{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>date</th>\n",
       "      <th>newcastle</th>\n",
       "      <th>HBA</th>\n",
       "      <th>ICI_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>Dec-23</td>\n",
       "      <td>146.25</td>\n",
       "      <td>117.38</td>\n",
       "      <td>118.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>Nov-23</td>\n",
       "      <td>132.15</td>\n",
       "      <td>139.80</td>\n",
       "      <td>118.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>Oct-23</td>\n",
       "      <td>121.10</td>\n",
       "      <td>123.96</td>\n",
       "      <td>121.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>Sep-23</td>\n",
       "      <td>160.01</td>\n",
       "      <td>133.13</td>\n",
       "      <td>116.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>Aug-23</td>\n",
       "      <td>156.00</td>\n",
       "      <td>179.90</td>\n",
       "      <td>114.57</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>140</th>\n",
       "      <td>2012</td>\n",
       "      <td>Apr-12</td>\n",
       "      <td>100.75</td>\n",
       "      <td>105.61</td>\n",
       "      <td>106.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2012</td>\n",
       "      <td>Mar-12</td>\n",
       "      <td>107.00</td>\n",
       "      <td>112.87</td>\n",
       "      <td>111.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2012</td>\n",
       "      <td>Feb-12</td>\n",
       "      <td>112.10</td>\n",
       "      <td>111.58</td>\n",
       "      <td>116.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2012</td>\n",
       "      <td>Jan-12</td>\n",
       "      <td>117.45</td>\n",
       "      <td>109.29</td>\n",
       "      <td>115.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2011</td>\n",
       "      <td>Dec-11</td>\n",
       "      <td>112.25</td>\n",
       "      <td>112.67</td>\n",
       "      <td>113.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>145 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year    date  newcastle     HBA   ICI_1\n",
       "0    2023  Dec-23     146.25  117.38  118.48\n",
       "1    2023  Nov-23     132.15  139.80  118.75\n",
       "2    2023  Oct-23     121.10  123.96  121.70\n",
       "3    2023  Sep-23     160.01  133.13  116.50\n",
       "4    2023  Aug-23     156.00  179.90  114.57\n",
       "..    ...     ...        ...     ...     ...\n",
       "140  2012  Apr-12     100.75  105.61  106.26\n",
       "141  2012  Mar-12     107.00  112.87  111.01\n",
       "142  2012  Feb-12     112.10  111.58  116.55\n",
       "143  2012  Jan-12     117.45  109.29  115.64\n",
       "144  2011  Dec-11     112.25  112.67  113.00\n",
       "\n",
       "[145 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../coal-price-data/coal_price_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.ICI_1\n",
    "x = df.newcastle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intercept, r, p, std_err = stats.linregress(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def myfunc(x):\n",
    "    return slope * x + intercept\n",
    "\n",
    "mymodel = list(map(myfunc, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y)\n",
    "plt.plot(x, mymodel)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}