Chapter03/Exercise3.02/Exercise_3_02.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aUcSSVlSOGoA"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kDn9hp3dPX5G"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# load the data\n",
    "#\n",
    "df = pd.read_csv('../Datasets/synth_temp.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "hBtKePSxQDOV",
    "outputId": "8b93ebe9-6986-489a-f7c2-56e237b9db88"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# slice 1902 and forward\n",
    "#\n",
    "df = df.loc[df.Year > 1901]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "JjrL_hZYQdk9",
    "outputId": "f6d27523-64af-4ced-85e2-2b158dfa97ed"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RgnAvTemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1902</td>\n",
       "      <td>17.385044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1903</td>\n",
       "      <td>17.222163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1904</td>\n",
       "      <td>17.217215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1905</td>\n",
       "      <td>17.817502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1906</td>\n",
       "      <td>17.386445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1907</td>\n",
       "      <td>17.384588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1908</td>\n",
       "      <td>17.388942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1909</td>\n",
       "      <td>17.662632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1910</td>\n",
       "      <td>17.457570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1911</td>\n",
       "      <td>17.095512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1912</td>\n",
       "      <td>17.356156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1913</td>\n",
       "      <td>17.175210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      RgnAvTemp\n",
       "Year           \n",
       "1902  17.385044\n",
       "1903  17.222163\n",
       "1904  17.217215\n",
       "1905  17.817502\n",
       "1906  17.386445\n",
       "1907  17.384588\n",
       "1908  17.388942\n",
       "1909  17.662632\n",
       "1910  17.457570\n",
       "1911  17.095512\n",
       "1912  17.356156\n",
       "1913  17.175210"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# roll up by year\n",
    "#\n",
    "df_group_year = df.groupby(['Year']).agg({'RgnAvTemp' : 'mean'})\n",
    "#\n",
    "df_group_year.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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    "colab": {
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    "id": "orClyQArGI8l",
    "outputId": "3d529d43-6e7a-4082-f0e1-58300eee50c5"
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   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AvTemp</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1902</td>\n",
       "      <td>17.385044</td>\n",
       "      <td>1902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1903</td>\n",
       "      <td>17.222163</td>\n",
       "      <td>1903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1904</td>\n",
       "      <td>17.217215</td>\n",
       "      <td>1904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1905</td>\n",
       "      <td>17.817502</td>\n",
       "      <td>1905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1906</td>\n",
       "      <td>17.386445</td>\n",
       "      <td>1906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AvTemp  Year\n",
       "Year                 \n",
       "1902  17.385044  1902\n",
       "1903  17.222163  1903\n",
       "1904  17.217215  1904\n",
       "1905  17.817502  1905\n",
       "1906  17.386445  1906"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# add the Year column so we can use that in a model\n",
    "#\n",
    "df_group_year['Year'] = df_group_year.index\n",
    "df_group_year = df_group_year.rename(columns = {'RgnAvTemp' : 'AvTemp'})\n",
    "df_group_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "xfk9mnWCPudD",
    "outputId": "e6d6b9fb-4fb6-41ee-a0d9-0fb37ad3668f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model slope =  0.02352237024970654\n",
      "model intercept =  -27.88736502793287\n",
      "r squared =  0.8438499294671093\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# construct the model and inspect results\n",
    "#\n",
    "linear_model = LinearRegression(fit_intercept = True)\n",
    "linear_model.fit(df_group_year['Year'].values.reshape((-1, 1)),\n",
    "                 df_group_year.AvTemp)\n",
    "print('model slope = ', linear_model.coef_[0])\n",
    "print('model intercept = ', linear_model.intercept_)\n",
    "r2 = linear_model.score(df_group_year['Year'].values.reshape((-1, 1)),\n",
    "                 df_group_year.AvTemp)\n",
    "print('r squared = ', r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "23ISdFd3QOyk"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# generate predictions for visualization\n",
    "#\n",
    "pred_X = df_group_year.loc[:, 'Year']\n",
    "pred_Y = linear_model.predict(df_group_year['Year'].values.reshape((-1, 1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 608
    },
    "colab_type": "code",
    "id": "x8g_7JnVLogz",
    "outputId": "2bfc38dc-ef95-4ad6-ae0f-9ce8b77ff6d4"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 7))\n",
    "ax = fig.add_axes([1, 1, 1, 1]);\n",
    "#\n",
    "# Raw data\n",
    "#\n",
    "raw_plot_data = df[df.Year > 1901]\n",
    "ax.scatter(raw_plot_data.Year, \n",
    "           raw_plot_data.RgnAvTemp, \n",
    "           label = 'Raw Data', \n",
    "           c = 'red',\n",
    "           s = 1.5)\n",
    "#\n",
    "# Annual averages\n",
    "#\n",
    "ax.scatter(df_group_year.Year, \n",
    "           df_group_year.AvTemp, \n",
    "           label = 'Annual average', \n",
    "           c = 'k',\n",
    "           s = 10)\n",
    "#\n",
    "# linear fit\n",
    "#\n",
    "ax.plot(pred_X, pred_Y, \n",
    "        c = \"blue\",\n",
    "        linestyle = '-.',\n",
    "        linewidth = 4,\n",
    "        label = 'linear fit')\n",
    "#\n",
    "ax.set_title('Mean Air Temperature Measurements',\n",
    "             fontsize = 16)\n",
    "#\n",
    "# make the ticks include the first and last years\n",
    "#\n",
    "tick_years = [1902] + list(range(1910, 2011, 10))\n",
    "ax.set_xlabel('Year', \n",
    "              fontsize = 14)\n",
    "ax.set_ylabel('Temperature ($^\\circ$C)', \n",
    "              fontsize = 14)\n",
    "ax.set_ylim(15, 21)\n",
    "ax.set_xticks(tick_years)\n",
    "ax.tick_params(labelsize = 12)\n",
    "ax.legend(fontsize = 12)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Exercise 2.02.ipynb",
   "provenance": []
  },
  "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}