weather-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our goal is to predict the probability of Rainfall based on the features at hand. The dataset is ordered by date, in an ascending order. \n",
    "\n",
    "The dataset has been obtained from the Australian Bureau of Meteorology. We have added an extra column to the existing dataset called 'Rain Tomorrow' which is the target variable. Below are the links to the dataset;\n",
    "\n",
    "https://www.kaggle.com/filhypedeeplearning/2020-sydney-daily-weather-observations\n",
    "\n",
    "http://www.bom.gov.au/climate/data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import datetime\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, StratifiedKFold\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import xgboost as xgb\n",
    "from xgboost import XGBClassifier\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = r'C:\\Users\\Geo\\Downloads\\2020 Sydney Daily Weather Observations'\n",
    "files = glob.iglob(os.path.join(path, \"*.csv\")) \n",
    "\n",
    "df = pd.concat((pd.read_csv(file, index_col='Date', parse_dates=True, \n",
    "                            dayfirst=True, encoding='cp1250') for file in files)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overview of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Minimum temperature (°C)</th>\n",
       "      <th>Maximum temperature (°C)</th>\n",
       "      <th>Rainfall (mm)</th>\n",
       "      <th>Evaporation (mm)</th>\n",
       "      <th>Sunshine (hours)</th>\n",
       "      <th>Direction of maximum wind gust</th>\n",
       "      <th>Speed of maximum wind gust (km/h)</th>\n",
       "      <th>Time of maximum wind gust</th>\n",
       "      <th>9am Temperature (°C)</th>\n",
       "      <th>9am relative humidity (%)</th>\n",
       "      <th>...</th>\n",
       "      <th>9am wind direction</th>\n",
       "      <th>9am wind speed (km/h)</th>\n",
       "      <th>9am MSL pressure (hPa)</th>\n",
       "      <th>3pm Temperature (°C)</th>\n",
       "      <th>3pm relative humidity (%)</th>\n",
       "      <th>3pm cloud amount (oktas)</th>\n",
       "      <th>3pm wind direction</th>\n",
       "      <th>3pm wind speed (km/h)</th>\n",
       "      <th>3pm MSL pressure (hPa)</th>\n",
       "      <th>Rain Tomorrow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <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>2020-01-01</th>\n",
       "      <td>18.9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>SSW</td>\n",
       "      <td>39.0</td>\n",
       "      <td>23:28</td>\n",
       "      <td>20.9</td>\n",
       "      <td>68.0</td>\n",
       "      <td>...</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>24.9</td>\n",
       "      <td>60.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>24</td>\n",
       "      <td>1013.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>19.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>SSW</td>\n",
       "      <td>35.0</td>\n",
       "      <td>23:02</td>\n",
       "      <td>22.6</td>\n",
       "      <td>64.0</td>\n",
       "      <td>...</td>\n",
       "      <td>ESE</td>\n",
       "      <td>17</td>\n",
       "      <td>1019.8</td>\n",
       "      <td>27.2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>20</td>\n",
       "      <td>1017.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>21.9</td>\n",
       "      <td>29.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.2</td>\n",
       "      <td>NE</td>\n",
       "      <td>43.0</td>\n",
       "      <td>16:22</td>\n",
       "      <td>24.1</td>\n",
       "      <td>79.0</td>\n",
       "      <td>...</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9</td>\n",
       "      <td>1017.5</td>\n",
       "      <td>27.6</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>E</td>\n",
       "      <td>26</td>\n",
       "      <td>1012.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Minimum temperature (°C)  Maximum temperature (°C)  Rainfall (mm)  \\\n",
       "Date                                                                            \n",
       "2020-01-01                      18.9                      27.3            0.0   \n",
       "2020-01-02                      19.3                      29.0            0.0   \n",
       "2020-01-03                      21.9                      29.4            0.0   \n",
       "\n",
       "            Evaporation (mm)  Sunshine (hours)  \\\n",
       "Date                                             \n",
       "2020-01-01              14.2               4.5   \n",
       "2020-01-02               7.2               5.3   \n",
       "2020-01-03               NaN               8.2   \n",
       "\n",
       "           Direction of maximum wind gust   Speed of maximum wind gust (km/h)  \\\n",
       "Date                                                                            \n",
       "2020-01-01                             SSW                               39.0   \n",
       "2020-01-02                             SSW                               35.0   \n",
       "2020-01-03                              NE                               43.0   \n",
       "\n",
       "           Time of maximum wind gust  9am Temperature (°C)  \\\n",
       "Date                                                         \n",
       "2020-01-01                     23:28                  20.9   \n",
       "2020-01-02                     23:02                  22.6   \n",
       "2020-01-03                     16:22                  24.1   \n",
       "\n",
       "            9am relative humidity (%)  ...  9am wind direction  \\\n",
       "Date                                   ...                       \n",
       "2020-01-01                       68.0  ...                   S   \n",
       "2020-01-02                       64.0  ...                 ESE   \n",
       "2020-01-03                       79.0  ...                 WNW   \n",
       "\n",
       "           9am wind speed (km/h) 9am MSL pressure (hPa)  3pm Temperature (°C)  \\\n",
       "Date                                                                            \n",
       "2020-01-01                    11                 1015.0                  24.9   \n",
       "2020-01-02                    17                 1019.8                  27.2   \n",
       "2020-01-03                     9                 1017.5                  27.6   \n",
       "\n",
       "            3pm relative humidity (%)  3pm cloud amount (oktas)  \\\n",
       "Date                                                              \n",
       "2020-01-01                       60.0                       5.0   \n",
       "2020-01-02                       51.0                       7.0   \n",
       "2020-01-03                       68.0                       0.0   \n",
       "\n",
       "            3pm wind direction 3pm wind speed (km/h)  3pm MSL pressure (hPa)  \\\n",
       "Date                                                                           \n",
       "2020-01-01                 ESE                    24                  1013.5   \n",
       "2020-01-02                 ESE                    20                  1017.4   \n",
       "2020-01-03                   E                    26                  1012.5   \n",
       "\n",
       "            Rain Tomorrow  \n",
       "Date                       \n",
       "2020-01-01            0.0  \n",
       "2020-01-02            0.0  \n",
       "2020-01-03            0.0  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe contains **366** rows, the total number of days in 2020 (leap year), and **21** distinct columns (excluding the 'Date' column). The 'Rain Tomorrow' column has been manually added in order to be used in our analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(366, 21)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the descriptive statistics of this dataframe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Minimum temperature (°C)</th>\n",
       "      <th>Maximum temperature (°C)</th>\n",
       "      <th>Rainfall (mm)</th>\n",
       "      <th>Evaporation (mm)</th>\n",
       "      <th>Sunshine (hours)</th>\n",
       "      <th>Speed of maximum wind gust (km/h)</th>\n",
       "      <th>9am Temperature (°C)</th>\n",
       "      <th>9am relative humidity (%)</th>\n",
       "      <th>9am cloud amount (oktas)</th>\n",
       "      <th>9am MSL pressure (hPa)</th>\n",
       "      <th>3pm Temperature (°C)</th>\n",
       "      <th>3pm relative humidity (%)</th>\n",
       "      <th>3pm cloud amount (oktas)</th>\n",
       "      <th>3pm wind speed (km/h)</th>\n",
       "      <th>3pm MSL pressure (hPa)</th>\n",
       "      <th>Rain Tomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>364.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.670219</td>\n",
       "      <td>23.510714</td>\n",
       "      <td>4.285083</td>\n",
       "      <td>5.382865</td>\n",
       "      <td>6.525414</td>\n",
       "      <td>42.663014</td>\n",
       "      <td>17.531421</td>\n",
       "      <td>77.649315</td>\n",
       "      <td>4.526027</td>\n",
       "      <td>1018.417760</td>\n",
       "      <td>21.991758</td>\n",
       "      <td>60.728022</td>\n",
       "      <td>4.616438</td>\n",
       "      <td>19.598361</td>\n",
       "      <td>1016.027869</td>\n",
       "      <td>0.438356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.354354</td>\n",
       "      <td>4.786657</td>\n",
       "      <td>13.519783</td>\n",
       "      <td>3.317315</td>\n",
       "      <td>4.020979</td>\n",
       "      <td>14.720617</td>\n",
       "      <td>4.456772</td>\n",
       "      <td>15.420757</td>\n",
       "      <td>2.812356</td>\n",
       "      <td>7.478648</td>\n",
       "      <td>4.737866</td>\n",
       "      <td>17.664775</td>\n",
       "      <td>2.689163</td>\n",
       "      <td>7.990741</td>\n",
       "      <td>7.517114</td>\n",
       "      <td>0.496867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.200000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>997.400000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>992.200000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10.900000</td>\n",
       "      <td>19.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1013.450000</td>\n",
       "      <td>18.600000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>1010.650000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.800000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1018.650000</td>\n",
       "      <td>21.900000</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1016.500000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.275000</td>\n",
       "      <td>26.500000</td>\n",
       "      <td>2.550000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>9.900000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1023.850000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1021.675000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25.300000</td>\n",
       "      <td>41.200000</td>\n",
       "      <td>164.800000</td>\n",
       "      <td>25.400000</td>\n",
       "      <td>13.400000</td>\n",
       "      <td>106.000000</td>\n",
       "      <td>31.200000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1038.300000</td>\n",
       "      <td>40.800000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>1035.100000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Minimum temperature (°C)  Maximum temperature (°C)  Rainfall (mm)  \\\n",
       "count                366.000000                364.000000     362.000000   \n",
       "mean                  14.670219                 23.510714       4.285083   \n",
       "std                    4.354354                  4.786657      13.519783   \n",
       "min                    6.200000                 13.600000       0.000000   \n",
       "25%                   10.900000                 19.700000       0.000000   \n",
       "50%                   14.800000                 23.400000       0.000000   \n",
       "75%                   18.275000                 26.500000       2.550000   \n",
       "max                   25.300000                 41.200000     164.800000   \n",
       "\n",
       "       Evaporation (mm)  Sunshine (hours)  Speed of maximum wind gust (km/h)  \\\n",
       "count        356.000000        362.000000                         365.000000   \n",
       "mean           5.382865          6.525414                          42.663014   \n",
       "std            3.317315          4.020979                          14.720617   \n",
       "min            0.000000          0.000000                          17.000000   \n",
       "25%            3.000000          2.800000                          31.000000   \n",
       "50%            4.800000          7.600000                          41.000000   \n",
       "75%            7.200000          9.900000                          50.000000   \n",
       "max           25.400000         13.400000                         106.000000   \n",
       "\n",
       "       9am Temperature (°C)  9am relative humidity (%)  \\\n",
       "count            366.000000                 365.000000   \n",
       "mean              17.531421                  77.649315   \n",
       "std                4.456772                  15.420757   \n",
       "min                8.100000                  36.000000   \n",
       "25%               13.800000                  67.000000   \n",
       "50%               17.950000                  80.000000   \n",
       "75%               21.000000                  90.000000   \n",
       "max               31.200000                 100.000000   \n",
       "\n",
       "       9am cloud amount (oktas)  9am MSL pressure (hPa)  3pm Temperature (°C)  \\\n",
       "count                365.000000              366.000000            364.000000   \n",
       "mean                   4.526027             1018.417760             21.991758   \n",
       "std                    2.812356                7.478648              4.737866   \n",
       "min                    0.000000              997.400000             10.500000   \n",
       "25%                    1.000000             1013.450000             18.600000   \n",
       "50%                    6.000000             1018.650000             21.900000   \n",
       "75%                    7.000000             1023.850000             24.900000   \n",
       "max                    8.000000             1038.300000             40.800000   \n",
       "\n",
       "       3pm relative humidity (%)  3pm cloud amount (oktas)  \\\n",
       "count                 364.000000                365.000000   \n",
       "mean                   60.728022                  4.616438   \n",
       "std                    17.664775                  2.689163   \n",
       "min                    19.000000                  0.000000   \n",
       "25%                    50.000000                  2.000000   \n",
       "50%                    60.500000                  6.000000   \n",
       "75%                    71.000000                  7.000000   \n",
       "max                   100.000000                  8.000000   \n",
       "\n",
       "       3pm wind speed (km/h)  3pm MSL pressure (hPa)  Rain Tomorrow  \n",
       "count             366.000000              366.000000     365.000000  \n",
       "mean               19.598361             1016.027869       0.438356  \n",
       "std                 7.990741                7.517114       0.496867  \n",
       "min                 2.000000              992.200000       0.000000  \n",
       "25%                15.000000             1010.650000       0.000000  \n",
       "50%                19.000000             1016.500000       0.000000  \n",
       "75%                24.000000             1021.675000       1.000000  \n",
       "max                59.000000             1035.100000       1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see on the above table that on average there were 6.5 hours of sunshine in Sydney across the whole year. The existence of **outliers** in some features can also be confirmed by looking at the descriptive statistics. For instance, in the Rainfall column we see that the mean is 4.28mm and that the maximum value is 164.8mm, which of course is way off the mean.\n",
    "\n",
    "The total number of missing values of each column can be found below. The feature with the most missing values is 'Evaporation (mm)' with 10 NAs in total. Before further proceeding with our analysis, we need to find an appropriate way to handle the missing data. Since the dataset is small, it is preferable to not remove the NAs but rather impute them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Minimum temperature (°C)              0\n",
       "Maximum temperature (°C)              2\n",
       "Rainfall (mm)                         4\n",
       "Evaporation (mm)                     10\n",
       "Sunshine (hours)                      4\n",
       "Direction of maximum wind gust        1\n",
       "Speed of maximum wind gust (km/h)     1\n",
       "Time of maximum wind gust             1\n",
       "9am Temperature (°C)                  0\n",
       "9am relative humidity (%)             1\n",
       "9am cloud amount (oktas)              1\n",
       "9am wind direction                    0\n",
       "9am wind speed (km/h)                 0\n",
       "9am MSL pressure (hPa)                0\n",
       "3pm Temperature (°C)                  2\n",
       "3pm relative humidity (%)             2\n",
       "3pm cloud amount (oktas)              1\n",
       "3pm wind direction                    0\n",
       "3pm wind speed (km/h)                 0\n",
       "3pm MSL pressure (hPa)                0\n",
       "Rain Tomorrow                         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using 'ffill' we are carrying the last observation forward thus replacing the NA value with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make some interesting observations by looking at the fluctuation of both the Maximum and Minimum temperatures across the whole year. It is noticable that they follow a similar trend and that there are a few outliers in the Maximum Temperature column that skew the mean upwards. We don't observe any extreme values in the other column though but we will verify this claim in the next plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_temp = df['Maximum temperature (°C)']\n",
    "min_temp = df['Minimum temperature (°C)']\n",
    "\n",
    "min_temp.plot(figsize=(12,7), legend=True)\n",
    "max_temp.plot(figsize=(12,7), color='r', legend=True)\n",
    "plt.title('Maximum and Minimum Temperature across 2020 per day')\n",
    "plt.ylabel('Temperature (°C)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In line with our expectations, the boxplot confirms the existence of outliers in the first variable, temperatures approximately over 37 degrees Celcius. There are no outliers observed in the 'Minimum temperature' feature though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(9,9), sharex=True)\n",
    "ax1.boxplot(max_temp, notch=True)\n",
    "ax1.set_title('Boxplot of Maximum Temperature', fontsize=15, fontweight='black', color='#333F4B')\n",
    "ax1.set_ylabel('Temperature (°C)', fontsize=12)\n",
    "\n",
    "#2nd plot\n",
    "ax2.boxplot(min_temp, notch=True)\n",
    "ax2.set_title('Boxplot of Minimum Temperature', fontsize=15, fontweight='black', color='#333F4B')\n",
    "plt.subplots_adjust(left=7, right=9, wspace=0.15)\n",
    "plt.xticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But when were these extremely high temperatures recorded? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#subsetting the df\n",
    "over_37 = max_temp[max_temp>37]\n",
    "dates = over_37.index.strftime(\"%b %d %Y\")\n",
    "\n",
    "#barh plot\n",
    "fig, ax = plt.subplots(figsize=(7,5))\n",
    "ax.bar(dates, over_37)\n",
    "ax.set_title('Highest temperatures in 2020')\n",
    "ax.set_ylabel('Temperature (°C)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of these values was captured in March which is at the beginning of Autumn in Australia.\n",
    "\n",
    "Based on the descriptive statistics we checked earlier, the 9am MSL pressure (hPa) feature appeared to follow, to a certain extent, a normal distribution. We can verify that by plotting a histogram and drawing the line as below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rIutxagadwlZxAZCiqhujH74xxphoiehOZVWdAkxJN29A2PQBnFpARq+dA5yT+xCNMfkt/LLWaChMPbKuXbuW7t27s3jxYgYPHsz9999/eNm0adO4++67CQaD9OrVi/79nduwXn31VYYNG8aGDRvYvn07FStWBJw+mbp27crmzZsJBALcf//9dO/ePdNtt2vXjo0bN7JypXN6tV+/fkyaNImEhAROOukkxowZwzHHHJOP7/5IdqeyMaZYO/bYY3nllVeOSATgdJd9xx13MHXqVFavXs24ceNYvdq5/er8889n5syZ/+lZ9bXXXqN+/fosW7aMOXPmcN9995Gamprhdj/55BOOPvroI+a1bNmSlStXsnz5curWrcszzzwTxXeaPUsIxpgCl5ycTL169ejVqxennnoqXbp0YebMmZx//vnUqVOH+fPnA0432T169OCss87ijDPO4PPPPz/8+ubNm9O4cWMaN27M999/DziD6lx00UV06NCBevXq0aVLF7K7+bZy5cqcddZZ/+nHaP78+dSuXZtatWqRkJBAp06dDm//jDPOIDEx8T/rEhH+/vtvVJW9e/dy7LHHEhf334aYvXv38tJLL/Hoo48eMb9Vq1aHy59zzjmkpKREsDejxxKCMcYT69ev5+6772b58uWsXbuWDz74gLlz5/LCCy/w9NNPAzB48GAuueQSFixYwFdffUW/fv3Yt28flStXZsaMGSxevJgJEyZw1113HV7vkiVLGDZsGKtXr2bjxo189913AAwYMICkpPTXw2Ru69atVK/+7wWW1apVY+vWrVm+pk+fPqxZs4YqVarQsGFDXn75ZXy+//7MPvbYY9x3331Zjr0wevRo2rRpE3G80WC9nRpjPFGzZk0aNmwIQIMGDWjRogUiQsOGDUlOTgbgyy+/JCkpiRdeeAGAAwcOsHnzZqpUqUKfPn1YunQpfr+fn3766fB6mzZtSrVq1QBo1KgRycnJNGvWjEGDBuUovoxqFtn1xjp9+nQaNWrE7Nmz2bBhAy1btqR58+aULVv2cJmlS5eyfv16hg4devh9pjd48GDi4uLo0qVLjmLOK0sIplAriKElo7UNGwbzSCVKlDg87fP5Dj/3+XwEAgHA+VH++OOPOfnkk4947cCBAznuuONYtmwZoVCIkiVLZrhev99/eF05Va1aNbZs+beThpSUFKpUqZLla8aMGUP//v0REWrXrk3NmjVZu3YtTZs2PVzmhx9+YNGiRSQmJhIIBPjjjz+46KKLmDNnDgDvvPMOkydPZtasWbnuDjy3rMnIGBOzWrduzfDhww8frS9ZsgRwruY54YQT8Pl8jB07lmAwGPVtn3XWWfz8889s2rSJ1NRUxo8fT7t27bJ8TY0aNZg1axYAv//+O+vWraNWrSP787ztttvYtm0bycnJzJ07l7p16x5OBtOmTWPIkCEkJSV5MpSn1RCMMTFbY3nssce45557OO2001BVEhMTmTx5MrfffjvXXnstH330ERdffDGlS5fOdl0DBgygSZMm//lR/+2332jSpAl//fUXPp/v8PmHsmXL8uqrr9K6dWuCwSA9evSgQYMGALzyyis899xz/Pbbb5x22mlcfvnljBw5kscee4ybbrqJhg0boqoMGTLk8CWpjRo1YunSpVnG2KdPHw4ePEjLli0B58RyJF14R0u23V8XNOv+2uREcWkysu6vTTgvu782xhhTDFhCMMYYA1hCMKbYirXmYhOZ/Py/WUIwphgqWbIkO3futKRQyKgqO3fuPOIy22iyq4yMKYaqVatGSkoKNiBV4VOyZMnDN95FmyUEY4qh+Ph4atas6XUYJsZYk5ExxhjAEoIxxhiXJQRjjDGAJQRjjDEuSwjGGGMASwjGGGNcESUEEblMRNaJyHoR6Z/B8hIiMsFdPk9EEsOWnSYiP4jIKhFZISL5c0eFMcaYPMk2IYiIH3gNaAPUBzqLSP10xXoCu1W1NjAUGOK+Ng54D7hVVRsAFwFpUYveGGNM1ERyY1pTYL2qbgQQkfFAe2B1WJn2wEB3eiLwqjhD/bQClqvqMgBV3RmluI2JKeHdU4fzuptrY3IikiajqsCWsOcp7rwMy6hqANgDVADqAioi00VksYg8kNEGRKS3iCwUkYV2K70xxngjkoSQ0aCe6XvEyqxMHNAM6OL+vVpEWvynoOoIVW2iqk0qVaoUQUjGGGOiLZKEkAJUD3teDdiWWRn3vEE5YJc7/2tV3aGq+4EpQOO8Bm2MMSb6IkkIC4A6IlJTRBKATkBSujJJQDd3ugMwW51+dacDp4lIKTdRXMiR5x6MMcbEiGxPKqtqQET64Py4+4HRqrpKRAYBC1U1CRgFjBWR9Tg1g07ua3eLyEs4SUWBKaqa8dk3Y4wxnoqo+2tVnYLT3BM+b0DY9AGgYyavfQ/n0lNjjDExzO5UNsYYA1hCMMYY47KEYIwxBrCEYIwxxmUJwRhjDGAJwRhjjMsSgjHGGMASgjHGGJclBGOMMYAlBGOMMS5LCMYYY4AI+zIyxkAJUuGPtfDnL7D7Fwimgj8efH5a+Lbxs1YjRSsRsuMsU0hZQjAFJlrDQ2Y2XGU0HdpGXdnCrf4lXOBbThPfOng9mGH5UQnO3380gcWhOswIncmM4JkRxVoQ7yc/ZPb/tGFACy9LCMakF0ilne87boybQRPfTwCsCdVgdLANq0MnskUrs0Urs3BgWwgFIZjK1U+Pp7ZvK/VkC819yxkY/y4D499lXqgebwXaMit0Bmo1BxPjLCEYc0goCEveg6+e5pWE39gYOp5BaTcwKXgO2yn/3/Ilyx2eXKJ1WBKsc/h5ovzKZb4FdI2byciEF9kQOoHhgav5LHQ+GY84a4z3LCEYA7BxDkx/BH5fCdXPptuubnwTapjro/pkPYE3gu14K9iWy33zuDVuEsMSXqdLaCaPp90U1dCNiRarw5ri7eBe+LwPvNseDv4FHd+GHtP5OnR6VJp4gviZFDqPK1IH0y+tN7XkVyYlPALTHoK0A3mP35goshqCKb62zIdPbnauGGp2L1zYH+JL5sumFB8fBS9ierAJ/eI+5IYfX4fkb+Ha0fmyPWNyw2oIpvhRhR//B6NbQygE3afApQPzLRmE+4ujeSzQAzpPgD1bYcSFXOP7Jt+3a0wkIkoIInKZiKwTkfUi0j+D5SVEZIK7fJ6IJLrzE0XkHxFZ6j7eiG74xuRQIBUm3QXT+sPJl8Nt38GJ5xV8HCdf5my76pm8lPAG/eLGI4QKPg5jwmSbEETED7wGtAHqA51FpH66Yj2B3apaGxgKDAlbtkFVG7mPW6MUtzE5t38XjL0KFr8Lze+D68ZCybLexVO2CtzwKR8ELuGOuCSGxw93bn4zxiORnENoCqxX1Y0AIjIeaA+sDivTHhjoTk8EXhURu7bOxI69f8DYq2HHT3DNW3DadV5H5PDH83CgJ5v0eB6KG8dxCbvpkfoAf1PK68hMMRRJk1FVYEvY8xR3XoZlVDUA7AEquMtqisgSEflaRJpntAER6S0iC0Vk4fbt23P0BozJ1p6tMKYN7NoI/zchdpLBYcJbwSvok3YnjWQDYxOeoSz7vA7KFEORJISMjvQ1wjK/AjVU9QygL/CBiPynjq6qI1S1iao2qVSpUgQhGROh3b/AmMucGkLXT+CkS7yOKFNTQudwW9o91Jdk3k8Y7DRxGVOAIkkIKUD1sOfVgG2ZlRGROKAcsEtVD6rqTgBVXQRsAOrmNWhjIvL3b879BQf2wI2fw4nneh1RtmaGzqR3Wl/qylZ4t50TuzEFJJKEsACoIyI1RSQB6AQkpSuTBHRzpzsAs1VVRaSSe1IaEakF1AE2Rid0Y7Kwf5dzzmDvH9DlY6ja2OuIIjYndAa90/o6PauO6wxp/3gdkikmsk0I7jmBPsB0YA3woaquEpFBItLOLTYKqCAi63Gahg5dmnoBsFxEluGcbL5VVa0ebPLXwb3wfkfYuR46fwDVz/I6ohz7OnQ6XP0G/PI9TOyBn4x7WTUmmiK6U1lVpwBT0s0bEDZ9AOiYwes+Bj7OY4zGRC4UhI97wrYlcP1YqHWR1xHlXsMO8M9umHI/z8Tt44FAb6xjPJOf7E5lU7RMfwR+mgaXPwf1ikBf/E1vhgsf5Lq4r7nNP8nraEwRZwnBFB3z34J5/4Nz7oCzenkdTfRc9BBJwXN5MH48rX0LvI7GFGGWEEyR0Ny3HKY+4HRH0epJr8OJLhH6pd3CklBthsa/Dr8u8zoiU0RZb6cmW5EMiejlsInV5A+Gxw9nTbAq1y7rwP5l045Ynj6ezIasjOXhHg+SQO/UvnxW4jFKjesMvb+Go2P/nh0bTrNwsRqCKdRKcpA344ciKLek9WU/+d9jqVe2cwy9U++D/Tvh4x7OCXRjosgSginElMHxozhFNnN3Wh8263FeB5TvVmkitH0RNn0Ds5/yOhxTxFhCMIVWV/9MrvXPZWjgWuaEGnkdTsE5oys07gZzX4K1GTd/GZMblhBMoVRfknksbixfBU/n1eBVXodT8No8Byc0gk9vg93JXkdjighLCKbQKc0/DI8fzm7KcF/abVEZ+7jQiS8J170DKHzcC4JpXkdkioBi+E0yhZoqT8aPIVF+4+7UPuzCwwFuvFY+Ea58GVIWwFdPex2NKQIsIZjCZdl4rvHP5eXAtczTU7yOxnunXgONb4S5Q2HjHK+jMYWcJQRTeOxOhin9mBeqVzzPG2TmsiFQsS580hv27fQ6GlOIWUIwhUMoCJ/cAiL0Tb2NkH10/5VQCjqMdjrCm3w3aPrxq4yJjH2rTOEwdyhs+REuf4GtxP4dugXu+FPhkkdhzSRYNt7raEwhZQnBxL5tS2DOM9DgmhgcDzmGnNsHapwHU/o5Q4cak0OWEExsCxyET2+F0pXhipdAbDyATPn8zqA6AJ/dBqGQt/GYQscSgoltc56B7Wuh3StwVHmvo4l95U+ENs/CL9/B/BFeR2MKGUsIJnalLITvXoYzboA6Lb2OpvBo1AXqtIKZA2HnBq+jMYWIJQQTm9IOOM0eZapA68FeR1O4iDg3rPkT4PM+1nRkIhZRQhCRy0RknYisF5H+GSwvISIT3OXzRCQx3fIaIrJXRO6PTtimyJvzNOz4CdoPh5LlvI6m8ClbBS57BjZ/Dwve8joaU0hkmxBExA+8BrQB6gOdRaR+umI9gd2qWhsYCgxJt3woMDXv4ZpiYeti+H64cwfuSZd4HU3h1ej//m062rXJ62hMIRBJDaEpsF5VN6pqKjAeaJ+uTHvgHXd6ItBCxLkcRESuAjYCq6ITsinSgmmQdCccfRy0LGJDYRY0EbhiGIgfJt9jN6yZbEUyhGZVYEvY8xTg7MzKqGpARPYAFUTkH+BBoCWQaXORiPQGegPUqFEj4uBN5GJtKMNM4/luGPy+EjqNg6OOybB8QcSUH+XzW6ZDg177BHzRF5a+T+KEf6/Uympo0Vj4jJiCF0kNIaMLv9MfamRW5glgqKruzWoDqjpCVZuoapNKlewu1GJr+zr4+jnnBrR6l3sdTdFxZnfnhrXpD1OJP72OxsSwSBJCClA97Hk1YFtmZUQkDigH7MKpSTwnIsnAPcDDItInjzGboigUgqS7IKG0M/iLiR6fz7mPI+0AA+Pf9jgYE8siSQgLgDoiUlNEEoBOQFK6MklAN3e6AzBbHc1VNVFVE4FhwNOq+mqUYjdFyeK3nb6KWj8NR1stMeoq1oELH6Ctfz6X+hZ5HY2JUdkmBFUNAH2A6cAa4ENVXSUig0SknVtsFM45g/VAX+A/l6Yak6m/foUZj0PNC+D0zl5HU3SdfzdrQtUZFD+G0vzjdTQmBkVyUhlVnQJMSTdvQNj0AaBjNusYmIv4THEw7UGnz6IrhllfRfnJH8/Dab34OGEgfeMm4lTmjfmX3alsPNXCtwhWfw4XPgAVTvI6nCJvidbhveCl3OSf5vQia0wYSwjGM6U4wKD4t6HSKXDeXV6HU2w8H7ieHZRzTuIHA16HY2KIJQTjmbvjPqaq7IQrh0FcgtfhFBt/U4qBad3gt+XWI6o5giUE44l6spme/qmMC1wMNc7xOpxiZ2qoKdRpDV8Nhj1bvQ7HxAhLCKbACSGejh/JHkozJJdm6SMAAB2sSURBVNDJ63CKKYHLn3PGqp72oNfBmBhhCcEUuE7+r2jsW8/gtC78SRmvwym+yic6J/PXTIJ107yOxsQASwimQFVgD/3jxvFj6BQ+CTX3Ohxzbh+oVA+m9OMoDngdjfGYJQRToB6O/4CjOMgjaT3IuAssU6DiEuCKobBnM33iPvM6GuMxSwimwJwta7jW/y1vBduyQat6HY455MTz4PT/42b/F5wkdoK5OLOEYApGIJUn40ezJVSJ4YGrvY7GpNfqSfZTkqfixvDfzoxNcWEJwRSMH1+jrm8rjwe6cYASXkdj0itdkSGBTpzrX81Vvu+8jsZ4xBKCyX9/boavn+PL4JnMDjX2OhqTifHBi1kSqs0j8e/BP7u9Dsd4wBKCyX/THgLgibQbPQ7EZEXx8WhaD47lb5j9lNfhGA9E1NupKZxiYojHn6bD2slw6UC2TrZxDnIis/9ftIa6zGj9qzSRd4Ot6L5gFDTqAlUjq9HFxGfN5JnVEEz+SfsHpvSDiifDOXd4HY2J0EuBjnB0ZfjiPudOZlNsWEIw+efbl+DPX6Dti9Z5XSHyN6Wg1WDYthgWve11OKYAWUIw+WPHevhuGDS8DmraHcmFTsMOkNgcZj0Be7d7HY0pIJYQTPSpwpT7IO4oaGUnJwslEadml7ofZgzIvrwpEiwhmOhb9SlsnAMtHoMyx3kdjcmtSifDeXfCsg8g2e5NKA4iSggicpmIrBOR9SLSP4PlJURkgrt8nogkuvObishS97FMROwW1SLuaPY7l5mecDo06eF1OCavLugH5Wo4J5iDaV5HY/JZtglBRPzAa0AboD7QWUTqpyvWE9itqrWBocAQd/5KoImqNgIuA94UEbvUtQi7N+5j2Ps7tB0KPr/X4Zi8SigFbYbA9jXw4+teR2PyWSQ1hKbAelXdqKqpwHigfboy7YF33OmJQAsREVXdr6qHBm0tiXWSUqTVl2Rn8PYm3aHamV6HY6Kl3uVw8uUw51n4c4vX0Zh8FElCqAqEfwpS3HkZlnETwB6gAoCInC0iq4AVwK1hCeIwEektIgtFZOH27XZFQ2EkhHgqfjS7KAMt7CRkkXPZs87FAtP+02JsipBIEkJGndanP9LPtIyqzlPVBsBZwEMiUvI/BVVHqGoTVW1SqZLdzVoYHRoF7em0LnBUea/DMdFW/kRndLW1k210tSIskoSQAlQPe14N2JZZGfccQTlgV3gBVV0D7ANOzW2wJkbt3c6DceP5IVifT0PNvI7G5Jew0dVI3e91NCYfRJIQFgB1RKSmiCQAnYCkdGWSgG7udAdgtqqq+5o4ABE5ETgZSI5K5CZ2zBhAKQ7waKA7NgpaERaXAG1fgj2b4ZvnvI7G5INsE4Lb5t8HmA6sAT5U1VUiMkhE2rnFRgEVRGQ90Bc41NDYDFgmIkuBT4HbVXVHtN+E8dCmb2HZBzYKWnGReD6c/n/w/XBqS4rX0Zgoi+gSUFWdAkxJN29A2PQBoGMGrxsLjM1jjCZWBQ7C5HvhmBMZ/pvdYlJstHoSfprK4MBoOqU+itr9rUWG/SdN7n33Muz8Gdq+ZKOgFSelK0LLQZztW0sH/zdeR2OiyBKCyZ2dG+CbF6DB1VDnUq+jMQWtUVfmherxcNwHHMtfXkdjosQSgsk5VfiiL8SVcK5PN8WPz8cjaT0ozT88Ev++19GYKLFuJIqhvIy4ldj/C672fcvQhDk8lnYTYwcviur6M1tPXsTiaF4FGVNet5X566vxZvBK7oz7jInBC/gh1CBP20m/rfDPTk7nm9yxGoLJkfL8xaPx77E4VJv3gtZUVNy9GriK5NBxPB03khKkeh2OySNLCCZHHon/gLLs56G0XnZ1ieEgCTwc6ElN3+/cGfep1+GYPLJvtInYeb6VdPB/w5vBK1inNbwOx8SI70On8lHgAm7xT6aebPY6HJMHlhBMREqQyuC4UWwKHcfwgN1zYI40ONCFPZTmmfiR+Ah5HY7JJUsIJiL3xn1MTd/vPBzoxUESvA7HxJg/KcOgtBs4w7eeG/1feh2OySVLCCZbp8pGbvZPZlzg4qhcSWKKpqTQecwJnk6/uAlUE+vGvjCyhGCyFkzjufi32EE5ngn8n9fRmJgmPJzWE0V4Om4kNh5W4WMJwWTtu5ep7/uFx9K68xelvY7GxLhtVOTZQGcu8K+wbi0KIUsIJnPb18HXz/FFsClfhs7yOhpTSLwfbMG8UD0eixtLJf70OhyTA5YQTMZCQfjsdkgozeNp3b2OxhQiio/+aTdTgjSeih+NNR0VHpYQTMZ+eA22LoTLn2cH5byOxhQym/QEXgx0pLV/Ie1833sdjomQJQTzX9t/gtlPQb0r4NRrvY7GFFKjgpezOFSbJ+LfsaajQsISgjlSKAif3wHxRznDJYoNiWlyJ4SPfmm3UIqDDI4f5fSSa2KaJQRzpO9fgZT5cPnzUOY4r6MxhdwGrcoLgY608i+C5R96HY7JhiUE86/fVsLswVC/PTT8z4ioxuTKqODlLAzVhan9YM9Wr8MxWYgoIYjIZSKyTkTWi0j/DJaXEJEJ7vJ5IpLozm8pIotEZIX795Lohm+iJnAQPr0FjioPbYdaU5GJmhA+7ku7FYIB+Px2CFlfR7Eq24QgIn7gNaANUB/oLCL10xXrCexW1drAUGCIO38HcKWqNgS6AWOjFbiJsjnPwu8rod0rULqC19GYIuYXPR5aPwUb58CCt7wOx2QikhpCU2C9qm5U1VRgPNA+XZn2wDvu9ESghYiIqi5R1W3u/FVASRGx0dhjzS8/wHfD4IyucHIbr6MxRdWZ3aFOK5gxwLmSzcScSIbQrApsCXueApydWRlVDYjIHqACTg3hkGuBJap6MP0GRKQ30BugRg3rZz8j+TZU4IE98ElvOKaGZ+Mjx+IQl4VFfu27fFmvCLQbDq+fy4rhHbkmdRBpxGX6ec5pDDacZt5FUkPIqDE5/fVjWZYRkQY4zUi3ZLQBVR2hqk1UtUmlSpUiCMlEzRf3w19b4ZqRUKKM19GYoq7M8dDuFRr6krkvzq46ijWRJIQUoHrY82rAtszKiEgcUA7Y5T6vBnwK3KiqG/IasIme9r65sOJDuKg/VLe+ikwBOeVK3g+04Na4yZzvW+F1NCZMJAlhAVBHRGqKSALQCUhKVyYJ56QxQAdgtqqqiBwDfAE8pKrfRStok3fV5XeejB8D1c+BZn29DscUM08GurI+VIWX4v8H+3Z6HY5xZZsQVDUA9AGmA2uAD1V1lYgMEpF2brFRQAURWQ/0BQ5dmtoHqA08JiJL3UflqL8LkyPxBBgePxwQuGYE+CM5lWRM9BygBHel9eEY9jqXotpdzDEhovsQVHWKqtZV1ZNUdbA7b4CqJrnTB1S1o6rWVtWmqrrRnf+UqpZW1UZhjz/y7+2YSDwQN55Gvo30S+sN5U/0OhxTTK3WRGfQpZ+mOZ0pGs/ZncrFTAvfIm6Om8LbgVZMDzX1OhxTzL0dbO10ojjzcdiywOtwij1LCMVIFXbwQvybrAwl2nCYJkYItH8NylaBid0px16vAyrWLCEUEwmk8XrCMPwE6ZN2JwdJ8DokYxxHHQMd3oa/f+PF+P8hWNcWXrGEUEwMiHvXPW9wK8l6gtfhGHOkamdC68Fc6l/C7f70FzGagmIJoRi4xvcNXeNm8UbgSqbb2MgmVjXtzWfB87gv7iMu8C3zOppiyRJCEVdfkhkcP5ofgvV5PnCd1+EYkzkRHkrrxTqtxsvxr1FNtnsdUbFjCaEIq8AeRiS8xG6O5s60Owni9zokY7L0DyW5Ne1e/IR4M/4ljuKA1yEVK5YQiqpAKq8nvExF9tA7tS87KOd1RMZE5Bc9nrvS+nCKbOaF+DfsJHMBsoRQVE19gLN9a3kg7RZWai2vozEmR+aEGvFMoDNt/fO5y/+p1+EUG9ZnQVE0701YNIb/Ba4kKXSe19EYkytvBdtSz7eFe+M/5ietxtRQ+l73TbRZDaGoWTcVpvWHk9vyfOB6r6MxJg+Eh9N6sihUh6Hxr9NI1nsdUJFnCaEo2bYEJvaAE06Ha98iZP9eU8gdJIHeqX35XcszMuEFasjvXodUpNkvRlHx5xb4oBOUqgCdJ0BCaa8jMiYqdlKOm9IexE+IMfHPwf5dXodUZBW5cwixPIxetGJLP7Rgef5iYsITVJK/6JD6OD8NXljgMUWyflO85eWzsElP4ObUvryf8AyM6wQ3fAYJpXK83Vj7TYg1VkMo5EpxgDEJz1NVdtAz9X5+0urZv8iYQmih1uOetNthy3z4qBsE07wOqcixhFCIxRPgf/HDOFU20SftLhZoPa9DMiZfTQ2dDVcMhZ+/hM9us3sUoswSQiHlJ8jL8a9yoX85DwV6MTN0ptchGVMwmnSHFgNgxUc8EfcOYKOtRUuRO4dQHPgIMTT+dS73z+eJtBv4KHiR1yEZU7Ca9YV/dnPj98NJJY6nAl0B8TqqQs9qCIWMEOK5+BG08//AM2mdGRNs43VIxhQ8EWj5JGMCrekVN5X+ceOwmkLeRZQQROQyEVknIutFpH8Gy0uIyAR3+TwRSXTnVxCRr0Rkr4i8Gt3Qix8fIZ6PH0EH/ze8mNaBN4NXeh2SMd4R4YnAjYwNXMqtcZN5IG4ClhTyJtsmIxHxA68BLYEUYIGIJKnq6rBiPYHdqlpbRDoBQ4DrgQPAY8Cp7sPkUhwBhsa/zpX+H3kprQPDg1d7HZIxMUAYELgJH8rtcUmUII0nA129DqrQiuQcQlNgvapuBBCR8UB7IDwhtAcGutMTgVdFRFR1HzBXRGpHL+RiKHCQ1+JfobV/IU+ndWaE1QyMOUzx8WigOweJp2fcVEpxAEJtwGfdvedUJE1GVYEtYc9T3HkZllHVALAHqBCNAIu9A3/B+x1o7V/IgLRulgyMyYDiY1DgBl4JXEXnuK/g414QSPU6rEInkhpCRqfu0zfURVIm8w2I9AZ6A9SoUSPSlxV9f/8G73eAP9bQN/VWPgld4HVExsQw4aXAdezVo3h41TjYvwOuf8/roAqVSGoIKUD47a/VgG2ZlRGROKAcEHGHI6o6QlWbqGqTSpUqRfqyom3HzzCqJezcCJ0nWDIwJkIjglfC1W/CL9/D6DYcz06vQyo0IkkIC4A6IlJTRBKATkBSujJJQDd3ugMwW1XtdH9ubZgNI1tA6n64aRLUudTriIwpXE7vBF0+gj8380mJx2kgm7yOqFDINiG45wT6ANOBNcCHqrpKRAaJSDu32CiggoisB/oChy9NFZFk4CXgJhFJEZH6UX4PRcv8t+C9DlC2Ktw8G6raHcjG5MpJl0CPqSjCxIQnuML3g9cRxbyI7lRW1SnAlHTzBoRNHwA6ZvLaxDzEV3ykHYBpD8Kit6HuZXDtSChRxuuojCncjm9I+4NP8b+EobyaMBxmJcDFj9gVSJmwO5Vjwe5kGN3KSQbN7oVOH1gyMCZKdlCOLqmPMD5wEXz7Irx3Dezd7nVYMckSgtfWTYU3L4RdydBpHFw60I5ejImyVOLpH7gZ2g2HzT/CG80g+Tuvw4o5lhC8krofJt/rDPZxTHW4ZQ7Uu9zrqIwpwgQa3wi9ZjojCr5zBcwebOMqhLGE4IEGsglGXAgLR8O5faDXLDi2ltdhGVM8HN8Qes+B066Hb56DUa2cy7xN8en+Oi/D6GU29F/4eiIpU4JU7or7hFv8k+FAZWcYwJMuzvF284sNNWi8kB+f88zWeeT8K2njq8zTW0dR/o3mcMkjcPZt4C/Yn8VY+t5ZDaGg/PIDXyQ8zB1xSXwSbA53/JhlMjDG5L+pobNpfXAI1LoIvnzUuf/n1+Veh+UZSwj57e/f4dNbYcxllJRUbkjtzwOBW+Co8l5HZowB/qA8dB4HHd+Gv7bCiItg6oPwz26vQytwxabJqKCVIJUb/DPg1VshcACa30fLGQ34h5Jeh2aMSU8EGlwNNS+EWYNg3puw4iNnqM4zbig2V/5ZDSHKhBBX+eYyq8T9PBr/PlQ/G27/EVoMsGRgTKwrdSxcOQxu+Roq1oVJd8P/zoe1X0Ax6I3HEkKUCCHa+OYxJeFhhiW8zp96NF1SH4KuE6HCSV6HZ4zJiRNOh+5ToeM7EEqD8f/nXI20flaRTgzWZJRXwTRY/TnTEh7nZF8KG0IncFfqHUwKnYtavjWm8BKBBldBvStg6XswZ4hzl3OVxnBBP6eLGV/R+o5bQsilsuyD715x2hr/SsFHVe5K7cPk0DmELBEYU3T44+DMm+D0zrD0A5g7FMZ3hgp14JxbnfkJpb2OMiosIeRQQ9lIF/9M2vl/gBkHIbE5tH2RVmPSrEZgTFEWVwKadHdOMq/6FH58Db64D2Y96SSFM7tB5VO8jjJPLCFEoBK7aef/nmv8c2ng+4X9WoKk4Ll0uv0JqNIIAKVgbyIzxnjEHwendYSGHWDLPJj3BiwYCfP+B9WaQqPOUP8q5wR1IWMJITN7t8PaybD6c34sMQe/KMtCtXgs7SY+Czbjb0rRyU0GxphiSARqnOM89u2AZeNg8Vinj7IpD0Cdlk5iqNsajjrG62gjYgnhEFX4Yw38/KXz2PwDaAjK1+R/wXZ8GmzGBq3qdZTGmFhUuiKcd6fTN9lvy2H5h7DyE1g3BXxxTtPyyW2g9qUxfdVh8U4Ie1Jg07ew6RvY9LVzlyI4nV81vx/qt4PjTuWFh6ZkvR5jjAGn1nDC6c6j5ZOwdRGsneTcxzD1AadM+ZpOVxmJzZxHDCk2CaEkB6kvv3C6bwN8NBG2zIe/UpyFRx3r/GMufADqtIKyVbwN1hhT+Pl8UP0s59FyEOza6NzHsH4mrJgIi8YAMCfhOBZrHRaH6sAftTw9MV3kEoKPEFVlO7VlG3y7Dv5YDb+tZFWJtfjFvaFkS1XnDuLqd0Li+VC5QZG7ntgYE2OOrQVNa0HTmyEYcJqWkr9l3bQkmvtWcI1/LszZDde941mIRSch/LoMPr2VNSV+poS4A17MAsrVgOPq8+qvp7AiVJMVoZrMG3iDp6EaY4o5fxxUbQxVG3PLpNqAUk12MPeS8z0NK6KEICKXAS8DfmCkqj6bbnkJ4F3gTGAncL2qJrvLHgJ6AkHgLlWdHrXow5WqAOUTGbOtFhu0ChtCVfjk8R5QshwAQwt4bAFjjImckKKVoGIdT6PINiGIiB94DWgJpAALRCRJVVeHFesJ7FbV2iLSCRgCXC8i9YFOQAOgCjBTROqqajDab4Ry1aDzOJ4N/+F3k4ExxpjsRdJw3hRYr6obVTUVGA+0T1emPXCo4Wsi0EJExJ0/XlUPquomYL27PmOMMTFGNJue+0SkA3CZqvZyn98AnK2qfcLKrHTLpLjPNwBnAwOBH1X1PXf+KGCqqk5Mt43eQG/36cnAury/taioCOzwOoh0LKbIxWJcFlNkLKbIHYrrRFWtlJcVRXIOQTKYlz6LZFYmkteiqiOAERHEUqBEZKGqNvE6jnAWU+RiMS6LKTIWU+SiGVckTUYpQPWw59WAbZmVEZE4oBywK8LXGmOMiQGRJIQFQB0RqSkiCTgniZPSlUkCurnTHYDZ6rRFJQGdRKSEiNQE6gDzoxO6McaYaMq2yUhVAyLSB5iOc9npaFVdJSKDgIWqmgSMAsaKyHqcmkEn97WrRORDYDUQAO7IlyuM8k/MNWNhMeVELMZlMUXGYopc1OLK9qSyMcaY4sH6azDGGANYQjDGGOMqVglBREaLyB/ufROH5h0rIjNE5Gf3b3l3vojIKyKyXkSWi0jjsNd0c8v/LCLdMtqWR3EFRWSp+0h/4j8/Y6onIj+IyEERuT/dei4TkXVuvP1jJKZkEVnh7qeFBRhTF/d/tlxEvheR08Ne49V+yiqmqO2nXMTV3o1pqYgsFJFmYa+J2vcvijF58t0LW36WG0OHsHk530+qWmwewAVAY2Bl2LzngP7udH9giDt9OTAV516Kc4B57vxjgY3u3/LudHmv43KX7fVoX1UGzgIGA/eHlfcDG4BaQAKwDKjvZUzusmSgogf76bxDnxWgTdhnysv9lGFM0d5PuYjraP49x3kasNadjur3Lxoxuc89+e6FfX5mA1OADnnZT1F5A4XpASSm29HrgBPc6ROAde70m0Dn9OWAzsCbYfOPKOdVXNH+UOYkprDlAzkyIZwLTA97/hDwkJcxufOSie4PXY5icueXB7bGyn5KH1N+7Kc8xHUusMadjvr3L68xuc89++4B9wB3AG/zb0LI1X4qVk1GmThOVX8FcP9WdudXBbaElUtx52U23+u4AEq6VdkfReSqAowpMwWxr3IaEzh3y38pIovE6TYl2iKJqSdOTQ9iZz+FxwT5v5+yjEtErhaRtcAXQA93tqf7KpOYwKPvnohUBa4G3khXPlf7qeiMhxB9eeqOIx9ltf0aqrpNRGoBs0VkhapuKMDY0vN6X2XmfHc/VQZmiMhaVf2moDYuIhfj/PgeaoP2fD9lEBN4vJ9U9VPgUxG5AHgSuBSP91UmMYF3371hwIOqGhQ5Ytfkaj9ZDQF+F5ETANy/f7jzM+t2o6C648hpXKjqob8bgTnAGQUUU2YKYl/lNKbw/fQH8CnR74E305hE5DRgJNBeVXe6sz3dT5nEVBD7Kcu4wuL4BjhJRCoSI5+pdDF5+d1rAowXkWScXiJed2soudpPlhCO7HajG/B52PwbxXEOsMetqk0HWolIefdMfyt3nqdxufGUAHA/pOfj3CFeEDFlJpJuTwo0JhEpLSJlDk3j/P9WZvWaaMUkIjWAT4AbVPWnsPKe7afMYiqg/ZRVXLXFPeQV50q6BJzBtwri+5ejmLz87qlqTVVNVNVEnKEHblfVz8jtformiZBYfwDjgF+BNJwM2hOogDPY5s/u32PdsoIzMNAGYAXQJGw9PXDGdlgPdI+FuHCuFlmBc4XKCqBnAcZ0vFvmL+BPd7qsu+xy4Cc33ke8jgnnSp5l7mNVAcc0EtgNLHUfC8PW49V+yjCmaO+nXMT1oLvdpcAPQLP8+P5FIyY8/O6le93buCeVc7ufrOsKY4wxgDUZGWOMcVlCMMYYA1hCMMYY47KEYIwxBrCEYIwxxmUJwRQaIqIiMjbseZyIbBeRye7z40RksogsE5HVIjLFnZ8Y3nOkMSZj1nWFKUz2AaeKyFGq+g/QEtgatnwQMENVX4bDd+BGjYjEqWogmuvMYlt+LVzDzZoiwGoIprCZCrR1pzvj3MRzyAk4N/IAoKrLI12piLwtIm+IyLci8pOIXOHOv0lEPhKRScCX7rx+IrJAnL7xn3DnlRaRL9zayUoRud6d/6xbW1kuIi+EbSu83/q97t+LROQrEfkA5wYnRKSriMwXp5/9N0XEn8P9ZUzErIZgCpvxwAC3meg0YDTQ3F32GjBBRPoAM4Ex6vYxE6FE4ELgJOArEantzj8XOE1Vd4lIK6AOTr8+AiS5HZ1VArapalsAESknIsfi9ERZT1VVRI6JIIamwKmquklETgGux+lkLk1EXge6AO/m4D0ZEzGrIZhCxT3qT8SpHUxJt2w6TpcLbwH1gCUiUikHq/9QVUOq+jPOgCL13PkzVHWXO93KfSwBFrtl6uAc0V8qIkNEpLmq7sHpNuMAMFJErgH2RxDDfFXd5E63AM4EFojIUvd5rRy8H2NyxGoIpjBKAl4ALsLp4+Uw94f7A+ADtxZxAbAowvWm78fl0PN9YfMEeEZV30z/YhE5E6dPomdE5EtVHSQiTXF+yDsBfYBLgADuwZjbWVpC2GrSb+sdVX0owviNyROrIZjCaDQwSFVXhM8UkUtEpJQ7XQan6WdzDtbbUUR8InISzpH4ugzKTAd6iMjR7naqikhlEakC7FfV93CSVWO3TDlVnYIzqlUjdx3JOEf+AO2B+EzimQV0EGc8gkPj6p6Yg/djTI5YDcEUOqqaArycwaIzgVdF5NAR+EhVXSAiicDJIpISVvZeVf0o3evXAV8DxwG3quoBOXLQEVT1S7dt/wd32V6gK1AbeF5EQji9VN4GlAE+F5GSOEf797qrecudPx/nR38fGVDV1SLyKM6oZT53vXcAv2S1f4zJLevt1BicK3+Ayao60etYjPGKNRkZY4wBrIZgjDHGZTUEY4wxgCUEY4wxLksIxhhjAEsIxhhjXJYQjDHGAPD/BH+rCeir7r8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "morning_msl_pressure = df['9am MSL pressure (hPa)']\n",
    "\n",
    "n, bins, patches = plt.hist(morning_msl_pressure, 100, density=1)\n",
    "mu = np.mean(morning_msl_pressure)\n",
    "sigma = np.std(morning_msl_pressure)\n",
    "plt.plot(bins, stats.norm.pdf(bins, mu, sigma))\n",
    "plt.xlabel('MSL pressure')\n",
    "plt.legend(['sd: ' + str(round(sigma, 2)), 'mean: ' + str(round(mu, 2))])\n",
    "plt.title('Distribution - 9am MSL pressure (hPa)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean: 1018.65\n",
      "Median: 1018.418\n"
     ]
    }
   ],
   "source": [
    "print('Mean:', round(morning_msl_pressure.median(), 3))\n",
    "print('Median:', round(morning_msl_pressure.mean(), 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize how many days it rained in the city in 2020. To begin with, we define a function to calculate the number of days it rained. We find that there was rainfall in **162** days, while there wasn't any in the remaining **204**. This can also be expressed as a percentage over the total number of days, which is illustrated in the boxplot below (**44%** and **56%** respectively). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rain(col):\n",
    "    rained, not_rained = 0, 0\n",
    "    for i in col:\n",
    "        if i!=0:\n",
    "            rained+=1\n",
    "        else:\n",
    "            not_rained+=1\n",
    "    return rained, not_rained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "number_rain_days = [rain(df['Rainfall (mm)'])[0], rain(df['Rainfall (mm)'])[1]]\n",
    "rain_percentage = df.iloc[:,-1].value_counts(normalize=True)\n",
    "\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax1, ax2 = fig.subplots(1,2)\n",
    "ax1.bar(np.arange(0,2), number_rain_days, width=0.5, tick_label=('Rain', 'No Rain'),\n",
    "        color=['lightseagreen','gray'])\n",
    "ax1.set_title('Days of Rainfall in 2020 in Sydney')\n",
    "ax1.set_ylabel('Number of days', fontsize=12)\n",
    "\n",
    "\n",
    "ax2.bar(np.arange(0,2), rain_percentage, width=0.5, tick_label=('No Rain', 'Rain'),\n",
    "        color=['r','b'])\n",
    "ax2.set_title('% of days of Rainfall in 2020 in Sydney')\n",
    "ax2.set_ylabel('% of days of rainfall', fontsize=12)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It would be interesting to see the months with the most hours of sunshine on average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8ODMfLP2X1bYtK20jiohDI6IbEd0//GnFaN0kSZIkaZ3VlxAZER+iWrb69fJ6CjA9M2+r9ZkMnAZ8ptY+0vWMWRuzI9US17c26f+YDZmLMrOVma2nTdpsHGckSZIkSeuGNR4iI2IBMAi8sSwxBXghcOmwrouAWzPz07W2ZcCMsp/JwMaUJbMRMR04Bzg4M39a619fMjsdWI4kSZIk6XFZoyEyIuYCRwCvzMz7a5vmUlvaGhFHUwXEdw/bxXlUN84BeC3w/czMsuz1fOADmfmjoc6ZeSdwX0TsUe7KejBw7mo+LUmSJElaZ/TyKz5OAy4HtouIZRGxEPgcsCFwUURcExH/t3QfAP6rjJsOfAiYDVxd+r259DsJmBoRS4H3AkeW9sOBbYF/Lv2viYgtyra3AScCS4Gf8ujrMCVJkiRJ4zC5VzvOzANGaD5peEMJjXdn5h/LuGWMfC0jmfkAsN8I7UcDR48ypgvs1LxySZIkSdJoehYimyqhcd4qO0qSJEmS+q6fX/EhSZIkSXqSMURKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhqb3O8CJqpn7jiF93dn9LsMSZIkSZpQnImUJEmSJDVmiJQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNWaIlCRJkiQ15ld8jGL58uV85CMf6XcZkiRJktZSRx11VL9LeFyciZQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNWaIlCRJkiQ11rMQGREnR8RdEXF9rW2/iLghIh6OiNYIYxZHxEYRcX5E3FT6HlPb/tSIOCMilkbElRExs7S/tIy9rjzuXRuza2lfGhGfiYjo1TlLkiRJ0tqulzORpwBzh7VdD7wa+MHwziUQ/hJYCfxbZm4P7AK8ICLmlW4LgXsyc1vgeOCTpf1u4BWZ+TxgAfDV2q6/ABwKzCo/w2uSJEmSJDXUsxCZmT8AVgxrW5KZN48yZB7w3cy8PzMvKf0fAq4Gppc+84FTy/OzgBdHRGTmjzNzeWm/AfiLMms5DdgoMy/PzAS+Auy7us5RkiRJktY1E+mayLnAd+sNEbEJ8Arg4tK0FXAHQGauBH4HTB22n9cAP87MB0v/ZbVty0qbJEmSJOlxmNzvAgAiYgowPTNvq7VNBk4DPlNrH+l6xqyN2ZFqies+TfqPUMehVEtf2XjjjcdzCpIkSZK0TpgoM5EvBC4d1rYIuDUzP11rWwbMgD+HzI0pS2YjYjpwDnBwZv601n96bfx0YDmjyMxFmdnKzNb666//BE5HkiRJktZOEyVEzgUuGHoREUdTBcR3D+t3HtWNcwBeC3w/M7Msez0f+EBm/mioc2beCdwXEXuUu7IeDJzbu9OQJEmSpLVbL7/i4zTgcmC7iFgWEQsj4lURsQz4G+D8iLiwdB8A/quMmw58CJgNXB0R10TEm0u/k4CpEbEUeC9wZGk/HNgW+OfS/5qI2KJsextwIrAU+Cm1sCpJkiRJGp+eXROZmQeMsumc+osSGu/OzD+WccsY+VpGMvMBYL8R2o8Gjh5lTBfYqXnlkiRJkqTR9P3GOiU0zltlR0mSJElS302UayIlSZIkSU8ChkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY5GZ/a5hQmq1WtntdvtdhiRJkiT1RUQszszW8HZnIiVJkiRJjRkiJUmSJEmNGSIlSZIkSY0ZIiVJkiRJjRkiJUmSJEmNGSIlSZIkSY1N7ncBE9Uttz/E3m+/vd9lSJIkSavN9z+/db9L0FrAmUhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSYz0LkRFxckTcFRHX19o2i4iLIuLW8rhpbdt6EbE4ImZExCURsSQiboiId61qfES8MSKuLT+XRcTza2PmRsTNEbE0Io7s1flKkiRJ0rqglzORpwBzh7UdCVycmbOAi8vrIXsClwErgfdl5g7AHsA7ImL2Ksb/DHhRZs4BPgYsAoiIScAJwDxgNnBAbV+SJEmSpHHqWYjMzB8AK4Y1zwdOLc9PBfatbZsLXJCZd2bm1WUf9wFLgK3GGp+Zl2XmPaX9CmB6eb4bsDQzb8vMh4DTyz4kSZIkSY/Dmr4m8hmZeSdAedyitm0voFPvHBEzgV2AKxuMH7IQuKA83wq4o7ZtGY8E0seIiEMjohsR3Yf+ODz/SpIkSZIm97sAgIjYEliRmffX2jYAvgW8OzPvbbifvahC5J5DTSN0y9HGZ+YiylLYjbaYM2o/SZIkSVpXremZyF9FxDSA8nhXaZ8HXDjUKSLWowqQX8/MsxuMJyLmACcC8zPzN6V5GTCjNn46sHy1npEkSZIkrUPWdIg8D1hQni8Azi3P51KWoEZEACcBSzLzuCbjI2Jr4GzgoMy8pdb/KmBWRGwTEVOA/cs+JEmSJEmPQy+/4uM04HJgu4hYFhELgWOAl0bErcBLgWPKHVRnZeZNZegLgIOAvSPimvLz8rLtMeNL+78AU4HPl/5dgMxcCRxONcu5BPhmZt7Qq3OWJEmSpLVdz66JzMwDRtn04vqLiNiT6o6qQ+MuZeRrGSnLVF88QvubgTePMuY7wHeaVS1JkiRJGkvfb6xTQuOl/a5DkiRJkrRqa/qaSEmSJEnSk5ghUpIkSZLUmCFSkiRJktSYIVKSJEmS1JghUpIkSZLUmCFSkiRJktSYIVKSJEmS1JghUpIkSZLU2OR+FzBRPXfrKXz/81v3uwxJkiRJmlCciZQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNeb3RI7i7vuX8KWrd+13GZIkSZIeh7f81eJ+l7DWciZSkiRJktSYIVKSJEmS1JghUpIkSZLUmCFSkiRJktSYIVKSJEmS1JghUpIkSZLUmCFSkiRJktSYIVKSJEmS1JghUpIkSZLUmCFSkiRJktSYIVKSJEmS1FhPQ2REzI2ImyNiaUQcWdr2joirI+L6iDg1IibX+q8XEYsjYkZEXBIRSyLihoh4V63PZhFxUUTcWh43Le1vjIhry89lEfH8seqQJEmSJI1fz0JkREwCTgDmAbOBAyJiNnAqsH9m7gT8AlhQG7YncBmwEnhfZu4A7AG8o4wFOBK4ODNnAReX1wA/A16UmXOAjwGLVlGHJEmSJGmcejkTuRuwNDNvy8yHgNOB1wAPZuYtpc9FpW3IXOCCzLwzM68GyMz7gCXAVqXPfKogSnnct/S7LDPvKe1XANPHqGP+6j1VSZIkSVo39DJEbgXcUXu9DHgmsF5EtErba4EZtT57AZ36TiJiJrALcGVpekZm3glQHrcY4dgLgQvGqGOrx4yojnVoRHQjonvfPSvHODVJkiRJWjdNXnWXxy1GaHsY2B84PiKeCnyPaukqEbElsCIz7//zDiI2AL4FvDsz72100Ii9qELknmPUkSONzcxFlGWwM2c/bcQ+kiRJkrQu62WIXMajZxmnA8sz83LghQARsQ/w3LJ9HnDhUOeIWI8qQH49M8+u7edXETEtM++MiGnAXbUxc4ATgXmZ+Zux6lgN5ydJkiRJ65xeLme9CpgVEdtExBSqGcjzImILgDITeQTwf0v/uZQlqBERwEnAksw8bth+z+ORm/EsAM4tY7YGzgYOql1zOWodq/VMJUmSJGkd0bOZyMxcGRGHU80uTgJOzswbIuLYiBikCrBfyMzvlzuozsrMm8rwFwAHAddFxDWl7YOZ+R3gGOCbEbEQuB3Yr2z/F2Aq8Pkqg7IyM1uj1dGr85YkSZKktVlk9v/Sv4jYEzgwMw/rdy1DZs5+Wn7oa9v3uwxJkiRJj8Nb/mpxv0t40ouIxZnZGt7ey2siG8vMS4FL+12HJEmSJGlsvbwmUpIkSZK0ljFESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIam9zvAiaqp6+/A2/5q26/y5AkSZKkCcWZSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY35P5CgWL19OfOQj/S5DkiRJUp/lUUf1u4QJxZlISZIkSVJjhkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjhkhJkiRJUmOGSEmSJElSY4ZISZIkSVJjPQ2RETE3Im6OiKURcWRpe3FEXB0R10TEpRGxba3/tIj4XkTsHBGXR8QNEXFtRLy+1mebiLgyIm6NiDMiYkppf29E3Fj6XxwRz6qNWVD63xoRC3p5zpIkSZK0NutZiIyIScAJwDxgNnBARMwGvgC8MTN3Br4BfLg2bC5wIXA/cHBm7ljaPh0Rm5Q+nwSOz8xZwD3AwtL+Y6CVmXOAs4BPlTo2A44Cdgd2A46KiE17c9aSJEmStHbr5UzkbsDSzLwtMx8CTgfmAwlsVPpsDCyvjZkLXJCZt2TmrQCZuRy4C9g8IgLYmyokApwK7Fv6XZKZ95f2K4Dp5fnLgIsyc0Vm3gNcVI4jSZIkSRqnyT3c91bAHbXXy6hmA98MfCci/gjcC+wBf5653C4zb6zvJCJ2A6YAPwWmAr/NzJW1fW41wrEXAheMUcdIYyRJkiRJq9DLmcgYoS2B9wAvz8zpwJeB48q23YErH7WDiGnAV4E3ZebDY+yzPuZAoAUcu4o6HltwxKER0Y2ILvffP1IXSZIkSVqn9TJELgNm1F5Pp1qW+vzMHAqLZwB/W57PA7471DkiNgLOBz6cmVeU5ruBTSJiaAZ1OrXlsBHxEuBDwCsz88Ex6qgvof2zzFyUma3MbLH++uM5V0mSJElaJ/QyRF4FzCp3U50C7A+cB2wcEc8tfV4KLCnPXwxcDFD6nwN8JTPPHNphZiZwCfDa0rQAOLeM2QX4IlWAvKtWx4XAPhGxabmhzj6lTZIkSZI0Tj27JjIzV0bE4VSBbRJwcmb+JCLeAnwrIh6murvqP0TE5sADmXlvGf464O+AqRFxSGk7JDOvAY4ATo+Io6nuyHpS2X4ssAFwZnX/HW7PzFdm5oqI+BhVqAX4aGau6NV5S5IkSdLaLKrJvT4XUV3HOD0zj+l3LUNiyy2Tt76132VIkiRJ6rM86qh+l9AXEbE4M1vD23t5d9bGMvNr/a5BkiRJkrRqvbwmUpIkSZK0ljFESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhqb3O8CJqpdt9yS7lFH9bsMSZIkSZpQnImUJEmSJDVmiJQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNWaIlCRJkiQ15ld8jOaBxXBT9LsKSZKkVds++12BpHWIM5GSJEmSpMYMkZIkSZKkxgyRkiRJkqTGDJGSJEmSpMYMkZIkSZKkxgyRkiRJkqTGDJGSJEmSpMYMkZIkSZKkxgyRkiRJkqTGDJGSJEmSpMYMkZIkSZKkxnoaIiNibkTcHBFLI+LI0vbDiLim/CyPiG/X+q8XEYsjYkZEXBIRSyLihoh4V63PZhFxUUTcWh43Le1vjIhry89lEfH8seqQJEmSJI1fz0JkREwCTgDmAbOBAyJidma+MDN3zsydgcuBs2vD9gQuA1YC78vMHYA9gHdExOzS50jg4sycBVxcXgP8DHhRZs4BPgYsGquOXp23JEmSJK3NejkTuRuwNDNvy8yHgNOB+UMbI2JDYG/g27Uxc4ELMvPOzLwaIDPvA5YAW5U+84FTy/NTgX1Lv8sy857SfgUwvUkdkiRJkqTmehkitwLuqL1exiNBEOBVVDOK99ba9gI69Z1ExExgF+DK0vSMzI+0rPEAACAASURBVLwToDxuMcKxFwIXNKyjfqxDI6IbEd1f3zNSD0mSJElat/UyRMYIbVl7fgBw2p87R2wJrMjM+2ttGwDfAt49LGyOftCIvahC5BEN63ikMXNRZrYys7X5pk2OJkmSJEnrll6GyGXAjNrr6cBygIiYSrXM9Pza9nnAhUMvImI9qgD59cysXzf5q4iYVvpMA+6qjZkDnAjMz8zfrKoOSZIkSdL49DJEXgXMiohtImIKsD9wXtm2H9DOzAdq/edSlqBGRAAnAUsy87hh+z0PWFCeLwDOLWO2prpJz0GZeUvDOiRJkiRJ4zC5VzvOzJURcTjV7OIk4OTMvKFs3h84ZqhvuYPqrMy8qTS9ADgIuC4iriltH8zM75Rx34yIhcDtVIEU4F+AqcDnqwzKyrI0daw6JEmSJEnjEJkjXh64ZouI2BM4MDMP63ctQ1o7RXbP6ncVkiRJDWzf/89zktY+EbE4M1vD2xvNREbEFsA7gefUx2Tm61ZHcZl5KXDp6tiXJEmSJKl3mi5n/RbVdzX+J/Cn3pUjSZIkSZrImobITTPz0J5WIkmSJEma8JrenfX68j2OkiRJkqR12JgzkRFxJpDAxlR3Sv0R8Oev5Vhd10RKkiRJkp4cVrWctV17/o1eFiJJkiRJmvjGDJGZeSpAROydmd+vb4uIvXtZmCRJkiRp4ml6TeS/jdB27OosRJIkSZI08a3qmshtgecCG0XEy2ubNgbW72VhkiRJkqSJZ1XXRL4AOAR4BvD+Wvu9wD/2qKaJ4S92he27/a5CkiRJkiaUJtdEnhoRh2TmKWumJEmSJEnSRLWqmUgAMvOUiHgO8Jz6mMz8Tq8KkyRJkiRNPI1CZER8AngLsAT4U2lOwBApSZIkSeuQRiESeB3wnMy8t5fFSJIkSZImtqZf8XGnAVKSJEmS1HQm8vKIOA04E3hgqNFrIiVJkiRp3dI0RP51eXxnrc1rIiVJkiRpHROZ2e8aJqSI8BcjSZImDD+zSVrTImJxZraGtzediSQiXga8hGoG8qLMvGg11idJkiRJehJodGOdiPgn4N+B3wK/A46LiH/sZWGSJEmSpImn0XLWiLgWeEFm3ldebwj8KDPn9Li+vnE5qyRJmkhczippTRttOWvTr/iIoQAJUJ7H6ipOkiRJkvTk0PSayKsi4svAl6iuiXwL0O1ZVZIkSZKkCanpTOQ7gV8BnwE+W56/o1dFSZIkSZImpjGviYyItw9vKo8JkJmf71Fdfec1kZIkaSLxmkhJa9rj/YqPz1EtW72ex14D6X/JJEmSJGkds6oQuRA4GNgROBU4LTPv6XlVkiRJkqQJacxrIjPzy5m5F/B6YAvgRxFxRkQ8b41UJ0mSJEmaUBrdWCczfw4cT3Vjnb2A3XtYkyRJkiRpghozREZlbkScAVwOPBPYPTNPbLLzMvbmiFgaEUfW9vnxiLglIpZExP+q9V8vIhZHxIyIuKRsvyEi3lXrs1lEXBQRt5bHTUv7GyPi2vJzWUQ8f6w6JEmSJEnjt6q7s/4SuBM4BbiEYTfTycwbxxg7CbgFeCmwDLgKOIBqFnMv4JDMfDgitsjMu8qYvYBXA58ApmXm1RGxIbAY2Dczb4yITwErMvOYEgg3zcwjIuJvgSWZeU9EzAP+NTN3H62OsWovtXjjIEmSNGF4d1ZJa9rjvTvr/wOmAu8D3suj79CawLPHGLsbsDQzbysFnA7MB14FvCEzHwYYCpDFXOCCzLyTKrySmfdFxBJgK+DGso+B0v9UoAMckZmX1fZzBTB9FXWMGSIlSZIkSY81ZojMzJlPYN9bAXfUXi+jmoV8DvD6iHgV8Gvgf2XmraXPXsBH6juJiJnALsCVpekZJWSSmXdGxBYjHHshcMEq6niMiDgUOLTBuUmSJEnSOmlVM5FPxPDvlYRq9vKpwAOZ2YqIVwMnAy+MiC2plqne/+cdRGwAfAt4d2be2+ig1ZLYhcCeq6jjsY2Zi4BFZT+uGZEkSZKkYRrdnfVxWgbMqL2eDiwv7d8qbecAc8rzecCFQ50jYr3S7+uZeXZtP7+KiGmlzzTgrtqYOcCJwPzM/M0q6pAkSZIkjVMvQ+RVwKyI2CYipgD7A+cB3wb2Ln1eRHXTGyjXQ0J1B1fgJKob5Rw3bL/nAQvK8wXAuWXM1sDZwEGZeUut/2h1SJIkSZLGacy7sz7hnUe8HPg0MAk4OTM/HhGbAF8HtgZ+DxwGXA8szsydy7g9gR8C1wEPl919MDO/ExFTgW+W8bcD+2Xmiog4EXgN8IvSf+XQnYRGqqNB7S5nlSRJE4Z3Z5W0po12d9aehsimSmg8MDMP63ctQwyRkiRpIpkIn9kkrVsmdIiciAyRkiRpIvEzm6Q1bbQQ2ctrIiVJkiRJaxlDpCRJkiSpMUOkJEmSJKkxQ6QkSZIkqTFDpCRJkiSpMUOkJEmSJKkxQ6QkSZIkqTFDpCRJkiSpscn9LmCi2nXXXel2u/0uQ5IkSZImFGciJUmSJEmNGSIlSZIkSY0ZIiVJkiRJjRkiJUmSJEmNGSIlSZIkSY0ZIiVJkiRJjRkiJUmSJEmN+T2Ro1mxGL4R/a5CkiTpyeEN2e8KJK0hzkRKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhrraYiMiLkRcXNELI2II0vbKRHxs4i4pvzsXOu/XkQsjogZEXFJRCyJiBsi4l21PptFxEURcWt53LS0vzEiri0/l0XE88eqQ5IkSZI0fj0LkRExCTgBmAfMBg6IiNll8/szc+fyc01t2J7AZcBK4H2ZuQOwB/CO2tgjgYszcxZwcXkN8DPgRZk5B/gYsKhBHZIkSZKkcejlTORuwNLMvC0zHwJOB+avYsxc4ILMvDMzrwbIzPuAJcBWpc984NTy/FRg39Lvssy8p7RfAUx/AnVIkiRJkkbQyxC5FXBH7fUyHgmCHy/LTo+PiKfW+uwFdOo7iYiZwC7AlaXpGZl5J0B53GKEYy8ELmhQhyRJkiRpHHoZImOEtgQ+AGwP/DWwGXAEQERsCazIzPv/vIOIDYBvAe/OzHsbHTRiL6oQecQq6hhp7KER0Y2I7q/va3I0SZIkSVq39DJELgNm1F5PB5aXpaqZmQ8CX6ZabgrVNYsXDnWOiPWoAuTXM/Ps2n5+FRHTSp9pwF21MXOAE4H5mfmbseoYqeDMXJSZrcxsbb7huM9XkiRJktZ6vQyRVwGzImKbiJgC7A+cVwuAQXU94/Wl/1zKEtSy7SRgSWYeN2y/5wELyvMFwLllzNbA2cBBmXnLqupYrWcqSZIkSeuIyb3acWaujIjDqWYXJwEnZ+YNEfH9iNicapnpNcBh5Q6qszLzpjL8BcBBwHURMXT31g9m5neAY4BvRsRC4HZgv7L9X4CpwOerDMrKMqs4Yh29Om9JkiRJWptF5oiXB67ZIiL2BA7MzMP6XcuQ1rMju0f3uwpJkqQniTf0/zOlpNUrIhZnZmt4e89mIscjMy8FLu13HZIkSZKksfXymkhJkiRJ0lrGEClJkiRJaswQKUmSJElqzBApSZIkSWrMEClJkiRJaswQKUmSJElqzBApSZIkSWrMEClJkiRJaswQKUmSJElqbHK/C5iwNtsV3tDtdxWSJEmSNKE4EylJkiRJaswQKUmSJElqzBApSZIkSWrMEClJkiRJaswQKUmSJElqzBApSZIkSWrMr/gYxX33LabTiX6XIUmSpGJgIPtdgiSciZQkSZIkjYMhUpIkSZLUmCFSkiRJktSYIVKSJEmS1JghUpIkSZLUmCFSkiRJktSYIVKSJEmS1JghUpIkSZLUmCFSkiRJktSYIVKSJEmS1JghUpIkSZLUWE9DZETMjYibI2JpRBw5bNtnI+L3w9qmRcT3ImLniLg8Im6IiGsj4vW1PttExJURcWtEnBERU0r7eyPixtL/4oh4Vm3MgtL/1ohY0MtzliRJkqS1Wc9CZERMAk4A5gGzgQMiYnbZ1gI2GWHYXOBC4H7g4MzcsbR9OiKG+n8SOD4zZwH3AAtL+4+BVmbOAc4CPlWOtRlwFLA7sBtwVERsuppPV5IkSZLWCb2cidwNWJqZt2XmQ8DpwPwSLo8F/mmEMXOBCzLzlsy8FSAzlwN3AZtHRAB7U4VEgFOBfUu/SzLz/tJ+BTC9PH8ZcFFmrsjMe4CLynEkSZIkSePUyxC5FXBH7fWy0nY4cF5m3lnvXMLldpl547D23YApwE+BqcBvM3PlsH0OtxC4YBV1PEZEHBoR3Yjo/u53qz5BSZIkSVrXTO7hvmOEtqcC+wEDI2zbHbjyUTuImAZ8FViQmQ+XmcjhctiYA4EW8KIx6sgR2sjMRcAigO22ixH7SJIkSdK6rJczkcuAGbXX04GfA9sCSyPi58D6EbG0bJ8HfHeoc0RsBJwPfDgzryjNdwObRMTk2j6X18a8BPgQ8MrMfHCMOpYjSZIkSRq3XobIq4BZ5W6qU4D9gW9n5jMzc2ZmzgTuz8xtS/8XAxcDlP7nAF/JzDOHdpiZCVwCvLY0LQDOLWN2Ab5IFSDvqtVxIbBPRGxabqizT2mTJEmSJI1Tz0JkuW7xcKrAtgT4ZmbeMFLfiNgceCAz7y1NrwP+DjgkIq4pPzuXbUcA7y0zmFOBk0r7scAGwJml/3mljhXAx6hC7VXAR0ubJEmSJGmcoprc63MR1XWM0zPzmH7XMmS77SK/+MV+VyFJkqQhAwP9/9wqrUsiYnFmtoa39/LGOo1l5tf6XYMkSZIkadV6eU2kJEmSJGktY4iUJEmSJDVmiJQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDVmiJQkSZIkNWaIlCRJkiQ1ZoiUJEmSJDU2ud8FTFQbbrgrAwPdfpchSZIkSROKM5GSJEmSpMYMkZIkSZKkxgyRkiRJkqTGDJGSJEmSpMYMkZIkSZKkxgyRkiRJkqTGDJGSJEmSpMb8nsjRLF4MEf2uQpIkqbnMflcgaR3gTKQkSZIkqTFDpCRJkiSpMUOkJEmSJKkxQ6QkSZIkqTFDpCRJkiSpMUOkJEmSJKkxQ6QkSZIkqTFDpCRJkiSpMUOkJEmSJKkxQ6QkSZIkqTFDpCRJkiSpsZ6GyIiYGxE3R8TSiDiytJ0UET+JiGsj4qyI2KDWf1pEfC8ido6IyyPihtLv9bU+20TElRFxa0ScERFTSvt7I+LG0v/iiHhWbcyC0v/WiFjQy3OWJEmSpLVZz0JkREwCTgDmAbOBAyJiNvCezHx+Zs4BbgcOrw2bC1wI3A8cnJk7lrZPR8Qmpc8ngeMzcxZwD7CwtP8YaJX9ngV8qtSxGXAUsDuwG3BURGzao9OWJEmSpLVaL2cidwOWZuZtmfkQcDowPzPvBYiIAP4SyNqYucAFmXlLZt4KkJnLgbuAzcuYvalCIsCpwL6l3yWZeX9pvwKYXp6/DLgoM1dk5j3AReU4kiRJkqRx6mWI3Aq4o/Z6WWkjIr4M/A+wPfDZ0jYJ2C4zb6zvJCJ2A6YAPwWmAr/NzJXD9znMQuCCVdUxXEQcGhHdiOj+uuFJSpIkSdK6pJchMkZoS4DMfBOwJbAEGLrecXfgykftIGIa8FXgTZn58Fj7rI05EGgBx66qjsc0Zi7KzFZmtjYfqYMkSZIkreN6GSKXATNqr6cDy4deZOafgDOA15SmecB3h7ZHxEbA+cCHM/OK0nw3sElETB5pnxHxEuBDwCsz88EmdUiSJEmSmutliLwKmFXupjoF2B84LyK2hT9fE/kK4KbS/8XAxWXbFOAc4CuZeebQDjMzgUuA15amBcC5ZcwuwBepAuRdtTouBPaJiE3LDXX2KW2SJEmSpHGavOouj09mroyIw6kC2yTgZKrlqz8ss4wB/AR4W0RsDjwwdNMd4HXA3wFTI+KQ0nZIZl4DHAGcHhFHU92R9aSy/VhgA+DMKp9ye2a+MjNXRMTHqEItwEczc0WvzluSJEmS1mZRTe71uYjqOsbpmXlMv2sZ0orIbr+LkCRJGo8J8LlO0tojIhZnZmt4e89mIscjM7/W7xokSZIkSavWy2siJUmSJElrGUOkJEmSJKkxQ6QkSZIkqTFDpCRJkiSpMUOkJEmSJKkxQ6QkSZIkqTFDpCRJkiSpMUOkJEmSJKmxyf0uYMLadVfodvtdhSRJkiRNKM5ESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSZIaM0RKkiRJkhozREqSJEmSGjNESpIkSdL/b+/eg+0q6zOOfx8SqFxsRUGam4I1XqgFkUipYZSotIFasdMbFJA6WrCKgto6UTtSBzvjH2ptHWlBQFNLRQpxpAoiRagCgiThmgaUmxKJYr1wqW1p5Nc/9hvdyZyTrCDnrGPW9zOz56y19rvW+u33BPZ+zvuutdWZ3xM5iVWrIOm7CkmSpO1XVd8VSHosHImUJEmSJHVmiJQkSZIkdWaIlCRJkiR1ZoiUJEmSJHVmiJQkSZIkdWaIlCRJkiR1ZoiUJEmSJHVmiJQkSZIkdWaIlCRJkiR1ZoiUJEmSJHU2pSEyydIktye5I8mytu3ctu3WJOck2XGs/Y5JViVZkOSKJGuTrEly8libJye5LMnX28/d2/ZjktzcHtck2X9LdUiSJEmStt2Uhcgks4CPAIcD+wJHJ9kXOBd4DvBrwM7A68Z2OwS4BtgAvK2qngscDLyx7QuwDLi8qhYCl7d1gLuBl1TVfsBpwJlbqUOSJEmStI2mciTyIOCOqrqrqh4BzgOOrKqLqwG+Cswf22cpcElVra+q1QBV9RCwFpjX2hwJLG/Ly4FXtXbXVNUP2vZrx447YR1T8HolSZIkabs3lSFyHnDv2Po6fhoEadNYjwM+P9ZmCXDl+EGS7A0cAFzXNu1VVesB2s+nTnDu1wKXdKlDkiRJktTd7Ck8dibYVmPLpwNfqqovAySZC3y/qn70kwMkuwEXAqdU1YOdTposYRQiD+lYx/i+JwAnjNae1uV0kiRJkjQoUzkSuQ5YMLY+H7gPIMmpwJ7AW8eePxy4dONKG6m8EDi3qlaMtftOkjmtzRzg/rF99gPOYjRt9ntbq2NzVXVmVS2qqkWj8iRJkiRJ46YyRF4PLEyyT5KdgKOAi5K8Dvgt4OiqenSs/VLaFNQkAc4G1lbVBzc77kXA8W35eOAzbZ+nASuA46rqa1ur43F8nZIkSZI0GFM2nbWqNiQ5idHo4izgnKpak+Qm4BvAV0ZZkRXAXwMLq+q2tvtiRtdL3pLkxrbtnVV1MfA+4PwkrwW+CfxBe/7dwFOA09txN7RRxQnrmKrXLUmSJEnbs4xuktpzEckhwLFV9fq+a9koWVSwsu8yJEmStlsz4GOopC1Ismp0qd+mpvLGOp1V1VXAVX3XIUmSJEnasqm8JlKSJEmStJ0xREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6m913ATPVgQfCypV9VyFJkiRJM4sjkZIkSZKkzgyRkiRJkqTODJGSJEmSpM4MkZIkSZKkzgyRkiRJkqTODJGSJEmSpM5SVX3XMCNlbooT+65CkiTNFHWqn5kkDUuSVVW1aPPtjkRKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOpvSEJlkaZLbk9yRZFnbdlJbryR7bNZ+xySrkixIckWStUnWJDl5rM2Tk1yW5Ovt5+5t+zFJbm6Pa5Lsv6U6JEmSJEnbbspCZJJZwEeAw4F9gaOT7AtcDbwc+MYEux0CXANsAN5WVc8FDgbe2PYFWAZcXlULgcvbOsDdwEuqaj/gNODMrdQhSZIkSdpGUzkSeRBwR1XdVVWPAOcBR1bVDVV1zyT7LAUuqar1VbUaoKoeAtYC81qbI4HlbXk58KrW7pqq+kHbfi0wf0t1PF4vUpIkSZKGZCpD5Dzg3rH1dfw0CE5mCXDl+IYkewMHANe1TXtV1XqA9vOpExzntcAl21pHkhOSrEyykh9tpVJJkiRJGqDZU3jsTLCtJm2czAW+X1U/Gtu2G3AhcEpVPdjppMkSRiHykG2to6rOZOM02LmZtFZJkiRJGqqpHIlcBywYW58P3LeF9ocDl25cSbIjowB5blWtGGv3nSRzWps5wP1j++wHnMVo2uz3HmMdkiRJkqRJTGWIvB5YmGSfJDsBRwEXbaH9UtoU1CQBzgbWVtUHN2t3EXB8Wz4e+Ezb52nACuC4qvraz1CHJEmSJGkSUxYiq2oDcBKj0cW1wPlVtSbJm5OsYzQieHOSs9odVBdW1W1t98XAccBLk9zYHke0594HHJbk68BhbR3g3cBTgNNb+5VbqmOqXrckSZIkbc9S1f+lf0kOAY6tqtf3XctGmZvixL6rkCRJM0Wd2v9nJkmaTklWVdWizbdP5Y11Oquqq4Cr+q5DkiRJkrRlU3lNpCRJkiRpO2OIlCRJkiR1ZoiUJEmSJHVmiJQkSZIkdWaIlCRJkiR1ZoiUJEmSJHVmiJQkSZIkdWaIlCRJkiR1ZoiUJEmSJHU2u+8CZqoD5x7IylNX9l2GJEmSJM0ojkRKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6M0RKkiRJkjozREqSJEmSOjNESpIkSZI6S1X1XcOMlOQh4Pa+6xiwPYD/7LuIAbP/+2X/98/fQb/s/37Z//2y//tl/2/q6VW15+YbZ/dRyc+J26tqUd9FDFWSlfZ/f+z/ftn//fN30C/7v1/2f7/s/37Z/904nVWSJEmS1JkhUpIkSZLUmSFycmf2XcDA2f/9sv/7Zf/3z99Bv+z/ftn//bL/+2X/d+CNdSRJkiRJnTkSKUmSJEnqzBC5mSRLk9ye5I4ky/quZ2iSnJPk/iS39l3LECVZkOSKJGuTrElyct81DUmSJyT5apKbWv+/p++ahijJrCQ3JPls37UMTZJ7ktyS5MYkK/uuZ4iSPCnJBUlua+8Fv9F3TUOR5Nnt3/7Gx4NJTum7riFJ8pb2/ntrkk8meULfNc1UTmcdk2QW8DXgMGAdcD1wdFX9R6+FDUiSFwMPA/9YVc/ru56hSTIHmFNVq5M8EVgFvMr/BqZHkgC7VtXDSXYErgJOrqprey5tUJK8FVgE/GJVvaLveoYkyT3AoqryO9p6kmQ58OWqOivJTsAuVfXDvusamvaZ9FvAr1fVN/quZwiSzGP0vrtvVf13kvOBi6vq4/1WNjM5Ermpg4A7ququqnoEOA84sueaBqWqvgR8v+86hqqq1lfV6rb8ELAWmNdvVcNRIw+31R3bw7/0TaMk84HfBs7quxZpuiX5ReDFwNkAVfWIAbI3LwPuNEBOu9nAzklmA7sA9/Vcz4xliNzUPODesfV1+AFaA5Vkb+AA4Lp+KxmWNpXyRuB+4LKqsv+n14eAtwOP9l3IQBXwhSSrkpzQdzED9Azgu8DH2pTus5Ls2ndRA3UU8Mm+ixiSqvoW8H7gm8B64IGq+kK/Vc1chshNZYJtjgJocJLsBlwInFJVD/Zdz5BU1Y+r6vnAfOCgJE7rniZJXgHcX1Wr+q5lwBZX1QuAw4E3tkscNH1mAy8A/r6qDgD+C/D+ENOsTSN+JfAvfdcyJEl2ZzQDcR9gLrBrkmP7rWrmMkRuah2wYGx9Pg5ja2DatXgXAudW1Yq+6xmqNoXsSmBpz6UMyWLgle26vPOAlyb5p35LGpaquq/9vB/4NKPLTDR91gHrxmZAXMAoVGp6HQ6srqrv9F3IwLwcuLuqvltV/wesAF7Uc00zliFyU9cDC5Ps0/4KdBRwUc81SdOm3djlbGBtVX2w73qGJsmeSZ7Ulndm9IZ2W79VDUdVvaOq5lfV3oz+///FqvKv0NMkya7thl60KZS/CXin7mlUVd8G7k3y7LbpZYA3Vpt+R+NU1j58Ezg4yS7t89DLGN0bQhOY3XcBM0lVbUhyEnApMAs4p6rW9FzWoCT5JHAosEeSdcCpVXV2v1UNymLgOOCWdl0ewDur6uIeaxqSOcDydle+HYDzq8qvmdBQ7AV8evTZjdnAP1fV5/staZDeBJzb/ph+F/CanusZlCS7MPqWgBP7rmVoquq6JBcAq4ENwA3Amf1WNXP5FR+SJEmSpM6czipJkiRJ6swQKUmSJEnqzBApSZIkSerMEClJkiRJ6swQKUmSJEnqzBApSdruJLknyfM227YyyaE9lTSpJB9LsibJp3o6/18lef8kz70+yVumuyZJ0szm90RKkvQYJJldVRt+xmPsBfwe8KSqevTxqezxU1X/0HcNkqSZx5FISdLgJNkryaeT3JzkliSvHnuukuw20Xpb/oskVwKnJnlRktVJbmyjiUdPcr5Xt/Pc3M771CRPBK4AdgFWbz7il2SHJKcnuS3JTUmubtsPTbJyrN1P1tvyjUnOaOe6Kclz23PPTvKVtu3WJH8+drp5SS5u5/pc+8LzTUYpk/xJki8k+VR7rVcn+eWxOt6e5KutP/51/DlJ0vbFkUhJ0vbqgiT/M7b+rLHlvwNurarfTTKHUYhbXVW3djjuDlV1KECSzwB/U1WfSBLglzZv3KbVvg84sKrWJzkN+HBV/VGSI4CVVfX8Cc6zP/By4DlV9WiS3TvUBvCrwGuq6sQk7wL+EjgGeANwcVWd1uoaP94i4IXAA8Clrf1HJzj2Q933NAAAAj1JREFUC4H9qureJB8F3gS8K8mxwDOBg1utfwZ8oB1HkrSdMURKkrZXvz8eCsdH7xiFs7cBtGD3OWAJ0CVELh9bvgJ4R5KnA5dV1XUTtF/CKLytb+tnADd1OM9dwCzg7CRfBD7bYR+A26vqhrZ8LfA7bflLwPuT7NTqvmJsn0ur6ocASa4DfmWSY19dVfeOHfuwtvxKRkF09ShLM5tRIJUkbYeczipJGqqaZP3HtPfHJE+YYL+Hf7JD1YcYhbTvAh9O8t4J2mcL55q8uKoHGI0qfgrYD1jTpohuYNP3781rHB99/THtD8ZVdSGwGLgTWAZ8Ymv7TGCydgHeW1XPb4/nVdXiLb9CSdLPK0OkJGmI/g04AaAFsyP46cjcnYymbQL88ZYOkuRZVXVnVZ0B/C1w0ATNLgeOGLtG8E/b+bcoyZ7AzlX1eUah7wHgGcDdwDOS7N6m0E54HeYEx3sm8O2q+jjwnklqfawuAt6wcYpskl9Isv/jeHxJ0gzidFZJ0hC9GTgjyc2MRtGWVdWa9txb2nPr2foU0jcnWQI8Avwvo2sEN1FVa5K8A7gsSTGapnpihxoXAB9NMpvR+/UlwLXtmsMPAKsYBcrrGY1Ybs0fAsckeYTRSOjJHfbppF0Tugfw72066w7A6XSbtitJ+jmTqq3OqJEkSZIkCXA6qyRJkiRpGxgiJUmSJEmdGSIlSZIkSZ0ZIiVJkiRJnRkiJUmSJEmdGSIlSZIkSZ0ZIiVJkiRJnRkiJUmSJEmd/T8C56JLktFdvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_sunshine_per_month = df['Sunshine (hours)'].groupby(pd.Grouper(freq=\"M\")).mean() \n",
    "locs = np.array(np.arange(0,12))\n",
    "months = np.unique(df.index.strftime('%m/%Y').tolist())\n",
    "colormap = np.array(['g', 'b', 'r', 'y', 'orange', 'black', 'gold', 'teal', \n",
    "                     'yellowgreen', 'royalblue', 'gray', 'blueviolet'])\n",
    "\n",
    "plt.figure(figsize=(15, 9))\n",
    "plt.barh(months, avg_sunshine_per_month, color=colormap[locs])\n",
    "plt.title('Hours of Sunshine on average per month', fontsize=13, fontweight='black', color = '#333F4B')\n",
    "plt.xlabel('Hours of sunshine', fontsize=11)\n",
    "plt.ylabel('Month', fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The months that the most hours of sunshine in 2020 were observed are between August and November."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have already conducted some data cleaning but there is some more work to be done. Machine Learning models cannot discern non-numeric data therefore we need to convert those columns to the right datatype. We define a function below which counts the number of columns that contain non-numeric values and at the same time saves their index in a list. This function would be even more useful in larger, higher dimensional datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_str_columns(dataframe):\n",
    "    str_cols, count = list(), 0\n",
    "    for y, z in enumerate(dataframe):\n",
    "        if any(isinstance(i, str) for i in dataframe.iloc[:,y]):\n",
    "            str_cols.append(y)\n",
    "            count+=1\n",
    "    return count, str_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of non-numeric columns: 5\n",
      "Index of non-numeric columns: [5, 7, 11, 12, 17]\n"
     ]
    }
   ],
   "source": [
    "print(f'Total of non-numeric columns: {count_str_columns(df)[0]}')\n",
    "print(f'Index of non-numeric columns: {count_str_columns(df)[1]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also another way that can be used to find columns that contain string values;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Direction of maximum wind gust</th>\n",
       "      <th>Time of maximum wind gust</th>\n",
       "      <th>9am wind direction</th>\n",
       "      <th>9am wind speed (km/h)</th>\n",
       "      <th>3pm wind direction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>SSW</td>\n",
       "      <td>23:28</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>ESE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>SSW</td>\n",
       "      <td>23:02</td>\n",
       "      <td>ESE</td>\n",
       "      <td>17</td>\n",
       "      <td>ESE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NE</td>\n",
       "      <td>16:22</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>S</td>\n",
       "      <td>22:23</td>\n",
       "      <td>E</td>\n",
       "      <td>9</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>SSW</td>\n",
       "      <td>23:05</td>\n",
       "      <td>S</td>\n",
       "      <td>20</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Direction of maximum wind gust  Time of maximum wind gust  \\\n",
       "Date                                                                   \n",
       "2020-01-01                             SSW                     23:28   \n",
       "2020-01-02                             SSW                     23:02   \n",
       "2020-01-03                              NE                     16:22   \n",
       "2020-01-04                               S                     22:23   \n",
       "2020-01-05                             SSW                     23:05   \n",
       "\n",
       "           9am wind direction 9am wind speed (km/h) 3pm wind direction  \n",
       "Date                                                                    \n",
       "2020-01-01                  S                    11                ESE  \n",
       "2020-01-02                ESE                    17                ESE  \n",
       "2020-01-03                WNW                     9                  E  \n",
       "2020-01-04                  E                     9                  E  \n",
       "2020-01-05                  S                    20                  S  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include='object').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is not a high dimensional dataset we can cross-check this by looking at the summary of the dataframe. There are **5** non-numeric columns, at the indices that we previously returned. The object dtype means that the values in the corresponding column are stored as strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 366 entries, 2020-01-01 to 2020-12-31\n",
      "Data columns (total 21 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   Minimum temperature (°C)           366 non-null    float64\n",
      " 1   Maximum temperature (°C)           366 non-null    float64\n",
      " 2   Rainfall (mm)                      366 non-null    float64\n",
      " 3   Evaporation (mm)                   366 non-null    float64\n",
      " 4   Sunshine (hours)                   366 non-null    float64\n",
      " 5   Direction of maximum wind gust     366 non-null    object \n",
      " 6   Speed of maximum wind gust (km/h)  366 non-null    float64\n",
      " 7   Time of maximum wind gust          366 non-null    object \n",
      " 8   9am Temperature (°C)               366 non-null    float64\n",
      " 9   9am relative humidity (%)          366 non-null    float64\n",
      " 10  9am cloud amount (oktas)           366 non-null    float64\n",
      " 11  9am wind direction                 366 non-null    object \n",
      " 12  9am wind speed (km/h)              366 non-null    object \n",
      " 13  9am MSL pressure (hPa)             366 non-null    float64\n",
      " 14  3pm Temperature (°C)               366 non-null    float64\n",
      " 15  3pm relative humidity (%)          366 non-null    float64\n",
      " 16  3pm cloud amount (oktas)           366 non-null    float64\n",
      " 17  3pm wind direction                 366 non-null    object \n",
      " 18  3pm wind speed (km/h)              366 non-null    int64  \n",
      " 19  3pm MSL pressure (hPa)             366 non-null    float64\n",
      " 20  Rain Tomorrow                      366 non-null    float64\n",
      "dtypes: float64(15), int64(1), object(5)\n",
      "memory usage: 62.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the said features, we notice that the '9am wind speed (km/h)' column should have been a continuous variable. We need to check if there are any discrepancies in the values of the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Direction of maximum wind gust</th>\n",
       "      <th>Time of maximum wind gust</th>\n",
       "      <th>9am wind direction</th>\n",
       "      <th>9am wind speed (km/h)</th>\n",
       "      <th>3pm wind direction</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-01</th>\n",
       "      <td>SSW</td>\n",
       "      <td>23:28</td>\n",
       "      <td>S</td>\n",
       "      <td>11</td>\n",
       "      <td>ESE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>SSW</td>\n",
       "      <td>23:02</td>\n",
       "      <td>ESE</td>\n",
       "      <td>17</td>\n",
       "      <td>ESE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NE</td>\n",
       "      <td>16:22</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>S</td>\n",
       "      <td>22:23</td>\n",
       "      <td>E</td>\n",
       "      <td>9</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>SSW</td>\n",
       "      <td>23:05</td>\n",
       "      <td>S</td>\n",
       "      <td>20</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Direction of maximum wind gust  Time of maximum wind gust  \\\n",
       "Date                                                                   \n",
       "2020-01-01                             SSW                     23:28   \n",
       "2020-01-02                             SSW                     23:02   \n",
       "2020-01-03                              NE                     16:22   \n",
       "2020-01-04                               S                     22:23   \n",
       "2020-01-05                             SSW                     23:05   \n",
       "\n",
       "           9am wind direction 9am wind speed (km/h) 3pm wind direction  \n",
       "Date                                                                    \n",
       "2020-01-01                  S                    11                ESE  \n",
       "2020-01-02                ESE                    17                ESE  \n",
       "2020-01-03                WNW                     9                  E  \n",
       "2020-01-04                  E                     9                  E  \n",
       "2020-01-05                  S                    20                  S  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:, count_str_columns(df)[1]].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['11' '17' '9' '20' '4' '15' '24' '13' 'Calm' '2' '22' '28' '6' '19' '7'\n",
      " '35' '43' 4 17 15 28 13 19 11 41 22 7 9 6 20 2 33 35 24 31 30 26 '39'\n",
      " '26' 43 '30'] \n",
      "\n",
      "Instances of Calm: 5\n"
     ]
    }
   ],
   "source": [
    "print(df.iloc[:, 12].unique(),'\\n')\n",
    "print('Instances of Calm:', df.iloc[:, 12][df.iloc[:, 12]=='Calm'].count())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is indeed a value called 'Calm' which is present in 5 different instances. We need to convert the instances of this value into numerical ones. Based on the Beaufort scale, wind speeds less than 2 km/h are classified as Calm. We will therefore convert the corresponding values to 1 km/h and subsequently convert all the values of this column into integers. Moreover, the 'Time of maximum wind gust' column is converted to datetime since it contains the hours that the phenomenon occured.\n",
    "\n",
    "For more information on the Beaufort scale:  https://en.wikipedia.org/wiki/Beaufort_scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df.iloc[:,12]=='Calm'), ('9am wind speed (km/h)')] = 1\n",
    "df['9am wind speed (km/h)'] = df.iloc[:,12].astype('int64')\n",
    "df['Time of maximum wind gust'] = pd.to_datetime(df['Time of maximum wind gust']).dt.hour\n",
    "\n",
    "\n",
    "ohe = OneHotEncoder(sparse=False)\n",
    "df['3pm wind direction'] = ohe.fit_transform(np.array(df['3pm wind direction']).reshape(-1,1))\n",
    "df['Direction of maximum wind gust '] = ohe.fit_transform(np.array(df['Direction of maximum wind gust ']).reshape(-1,1))\n",
    "df['9am wind direction'] = ohe.fit_transform(np.array(df['9am wind direction']).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we define the target variable, y, and the dependent variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Rain Tomorrow'], axis=1).to_numpy()\n",
    "y = np.array(df.iloc[:,-1].values).astype('int64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to test which model produces more robust results. Four algorithms will be tested, **Naive Bayes**, **Decision Tree**, **XGBoost** and **Logistic Regression**. For the algorithm that performs better, we will tune its hyper-parameters to achieve the optimal accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Occurence of values in the target array: [185 145]\n"
     ]
    }
   ],
   "source": [
    "random_state = 15\n",
    "#initiating the models \n",
    "models = []\n",
    "scores = []\n",
    "gnb = GaussianNB()\n",
    "dt = DecisionTreeClassifier(random_state=random_state)\n",
    "xgb = XGBClassifier(use_label_encoder=False, random_state=random_state, verbosity=0) \n",
    "lr = LogisticRegression(max_iter=20000,random_state=random_state)\n",
    "models.extend([gnb, dt, xgb, lr])\n",
    "\n",
    "#using Startified cv\n",
    "kf = StratifiedKFold(n_splits=10, random_state=random_state, shuffle=True)\n",
    "\n",
    "#splitting the data ()\n",
    "for fold, (train_index, test_index) in enumerate(kf.split(X, y), 1):\n",
    "    X_train = X[train_index]\n",
    "    y_train = y[train_index]\n",
    "    X_test = X[test_index]\n",
    "    y_test = y[test_index]\n",
    "print('Occurence of values in the target array:', np.bincount(y_train))\n",
    "        \n",
    "for model in models:\n",
    "    model.fit(X_train, y_train)  \n",
    "    cvs = cross_val_score(model, X_train, y_train, cv=kf, scoring='accuracy', verbose=0)\n",
    "    scores.append(cvs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A boxplot can help us to visualize the performance of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml_names = ['Naive Bayes', 'Decision Tree', 'XGB', 'Logistic Regression']\n",
    "fig, ax = plt.subplots(1, 1, figsize=(7,5))\n",
    "ax.boxplot(scores, labels=ml_names)\n",
    "ax.set_title('Algorithm Comparison')\n",
    "ax.set_ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most robust model is Logistic Regression with an accuracy of **80%**. Naive Bayes' performance is on par with XGB's  (**approximately 78%**), while the Decision Tree classifier performs poorly (**~73%**) compared to the other 3 models."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}