{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load weather data and station metadata"
   ]
  },
  {
   "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>BROCKVILLE PCC;MEAN_TEMPERATURE</th>\n",
       "      <th>BROCKVILLE PCC;MIN_TEMPERATURE</th>\n",
       "      <th>BROCKVILLE PCC;MAX_TEMPERATURE</th>\n",
       "      <th>BROCKVILLE PCC;TOTAL_PRECIPITATION</th>\n",
       "      <th>POWELL RIVER A;MEAN_TEMPERATURE</th>\n",
       "      <th>POWELL RIVER A;MIN_TEMPERATURE</th>\n",
       "      <th>POWELL RIVER A;MAX_TEMPERATURE</th>\n",
       "      <th>POWELL RIVER A;TOTAL_PRECIPITATION</th>\n",
       "      <th>STONY MOUNTAIN;MEAN_TEMPERATURE</th>\n",
       "      <th>STONY MOUNTAIN;MIN_TEMPERATURE</th>\n",
       "      <th>...</th>\n",
       "      <th>CHATHAM POINT;MAX_TEMPERATURE</th>\n",
       "      <th>CHATHAM POINT;TOTAL_PRECIPITATION</th>\n",
       "      <th>GREENWOOD A;MEAN_TEMPERATURE</th>\n",
       "      <th>GREENWOOD A;MIN_TEMPERATURE</th>\n",
       "      <th>GREENWOOD A;MAX_TEMPERATURE</th>\n",
       "      <th>GREENWOOD A;TOTAL_PRECIPITATION</th>\n",
       "      <th>BROOKS;MEAN_TEMPERATURE</th>\n",
       "      <th>BROOKS;MIN_TEMPERATURE</th>\n",
       "      <th>BROOKS;MAX_TEMPERATURE</th>\n",
       "      <th>BROOKS;TOTAL_PRECIPITATION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOCAL_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1990-01-01</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>9.3</td>\n",
       "      <td>8.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-2.2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.2</td>\n",
       "      <td>13.2</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.6</td>\n",
       "      <td>-15.2</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>0.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.9</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>20.2</td>\n",
       "      <td>-1.7</td>\n",
       "      <td>-6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>-15.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.9</td>\n",
       "      <td>11.8</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-7.1</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-12.3</td>\n",
       "      <td>-15.1</td>\n",
       "      <td>-9.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-18.5</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>...</td>\n",
       "      <td>8.5</td>\n",
       "      <td>34.2</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10.3</td>\n",
       "      <td>-15.1</td>\n",
       "      <td>-5.4</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>-26.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.4</td>\n",
       "      <td>-7.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>-27.3</td>\n",
       "      <td>-37.3</td>\n",
       "      <td>-17.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>-27.7</td>\n",
       "      <td>-20.2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.3</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-29.5</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-24.9</td>\n",
       "      <td>-31.3</td>\n",
       "      <td>-18.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-31.5</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-27.3</td>\n",
       "      <td>-33.3</td>\n",
       "      <td>-21.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>-3.8</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.4</td>\n",
       "      <td>-32.3</td>\n",
       "      <td>-38.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>3.9</td>\n",
       "      <td>0.6</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.3</td>\n",
       "      <td>-19.4</td>\n",
       "      <td>-33.9</td>\n",
       "      <td>-4.9</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11689 rows × 428 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            BROCKVILLE PCC;MEAN_TEMPERATURE  BROCKVILLE PCC;MIN_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                    \n",
       "1990-01-01                             -5.0                           -10.0   \n",
       "1990-01-02                             -4.0                            -9.0   \n",
       "1990-01-03                              0.5                            -4.0   \n",
       "1990-01-04                              4.0                             2.0   \n",
       "1990-01-05                             -3.0                            -4.0   \n",
       "...                                     ...                             ...   \n",
       "2021-12-28                             -3.0                            -9.0   \n",
       "2021-12-29                             -2.5                            -4.0   \n",
       "2021-12-30                             -1.0                            -3.0   \n",
       "2021-12-31                              NaN                             NaN   \n",
       "2022-01-01                             -0.5                            -2.5   \n",
       "\n",
       "            BROCKVILLE PCC;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                   \n",
       "1990-01-01                             0.0   \n",
       "1990-01-02                             1.0   \n",
       "1990-01-03                             5.0   \n",
       "1990-01-04                             6.0   \n",
       "1990-01-05                            -2.0   \n",
       "...                                    ...   \n",
       "2021-12-28                             3.0   \n",
       "2021-12-29                            -1.0   \n",
       "2021-12-30                             1.0   \n",
       "2021-12-31                             NaN   \n",
       "2022-01-01                             1.5   \n",
       "\n",
       "            BROCKVILLE PCC;TOTAL_PRECIPITATION  \\\n",
       "LOCAL_DATE                                       \n",
       "1990-01-01                                 0.0   \n",
       "1990-01-02                                 0.0   \n",
       "1990-01-03                                 0.0   \n",
       "1990-01-04                                 2.4   \n",
       "1990-01-05                                 0.0   \n",
       "...                                        ...   \n",
       "2021-12-28                                 0.0   \n",
       "2021-12-29                                 0.0   \n",
       "2021-12-30                                 0.0   \n",
       "2021-12-31                                 NaN   \n",
       "2022-01-01                                 8.8   \n",
       "\n",
       "            POWELL RIVER A;MEAN_TEMPERATURE  POWELL RIVER A;MIN_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                    \n",
       "1990-01-01                              3.6                             2.0   \n",
       "1990-01-02                              0.9                            -2.0   \n",
       "1990-01-03                              3.7                             1.9   \n",
       "1990-01-04                              5.9                             3.8   \n",
       "1990-01-05                              5.7                             2.2   \n",
       "...                                     ...                             ...   \n",
       "2021-12-28                             -5.0                            -7.0   \n",
       "2021-12-29                             -7.5                           -12.0   \n",
       "2021-12-30                             -3.3                            -5.0   \n",
       "2021-12-31                             -6.0                           -10.5   \n",
       "2022-01-01                             -3.8                           -10.5   \n",
       "\n",
       "            POWELL RIVER A;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                   \n",
       "1990-01-01                             5.2   \n",
       "1990-01-02                             3.8   \n",
       "1990-01-03                             5.5   \n",
       "1990-01-04                             8.0   \n",
       "1990-01-05                             9.2   \n",
       "...                                    ...   \n",
       "2021-12-28                            -3.0   \n",
       "2021-12-29                            -3.0   \n",
       "2021-12-30                            -1.5   \n",
       "2021-12-31                            -1.5   \n",
       "2022-01-01                             3.0   \n",
       "\n",
       "            POWELL RIVER A;TOTAL_PRECIPITATION  \\\n",
       "LOCAL_DATE                                       \n",
       "1990-01-01                                 0.0   \n",
       "1990-01-02                                 1.8   \n",
       "1990-01-03                                 3.2   \n",
       "1990-01-04                                 1.0   \n",
       "1990-01-05                                14.0   \n",
       "...                                        ...   \n",
       "2021-12-28                                 0.6   \n",
       "2021-12-29                                 8.0   \n",
       "2021-12-30                                 0.0   \n",
       "2021-12-31                                 0.0   \n",
       "2022-01-01                                11.4   \n",
       "\n",
       "            STONY MOUNTAIN;MEAN_TEMPERATURE  STONY MOUNTAIN;MIN_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                    \n",
       "1990-01-01                            -13.0                           -25.0   \n",
       "1990-01-02                            -10.5                           -13.0   \n",
       "1990-01-03                            -15.0                           -18.0   \n",
       "1990-01-04                            -21.0                           -25.0   \n",
       "1990-01-05                            -18.5                           -25.0   \n",
       "...                                     ...                             ...   \n",
       "2021-12-28                            -22.0                           -26.5   \n",
       "2021-12-29                            -30.0                           -35.0   \n",
       "2021-12-30                            -29.5                           -36.0   \n",
       "2021-12-31                            -31.5                           -35.0   \n",
       "2022-01-01                            -32.3                           -38.0   \n",
       "\n",
       "            ...  CHATHAM POINT;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE  ...                                  \n",
       "1990-01-01  ...                            3.5   \n",
       "1990-01-02  ...                            4.2   \n",
       "1990-01-03  ...                            5.5   \n",
       "1990-01-04  ...                            6.9   \n",
       "1990-01-05  ...                            8.5   \n",
       "...         ...                            ...   \n",
       "2021-12-28  ...                           -3.0   \n",
       "2021-12-29  ...                           -1.5   \n",
       "2021-12-30  ...                            0.0   \n",
       "2021-12-31  ...                           -0.5   \n",
       "2022-01-01  ...                            4.0   \n",
       "\n",
       "            CHATHAM POINT;TOTAL_PRECIPITATION  GREENWOOD A;MEAN_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                    \n",
       "1990-01-01                                1.2                           2.2   \n",
       "1990-01-02                               13.2                          -3.3   \n",
       "1990-01-03                               20.2                          -1.7   \n",
       "1990-01-04                               11.8                          -0.3   \n",
       "1990-01-05                               34.2                          -0.7   \n",
       "...                                       ...                           ...   \n",
       "2021-12-28                                0.0                          -2.4   \n",
       "2021-12-29                                5.0                           0.2   \n",
       "2021-12-30                                0.0                           0.4   \n",
       "2021-12-31                                0.0                           0.9   \n",
       "2022-01-01                               29.2                           3.9   \n",
       "\n",
       "            GREENWOOD A;MIN_TEMPERATURE  GREENWOOD A;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                             \n",
       "1990-01-01                         -5.0                          9.3   \n",
       "1990-01-02                         -8.0                          1.4   \n",
       "1990-01-03                         -6.4                          3.1   \n",
       "1990-01-04                         -7.1                          6.6   \n",
       "1990-01-05                         -8.0                          6.6   \n",
       "...                                 ...                          ...   \n",
       "2021-12-28                         -7.2                          2.5   \n",
       "2021-12-29                         -1.5                          1.8   \n",
       "2021-12-30                         -1.5                          2.2   \n",
       "2021-12-31                          0.0                          1.8   \n",
       "2022-01-01                          0.6                          7.1   \n",
       "\n",
       "            GREENWOOD A;TOTAL_PRECIPITATION  BROOKS;MEAN_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                             \n",
       "1990-01-01                              8.4                      0.5   \n",
       "1990-01-02                              0.0                     -8.6   \n",
       "1990-01-03                              0.0                     -7.1   \n",
       "1990-01-04                              3.0                    -12.3   \n",
       "1990-01-05                              0.0                    -10.3   \n",
       "...                                     ...                      ...   \n",
       "2021-12-28                              4.2                    -27.3   \n",
       "2021-12-29                              0.4                    -24.0   \n",
       "2021-12-30                              0.0                    -24.9   \n",
       "2021-12-31                              3.0                    -27.3   \n",
       "2022-01-01                              5.3                    -19.4   \n",
       "\n",
       "            BROOKS;MIN_TEMPERATURE  BROOKS;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                   \n",
       "1990-01-01                    -2.2                     3.1   \n",
       "1990-01-02                   -15.2                    -1.9   \n",
       "1990-01-03                   -15.4                     1.2   \n",
       "1990-01-04                   -15.1                    -9.4   \n",
       "1990-01-05                   -15.1                    -5.4   \n",
       "...                            ...                     ...   \n",
       "2021-12-28                   -37.3                   -17.4   \n",
       "2021-12-29                   -27.7                   -20.2   \n",
       "2021-12-30                   -31.3                   -18.6   \n",
       "2021-12-31                   -33.3                   -21.3   \n",
       "2022-01-01                   -33.9                    -4.9   \n",
       "\n",
       "            BROOKS;TOTAL_PRECIPITATION  \n",
       "LOCAL_DATE                              \n",
       "1990-01-01                         0.0  \n",
       "1990-01-02                         0.0  \n",
       "1990-01-03                         0.0  \n",
       "1990-01-04                         0.0  \n",
       "1990-01-05                         0.7  \n",
       "...                                ...  \n",
       "2021-12-28                         0.2  \n",
       "2021-12-29                         0.0  \n",
       "2021-12-30                         0.2  \n",
       "2021-12-31                         0.0  \n",
       "2022-01-01                         0.0  \n",
       "\n",
       "[11689 rows x 428 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data = pd.read_csv('./weather_data.csv', index_col=0)\n",
    "weather_data.index = pd.DatetimeIndex(weather_data.index)\n",
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>FILENAME</th>\n",
       "      <th>PROVINCE_CODE</th>\n",
       "      <th>min_date</th>\n",
       "      <th>max_date</th>\n",
       "      <th>mean_temp_coverage</th>\n",
       "      <th>max_temp_coverage</th>\n",
       "      <th>total_precipitation_coverage</th>\n",
       "      <th>direction_max_gust_coverage</th>\n",
       "      <th>speed_max_gust_coverage</th>\n",
       "      <th>min_rel_humiudity_coverage</th>\n",
       "      <th>max_rel_humiudity_coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-75.666667</td>\n",
       "      <td>44.600000</td>\n",
       "      <td>BROCKVILLE PCC</td>\n",
       "      <td>station_4236_data.csv</td>\n",
       "      <td>ON</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.998624</td>\n",
       "      <td>0.999570</td>\n",
       "      <td>0.999398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-124.500278</td>\n",
       "      <td>49.834169</td>\n",
       "      <td>POWELL RIVER A</td>\n",
       "      <td>station_327_data.csv</td>\n",
       "      <td>BC</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.982635</td>\n",
       "      <td>0.982981</td>\n",
       "      <td>0.999914</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-97.166667</td>\n",
       "      <td>50.116667</td>\n",
       "      <td>STONY MOUNTAIN</td>\n",
       "      <td>station_3678_data.csv</td>\n",
       "      <td>MB</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.999825</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-82.933333</td>\n",
       "      <td>42.333333</td>\n",
       "      <td>WINDSOR RIVERSIDE</td>\n",
       "      <td>station_4715_data.csv</td>\n",
       "      <td>ON</td>\n",
       "      <td>1993-12-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999902</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-77.533333</td>\n",
       "      <td>44.116667</td>\n",
       "      <td>TRENTON A</td>\n",
       "      <td>station_5126_data.csv</td>\n",
       "      <td>ON</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.953286</td>\n",
       "      <td>0.953286</td>\n",
       "      <td>0.953114</td>\n",
       "      <td>0.889711</td>\n",
       "      <td>0.899948</td>\n",
       "      <td>0.926445</td>\n",
       "      <td>0.926531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>106</th>\n",
       "      <td>-93.966667</td>\n",
       "      <td>48.633333</td>\n",
       "      <td>BARWICK</td>\n",
       "      <td>station_3932_data.csv</td>\n",
       "      <td>ON</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.998912</td>\n",
       "      <td>0.999275</td>\n",
       "      <td>0.999637</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>-110.283333</td>\n",
       "      <td>54.416667</td>\n",
       "      <td>COLD LAKE A</td>\n",
       "      <td>station_2832_data.csv</td>\n",
       "      <td>AB</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.999914</td>\n",
       "      <td>0.999914</td>\n",
       "      <td>0.999914</td>\n",
       "      <td>0.908731</td>\n",
       "      <td>0.927346</td>\n",
       "      <td>0.999052</td>\n",
       "      <td>0.999224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>-125.445556</td>\n",
       "      <td>50.333194</td>\n",
       "      <td>CHATHAM POINT</td>\n",
       "      <td>station_153_data.csv</td>\n",
       "      <td>BC</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.999359</td>\n",
       "      <td>0.999680</td>\n",
       "      <td>0.999786</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>-64.916667</td>\n",
       "      <td>44.983333</td>\n",
       "      <td>GREENWOOD A</td>\n",
       "      <td>station_6354_data.csv</td>\n",
       "      <td>NS</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.999743</td>\n",
       "      <td>0.999743</td>\n",
       "      <td>0.999743</td>\n",
       "      <td>0.965780</td>\n",
       "      <td>0.966293</td>\n",
       "      <td>0.998460</td>\n",
       "      <td>0.998802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>-111.848897</td>\n",
       "      <td>50.555297</td>\n",
       "      <td>BROOKS</td>\n",
       "      <td>station_2180_data.csv</td>\n",
       "      <td>AB</td>\n",
       "      <td>1990-01-01</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>0.993552</td>\n",
       "      <td>0.993937</td>\n",
       "      <td>0.974112</td>\n",
       "      <td>0.494274</td>\n",
       "      <td>0.494274</td>\n",
       "      <td>0.344240</td>\n",
       "      <td>0.344048</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              x          y       STATION_NAME               FILENAME  \\\n",
       "0    -75.666667  44.600000     BROCKVILLE PCC  station_4236_data.csv   \n",
       "1   -124.500278  49.834169     POWELL RIVER A   station_327_data.csv   \n",
       "2    -97.166667  50.116667     STONY MOUNTAIN  station_3678_data.csv   \n",
       "3    -82.933333  42.333333  WINDSOR RIVERSIDE  station_4715_data.csv   \n",
       "5    -77.533333  44.116667          TRENTON A  station_5126_data.csv   \n",
       "..          ...        ...                ...                    ...   \n",
       "106  -93.966667  48.633333            BARWICK  station_3932_data.csv   \n",
       "107 -110.283333  54.416667        COLD LAKE A  station_2832_data.csv   \n",
       "108 -125.445556  50.333194      CHATHAM POINT   station_153_data.csv   \n",
       "109  -64.916667  44.983333        GREENWOOD A  station_6354_data.csv   \n",
       "110 -111.848897  50.555297             BROOKS  station_2180_data.csv   \n",
       "\n",
       "    PROVINCE_CODE    min_date    max_date  mean_temp_coverage  \\\n",
       "0              ON  1990-01-01  2022-01-01            0.998624   \n",
       "1              BC  1990-01-01  2022-01-01            0.982635   \n",
       "2              MB  1990-01-01  2022-01-01            0.999825   \n",
       "3              ON  1993-12-01  2022-01-01            1.000000   \n",
       "5              ON  1990-01-01  2022-01-01            0.953286   \n",
       "..            ...         ...         ...                 ...   \n",
       "106            ON  1990-01-01  2022-01-01            0.998912   \n",
       "107            AB  1990-01-01  2022-01-01            0.999914   \n",
       "108            BC  1990-01-01  2022-01-01            0.999359   \n",
       "109            NS  1990-01-01  2022-01-01            0.999743   \n",
       "110            AB  1990-01-01  2022-01-01            0.993552   \n",
       "\n",
       "     max_temp_coverage  total_precipitation_coverage  \\\n",
       "0             0.999570                      0.999398   \n",
       "1             0.982981                      0.999914   \n",
       "2             1.000000                      0.999912   \n",
       "3             1.000000                      0.999902   \n",
       "5             0.953286                      0.953114   \n",
       "..                 ...                           ...   \n",
       "106           0.999275                      0.999637   \n",
       "107           0.999914                      0.999914   \n",
       "108           0.999680                      0.999786   \n",
       "109           0.999743                      0.999743   \n",
       "110           0.993937                      0.974112   \n",
       "\n",
       "     direction_max_gust_coverage  speed_max_gust_coverage  \\\n",
       "0                       0.000000                 0.000000   \n",
       "1                       0.000000                 0.000000   \n",
       "2                       0.000000                 0.000000   \n",
       "3                       0.000000                 0.000000   \n",
       "5                       0.889711                 0.899948   \n",
       "..                           ...                      ...   \n",
       "106                     0.000000                 0.000000   \n",
       "107                     0.908731                 0.927346   \n",
       "108                     0.000000                 0.000000   \n",
       "109                     0.965780                 0.966293   \n",
       "110                     0.494274                 0.494274   \n",
       "\n",
       "     min_rel_humiudity_coverage  max_rel_humiudity_coverage  \n",
       "0                      0.000000                    0.000000  \n",
       "1                      0.000000                    0.000000  \n",
       "2                      0.000000                    0.000000  \n",
       "3                      0.000000                    0.000000  \n",
       "5                      0.926445                    0.926531  \n",
       "..                          ...                         ...  \n",
       "106                    0.000000                    0.000000  \n",
       "107                    0.999052                    0.999224  \n",
       "108                    0.000000                    0.000000  \n",
       "109                    0.998460                    0.998802  \n",
       "110                    0.344240                    0.344048  \n",
       "\n",
       "[109 rows x 14 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_metadata = pd.read_csv(\"./station_metadata.csv\", index_col=0)\n",
    "station_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize locations of included weather stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d32a2c590>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "ax = world.loc[world.continent == 'North America'].plot(color='white', edgecolor='black', figsize=(12,12))\n",
    "ax.scatter(station_metadata.x, station_metadata.y, s=5, color='red')\n",
    "ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data visualization\n",
    "\n",
    "Examine one series of each feature type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d332e7f50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_data[('BROCKVILLE PCC;MEAN_TEMPERATURE')].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d3360da50>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather_data[('BROCKVILLE PCC;TOTAL_PRECIPITATION')].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting feature subsets"
   ]
  },
  {
   "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>BROCKVILLE PCC;MAX_TEMPERATURE</th>\n",
       "      <th>POWELL RIVER A;MAX_TEMPERATURE</th>\n",
       "      <th>STONY MOUNTAIN;MAX_TEMPERATURE</th>\n",
       "      <th>WINDSOR RIVERSIDE;MAX_TEMPERATURE</th>\n",
       "      <th>TRENTON A;MAX_TEMPERATURE</th>\n",
       "      <th>PEACHLAND;MAX_TEMPERATURE</th>\n",
       "      <th>CASTLEGAR BCHPA DAM;MAX_TEMPERATURE</th>\n",
       "      <th>OUTLOOK PFRA;MAX_TEMPERATURE</th>\n",
       "      <th>SCOTT CDA;MAX_TEMPERATURE</th>\n",
       "      <th>COWAN;MAX_TEMPERATURE</th>\n",
       "      <th>...</th>\n",
       "      <th>MACTAQUAC PROV PARK;MAX_TEMPERATURE</th>\n",
       "      <th>LEROY;MAX_TEMPERATURE</th>\n",
       "      <th>WHISTLER;MAX_TEMPERATURE</th>\n",
       "      <th>GREEN ISLAND;MAX_TEMPERATURE</th>\n",
       "      <th>QUINSAM RIVER HATCHERY;MAX_TEMPERATURE</th>\n",
       "      <th>BARWICK;MAX_TEMPERATURE</th>\n",
       "      <th>COLD LAKE A;MAX_TEMPERATURE</th>\n",
       "      <th>CHATHAM POINT;MAX_TEMPERATURE</th>\n",
       "      <th>GREENWOOD A;MAX_TEMPERATURE</th>\n",
       "      <th>BROOKS;MAX_TEMPERATURE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOCAL_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>1990-01-01</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>9.3</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.8</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>-2.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-10.3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>-1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-7.5</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-13.5</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>6.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-12.2</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-13.5</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>-2.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>-14.5</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>8.5</td>\n",
       "      <td>6.6</td>\n",
       "      <td>-5.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>3.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-17.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-27.8</td>\n",
       "      <td>-29.5</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>-15.5</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-28.8</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-11.5</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-26.4</td>\n",
       "      <td>-28.5</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-28.3</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9.5</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-28.9</td>\n",
       "      <td>-29.4</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-16.5</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>-18.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.5</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-29.9</td>\n",
       "      <td>-30.3</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-10.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>-21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-26.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-4.5</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-22.6</td>\n",
       "      <td>-20.1</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-20.5</td>\n",
       "      <td>-22.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>-4.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11689 rows × 107 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            BROCKVILLE PCC;MAX_TEMPERATURE  POWELL RIVER A;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                   \n",
       "1990-01-01                             0.0                             5.2   \n",
       "1990-01-02                             1.0                             3.8   \n",
       "1990-01-03                             5.0                             5.5   \n",
       "1990-01-04                             6.0                             8.0   \n",
       "1990-01-05                            -2.0                             9.2   \n",
       "...                                    ...                             ...   \n",
       "2021-12-28                             3.0                            -3.0   \n",
       "2021-12-29                            -1.0                            -3.0   \n",
       "2021-12-30                             1.0                            -1.5   \n",
       "2021-12-31                             NaN                            -1.5   \n",
       "2022-01-01                             1.5                             3.0   \n",
       "\n",
       "            STONY MOUNTAIN;MAX_TEMPERATURE  WINDSOR RIVERSIDE;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                      \n",
       "1990-01-01                            -1.0                                NaN   \n",
       "1990-01-02                            -8.0                                NaN   \n",
       "1990-01-03                           -12.0                                NaN   \n",
       "1990-01-04                           -17.0                                NaN   \n",
       "1990-01-05                           -12.0                                NaN   \n",
       "...                                    ...                                ...   \n",
       "2021-12-28                           -17.5                                2.5   \n",
       "2021-12-29                           -25.0                                4.5   \n",
       "2021-12-30                           -23.0                                5.0   \n",
       "2021-12-31                           -28.0                                6.5   \n",
       "2022-01-01                           -26.5                                4.0   \n",
       "\n",
       "            TRENTON A;MAX_TEMPERATURE  PEACHLAND;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                         \n",
       "1990-01-01                        1.2                        NaN   \n",
       "1990-01-02                        0.8                        NaN   \n",
       "1990-01-03                        5.1                        NaN   \n",
       "1990-01-04                        5.7                        NaN   \n",
       "1990-01-05                        0.8                        NaN   \n",
       "...                               ...                        ...   \n",
       "2021-12-28                        NaN                        NaN   \n",
       "2021-12-29                        NaN                      -11.5   \n",
       "2021-12-30                        NaN                       -9.5   \n",
       "2021-12-31                        NaN                      -10.5   \n",
       "2022-01-01                        NaN                       -4.5   \n",
       "\n",
       "            CASTLEGAR BCHPA DAM;MAX_TEMPERATURE  OUTLOOK PFRA;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                      \n",
       "1990-01-01                                  3.0                           1.5   \n",
       "1990-01-02                                  1.5                          -8.0   \n",
       "1990-01-03                                  0.5                          -7.5   \n",
       "1990-01-04                                  2.0                         -13.5   \n",
       "1990-01-05                                  2.5                           4.5   \n",
       "...                                         ...                           ...   \n",
       "2021-12-28                                -10.0                         -27.8   \n",
       "2021-12-29                                -10.0                         -26.4   \n",
       "2021-12-30                                 -7.0                         -28.9   \n",
       "2021-12-31                                 -9.0                         -29.9   \n",
       "2022-01-01                                 -9.0                         -22.6   \n",
       "\n",
       "            SCOTT CDA;MAX_TEMPERATURE  COWAN;MAX_TEMPERATURE  ...  \\\n",
       "LOCAL_DATE                                                    ...   \n",
       "1990-01-01                       -0.5                    1.0  ...   \n",
       "1990-01-02                      -10.0                  -11.0  ...   \n",
       "1990-01-03                      -13.0                  -11.5  ...   \n",
       "1990-01-04                      -17.0                  -17.0  ...   \n",
       "1990-01-05                        3.5                  -14.5  ...   \n",
       "...                               ...                    ...  ...   \n",
       "2021-12-28                      -29.5                  -20.0  ...   \n",
       "2021-12-29                      -28.5                  -21.0  ...   \n",
       "2021-12-30                      -29.4                  -24.0  ...   \n",
       "2021-12-31                      -30.3                  -19.0  ...   \n",
       "2022-01-01                      -20.1                  -17.0  ...   \n",
       "\n",
       "            MACTAQUAC PROV PARK;MAX_TEMPERATURE  LEROY;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                               \n",
       "1990-01-01                                  3.0                    0.5   \n",
       "1990-01-02                                 -0.5                   -9.5   \n",
       "1990-01-03                                  3.5                  -13.5   \n",
       "1990-01-04                                  5.0                  -20.0   \n",
       "1990-01-05                                 -1.0                   -3.0   \n",
       "...                                         ...                    ...   \n",
       "2021-12-28                                 -3.0                  -27.0   \n",
       "2021-12-29                                  3.0                  -29.0   \n",
       "2021-12-30                                  1.0                  -27.0   \n",
       "2021-12-31                                  2.5                  -28.0   \n",
       "2022-01-01                                  NaN                  -22.0   \n",
       "\n",
       "            WHISTLER;MAX_TEMPERATURE  GREEN ISLAND;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                           \n",
       "1990-01-01                       2.0                           4.0   \n",
       "1990-01-02                      -2.8                           7.0   \n",
       "1990-01-03                      -0.1                           6.5   \n",
       "1990-01-04                       2.2                           8.0   \n",
       "1990-01-05                       4.2                           9.0   \n",
       "...                              ...                           ...   \n",
       "2021-12-28                     -15.5                          -3.0   \n",
       "2021-12-29                       NaN                           0.5   \n",
       "2021-12-30                       NaN                          -1.5   \n",
       "2021-12-31                     -10.5                           4.5   \n",
       "2022-01-01                       NaN                           6.0   \n",
       "\n",
       "            QUINSAM RIVER HATCHERY;MAX_TEMPERATURE  BARWICK;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                    \n",
       "1990-01-01                                     5.5                     -4.0   \n",
       "1990-01-02                                     5.0                     -0.5   \n",
       "1990-01-03                                     6.0                     -3.0   \n",
       "1990-01-04                                     9.0                    -11.0   \n",
       "1990-01-05                                     8.5                    -15.0   \n",
       "...                                            ...                      ...   \n",
       "2021-12-28                                     NaN                    -12.0   \n",
       "2021-12-29                                     NaN                    -19.0   \n",
       "2021-12-30                                     0.5                    -16.5   \n",
       "2021-12-31                                    -2.0                    -21.0   \n",
       "2022-01-01                                     3.0                    -20.5   \n",
       "\n",
       "            COLD LAKE A;MAX_TEMPERATURE  CHATHAM POINT;MAX_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                               \n",
       "1990-01-01                         -1.6                            3.5   \n",
       "1990-01-02                        -10.3                            4.2   \n",
       "1990-01-03                        -12.2                            5.5   \n",
       "1990-01-04                        -18.8                            6.9   \n",
       "1990-01-05                        -10.8                            8.5   \n",
       "...                                 ...                            ...   \n",
       "2021-12-28                        -28.8                           -3.0   \n",
       "2021-12-29                        -28.3                           -1.5   \n",
       "2021-12-30                        -26.0                            0.0   \n",
       "2021-12-31                        -29.0                           -0.5   \n",
       "2022-01-01                        -22.1                            4.0   \n",
       "\n",
       "            GREENWOOD A;MAX_TEMPERATURE  BROOKS;MAX_TEMPERATURE  \n",
       "LOCAL_DATE                                                       \n",
       "1990-01-01                          9.3                     3.1  \n",
       "1990-01-02                          1.4                    -1.9  \n",
       "1990-01-03                          3.1                     1.2  \n",
       "1990-01-04                          6.6                    -9.4  \n",
       "1990-01-05                          6.6                    -5.4  \n",
       "...                                 ...                     ...  \n",
       "2021-12-28                          2.5                   -17.4  \n",
       "2021-12-29                          1.8                   -20.2  \n",
       "2021-12-30                          2.2                   -18.6  \n",
       "2021-12-31                          1.8                   -21.3  \n",
       "2022-01-01                          7.1                    -4.9  \n",
       "\n",
       "[11689 rows x 107 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_name = \"MAX_TEMPERATURE\"\n",
    "weather_data[[col for col in weather_data.columns if feature_name in col]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting location subsets"
   ]
  },
  {
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BROCKVILLE PCC;MEAN_TEMPERATURE</th>\n",
       "      <th>BROCKVILLE PCC;MIN_TEMPERATURE</th>\n",
       "      <th>BROCKVILLE PCC;MAX_TEMPERATURE</th>\n",
       "      <th>BROCKVILLE PCC;TOTAL_PRECIPITATION</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LOCAL_DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>-5.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>-4.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>0.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>-2.5</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-01-01</th>\n",
       "      <td>-0.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11689 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            BROCKVILLE PCC;MEAN_TEMPERATURE  BROCKVILLE PCC;MIN_TEMPERATURE  \\\n",
       "LOCAL_DATE                                                                    \n",
       "1990-01-01                             -5.0                           -10.0   \n",
       "1990-01-02                             -4.0                            -9.0   \n",
       "1990-01-03                              0.5                            -4.0   \n",
       "1990-01-04                              4.0                             2.0   \n",
       "1990-01-05                             -3.0                            -4.0   \n",
       "...                                     ...                             ...   \n",
       "2021-12-28                             -3.0                            -9.0   \n",
       "2021-12-29                             -2.5                            -4.0   \n",
       "2021-12-30                             -1.0                            -3.0   \n",
       "2021-12-31                              NaN                             NaN   \n",
       "2022-01-01                             -0.5                            -2.5   \n",
       "\n",
       "            BROCKVILLE PCC;MAX_TEMPERATURE  BROCKVILLE PCC;TOTAL_PRECIPITATION  \n",
       "LOCAL_DATE                                                                      \n",
       "1990-01-01                             0.0                                 0.0  \n",
       "1990-01-02                             1.0                                 0.0  \n",
       "1990-01-03                             5.0                                 0.0  \n",
       "1990-01-04                             6.0                                 2.4  \n",
       "1990-01-05                            -2.0                                 0.0  \n",
       "...                                    ...                                 ...  \n",
       "2021-12-28                             3.0                                 0.0  \n",
       "2021-12-29                            -1.0                                 0.0  \n",
       "2021-12-30                             1.0                                 0.0  \n",
       "2021-12-31                             NaN                                 NaN  \n",
       "2022-01-01                             1.5                                 8.8  \n",
       "\n",
       "[11689 rows x 4 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_name = \"BROCKVILLE PCC\"\n",
    "weather_data[[col for col in weather_data.columns if station_name in col]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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