44.82      66.59      82.16\n",
       "December       64.70      75.11     114.49      37.80      62.00      65.41"
      ]
     },
     "execution_count": 16,
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   "source": [
    "month_order = ['January', 'February', 'March', 'April', 'May', 'June', \n",
    "               'July', 'August', 'September', 'October', 'November', 'December']\n",
    "month_short = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "\n",
    "# Extracting data\n",
    "query = \"\"\"\n",
    "WITH first AS (\n",
    "    SELECT  c1.month, c1.crude_oil_price AS Price2006, c2.crude_oil_price AS Price2009\n",
    "      FROM  crudeOilPrice AS c1\n",
    "INNER JOIN  crudeOilPrice AS c2\n",
    "        ON  c1.month = c2.month\n",
    "     WHERE  c1.year = 2006 AND c2.year = c1.year + 3),\n",
    "\n",
    "sec AS (\n",
    "    SELECT  c1.month, c1.crude_oil_price AS Price2012, c2.crude_oil_price AS Price2015\n",
    "      FROM  crudeOilPrice AS c1\n",
    "INNER JOIN  crudeOilPrice AS c2\n",
    "        ON  c1.month = c2.month\n",
    "     WHERE  c1.year = 2012 AND c2.year = c1.year + 3),\n",
    "\n",
    "third AS (\n",
    "    SELECT  c1.month, c1.crude_oil_price AS Price2018, c2.crude_oil_price AS Price2021\n",
    "      FROM  crudeOilPrice AS c1\n",
    "INNER JOIN  crudeOilPrice AS c2\n",
    "        ON  c1.month = c2.month\n",
    "     WHERE  c1.year = 2018 AND c2.year = c1.year + 3)\n",
    "     \n",
    "    SELECT  f.month, f.Price2006, f.Price2009, s.Price2012, s.Price2015, t.Price2018, t.Price2021\n",
    "      FROM  first AS f\n",
    " LEFT JOIN  sec AS s\n",
    "     USING  (month)\n",
    " LEFT JOIN  third AS t\n",
    "     USING  (month)\n",
    "\"\"\"\n",
    "\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='month').reindex(month_order)\n",
    "sqlDF"
   ]
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   "cell_type": "code",
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   ],
   "source": [
    "# Plotting Data\n",
    "def add_year(price_year, color):\n",
    "    price = price_year\n",
    "    name = price[5:] + ' Price'\n",
    "    fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF[price_year],\n",
    "                            name=name, line=dict(color=color, \n",
    "                                                            width=3, dash='dot')))\n",
    "fig = go.Figure()\n",
    "add_year('Price2006', 'firebrick')\n",
    "add_year('Price2009', 'royalblue')\n",
    "add_year('Price2012', 'goldenrod')\n",
    "add_year('Price2015', 'darkturquoise')\n",
    "add_year('Price2018', 'forestgreen')\n",
    "add_year('Price2021', 'darkgrey')\n",
    "fig.update_layout(title='Crude oil price with three years gap', width=850,\n",
    "                  yaxis_title = 'Price (US$/Barrel)',\n",
    "                  xaxis = dict(\n",
    "                      tickvals = month_order, \n",
    "                      ticktext = month_short\n",
    "                  )\n",
    "                  )\n",
    "fig.show()"
   ]
  },
  {
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2012 looks like an ouliear but then, we are viewing the year with three years gap. The price is clustered between 35 and 85(US$/Barrel).\n",
    "\n",
    "**Production Difference**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Prod2006</th>\n",
       "      <th>Prod2009</th>\n",
       "      <th>Prod2012</th>\n",
       "      <th>Prod2015</th>\n",
       "      <th>Prod2018</th>\n",
       "      <th>Prod2021</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</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>January</th>\n",
       "      <td>2.59</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.23</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>2.47</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>2.25</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.34</td>\n",
       "      <td>2.07</td>\n",
       "      <td>1.94</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>2.32</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.30</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>2.28</td>\n",
       "      <td>2.22</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.05</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>2.41</td>\n",
       "      <td>2.17</td>\n",
       "      <td>2.37</td>\n",
       "      <td>1.97</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>2.39</td>\n",
       "      <td>2.14</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.83</td>\n",
       "      <td>1.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>2.40</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>2.40</td>\n",
       "      <td>2.18</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.22</td>\n",
       "      <td>1.96</td>\n",
       "      <td>1.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>2.38</td>\n",
       "      <td>2.28</td>\n",
       "      <td>2.19</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.01</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>2.34</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.18</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>2.36</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.21</td>\n",
       "      <td>2.08</td>\n",
       "      <td>1.90</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           Prod2006  Prod2009  Prod2012  Prod2015  Prod2018  Prod2021\n",
       "month                                                                \n",
       "January        2.59      2.03      2.23      2.20      2.00      1.36\n",
       "February       2.47      2.06      2.40      2.21      2.01      1.42\n",
       "March          2.25      2.07      2.34      2.07      1.94      1.43\n",
       "April          2.32      1.86      2.30      2.03      1.97      1.37\n",
       "May            2.28      2.22      2.40      2.05      1.78      1.34\n",
       "June           2.41      2.17      2.37      1.97      1.78      1.31\n",
       "July           2.39      2.14      2.42      2.18      1.83      1.32\n",
       "August         2.40      2.12      2.48      2.12      2.00      1.24\n",
       "September      2.40      2.18      2.45      2.22      1.96      1.25\n",
       "October        2.38      2.28      2.19      2.21      2.01      1.23\n",
       "November       2.34      2.15      2.03      2.18      1.80      1.23\n",
       "December       2.36      2.05      2.21      2.08      1.90      1.20"
      ]
     },
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   ],
   "source": [
    "month_order = ['January', 'February', 'March', 'April', 'May', 'June', \n",
    "               'July', 'August', 'September', 'October', 'November', 'December']\n",
    "\n",
    "# Extracting Data\n",
    "query = \"\"\"\n",
    "WITH first AS (\n",
    "    SELECT  c1.month, c1.production AS Prod2006, c2.production AS Prod2009\n",
    "      FROM  crudeOilPrice AS c1\n",
    "INNER JOIN  crudeOilPrice AS c2\n",
    "        ON  c1.month = c2.month\n",
    "     WHERE  c1.year = 2006 AND c2.year = c1.year + 3),\n",
    "\n",
    "sec AS (\n",
    "    SELECT  c1.month, c1.production AS Prod2012, c2.production AS Prod2015\n",
    "      FROM  crudeOilPrice AS c1\n",
    "INNER JOIN  crudeOilPrice AS c2\n",
    "        ON  c1.month = c2.month\n",
    "     WHERE  c1.year = 2012 AND c2.year = c1.year + 3),\n",
    "\n",
    "third AS (\n",
    "    SELECT  c1.month, c1.production AS Prod2018, c2.production AS Prod2021\n",
    "      FROM  crudeOilPrice AS c1\n",
    "INNER JOIN  crudeOilPrice AS c2\n",
    "        ON  c1.month = c2.month\n",
    "     WHERE  c1.year = 2018 AND c2.year = c1.year + 3)\n",
    "     \n",
    "    SELECT  f.month, f.Prod2006, f.Prod2009, s.Prod2012, s.Prod2015, t.Prod2018, t.Prod2021\n",
    "      FROM  first AS f\n",
    " LEFT JOIN  sec AS s\n",
    "     USING  (month)\n",
    " LEFT JOIN  third AS t\n",
    "     USING  (month)\n",
    "\"\"\"\n",
    "\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='month').reindex(month_order)\n",
    "sqlDF"
   ]
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting data\n",
    "def add_year(price_year, color):\n",
    "    price = price_year\n",
    "    name = price[4:] + ' Production'\n",
    "    fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF[price_year],\n",
    "                            name=name, line=dict(color=color, \n",
    "                                                            width=3, dash='dot')))\n",
    "fig = go.Figure()\n",
    "add_year('Prod2006', 'firebrick')\n",
    "add_year('Prod2009', 'royalblue')\n",
    "add_year('Prod2012', 'goldenrod')\n",
    "add_year('Prod2015', 'darkturquoise')\n",
    "add_year('Prod2018', 'forestgreen')\n",
    "add_year('Prod2021', 'darkgrey')\n",
    "\n",
    "fig.update_layout(title='Crude oil production with three years gap', width=850,\n",
    "                  yaxis_title = 'Production (mbd)',\n",
    "                  xaxis = dict(\n",
    "                      tickvals = month_order, \n",
    "                      ticktext = month_short\n",
    "                  )\n",
    "                  )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the visual above, production started decreasing after 2012. \n",
    "\n",
    "### Other Insights\n",
    "\n",
    "* Summarizing the three numeric column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Min_Price</th>\n",
       "      <th>Avg_Price</th>\n",
       "      <th>Max_Price</th>\n",
       "      <th>Min_Production</th>\n",
       "      <th>Avg_Production</th>\n",
       "      <th>Max_Production</th>\n",
       "      <th>Min_Export</th>\n",
       "      <th>Avg_Export</th>\n",
       "      <th>Max_Export</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.28</td>\n",
       "      <td>78.17</td>\n",
       "      <td>138.74</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2.03</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.58</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   Min_Price  Avg_Price  Max_Price  Min_Production  Avg_Production  \\\n",
       "0      14.28      78.17     138.74            0.94            2.03   \n",
       "\n",
       "   Max_Production  Min_Export  Avg_Export  Max_Export  \n",
       "0            2.88        0.49        1.58        2.43  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting Summary\n",
    "query = \"\"\"\n",
    "SELECT  min(crude_oil_price) Min_Price,\n",
    "        ROUND(avg(crude_oil_price), 2) Avg_Price,\n",
    "        max(crude_oil_price) Max_Price,\n",
    "        min(production) Min_Production,\n",
    "        ROUND(avg(production), 2) Avg_Production,\n",
    "        max(production) Max_Production,\n",
    "        min(crude_oil_export) Min_Export,\n",
    "        ROUND(avg(crude_oil_export), 2) Avg_Export,\n",
    "        max(crude_oil_export) Max_Export\n",
    "  FROM  crudeOilPrice\n",
    "\"\"\"\n",
    "\n",
    "pd.read_sql_query(query, conn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Statistics Summary Of Crude Oil**\n",
    "\n",
    "**Price by month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min_Price</th>\n",
       "      <th>Avg_Price</th>\n",
       "      <th>Max_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>30.66</td>\n",
       "      <td>73.43</td>\n",
       "      <td>115.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>31.70</td>\n",
       "      <td>75.73</td>\n",
       "      <td>121.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>32.29</td>\n",
       "      <td>78.70</td>\n",
       "      <td>128.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>14.28</td>\n",
       "      <td>78.90</td>\n",
       "      <td>124.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>27.90</td>\n",
       "      <td>81.27</td>\n",
       "      <td>126.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>40.30</td>\n",
       "      <td>82.88</td>\n",
       "      <td>138.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>44.10</td>\n",
       "      <td>83.00</td>\n",
       "      <td>137.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>45.06</td>\n",
       "      <td>79.69</td>\n",
       "      <td>115.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>40.85</td>\n",
       "      <td>79.06</td>\n",
       "      <td>115.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>39.74</td>\n",
       "      <td>76.22</td>\n",
       "      <td>113.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>42.70</td>\n",
       "      <td>75.02</td>\n",
       "      <td>113.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>37.80</td>\n",
       "      <td>73.53</td>\n",
       "      <td>114.49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Min_Price  Avg_Price  Max_Price\n",
       "Month                                     \n",
       "January        30.66      73.43     115.24\n",
       "February       31.70      75.73     121.87\n",
       "March          32.29      78.70     128.00\n",
       "April          14.28      78.90     124.49\n",
       "May            27.90      81.27     126.57\n",
       "June           40.30      82.88     138.74\n",
       "July           44.10      83.00     137.74\n",
       "August         45.06      79.69     115.84\n",
       "September      40.85      79.06     115.73\n",
       "October        39.74      76.22     113.12\n",
       "November       42.70      75.02     113.92\n",
       "December       37.80      73.53     114.49"
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    "# Extracting data\n",
    "query = \"\"\"\n",
    "  SELECT month Month,\n",
    "         min(crude_oil_price) Min_Price,\n",
    "         ROUND(avg(crude_oil_price), 2) Avg_Price,\n",
    "         max(crude_oil_price) Max_Price\n",
    "    FROM crudeOilPrice\n",
    "GROUP BY month\n",
    "ORDER BY Avg_Price\n",
    "\"\"\"\n",
    "\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='Month').reindex(month_order)\n",
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   ],
   "source": [
    "# Plotting data\n",
    "def add_line(price, color):\n",
    "    fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF[price],\n",
    "                            name=price, mode='lines+markers+text',\n",
    "                            text = sqlDF[price], textposition='top center',\n",
    "                            line=dict(color=color, \n",
    "                                      width=3, dash='dot'),\n",
    "                            textfont=dict(\n",
    "                                size=10\n",
    "                            )))\n",
    "fig = go.Figure()\n",
    "add_line('Min_Price', 'firebrick')\n",
    "add_line('Avg_Price', 'royalblue')\n",
    "add_line('Max_Price', 'forestgreen')\n",
    "\n",
    "fig.update_layout(title='Summary of crude oil price by month', width=850,\n",
    "                  yaxis_title = 'Price (US$/Barrel)',\n",
    "                  xaxis = dict(\n",
    "                      tickvals = month_order, \n",
    "                      ticktext = month_short\n",
    "                  ))\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, we can see that average price of crude oil increase as the year starts and decrease towards the end.\n",
    "\n",
    "**Price by year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min_Price</th>\n",
       "      <th>Avg_Price</th>\n",
       "      <th>Max_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>59.49</td>\n",
       "      <td>66.67</td>\n",
       "      <td>75.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>55.57</td>\n",
       "      <td>74.96</td>\n",
       "      <td>95.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>45.87</td>\n",
       "      <td>101.02</td>\n",
       "      <td>138.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>44.95</td>\n",
       "      <td>63.90</td>\n",
       "      <td>78.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>75.06</td>\n",
       "      <td>80.90</td>\n",
       "      <td>92.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>97.96</td>\n",
       "      <td>113.76</td>\n",
       "      <td>124.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>98.06</td>\n",
       "      <td>113.72</td>\n",
       "      <td>128.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>105.55</td>\n",
       "      <td>110.99</td>\n",
       "      <td>118.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>63.28</td>\n",
       "      <td>100.40</td>\n",
       "      <td>114.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>37.80</td>\n",
       "      <td>52.65</td>\n",
       "      <td>65.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>30.66</td>\n",
       "      <td>43.81</td>\n",
       "      <td>53.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>46.39</td>\n",
       "      <td>54.09</td>\n",
       "      <td>65.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>62.00</td>\n",
       "      <td>72.66</td>\n",
       "      <td>79.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>59.10</td>\n",
       "      <td>65.85</td>\n",
       "      <td>73.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>14.28</td>\n",
       "      <td>41.89</td>\n",
       "      <td>66.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>54.87</td>\n",
       "      <td>70.12</td>\n",
       "      <td>84.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>88.71</td>\n",
       "      <td>109.23</td>\n",
       "      <td>130.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      Min_Price  Avg_Price  Max_Price\n",
       "Year                                 \n",
       "2006      59.49      66.67      75.15\n",
       "2007      55.57      74.96      95.05\n",
       "2008      45.87     101.02     138.74\n",
       "2009      44.95      63.90      78.25\n",
       "2010      75.06      80.90      92.79\n",
       "2011      97.96     113.76     124.49\n",
       "2012      98.06     113.72     128.00\n",
       "2013     105.55     110.99     118.81\n",
       "2014      63.28     100.40     114.60\n",
       "2015      37.80      52.65      65.08\n",
       "2016      30.66      43.81      53.48\n",
       "2017      46.39      54.09      65.11\n",
       "2018      62.00      72.66      79.59\n",
       "2019      59.10      65.85      73.65\n",
       "2020      14.28      41.89      66.68\n",
       "2021      54.87      70.12      84.11\n",
       "2022      88.71     109.23     130.10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting data\n",
    "query = \"\"\"\n",
    "  SELECT year Year,\n",
    "         min(crude_oil_price) Min_Price,\n",
    "         ROUND(avg(crude_oil_price), 2) Avg_Price,\n",
    "         max(crude_oil_price) Max_Price\n",
    "    FROM crudeOilPrice\n",
    "GROUP BY year\n",
    "ORDER BY Avg_Price\n",
    "\"\"\"\n",
    "\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='Year').sort_index()\n",
    "sqlDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dac= sqlDF.reset_index()\n",
    "dac['Year'] = dac['Year'].astype('int') \n",
    "dac\n",
    "dac.to_csv('price_year.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>59.49</td>\n",
       "      <td>66.67</td>\n",
       "      <td>75.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>55.57</td>\n",
       "      <td>74.96</td>\n",
       "      <td>95.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>45.87</td>\n",
       "      <td>101.02</td>\n",
       "      <td>138.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>44.95</td>\n",
       "      <td>63.90</td>\n",
       "      <td>78.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>75.06</td>\n",
       "      <td>80.90</td>\n",
       "      <td>92.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2011</td>\n",
       "      <td>97.96</td>\n",
       "      <td>113.76</td>\n",
       "      <td>124.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012</td>\n",
       "      <td>98.06</td>\n",
       "      <td>113.72</td>\n",
       "      <td>128.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013</td>\n",
       "      <td>105.55</td>\n",
       "      <td>110.99</td>\n",
       "      <td>118.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>63.28</td>\n",
       "      <td>100.40</td>\n",
       "      <td>114.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52.65</td>\n",
       "      <td>65.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2016</td>\n",
       "      <td>30.66</td>\n",
       "      <td>43.81</td>\n",
       "      <td>53.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017</td>\n",
       "      <td>46.39</td>\n",
       "      <td>54.09</td>\n",
       "      <td>65.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2018</td>\n",
       "      <td>62.00</td>\n",
       "      <td>72.66</td>\n",
       "      <td>79.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019</td>\n",
       "      <td>59.10</td>\n",
       "      <td>65.85</td>\n",
       "      <td>73.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020</td>\n",
       "      <td>14.28</td>\n",
       "      <td>41.89</td>\n",
       "      <td>66.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021</td>\n",
       "      <td>54.87</td>\n",
       "      <td>70.12</td>\n",
       "      <td>84.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022</td>\n",
       "      <td>88.71</td>\n",
       "      <td>109.23</td>\n",
       "      <td>130.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year  Min_Price  Avg_Price  Max_Price\n",
       "0   2006      59.49      66.67      75.15\n",
       "1   2007      55.57      74.96      95.05\n",
       "2   2008      45.87     101.02     138.74\n",
       "3   2009      44.95      63.90      78.25\n",
       "4   2010      75.06      80.90      92.79\n",
       "5   2011      97.96     113.76     124.49\n",
       "6   2012      98.06     113.72     128.00\n",
       "7   2013     105.55     110.99     118.81\n",
       "8   2014      63.28     100.40     114.60\n",
       "9   2015      37.80      52.65      65.08\n",
       "10  2016      30.66      43.81      53.48\n",
       "11  2017      46.39      54.09      65.11\n",
       "12  2018      62.00      72.66      79.59\n",
       "13  2019      59.10      65.85      73.65\n",
       "14  2020      14.28      41.89      66.68\n",
       "15  2021      54.87      70.12      84.11\n",
       "16  2022      88.71     109.23     130.10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data = pd.read_csv('price_year.csv', index_col=0)\n",
    "data"
   ]
  },
  {
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   "source": [
    "# Plotting Data\n",
    "def add_line(price, color):\n",
    "    fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF[price],\n",
    "                            name=price, line=dict(color=color, \n",
    "                                                    width=3, dash='dot')))\n",
    "fig = go.Figure()\n",
    "add_line('Min_Price', 'firebrick')\n",
    "add_line('Avg_Price', 'royalblue')\n",
    "add_line('Max_Price', 'forestgreen')\n",
    "\n",
    "fig.update_layout(title='Summary of crude oil price by year', width=950,\n",
    "                  yaxis_title = 'Price (US$/Barrel)'\n",
    "                  )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Production by month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min_Production</th>\n",
       "      <th>Avg_Production</th>\n",
       "      <th>Max_Production</th>\n",
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       "    <tr>\n",
       "      <th>Month</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.36</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.26</td>\n",
       "      <td>2.10</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1.24</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>1.22</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1.02</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1.16</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1.08</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>0.97</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>0.94</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.23</td>\n",
       "      <td>2.12</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1.23</td>\n",
       "      <td>2.04</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1.20</td>\n",
       "      <td>2.01</td>\n",
       "      <td>2.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "           Min_Production  Avg_Production  Max_Production\n",
       "Month                                                    \n",
       "January              1.36            2.10            2.59\n",
       "February             1.26            2.10            2.51\n",
       "March                1.24            2.02            2.44\n",
       "April                1.22            2.01            2.42\n",
       "May                  1.02            1.98            2.50\n",
       "June                 1.16            1.97            2.41\n",
       "July                 1.08            2.01            2.48\n",
       "August               0.97            2.02            2.50\n",
       "September            0.94            2.02            2.48\n",
       "October              1.23            2.12            2.88\n",
       "November             1.23            2.04            2.50\n",
       "December             1.20            2.01            2.58"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting Data\n",
    "query = \"\"\"\n",
    "  SELECT month Month,\n",
    "         min(production) Min_Production,\n",
    "         ROUND(avg(production), 2) Avg_Production,\n",
    "         max(production) Max_Production\n",
    "    FROM crudeOilPrice\n",
    "GROUP BY month\n",
    "ORDER BY Avg_Production\n",
    "\"\"\"\n",
    "\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='Month').reindex(month_order)\n",
    "sqlDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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     },
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     "output_type": "display_data"
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   ],
   "source": [
    "# Plotting Data\n",
    "def add_line(price, color):\n",
    "    fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF[price],\n",
    "                            name=price, mode= 'lines+markers+text',\n",
    "                            text= sqlDF[price], textposition='top center',\n",
    "                            line=dict(color=color, \n",
    "                                      width=3, dash='dot'),\n",
    "                            textfont=dict(\n",
    "                                size=10\n",
    "                            )))\n",
    "fig = go.Figure()\n",
    "add_line('Min_Production', 'firebrick')\n",
    "add_line('Avg_Production', 'royalblue')\n",
    "add_line('Max_Production', 'forestgreen')\n",
    "\n",
    "fig.update_layout(title='Summary of crude oil production by month', width=850,\n",
    "                  yaxis_title = 'Production (mbd)',\n",
    "                  xaxis = dict(\n",
    "                      tickvals = month_order, \n",
    "                      ticktext = month_short\n",
    "                  ))\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Production by year**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Min_Production</th>\n",
       "      <th>Avg_Production</th>\n",
       "      <th>Max_Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2.25</td>\n",
       "      <td>2.382500</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.96</td>\n",
       "      <td>2.201667</td>\n",
       "      <td>2.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.96</td>\n",
       "      <td>2.099167</td>\n",
       "      <td>2.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.86</td>\n",
       "      <td>2.110833</td>\n",
       "      <td>2.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2.21</td>\n",
       "      <td>2.467500</td>\n",
       "      <td>2.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>2.27</td>\n",
       "      <td>2.380833</td>\n",
       "      <td>2.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>2.03</td>\n",
       "      <td>2.318333</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>2.03</td>\n",
       "      <td>2.183333</td>\n",
       "      <td>2.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2.06</td>\n",
       "      <td>2.205833</td>\n",
       "      <td>2.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1.97</td>\n",
       "      <td>2.126667</td>\n",
       "      <td>2.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.50</td>\n",
       "      <td>1.816667</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.60</td>\n",
       "      <td>1.889167</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.78</td>\n",
       "      <td>1.915000</td>\n",
       "      <td>2.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.94</td>\n",
       "      <td>2.012500</td>\n",
       "      <td>2.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.755833</td>\n",
       "      <td>2.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>1.20</td>\n",
       "      <td>1.308333</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.94</td>\n",
       "      <td>1.143333</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Min_Production  Avg_Production  Max_Production\n",
       "Year                                                \n",
       "2006            2.25        2.382500            2.59\n",
       "2007            1.96        2.201667            2.32\n",
       "2008            1.96        2.099167            2.26\n",
       "2009            1.86        2.110833            2.28\n",
       "2010            2.21        2.467500            2.88\n",
       "2011            2.27        2.380833            2.51\n",
       "2012            2.03        2.318333            2.48\n",
       "2013            2.03        2.183333            2.29\n",
       "2014            2.06        2.205833            2.33\n",
       "2015            1.97        2.126667            2.22\n",
       "2016            1.50        1.816667            2.15\n",
       "2017            1.60        1.889167            2.01\n",
       "2018            1.78        1.915000            2.01\n",
       "2019            1.94        2.012500            2.11\n",
       "2020            1.42        1.755833            2.07\n",
       "2021            1.20        1.308333            1.43\n",
       "2022            0.94        1.143333            1.40"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting data\n",
    "query = \"\"\"\n",
    "  SELECT year Year,\n",
    "         min(production) Min_Production,\n",
    "         avg(production) Avg_Production,\n",
    "         max(production) Max_Production\n",
    "    FROM crudeOilPrice\n",
    "GROUP BY year\n",
    "ORDER BY Avg_Production\n",
    "\"\"\"\n",
    "\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='Year').sort_index()\n",
    "sqlDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# Plotting Data\n",
    "def add_line(price, color):\n",
    "    fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF[price],\n",
    "                            name=price, line=dict(color=color, \n",
    "                                                            width=3, dash='dot')))\n",
    "fig = go.Figure()\n",
    "add_line('Min_Production', 'firebrick')\n",
    "add_line('Avg_Production', 'royalblue')\n",
    "add_line('Max_Production', 'forestgreen')\n",
    "\n",
    "fig.update_layout(title='Summary of crude oil production by year', width=950,\n",
    "                  yaxis_title = 'Production (mbd)'\n",
    "                  )\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Let's look at years that its average is above the overall average for price, production and export**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year_avg_export</th>\n",
       "      <th>Overall_avg_export</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>1.93</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>1.75</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>1.65</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>1.66</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>2.02</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>1.93</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1.87</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>1.73</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>1.76</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1.68</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>1.37</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>1.44</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>1.47</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>1.56</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>1.31</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>0.86</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>0.69</td>\n",
       "      <td>1.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year_avg_export  Overall_avg_export\n",
       "Year                                     \n",
       "2006             1.93                1.58\n",
       "2007             1.75                1.58\n",
       "2008             1.65                1.58\n",
       "2009             1.66                1.58\n",
       "2010             2.02                1.58\n",
       "2011             1.93                1.58\n",
       "2012             1.87                1.58\n",
       "2013             1.73                1.58\n",
       "2014             1.76                1.58\n",
       "2015             1.68                1.58\n",
       "2016             1.37                1.58\n",
       "2017             1.44                1.58\n",
       "2018             1.47                1.58\n",
       "2019             1.56                1.58\n",
       "2020             1.31                1.58\n",
       "2021             0.86                1.58\n",
       "2022             0.69                1.58"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting Data\n",
    "query = \"\"\"\n",
    "SELECT Year, Year_avg_export, sub.Overall_avg_export\n",
    "  FROM (SELECT year Year, ROUND(avg(crude_oil_export), 2) Year_avg_export, \n",
    "               (SELECT ROUND(avg(crude_oil_export), 2) \n",
    "                  FROM crudeOilPrice) Overall_avg_export\n",
    "          FROM crudeOilPrice\n",
    "      GROUP BY year) sub\n",
    "-- WHERE Year_avg_export > sub.Overall_avg_export\n",
    "\"\"\"\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='Year').sort_index()\n",
    "sqlDF"
   ]
  },
  {
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   "execution_count": 32,
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     "metadata": {},
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   ],
   "source": [
    "# Plotting Data\n",
    "fig = go.Figure()\n",
    "fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF.Year_avg_export,\n",
    "                            name='Year_avg_export', mode= 'lines+markers+text',\n",
    "                            text= sqlDF.Year_avg_export, textposition='top right',\n",
    "                            line=dict(color='royalblue', \n",
    "                                        width=3, dash='dot'),\n",
    "                            textfont=dict(size=11)))\n",
    "\n",
    "fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF.Overall_avg_export,\n",
    "                            name='Overall_avg_export', line=dict(color='firebrick', \n",
    "                                                            width=3, dash='dot')))\n",
    "\n",
    "fig.update_layout(title='Comparing yearly average of crude oil exported with overall average, 1.58mbd', width=950,\n",
    "                  yaxis_title = 'Production (mbd)')\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting from 2016, the yearly average of crude oil exported has not pass the overall average of crude oil exported, And the gap is increasing. Do you think it will pass the mark in few years?\n",
    "\n",
    "**Comparing 2021 crude oil production with 2022**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_prod_2021</th>\n",
       "      <th>total_prod_2022</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.36</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1.43</td>\n",
       "      <td>1.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>1.37</td>\n",
       "      <td>1.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1.34</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1.31</td>\n",
       "      <td>1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1.32</td>\n",
       "      <td>1.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1.24</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "           total_prod_2021  total_prod_2022\n",
       "month                                      \n",
       "January               1.36             1.40\n",
       "February              1.42             1.26\n",
       "March                 1.43             1.24\n",
       "April                 1.37             1.22\n",
       "May                   1.34             1.02\n",
       "June                  1.31             1.16\n",
       "July                  1.32             1.08\n",
       "August                1.24             0.97\n",
       "September             1.25             0.94\n",
       "October               1.23              NaN\n",
       "November              1.23              NaN\n",
       "December              1.20              NaN"
      ]
     },
     "execution_count": 47,
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   ],
   "source": [
    "# Filtering monthly production\n",
    "query = \"\"\"\n",
    "WITH sub AS (\n",
    "    SELECT month, SUM(production) AS total_prod_2022\n",
    "                            FROM crudeOilPrice\n",
    "                           WHERE year = '2022'\n",
    "                        GROUP BY month)\n",
    "\n",
    "  SELECT c1.month, SUM(c1.production) AS total_prod_2021, sub.total_prod_2022\n",
    "    FROM crudeOilPrice AS c1\n",
    "    LEFT JOIN sub \n",
    "    USING(month)\n",
    "   WHERE year = '2021' \n",
    "GROUP BY c1.month\n",
    "\"\"\"\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='month').reindex(month_order)\n",
    "sqlDF"
   ]
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   ],
   "source": [
    "# Visualizing data\n",
    "def add_line(total_prod, color):\n",
    "    ex = total_prod\n",
    "    name = ex[-4:] + ' Production'\n",
    "    fig.add_trace(go.Scatter(x=sqlDF.index, y=sqlDF[total_prod],\n",
    "                            name=name, mode='lines+markers+text',\n",
    "                            text=sqlDF[total_prod], textposition='bottom left',\n",
    "                            line=dict(color=color, \n",
    "                                        width=3, dash='dot'),\n",
    "                            textfont=dict(size=10)))\n",
    "fig = go.Figure()\n",
    "add_line('total_prod_2021', 'royalblue')\n",
    "add_line('total_prod_2022', 'firebrick')\n",
    "\n",
    "\n",
    "fig.update_layout(title='Comparing year 2021 crude oil production with that of 2022', width=950,\n",
    "                  yaxis_title = 'Production (mbd)',\n",
    "                  xaxis= dict(\n",
    "                      tickvals= month_order,\n",
    "                      ticktext= month_short\n",
    "                  ))\n",
    "fig.update_yaxes(showticklabels=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above charts depicts that production is decreasing. Will it increase next month? We don't know untill we have the data for that month.\n",
    "\n",
    "**Change in production between 2021 and 2022**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
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       "      <th></th>\n",
       "      <th>total_prod_2021</th>\n",
       "      <th>total_prod_2022</th>\n",
       "      <th>Percentage_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
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       "    <tr>\n",
       "      <th>January</th>\n",
       "      <td>1.36</td>\n",
       "      <td>1.40</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>February</th>\n",
       "      <td>1.42</td>\n",
       "      <td>1.26</td>\n",
       "      <td>-16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>March</th>\n",
       "      <td>1.43</td>\n",
       "      <td>1.24</td>\n",
       "      <td>-19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>April</th>\n",
       "      <td>1.37</td>\n",
       "      <td>1.22</td>\n",
       "      <td>-15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>1.34</td>\n",
       "      <td>1.02</td>\n",
       "      <td>-32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>June</th>\n",
       "      <td>1.31</td>\n",
       "      <td>1.16</td>\n",
       "      <td>-15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>July</th>\n",
       "      <td>1.32</td>\n",
       "      <td>1.08</td>\n",
       "      <td>-24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>August</th>\n",
       "      <td>1.24</td>\n",
       "      <td>0.97</td>\n",
       "      <td>-27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>September</th>\n",
       "      <td>1.25</td>\n",
       "      <td>0.94</td>\n",
       "      <td>-31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>October</th>\n",
       "      <td>1.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>November</th>\n",
       "      <td>1.23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>December</th>\n",
       "      <td>1.20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "           total_prod_2021  total_prod_2022  Percentage_change\n",
       "month                                                         \n",
       "January               1.36             1.40                4.0\n",
       "February              1.42             1.26              -16.0\n",
       "March                 1.43             1.24              -19.0\n",
       "April                 1.37             1.22              -15.0\n",
       "May                   1.34             1.02              -32.0\n",
       "June                  1.31             1.16              -15.0\n",
       "July                  1.32             1.08              -24.0\n",
       "August                1.24             0.97              -27.0\n",
       "September             1.25             0.94              -31.0\n",
       "October               1.23              NaN                NaN\n",
       "November              1.23              NaN                NaN\n",
       "December              1.20              NaN                NaN"
      ]
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   "source": [
    "# Filtering monthly production\n",
    "query = \"\"\"\n",
    "WITH sub AS (\n",
    "    SELECT month, SUM(production) AS total_prod_2022\n",
    "                            FROM crudeOilPrice\n",
    "                           WHERE year = '2022'\n",
    "                        GROUP BY month)\n",
    "\n",
    "  SELECT c1.month, SUM(c1.production) AS total_prod_2021, sub.total_prod_2022,\n",
    "            ROUND((sub.total_prod_2022 - SUM(c1.production)) * 100) Percentage_change\n",
    "    FROM crudeOilPrice AS c1\n",
    "    LEFT JOIN sub \n",
    "    USING(month)\n",
    "   WHERE year = '2021' \n",
    "GROUP BY c1.month\n",
    "\"\"\"\n",
    "sqlDF = pd.read_sql_query(query, conn, index_col='month').reindex(month_order)\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(go.Bar(x=sqlDF.index, y=sqlDF.Percentage_change, text=sqlDF.Percentage_change))\n",
    "fig.update_layout(title='Percentage change of crude oil production between 2021 and 2022',\n",
    "                  yaxis_title= 'Pecentage change', width= 700,\n",
    "                  xaxis= dict(\n",
    "                      tickvals = month_order,\n",
    "                      ticktext= month_short\n",
    "                  ))\n",
    "fig.update_yaxes(showticklabels=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above bar chart is showing the percentage difference between months in 2021 and months in 2022. Production increase by 4% in 2022 compared to January in 2021, afterwards there has not been a positive difference between other months.\n",
    "\n",
    "Let's see the relationship between Nigeria Crude Oil export and other attributes\n",
    "\n",
    "Since SQLite doesn't have a built in correlation function and doen't support windows function. I will use Python to find the relationship between the numeric varaiable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crude_oil_price     0.199156\n",
       "production          1.000000\n",
       "crude_oil_export    1.000000\n",
       "Name: crude_oil_export, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"SELECT * FROM crudeOilPrice\"\n",
    "sqlDF = pd.read_sql_query(query, conn)  \n",
    "sqlDF.corr()[\"crude_oil_export\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.lmplot(data=sqlDF, x= 'production', y='crude_oil_price');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.lmplot(data=sqlDF, x= 'production', y='crude_oil_export');"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's interesting! Crude oil price has a perfect positive correlation. Which means we can use just the production column and traiin a model to predict future export of crude oil."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer to questions\n",
    "* The largest production was made in October, 2010. A total of 2.88mbd was produced.\n",
    "* The highest average production of crude oil was made in 2010.\n",
    "* The highest average price was in year 2011.\n",
    "* No. On no ocassion has Nigeria exported all its produced crude oil.\n",
    "* There are 22 count where the crude oil exported fell below 70% of total production.\n",
    "1. For crude oil price with three years gap, all years are clustered between 35 (US$/Barrel) to 84 (US$/Barrel) except for year 2012 which had 98.06 (US$/Barrel) as it minimum price.\n",
    "2. For crude oil production with three years gap, production starts falling after year 2012. Year 2021 looks like an outlier because its maximum production, 1.43mbd is way below the minimum, 1.78mbd, of 2018. \n",
    "\n",
    "## Summary\n",
    "* Average price of crude oil increases as the year begins and decrease as the year comes to an end.\n",
    "* Crude oil price is has been inreasing since 2020, currently (2022) the minimum price per barrel is $88.71 compare to the maximum price of $66.68 in year 2020.\n",
    "* There has been a decrease in oil production since year 2019. \n",
    "* According to [Vanguard news](https://www.vanguardngr.com/2022/04/nigerias-oil-production-rises-5-07-to-1-388mbd-in-q1/) published on April 19, 2022. Nigeria’s failure to boost crude oil production in the face of high price of oil in the international market has been largely blamed on high oil theft and pipeline vandalism in the Niger Delta region. \n",
    "* The Group Managing Director of the Nigerian National Petroleum Corporation (NNPC) Limited, Mallam Mele Kyari revealed earlier in April that oil production has crashed to 1.15 million barrels per day. According to him, “As we speak now, there is massive disruption to our operations as a result of the activities of vandals and criminals along our pipelines in the Niger Delta area. This has brought down our production to levels as low as we have never seen before. [Article](https://www.vanguardngr.com/2022/04/nigerias-oil-production-rises-5-07-to-1-388mbd-in-q1/)\n",
    "* Starting from 2016, the yearly average of crude oil exported has not pass the overall average of crude oil exported.\n",
    "* The percentage decrease of oil exported in each month of year 2022 compared to months in 2021 is currently above 14%, except for January where ther was 4% increase in exported crude oil.  \n",
    "* For this sample of data, there is a perfect linear relationship between production and crude oil exported.\n",
    "\n",
    "## Limitations\n",
    "* The Dataset consist of few records which starts from 2006 to 2022, this may affects our general view of oil production.\n",
    "* Our data has few features which is not sufficient enough to depict why there is a fall in oil production.\n",
    "* Samples for year 2022 is not complete, it stops at september.\n",
    "* This data did not meet the rule of thumb where at least 100 samples is required for each feature before proceeding to building an algorithm for prediction.\n",
    "\n"
   ]
  }
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