Sprint - Data Munging.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "prices = pd.read_csv(\"WTI_price.csv\", na_values = (\"NaN\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do some exploration and transform the data into something we can understand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Date</th>\n",
       "      <th>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan 02, 1986</td>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jan 03, 1986</td>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Jan 06, 1986</td>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jan 07, 1986</td>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Jan 08, 1986</td>\n",
       "      <td>25.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  Cushing, OK WTI Spot Price FOB (Dollars per Barrel)\n",
       "0  Jan 02, 1986                                              25.56  \n",
       "1  Jan 03, 1986                                              26.00  \n",
       "2  Jan 06, 1986                                              26.53  \n",
       "3  Jan 07, 1986                                              25.85  \n",
       "4  Jan 08, 1986                                              25.87  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dailies = prices['Cushing, OK WTI Spot Price FOB (Dollars per Barrel)']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we're converting the data into the index by which we can sort/arrange the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices.index = pd.to_datetime(prices['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices.drop('Date', axis = 1, inplace=True)"
   ]
  },
  {
   "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>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-01-02</th>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-03</th>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-06</th>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-07</th>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-08</th>\n",
       "      <td>25.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cushing, OK WTI Spot Price FOB (Dollars per Barrel)\n",
       "Date                                                           \n",
       "1986-01-02                                              25.56  \n",
       "1986-01-03                                              26.00  \n",
       "1986-01-06                                              26.53  \n",
       "1986-01-07                                              25.85  \n",
       "1986-01-08                                              25.87  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.plot(prices)\n",
    "plt.title(\"WTI Crude Oil Prices\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Price in USD\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's clean up the data and put it into a neat dataframe. For exploratory purposes, let's use the imports, exports, and consumption columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "imports = pd.read_csv(\"Imports.csv\", na_values = (\"NaN\"))\n",
    "exports = pd.read_csv(\"Exports.csv\", na_values = (\"NaN\"))\n",
    "consumption = pd.read_csv(\"Consumption.csv\", na_values = (\"NaN\"))\n",
    "imports.index = pd.to_datetime(imports['Date'])\n",
    "exports.index = pd.to_datetime(exports['Date'])\n",
    "consumption.index = pd.to_datetime(consumption['Date'])\n",
    "imports.drop('Date', axis = 1, inplace=True)\n",
    "exports.drop('Date', axis = 1, inplace=True)\n",
    "consumption.drop('Date', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data_predictors = pd.DataFrame(data = (imports[\"Weekly U.S. Commercial Crude Oil Imports Excluding SPR  (Thousand Barrels per Day)\"], exports['Weekly U.S. Exports of Crude Oil and Petroleum Products  (Thousand Barrels per Day)'], consumption['Weekly U.S. Product Supplied of Petroleum Products  (Thousand Barrels per Day)'])).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>Weekly U.S. Commercial Crude Oil Imports Excluding SPR  (Thousand Barrels per Day)</th>\n",
       "      <th>Weekly U.S. Exports of Crude Oil and Petroleum Products  (Thousand Barrels per Day)</th>\n",
       "      <th>Weekly U.S. Product Supplied of Petroleum Products  (Thousand Barrels per Day)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1982-08-20</th>\n",
       "      <td>3459.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-08-27</th>\n",
       "      <td>3354.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-09-24</th>\n",
       "      <td>3494.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-10-01</th>\n",
       "      <td>2954.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-10-08</th>\n",
       "      <td>3008.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Weekly U.S. Commercial Crude Oil Imports Excluding SPR  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                             \n",
       "1982-08-20                                             3459.0                                    \n",
       "1982-08-27                                             3354.0                                    \n",
       "1982-09-24                                             3494.0                                    \n",
       "1982-10-01                                             2954.0                                    \n",
       "1982-10-08                                             3008.0                                    \n",
       "\n",
       "            Weekly U.S. Exports of Crude Oil and Petroleum Products  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                              \n",
       "1982-08-20                                                NaN                                     \n",
       "1982-08-27                                                NaN                                     \n",
       "1982-09-24                                                NaN                                     \n",
       "1982-10-01                                                NaN                                     \n",
       "1982-10-08                                                NaN                                     \n",
       "\n",
       "            Weekly U.S. Product Supplied of Petroleum Products  (Thousand Barrels per Day)  \n",
       "Date                                                                                        \n",
       "1982-08-20                                                NaN                               \n",
       "1982-08-27                                                NaN                               \n",
       "1982-09-24                                                NaN                               \n",
       "1982-10-01                                                NaN                               \n",
       "1982-10-08                                                NaN                               "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data_predictors.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's put the predictos and response together in order to clean it up a bit more for some statistical analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data = pd.concat((model_data_predictors, prices), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_prices = model_data[\"Cushing, OK WTI Spot Price FOB (Dollars per Barrel)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_predictors = model_data.drop(\"Cushing, OK WTI Spot Price FOB (Dollars per Barrel)\", axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "1991-02-08    21.78\n",
       "1991-02-15    20.73\n",
       "1991-02-22    17.43\n",
       "1991-03-01    19.43\n",
       "1991-03-08    19.34\n",
       "Name: Cushing, OK WTI Spot Price FOB (Dollars per Barrel), dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Weekly U.S. Commercial Crude Oil Imports Excluding SPR  (Thousand Barrels per Day)</th>\n",
       "      <th>Weekly U.S. Exports of Crude Oil and Petroleum Products  (Thousand Barrels per Day)</th>\n",
       "      <th>Weekly U.S. Product Supplied of Petroleum Products  (Thousand Barrels per Day)</th>\n",
       "      <th>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1991-02-08</th>\n",
       "      <td>5542.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>16067.0</td>\n",
       "      <td>21.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-15</th>\n",
       "      <td>5527.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>15627.0</td>\n",
       "      <td>20.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-02-22</th>\n",
       "      <td>4934.0</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>15414.0</td>\n",
       "      <td>17.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-01</th>\n",
       "      <td>4983.0</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>16997.0</td>\n",
       "      <td>19.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-03-08</th>\n",
       "      <td>5430.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>16982.0</td>\n",
       "      <td>19.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Weekly U.S. Commercial Crude Oil Imports Excluding SPR  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                             \n",
       "1991-02-08                                             5542.0                                    \n",
       "1991-02-15                                             5527.0                                    \n",
       "1991-02-22                                             4934.0                                    \n",
       "1991-03-01                                             4983.0                                    \n",
       "1991-03-08                                             5430.0                                    \n",
       "\n",
       "            Weekly U.S. Exports of Crude Oil and Petroleum Products  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                              \n",
       "1991-02-08                                             1224.0                                     \n",
       "1991-02-15                                             1224.0                                     \n",
       "1991-02-22                                             1510.0                                     \n",
       "1991-03-01                                             1510.0                                     \n",
       "1991-03-08                                             1025.0                                     \n",
       "\n",
       "            Weekly U.S. Product Supplied of Petroleum Products  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                         \n",
       "1991-02-08                                            16067.0                                \n",
       "1991-02-15                                            15627.0                                \n",
       "1991-02-22                                            15414.0                                \n",
       "1991-03-01                                            16997.0                                \n",
       "1991-03-08                                            16982.0                                \n",
       "\n",
       "            Cushing, OK WTI Spot Price FOB (Dollars per Barrel)  \n",
       "Date                                                             \n",
       "1991-02-08                                              21.78    \n",
       "1991-02-15                                              20.73    \n",
       "1991-02-22                                              17.43    \n",
       "1991-03-01                                              19.43    \n",
       "1991-03-08                                              19.34    "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More exploration using different data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "stockpiles = pd.read_csv(\"Weekly_Cushing_OK_Ending_Stocks_excluding_SPR_of_Crude_Oil.csv\", skiprows = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "stockpiles.index = pd.to_datetime(stockpiles['Week of'])\n",
    "stockpiles.drop('Week of', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "stockpiles_prices = pd.concat((prices, stockpiles), sort = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-12</th>\n",
       "      <td>37.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-13</th>\n",
       "      <td>37.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-14</th>\n",
       "      <td>36.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-15</th>\n",
       "      <td>37.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-16</th>\n",
       "      <td>37.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cushing, OK WTI Spot Price FOB (Dollars per Barrel)\n",
       "Date                                                           \n",
       "2004-04-12                                              37.79  \n",
       "2004-04-13                                              37.09  \n",
       "2004-04-14                                              36.62  \n",
       "2004-04-15                                              37.74  \n",
       "2004-04-16                                              37.70  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices[\"2004-04-09\":\"2019-08-19\"].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we're just combining the prices and stockpiles and prices dataframes and ordering the rows by date."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "stockpiles = stockpiles.reindex(stockpiles.index[::-1])\n",
    "stockpiles.index.names = ['Date']\n",
    "stockpiles_prices = pd.concat((stockpiles[\"2004-04-09\":\"2019-08-19\"], prices[\"2004-04-09\":\"2019-08-19\"]), sort = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "stockpiles_prices = stockpiles_prices.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)</th>\n",
       "      <th>Weekly Cushing OK Ending Stocks excluding SPR of Crude Oil Thousand Barrels</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-12</th>\n",
       "      <td>37.79</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-13</th>\n",
       "      <td>37.09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-14</th>\n",
       "      <td>36.62</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-15</th>\n",
       "      <td>37.74</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Cushing, OK WTI Spot Price FOB (Dollars per Barrel)  \\\n",
       "Date                                                              \n",
       "2004-04-09                                                NaN     \n",
       "2004-04-12                                              37.79     \n",
       "2004-04-13                                              37.09     \n",
       "2004-04-14                                              36.62     \n",
       "2004-04-15                                              37.74     \n",
       "\n",
       "            Weekly Cushing OK Ending Stocks excluding SPR of Crude Oil Thousand Barrels  \n",
       "Date                                                                                     \n",
       "2004-04-09                                            11677.0                            \n",
       "2004-04-12                                                NaN                            \n",
       "2004-04-13                                                NaN                            \n",
       "2004-04-14                                                NaN                            \n",
       "2004-04-15                                                NaN                            "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stockpiles_prices.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning up the data and only including only rows where both columns have usable data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a385a16d8>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "stockpiles_prices_fill = stockpiles_prices.groupby(stockpiles_prices.index).sum()\n",
    "stockpiles_prices = stockpiles_prices.groupby(stockpiles_prices.index).sum()\n",
    "stockpiles_prices.replace(0, np.nan, inplace = True)\n",
    "stockpiles_prices.dropna(inplace = True)\n",
    "plt.figure(figsize = (12,8))\n",
    "plt.plot(stockpiles_prices[\"Weekly Cushing OK Ending Stocks excluding SPR of Crude Oil Thousand Barrels\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we're adding the stockpiles data to the original dataframe to get four total predictors for our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data[\"Weekly Cushing OK Ending Stocks excluding SPR of Crude Oil Thousand Barrels\"] = stockpiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Weekly U.S. Commercial Crude Oil Imports Excluding SPR  (Thousand Barrels per Day)</th>\n",
       "      <th>Weekly U.S. Exports of Crude Oil and Petroleum Products  (Thousand Barrels per Day)</th>\n",
       "      <th>Weekly U.S. Product Supplied of Petroleum Products  (Thousand Barrels per Day)</th>\n",
       "      <th>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)</th>\n",
       "      <th>Weekly Cushing OK Ending Stocks excluding SPR of Crude Oil Thousand Barrels</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2004-04-16</th>\n",
       "      <td>9676.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>20051.0</td>\n",
       "      <td>37.70</td>\n",
       "      <td>12512.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-23</th>\n",
       "      <td>10469.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>19553.0</td>\n",
       "      <td>37.22</td>\n",
       "      <td>12905.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-30</th>\n",
       "      <td>9791.0</td>\n",
       "      <td>967.0</td>\n",
       "      <td>18932.0</td>\n",
       "      <td>37.31</td>\n",
       "      <td>12731.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-05-07</th>\n",
       "      <td>9926.0</td>\n",
       "      <td>975.0</td>\n",
       "      <td>20706.0</td>\n",
       "      <td>39.98</td>\n",
       "      <td>13631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-05-14</th>\n",
       "      <td>9939.0</td>\n",
       "      <td>964.0</td>\n",
       "      <td>20606.0</td>\n",
       "      <td>41.42</td>\n",
       "      <td>14358.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Weekly U.S. Commercial Crude Oil Imports Excluding SPR  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                             \n",
       "2004-04-16                                             9676.0                                    \n",
       "2004-04-23                                            10469.0                                    \n",
       "2004-04-30                                             9791.0                                    \n",
       "2004-05-07                                             9926.0                                    \n",
       "2004-05-14                                             9939.0                                    \n",
       "\n",
       "            Weekly U.S. Exports of Crude Oil and Petroleum Products  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                              \n",
       "2004-04-16                                              967.0                                     \n",
       "2004-04-23                                              967.0                                     \n",
       "2004-04-30                                              967.0                                     \n",
       "2004-05-07                                              975.0                                     \n",
       "2004-05-14                                              964.0                                     \n",
       "\n",
       "            Weekly U.S. Product Supplied of Petroleum Products  (Thousand Barrels per Day)  \\\n",
       "Date                                                                                         \n",
       "2004-04-16                                            20051.0                                \n",
       "2004-04-23                                            19553.0                                \n",
       "2004-04-30                                            18932.0                                \n",
       "2004-05-07                                            20706.0                                \n",
       "2004-05-14                                            20606.0                                \n",
       "\n",
       "            Cushing, OK WTI Spot Price FOB (Dollars per Barrel)  \\\n",
       "Date                                                              \n",
       "2004-04-16                                              37.70     \n",
       "2004-04-23                                              37.22     \n",
       "2004-04-30                                              37.31     \n",
       "2004-05-07                                              39.98     \n",
       "2004-05-14                                              41.42     \n",
       "\n",
       "            Weekly Cushing OK Ending Stocks excluding SPR of Crude Oil Thousand Barrels  \n",
       "Date                                                                                     \n",
       "2004-04-16                                            12512.0                            \n",
       "2004-04-23                                            12905.0                            \n",
       "2004-04-30                                            12731.0                            \n",
       "2004-05-07                                            13631.0                            \n",
       "2004-05-14                                            14358.0                            "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data.dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_data.columns = [\"Imports\", \"Exports\", \"Consumption\", \"Price\", \"Stockpiles\"]\n",
    "model_data.dropna().to_csv(\"pca_data\")\n",
    "model_data.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now for some modelling and predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = model_data.drop(\"Price\", axis = 1)\n",
    "price = model_data[\"Price\"]\n",
    "features_list = list(features.columns)\n",
    "features = np.array(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we have to establish a baseline error. For this, we will make predictions using a time-series forecasting method known as long short-term memory (LSTM) on all of the prices data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range = (0, 1))\n",
    "\n",
    "prices_scaled = scaler.fit_transform(prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices = np.array(prices)\n",
    "prices_steps = []\n",
    "prices_labels = []\n",
    "for i in range(60, 7476):\n",
    "    prices_steps.append(prices_scaled[i-60:i, 0])\n",
    "    prices_labels.append(prices_scaled[i, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_steps, price_labels = np.array(prices_steps), np.array(prices_labels)\n",
    "prices_steps = np.reshape(prices_steps, (prices_steps.shape[0], prices_steps.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences = True, input_shape = (prices_steps.shape[1], 1)))\n",
    "model.add(Dropout(.2))\n",
    "model.add(LSTM(units=50, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50, return_sequences=True))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(LSTM(units=50))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(units = 1))\n",
    "model.compile(optimizer = 'adam', loss = 'mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/andrew/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /Users/andrew/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:1033: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead.\n",
      "\n",
      "Epoch 1/1\n",
      "7416/7416 [==============================] - 36s 5ms/step - loss: 0.0048\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a385a18d0>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(prices_steps, prices_labels, epochs = 1, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_prices = []\n",
    "for i in range(7476, 8476):\n",
    "    test_prices.append(prices_scaled[i-60:i, 0])\n",
    "\n",
    "test_prices = np.array(test_prices)\n",
    "test_prices = np.reshape(test_prices, (test_prices.shape[0], test_prices.shape[1], 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(test_prices)\n",
    "predictions = scaler.inverse_transform(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (13,9))\n",
    "plt.plot(prices[7476:8476], color = 'blue', label = 'Actual WTI Prices')\n",
    "plt.plot(predictions, color = 'red', label = 'Predicted WTI Prices')\n",
    "plt.title('WTI Crude Oil Price Prediction')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=1000, n_jobs=None,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "train_features, test_features, train_price, test_price = train_test_split(features, price, test_size = .25, random_state = 1)\n",
    "rf = RandomForestRegressor(n_estimators = 1000, random_state = 42)\n",
    "rf.fit(train_features, train_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93.26"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_rf = rf.predict(test_features)\n",
    "errors = (predictions_rf - test_price) ** 2\n",
    "rss = round(np.mean(errors),2)\n",
    "rss\n",
    "abs_errors = abs(predictions_rf - test_price)\n",
    "mape = 100 * (abs_errors / predictions_rf)\n",
    "accuracy = round(100 - np.mean(mape), 2)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}