Chapter03/Activity3.03/Activity_3_03.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": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "kDn9hp3dPX5G",
    "outputId": "006b2a21-9de2-42cb-c84b-1c05a924d9c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date  TempHighF  TempAvgF  TempLowF DewPointHighF DewPointAvgF  \\\n",
      "0  2013-12-21         74        60        45            67           49   \n",
      "1  2013-12-22         56        48        39            43           36   \n",
      "2  2013-12-23         58        45        32            31           27   \n",
      "3  2013-12-24         61        46        31            36           28   \n",
      "4  2013-12-25         58        50        41            44           40   \n",
      "\n",
      "  DewPointLowF HumidityHighPercent HumidityAvgPercent HumidityLowPercent  ...  \\\n",
      "0           43                  93                 75                 57  ...   \n",
      "1           28                  93                 68                 43  ...   \n",
      "2           23                  76                 52                 27  ...   \n",
      "3           21                  89                 56                 22  ...   \n",
      "4           36                  86                 71                 56  ...   \n",
      "\n",
      "  SeaLevelPressureAvgInches SeaLevelPressureLowInches VisibilityHighMiles  \\\n",
      "0                     29.68                     29.59                  10   \n",
      "1                     30.13                     29.87                  10   \n",
      "2                     30.49                     30.41                  10   \n",
      "3                     30.45                      30.3                  10   \n",
      "4                     30.33                     30.27                  10   \n",
      "\n",
      "  VisibilityAvgMiles VisibilityLowMiles WindHighMPH WindAvgMPH WindGustMPH  \\\n",
      "0                  7                  2          20          4          31   \n",
      "1                 10                  5          16          6          25   \n",
      "2                 10                 10           8          3          12   \n",
      "3                 10                  7          12          4          20   \n",
      "4                 10                  7          10          2          16   \n",
      "\n",
      "  PrecipitationSumInches               Events  \n",
      "0                   0.46  Rain , Thunderstorm  \n",
      "1                      0                       \n",
      "2                      0                       \n",
      "3                      0                       \n",
      "4                      T                       \n",
      "\n",
      "[5 rows x 21 columns]\n",
      "            Date  TempHighF  TempAvgF  TempLowF DewPointHighF DewPointAvgF  \\\n",
      "1314  2017-07-27        103        89        75            71           67   \n",
      "1315  2017-07-28        105        91        76            71           64   \n",
      "1316  2017-07-29        107        92        77            72           64   \n",
      "1317  2017-07-30        106        93        79            70           68   \n",
      "1318  2017-07-31         99        88        77            66           61   \n",
      "\n",
      "     DewPointLowF HumidityHighPercent HumidityAvgPercent HumidityLowPercent  \\\n",
      "1314           61                  82                 54                 25   \n",
      "1315           55                  87                 54                 20   \n",
      "1316           55                  82                 51                 19   \n",
      "1317           63                  69                 48                 27   \n",
      "1318           54                  64                 43                 22   \n",
      "\n",
      "      ... SeaLevelPressureAvgInches SeaLevelPressureLowInches  \\\n",
      "1314  ...                     29.97                     29.88   \n",
      "1315  ...                      29.9                     29.81   \n",
      "1316  ...                     29.86                     29.79   \n",
      "1317  ...                     29.91                     29.87   \n",
      "1318  ...                     29.97                     29.91   \n",
      "\n",
      "     VisibilityHighMiles VisibilityAvgMiles VisibilityLowMiles WindHighMPH  \\\n",
      "1314                  10                 10                 10          12   \n",
      "1315                  10                 10                 10          14   \n",
      "1316                  10                 10                 10          12   \n",
      "1317                  10                 10                 10          13   \n",
      "1318                  10                 10                 10          12   \n",
      "\n",
      "     WindAvgMPH WindGustMPH PrecipitationSumInches Events  \n",
      "1314          5          21                      0         \n",
      "1315          5          20                      0         \n",
      "1316          4          17                      0         \n",
      "1317          4          20                      0         \n",
      "1318          4          20                      0         \n",
      "\n",
      "[5 rows x 21 columns]\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# load data and inspect\n",
    "#\n",
    "df = pd.read_csv('../Datasets/austin_weather.csv')\n",
    "#\n",
    "print(df.head())\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "hBtKePSxQDOV",
    "outputId": "376019db-14b6-40c7-bb8e-7fc173cfae24"
   },
   "outputs": [],
   "source": [
    "df = df.loc[:, ['Date', 'TempAvgF']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "91njYskTbYvc",
    "outputId": "474e9dca-394b-4713-9622-5367c21da1ac"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# add some useful columns\n",
    "#\n",
    "df.loc[:, 'Year'] = df.loc[:, 'Date'].str.slice(0, 4).astype('int')\n",
    "df.loc[:, 'Month'] = df.loc[:, 'Date'].str.slice(5, 7).astype('int')\n",
    "df.loc[:, 'Day'] = df.loc[:, 'Date'].str.slice(8, 10).astype('int')\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "bb7yHHt-cXt_",
    "outputId": "b45ff4ee-b333-44eb-9f80-dea3a880cb65"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# set a 20 day window then use that to smooth \n",
    "# temperature in a new column\n",
    "window = 20\n",
    "df['20_d_mov_avg'] = df.TempAvgF.rolling(window).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "qm2_i2KdtLTY",
    "outputId": "f592a3b4-31bc-42c6-9673-628ea1ec7de2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index        Date  TempAvgF  Year  Month  Day  20_d_mov_avg  Day_of_Year\n",
      "0    376  2015-01-01        37  2015      1    1         52.70            1\n",
      "1    377  2015-01-02        41  2015      1    2         51.50            2\n",
      "2    378  2015-01-03        51  2015      1    3         50.65            3\n",
      "3    379  2015-01-04        43  2015      1    4         49.65            4\n",
      "4    380  2015-01-05        41  2015      1    5         48.95            5\n",
      "     index        Date  TempAvgF  Year  Month  Day  20_d_mov_avg  Day_of_Year\n",
      "360    736  2015-12-27        55  2015     12   27         61.10          361\n",
      "361    737  2015-12-28        41  2015     12   28         60.20          362\n",
      "362    738  2015-12-29        43  2015     12   29         59.25          363\n",
      "363    739  2015-12-30        49  2015     12   30         58.45          364\n",
      "364    740  2015-12-31        46  2015     12   31         57.45          365\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# now let's slice exactly one year on the\n",
    "# calendar start and end dates\n",
    "# we see from the previous output that \n",
    "# 2014 is the first year with complete data, \n",
    "# however it will still have NaN values for \n",
    "# the moving average, so we'll use 2015\n",
    "#\n",
    "df_one_year = df.loc[df.Year == 2015, :].reset_index()\n",
    "df_one_year['Day_of_Year'] = df_one_year.index + 1\n",
    "print(df_one_year.head())\n",
    "print(df_one_year.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 608
    },
    "colab_type": "code",
    "id": "RKtQn57hRMM8",
    "outputId": "2b0e4937-9a9e-4694-c846-3ae1caa9183a"
   },
   "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",
    "ax.scatter(df_one_year.Day_of_Year, \n",
    "           df_one_year.TempAvgF, \n",
    "           label = 'Raw Data', c = 'k')\n",
    "#\n",
    "# Moving averages\n",
    "#\n",
    "ax.plot(df_one_year.Day_of_Year,\n",
    "        df_one_year['20_d_mov_avg'], \n",
    "        c = 'r', \n",
    "        linestyle = '--', \n",
    "        label = f'{window} day moving average')\n",
    "#\n",
    "ax.set_title('Air Temperature Measurements',\n",
    "             fontsize = 16)\n",
    "ax.set_xlabel('Day', \n",
    "              fontsize = 14)\n",
    "ax.set_ylabel('Temperature ($^\\circ$F)', \n",
    "              fontsize = 14)\n",
    "ax.set_xticks(range(df_one_year.Day_of_Year.min(), \n",
    "                    df_one_year.Day_of_Year.max(), \n",
    "                    30))\n",
    "ax.tick_params(labelsize = 12)\n",
    "ax.legend(fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 609
    },
    "colab_type": "code",
    "id": "EZrW77l43Vbz",
    "outputId": "b9dec81d-728e-48a9-c34e-9b0e86ca332d"
   },
   "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>index</th>\n",
       "      <th>Date</th>\n",
       "      <th>TempAvgF</th>\n",
       "      <th>Year</th>\n",
       "      <th>Day</th>\n",
       "      <th>20_d_mov_avg</th>\n",
       "      <th>Day_of_Year</th>\n",
       "      <th>Feb</th>\n",
       "      <th>Mar</th>\n",
       "      <th>Apr</th>\n",
       "      <th>May</th>\n",
       "      <th>Jun</th>\n",
       "      <th>Jul</th>\n",
       "      <th>Aug</th>\n",
       "      <th>Sep</th>\n",
       "      <th>Oct</th>\n",
       "      <th>Nov</th>\n",
       "      <th>Dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>376</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>37</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>52.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>377</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>41</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>51.50</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>378</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>51</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>50.65</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>379</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>43</td>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>49.65</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>380</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>41</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>48.95</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>360</td>\n",
       "      <td>736</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>55</td>\n",
       "      <td>2015</td>\n",
       "      <td>27</td>\n",
       "      <td>61.10</td>\n",
       "      <td>361</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>361</td>\n",
       "      <td>737</td>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>41</td>\n",
       "      <td>2015</td>\n",
       "      <td>28</td>\n",
       "      <td>60.20</td>\n",
       "      <td>362</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>362</td>\n",
       "      <td>738</td>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>43</td>\n",
       "      <td>2015</td>\n",
       "      <td>29</td>\n",
       "      <td>59.25</td>\n",
       "      <td>363</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>363</td>\n",
       "      <td>739</td>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>49</td>\n",
       "      <td>2015</td>\n",
       "      <td>30</td>\n",
       "      <td>58.45</td>\n",
       "      <td>364</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>364</td>\n",
       "      <td>740</td>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>46</td>\n",
       "      <td>2015</td>\n",
       "      <td>31</td>\n",
       "      <td>57.45</td>\n",
       "      <td>365</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index        Date  TempAvgF  Year  Day  20_d_mov_avg  Day_of_Year  Feb  \\\n",
       "0      376  2015-01-01        37  2015    1         52.70            1    0   \n",
       "1      377  2015-01-02        41  2015    2         51.50            2    0   \n",
       "2      378  2015-01-03        51  2015    3         50.65            3    0   \n",
       "3      379  2015-01-04        43  2015    4         49.65            4    0   \n",
       "4      380  2015-01-05        41  2015    5         48.95            5    0   \n",
       "..     ...         ...       ...   ...  ...           ...          ...  ...   \n",
       "360    736  2015-12-27        55  2015   27         61.10          361    0   \n",
       "361    737  2015-12-28        41  2015   28         60.20          362    0   \n",
       "362    738  2015-12-29        43  2015   29         59.25          363    0   \n",
       "363    739  2015-12-30        49  2015   30         58.45          364    0   \n",
       "364    740  2015-12-31        46  2015   31         57.45          365    0   \n",
       "\n",
       "     Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec  \n",
       "0      0    0    0    0    0    0    0    0    0    0  \n",
       "1      0    0    0    0    0    0    0    0    0    0  \n",
       "2      0    0    0    0    0    0    0    0    0    0  \n",
       "3      0    0    0    0    0    0    0    0    0    0  \n",
       "4      0    0    0    0    0    0    0    0    0    0  \n",
       "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  \n",
       "360    0    0    0    0    0    0    0    0    0    1  \n",
       "361    0    0    0    0    0    0    0    0    0    1  \n",
       "362    0    0    0    0    0    0    0    0    0    1  \n",
       "363    0    0    0    0    0    0    0    0    0    1  \n",
       "364    0    0    0    0    0    0    0    0    0    1  \n",
       "\n",
       "[365 rows x 18 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "# use the month as a dummy variable\n",
    "#\n",
    "dummy_vars = pd.get_dummies(df_one_year['Month'], drop_first = True)\n",
    "dummy_vars.columns = ['Feb', 'Mar', 'Apr', 'May', 'Jun',\n",
    "                      'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "df_one_year = pd.concat([df_one_year, dummy_vars], \n",
    "                        axis = 1).drop('Month', axis = 1)\n",
    "df_one_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "hQZV2w2v39_b",
    "outputId": "78d41c36-cb8d-4e2c-db20-7fcd091c875e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model coefficients:  [ 0.03719346  1.57445204  9.35397321 19.16903518 22.02065629 26.80023439\n",
      " 30.17121033 30.82466482 25.6117698  15.71715435  1.542969   -4.06777548]\n",
      "model intercept:  48.34038858048261\n",
      "model r squared:  0.7834805472165678\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "# fit model using the month dummy vars\n",
    "#\n",
    "linear_model = LinearRegression(fit_intercept = True)\n",
    "linear_model.fit(\n",
    "    pd.concat([df_one_year.Day_of_Year,\n",
    "               df_one_year.loc[:, 'Feb':'Dec']], axis = 1),\n",
    "    df_one_year['TempAvgF'])\n",
    "print('model coefficients: ', linear_model.coef_)\n",
    "print('model intercept: ', linear_model.intercept_)\n",
    "print('model r squared: ',\n",
    "      linear_model.score(\n",
    "          pd.concat([df_one_year.Day_of_Year,\n",
    "                     df_one_year.loc[:, 'Feb':'Dec']], axis = 1),\n",
    "          df_one_year['TempAvgF']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-nPKsGiM7sYM"
   },
   "outputs": [],
   "source": [
    "#\n",
    "# make predictions using the data\n",
    "#\n",
    "y_pred = linear_model.predict(\n",
    "    pd.concat([df_one_year.Day_of_Year,\n",
    "               df_one_year.loc[:, 'Feb':'Dec']],\n",
    "              axis = 1))\n",
    "x_pred = df_one_year.Day_of_Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 608
    },
    "colab_type": "code",
    "id": "uOa2Pbp-8EzJ",
    "outputId": "217c2a89-301c-4dc9-f955-a595cc3ae99b"
   },
   "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",
    "ax.scatter(df_one_year.Day_of_Year, \n",
    "           df_one_year.TempAvgF, \n",
    "           label = 'Raw Data', c = 'k')\n",
    "#\n",
    "# Moving averages\n",
    "#\n",
    "ax.plot(df_one_year.Day_of_Year,\n",
    "        df_one_year['20_d_mov_avg'], \n",
    "        c = 'r', \n",
    "        linestyle = '--', \n",
    "        label = f'{window} day moving average')\n",
    "#\n",
    "# regression predictions\n",
    "#\n",
    "ax.plot(x_pred, y_pred,\n",
    "        c = \"blue\",\n",
    "        linestyle = '-.',\n",
    "        label = 'linear model w/dummy vars')\n",
    "ax.set_title('Air Temperature Measurements',\n",
    "             fontsize = 16)\n",
    "ax.set_xlabel('Day', \n",
    "              fontsize = 14)\n",
    "ax.set_ylabel('Temperature ($^\\circ$F)', \n",
    "              fontsize = 14)\n",
    "ax.set_xticks(range(df_one_year.Day_of_Year.min(), \n",
    "                    df_one_year.Day_of_Year.max(), \n",
    "                    30))\n",
    "ax.tick_params(labelsize = 12)\n",
    "ax.legend(fontsize = 12,\n",
    "          loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Activity 2.03_colab.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
}