Covid_19_Analysis - RanjithKrishna_Acharya.ipynb

{
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  {
   "cell_type": "code",
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   "id": "b90e5026",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries Used\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sea\n",
    "import plotly.express as px\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6388c294",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 98252 entries, 0 to 98251\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   SNo              98252 non-null  int64  \n",
      " 1   ObservationDate  98252 non-null  object \n",
      " 2   Province/State   67099 non-null  object \n",
      " 3   Country/Region   98252 non-null  object \n",
      " 4   Last Update      98252 non-null  object \n",
      " 5   Confirmed        98252 non-null  float64\n",
      " 6   Deaths           98252 non-null  float64\n",
      " 7   Recovered        98252 non-null  float64\n",
      "dtypes: float64(3), int64(1), object(4)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Importing and Exploring the Dataset\n",
    "data = pd.read_csv('covid_19_data.csv')\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e749533e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15926</th>\n",
       "      <td>15927</td>\n",
       "      <td>04/17/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>2020-04-17 23:38:48</td>\n",
       "      <td>13216.0</td>\n",
       "      <td>1400.0</td>\n",
       "      <td>550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56694</th>\n",
       "      <td>56695</td>\n",
       "      <td>07/04/2020</td>\n",
       "      <td>Risaralda</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>2020-07-05 04:33:46</td>\n",
       "      <td>587.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76668</th>\n",
       "      <td>76669</td>\n",
       "      <td>07/31/2020</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2020-08-01 04:36:27</td>\n",
       "      <td>511.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42887</th>\n",
       "      <td>42888</td>\n",
       "      <td>06/15/2020</td>\n",
       "      <td>Smolensk Oblast</td>\n",
       "      <td>Russia</td>\n",
       "      <td>2020-06-16 04:33:09</td>\n",
       "      <td>3868.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1469.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61231</th>\n",
       "      <td>61232</td>\n",
       "      <td>07/11/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>2020-07-12 04:34:30</td>\n",
       "      <td>64767.0</td>\n",
       "      <td>459.0</td>\n",
       "      <td>54919.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         SNo ObservationDate   Province/State Country/Region  \\\n",
       "15926  15927      04/17/2020              NaN         Sweden   \n",
       "56694  56695      07/04/2020        Risaralda       Colombia   \n",
       "76668  76669      07/31/2020          Unknown    Netherlands   \n",
       "42887  42888      06/15/2020  Smolensk Oblast         Russia   \n",
       "61231  61232      07/11/2020              NaN        Belarus   \n",
       "\n",
       "               Last Update  Confirmed  Deaths  Recovered  \n",
       "15926  2020-04-17 23:38:48    13216.0  1400.0      550.0  \n",
       "56694  2020-07-05 04:33:46      587.0    16.0      297.0  \n",
       "76668  2020-08-01 04:36:27      511.0    18.0        0.0  \n",
       "42887  2020-06-16 04:33:09     3868.0    51.0     1469.0  \n",
       "61231  2020-07-12 04:34:30    64767.0   459.0    54919.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sampling out the Data from the Dataset\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fa0ba6b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNo                    0\n",
       "ObservationDate        0\n",
       "Province/State     31153\n",
       "Country/Region         0\n",
       "Last Update            0\n",
       "Confirmed              0\n",
       "Deaths                 0\n",
       "Recovered              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the Null values in the Dataset\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "56abc31b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Unknown\n",
       "Name: Province/State, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the Province/State Column\n",
    "data['Province/State'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "743746bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>02/04/2020</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Germany</td>\n",
       "      <td>2020-02-03T20:53:02</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10320</th>\n",
       "      <td>10321</td>\n",
       "      <td>03/30/2020</td>\n",
       "      <td>Recovered</td>\n",
       "      <td>Canada</td>\n",
       "      <td>3/8/20 5:31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>466.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34170</th>\n",
       "      <td>34171</td>\n",
       "      <td>06/03/2020</td>\n",
       "      <td>Florida</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-06-04 02:33:14</td>\n",
       "      <td>58764.0</td>\n",
       "      <td>2566.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9200</th>\n",
       "      <td>9201</td>\n",
       "      <td>03/27/2020</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Kosovo</td>\n",
       "      <td>2020-03-27 23:27:48</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42102</th>\n",
       "      <td>42103</td>\n",
       "      <td>06/14/2020</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>2020-06-15 03:33:14</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1051.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         SNo ObservationDate Province/State Country/Region  \\\n",
       "730      731      02/04/2020        Unknown        Germany   \n",
       "10320  10321      03/30/2020      Recovered         Canada   \n",
       "34170  34171      06/03/2020        Florida             US   \n",
       "9200    9201      03/27/2020        Unknown         Kosovo   \n",
       "42102  42103      06/14/2020     Queensland      Australia   \n",
       "\n",
       "               Last Update  Confirmed  Deaths  Recovered  \n",
       "730    2020-02-03T20:53:02       12.0     0.0        0.0  \n",
       "10320          3/8/20 5:31        0.0     0.0      466.0  \n",
       "34170  2020-06-04 02:33:14    58764.0  2566.0        0.0  \n",
       "9200   2020-03-27 23:27:48       86.0     1.0        1.0  \n",
       "42102  2020-06-15 03:33:14     1065.0     6.0     1051.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing the NaN value in the Province/State Column with the 'Unknown' i.e. almost 31.75% of the given dataset\n",
    "data['Province/State'] = data['Province/State'].fillna(data['Province/State'].mode()[0])\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "165da8cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 98252 entries, 0 to 98251\n",
      "Data columns (total 8 columns):\n",
      " #   Column           Non-Null Count  Dtype         \n",
      "---  ------           --------------  -----         \n",
      " 0   SNo              98252 non-null  int64         \n",
      " 1   ObservationDate  98252 non-null  datetime64[ns]\n",
      " 2   Province/State   98252 non-null  object        \n",
      " 3   Country/Region   98252 non-null  object        \n",
      " 4   Last Update      98252 non-null  datetime64[ns]\n",
      " 5   Confirmed        98252 non-null  float64       \n",
      " 6   Deaths           98252 non-null  float64       \n",
      " 7   Recovered        98252 non-null  float64       \n",
      "dtypes: datetime64[ns](2), float64(3), int64(1), object(2)\n",
      "memory usage: 6.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# Changing the data type of the 'ObservationDate and Last Update' Column\n",
    "data['ObservationDate'] = pd.to_datetime(data['ObservationDate'], errors = 'coerce', dayfirst = True)\n",
    "data['Last Update'] = pd.to_datetime(data['Last Update'], errors = 'coerce', dayfirst = True)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1474bd41",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16737</th>\n",
       "      <td>16738</td>\n",
       "      <td>2020-04-20</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-04-20 23:44:50</td>\n",
       "      <td>14795.0</td>\n",
       "      <td>470.0</td>\n",
       "      <td>10631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69926</th>\n",
       "      <td>69927</td>\n",
       "      <td>2020-07-22</td>\n",
       "      <td>Sichuan</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-07-23 05:15:04</td>\n",
       "      <td>603.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>594.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97090</th>\n",
       "      <td>97091</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>Gifu</td>\n",
       "      <td>Japan</td>\n",
       "      <td>2020-08-29 04:28:19</td>\n",
       "      <td>551.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50816</th>\n",
       "      <td>50817</td>\n",
       "      <td>2020-06-26</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-06-27 04:33:35</td>\n",
       "      <td>7818.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54347</th>\n",
       "      <td>54348</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>La Libertad</td>\n",
       "      <td>Peru</td>\n",
       "      <td>2020-07-02 04:33:46</td>\n",
       "      <td>10148.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SNo ObservationDate Province/State  Country/Region  \\\n",
       "16737  16738      2020-04-20        Unknown         Austria   \n",
       "69926  69927      2020-07-22        Sichuan  Mainland China   \n",
       "97090  97091      2020-08-28           Gifu           Japan   \n",
       "50816  50817      2020-06-26         Oregon              US   \n",
       "54347  54348      2020-01-07    La Libertad            Peru   \n",
       "\n",
       "              Last Update  Confirmed  Deaths  Recovered  \n",
       "16737 2020-04-20 23:44:50    14795.0   470.0    10631.0  \n",
       "69926 2020-07-23 05:15:04      603.0     3.0      594.0  \n",
       "97090 2020-08-29 04:28:19      551.0     9.0      499.0  \n",
       "50816 2020-06-27 04:33:35     7818.0   202.0        0.0  \n",
       "54347 2020-07-02 04:33:46    10148.0   598.0        0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exploring the DataFrame\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0f48afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separating the Date and Hour from 'Last Update' Column into 2 separate column in the DataFrame\n",
    "data['Last Update_Date'] = data['Last Update'].dt.date\n",
    "data['Last Update_Hour'] = data['Last Update'].dt.hour\n",
    "data['Last Update_Date'] = pd.to_datetime(data['Last Update_Date'], errors = 'coerce', dayfirst = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "608036e3",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-01-22 17:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-01-22 17:00:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-01-22 17:00:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-01-22 17:00:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-01-22 17:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo ObservationDate Province/State  Country/Region         Last Update  \\\n",
       "0    1      2020-01-22          Anhui  Mainland China 2020-01-22 17:00:00   \n",
       "1    2      2020-01-22        Beijing  Mainland China 2020-01-22 17:00:00   \n",
       "2    3      2020-01-22      Chongqing  Mainland China 2020-01-22 17:00:00   \n",
       "3    4      2020-01-22         Fujian  Mainland China 2020-01-22 17:00:00   \n",
       "4    5      2020-01-22          Gansu  Mainland China 2020-01-22 17:00:00   \n",
       "\n",
       "   Confirmed  Deaths  Recovered Last Update_Date  Last Update_Hour  \n",
       "0        1.0     0.0        0.0       2020-01-22                17  \n",
       "1       14.0     0.0        0.0       2020-01-22                17  \n",
       "2        6.0     0.0        0.0       2020-01-22                17  \n",
       "3        1.0     0.0        0.0       2020-01-22                17  \n",
       "4        0.0     0.0        0.0       2020-01-22                17  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "87a40d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 223 Countries with Disease Spread\n"
     ]
    }
   ],
   "source": [
    "# Total no. of Countries with Disease Spread\n",
    "print('There are',data['Country/Region'].value_counts().count(),'Countries with Disease Spread')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "69b0f241",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1517276871.0 Confirmed Cases around the World\n",
      "There are 824611490.0 Recovered Cases around the World\n",
      "There are 68039737.0 Death Cases around the World\n"
     ]
    }
   ],
   "source": [
    "# Total no. of Confirmed, Recovered, Deaths Cases around the world\n",
    "print('There are',data['Confirmed'].sum(),'Confirmed Cases around the World')\n",
    "print('There are',data['Recovered'].sum(),'Recovered Cases around the World')\n",
    "print('There are',data['Deaths'].sum(),'Death Cases around the World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6cae18c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 624625644.0 Active Cases around the World\n",
      "There are 892651227.0 Closed Cases around the World\n"
     ]
    }
   ],
   "source": [
    "# Total no. of Active and Closed Cases around the world\n",
    "# active_cases = confirmed_cases - recovered_cases - death_cases\n",
    "# closed_cases = recovered_cases + death_cases\n",
    "active_cases = data['Confirmed'].sum() - data['Recovered'].sum() - data['Deaths'].sum()\n",
    "closed_cases = data['Recovered'].sum() + data['Deaths'].sum()\n",
    "\n",
    "print('There are',active_cases,'Active Cases around the World')\n",
    "print('There are',closed_cases,'Closed Cases around the World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0feead00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approx. no. of Confirmed Cases per Day around the world\n",
    "confirm_per_day = data.groupby(data['Last Update_Date'])['Confirmed'].sum()\n",
    "# confirm_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "677d09d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approx. no. of Recovered Cases per Day around the world\n",
    "recover_per_day = data.groupby(data['Last Update_Date'])['Recovered'].sum()\n",
    "# recover_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "61e57517",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approx. no. of Deaths Cases per Day around the world\n",
    "death_per_day = data.groupby(data['Last Update_Date'])['Deaths'].sum()\n",
    "# death_per_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "851f5104",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Approx. No. of Confirmed, Recovered, and Death Cases per Day around the World')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Approx. no. Confirmed, Recoverd, and Deaths Cases per Day around the world\n",
    "plt.figure(figsize = (10,4))\n",
    "plt.plot(confirm_per_day, color = 'Red')\n",
    "plt.plot(recover_per_day, color = 'Green')\n",
    "plt.plot(death_per_day, color = 'Black')\n",
    "plt.xlabel('Per Day')\n",
    "plt.ylabel('Cases')\n",
    "plt.legend(['Confirmed', 'Recovered', 'Death'], loc = 'best')\n",
    "plt.title('Approx. No. of Confirmed, Recovered, and Death Cases per Day around the World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "965f8649",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approx. no. of Confirmed cases per hour around the world\n",
    "confirm_per_hour = data.groupby(data['Last Update_Hour'])['Confirmed'].sum()\n",
    "# confirm_per_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83304ff6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approx. no. of Recovered cases per hour around the world\n",
    "recover_per_hour = data.groupby(data['Last Update_Hour'])['Recovered'].sum()\n",
    "# recover_per_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3e813198",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Approx. no. of Death cases per hour around the world\n",
    "death_per_hour = data.groupby(data['Last Update_Hour'])['Deaths'].sum()\n",
    "# death_per_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e09cc22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Approx. No. of Confirmed, Recoverd, and Death Cases per Hour around the World')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Approx. no. Confirmed, Recoverd, and Deaths Cases per Hour around the world\n",
    "plt.figure(figsize = (10, 4))\n",
    "plt.plot(confirm_per_hour, color = 'Red')\n",
    "plt.plot(recover_per_hour, color = 'Green')\n",
    "plt.plot(death_per_hour, color = 'Black')\n",
    "plt.xlabel('Per Hour')\n",
    "plt.ylabel('Cases')\n",
    "plt.legend(['Confirmed', 'Recovered', 'Death'], loc = 'best')\n",
    "plt.title('Approx. No. of Confirmed, Recoverd, and Death Cases per Hour around the World')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "14784357",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1208.0 confirmed cases in last 24 Hour\n"
     ]
    }
   ],
   "source": [
    "# No. of Confirmed Cases in 24 Hour\n",
    "# start_date_confirm = data.loc[(data['Last Update_Date'] == '2020-01-22')]\n",
    "# end_date_confirm = data.loc[(data['Last Update_Date'] == '2020-01-23')]\n",
    "# total_confirm_hour = start_date_confirm['Confirmed'].sum() + end_date_confirm['Confirmed'].sum()\n",
    "total_confirm_hour = data.loc[(data['Last Update_Date'] >= '2020-01-22') & (data['Last Update_Date'] <= '2020-01-23')]\n",
    "print('There are',total_confirm_hour['Confirmed'].sum(),'confirmed cases in last 24 Hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "44c98ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 58.0 recovered cases in last 24 Hour\n"
     ]
    }
   ],
   "source": [
    "# No. of Recoverd Cases in 24 Hour\n",
    "# start_date_recover = data.loc[(data['Last Update_Date'] == '2020-01-22')]\n",
    "# end_date_recover = data.loc[(data['Last Update_Date'] == '2020-01-23')]\n",
    "# total_recover_hour = start_date_recover['Recovered'].sum() + end_date_recover['Recovered'].sum()\n",
    "total_recover_hour = data.loc[(data['Last Update_Date'] >= '2020-01-22') & (data['Last Update_Date'] <= '2020-01-23')]\n",
    "print('There are',total_recover_hour['Recovered'].sum(),'recovered cases in last 24 Hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fa105c6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 35.0 death cases in last 24 Hour\n"
     ]
    }
   ],
   "source": [
    "# No. of Death Cases in 24 Hour\n",
    "# start_date_death = data.loc[(data['Last Update_Date'] == '2020-01-22')]\n",
    "# end_date_death = data.loc[(data['Last Update_Date'] == '2020-01-23')]\n",
    "# total_death_hour = start_date_death['Deaths'].sum() + end_date_death['Deaths'].sum()\n",
    "total_death_hour = data.loc[(data['Last Update_Date'] >= '2020-01-22') & (data['Last Update_Date'] <= '2020-01-23')]\n",
    "print('There are',total_death_hour['Deaths'].sum(),'death cases in last 24 Hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96d346ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region\n",
       "Mainland China    1186.0\n",
       "Thailand             5.0\n",
       "Japan                3.0\n",
       "Macau                3.0\n",
       "Hong Kong            2.0\n",
       "South Korea          2.0\n",
       "Taiwan               2.0\n",
       "US                   2.0\n",
       "Vietnam              2.0\n",
       "Singapore            1.0\n",
       "Australia            0.0\n",
       "Brazil               0.0\n",
       "Colombia             0.0\n",
       "Malaysia             0.0\n",
       "Mexico               0.0\n",
       "Name: Confirmed, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 15 Countries as per no. of Confirmed Cases in Last 24 Hours\n",
    "data.loc[(data['Last Update_Date'] >= '2020-01-22') & (data['Last Update_Date'] <= '2020-01-23')].groupby('Country/Region')['Confirmed'].sum().sort_values(ascending = False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "38ab77bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region\n",
       "Mainland China    58.0\n",
       "Australia          0.0\n",
       "Brazil             0.0\n",
       "Colombia           0.0\n",
       "Hong Kong          0.0\n",
       "Japan              0.0\n",
       "Macau              0.0\n",
       "Malaysia           0.0\n",
       "Mexico             0.0\n",
       "Philippines        0.0\n",
       "Singapore          0.0\n",
       "South Korea        0.0\n",
       "Taiwan             0.0\n",
       "Thailand           0.0\n",
       "US                 0.0\n",
       "Name: Recovered, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 15 Countries as per no. of Recovered Cases in Last 24 Hours\n",
    "data.loc[(data['Last Update_Date'] >= '2020-01-22') & (data['Last Update_Date'] <= '2020-01-23')].groupby('Country/Region')['Recovered'].sum().sort_values(ascending = False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82f1c4db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region\n",
       "Mainland China    35.0\n",
       "Australia          0.0\n",
       "Brazil             0.0\n",
       "Colombia           0.0\n",
       "Hong Kong          0.0\n",
       "Japan              0.0\n",
       "Macau              0.0\n",
       "Malaysia           0.0\n",
       "Mexico             0.0\n",
       "Philippines        0.0\n",
       "Singapore          0.0\n",
       "South Korea        0.0\n",
       "Taiwan             0.0\n",
       "Thailand           0.0\n",
       "US                 0.0\n",
       "Name: Deaths, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 15 Countries as per no. of Death Cases in Last 24 Hours\n",
    "data.loc[(data['Last Update_Date'] >= '2020-01-22') & (data['Last Update_Date'] <= '2020-01-23')].groupby('Country/Region')['Deaths'].sum().sort_values(ascending = False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "06beda48",
   "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>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Last Update_Date</th>\n",
       "      <th>Last Update_Hour</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84673</th>\n",
       "      <td>84674</td>\n",
       "      <td>2020-11-08</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>US</td>\n",
       "      <td>2020-08-12 04:27:29</td>\n",
       "      <td>138743.0</td>\n",
       "      <td>2238.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-08-12</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78218</th>\n",
       "      <td>78219</td>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Austria</td>\n",
       "      <td>2020-08-04 04:41:59</td>\n",
       "      <td>21385.0</td>\n",
       "      <td>718.0</td>\n",
       "      <td>19063.0</td>\n",
       "      <td>2020-08-04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82222</th>\n",
       "      <td>82223</td>\n",
       "      <td>2020-08-08</td>\n",
       "      <td>Drenthe</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>2020-08-09 04:34:54</td>\n",
       "      <td>566.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2020-08-09</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89490</th>\n",
       "      <td>89491</td>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>2020-08-19 04:27:00</td>\n",
       "      <td>2490.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>2020-08-19</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42617</th>\n",
       "      <td>42618</td>\n",
       "      <td>2020-06-15</td>\n",
       "      <td>Hubei</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>2020-06-16 04:33:09</td>\n",
       "      <td>68135.0</td>\n",
       "      <td>4512.0</td>\n",
       "      <td>63623.0</td>\n",
       "      <td>2020-06-16</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         SNo ObservationDate  Province/State  Country/Region  \\\n",
       "84673  84674      2020-11-08  North Carolina              US   \n",
       "78218  78219      2020-03-08         Unknown         Austria   \n",
       "82222  82223      2020-08-08         Drenthe     Netherlands   \n",
       "89490  89491      2020-08-18         Unknown     South Sudan   \n",
       "42617  42618      2020-06-15           Hubei  Mainland China   \n",
       "\n",
       "              Last Update  Confirmed  Deaths  Recovered Last Update_Date  \\\n",
       "84673 2020-08-12 04:27:29   138743.0  2238.0        0.0       2020-08-12   \n",
       "78218 2020-08-04 04:41:59    21385.0   718.0    19063.0       2020-08-04   \n",
       "82222 2020-08-09 04:34:54      566.0    41.0        0.0       2020-08-09   \n",
       "89490 2020-08-19 04:27:00     2490.0    47.0     1175.0       2020-08-19   \n",
       "42617 2020-06-16 04:33:09    68135.0  4512.0    63623.0       2020-06-16   \n",
       "\n",
       "       Last Update_Hour  weekday  \n",
       "84673                 4        2  \n",
       "78218                 4        1  \n",
       "82222                 4        6  \n",
       "89490                 4        2  \n",
       "42617                 4        1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making a weekdays column in the DataFrame 0, 1, 2, 3, 4, 5, 6 - (Mon, Tue, Wed, Thurs, Fri, Sat, Sun)\n",
    "data['weekday'] = data['Last Update_Date'].dt.weekday\n",
    "data.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a9be76c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weekday\n",
      "0    16050027.0\n",
      "1    16504181.0\n",
      "2    16918477.0\n",
      "3    17463530.0\n",
      "4    18028383.0\n",
      "5    18566520.0\n",
      "6    19125691.0\n",
      "Name: Confirmed, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Weekly Growth of Confirmed Cases in INDIA')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Weekly growth of different types of cases in India (CONFIRMED)\n",
    "plt.figure(figsize = (10, 4))\n",
    "india = data.loc[data['Country/Region'] == 'India']\n",
    "print(india.groupby(data['weekday'])['Confirmed'].sum())\n",
    "plt.plot(india.groupby(data['weekday'])['Confirmed'].sum(), color = 'Red', marker = 'o')\n",
    "plt.xlabel('Weekday')\n",
    "plt.ylabel('Confirmed')\n",
    "plt.legend(['Confirmed'], loc = 'best')\n",
    "plt.title('Weekly Growth of Confirmed Cases in INDIA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a705d196",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weekday\n",
      "0    10650949.0\n",
      "1    11019865.0\n",
      "2    11360598.0\n",
      "3    11767569.0\n",
      "4    12183667.0\n",
      "5    12586618.0\n",
      "6    13016289.0\n",
      "Name: Recovered, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Weekly Growth of Recovered Cases in INDIA')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8+PEqfyOeTTu9ffv2Kx23TZs2mDJlCtatW4f09PRqH+fZ35a8vLwX9jt06BCysrIwYcIEsW3ChAlISEhAUlJSte+fiF6OxdVLnD59GsOHD4e5uTlkMhn27dtXpf2fXWvnnz9NmjSpncBEEpHJZOjWrZt4blV0dDRMTU3Rtm1bAKrF1bP/Pvugf+PGDQiCADs7O5UixMTEBNeuXRMXPrhx4wYAwNfXt1y/DRs2oLi4uNy5FsOHD4e+vj6OHDkCAwODSj2WqVOnIjo6Wiwidu3ahdLSUkyZMuWF+z37IJqfn6/S3r17dxw9ehRHjx7Fm2++WeG+NjY2Kref3Xe7du3K9e3QoYNYVAJPPwCWlZXh7NmzuH79Oh48eAAvLy/07NlTpbhycHBQKXhextLSUuX2sw+d/zw3qLq5q+PDDz/E0aNHsXfvXkydOhU5OTlQU/vfP2WZmZnIzs7GunXryr1Hpk+fDuB/C2ncunUL7dq1g7r682fI3717F+bm5uWKjA4dOqg8XuDpB/Po6Ghx2e1Tp07hwYMHGD9+vNjnxo0byMnJgampabl8+fn5YrZn/vm+eHaMw4cPl9u/f//+Ko/v7t27MDMzK1fsV/TaVMTAwOClH+L/LikpCaNGjYKhoSEMDAxgYmIiTpF99ntpY2ODRYsWYcOGDWjevDkGDhyIoKAgld/byv49qMyxqqo67/nKWrZsGcrKyl7p3Ktnf1teVvRu3boVNjY20NLSws2bN3Hz5k3Y2tpCV1dX5YsjIqp5POfqJQoKCuDi4oIZM2bA29u7yvsvWbIEfn5+Km39+vUTv2Ulakx69OiBn376CVeuXBHPt3qmW7du4nkqZ86cgbm5Odq0aQPg6Qn+MpkMhw4dglwuL3fcZx8On41KffXVV3B1da0wwz8/SI4ePRphYWHYtm3bCxej+DsfHx8sXLgQ27Ztw3/+8x9s3boV7u7uL/1Q+uyk/8TERLi4uIjtf//gu3Xr1gr3fZUV4Nzd3aGtrY3Tp0/D0tISpqamsLe3h5eXF3744QcUFxcjKipKZZn8yqjotQBQqZPpa0vHjh3F53LkyJEoLCzE7Nmz0aNHD1hYWIjvkcmTJz/33KJn5/3VtPHjx2Pp0qXYtWsX3n33XezcuROGhoYYNGiQ2EepVMLU1PS5H3D/PnoJVPy+UCqVGDBgwHNXR7S3t3+FR/E/7du3R3x8PEpKSl66cmV2djZ69eoFAwMD/N///R9sbW2hra2Nixcv4t///rfKiPKqVaswbdo07N+/H7/++isWLFiAlStX4ty5c2jdunWl/x5U5lhVVZvv+TZt2mDy5MlYt24d3n///WodIzExEQDEL60qkpubi59++glFRUWws7Mrtz08PByffvrpC0fhiaj6WFy9xODBgzF48ODnbi8uLsYHH3yA7du3Izs7G05OTvjiiy/EE/r19PRU/iFISEjA1atXERISUtvRierc3693FR0dLS4cAABubm7Q0tLCqVOnEBMTgyFDhojbni0cYGNj88IPhra2tgCefqP+7AP2y3z11VdQV1fH22+/DX19fUycOPGl+xgZGWHo0KHYtm0bJk2ahOjoaHGRgBcZPHgw5HK5uN+reDZ18Pr16+W2/fHHH2jevLk4Aq6pqYkuXbogKioKlpaW4lQmLy8vFBcXY9u2bbh//z569uypcpza+HBVldw14fPPP8fevXvx6aefIiQkBCYmJtDX14dCoXjpe8TW1hYxMTEoLS197mIFVlZWOHbsGPLy8lRGC55Nc/v7FE8bGxt06dIFO3bswPz58xEZGYmRI0dCS0tL5T6PHTuG7t27V7ugtrW1RX5+/ksfn5WVFY4fP478/HyVf4cqem0qMnz4cJw9exZ79uxRmV5WkVOnTiErKwuRkZEq77Pk5OQK+3fs2BEdO3bEsmXL8Pvvv6N79+4ICQnBihUrKv33oDLHqm+WLVuGrVu34osvvqjyvvn5+di7dy8sLCzEkdOKREZGoqioCMHBweUW7rl+/TqWLVuG6OjoRncZDKL6gtMCX9H8+fNx9uxZRERE4PLlyxg7diwGDRokTl/6pw0bNojfKBM1Ns9GULZt24bU1FSVkSstLS107twZQUFBKCgoUPmH3dvbG3K5HB9//HG5b4gFQUBWVhaApwWara0tvv7663JT7wBUuEy4TCbDunXrMGbMGPj6+qosWf4iU6ZMwdWrV/Hee+9BLpdX6mLHlpaWmDFjBg4dOoQ1a9ZU2Key34CbmZnB1dUVYWFhKktnJyYm4tdff1UpToGnhVRMTAxOnjwp/n1p3rw5OnToIH6Q++ffnSZNmlR6We7KqmruV2Vra4vRo0cjNDQUGRkZkMvlGD16NPbs2SN+y/93f3+PjB49Gg8fPqzwtXr2Og0ZMgQKhaJcn2+//RYymazcl2/jx4/HuXPnsHHjRjx8+FBlSiAAjBs3DgqFAp988km5+ywrK6vU6zFu3DicPXsWR44cKbctOzsbZWVlYvaysjIEBweL2xUKRaUvZuvn5wczMzMsXrwYf/75Z7ntDx48EAuYZyM+f39/l5SU4IcfflDZJzc3V8z3TMeOHaGmpobi4mIAlf97UJlj1Te2traYPHky1q5di4yMjErv9+TJE0yZMgWPHj3CBx988MIvRrZu3Yo2bdrAz88PY8aMUflZsmQJ9PT0ODWQqBZx5OoVpKSkYNOmTUhJSRGXVl2yZAkOHz6MTZs24bPPPlPpX1RUhG3btlV7OgBRfaepqQkPDw9ERUVBS0sLbm5uKtu7deuGVatWAVC9eLCtrS1WrFiBpUuX4s6dOxg5ciT09fWRnJyMvXv3Ys6cOViyZAnU1NSwYcMGDB48GI6Ojpg+fTpatWqF1NRUnDx5EgYGBvjpp5/K5VJTU8PWrVsxcuRIjBs3DgcPHkTfvn1f+FiGDh0KY2Nj7Nq1C4MHD4apqWmlnoPAwEAkJyfjnXfeQUREBIYPHw5TU1M8fPgQ0dHR+Omnnyp9zstXX32FwYMHo2vXrpg5c6a4pLmhoSE++ugjlb5eXl749NNP8ddff6kUUT179sTatWthbW1dbpqUm5sbgoODsWLFCrRt2xampqYvfV5qOndNeO+997Bz504EBgbi888/x+eff46TJ0/C09MTs2fPhoODAx49eoSLFy/i2LFjePToEYCn59Zt3rwZixYtwvnz5+Hl5YWCggIcO3YMb7/9NkaMGIHhw4ejT58++OCDD3Dnzh24uLjg119/xf79+/Huu++Ko6nPjBs3DkuWLMGSJUtgZGRUbnSpV69emDt3LlauXIn4+Hi8+eab0NDQwI0bN7Br1y6sXr36pUufv/feezhw4ACGDRuGadOmwc3NDQUFBbhy5Qp2796NO3fuoHnz5hg+fDi6d++O999/H3fu3IGDgwMiIyMrfU5Ss2bNsHfvXnFxlMmTJ4u/0xcvXsT27dvRtWtXAE9/t5s1awZfX18sWLAAMpkMW7ZsKVccnThxAvPnz8fYsWNhb2+PsrIybNmyRSyKgcr/PajMseqjDz74AFu2bMH169crXE49NTVVnD6cn5+Pq1evYteuXcjIyMDixYtfOL05LS0NJ0+eLLcAyzNaWloYOHAgdu3ahe+++65OL6ZM9Nqo8/UJGzAAwt69e8XbP//8swBAaNKkicqPurq6MG7cuHL7h4eHC+rq6kJGRkYdpiaqW0uXLhUACN26dSu3LTIyUgAg6OvrC2VlZeW279mzR+jRo4f4u9S+fXvB399fuH79ukq/S5cuCd7e3oKxsbGgpaUlWFlZCePGjROOHz8u9vn7UuzPFBYWCr169RL09PSEc+fOCYJQfknmv3v77bcFAEJ4eHiVnoOysjJh06ZNQt++fQUjIyNBXV1daN68udCvXz8hJCREePLkidj32RLQX331VYXHOnbsmNC9e3dBR0dHMDAwEIYPHy5cvXq1XL9ny9v/87ndunWrAECYMmVKuX0yMjKEoUOHCvr6+gIAcVn2vy8h/XfPlps+efLkS5+DyuSuzlLsz+vbu3dvwcDAQFwm/v79+4K/v79gYWEhaGhoCC1bthT69esnrFu3TmW/wsJC4YMPPhBsbGzEfmPGjBFu3bol9snLyxMWLlwomJubCxoaGoKdnZ3w1VdfPXdZ/u7duwsAhFmzZj338axbt05wc3MTdHR0BH19faFjx47Cv/71LyEtLU3s86JlufPy8oSlS5cKbdu2FTQ1NYXmzZsL3bp1E77++muhpKRE7JeVlSVMmTJFMDAwEAwNDYUpU6aIS5i/bCn2Z9LS0oSFCxcK9vb2gra2tqCrqyu4ubkJn376qZCTkyP2i46OFt544w1BR0dHMDc3F/71r38JR44cUXnP3L59W5gxY4Zga2sraGtrC0ZGRkKfPn2EY8eOlbvfl/09qMqx/ul5S7FX9HsIQFi+fPkLj/eypdgrun8AFS7FDkAAIMhkMsHAwEBwdHQUZs+eLcTExFR43/jbUuyrVq0SAKj8Lfyn0NDQcsu5E1HNkQmChGcmNzAymQx79+7FyJEjAQA7duzApEmTkJSUVO4kWD09PZULSQJPF7IwMDDA3r176yoyEb2ChQsX4scff0RGRgZ0dXWljkNERET1HKcFvoJOnTpBoVCIyx6/SHJyMk6ePFnp8z2ISFpFRUXYunUrRo8ezcKKiIiIKoXF1Uvk5+fj5s2b4u3k5GTEx8fDyMgI9vb2mDRpEqZOnYpVq1ahU6dOyMzMxPHjx+Hs7IyhQ4eK+23cuBFmZmYvXHmQiKT34MEDHDt2DLt370ZWVhYCAgKkjkREREQNBIurl4iNjUWfPn3E24sWLQLw9CKmoaGh2LRpE1asWIHFixcjNTUVzZs3xxtvvIFhw4aJ+yiVSoSGhmLatGnPvYYGEdUPV69exaRJk2BqaorvvvvuudfTIiIiIvonnnNFRERERERUA3idKyIiIiIiohrA4oqIiIiIiKgG8JyrCiiVSqSlpUFfX/+FV0EnIiIiIqLGTRAE5OXlwdzcHGpqLx6bYnFVgbS0NFhYWEgdg4iIiIiI6om//voLrVu3fmEfFlcV0NfXB/D0CTQwMJA4DRERERERSSU3NxcWFhZijfAiLK4q8GwqoIGBAYsrIiIiIiKq1OlCXNCCiIiIiIioBrC4IiIiIiIiqgEsroiIiIiIiGoAz7mqJkEQUFZWBoVCIXUUqiUaGhqQy+VSxyAiIiKiBoLFVTWUlJQgPT0dhYWFUkehWiSTydC6dWvo6elJHYWIiIiIGgAWV1WkVCqRnJwMuVwOc3NzaGpq8kLDjZAgCMjMzMS9e/dgZ2fHESwiIiIieikWV1VUUlICpVIJCwsL6OrqSh2HapGJiQnu3LmD0tJSFldEREREdUShVCAqJQrpeekw0zeDl6UX5GoN47MYi6tqUlPjWiCNHUckiYiIiOpW5LVIBBwOwL3ce2Jba4PWWD1oNbw7eEuYrHJYIRARERERkeQir0VizM4xKoUVAKTmpmLMzjGIvBYpUbLKk7S4On36NIYPHw5zc3PIZDLs27fvhf3PnDmD7t27w9jYGDo6Omjfvj2+/fbbcv2CgoJgbW0NbW1teHp64vz587X0CIiIiIiI6FUplAoEHA6AAKHctmdt7x5+Fwpl/V6pW9LiqqCgAC4uLggKCqpU/yZNmmD+/Pk4ffo0rl27hmXLlmHZsmVYt26d2GfHjh1YtGgRli9fjosXL8LFxQUDBw7EgwcPauthVJtCqcCpO6ew/cp2nLpzqt6/WRqb0NBQNG3aVOoYRERERK+9qJSociNWfydAwF+5fyEqJaoOU1WdpMXV4MGDsWLFCowaNapS/Tt16oQJEybA0dER1tbWmDx5MgYOHIioqP89yd988w1mz56N6dOnw8HBASEhIdDV1cXGjRtr62FUS+S1SFivtkafsD6YGDkRfcL6wHq1da0Od06bNg0ymQwymQwaGhqwsbHBv/71LxQVFdXafRIRERERvUhGfga+P/99pfqm56XXcppX06DPubp06RJ+//139OrVC8DTlfzi4uLQv39/sY+amhr69++Ps2fPPvc4xcXFyM3NVfmpTVLOJx00aBDS09Nx+/ZtfPvtt1i7di2WL19ea/dX2xQKBZRKpdQxiIiIiKiKYtNiMWXvFFh+a1npz79m+ma1nOrVNMjiqnXr1tDS0oK7uzv8/f0xa9YsAMDDhw+hUCjQokULlf4tWrRARkbGc4+3cuVKGBoaij8WFhZVyiMIAgpKCir1k1uUiwWHFrxwPmnAoQDkFuW+9FiCUP4YL6OlpYWWLVvCwsICI0eORP/+/XH06FEAT6/htXLlStjY2EBHRwcuLi7YvXu3yv5JSUkYNmwYDAwMoK+vDy8vL9y6dUvc///+7//E18fV1RWHDx8W9+3WrRv+/e9/qxwvMzMTGhoaOH36NICnhe6SJUvQqlUrNGnSBJ6enjh16pTY/9lUvgMHDsDBwQFaWlpISUl56X7P9rW0tISuri5GjRqFrKysKj9/RERERFR9Zcoy7Ezaie4bu8NjvQe2Xt6KUmUp3mj1Box0jCBDxas1yyCDhYEFvCy96jhx1TTIpdijoqKQn5+Pc+fO4f3330fbtm0xYcKEah9v6dKlWLRokXg7Nze3SgVWYWkh9FbqVfv+/06AgHt592D4heFL++YvzUcTzSbVvq/ExET8/vvvsLKyAvC0yNy6dStCQkJgZ2eH06dPY/LkyTAxMUGvXr2QmpqKnj17onfv3jhx4gQMDAwQHR2NsrIyAMDq1auxatUqrF27Fp06dcLGjRvx1ltvISkpCXZ2dpg0aRK+/PJLfP755+Iy5zt27IC5uTm8vJ7+osyfPx9Xr15FREQEzM3NsXfvXgwaNAhXrlyBnZ0dAKCwsBBffPEFNmzYAGNjY5iamr50v5iYGMycORMrV67EyJEjcfjw4QY9YkdERETUkGQVZmH9xfUIuhAkzt7SUNPAOMdxCPAMgEcrD3F2lwwylYGIZwVX4KDAen+9K5lQneGPWiCTybB3716MHDmySvutWLECW7ZswfXr11FSUgJdXV3s3r1b5Ti+vr7Izs7G/v37K3XM3NxcGBoaIicnBwYGBirbioqKkJycDBsbG2hrawMACkoKaqy4qoqqFlfTpk3D1q1boa2tjbKyMhQXF0NNTQ07d+7EsGHDYGRkhGPHjqFr167iPrNmzUJhYSHCw8Pxn//8BxEREbh+/To0NDTKHb9Vq1bw9/fHf/7zH7GtS5cu8PDwQFBQEDIzM2Fubo4TJ06IxVS3bt3Qs2dPfP7550hJSUGbNm2QkpICc3Nz8Rj9+/dHly5d8NlnnyE0NBTTp09HfHw8XFxcAKBS+02cOBE5OTn45ZdfxO0+Pj44fPgwsrOzK3y+KnqtiYiIiKjyEh8k4ruY77D18lY8KXsCADDRNYGfux/muc8rN82voutcWRhYIHBQoGTXuXpRbfBPDXLk6u+USiWKi4sBAJqamnBzc8Px48fF4kqpVOL48eOYP39+rWXQ1dBF/tL8SvU9ffc0hoQPeWm/gxMPoqdVz5feb1X16dMHwcHBKCgowLfffgt1dXWMHj0aSUlJKCwsxIABA1T6l5SUoFOnTgCA+Ph4eHl5VVhY5ebmIi0tDd27d1dp7969OxISEgAAJiYmePPNN7Ft2zZ4eXkhOTkZZ8+exdq1awEAV65cgUKhgL29vcoxiouLYWxsLN7W1NSEs7OzeLsy+127dq3cwildu3ZVmbZIRERERK9OoVTglxu/YHXMapxIPiG2u7Z0RYBnAHycfKCtXvEX194dvDGi3QhEpUQhPS8dZvpm8LL0qvcjVs9IWlzl5+fj5s2b4u3k5GTEx8fDyMgIlpaWWLp0KVJTU7F582YAT69fZWlpifbt2wN4ep2sr7/+GgsWLBCPsWjRIvj6+sLd3R1dunRBYGAgCgoKMH369Fp7HDKZrNIjSG/avonWBq2Rmpta4XlXMsjQ2qA13rR9s1beRE2aNEHbtm0BABs3boSLiwt+/PFHODk5AQB++eUXtGrVSmUfLS0tAICOjs4r3/+kSZOwYMECfP/99wgPD0fHjh3RsWNHAE/fD3K5HHFxcZDLVR+7nt7/RgZ1dHTEaYVV2Y+IiIiIak9ucS42XtqI789/j9uPbwMA1GRqGNV+FAI8A9DDsofKZ7jnkavJ0du6dy2nrR2SFlexsbHo06ePePvZeU++vr4IDQ1Feno6UlJSxO1KpRJLly5FcnIy1NXVYWtriy+++AJz584V+4wfPx6ZmZn48MMPkZGRIS6q8M9FLqQiV5Nj9aDV9WI+qZqaGv7zn/9g0aJF+PPPP8XFIZ6tvvhPzs7OCAsLQ2lpabnRKwMDA5ibmyM6Olpl/+joaHTp0kW8PWLECMyZMweHDx9GeHg4pk6dKm7r1KkTFAoFHjx4IE4brIzK7NehQwfExMSotJ07d67S90FEREREFbuRdQPfn/8em+I3Ib/k6WyuptpNMbvzbPh7+MOqqZXECetOvTnnqj6p6jlX1SHFfNJp06YhOzsb+/btE9vKyspgbW2Nd999F9nZ2QgJCcGqVavQo0cP5OTkIDo6GgYGBvD19UVWVhbatWuHXr16YenSpTA0NMS5c+fQpUsXtGvXDoGBgVi+fDnWrVsHV1dXbNq0Cd988424oMUzkydPRmJiIi5fvow7d+7A0tJSZVt0dDRWrVqFTp06ITMzE8ePH4ezszOGDh2K0NBQMevfvWy/c+fOoXv37vjiiy8wYsQIHDlyBP/9738hCALPuSIiIiKqIkEQcOz2MayOWY2DNw6KAwYdmnfAAs8FmOI85ZUWXqtPXqtzrhqq+jKfVF1dHfPnz8eXX36J5ORkmJiYYOXKlbh9+zaaNm2Kzp07iwtUGBsb48SJE3jvvffQq1cvyOVyuLq6iudZLViwADk5OVi8eDEePHgABwcHHDhwQKWwAp5ODRwyZAh69uypUlgBwKZNm7BixQosXrwYqampaN68Od544w0MGzbshY/jZfu98cYbWL9+PZYvX44PP/wQ/fv3x7Jly/DJJ5/U1FNJRERE1OgVlBRgy+Ut+C7mO1x7eE1sH2I3BAGeARjQZkClpv41Vhy5qkBdjFxR/cfXmoiIiOipu9l3EXQhCBsubsDjoscAAD1NPUx3nY53urwDO2O7lxyh4eLIFRERERERvRJBEHAm5QxWx6zG3j/2QikoAQBtmrXBO13ewXTX6TDUfvm1WV8nLK6IiIiIiEhUXFaMiMQIrI5ZjUsZl8T2vjZ9EeAZgKF2QxvM0uh1jcUVEREREREhPS8dwbHBWBu3Fg8KHgAAtNW1MbnjZCzwXICOLTpKnLD+Y3FFRERERPQau5B6AatjVmNn0k6UKksBAK0NWsPfwx+zO8+Gsa6xxAkbDhZX1cR1QBo/vsZERETUWJUqShF5LRKrY1bj7L2zYns3i24I8AzAqPajoCHXeMERqCIsrqro2cVzCwsLoaOjI3Eaqk0lJSUAALmcc4qJiIiocXhY+BDr49Yj6EIQUvNSAQAaahoY7zQeAZ4BcDd3lzhhw8biqorkcjmaNm2KBw+ezkPV1dV9rdfyb6yUSiUyMzOhq6sLdXX+mhAREVHDduX+FayOWY1tV7ahqKwIAGDaxBR+bn7wc/eDmb6ZxAkbB35qrIaWLVsCgFhgUeOkpqYGS0tLFs9ERETUICmUCvz8589YHbMaJ++cFNs7m3VGgGcAxjuOh5a6loQJGx8WV9Ugk8lgZmYGU1NTlJaWSh2HaommpibU1NSkjkFERERUJTlFOdh4aSPWXFiD249vAwDUZGrw7uCNAM8AdLfozi+PawmLq1cgl8t5Pg4RERER1Qt/Zv2J72O+R2hCKPJL8gEAzbSbYXbn2fDv4g9LQ0uJEzZ+LK6IiIiIiBooQRDw661fsTpmNQ7dPCS2O5g4YEGXBZjsPBlNNJtImPD1wuKKiIiIiKiBKSgpwOaEzfju/Hf44+EfAAAZZBhqPxQBngHoZ9OPU/8kwOKKiIiIiKiBuJt9F2vOr8GGSxuQXZQNANDX1Md01+l4x/MdtDVqK23A1xyLKyIiIiKiekwQBESlRGF1zGrs+2MflIISAGDbzBbvdHkH0ztNh4GWgcQpCWBxRURERERULxWVFWH7le347vx3iM+IF9v72fRDgGcAhtgNgVyNi6vVJyyuiIiIiIjqkbS8NARfCMbauLXILMwEAOio62CK8xQs8FwAR1NHiRPS87C4IiIiIiKqB86nnsfqmNXYmbQTZcoyAICFgQX8Pfwxq/MsGOsaS5yQXobFFRERERGRREoVpdhzbQ9Wx6zGuXvnxPbuFt0R4BmAUR1GQV2NH9kbCr5SRERERER1LLMgE+vi1uGH2B+QlpcGANBQ04CPkw8CPAPgZu4mcUKqDhZXRERERER15PL9y1h9bjW2XdmGYkUxAKBFkxaY5z4Pc93noqVeS4kT0qtgcUVEREREVIsUSgV++vMnrI5ZjVN3TontbmZuCPAMwDjHcdBS15IuINUYFldERERERLUguygbGy9txJrza5CcnQwAkMvk8O7gjQDPAHSz6AaZTCZxSqpJLK6IiIiIiGrQ9YfX8V3MdwhLCENBaQEAwEjHCLM7z4a/hz8sDC0kTki1hcUVEREREdErUgpK/HrrV6yOWY3DNw+L7Y4mjgjwDMAk50nQ1dCVMCHVBRZXRERERETVlF+Sj80Jm/FdzHe4nnUdACCDDMPshyHAMwB9bfpy6t9rhMUVEREREVEVJT9ORtCFIGy4uAE5xTkAAH1NfczoNAPzu8xHW6O2EickKbC4IiIiIiKqBEEQ8Nvd37A6ZjUOXD8ApaAEALQ1aot3uryDaa7TYKBlIHFKkhKLKyIiIiKiFygqK0L4lXB8F/MdEu4niO0D2gxAgGcABtsNhppMTcKEVF+wuCIiIiIiqkBaXhp+uPAD1satxcPChwAAHXUdTHWZigWeC+Bg4iBxQqpvWFwREREREf3NuXvn8F3Md9h1dRfKlGUAAAsDC8zvMh+zOs+CkY6RxAmpvmJxRURERESvvRJFCXZf3Y3VMatxPvW82O5l6YUFngswsv1IqKvxozO9GN8hRERERPTayizIxNq4tfjhwg9Iz08HAGjKNTHBaQIWeC5AZ7POEiekhoTFFRERERG9dhIyErA6ZjXCr4SjWFEMAGip1xLz3OdhrttctNBrIXFCaohYXBERERHRa0GhVGD/9f34LuY7/Hb3N7Hd3dwdAZ4BGOc4DppyTQkTUkPH4oqIiIiIGrXHTx7jx0s/Ys35NbibcxcAIJfJMcZhDBZ4LkDX1l0hk8kkTkmNAYsrIiIiImqwFEoFolKikJ6XDjN9M3hZekGuJgcA/PHwD3wX8x3CEsJQWFoIADDWMcYctzl42+NttDZoLWV0aoRYXBERERFRgxR5LRIBhwNwL/ee2NbaoDV8XXwRmxaLI7eOiO1Opk4I8AzApI6ToKOhI0Vceg3IBEEQpA5R3+Tm5sLQ0BA5OTkwMDCQOg4RERER/UPktUiM2TkGAp7/UVYGGYa3G44AzwD0se7DqX9ULVWpDThyRUREREQNikKpQMDhgBcWVnqaeoibHQf75vZ1mIxed2pSByAiIiIiqoqTySdVpgJWJL8kH2n5aXWUiOgpjlwRERERUYOQkpOC9XHr8f357yvVPz0vvZYTEalicUVERERE9ZZCqcDhm4cREheCgzcOQikoK72vmb5ZLSYjKo/FFRERERHVOxn5Gfjx4o9Yd3EdUnJSxPa+Nn0xu/NsLPl1CdLy0io870oGGVobtIaXpVddRiZicUVERERE9YNSUOJk8kmExIVg3x/7UKYsAwA0026G6a7TMcdtDto1bwcA0JRrYszOMZBBplJgyfB0RcDAQYHi9a6I6gqLKyIiIiKSVFZhFkLjQ7E2bi1uPLohtnez6AY/Nz+McRhT7tpU3h28sXvc7gqvcxU4KBDeHbzrLD/RM7zOVQV4nSsiIiKi2iUIAn7/63eExIVgV9IuFCuKAQD6mvqY4jwFc93nwrmF80uPo1AqEJUShfS8dJjpm8HL0osjVlSjqlIbSLoU++nTpzF8+HCYm5tDJpNh3759L+wfGRmJAQMGwMTEBAYGBujatSuOHDmi0uejjz6CTCZT+Wnfvn0tPgoiIiIiqqycohwEnQ+Cc4gzemzqga2Xt6JYUYxOLTth3bB1SFuchqChQZUqrABAriZHb+vemNBxAnpb92ZhRZKSdFpgQUEBXFxcMGPGDHh7v3zo9vTp0xgwYAA+++wzNG3aFJs2bcLw4cMRExODTp06if0cHR1x7Ngx8ba6Omc/EhEREUkpLi0OIbEhCE8MR2FpIQBAR10HE5wmwM/dD+7m7pDJZBKnJHo1klYdgwcPxuDBgyvdPzAwUOX2Z599hv379+Onn35SKa7U1dXRsmXLmopJRERERNVQUFKAiMQIhMSFIDYtVmx3MHGAn5sfprhMQVPtptIFJKphDXpIR6lUIi8vD0ZGRirtN27cgLm5ObS1tdG1a1esXLkSlpaWzz1OcXExiouLxdu5ubm1lpmIiIiosUt8kIi1sWux+fJm5BY//VylKdfEGIcx8HPzQw/LHhylokapQRdXX3/9NfLz8zFu3DixzdPTE6GhoWjXrh3S09Px8ccfw8vLC4mJidDX16/wOCtXrsTHH39cV7GJiIiIGp2isiLsuboHIXEhOJNyRmy3bWaLuW5zMc11GkyamEiYkKj21ZvVAmUyGfbu3YuRI0dWqn94eDhmz56N/fv3o3///s/tl52dDSsrK3zzzTeYOXNmhX0qGrmysLDgaoFEREREL3Ej6wbWxa3DpvhNyHqSBQCQy+QY0X4E/Nz80K9NP6jJJF1DjeiVVGW1wAY5chUREYFZs2Zh165dLyysAKBp06awt7fHzZs3n9tHS0sLWlpaNR2TiIiIqFEqVZTiwPUDCIkLwbHb/1tErLVBa8zpPAczO8+Eub65hAmJpNHgiqvt27djxowZiIiIwNChQ1/aPz8/H7du3cKUKVPqIB0RERFR45WSk4L1ceux4dIGZORnAABkkGGw3WD4uflhsN1gqKs1uI+XRDVG0nd/fn6+yohScnIy4uPjYWRkBEtLSyxduhSpqanYvHkzgKdTAX19fbF69Wp4enoiI+PpL7WOjg4MDQ0BAEuWLMHw4cNhZWWFtLQ0LF++HHK5HBMmTKj7B0hERETUwCmUChy+eRghcSE4eOMglIISANCiSQvM7DQTs91mw7qptbQhieoJSYur2NhY9OnTR7y9aNEiAICvry9CQ0ORnp6OlJQUcfu6detQVlYGf39/+Pv7i+3P+gPAvXv3MGHCBGRlZcHExAQ9evTAuXPnYGLCEyiJiIiIKis9Lx0bL23EuovrkJLzv89jfW36ws/NDyPaj4CmXFPChET1T71Z0KI+qcpJa0RERESNhVJQ4mTySYTEhWDfH/tQpiwDADTTbobprtMxx20O2jVvJ3FKorrV6Be0ICIiIqKak1WYhdD4UKyNW4sbj26I7d0susHPzQ9jHMZAR0NHwoREDQOLKyIiIqLXkCAI+P2v3xESF4JdSbtQrHh6WRp9TX1McZ6Cue5z4dzCWeKURA0LiysiIiKi10hOUQ62Xt6KkLgQJD5IFNs7teyEee7zMKHjBOhp6kmYkKjhYnFFRERE9BqIS4tDSGwIwhPDUVhaCADQUdfBBKcJ8HP3g7u5O2QymcQpiRo2FldEREREjVRBSQEiEiMQEheC2LRYsd3BxAF+bn6Y4jIFTbWbSheQqJFhcUVERETUyCQ+SMTa2LXYfHkzcotzAQCack2MdRgLP3c/dLfozlEqolrA4oqIiIioESgqK8Keq3sQEheCMylnxHbbZraY6zYX01ynwaQJr/tJVJtYXBERERE1YDeybmBd3Dpsit+ErCdZAAC5TI4R7UfAz80P/dr0g5pMTeKURK8HFldEREREDUypohQHrh9ASFwIjt0+JrZbGFhgdufZmNl5Jsz1zSVMSPR6YnFFRERE1ECk5KRgfdx6bLi0ARn5GQAAGWQYYjcEfu5+GNx2MORqcolTEr2+WFwRERER1WMKpQKHbx5GSFwIDt44CKWgBAC0aNICszrPwqzOs2Dd1FrakEQEgMUVERERUb2UnpeOjZc2Yt3FdUjJSRHb+9n0g5+7H95q9xY05ZoSJiSif2JxRURERFRPKAUlTiSfQEhsCPZf348yZRkAwEjHCNNdp2OO2xzYG9tLnJKInofFFREREZHEHhY+RGh8KNbGrcXNRzfF9u4W3eHn7ocxDmOgra4tYUIiqgwWV0REREQSEAQBv//1O0LiQrAraReKFcUAAH1NfUx1mYq5bnPRsUVHiVMSUVWwuCIiIiKqQzlFOdhyeQtCYkOQlJkktnc264x57vPg4+QDPU09CRMSUXWxuCIiIiKqA7FpsQiJDcH2xO0oLC0EAOio62Bix4nwc/eDu7m7xAmJ6FWxuCIiIiKqJQUlBdieuB0hsSGIS48T2x1NHOHn7ofJzpPRVLupdAGJqEaxuCIiIiKqYYkPEhESG4Itl7cgtzgXAKAp18RYh7Hwc/dDd4vukMlkEqckoprG4oqIiIioBhSVFWH31d0IiQ1B9F/RYntbo7aY6zYX01ynoblucwkTElFtY3FFRERE9ApuZN3A2ri12BS/CY+ePAIAyGVyjGw/En7ufuhr0xdqMjWJUxJRXWBxRURERFRFpYpS7L++HyGxITiefFxstzCwwBy3OZjRaQbM9c0lTEhEUmBxRURERFRJd7PvYv3F9fjx0o/IyM8AAMggwxC7IfBz98PgtoMhV5NLnJKIpMLiioiIiOgFFEoFDt08hJDYEBy8cRACBABAiyYtMKvzLMzuPBtWTa0kTklE9QGLKyIiIqIKpOel48dLP2L9xfVIyUkR2/vZ9IOfux9GtBsBDbmGhAmJqL5hcUVERESvBYVSgaiUKKTnpcNM3wxell7lpvApBSVOJJ9ASGwI9l/fjzJlGQDASMcI012nY47bHNgb20sRn4gaABZXRERE1OhFXotEwOEA3Mu9J7a1NmiN1YNWw7uDNx4WPkRofCjWxq3FzUc3xT7dLbrDz90PYxzGQFtdW4roRNSAsLgiIiKiRi3yWiTG7Bwjniv1TGpuKkbvHA0vSy/EpMagRFECADDQMsAU5ymY6zYXHVt0lCIyETVQLK6IiIio0VIoFQg4HFCusAIgtkWlRAEA3MzcMM99HnycfNBEs0md5iSixoHFFRERETVaUSlRKlMBnydkaAjmus+tg0RE1JjxcuFERETUaP2V81el+hloGdRyEiJ6HXDkioiIiBqde7n3sDZ2LYIuBFWqv5m+WS0nIqLXAYsrIiIiahQEQcCpO6cQdCEI+/7YB4WgAACoydSgFJQV7iODDK0NWsPL0qsuoxJRI8XiioiIiBq0vOI8bLm8BUEXgnA186rY3suqF/w9/CFAgM9uHwBQWdhCBhkAIHBQYLnrXRERVQeLKyIiImqQrmVeww8XfkBYQhjySvIAAE00mmCK8xT4d/GHk6mT2FddTb3C61wFDgqEdwfvOs9ORI2TTBCE8muTvuZyc3NhaGiInJwcGBjwBFciIqL6okxZhp+u/4SgC0E4nnxcbG9n3A5ve7wNXxdfGGobVrivQqlAVEoU0vPSYaZvBi9LL45YEdFLVaU24MgVERER1XsPCh5gw8UNCIkNwV+5T1cAVJOpYbj9cMzvMh/9bPpBJpO98BhyNTl6W/eug7RE9LpicUVERET1kiAIiEmNQdCFIOxM2okSRQkAoLluc8zuPBtz3ebCqqmVxCmJiP6HxRURERHVK09KnyAiMQJrLqzBxfSLYnuXVl0w32M+xjqOhba6toQJiYgqxuKKiIiI6oXkx8kIjg3Gj5d+xKMnjwAAWnIt+Dj5wN/DHx6tPCROSET0YpUurpo1a/bSuczPPHr0qNqBiIiI6PWhFJT49davWHN+DQ7eOCgulW5laIW3Pd7GjE4z0Fy3ucQpiYgqp9LFVWBgoPj/WVlZWLFiBQYOHIiuXbsCAM6ePYsjR47gv//9b42HJCIiosbl8ZPH2BS/CcGxwbj56KbYPtB2IPw9/DHEbghX8iOiBqdaS7GPHj0affr0wfz581Xa16xZg2PHjmHfvn01lU8SXIqdiIiodsRnxCPofBC2XdmGJ2VPAACGWoaY7jod8zzmwd7YXuKERESqqlIbVKu40tPTQ3x8PNq2bavSfvPmTbi6uiI/P7+qh6xXWFwRERHVnBJFCSKvRWLN+TWI/itabHdu4Qx/D39M6jgJTTSbSJiQiOj5av06V8bGxti/fz8WL16s0r5//34YGxtX55BERETUyKTmpmJt3Fqsi1uH+wX3AQDqauoY3WE0/D380cOyR6XP5yYiagiqVVx9/PHHmDVrFk6dOgVPT08AQExMDA4fPoz169fXaEAiIiJqOARBwG93f0PQhSDsvbYXCkEBADDTM8Nct7mY4zYHZvpmEqckIqod1Squpk2bhg4dOuC7775DZGQkAKBDhw44c+aMWGwRERHR6yO/JB9bErYg6EIQkjKTxPaeVj3h7+GPUe1HQUOuIWFCIqLaV61zrho7nnNFRERUOX88/AM/XPgBYQlhyC3OBQDoauhiivMU+Hv4o2OLjhInJCJ6NVWpDdSqeye3bt3CsmXLMHHiRDx48AAAcOjQISQlJb1kz/85ffo0hg8fDnNzc8hkspeuMhgZGYkBAwbAxMQEBgYG6Nq1K44cOVKuX1BQEKytraGtrQ1PT0+cP3++So+NiIiInq9MWYZ9f+zDgC0D0CGoA74//z1yi3Nhb2yPwIGBSF2UipBhISysiOi1U63i6rfffkPHjh0RExODPXv2iKsDJiQkYPny5ZU+TkFBAVxcXBAUFFSp/qdPn8aAAQNw8OBBxMXFoU+fPhg+fDguXbok9tmxYwcWLVqE5cuX4+LFi3BxccHAgQPFApCIiIiqJ7MgEyujVqLN6jYYtWMUjt0+BjWZGt5q9xZ+nfwrrvlfQ8AbAWiq3VTqqEREkqjWtMCuXbti7NixWLRoEfT19ZGQkIA2bdrg/Pnz8Pb2xr1796oeRCbD3r17MXLkyCrt5+joiPHjx+PDDz8EAHh6esLDwwNr1qwBACiVSlhYWOCdd97B+++/X6ljclogERHRU4Ig4HzqeQRdCMKOpB0oUZQAAIx1jDGr8yz4ufvBuqm1tCGJiGpRrS/FfuXKFYSHh5drNzU1xcOHD6tzyGpRKpXIy8uDkZERAKCkpARxcXFYunSp2EdNTQ39+/fH2bNnn3uc4uJiFBcXi7dzc3NrLzQREVED8KT0CXYk7UDQhSDEpsWK7R7mHvD38Md4p/HQVteWMCERUf1TreKqadOmSE9Ph42NjUr7pUuX0KpVqxoJVhlff/018vPzMW7cOADAw4cPoVAo0KJFC5V+LVq0wB9//PHc46xcuRIff/xxrWYlIiJqCJIfJyMkNgQbLm3AoyePAABaci2MdxoPfw9/dGnVReKERET1V7WKKx8fH/z73//Grl27IJPJoFQqER0djSVLlmDq1Kk1nbFC4eHh+Pjjj7F//36Ympq+0rGWLl2KRYsWibdzc3NhYWHxqhGJiIgaBKWgxNFbR7Hmwhr88ucvEPD0jAFLQ0u87f42Znaeiea6zSVOSURU/1WruPrss8/g7+8PCwsLKBQKODg4QKFQYOLEiVi2bFlNZywnIiICs2bNwq5du9C/f3+xvXnz5pDL5bh//75K//v376Nly5bPPZ6Wlha0tLRqLS8REVF9lF2UjdD4UARdCMLNRzfF9jdt34S/hz+G2g2FXE0uYUIiooalysWVIAjIyMjAd999hw8//BBXrlxBfn4+OnXqBDs7u9rIqGL79u2YMWMGIiIiMHToUJVtmpqacHNzw/Hjx8WFMZRKJY4fP4758+fXejYiIqKGICEjAUEXgrDtyjYUlhYCAAy0DDDddTre9ngb9sb2EickImqYqlVctW3bFklJSbCzs3ul6XP5+fm4efN/35QlJycjPj4eRkZGsLS0xNKlS5GamorNmzcDeDoV0NfXF6tXr4anpycyMjIAADo6OjA0NAQALFq0CL6+vnB3d0eXLl0QGBiIgoICTJ8+vdo5iYiIGroSRQkir0Ui6EIQzqScEds7mnaEv4c/JjlPgp6mnoQJiYgavioXV2pqarCzs0NWVtYrj1TFxsaiT58+4u1n5z35+voiNDQU6enpSElJEbevW7cOZWVl8Pf3h7+/v9j+rD8AjB8/HpmZmfjwww+RkZEBV1dXHD58uNwiF0RERK+DtLw0rI1di3UX1yEj/+mXkupq6vDu4A1/D394WXpBJpNJnJKIqHGo1nWufvrpJ3z55ZcIDg6Gk5NTbeSSFK9zRUREDZkgCIhKicKa82uw94+9KFOWAQBa6rXEXLe5mOM2B+b65hKnJCJqGKpSG1SruGrWrBkKCwtRVlYGTU1N6OjoqGx/9OhRVQ9Zr7C4IiKihii/JB9bL29F0IUgJD5IFNu9LL3g7+GPUR1GQVOuKWFCIqKGp9YvIhwYGFid3YiIiKgWXH94HT9c+AGhCaHILc4FAOhq6GJyx8nw7+IP5xbOEickIno9VKu48vX1rekcREREVAUKpQI///kzgi4E4ejto2K7nZEd3vZ4G9Ncp6GpdlPpAhIRvYaqVVwBwK1bt7Bp0ybcunULq1evhqmpKQ4dOgRLS0s4OjrWZEYiIiL6/zILMvHjpR8RHBuMlJyniz7JIMMw+2Hw9/DHANsBUJOpSZySiOj1VK3i6rfffsPgwYPRvXt3nD59Gp9++ilMTU2RkJCAH3/8Ebt3767pnERERK+186nnseb8GuxI2oESRQkAwFjHGDM7zYSfux9smtlInJCIiKpVXL3//vtYsWIFFi1aBH19fbG9b9++WLNmTY2FIyIiep0VlRVhR+IOrLmwBrFpsWK7u7k75nvMxzjHcdDR0HnBEYiIqC5Vq7i6cuUKwsPDy7Wbmpri4cOHrxyKiIjodXYn+w5CYkOw4eIGZD3JAgBoyjUx3nE85neZjy6tukickIiIKlKt4qpp06ZIT0+HjY3qFIRLly6hVatWNRKMiIjodaIUlDh2+xjWnF+Dn//8GQKeXinF0tASfm5+mNV5FkyamEickoiIXqRaxZWPjw/+/e9/Y9euXZDJZFAqlYiOjsaSJUswderUms5IRETUaGUXZSM0PhQ/XPgBNx7dENv7t+mP+R7zMcx+GORqcgkTEhFRZVWruPrss8/g7+8PCwsLKBQKODg4QKFQYOLEiVi2bFlNZyQiImp0Lt+/jKDzQdh6ZSsKSwsBAAZaBpjmMg1ve7yNds3bSZyQiIiqSiYIglDdnVNSUpCYmIj8/Hx06tQJdnZ2NZlNMlW5CjMREVFllSpKsfePvVhzfg2iUqLEdkcTR8zvMh+TnSdDT1NPwoRERPRPVakNqjVydebMGfTo0QOWlpawtLSsVkgiIqLXRVpeGtbFrcO6uHVIz08HAMhlcnh38Ia/hz96WvWETCaTOCUREb2qahVXffv2RatWrTBhwgRMnjwZDg4ONZ2LiIioQRMEAVEpUQi6EITIa5EoU5YBAFrqtcScznMwx20OWhlwESgiosakWsVVWloaIiIisH37dnz++edwdnbGpEmTMGHCBLRu3bqmMxIRETUY+SX52HZ5G4IuBOHKgytiew/LHvD38Id3B29oyjUlTEhERLXllc65AoDk5GSEh4dj+/bt+OOPP9CzZ0+cOHGipvJJgudcERFRVf2Z9Sd+uPADQuNDkVOcAwDQUdfBZOfJ8Pfwh0tLF4kTEhFRdVSlNnjl4goAFAoFDh06hP/+97+4fPkyFArFqx5SUiyuiIioMhRKBX658QuCLgTh11u/iu1tjdribfe3Mc11GprpNJMwIRERvapaX9DimejoaGzbtg27d+9GUVERRowYgZUrV77KIYmIiOoFhVKBqJQopOelw0zfDF6WXuL1ph4WPsSPF39EcGww7ubcBQDIIMNQ+6Hw9/DHm7ZvQk2mJmV8IiKSQLWKq6VLlyIiIgJpaWkYMGAAVq9ejREjRkBXV7em8xEREdW5yGuRCDgcgHu598S21gat8Y7HO7j68CoiEiNQrCgGABjpGGFmp5mY5z4PNs1spIpMRET1QLWmBXbv3h2TJk3CuHHj0Lx589rIJSlOCyQien1FXovEmJ1jIODF/zx2NuuM+R7z4ePkAx0NnTpKR0REda3WpwVGR0dXKxgREVF9plAqEHA44IWFla6GLn6d/Cu6WXTjtamIiEhFtc+5unXrFgIDA3Ht2jUAgIODAwICAmBra1tj4YiIiOrSqTunVKYCVqSwtBClylIWVkREVE61zrY9cuQIHBwccP78eTg7O8PZ2RkxMTFwdHTE0aNHazojERFRrbqffx+fRX0Gn90+leqfnpdey4mIiKghqtbI1fvvv4+FCxfi888/L9f+73//GwMGDKiRcERERLVFEAScvnsawbHBiLwWiVJlaaX3NdM3q8VkRETUUFVr5OratWuYOXNmufYZM2bg6tWrrxyKiIiotuQU5eD7mO/hFOyE3mG9sSNpB0qVpfBs5YmNb21EK/1WkKHiKX8yyGBhYAEvS6+6DU1ERA1CtUauTExMEB8fDzs7O5X2+Ph4mJqa1kgwIiKimnQx/SKCLwQjPDEchaWFAJ4uTjGp4yTMc5+HTmadAACG2oYYs3MMZJCpLGzxrOAKHBQoXu+KiIjo76pVXM2ePRtz5szB7du30a1bNwBPVxD84osvsGjRohoNSEREVF1PSp9gZ9JOBMcGIyY1Rmx3MHHAPPd5mOI8BYbahir7eHfwxu5xuyu8zlXgoEB4d/Cus/xERNSwVOs6V4IgIDAwEKtWrUJaWhoAwNzcHO+99x4WLFjQ4FdQ4nWuiIgathtZNxASG4JN8ZvwuOgxAEBDTQOjHUZjnvs8eFl6vfTfKoVSgaiUKKTnpcNM3wxell4csSIieg1VpTaoVnH1d3l5eQAAfX39VzlMvcLiioio4SlTluHA9QMIjg3GsdvHxHYrQyvMdZuLGZ1moIVeCwkTEhFRQ1TrFxFOTk5GWVkZ7OzsVIqqGzduQENDA9bW1tU5LBERUZWl5qZi/cX1WH9xPdLyns6mkEGGIXZDMM99Hga1HcQRJyIiqhPVKq6mTZuGGTNmlFvQIiYmBhs2bMCpU6dqIhsREVGFlIISx28fR3BsMA5cPwCFoAAAmDYxxcxOMzHHbQ6sm1pLG5KIiF471SquLl26hO7du5drf+ONNzB//vxXDkVERFSRrMIshMaHIiQuBDcf3RTbe1r1xDz3efDu4A1NuaaECYmI6HVWreJKJpOJ51r9XU5ODhQKxSuHIiIiekYQBMSkxiA4Nhg7EnegWFEMADDQMsBU56nwc/eDo6mjxCmJiIiqWVz17NkTK1euxPbt2yGXP53HrlAosHLlSvTo0aNGAxIR0espvyQf4VfCERwbjPiMeLG9U8tOmOc+DxM6ToCepp50AYmIiP6hWsXVF198gZ49e6Jdu3bw8np6lfqoqCjk5ubixIkTNRqQiIheL0kPkhAcG4wtl7cgtzgXAKCtro3xjuMxz30eurTq0uAv+UFERI1TtYorBwcHXL58GWvWrEFCQgJ0dHQwdepUzJ8/H0ZGRjWdkYiIGrnismJEXotEcGwwolKixHY7Izv4ufthmus0GOnw3xciIqrfXvk6V40Rr3NFRFQ37mTfwdrYtfjx0o/ILMwEAMhlcrzV7i3Mc5+Hfm36QU2mJnFKIiJ6ndX6da6Ap9MA165di9u3b2PXrl1o1aoVtmzZAhsbG553RUREz6VQKnDo5iEExwbj0I1DEPD0Oz5zfXPM7jwbszvPRiuDVhKnJCIiqrpqFVd79uzBlClTMGnSJFy8eBHFxU9XbsrJycFnn32GgwcP1mhIIiJq+O7n38ePl37Eurh1uJtzV2zv36Y/5rnPw3D74dCQa0iYkIiI6NVUq7hasWIFQkJCMHXqVERERIjt3bt3x4oVK2osHBERNWyCIOD03dMIjg1G5LVIlCpLAQDNtJthuut0+Ln7wc7Y7iVHISIiahiqVVxdv34dPXv2LNduaGiI7OzsV81EREQNXE5RDrZc3oLg2GBczbwqtnu28sQ893kY5zgOOho6EiYkIiKqedUqrlq2bImbN2/C2tpapf3MmTNo06ZNTeQiIqIG6FL6JQTHBmPblW0oLC0EAOhq6GJSx0mY5z4Pncw6SZyQiIio9lSruJo9ezYCAgKwceNGyGQypKWl4ezZs1i8eDE+/PDDms5IRET12JPSJ9iZtBPBscGISY0R2x1MHDDPfR6mOE+BobahhAmJiIjqRrWKq/fffx9KpRL9+vVDYWEhevbsCS0tLbz33nuYNWtWTWckIqJ66EbWDYTEhmBT/CY8LnoMANBQ08Boh9GY5z4PXpZevNgvERG9Vl7pOlclJSW4efMm8vPz4eDggLVr1+Krr75CRkZGTWasc7zOFRFRxcqUZThw/QCCY4Nx7PYxsd3K0Apz3eZiRqcZaKHXQsKERERENavWrnNVXFyMjz76CEePHhVHqkaOHIlNmzZh1KhRkMvlWLhw4SuFJyKi+ic1NxXrL67H+ovrkZaXBgCQQYYhdkMwz30eBrUdBLmaXOKURERE0qpScfXhhx9i7dq16N+/P37//XeMHTsW06dPx7lz57Bq1SqMHTsWcjn/cSUiagyUghLHbx9HcGwwDlw/AIWgAACYNjHFzE4zMcdtDqybWksbkoiIqB6pUnG1a9cubN68GW+99RYSExPh7OyMsrIyJCQkcF49EVEjkVWYhdD4UKyNW4sbj26I7T2temKe+zx4d/CGplxTwoRERET1U5WKq3v37sHNzQ0A4OTkBC0tLSxcuJCFFRFRAycIAmJSYxAcG4wdiTtQrCgGABhoGWCq81T4ufvB0dRR4pRERET1m1pVOisUCmhq/u/bSnV1dejp6VX7zk+fPo3hw4fD3NwcMpkM+/bte2H/9PR0TJw4Efb29lBTU8O7775brk9oaChkMpnKj7a2drUzEhE1ZgUlBVgftx5u69zQ9ceu2JywGcWKYri2dMW6YeuQuigV3w/5noUVERFRJVRp5EoQBEybNg1aWloAgKKiIvj5+aFJkyYq/SIjIyt1vIKCAri4uGDGjBnw9vZ+af/i4mKYmJhg2bJl+Pbbb5/bz8DAANevXxdvc2SNiEhV0oMkhMSGYPPlzcgtzgUAaMm1MN5pPOa5z4NnK0/+7SQiIqqiKhVXvr6+KrcnT578Snc+ePBgDB48uNL9ra2tsXr1agDAxo0bn9tPJpOhZcuWr5SNiKixKVGUIPJaJIJjg3H67mmxva1RW/i5+WGa6zQY6xpLmJCIiKhhq1JxtWnTptrKUaPy8/NhZWUFpVKJzp0747PPPoOj4/OntBQXF6O4uFi8nZubWxcxiYjqxJ3sO1gXtw4/XvoRDwoeAADkMjneavcW5rnPQ782/aAmq9IscSIiIqpAlYqrhqBdu3bYuHEjnJ2dkZOTg6+//hrdunVDUlISWrduXeE+K1euxMcff1zHSYmIao9CqcDhm4cRHBuMgzcOQsDT68Wb65tjdufZmN15NloZtJI4JRERUePS6Iqrrl27omvXruLtbt26oUOHDli7di0++eSTCvdZunQpFi1aJN7Ozc2FhYVFrWclIqppDwoe4MeLP2Jt3Frczbkrtvdv0x/z3OdhuP1waMg1JExIRETUeDW64uqfNDQ00KlTJ9y8efO5fbS0tMRFOoiIGhpBEBCVEoXg2GDsuboHpcpSAEAz7WaY7jodc93nwt7YXuKUREREjV+jL64UCgWuXLmCIUOGSB2FiKhG5RTlYMvlLQiJDUFSZpLY7tnKE/Pc52Gc4zjoaOhImJCIiOj1ImlxlZ+frzKilJycjPj4eBgZGcHS0hJLly5FamoqNm/eLPaJj48X983MzER8fDw0NTXh4OAAAPi///s/vPHGG2jbti2ys7Px1Vdf4e7du5g1a1adPjYiotpyKf0SgmODEX4lHAWlBQAAXQ1dTOo4CfPc56GTWSeJExIREb2eJC2uYmNj0adPH/H2s/OefH19ERoaivT0dKSkpKjs06nT/z40xMXFITw8HFZWVrhz5w4A4PHjx5g9ezYyMjLQrFkzuLm54ffffxeLLyKihuhJ6RPsTNqJ4NhgxKTGiO0OJg6Y5z4PU5ynwFDbUMKEREREJBMEQZA6RH2Tm5sLQ0ND5OTkwMDAQOo4RPQau5F1AyGxIQhNCMWjJ48AABpqGhjtMBrz3OfBy9KLF/slIiKqRVWpDRr9OVdERA1NmbIMB64fQHBsMI7dPia2WxlaYa7bXMzoNAMt9FpImJCIiIgqwuKKiKieSM1NxfqL67H+4nqk5aUBAGSQYbDdYLzt/jYGtR0EuZpc4pRERET0PCyuiIgkpBSUOH77OIJjg3Hg+gEoBAUAwETXBDM7zcQctzmwaWYjcUoiIiKqDBZXREQSyCrMQmh8KNbGrcWNRzfEdi9LL8xznwfvDt7QUuf194iIiBoSFldERHVEEATEpMYgODYYOxJ3oFhRDADQ19THVJep8HP3g5Opk8QpiYiIqLpYXBERvSKFUoGolCik56XDTN8MXpZeKudGFZQUIPxKOIJjg3Ep45LY7trSFfPc52Fix4nQ09STIjoRERHVIBZXRESvIPJaJAIOB+Be7j2xrbVBa6wetBrtm7dH8IVgbL68GbnFuQAALbkWxjuNxzz3efBs5cll1ImIiBoRFldERNUUeS0SY3aOgQDVywXey72H0TtHq7S1NWoLPzc/THOdBmNd47qMSURERHWExRURUTUolAoEHA4oV1j908h2I/G2x9vo16Yf1GRqdZSOiIiIpMDiioioGqJSolSmAj5PwBsB6G3du/YDERERkeRYXBERVcH9/PvYfXU3vj//faX6p+el13IiIiIiqi9YXBERvcSjJ4+w99peRCRF4ETyCSgFZaX3NdM3q8VkREREVJ+wuCIiqkBecR72X9+PiMQI/HrrV5QqS8VtXVp1wTiHcVh1dhUy8jMqPO9KBhlaG7SGl6VXXcYmIiIiCbG4IiL6/56UPsEvN35BRGIEfrnxC4rKisRtLi1c4OPkg3GO49CmWRsAgE0zG4zZOQYyyFQKLBmeLq8eOChQ5XpXRERE1LixuCKi11qJogS/3voVEYkR2H99P/JL8sVt9sb2mOA0AeMdx6ODSYdy+3p38MbucbsrvM5V4KBAeHfwrpPHQERERPWDTBCEF68j/BrKzc2FoaEhcnJyYGBgIHUcIqphZcoynLpzChGJEdhzbQ+yi7LFbVaGVvBx8oGPkw9cWrhU6iK/CqUCUSlRSM9Lh5m+GbwsvThiRURE1EhUpTbgyBURvRaUghK///U7IhIjsOvqLjwoeCBuM9MzwzjHcfBx8oFnK89KFVR/J1eTc7l1IiIiYnFFRI2XIAiIS49DRGIEdiTtUJm6Z6xjjDEOY+Dj5MORJiIiIqoRLK6IqFERBAGJDxIRkRiBiKQI3H58W9xmoGUA7w7e8HH0QV+bvtCQa0iYlIiIiBobFldE1Cj8mfUndiTuQERSBK5mXhXbdTV08Va7t+Dj6IOBbQdCW11bwpRERETUmLG4IqIG6272XexM2omIpAhcTL8otmvKNTHEbgh8HH0wzH4Ymmg2kTAlERERvS5YXBFRg5KRn4FdSbsQkRSB3//6XWyXy+QYYDsAPo4+GNl+JAy1DSVMSURERK8jFldEVO9lFWZhz7U9iEiMwG93f4NSUAJ4erHeXta94OPog9EOo9Fct7nESYmIiOh1xuKKiOql3OJc7PtjHyISI3D09lGUKcvEbV1bd8V4x/EY6zgW5vrmEqYkIiIi+h8WV0RUbxSWFuLnP39GRGIEDt44iGJFsbitU8tO8HHywTjHcbBuai1dSCIiIqLnYHFFRJIqLivGkVtHEJEYgQPXD6CgtEDc1r55e0xwmoDxjuPRrnk7CVMSERERvRyLKyKqc2XKMpxIPoGIxAhEXotETnGOuM2mqQ18nHzg4+SDjqYdIZPJJExKREREVHksroioTigFJaLuRiEiMQK7r+3Gw8KH4jZzfXOMdxwPHycfeJh7sKAiIiKiBonFFRHVGkEQcD71PCISI7Dz6k6k5aWJ20x0TTDGYQx8nHzQw7IH1GRqEiYlIiIienUsroioRgmCgMv3LyMiMQIRSRG4k31H3GaoZYjRHUbDx8kHfWz6QF2Nf4KIiIio8eAnGyKqEdcfXhcLqj8e/iG2N9FoghHtR8DH0Qdv2r4JLXUtCVMSERER1R4WV0RUbXey72BH4g5EJEUgPiNebNeSa2Go/VD4OPpgqP1Q6GroSheSiIiIqI6wuCKiKknLS8POpJ3YkbQD5+6dE9vV1dTxpu2b8HH0wYj2I2CgZSBhSiIiIqK6x+KKiF4qsyATe67tQURiBE7fPQ0BAgBATaaG3ta94ePoA+8O3jDWNZY4KREREZF0WFwRUYWyi7Kx7499iEiMwLHbx6AQFOK27hbd4ePkgzEOY9BSr6WEKYmIiIjqDxZXRCQqKCnAT3/+hIjECBy6eQglihJxm5uZG3ycfDDOcRwsDS0lTElERERUP7G4InrNFZUV4fDNw4hIjMBPf/6EwtJCcZuDiQMmOE3AeMfxsDO2kzAlERERUf3H4oroNVSqKMWx28ewI2kH9v6xF7nFueI222a28HHygY+TD5xMnSRMSURERNSwsLgiek0olAqcvnsaEYkR2HNtD7KeZInbWhu0xnjH8fBx8oGbmRtkMpmESYmIiIgaJhZXRI2YIAg4d+8cIhIjsPPqTmTkZ4jbTJuYYpzDOPg4+aCrRVeoydQkTEpERETU8LG4ImpkBEFAfEY8IhIjsCNpB+7m3BW3NdNuhtEdRsPHyQe9rHtBXY1/AoiIiIhqCj9ZETUSVzOvYkfiDkQkReDPrD/Fdj1NPYxsPxI+jj4YYDsAmnJNCVMSERERNV4srogasNuPb4sF1eX7l8V2bXVtDLMfBh9HHwyxGwIdDR0JUxIRERG9HlhcETUw93LvYWfSTkQkRuBC2gWxXUNNAwPbDoSPow/eavcW9LX0JUxJRERE9PphcUXUADwoeIDdV3cjIjECUSlRYruaTA39bPrBx8kHo9qPQjOdZhKmJCIiInq9sbgiqmMKpQJRKVFIz0uHmb4ZvCy9IFeTl+v3+Mlj7P1jLyISI3A8+TiUglLc5mXpBR8nH4zuMBot9FrUZXwiIiIieg4WV0R1KPJaJAIOB+Be7j2xrbVBa6wetBreHbyRX5KPA9cPICIxAodvHkapslTs52HuAR8nH4x1GAsLQwsp4hMRERHRC7C4IqojkdciMWbnGAgQVNpTc1MxeudodG3dFfEZ8XhS9kTc1tG0I3ycfDDecTxsjWzrOjIRERERVQGLK6I6oFAqEHA4oFxhBUBsO3vvLADAzshOLKgcTR3rNCcRERERVZ+alHd++vRpDB8+HObm5pDJZNi3b98L+6enp2PixImwt7eHmpoa3n333Qr77dq1C+3bt4e2tjY6duyIgwcP1nx4oiqISolSmQr4POuGrcP1+dfxf33+j4UVERERUQMjaXFVUFAAFxcXBAUFVap/cXExTExMsGzZMri4uFTY5/fff8eECRMwc+ZMXLp0CSNHjsTIkSORmJhYk9GJKi01NxVrY9dWqq+eph5kMlktJyIiIiKi2iATBKH8PCUJyGQy7N27FyNHjqxU/969e8PV1RWBgYEq7ePHj0dBQQF+/vlnse2NN96Aq6srQkJCKjxWcXExiouLxdu5ubmwsLBATk4ODAwMqvxYiJ6UPsG+P/YhLCEMR28fVVnp70VO+p5Eb+vetRuOiIiIiCotNzcXhoaGlaoNJB25qg1nz55F//79VdoGDhyIs2fPPneflStXwtDQUPyxsOBKbFR1giAgOiUasw/MRstVLTExciKO3DoCpaBEd4vuaKbdDDJUPColgwwWBhbwsvSq49REREREVFMa3YIWGRkZaNFC9bo/LVq0QEZGxnP3Wbp0KRYtWiTefjZyRVQZd7PvYnPCZmy+vBk3H90U260MreDr4oupLlNha2QrrhYog0xlYYtnBVfgoMAKr3dFRERERA1DoyuuqkNLSwtaWlpSx6AGJL8kH3uu7kFoQihO3TkltjfRaIKxjmPh6+KLnlY9oSb73+Cwdwdv7B63u8LrXAUOCoR3B++6fAhEREREVMMaXXHVsmVL3L9/X6Xt/v37aNmypUSJqLFQCkqcunMKYQlh2HN1DwpKCwA8HXnqY9MHvi6+8O7gDT1Nvecew7uDN0a0G4GolCik56XDTN8MXpZeHLEiIiIiagQaXXHVtWtXHD9+XGWZ9qNHj6Jr167ShaIG7UbWDXHaX0pOithuZ2QHXxdfTHGZAktDy0ofT64m56IVRERERI2QpMVVfn4+bt783zkqycnJiI+Ph5GRESwtLbF06VKkpqZi8+bNYp/4+Hhx38zMTMTHx0NTUxMODg4AgICAAPTq1QurVq3C0KFDERERgdjYWKxbt65OHxs1bNlF2diZtBNhCWH4/a/fxXZDLUOMdxwPX1dfdG3dlcumExEREZFI0qXYT506hT59+pRr9/X1RWhoKKZNm4Y7d+7g1KlT4raKPsxaWVnhzp074u1du3Zh2bJluHPnDuzs7PDll19iyJAhlc5VleUWqfFQKBU4evsowhLCsO+PfSgqKwIAqMnUMNB2IHxdfPFWu7ego6EjcVIiIiIiqitVqQ3qzXWu6hMWV6+XpAdJCEsIw9bLW5Geny62O5o4wtfFF5OcJ8Fc31zChEREREQklarUBo3unCuiysgqzML2xO0ISwhDbFqs2G6sY4yJHSfC18UXnc06c9ofEREREVUaiyt6bZQqSnHwxkGEJYTh5z9/RqmyFACgrqaOoXZD4evii6H2Q6Ep15Q4KRERERE1RCyuqFETBAHxGfEISwhD+JVwZBZmits6teyEaa7TMMFpAkyamEiYkoiIiIgaAxZX1Cjdz7+PbVe2ITQ+FFceXBHbWzRpgcnOk+Hr4ouOLTpKmJCIiIiIGhsWV9RoFJUV4afrPyEsIQyHbx6GQlAAADTlmhjRbgR8XXwxsO1AqKvxbU9ERERENY+fMqlBEwQB51PPIywhDNsTtyO7KFvc9kbrN+Dr4ovxjuPRTKeZdCGJiIiI6LXA4ooapHu597AlYQvCEsJwPeu62N7aoDWmOE+Br4sv2jVvJ2FCIiIiInrdsLiiBqOwtBB7r+1FWEIYjt0+BgFPL9Gmo66D0Q6j4eviiz7WfSBXk0uclIiIiIheRyyuqF4TBAFRKVEIiw/Drqu7kFeSJ27radUTvi6+GOMwBgZavNgzEREREUmLxRXVS8mPk7E5YTM2X96M249vi+02TW0w1WUqprpMRZtmbSRMSERERESkisUV1Rt5xXnYdXUXwhLCcPruabFdX1MfYx3GwtfVFz0se0BNpiZhSiIiIiKiirG4IkkplAqcvHMSYQlhiLwWicLSQgCADDL0a9MP01ymYVSHUdDV0JU4KRERERHRi7G4Ikn8mfUnwuLDsPnyZtzLvSe2tzNuB18XX0x2ngwLQwsJExIRERERVQ2LK6ozj588xo6kHQhLCMO5e+fE9qbaTeHj6ANfV194tvKETCaTMCURERERUfWwuKJaVaYsw6+3fkVYQhj2/7EfxYpiAIBcJsegtoPg6+KL4e2GQ1tdW+KkRERERESvhsUV1Yor968gLCEM265sQ0Z+htje0bQjfF18Mcl5ElrqtZQwIRERERFRzWJxRTUmsyAT2xO3IywhDBfTL4rtzXWbY1LHSfB18YVrS1dO+yMiIiKiRonFFb2SEkUJDt44iND4UPxy4xeUKcsAABpqGhhmPwy+Lr4YbDcYmnJNiZMSEREREdUuFldUZYIg4GL6RYQlhCH8SjiynmSJ29zM3ODr4osJHSeguW5zCVMSEREREdUtFldUael56dh2ZRtC40ORlJkktpvpmWGy82T4uvjC0dRRwoRERERERNJhcUUvVFRWhP1/7EdYQhiO3DoCpaAEAGjJtTCy/UhMc52G/m36Q12NbyUiIiIier3xEzGVIwgCzt07h7CEMEQkRiCnOEfc1s2iG3xdfDHOcRyaajeVLiQRERERUT3D4opEKTkp2JKwBZsvb8afWX+K7RYGFpjqMhVTXabC3thewoRERERERPUXi6vXXEFJASKvRSIsIQwnkk9AgAAA0NXQxRiHMfB18UVv695Qk6lJnJSIiIiIqH5jcfUaUgpKRN2NQmhCKHZf3Y38knxxW2/r3vB18cXoDqOhr6UvYUoiIiIiooaFxdVr5NajW9icsBmbL2/Gnew7YrttM1tx2p91U2vJ8hERERERNWQsrhq53OJc7ErahdCEUJxJOSO2G2gZYJzDOPi6+qK7RXfIZDIJUxIRERERNXwsrhohhVKB48nHEZYQhr3X9uJJ2RMAgAwyDLAdAF8XX4xsPxK6GroSJyUiIiIiajxYXDUifzz8A2HxYdhyeQtS81LF9g7NO8DXxReTnSejlUErCRMSERERETVeLK7qMYVSgaiUKKTnpcNM3wxell6Qq8lV+jx68ggRiREISwjD+dTzYnsz7WaY4DQB01ynwd3cndP+iIiIiIhqGYureiryWiQCDgfgXu49sa21QWusHrQaw+2H48itIwhLCMOB6wdQoigBAMhlcgyxGwJfF18Msx8GLXUtqeITEREREb12WFzVQ5HXIjFm5xjxmlPPpOamYvTO0TDQMkBuca7Y7tLCBb4uvpjYcSJa6LWo67hERERERAQWV/WOQqlAwOGAcoUVALEttzgXzXWaY7LzZPi6+sK1pWsdpyQiIiIion9icVXPRKVEqUwFfJ7to7ejv23/OkhERERERESVoSZ1AFKVnpdeqX6ZhZm1nISIiIiIiKqCxVU9Y6ZvVqP9iIiIiIiobrC4qme8LL3Q2qA1ZKh46XQZZLAwsICXpVcdJyMiIiIiohdhcVXPyNXkWD1oNQCUK7Ce3Q4cFFjueldERERERCQtFlf1kHcHb+wetxutDFqptLc2aI3d43bDu4O3RMmIiIiIiOh5ZIIglF/z+zWXm5sLQ0ND5OTkwMDAQLIcCqUCUSlRSM9Lh5m+GbwsvThiRURERERUh6pSG3Ap9npMriZHb+veUscgIiIiIqJK4LRAIiIiIiKiGsDiioiIiIiIqAawuCIiIiIiIqoBLK6IiIiIiIhqAIsrIiIiIiKiGsDiioiIiIiIqAZwKfYKPLv0V25ursRJiIiIiIhISs9qgspcHpjFVQXy8vIAABYWFhInISIiIiKi+iAvLw+GhoYv7CMTKlOCvWaUSiXS0tKgr68PmUwmaZbc3FxYWFjgr7/+eukVoanh4Ova+PA1bZz4ujY+fE0bJ76ujU99ek0FQUBeXh7Mzc2hpvbis6o4clUBNTU1tG7dWuoYKgwMDCR/Y1HN4+va+PA1bZz4ujY+fE0bJ76ujU99eU1fNmL1DBe0ICIiIiIiqgEsroiIiIiIiGoAi6t6TktLC8uXL4eWlpbUUagG8XVtfPiaNk58XRsfvqaNE1/XxqehvqZc0IKIiIiIiKgGcOSKiIiIiIioBrC4IiIiIiIiqgEsroiIiIiIiGoAiysiIiIiIqIawOKqngsKCoK1tTW0tbXh6emJ8+fPSx2JXsHp06cxfPhwmJubQyaTYd++fVJHole0cuVKeHh4QF9fH6amphg5ciSuX78udSx6BcHBwXB2dhYvXNm1a1ccOnRI6lhUgz7//HPIZDK8++67UkehV/DRRx9BJpOp/LRv317qWPSKUlNTMXnyZBgbG0NHRwcdO3ZEbGys1LEqjcVVPbZjxw4sWrQIy5cvx8WLF+Hi4oKBAwfiwYMHUkejaiooKICLiwuCgoKkjkI15LfffoO/vz/OnTuHo0ePorS0FG+++SYKCgqkjkbV1Lp1a3z++eeIi4tDbGws+vbtixEjRiApKUnqaFQDLly4gLVr18LZ2VnqKFQDHB0dkZ6eLv6cOXNG6kj0Ch4/fozu3btDQ0MDhw4dwtWrV7Fq1So0a9ZM6miVxqXY6zFPT094eHhgzZo1AAClUgkLCwu88847eP/99yVOR69KJpNh7969GDlypNRRqAZlZmbC1NQUv/32G3r27Cl1HKohRkZG+OqrrzBz5kypo9AryM/PR+fOnfHDDz9gxYoVcHV1RWBgoNSxqJo++ugj7Nu3D/Hx8VJHoRry/vvvIzo6GlFRUVJHqTaOXNVTJSUliIuLQ//+/cU2NTU19O/fH2fPnpUwGRG9SE5ODoCnH8ap4VMoFIiIiEBBQQG6du0qdRx6Rf7+/hg6dKjKv63UsN24cQPm5uZo06YNJk2ahJSUFKkj0Ss4cOAA3N3dMXbsWJiamqJTp05Yv3691LGqhMVVPfXw4UMoFAq0aNFCpb1FixbIyMiQKBURvYhSqcS7776L7t27w8nJSeo49AquXLkCPT09aGlpwc/PD3v37oWDg4PUsegVRERE4OLFi1i5cqXUUaiGeHp6IjQ0FIcPH0ZwcDCSk5Ph5eWFvLw8qaNRNd2+fRvBwcGws7PDkSNHMG/ePCxYsABhYWFSR6s0dakDEBE1Fv7+/khMTOSc/0agXbt2iI+PR05ODnbv3g1fX1/89ttvLLAaqL/++gsBAQE4evQotLW1pY5DNWTw4MHi/zs7O8PT0xNWVlbYuXMnp/A2UEqlEu7u7vjss88AAJ06dUJiYiJCQkLg6+srcbrK4chVPdW8eXPI5XLcv39fpf3+/fto2bKlRKmI6Hnmz5+Pn3/+GSdPnkTr1q2ljkOvSFNTE23btoWbmxtWrlwJFxcXrF69WupYVE1xcXF48OABOnfuDHV1dairq+O3337Dd999B3V1dSgUCqkjUg1o2rQp7O3tcfPmTamjUDWZmZmV+xKrQ4cODWq6J4urekpTUxNubm44fvy42KZUKnH8+HHO+yeqRwRBwPz587F3716cOHECNjY2UkeiWqBUKlFcXCx1DKqmfv364cqVK4iPjxd/3N3dMWnSJMTHx0Mul0sdkWpAfn4+bt26BTMzM6mjUDV179693OVM/vzzT1hZWUmUqOo4LbAeW7RoEXx9feHu7o4uXbogMDAQBQUFmD59utTRqJry8/NVvlFLTk5GfHw8jIyMYGlpKWEyqi5/f3+Eh4dj//790NfXF8+JNDQ0hI6OjsTpqDqWLl2KwYMHw9LSEnl5eQgPD8epU6dw5MgRqaNRNenr65c7D7JJkyYwNjbm+ZEN2JIlSzB8+HBYWVkhLS0Ny5cvh1wux4QJE6SORtW0cOFCdOvWDZ999hnGjRuH8+fPY926dVi3bp3U0SqNxVU9Nn78eGRmZuLDDz9ERkYGXF1dcfjw4XKLXFDDERsbiz59+oi3Fy1aBADw9fVFaGioRKnoVQQHBwMAevfurdK+adMmTJs2re4D0St78OABpk6divT0dBgaGsLZ2RlHjhzBgAEDpI5GRH9z7949TJgwAVlZWTAxMUGPHj1w7tw5mJiYSB2NqsnDwwN79+7F0qVL8X//93+wsbFBYGAgJk2aJHW0SuN1roiIiIiIiGoAz7kiIiIiIiKqASyuiIiIiIiIagCLKyIiIiIiohrA4oqIiIiIiKgGsLgiIiIiIiKqASyuiIiIiIiIagCLKyIiIiIiohrA4oqIiIiIiKgGsLgiIiKqQO/evfHuu+9WaZ9p06Zh5MiRtZKHiIjqPxZXRERUr4WEhEBfXx9lZWViW35+PjQ0NNC7d2+VvqdOnYJMJsOtW7fqOCURERGLKyIiquf69OmD/Px8xMbGim1RUVFo2bIlYmJiUFRUJLafPHkSlpaWsLW1lSIqERG95lhcERFRvdauXTuYmZnh1KlTYtupU6cwYsQI2NjY4Ny5cyrtffr0gVKpxMqVK2FjYwMdHR24uLhg9+7dKsdNTEzE4MGDoaenhxYtWmDKlCl4+PDhc3P88ssvMDQ0xLZt2wAACoUCixYtQtOmTWFsbIx//etfEARBZZ/Dhw+jR48eYp9hw4apjKr17dsX8+fPV9knMzMTmpqaOH78eJWfKyIikhaLKyIiqvf69OmDkydPirdPnjyJ3r17o1evXmL7kydPEBMTgz59+mDlypXYvHkzQkJCkJSUhIULF2Ly5Mn47bffAADZ2dno27cvOnXqhNjYWBw+fBj379/HuHHjKrz/8PBwTJgwAdu2bcOkSZMAAKtWrUJoaCg2btyIM2fO4NGjR9i7d6/KfgUFBVi0aBFiY2Nx/PhxqKmpYdSoUVAqlQCAWbNmITw8HMXFxeI+W7duRatWrdC3b9+aewKJiKhOyIR/fs1GRERUz2zYsAHvvvsusrOz8eTJExgZGSEtLQ3Hjh1DSEgIfvvtN5w4cQL9+vXDnTt34ODggGPHjqFr167iMWbNmoXCwkKEh4djxYoViIqKwpEjR8Tt9+7dg4WFBa5fvw57e3v07t0brq6usLOzwwcffID9+/ejV69eYn9zc3MsXLgQ7733HgCgrKwMNjY2cHNzw759+yp8HA8fPoSJiQmuXLkCJycnFBUVwdzcHCEhIWJh5+LiAm9vbyxfvrwWnkkiIqpN6lIHICIiepnevXujoKAAFy5cwOPHj2Fvbw8TExP06tUL06dPR1FREU6dOoU2bdogPz8fhYWFGDBggMoxSkpK0KlTJwBAQkICTp48CT09vXL3devWLdjb2wMAdu/ejQcPHiA6OhoeHh5in5ycHKSnp8PT01NsU1dXh7u7u8rUwBs3buDDDz9ETEwMHj58KI5YpaSkwMnJCdra2pgyZQo2btyIcePG4eLFi0hMTMSBAwdq7skjIqI6w+KKiIjqvbZt26J169Y4efIkHj9+LI4gmZubw8LCAr///jtOnjyJvn37Ij8/H8DTc6RatWqlchwtLS0AT1cbHD58OL744oty92VmZib+f6dOnXDx4kVs3LgR7u7ukMlkVco9fPhwWFlZYf369TA3N4dSqYSTkxNKSkrEPrNmzYKrqyvu3buHTZs2oW/fvrCysqrS/RARUf3A4oqIiBqEPn364NSpU3j8+LE4FQ8AevbsiUOHDuH8+fOYN28eHBwcoKWlhZSUFJVpfH/XuXNn7NmzB9bW1lBXf/4/hba2tli1ahV69+4NuVyONWvWAAAMDQ1hZmaGmJgY9OzZE8DTaYFxcXHo3LkzACArKwvXr1/H+vXr4eXlBQA4c+ZMufvo2LEj3N3dsX79eoSHh4v3QUREDQ+LKyIiahD69OkDf39/lJaWqhRNvXr1wvz581FSUoI+ffpAX18fS5YswcKFC6FUKtGjRw/k5OQgOjoaBgYG8PX1hb+/P9avX48JEybgX//6F4yMjHDz5k1ERERgw4YNkMvl4vHt7e3FBTTU1dURGBgIAAgICMDnn38OOzs7tG/fHt988w2ys7PF/Zo1awZjY2OsW7cOZmZmSElJwfvvv1/hY5s1axbmz5+PJk2aYNSoUbXy/BERUe3jaoFERNQg9OnTB0+ePEHbtm3RokULsb1Xr17Iy8sTl2wHgE8++QT//e9/sXLlSnTo0AGDBg3CL7/8AhsbGwBPpxNGR0dDoVDgzTffRMeOHfHuu++iadOmUFMr/09ju3btcOLECWzfvh2LFy8GACxevBhTpkyBr68vunbtCn19fZXCSE1NDREREYiLi4OTkxMWLlyIr776qsLHNmHCBKirq2PChAnQ1tauseeMiIjqFlcLJCIiktidO3dga2uLCxcuiNMKiYio4WFxRUREJJHS0lJkZWVhyZIlSE5ORnR0tNSRiIjoFXBaIBERkUSio6NhZmaGCxcuICQkROo4RET0ijhyRUREREREVAM4ckVERERERFQDWFwRERERERHVABZXRERERERENYDFFRERERERUQ1gcUVERERERFQDWFwRERERERHVABZXRERERERENYDFFRERERERUQ34f63cIC2cODkGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Weekly growth of different types of cases in India (RECOVERED)\n",
    "plt.figure(figsize = (10, 4))\n",
    "india = data.loc[data['Country/Region'] == 'India']\n",
    "print(india.groupby(data['weekday'])['Recovered'].sum())\n",
    "plt.plot(india.groupby(data['weekday'])['Recovered'].sum(), color = 'Green', marker = 'o')\n",
    "plt.xlabel('Weekday')\n",
    "plt.ylabel('Recovered')\n",
    "plt.legend(['Recovered'], loc = 'best')\n",
    "plt.title('Weekly Growth of Recovered Cases in INDIA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c542963e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weekday\n",
      "0    358688.0\n",
      "1    367078.0\n",
      "2    376699.0\n",
      "3    385945.0\n",
      "4    396013.0\n",
      "5    404946.0\n",
      "6    414087.0\n",
      "Name: Deaths, dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Weekly Growth of Deaths Cases in INDIA')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Weekly growth of different types of cases in India (DEATHS)\n",
    "plt.figure(figsize = (10, 4))\n",
    "india = data.loc[data['Country/Region'] == 'India']\n",
    "print(india.groupby(data['weekday'])['Deaths'].sum())\n",
    "plt.plot(india.groupby(data['weekday'])['Deaths'].sum(), color = 'Black', marker = 'o')\n",
    "plt.xlabel('Weekday')\n",
    "plt.ylabel('Deaths')\n",
    "plt.legend(['Deaths'], loc = 'best')\n",
    "plt.title('Weekly Growth of Deaths Cases in INDIA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c73ace6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Mean of 7 Days in Confirmed, Recovered and Death Cases : 216753839.0 , 117801641.0 , 9719962.0\n"
     ]
    }
   ],
   "source": [
    "# 7 Days rolling mean of daily increase of Confirmed, Recovered and Death Cases\n",
    "week_data = data.groupby(data['weekday']).sum()\n",
    "print('The Mean of 7 Days in Confirmed, Recovered and Death Cases :',round(week_data.mean()['Confirmed'], 0),',',round(week_data.mean()['Recovered'], 0),',',round(week_data.mean()['Deaths'], 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3050d029",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 17 countries with more than 100 Confirmed Cases and No Deaths\n"
     ]
    },
    {
     "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>SNo</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Last Update_Hour</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</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>Bhutan</th>\n",
       "      <td>7251693</td>\n",
       "      <td>9320.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6199.0</td>\n",
       "      <td>1336</td>\n",
       "      <td>532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cambodia</th>\n",
       "      <td>7316256</td>\n",
       "      <td>25630.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21357.0</td>\n",
       "      <td>1572</td>\n",
       "      <td>628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dominica</th>\n",
       "      <td>7167098</td>\n",
       "      <td>2667.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>1051</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Eritrea</th>\n",
       "      <td>7175654</td>\n",
       "      <td>21415.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13921.0</td>\n",
       "      <td>1072</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>French Guiana</th>\n",
       "      <td>78532</td>\n",
       "      <td>117.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>128</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Grenada</th>\n",
       "      <td>7170048</td>\n",
       "      <td>3256.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2620.0</td>\n",
       "      <td>1051</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guadeloupe</th>\n",
       "      <td>57307</td>\n",
       "      <td>187.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>110</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Holy See</th>\n",
       "      <td>7243661</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1138.0</td>\n",
       "      <td>1304</td>\n",
       "      <td>507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Laos</th>\n",
       "      <td>7158244</td>\n",
       "      <td>2921.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2181.0</td>\n",
       "      <td>1023</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macau</th>\n",
       "      <td>7368772</td>\n",
       "      <td>7731.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6367.0</td>\n",
       "      <td>1769</td>\n",
       "      <td>647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mongolia</th>\n",
       "      <td>7249663</td>\n",
       "      <td>26762.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18672.0</td>\n",
       "      <td>1215</td>\n",
       "      <td>499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reunion</th>\n",
       "      <td>67279</td>\n",
       "      <td>137.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Kitts and Nevis</th>\n",
       "      <td>7156905</td>\n",
       "      <td>2333.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1840.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Lucia</th>\n",
       "      <td>7233714</td>\n",
       "      <td>3071.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2655.0</td>\n",
       "      <td>1103</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saint Vincent and the Grenadines</th>\n",
       "      <td>7234009</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3684.0</td>\n",
       "      <td>1179</td>\n",
       "      <td>510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seychelles</th>\n",
       "      <td>7234617</td>\n",
       "      <td>8133.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4854.0</td>\n",
       "      <td>1138</td>\n",
       "      <td>494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timor-Leste</th>\n",
       "      <td>7185258</td>\n",
       "      <td>3316.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2905.0</td>\n",
       "      <td>1051</td>\n",
       "      <td>470</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      SNo  Confirmed  Deaths  Recovered  \\\n",
       "Country/Region                                                            \n",
       "Bhutan                            7251693     9320.0     0.0     6199.0   \n",
       "Cambodia                          7316256    25630.0     0.0    21357.0   \n",
       "Dominica                          7167098     2667.0     0.0     2253.0   \n",
       "Eritrea                           7175654    21415.0     0.0    13921.0   \n",
       "French Guiana                       78532      117.0     0.0        6.0   \n",
       "Grenada                           7170048     3256.0     0.0     2620.0   \n",
       "Guadeloupe                          57307      187.0     0.0        0.0   \n",
       "Holy See                          7243661     1748.0     0.0     1138.0   \n",
       "Laos                              7158244     2921.0     0.0     2181.0   \n",
       "Macau                             7368772     7731.0     0.0     6367.0   \n",
       "Mongolia                          7249663    26762.0     0.0    18672.0   \n",
       "Reunion                             67279      137.0     0.0        0.0   \n",
       "Saint Kitts and Nevis             7156905     2333.0     0.0     1840.0   \n",
       "Saint Lucia                       7233714     3071.0     0.0     2655.0   \n",
       "Saint Vincent and the Grenadines  7234009     4640.0     0.0     3684.0   \n",
       "Seychelles                        7234617     8133.0     0.0     4854.0   \n",
       "Timor-Leste                       7185258     3316.0     0.0     2905.0   \n",
       "\n",
       "                                  Last Update_Hour  weekday  \n",
       "Country/Region                                               \n",
       "Bhutan                                        1336      532  \n",
       "Cambodia                                      1572      628  \n",
       "Dominica                                      1051      470  \n",
       "Eritrea                                       1072      475  \n",
       "French Guiana                                  128       46  \n",
       "Grenada                                       1051      470  \n",
       "Guadeloupe                                     110       25  \n",
       "Holy See                                      1304      507  \n",
       "Laos                                          1023      470  \n",
       "Macau                                         1769      647  \n",
       "Mongolia                                      1215      499  \n",
       "Reunion                                        160       32  \n",
       "Saint Kitts and Nevis                         1000      469  \n",
       "Saint Lucia                                   1103      485  \n",
       "Saint Vincent and the Grenadines              1179      510  \n",
       "Seychelles                                    1138      494  \n",
       "Timor-Leste                                   1051      470  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Countries with more than 100 Confirmed Cases and No Deaths with considerably high Recovery Rate\n",
    "country = data.groupby(data['Country/Region']).sum()\n",
    "\n",
    "print('There are',country.loc[(country['Confirmed'] > 100) & (country['Deaths'] == 0)]['SNo'].count(),'countries with more than 100 Confirmed Cases and No Deaths')\n",
    "country.loc[(country['Confirmed'] > 100) & (country['Deaths'] == 0)]"
   ]
  }
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