11_Nijat_Hajiyev.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# istifade olunacaq kitabxanalari import edin\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from plotly import __version__\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>1.22.2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>1.22.2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>1.22.2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>1.22.2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>1.22.2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>48875</th>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>7.27.2020</td>\n",
       "      <td>865</td>\n",
       "      <td>14</td>\n",
       "      <td>734</td>\n",
       "      <td>117</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48876</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>7.27.2020</td>\n",
       "      <td>1691</td>\n",
       "      <td>483</td>\n",
       "      <td>833</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48877</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>7.27.2020</td>\n",
       "      <td>354</td>\n",
       "      <td>7</td>\n",
       "      <td>328</td>\n",
       "      <td>19</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48878</th>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>7.27.2020</td>\n",
       "      <td>7235</td>\n",
       "      <td>60</td>\n",
       "      <td>6028</td>\n",
       "      <td>1147</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48879</th>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>7.27.2020</td>\n",
       "      <td>505</td>\n",
       "      <td>12</td>\n",
       "      <td>128</td>\n",
       "      <td>365</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48880 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Country/Region        Lat       Long       Date  Confirmed  \\\n",
       "0                Afghanistan  33.939110  67.709953  1.22.2020          0   \n",
       "1                    Albania  41.153300  20.168300  1.22.2020          0   \n",
       "2                    Algeria  28.033900   1.659600  1.22.2020          0   \n",
       "3                    Andorra  42.506300   1.521800  1.22.2020          0   \n",
       "4                     Angola -11.202700  17.873900  1.22.2020          0   \n",
       "...                      ...        ...        ...        ...        ...   \n",
       "48875  Sao Tome and Principe   0.186400   6.613100  7.27.2020        865   \n",
       "48876                  Yemen  15.552727  48.516388  7.27.2020       1691   \n",
       "48877                Comoros -11.645500  43.333300  7.27.2020        354   \n",
       "48878             Tajikistan  38.861000  71.276100  7.27.2020       7235   \n",
       "48879                Lesotho -29.610000  28.233600  7.27.2020        505   \n",
       "\n",
       "       Deaths  Recovered  Active             WHO Region  \n",
       "0           0          0       0  Eastern Mediterranean  \n",
       "1           0          0       0                 Europe  \n",
       "2           0          0       0                 Africa  \n",
       "3           0          0       0                 Europe  \n",
       "4           0          0       0                 Africa  \n",
       "...       ...        ...     ...                    ...  \n",
       "48875      14        734     117                 Africa  \n",
       "48876     483        833     375  Eastern Mediterranean  \n",
       "48877       7        328      19                 Africa  \n",
       "48878      60       6028    1147                 Europe  \n",
       "48879      12        128     365                 Africa  \n",
       "\n",
       "[48880 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# covid datasini oxuyun\n",
    "data1 = pd.read_csv(r'C:\\Users\\nijat\\Desktop\\PYTHON - KURS\\Ders 11 - practice\\covid.csv')\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
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       "      <td>Azerbaijan</td>\n",
       "      <td>33.939110</td>\n",
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       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>41.153300</td>\n",
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       "      <th>2</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>28.033900</td>\n",
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       "      <td>Azerbaijan</td>\n",
       "      <td>42.506300</td>\n",
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       "      <td>Europe</td>\n",
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       "      <th>4</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
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       "      <td>0</td>\n",
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       "      <td>Azerbaijan</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>865</td>\n",
       "      <td>14</td>\n",
       "      <td>734</td>\n",
       "      <td>117</td>\n",
       "      <td>Africa</td>\n",
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       "    <tr>\n",
       "      <th>48876</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>1691</td>\n",
       "      <td>483</td>\n",
       "      <td>833</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48877</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>354</td>\n",
       "      <td>7</td>\n",
       "      <td>328</td>\n",
       "      <td>19</td>\n",
       "      <td>Africa</td>\n",
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       "    <tr>\n",
       "      <th>48878</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>7235</td>\n",
       "      <td>60</td>\n",
       "      <td>6028</td>\n",
       "      <td>1147</td>\n",
       "      <td>Europe</td>\n",
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       "    <tr>\n",
       "      <th>48879</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>505</td>\n",
       "      <td>12</td>\n",
       "      <td>128</td>\n",
       "      <td>365</td>\n",
       "      <td>Africa</td>\n",
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       "<p>48880 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "      Country/Region        Lat       Long       Date  Confirmed  Deaths  \\\n",
       "0         Azerbaijan  33.939110  67.709953 2020-01-22          0       0   \n",
       "1         Azerbaijan  41.153300  20.168300 2020-01-22          0       0   \n",
       "2         Azerbaijan  28.033900   1.659600 2020-01-22          0       0   \n",
       "3         Azerbaijan  42.506300   1.521800 2020-01-22          0       0   \n",
       "4         Azerbaijan -11.202700  17.873900 2020-01-22          0       0   \n",
       "...              ...        ...        ...        ...        ...     ...   \n",
       "48875     Azerbaijan   0.186400   6.613100 2020-07-27        865      14   \n",
       "48876     Azerbaijan  15.552727  48.516388 2020-07-27       1691     483   \n",
       "48877     Azerbaijan -11.645500  43.333300 2020-07-27        354       7   \n",
       "48878     Azerbaijan  38.861000  71.276100 2020-07-27       7235      60   \n",
       "48879     Azerbaijan -29.610000  28.233600 2020-07-27        505      12   \n",
       "\n",
       "       Recovered  Active             WHO Region  \n",
       "0              0       0  Eastern Mediterranean  \n",
       "1              0       0                 Europe  \n",
       "2              0       0                 Africa  \n",
       "3              0       0                 Europe  \n",
       "4              0       0                 Africa  \n",
       "...          ...     ...                    ...  \n",
       "48875        734     117                 Africa  \n",
       "48876        833     375  Eastern Mediterranean  \n",
       "48877        328      19                 Africa  \n",
       "48878       6028    1147                 Europe  \n",
       "48879        128     365                 Africa  \n",
       "\n",
       "[48880 rows x 9 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dtypes baxiriq (change Date column to date format)\n",
    "data1['Date'] = pd.to_datetime(data1['Date']) \n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nijat\\AppData\\Local\\Temp\\ipykernel_17736\\3141792533.py:2: FutureWarning:\n",
      "\n",
      "Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "\n"
     ]
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       "      <td>Azerbaijan</td>\n",
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       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.362056</td>\n",
       "      <td>23.445506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.691738e+04</td>\n",
       "      <td>887.025655</td>\n",
       "      <td>7.928616e+03</td>\n",
       "      <td>8.101734e+03</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>24.971423</td>\n",
       "      <td>70.565423</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.275416e+05</td>\n",
       "      <td>6325.533632</td>\n",
       "      <td>5.490390e+04</td>\n",
       "      <td>7.640442e+04</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-51.796300</td>\n",
       "      <td>-135.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-1.400000e+01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.789790</td>\n",
       "      <td>-16.237775</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>23.529288</td>\n",
       "      <td>21.375600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.670000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>2.600000e+01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>41.227185</td>\n",
       "      <td>81.641348</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.504250e+03</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>6.600000e+02</td>\n",
       "      <td>5.922500e+02</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>71.706900</td>\n",
       "      <td>178.065000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.290259e+06</td>\n",
       "      <td>148011.000000</td>\n",
       "      <td>1.846641e+06</td>\n",
       "      <td>2.816444e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country/Region           Lat          Long                 Date  \\\n",
       "count           48880  48880.000000  48880.000000                48880   \n",
       "unique              1           NaN           NaN                  188   \n",
       "top        Azerbaijan           NaN           NaN  2020-01-22 00:00:00   \n",
       "freq            48880           NaN           NaN                  260   \n",
       "first             NaN           NaN           NaN  2020-01-22 00:00:00   \n",
       "last              NaN           NaN           NaN  2020-07-27 00:00:00   \n",
       "mean              NaN     21.362056     23.445506                  NaN   \n",
       "std               NaN     24.971423     70.565423                  NaN   \n",
       "min               NaN    -51.796300   -135.000000                  NaN   \n",
       "25%               NaN      7.789790    -16.237775                  NaN   \n",
       "50%               NaN     23.529288     21.375600                  NaN   \n",
       "75%               NaN     41.227185     81.641348                  NaN   \n",
       "max               NaN     71.706900    178.065000                  NaN   \n",
       "\n",
       "           Confirmed         Deaths     Recovered        Active WHO Region  \n",
       "count   4.888000e+04   48880.000000  4.888000e+04  4.888000e+04      48880  \n",
       "unique           NaN            NaN           NaN           NaN          6  \n",
       "top              NaN            NaN           NaN           NaN     Europe  \n",
       "freq             NaN            NaN           NaN           NaN      14852  \n",
       "first            NaN            NaN           NaN           NaN        NaN  \n",
       "last             NaN            NaN           NaN           NaN        NaN  \n",
       "mean    1.691738e+04     887.025655  7.928616e+03  8.101734e+03        NaN  \n",
       "std     1.275416e+05    6325.533632  5.490390e+04  7.640442e+04        NaN  \n",
       "min     0.000000e+00       0.000000  0.000000e+00 -1.400000e+01        NaN  \n",
       "25%     4.000000e+00       0.000000  0.000000e+00  0.000000e+00        NaN  \n",
       "50%     1.670000e+02       2.000000  2.900000e+01  2.600000e+01        NaN  \n",
       "75%     1.504250e+03      30.000000  6.600000e+02  5.922500e+02        NaN  \n",
       "max     4.290259e+06  148011.000000  1.846641e+06  2.816444e+06        NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics\n",
    "data1.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# descriptive stats-dan outlier-leri teyin edin\n",
    "plt.figure(figsize=(16,9))\n",
    "sns.boxplot(data=data1['Deaths'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9VQwMDMRLL70UH/nIR2rR8aCDDopt27bFvHnzIiLihRdeiJ6enhgZGan9t/65uXPnxosvvhhFUURPT0/cdttt8Zu/+ZvxwgsvRETEvHnzYt26dXHJJZfE1q1bm46nfruLL764tm+r8VfH0SqqNjtW47luueWWiIhYuXJlbN26teUx586dG9u2bWv5fPX7+rmrn6eiKGLbtm2jtq9uN14UbnWe+rlvdvxm89Bs7g866KB48cUXa9cYES2PUz8X9ff6rrvuiqGhoTFrqPGaqvNRv3+z+Wg1B/Pnz49vfOMbtetonINmX1fXeMT/v8eNz9cfs/H7xjVf/W+7/ZpdV/3rrd7OnTtrY2u2Phu3b9xnvDG2O18n89PsGM3s3LkzhoeHR73m69fleNcznvp1Vb/eJqLZtZcdT5ljR0TL8403lsbnGtda47g7ua7G+zN37tyoVCpN73O74zW71/XvMa3W0ETnv9l6H2//Tuej3TXu6TGqOlnDZdZP2Xkss9bKrINOlF3L7cZcZkxl1s1kvh47mZPG10+Z98apfB/rxjm7tX67pRtj6PbPXXv6Op+O+1DW3jjmMmbifc0+51Wd9rXSf9T39ttvj9WrV8enPvWp2LRpUyxdujRWrFgRmzdv3qMBA3lVw8ILL7wQt956a0REfPrTn46RkZHa89X/WX/hhRdqH06rz1f/W//ctm3bascdGRmJT37yk6PC3QsvvBCf/vSnm8a8+gA03naN29ePt5lmx2o816233hq33HLLmGtsPGZ1Plo9X/2+fu7q56kxaNVvN96v97Q6T/3cNzt+vfHmtBr9qtfYLvpVt6u/B3/4h38YEWPXUKPqc/X7119Xq3PXX3P9dTTOQauvG+9x4/ONx6z/vnHN19/T8fZrdl31r7d69WNrtj6babZmm42x3fk6mZ9WYxjvuNV96+9pmWPVq19X9ettIppd+2TpZF7rz1fmuca11jjuTq6r8f5s27ZtQmNrdqx2r83q/hOd/2brvdPXR5nXwWQfo6qTNVxm/ZSdx8lahxN5vZRdy+3OXWZMZdbNZL4e22n2+ilzjKl8H+vGObu1frulG2Po9s9dM2l8U2VvHHMZM/G+Zp/zskr/jr9TTjklTjjhhPja175We+yoo46Kc889Nz7/+c+33T/z7/hbtmzZdA8BZry+vr646qqr4vrrr5/uoUyLnp6eqFQqsXv37ukeyl7t0ksvjb/4i7+Y7mE01dvbGxExI+5xX19frF+/PhYuXBgRET/72c/iwgsvbDm23t7e+Mu//Mva9p3sU/Z87ean8RjN/OxnP4vf+I3fqP3PdSvNrmc8jz32WFx11VVjHr/xxhvjxBNP7OgY9WNsvPZOrm2ix+7t7Y2iKEbNSfV8EdFyLM2ea1Q/7k6uq5M108nYWp2vnb6+vrj++uvjd3/3d0vP/3jna7b/ROdjMua02Xg6WcOdvi4nMo/jjTOi3Dos+3ops+46uYdlrr3Muik7R51cQyut3is7fW+cyvexbpyz7LEmun6nai7KjG+yxtDtn7sm43Xe7ftQ1t445jJm4n3NPuf1puR3/L3++uvx+OOPx/Lly0c9vnz58nj44Yeb7vPaa6/F9u3bR/3ISPSDzuzatSu++MUvTvcwps3IyMiMCEJ7u5ka/SLe/B/nmXKPi6KItWvXRlEUta/Hs3v37tr2jftP1vnazU+7cxZFEWvWrGkb/Zpdz3hGRkbiuuuua/rcdddd19H56sfY7NrLzmeZY+/evXvMGKtz1WosrZ5rdc6RkZG211X9ut1roN3Y6s9X9vXU6l52srbKPN/JfW63TZk5bTeeTtZwmddlURRx3XXXNf0d083mqd0416xZ0/S5duugk9dLJ+9vzY453pg7vfZ2567e407uZbM5ancN423X6r2yk/fGqXwf68Y5yx5rvO0nY43uqW7cj+n4uWsyXufdvA9l7Y1jLmMm3tfscz5RpcLf1q1bY/fu3bFgwYJRjy9YsCCee+65pvt8/vOfjzlz5tR+LFq0aOKjBVKYKVEEstu9e3c8+uijsXnz5ti8eXM8+uijbV9/1e0jorZPp9FrIucb7xjNbN68OR577LGOjzfeseo98sgjLX9xcvv27fHII490fM5W197u2vbk2M3s3r07HnvssZZjafVcs+M8+uij8cgjj7S9rur49nRs9ecra2RkJLZv3156/tut98b9O7nP7bYpM6ftrqeTNVx2/Wzfvn3MfLSax3bjfOyxx0qtwzKvl06vq8w97PTa2517ZGSk43vZbI7aXUMr7d4r2x1jKt/HunHOsscab/vJWKN7qhv3Yzp+7pqM13k370NZe+OYy5iJ9zX7nE9U6b/jL+LNvzy8XlEUYx6r+r3f+714+eWXaz+eeeaZiZwSSKT6R4qAqdXb2xsnn3xyHHbYYXHYYYfFSSed1Pb1V90+Imr79PR09nFhIucb7xjNHHbYYaX+2O14x6p3yimntPwjEnPmzIlTTjml43O2uvZ217Ynx26mt7c3TjrppJZjafVcs+OcfPLJccopp7S9rur49nRs9ecrq6enJwYHB0vPf7v13rh/J/e53TZl5rTd9XSyhsuun8HBwTHz0Woe243zxBNPLLUOy7xeOr2uMvew02tvd+6enp6O72WzOWp3Da20e69sd4ypfB/rxjnLHmu87Sdjje6pbtyP6fi5azJe5928D2XtjWMuYybe1+xzPlGlwt/8+fOjt7d3zO/ue/7558f8LsCq/v7+GBwcHPUjowceeGC6hwB7hb6+vrj66qunexjTpqenR/icBJdeeul0D6Gl3t7eGXOPK5VKXHbZZVGpVGpfj6e3t7e2feP+k3W+dvPT7pyVSiVWr17dUYxsvJ7x9PT0tPyHPK699tqO42d1jM2uvex8ljl2b2/vmDFW56rVWFo91+qcPT09ba+r+nW710C7sdWfr+zrqaenJ6699tpxx9lqTGWe7+Q+t9umzJy2G08na7jM67JSqcS11147Zj5azVO7ca5evbrpc+3WQSevl07e35odc7wxd3rt7c5dvced3Mtmc9TuGsbbrtV7ZSfvjVP5PtaNc5Y91njbT8Ya3VPduB/T8XPXZLzOu3kfytobx1zGTLyv2ed8okqFv1mzZsWSJUvivvvuG/X4fffdF6effvqkDmxvdNttt033EGBGq1QqccEFF8TZZ58dxx577KQe+61vfeuYx4499tiO3twncyztjrVy5cq44IIL0v+k0+ncT8Rxxx0X559//qSvoUaVSqX0dVQqlRgeHm55jxuP2ek5JrJf9fX2i7/4i7XHFi5cOO76Gx4eHrV9J/uUOV8n89N4jGYWLlwYw8PD427T6nrGc+KJJ45ZV8cdd1yccMIJHR+jfoyN197JtU302MPDwzE8PNz0fOONpdlzjWutftydXFd1m1Y6Hdt4x2q1/qv7L1myZELz32q9t9q/zHxMxpy2u55O1nAnr8uJzmPZtdbpOuhE2bXcbsxlrr3MupnM12Mnc9LsvbLT98apfB/rxjnLHmuq1+ie6sYYuv1z12S8zrt9H8raG8dcxky8r9nnfCJK/1HfK664Ir7xjW/EN7/5zXjqqafi8ssvj82bN8cnPvGJqRgfkED1TXf+/Pm1/4H73Oc+V/tV6J6enpg7d25tm3nz5tUer/9v/XPz5s2rHbenpye+/OUv156rbvu5z31u1GON42m3XVXjOOp/9bzdsRqfv+CCC2J4eHjMNdZvV6lUavPR7Pn67+vnrn6eqo/VX0Pjds3G2HieZnPf7Pj1xpvTgw46qPb1vHnzxj1OdSyN97r6l9c3rqFG1cfq968/dqtz119z/XU0zkGrrxvvcePzjces/77VWmu3X7Prqn+91Wu2/sbbvtU+zcbY7nydzM94wajVcSPGrqcyx6rXuK5a/WMJZcc40fGUOfZ45yvzXONaaxx3J9fV7P5MZGzNjtXutVndf6Lz32y9d/r6KPM6mOxjVHWyhsusn7LzOFnrcCKvl7Jrud25y4ypzLqZzNdjO81eP2WOMZXvY904Z7fWb7d0Ywzd/rlrJo1vquyNYy5jJt7X7HNeVunw97GPfSzWrFkT1113XbznPe+JjRs3xne/+91YvHjxVIwPKGn+/PkRMTbg1Ovr64vZs2fH7NmzR4Wl2bNnR39/f0S8+cf0+/r6avv09/fXnq9UKtHf3x9DQ0OxbNmyqFQq0dfXF5VKJZYtWxZDQ0O14w8NDcXKlStjwYIFcfnll8fAwEBERAwNDcXw8HD09PTE8PBwXHXVVbFgwYK44oor4sorr4wFCxbE8PDwqP/WP3fllVfGypUra/tXHxsaGoqhoaG44oorav9dsGBBbZyzZ8+OlStXjtmuum913PXjbxzH8PBwbf/xjtXs+YGBgRgYGKiNq3rMlStXxuzZs6NSqcTKlStr89Hs+VWrVo269sZtr7jiirjqqqtiaGiodr8at6tea+MYq183Xnv93Ncff2hoKFatWjVqn8a5P+aYYyLizX/9/Hd+53dq+1155ZW141T3bbxPze710NBQ0zW0atWq6OnpiWXLlo2aj/r9jznmmOjp6Rk1x9VzVtd0db/LL7981HU0zkGzr6trvP4eNz5ff8zG75uttU72W7Zs2Zjrqn+91Wtcf43rs5lma7bZGNudr5P5aTWGZsetf63Vr6d21zOexnVVXW8T0ezaJ0u7eW08X5nnGtda47g7ua5m96fVfW53vMZjNb7HtFpDE53/Zut9vP07nY/JmNNOrqeTNVxm/ZSdx8lahxN5vZRdy+3OXWZMZdbNZL4eO5mTxtdPmWNM5ftYN87ZrfXbLd0YQ7d/7ppJ45sqe+OYy5iJ9zX7nJdVKbr87xlv37495syZEy+//HK6v+9vy5Ytcf7559e+v/fee2P//fefxhEBAAAAkE2nfW1C/6ovAAAAADCzCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwN4n6+/trX996660xMDAwjaMBAAAAYF8m/E2iSqVS+3r//fcf9T0AAAAAdJPwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwN4kGBgaafg0AAAAA3dY33QPIZGBgIO69997a1wAAAAAwXYS/SVSpVGL//fef7mEAAAAAgD/qCwAAAAAZCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJCX8AAAAAkJDwBwAAAAAJ9XX7hEVRRETE9u3bu31qAAAAANjrVbtatbO10vXwt2PHjoiIWLRoUbdPDQAAAABp7NixI+bMmdPy+UrRLg1OspGRkXj22WfjwAMPjEql0s1Td8X27dtj0aJF8cwzz8Tg4OB0D4eErDGmkvXFVLPGmErWF1PNGmOqWWNMJesrl6IoYseOHXHooYdGT0/rv8mv67/jr6enJxYuXNjt03bd4OCgFxJTyhpjKllfTDVrjKlkfTHVrDGmmjXGVLK+8hjvd/pV+cc9AAAAACAh4Q8AAAAAEhL+Jll/f39ce+210d/fP91DISlrjKlkfTHVrDGmkvXFVLPGmGrWGFPJ+to3df0f9wAAAAAApp7f8QcAAAAACQl/AAAAAJCQ8AcAAAAACQl/AAAAAJCQ8AcAAAAACQl/bXz1q1+Nt7/97TEwMBBLliyJf/mXfxl3+wcffDCWLFkSAwMD8Y53vCP+/M//fMw2d955Zxx99NHR398fRx99dNx9991TNXz2AmXW2F133RUf/OAH4+CDD47BwcE47bTT4p/+6Z9GbbN+/fqoVCpjfuzcuXOqL4UZqswae+CBB5qun//8z/8ctZ33MarKrK+Pf/zjTdfXu971rto23sOo2rhxY5xzzjlx6KGHRqVSie985ztt9/E5jDLKrjGfwyir7BrzOYwyyq4vn8P2XcLfOG6//fZYvXp1fOpTn4pNmzbF0qVLY8WKFbF58+am2//Xf/1XfPjDH46lS5fGpk2b4vd///fjt3/7t+POO++sbfO9730vPvaxj8WqVavi3//932PVqlVx3nnnxSOPPNKty2IGKbvGNm7cGB/84Afju9/9bjz++ONx1llnxTnnnBObNm0atd3g4GBs2bJl1I+BgYFuXBIzTNk1VvWDH/xg1Pr5pV/6pdpz3seoKru+1q5dO2pdPfPMMzF37tz46Ec/Omo772FERLzyyivx7ne/O/7sz/6so+19DqOssmvM5zDKKrvGqnwOoxNl15fPYfuwgpZOPvnk4hOf+MSox4488sjimmuuabr91VdfXRx55JGjHrv00kuLU089tfb9eeedV3zoQx8atc3ZZ59d/Pqv//okjZq9Sdk11szRRx9dfPazn619/61vfauYM2fOZA2RvVzZNXb//fcXEVG8+OKLLY/pfYyqPX0Pu/vuu4tKpVL893//d+0x72E0ExHF3XffPe42PoexJzpZY834HEanOlljPocxURN5D/M5bN/hd/y18Prrr8fjjz8ey5cvH/X48uXL4+GHH266z/e+970x25999tnx2GOPxRtvvDHuNq2OSV4TWWONRkZGYseOHTF37txRj//85z+PxYsXx8KFC+NXfuVXxvxKNPuGPVljxx9/fBxyyCHx/ve/P+6///5Rz3kfI2Jy3sPWrVsXH/jAB2Lx4sWjHvcexkT4HEa3+RzGVPE5jG7wOWzfIfy1sHXr1ti9e3csWLBg1OMLFiyI5557ruk+zz33XNPtd+3aFVu3bh13m1bHJK+JrLFGN910U7zyyitx3nnn1R478sgjY/369fF3f/d3cdttt8XAwECcccYZ8aMf/WhSx8/MN5E1dsghh8TXv/71uPPOO+Ouu+6KI444It7//vfHxo0ba9t4HyNiz9/DtmzZEvfee29ccsklox73HsZE+RxGt/kcxmTzOYxu8Tls39I33QOY6SqVyqjvi6IY81i77RsfL3tMcpvoerjtttviM5/5TPzt3/5tvPWtb609fuqpp8app55a+/6MM86IE044Ib785S/Hn/7pn07ewNlrlFljRxxxRBxxxBG170877bR45pln4sYbb4z3ve99EzomuU10Laxfvz6Ghobi3HPPHfW49zD2hM9hdIvPYUwFn8PoFp/D9i1+x18L8+fPj97e3jG/cvL888+P+RWWql/4hV9oun1fX1/Mmzdv3G1aHZO8JrLGqm6//fa4+OKL46//+q/jAx/4wLjb9vT0xEknneRXafZBe7LG6p166qmj1o/3MSL2bH0VRRHf/OY3Y9WqVTFr1qxxt/UeRqd8DqNbfA6jm3wOY7L5HLbvEf5amDVrVixZsiTuu+++UY/fd999cfrppzfd57TTThuz/YYNG+LEE0+M/fbbb9xtWh2TvCayxiLe/BXmj3/843HrrbfGL//yL7c9T1EU8f3vfz8OOeSQPR4ze5eJrrFGmzZtGrV+vI8RsWfr68EHH4wf//jHcfHFF7c9j/cwOuVzGN3gcxjd5nMYk83nsH1Q9/89kb3Ht7/97WK//fYr1q1bVzz55JPF6tWri9mzZ9f+1ZtrrrmmWLVqVW37n/zkJ8UBBxxQXH755cWTTz5ZrFu3rthvv/2Kv/mbv6lt89BDDxW9vb3F9ddfXzz11FPF9ddfX/T19RX/+q//2vXrY/qVXWO33npr0dfXV3zlK18ptmzZUvvx0ksv1bb5zGc+U/zjP/5j8fTTTxebNm0qLrrooqKvr6945JFHun59TL+ya+xP/uRPirvvvrv44Q9/WDzxxBPFNddcU0REceedd9a28T5GVdn1VbVy5crilFNOaXpM72FU7dixo9i0aVOxadOmIiKKL33pS8WmTZuKn/70p0VR+BzGniu7xnwOo6yya8znMMoou76qfA7b9wh/bXzlK18pFi9eXMyaNas44YQTigcffLD23IUXXliceeaZo7Z/4IEHiuOPP76YNWtW8ba3va342te+NuaYd9xxR3HEEUcU++23X3HkkUeOeiNn31NmjZ155plFRIz5ceGFF9a2Wb16dXHYYYcVs2bNKg4++OBi+fLlxcMPP9zFK2KmKbPGvvCFLxSHH354MTAwUBx00EHFe9/73uIf/uEfxhzT+xhVZX+efOmll4r999+/+PrXv970eN7DqLr//vvH/TnP5zD2VNk15nMYZZVdYz6HUcZEfp70OWzfVCmK//e3HgMAAAAAafg7/gAAAAAgIeEPAAAAABIS/gAAAAAgIeEPAAAAABIS/gAAAAAgIeEPAAAAABIS/gAAAAAgIeEPAAAAACbRxo0b45xzzolDDz00KpVKfOc73yl9jKIo4sYbb4x3vvOd0d/fH4sWLYo//uM/LnWMvtJnBQAAAABaeuWVV+Ld7353XHTRRfGrv/qrEzrGZZddFhs2bIgbb7wxjj322Hj55Zdj69atpY5RKYqimNDZAQAAAIBxVSqVuPvuu+Pcc8+tPfb666/HH/zBH8Qtt9wSL730UhxzzDHxhS98IZYtWxYREU899VQcd9xx8cQTT8QRRxwx4XP7o74AAAAA0EUXXXRRPPTQQ/Htb387/uM//iM++tGPxoc+9KH40Y9+FBER99xzT7zjHe+Iv//7v4+3v/3t8ba3vS0uueSS2LZtW6nzCH8AAAAA0CVPP/103HbbbXHHHXfE0qVL4/DDD4+rrroq3vve98a3vvWtiIj4yU9+Ej/96U/jjjvuiJtvvjnWr18fjz/+ePzar/1aqXP5O/4AAAAAoEv+7d/+LYqiiHe+852jHn/ttddi3rx5ERExMjISr732Wtx888217datWxdLliyJH/zgBx3/8V/hDwAAAAC6ZGRkJHp7e+Pxxx+P3t7eUc+95S1viYiIQw45JPr6+kbFwaOOOioiIjZv3iz8AQAAAMBMc/zxx8fu3bvj+eefj6VLlzbd5owzzohdu3bF008/HYcffnhERPzwhz+MiIjFixd3fC7/qi8AAAAATKKf//zn8eMf/zgi3gx9X/rSl+Kss86KuXPnxmGHHRYrV66Mhx56KG666aY4/vjjY+vWrfHP//zPceyxx8aHP/zhGBkZiZNOOine8pa3xJo1a2JkZCR+67d+KwYHB2PDhg0dj0P4AwAAAIBJ9MADD8RZZ5015vELL7ww1q9fH2+88Ub80R/9Udx8883xP//zPzFv3rw47bTT4rOf/Wwce+yxERHx7LPPxic/+cnYsGFDzJ49O1asWBE33XRTzJ07t+NxCH8AAAAAkFDPdA8AAAAAAJh8wh8AAAAAJCT8AQAAAEBCwh8AAAAAJCT8AQAAAEBCwh8AAAAAJCT8AQAAAEBCwh8AAAAAJCT8AQAAAEBCwh8AAAAAJCT8AQAAAEBC/wee5QR3UYIFpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.boxplot(data=data1['Recovered'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.boxplot(data=data1['Confirmed'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.boxplot(data=data1['Active'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numeric deyerler uchun boxplot ile outlier-leri gosterin\n",
    "\n",
    "\n",
    "# YUXARIDA GOSTERILDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country/Region    0\n",
       "Lat               0\n",
       "Long              0\n",
       "Date              0\n",
       "Confirmed         0\n",
       "Deaths            0\n",
       "Recovered         0\n",
       "Active            0\n",
       "WHO Region        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data-da missing deyerleri yoxlayin\n",
    "data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lat</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.128297</td>\n",
       "      <td>0.036896</td>\n",
       "      <td>0.070454</td>\n",
       "      <td>0.015522</td>\n",
       "      <td>0.044604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Long</th>\n",
       "      <td>-0.128297</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.078849</td>\n",
       "      <td>-0.101223</td>\n",
       "      <td>-0.052330</td>\n",
       "      <td>-0.085638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Confirmed</th>\n",
       "      <td>0.036896</td>\n",
       "      <td>-0.078849</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.912368</td>\n",
       "      <td>0.895503</td>\n",
       "      <td>0.950256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>0.070454</td>\n",
       "      <td>-0.101223</td>\n",
       "      <td>0.912368</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.763099</td>\n",
       "      <td>0.891863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recovered</th>\n",
       "      <td>0.015522</td>\n",
       "      <td>-0.052330</td>\n",
       "      <td>0.895503</td>\n",
       "      <td>0.763099</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.713088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Active</th>\n",
       "      <td>0.044604</td>\n",
       "      <td>-0.085638</td>\n",
       "      <td>0.950256</td>\n",
       "      <td>0.891863</td>\n",
       "      <td>0.713088</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Lat      Long  Confirmed    Deaths  Recovered    Active\n",
       "Lat        1.000000 -0.128297   0.036896  0.070454   0.015522  0.044604\n",
       "Long      -0.128297  1.000000  -0.078849 -0.101223  -0.052330 -0.085638\n",
       "Confirmed  0.036896 -0.078849   1.000000  0.912368   0.895503  0.950256\n",
       "Deaths     0.070454 -0.101223   0.912368  1.000000   0.763099  0.891863\n",
       "Recovered  0.015522 -0.052330   0.895503  0.763099   1.000000  0.713088\n",
       "Active     0.044604 -0.085638   0.950256  0.891863   0.713088  1.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Korrelasiya cedvelini hem table hem de plot formatinda chixarin\n",
    "\n",
    "data1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AIK65cO7VCbrV6YXXq7ca5/308RMvn7wkr1PKeyZTldyo6OsOHDjAihUr+OabbzAyMqJ27dpMnjyZuXPnsmXLlq+WNzU1xczMTO2QIQLdCA9/j5ubh+p49OgZ3t6+NGpYR5XH2NiYOrWrceXKzS+cSTsKhULVANGH6Khont57SpU6ldTSq9SpxP2bDxMs88D1oUb+qnUr8/ju0wTHxFUUcRNn42vVpTnBASFcPnk1+RVIRdFR0Ty7/xzn2hXV0p1rV+TRzUcJlnnk+lgjf6U6zjy790zjtanbug7GJsac2nNKt4GnQHRUNI/vPaVaXfVJwtXqVubujQcJlrnr+kAjf/V6VXh890mS5wsA3Ll+j/yF8qml5SuYD+83aT+JMO598YzKdZzV0ivXcebBF94X8fNXqVuJJ/fi3hevXnjSvUFfejfprzouHr/Mrct36N2kf4IrFACMTYzJXyQ/gemw1ywz06oxEBQUhJOTExA3PyAoKK6FXatWLc6fTx8ziJPqw4ePPHnmxpNnbgB4vfXlyTM3vH0S/kXOiJYs/Z3xPw6jbdtmlCpVjHVrF/Lhw0e2bd+ryrN+3WLmzB6vemxsbEy5cqUoV64UJibGOOZ2oFy5UhQqVECVZ/as8dSqWYX8+fNQunRxZs38kbp1q7Nt2560rJ6GbWv+pE23lrTq0pwChfMxYvoQ7B3t2btpPwA/TOjP1MUTVPn3bNqPQx57RkwbTIHC+WjVpTmtu7Zg66rPE+16Du1GlTrO5M6Xi/yF89F1QCdafNOUo3vUV1QoFApadm7G4T+PEROT/r5Z7P7fHpp3aUazzk3IVzgvP0wbiJ2jHQc2x628+P7HPvy4cKwq/8HNB7HLY8+gqQPIVzgvzTo3oVnnpvy5erfGuZt3acal45cJi7e6QN82r95B+26tadu1JU5F8jN6xnAcHO3ZtSnu93/YxEHMWjpZlX/Xpn3kyuPA6OnDcCqSn7ZdW9Kuays2rfw8+c7I2IiipYpQtFQRjI2NsctlS9FSRchbwPHzddfsoIxzKfoO70neAo40a9+Yjj3asGO9ft4fO/73J627tqBl52bkL5yP4dMHx70v/oib0DhofD8mL/78N2DfHwdwyGPPsGk/kL9wPlp2bkarLs3ZtipuUmxkRBTuTz3UjvCwcD6Ef8D9qQfRUdEADJkyiPLVypIrrwMlKxRn9prpZM+RjcN/Hk/7FyE5ZJjg6woWLIiHhwf58+enZMmS7Ny5kypVqnDgwAG15YYZwYMnz+k77HOX9i9L1wDQtnkj5kwera+wdOrX31aQNWsWli2Zi6WlOdev36Z5y25qewzky5ub2H9NmMmd2x7XG5/frKNH/8Do0T9w7txlGjbuBMQNNWxYv4RcuewIDX3H/fuPadnqO06e0u/68pP7z2Buacb3o3phbWfFy6fuuHT/UbWm3MbOGgdHe1V+79c+uHQfz8gZQ+jYux0BvoEsmLJUtawQIGu2rIydOwrbXLZEfIrglZsn04fN4eT+M2rXrlzHmVx5HDiw/XDaVDaZzh44h5llTrqP+A4rOys8nr5iYq/J+P29/4KVvRV2jraq/D6vfZnUazI/TB1Im56tCfQNYvm0laplhf9wdHKkTJXSjOs2gfTm+F+nMLc0Y4BLH2zsrHnx5CXDvhuD95u/fx/s1X8f3np6M+y7MYyeMZxv+3TA3zeAXyYvUi0rhLjNhHac2qB63GtwN3oN7sbNy7fo32EYELf8cHTfCQybOIgBLr3x8vTm1ymLObJHPx+Cp/afxczSjD6jev79vvBgTI8J+P79vrC2t8I+t50qv/drH8b0mMDw6UPo0KstAb6BLJq6TGOPga+xy2XDjOWTMbcyJyQwlIe3HjGg9VDVddOtTDaBUKHUYhbZwoULMTQ0ZPjw4Zw5c4aWLVsSExNDdHQ0CxYsYMSI5O+4FhXwMtll/ouy5q6t7xDShUo2+p+EmB7kMJThM4DAKP1s1JPeZJPfB5VLXqdT9fyfru74eqYkylKts87OlVq06hkYNWqU6v/169fnyZMn3Lx5E1tbW9av19y4RAghhMhQMkj3vq7o5BbG+fLlo0OHDpiZmbFx40ZdnFIIIYTQH7lRkRBCCCEyE62GCYQQQoj/tAzyjV5XpDEghBBCxCN3LfyCDh06fPH5kJCQlMQihBBCCD1IVmPga3sImJub07NnzxQFJIQQQuidDBMkTpYNCiGEyBQy2dJCmTMghBBCxJfJegZkaaEQQgiRyUnPgBBCCBGfDBMIIYQQmZwMEwghhBAiM5GeASGEECI+GSYQQgghMjkZJhBCCCFEZiI9A0IIIUR8maxnQBoDQgghRHyZbM6ADBMIIYQQmZz0DAghhBDxyTCBEEIIkcllsmECaQwIIYQQ8WWyngGZMyCEEEJkctIzIIQQQsQnwwRCCCFEJpfJhgnSTWMga+7a+g4hXfj49oK+Q0gX7J2a6juEdCEiJkrfIaQLFqbZ9R1CuhAe9UnfIYj/KJkzIIQQQsQXG6u7I5lWrFiBk5MTWbJkwdnZmQsXvvwlccuWLZQrV45s2bKRK1cu+vTpQ2BgYLKuKY0BIYQQIj6lUndHMuzYsYORI0cyadIkbt++Te3atWnevDmenp4J5r948SI9e/bk+++/5+HDh/z555/cuHGDfv36Jeu60hgQQgghUlFERARhYWFqR0RERIJ5FyxYwPfff0+/fv0oUaIEixYtIm/evKxcuTLB/FevXqVAgQIMHz4cJycnatWqxcCBA7l582ayYpTGgBBCCBGfDocJ5s2bh7m5udoxb948jUtGRkbi6upKkyZN1NKbNGnC5cuXEwyzRo0avHnzhsOHD6NUKvH19WXXrl20bNkyWdVNNxMIhRBCiHRDh6sJJkyYjIuLi1qaqampRr6AgABiYmKwt7dXS7e3t8fHxyfBc9eoUYMtW7bQuXNnPn36RHR0NG3atGHp0qXJilF6BoQQQohUZGpqipmZmdqRUGPgHwqFQu2xUqnUSPvHo0ePGD58OFOnTsXV1ZWjR4/i7u7OoEGDkhWj9AwIIYQQ8elh0yEbGxsMDQ01egH8/Pw0egv+MW/ePGrWrMnYsWMBKFu2LNmzZ6d27drMnj2bXLlyJena0jMghBBCxKeHpYUmJiY4Oztz4sQJtfQTJ05Qo0aNBMt8+PABAwP1j3JDQ0MgrkchqaRnQAghhIgvmUsCdcXFxYUePXpQqVIlqlevzpo1a/D09FR1+0+YMAEvLy82bdoEQOvWrenfvz8rV66kadOmeHt7M3LkSKpUqULu3LmTfF1pDAghhBDpROfOnQkMDGTmzJl4e3tTunRpDh8+TP78+QHw9vZW23Ogd+/evHv3jmXLljF69GgsLCxo0KABP//8c7Kuq1Ampx8hFRmZOOo7hHRBtiOOI9sRx5HtiOPIdsRxZDviz0LD3VL1/B/Xj9PZubL2+UVn50ot0jMghBBCxJfJblQkEwiFEEKITE56BoQQQoj49LC0UJ+kMSCEEELEo4xNF9Pp0owMEwghhBCZnPQMCCGEEPFlsgmE0hgQQggh4pM5A1+3f//+BNMVCgVZsmShcOHCODk5pSgwIYQQQqQNrRoD7dq1Q6FQaOx7/E+aQqGgVq1a7Nu3D0tLS50EKoQQQqQZmUD4dSdOnKBy5cqcOHGC0NBQQkNDOXHiBFWqVOHgwYOcP3+ewMBAxowZo+t4hRBCiNSnhxsV6ZNWPQMjRoxgzZo1andRatiwIVmyZGHAgAE8fPiQRYsW0bdvX50FKoQQQqSZDPIhrita9Qy4ublhZmamkW5mZsbLly8BKFKkCAEBASmLTgghhBCpTqvGgLOzM2PHjsXf31+V5u/vz7hx46hcuTIAz58/J0+ePLqJUgghhEhLSqXujgxAq2GCtWvX0rZtW/LkyUPevHlRKBR4enpSsGBB/vrrLwDCw8OZMmWKToMVQggh0oQME3xdsWLFePz4MX/99RfDhw9n6NCh7N+/n4cPH1K0aFEgbsVBjx49dBpsck2d4oKnhyvvQl9w6sSflCxZ9Iv5S5Ysys4da3jx7CrRkV4MH9ZPI8/AAT255XqCoIAnBAU84eL5/TRrWj+1qpBmbt65z5Bx06jf5jtK12zOqfOX9R1Siv04YRgPn13Ey+8++w9vpnjxwl8t07pNU67cOIJ3wEOu3DhCy9aN1Z6/8+AMQe+eaxy/zJ+mymNra82yVT/z8NlF3vje4889aylYKL/O66dLEyeN5IXbNQICn3Dk6HZKlCjyxfwlShRhy9aVPHp8kfcfPBgyJGPMD3L5cTCuj87w4q0rfx5YT9Hihb5apkXrxpy5sp+XPrc5c2U/zVo21DinV/BDteP2k3Oq542MjJg43YWTl/by/M0NXB+dYfHKudg72Oq8fkk1fuJwnjy/jI//Qw4e2ULxr/y8Adq0bcq1m0fxC3zEtZtHadW6idrzhoaGTJ7qwr0HZ/Hxf8jd+2cYN34oCoVCLV/RYoXYtmM1nl53eON9l5Ond5EnTy6d1k8kn9bbESsUCpo1a8bw4cMZMWIETZs2xcAg/exuPHbMYEaOGMDwkZOpVqMlPr7+HD28jRw5Er8verasWXF/6cnEyXPx9vZNMI+XlzeTJs2javUWVK3egjNnL7Fn97qvNjTSu48fP1GscEEmugzWdyg6MXzUAAYP7cuPY2bSqG4H/Hz92b1/wxd//pWrlGftxkXs2L6POjVas2P7PtZtXIxzpXKqPA3rdaR4oeqqo33rXgD8tfeIKs/m7SspUCAv3bv8QL1abXn9+i17928kW7asqVfhFHBxGcSwYd/j4jKVOrXb4Ovrz4GDm7/4WmXNlhUPd0+mTvkZHx+/NIxWe4NHfM+Awb2YPG4OLRt2xt8vgG17fid7jmyJlnGuXI6V635j9879NK7dgd0797Nq/XwqOJdRy/fk8XPKF6urOhrWbKd6Lmu2LJQpW4LFv66iWb1O9O85goKFCrB+67LUquoXjRw1gCFD+zJ29HTq122Pn28A+/Zv/Mp7owLrNy5hx7Z91Kzeih3b9rFh0xK198Yol4H0/b4rY0ZPp4pzE6ZO+ZnhI/oz8IdeqjxOTvk4dnwHz5+9pFXzbtSq3opffl7Gp4jIVK2zVmKVujsyAIUy/mYBSXTq1ClOnTqFn58fsfG6U9atW5fs8xmZOGoTRqJev7rFkqW/8+tvKwAwMTHh7Zs7TJg4l//9vvmr5V88u8qSpb+zZOnvX83r5/OAH8fPZv2G7SmO++PbCyk+R0qVrtmcxfOm0LBOja9nTiX2Tk1TVP7R80usWrGRJQvXAHE//6duV5g+9Vc2rk/457R2wyJy5szBtx0/9wj9uWctISFh9O87KsEyc3+aRJNm9alUvhEAhQoX4MbtE9So3JwnT14AYGBgwDP3q8yY+it/bPwzWfWIiIlKVn5tuL28zvJl61iwYBUQ91q5e9xkypSfWLd261fLP3p8keXL1rF8efLf90llYZr4B1VS3Xp8lt9X/cGKxWsBMDEx5s6z88ydvoDNGxL+uaxc+xs5zHLQo9MgVdrmP1cTGhrGkH5jgbiegWYtG9KkTsckx1KuQmkOn95B5TKNePvGO8nlwqM+JTlvYp6+uMLK5etZ9K/3xvOX15g+9RfWr9uWYJn1G5eQM2cOvunwuQdo9971hISE8n2fkQDs+PN/+PsFMHTIBFWeP7Ys58OHjwzsH7fMfN2GxURFRakep0RouFuKz/ElH37VXW9XtrGp997QFa2+ys+YMYMmTZpw6tQpAgICCA4OVjv0zckpH7ly2XPi5OeuusjISM5fuEr16pV0dh0DAwO+/bYN2bNn4+o1V52dV6RM/gJ5cXCw48ypi6q0yMhILl26TpVqFRItV7lKBc6cvqiWdvrUBapUTbiMsbExnbq0YcvmXao0ExMTALVvOrGxsURGRlFVh797ulLg79fq1KnPjdDIyEguXrxGtarOeoxMt/Llz4O9gy3nTl9SpUVGRnH10k0qVUn8d8K5SnnOn1YfMjt3+hKVqpRXS3MqmA/XR2e4cucYK9b+Sr78X548bWaWg9jYWMJCw5JfmRT45+d9Ov574+I1qlStmGi5ylUqcPqU+heVUyfPq5W5euUmderVoFDhAgCULl2catUrcfx43N9hhUJBk6b1ePHCgz371vPC/TqnzuymZSv1oTihH1pNIFy1ahUbNmzQek5AREQEERERamn/7FyoCw72dgD4+qovbfT19Sd/vpSvcChdujgXz+8nSxZTwsPf802nfjx+/DzF5xW6YW9vA4C/n/rP398vgLx5E++BsrO3wc8vUC3Nzy8QO/uEx3ZbtmqEubkZ2zbvUaU9f/YSz1dvmDp9NKNGTOHD+48MHtYHBwc7HBI5jz7Z/x2Tr5+/Wrqfnz/58v53VgPZ/f07EeCv/vP19wskT97ciZaztbPBP34Z/0Bs7WxUj2+73mPEDxN56eaBra01w8cM5K9jW2hQvQ3BwaEa5zQ1NWHCtFHs3XWI8HfvU1KtZPvnd9kv/nvDP5C8X3gd7O1tNMr4+QWo3msACxesxswsJzdvnSAmJgZDQ0NmzZjP7j8PAHFzaXLmzMEol4HMnrmAaVN+oVHjOmzeuoJWLb7j0sXruqqmbmSQ7n1d0apnIDIyUm3DoeSaN28e5ubmaocy9p3W5+vatT0hQc9Uh7FxXBsnse2SU+rpUzecKzehZq3WrF6ziXVrF311wpVIPd982wZP7zuqw8jIGNDu55+cMt17duLkifNqY+bR0dH06j6UQoWdcH/tipffPWrVqsqJY2eJiYnRpno61blzW3z9HqoOY+O41yr+8ieFQoGSjPvHsH2nljx7fUN1GBlp/zdBs4x62pmTFzl84ARPHj3nwrmr9OwcN++mU9d2GucyMjJixdrfMDAwYOKYWdpULVk6fdsGL597qiPRv40ovroCLv7Tca/d58cdv2nFt13a0a/vKOrUasOgAWMZNrwfXbt1AFDNKTt86CQrlq/n/v3HLFywmqNHTtP3+24pqmdqUMbG6uzICLTqGejXrx9bt27VeunghAkTcHFxUUuztC6u1bkADhw4zvXrt1WPTU3jumodHGzV/lDb2dng65fyjZCioqJwc/MAwPXWPSo5l2fY0H4MHvJjis8tku/o4VO43ryjemz6d1e9nb0tvr6fv/Ha2Frj55/4z9/PV/2bDoCtrZVGDwNAnry5qVu/Bj2/G6Lx3N07D6lbsw05zXJgYmJCYEAQJ07v4vbt+8mtms4dOnSSGzfuqB7/816xt7fDx+fza2Vra4Ofb8bdNOz4kTPcvvn59TYxjWv02Nqp18vG1kqjt+Df/P0CsLNT/52wsbH+YpmPHz7y5NEznArlU0s3MjJi1fr55Mufh2/b9EmTXoEjh0/hevOu6rGJ6ucd/71hpfHN/998fQOwt4v/3rBWKzNz9ngWLljF7l0HAXj08Bl58zniMmYQ27buITAwmKioKNVcmn88e+pGtXQ4hJbZaNUY+PTpE2vWrOHkyZOULVv287eLvy1YsOCL5U1NTTE1NVVLS8kQQXj4e8LD1d9Y3t6+NGpYhzt3HgJx47t1aldjwsS5Wl8nMQqFQvVHVaS9hH7+Pj5+1GtQk/v3HgFxP/+aNaswfeqviZ7nxvXb1Ktfk5XLN6jS6jeoxfVrtzXyfte9I/7+gRw/ejbR870LCwegYKH8lK9YmrmzFyW9UqkksdeqQYNa3L37+b1Sq1ZVpkz5SR8h6sT78A+8D/dUS/P18adO/Ro8vP8EiKtntZqVmDs98b9XrtfvULt+df63cpMqrU6DGty8fifRMiYmxhQpWpBrV26p0v5pCDgVyk+n1n0SHD5IDYn9vOs3qMW9f783alVl+tRfEj3Pjeu3qd+gFiuWr1elNWhYm+vXPtcxW9YsGt+CY2NiMFDE9QhERUVxy/U+RYqo39G2UBEnXr/20q6CqSmTDRNo1Ri4d+8e5cuXB+DBgwdqz+lq3D+lliz9nfE/DuP5C3devHBn/I/D+PDhI9u271XlWb9uMW/fejNpctwfPWNjY9USQRMTYxxzO1CuXCnCw9+regJmzxrP0aOnef3mLTlz5qDzt22pW7c6LVt9l+Z11KUPHz7i+eat6rHXW1+ePHPD3CwnuRzs9BiZdlat2IjL6EG8dPPg5QsPRo35gQ8fP6rGLwFWrP4Fb29fZk2fD8DqlRs5eHQrw0cN4MihkzRv2Yi69WvQoklXtXMrFAq6de/I9q17E+z6b9uuGQEBQbx5403JUkWZ9/NkDh88qTE5Mb1YvmwdY8YO4YWbB24v3Bk7dggfP35k546/VHn+97/5vH3ry7RpcR8YxsbGqqExExNjcue2p2zZkoSHv+fly1d6qcfX/L7qD4a59Mfd7RXuL18xzGUAHz98Yu+uQ6o8i1fOxdvbj59mLgJg7erN7D60kcEjvufY4dM0bdGA2nWr0b755/lSU2aO4cTRs3i98cbG1ooRYwaRI2cO/ty+D4hbf79m40LKlCtBry5DMDQ0VM05CAkOJSoq9VeM/NvK5etxGfMDbm4euLl5MHrMD3z8+JE/d36+Nf2qNb/h/daHGdN/iyuzYgNHjm1j5KgBHDp0kpYtG1Gvfg2aNu6sKnPkyGlGjx3M69dvefL4OWXLlWLIsL5s3vR5gu2Sxf9j/cbFXL50gwvnr9KwcR2aN29Ay+bpb5gAZcbo3tcVrRoDZ86c0XUcOvfrbyvImjULy5bMxdLSnOvXb9O8ZTe1VnK+vLnVlkXmzm2P643jqsejR//A6NE/cO7cZRo27gTEDTVsWL+EXLnsCA19x/37j2nZ6jtOntL/ksCUePDkOX2HfR7m+GVp3LKjts0bMWfyaH2FpbUlC9eQNYspvy6YjoWFOa437/JN2z5qP/88eXMT+69Bz+vXbtOv9ygmTh3JxMkj8HB/zfe9R6p1swLUq1+TvPkc2fLHLhJi72DH7HkTsbWzxtfHnx3b9vHrz8tTp6I6sGDBKrJkzcKiRbOwsDDnxo07tGndI95r5Ujsv74p5cplz5Wrh1WPR44ayMhRAzl//irNm3VJ0/iTasXitWTJYsrc36ZgbmHGbdd7dOvYn/fhH1R5cufJpVbPm9fvMPj7sYybNIyxE4fxyt2TH/qO4bbr5yGIXI72LP/9V6ysLQkMCOLWzXu0btINr9dxSwZz5banaYsGAJy48HmyKcA3rXpz5dKN1Ky2hkUL15AlaxbmL5yBhYU5N2/eoX3b3vF+3rnU/jZev3aLvr1HMHmqC5OmjMLd3ZM+vYarvTfGjZnBpCmjmL9wJra21vh4+7J+3XZ+nrdUlefggeOMGjEFl9E/8POvU3n+/CU9vhvC1SvpcDVWJusZ0HqfgX+8efMGhUKBo2PK9gnQ9T4DGVV62GcgPUjpPgP/FWmxz0BGoIt9Bv4LdLHPwH9Fau8z8H6m7np7s0/dorNzpRatVhPExsYyc+ZMzM3NyZ8/P/ny5cPCwoJZs2ZpbEAkhBBCZDixsbo7MgCthgkmTZrE2rVr+emnn6hZsyZKpZJLly4xffp0Pn36xJw5c3QdpxBCCJF2MtkwgVaNgY0bN/L777/Tpk0bVVq5cuVwdHRk8ODB0hgQQgghMhCtGgNBQUEUL665L0Dx4sUJCgpKcVBCCCGEXmWy1QRazRkoV64cy5Zp3nFr2bJllC1bNsVBCSGEEHqVye5aqFXPwC+//ELLli05efIk1atXR6FQcPnyZV6/fs3hw4e/fgIhhBBCpBta9QzUrVuXZ8+e0b59e0JCQggKCqJDhw48fPiQ9evXf/0EQgghRDqW2e5NkOJ9Bv7t7t27VKxYUasbssg+A3Fkn4E4ss9AHNlnII7sMxBH9hn4LLX3GQj/sYPOzpXj5z1fz6RnWvUMCCGEEOK/Q6s5A0IIIcR/WgaZ+Kcr0hgQQggh4stkSwuT1Rjo0OHLYyghISEpiUUIIYRIH6RnIHHm5uZffb5nz54pCkgIIYQQaStZjQFZNiiEECIzUErPgBBCCJHJZbLGgCwtFEIIITI56RkQQggh4ssgOwfqijQGhBBCiPhkmEAIIYQQmYn0DAghhBDxZbKeAWkMCCGEEPHo8B5+GYIMEwghhBCZnPQMCCGEEPHJMIEQQgiRyUljIGGWlpYoFIok5Q0KCtI6ICGEEELfZDviRCxatEj1/8DAQGbPnk3Tpk2pXr06AFeuXOHYsWNMmTJFq0Aq2RTRqtx/jb1TU32HkC74uh/TdwjpQsybR/oOIX2I/KjvCNIFhaWjvkMQ/1FJbgz06tVL9f+OHTsyc+ZMhg4dqkobPnw4y5Yt4+TJk4waNUq3UQohhBBpKZP1DGi1muDYsWM0a9ZMI71p06acPHkyxUEJIYQQehWrwyMD0KoxYG1tzd69ezXS9+3bh7W1dYqDEkIIIUTa0aoxMGPGDMaPH0/Lli2ZPXs2s2fPplWrVkyYMIEZM2boOkYhhBAiTSljlTo7kmvFihU4OTmRJUsWnJ2duXDhwhfzR0REMGnSJPLnz4+pqSmFChVi3bp1ybqmVksLe/fuTYkSJViyZAl79uxBqVRSsmRJLl26RNWqVbU5pRBCCJF+6GnOwI4dOxg5ciQrVqygZs2arF69mubNm/Po0SPy5cuXYJlvv/0WX19f1q5dS+HChfHz8yM6OjpZ11Uo08mei9Vy19N3COnCs3de+g4hXZDVBHFkNcHfZDUBIKsJ/s20SI1UPX9I1/o6O5fFtjNJzlu1alUqVqzIypUrVWklSpSgXbt2zJs3TyP/0aNH6dKlCy9fvsTKykrrGLXejtjNzY3JkyfTrVs3/Pz8VEE9fPhQ62CEEEKIdEGHEwgjIiIICwtTOyIiIjQuGRkZiaurK02aNFFLb9KkCZcvX04wzP3791OpUiV++eUXHB0dKVq0KGPGjOHjx+Q1oLVqDJw7d44yZcpw7do1du/eTXh4OAD37t1j2rRp2pxSCCGESDd0OWdg3rx5mJubqx0JfcsPCAggJiYGe3t7tXR7e3t8fHwSjPPly5dcvHiRBw8esHfvXhYtWsSuXbsYMmRIsuqrVWNg/PjxzJ49mxMnTmBiYqJKr1+/PleuXNHmlEIIIcR/0oQJEwgNDVU7JkyYkGj++Lv9KpXKRHcAjo2NRaFQsGXLFqpUqUKLFi1YsGABGzZsSFbvgFYTCO/fv8/WrVs10m1tbQkMDNTmlEIIIUT6ocP9AUxNTTE1Nf1qPhsbGwwNDTV6Afz8/DR6C/6RK1cuHB0dMTc3V6WVKFECpVLJmzdvKFIkabv7atUzYGFhgbe3t0b67du3cXSUCS5CCCEyNn0sLTQxMcHZ2ZkTJ06opZ84cYIaNRKeMFmzZk3evn2rGq4HePbsGQYGBuTJkyfJ19aqMdCtWzd+/PFHfHx8UCgUxMbGcunSJcaMGUPPnj21OaUQQgiRfuhpB0IXFxd+//131q1bx+PHjxk1ahSenp4MGjQIiBty+PfnbLdu3bC2tqZPnz48evSI8+fPM3bsWPr27UvWrFmTfF2thgnmzJlD7969cXR0VO0xEBMTQ7du3Zg8ebI2pxRCCCEyvc6dOxMYGMjMmTPx9vamdOnSHD58mPz58wPg7e2Np6enKn+OHDk4ceIEw4YNo1KlSlhbW/Ptt98ye/bsZF03RfsMuLm5cfv2bWJjY6lQoUKSxyYSIvsMxJF9BuLIPgNxZJ+Bv8k+A4DsM/Bvqb3PQGDrujo7l/WBczo7V2rRqmfgH4UKFaJQoUK6ikUIIYRIHzLIDYZ0RavGgFKpZNeuXZw5cwY/Pz9iY9VftT179ugkOCGEEEKkPq0aAyNGjGDNmjXUr18fe3v7RNc/CiGEEBmRUnoGvm7z5s3s2bOHFi1a6DoeIYQQQv8yWWNAq6WF5ubmFCxYUNexCCGEEEIPtGoMTJ8+nRkzZiT7RghCCCFERqCM1d2REWg1TNCpUye2bduGnZ0dBQoUwNjYWO35W7du6SQ4IYQQQh8yyoe4rmjVGOjduzeurq50795dJhAKIYT4z5HGQBIcOnSIY8eOUatWLV3HI4QQQog0plVjIG/evJiZmek6FiGEECJ9UGauHm+tGgPz589n3LhxrFq1igIFCug4JO107NWW737ogrWdNe7P3Fk4dRl3r99PNH+FauUYMX0wTkWdCPANYPOK7ez9Y7/q+XrNa9NreHfyFHDEyNiQ1+5ebF21g6O7P99Nau+17eTK66Bx7l0b9vLbxMW6rWAy/DhhGD37dMbCwhzXm3cZ5zKdJ09efLFM6zZNmThlJAWc8uHh7snsmQs4dOBzXe88OEO+/Jp3wPp9zWbGjZ4BgK2tNdNmjaN+g5qYm5tx5dINfhw7k5dur3Rav9R088591m/dxaMnL/APDGLxvCk0rJO6256mtR3HLrHhwFkCQsIolMeBcb3aUrFE4quDth+7yPajl3jrH4SDjSX92zeidd1KqudfvPZhxc6jPHZ/w1v/YMb2bEv3lnXSoCa6tePkNTYcukBAaDiFHO0Y170FFYsVSDT/9hNX2X7yGm/9g3GwtqB/27q0rlUh7QLWke2HTrNhzxECgkIolM+Rcf274Vy6aOL5D55i28FTvPULwMHWmv7ftqJNw5qq5/86eZEpi9ZqlLuxZw2mJsYa6emVDBMkQffu3fnw4QOFChUiW7ZsGhMIg4KCdBJcUjVqU5+RM4by68RF3Lt+n3Y92rBwyy90rdcLXy8/jfy58jqwYPNP/LXlENOHzqFslTKMnTuSkMAQzhw+D0BYyDs2LP6DVy88iYqKpmaj6kxeOJ7ggBCunbsBQJ/mAzEwNFSdt1BxJ5bumM9pPe5DPXzUAAYP7cuQH37E7bk7o8cNZvf+DVSt2JTw8PcJlqlcpTxrNy5i7uxFHDpwgpatG7Nu42JaNOmK6827ADSs1xFDg8+LT0qULMreAxv5a+8RVdrm7SuJioqme5cfePcunMFD+7J3/0aqV27Ohw8ZY+XJx4+fKFa4IO1aNGHUpOTd6CMjOHr5Nr9s/ItJ33egfDEndp28wuB5/2PvgnHksrHUyL/z+GWWbDvM1AGdKF0oH/dfeDJzzZ/kzJGVes6lAPgUEUkee2saVyvHb5v+Susq6cTRq/f5ZfNhJvVuTfki+dh15gaDf93E3p+Gk8vGQiP/zpPXWLLzBFO/b0fpgo7cd3vDzHX7yJktK/UqFk/7Cmjp6Plr/PK/rUz6oQcVShbhzyNnGTx9AftWzCGXnbVG/h2HT7N44y6mDetN6aJO3H/qzoxlGzDLkZ16Vcur8uXIlpX9q+eplc1IDYHMSKvGwKJFi3QcRsp0HdCJA9sOs3/rIQAWTVtGtXqV6dCzLSvn/U8jf4eebfD18mPRtGUAeLzwpHjZYnQb1FnVGLh15Y5amZ1rd9Py26aUq1JG1RgICQpVy9NzaDdeu3tplE1Lgwb3Yv5vKzm4/zgAgwf+yFO3K3Ts1JqN67cnUqY3Z09fYtH81QAsmr+amjWrMGhwb/r3HQVAYIB6A2+ky0Beur3i0sXrABQqXIDKVSpQo3JzVS/EmFHTeOZ+lY6dWvHHxj9Tpb66Vrt6ZWpXr6zvMFLNH4fO075BFTo0rAbAuN7tuHz3KTuPX2ZEt5Ya+Q9euMk3jarTrEbcN9489tbcf/6K9X+dVjUGShfOR+nC+QBYsu1QGtVEt/44con2dZ3pUC+ux2Nc95Zcvv+CnaeuM6JzE438By/d4ZsGlWlWrQwAeeysuO/2mvWHzmeoxsCmfcdp37gOHZvG3ZTnxwHduHzrATsPn2ZE704a+Q+evsw3zevRrE5VAPI42HHvqRvrdx9WawwoFGBjaZ4mdUgtylgZJviiqKgozp49y5QpU9LFxkNGxkYUK1uMTcu2qqVfO3eDMpVKJVimtHMp1Qe6Kv/Z67Tp2gJDI0NiomM0ylSqVZF8hfKyfM7qRONo1rEx21bv1LImKZe/QF4cHOw4c+qiKi0yMpJLl65TpVqFRBsDlatUYOXy9Wppp09dYNDg3gnmNzY2plOXNqxY9rmMiYkJEPct8R+xsbFERkZRtXqlDNMY+C+Lio7m8cs39G3bQC29erli3H3mkWCZyKgYTIzV/0yYmhjz4MVroqJjMDYyTLBcRhIVHc1jj7f0ba0+tFG9dGHuPvdMsExkdAKvi7ExD9y8MszrEhUVzeMXHnz/jfpOstUrlOLOE7cEy0RGRWMaryc4i4kx95+9JCo6GmOjuNfkw8cImvYZQ0xsLMUL5mNI9/aUKJQ/dSqSSjLbMEGyNx0yNjZm7969qRGLViyszDEyMiQoIFgtPcg/GGs7qwTLWNtaEeQfL39AMEbGRlhYfW7NZs+ZndPPj3Dx1Unmb/qJ+ZOXcP28a4LnrNusFjnMcnBo59EU1kh79vY2APj7Bail+/sFYG9nm2g5O3sb/PwC1dL8/AKxs0+4TMtWjTA3N2Pb5s83pHr+7CWer94wdfpozC3MMDY2ZoTLABwc7HBI5DwibQWHvScmNhZr8xxq6dbmOQgIeZdgmRrlirH39DUevXyNUqnkodtr9p29TnRMDCHvEh52ymiC332Ie13M4r8u2QkIDU+wTI0yhdl79iaP3L3iXpeXXuw77xr3uoR/SIuwUyw47F1cvS3VJ4NbW5oTEByaYJkaFUuz5/h5Hr3wiKv3c3f2nrxIdHQMIWFxr1WBPLmYNep7lkwZzs9jB2FibEyvcXN55eWT6nUS2tNqmKB9+/bs27cPFxcXrS4aERFBRESEWlqsMhYDhVYbIgJxd1L8N4VCQbykr+aPS/+c9iH8Az0b9yNr9qxUrlWREdOG8PaVd4LDAK27tuDqmWsE+AZqPJdavvm2DQsWz1Q97vLNACCx1+ILL0Yyy3Tv2YmTJ87j4/N5PkZ0dDS9ug9lyfJ5uL92JTo6mnNnLnPi2NnkVEmkgfj7giiVcd26CRnQsTEBIWH0mLwEpRKszHPQpm5lNuw/g4HBf6sbNf5r8MXXpV19AkLD6TFj9d+vS3ba1K7IhkMXMMhg+64oiP/7oEy03gO7tCEwOJTuo2ejVCqxtjCjbcOarN99BIO/5xSVK16IcsU/39q+QsnCdB4xnW0HTzF+4HepVg9dU8pqgq8rXLgws2bN4vLlyzg7O5M9e3a154cPH/7F8vPmzWPGjBlqaY458pMnZ4FkxxISFEp0dAzWtuq9AJY2FgT5JzyRMdA/SKPXwNLaguioaEL/1SJWKpW88fAC4PnDFxQokp+ew7ppNAYcHO2pXNuZ8f2mJjv+lDh6+BSuNz/HYvp3V72dvS2+vv6qdBtba/z8A+IXV/HzDVD1KvzD1tZKo4cBIE/e3NStX4Oe3w3ReO7unYfUrdmGnGY5MDExITAgiBOnd3H7duKrOkTasTTLjqGBgUYvQFBYONbmORMsk8XEmJk/dGFK/04Ehb7DxtKM3Sevkj2rKZY5sydYJqOxzJkt7nWJ1wsQFPZeo7fgH1lMjJnZvwNT+rQlKCwcG4uc7D59g+xZTLHMmS0twk4xS7OccfWO1wsQFBKGtUXC4/1ZTE2YOfJ7pgztRWBIGLaWFuw6dpbsWbNgmchrZWBgQKkiTrx666vzOqQmGSZIgt9//x0LCwtcXV1Zs2YNCxcuVB1JmVw4YcIEQkND1Y7cOfJpEwrRUdE8vfeUKnUqqaVXqVOJ+zcfJljmgetDjfxV61bm8d2nCc4XUFF8Hhv/t1ZdmhMcEMLlk1eTX4EUCA9/j/tLT9Xx5MkLfHz8qNfg8zIfY2NjataswvWrtxM9z43rt6lXv6ZaWv0Gtbh+TbPMd9074u8fyPGjZxM937uwcAIDgihYKD/lK5bmyKFTya+c0DljIyNKFMzD1XvP1NKv3ntGuaIFvlLWEHtrCwwNDDh6+TZ1KpZUfRPM6IyNjChRIDdXH6gvv7364AXlinz575KxkSH2VuZxr8vV+9SpUCzDvC7GxkaUKFyAK3fU/05evfOI8v/6Zp9gWSMjHGysMDQ04Oj569SpUi7ReiuVSp66e2KbwScU/tdp1TPg7u6eoouamppiamqqlpaSIYJta/5k2pKJPL73lAc3H9K2e2vsHe3Zuylu34AfJvTH1sGGmSPilrrs2bSfb/q0Z8S0wfy15SClK5WiddcWTB08S3XOnkO78eTeU954vMXYxJgaDarS4pum/DJhodq1FQoFLTs34/Cfx4iJ+UJDIo2sWrERl9GDeOnmwcsXHowa8wMfPn5k958HVHlWrP4Fb29fZk2fD8DqlRs5eHQrw0cN4MihkzRv2Yi69WvQoklXtXMrFAq6de/I9q17E6xr23bNCAgI4s0bb0qWKsq8nydz+OBJzpy+qJE3vfrw4SOeb96qHnu99eXJMzfMzXKSy8FOj5HpRo+WdZi0bBslC+WhXJEC7D51Fe+AYDo1rg7A4q2H8AsKZc7QbgB4vPXngZsnZQrnI+z9R/44eI4Xr32YNfjz70ZUdDRub3z//n8MfsGhPPHwIlsWU/I52GgGkQ71aF6TSat2UdLJkXKF87L7zE28A0Pp1DBuZcniHcfxCw5jzqBvAPDwDuDByzeUKZSHsPef+OPIJV54+TJrYEd9ViPZerZrwsQF/6NU4QKUK1GYXUfP4e0fSKcW9QFYvOFPfANDmDu6PwAeXj48ePaSMkULEhb+gT/2HePFqzfMHtVPdc6VW/dRtlgh8jvaE/7hE1v3n+Dpy9dMHNRDL3XUlqwmyIBO7j+DuaUZ34/qhbWdFS+fuuPS/Ud8vOL+QNnYWePgaK/K7/3aB5fu4xk5Ywgde7cjwDeQBVOWqpYVAmTNlpWxc0dhm8uWiE8RvHLzZPqwOZzcf0bt2pXrOJMrjwMHth9Om8p+xZKFa8iaxZRfF0xXbTr0Tds+ansM5Mmbm9h/zQe4fu02/XqPYuLUkUycPAIP99d833ukao+Bf9SrX5O8+RzZ8seuBK9t72DH7HkTsbWzxtfHnx3b9vHrz8tTp6Kp5MGT5/Qd9qPq8S9L1wDQtnkj5kwera+wdKZZjQqEvvvAmt0n8A8Oo3DeXCwf34/cfw+zBYSE4RMYosofGxvLpoNnefXWHyNDQyqXKsSmWcNw/Ncwm19QGJ1/XKB6vPHAWTYeOEulkoVYO21wGtUsZZpVK0No+AfW7DuDf8g7CuexZ/mYHuT+e++FgJB3mq/LkUu88g7AyNCAyiUKsmnqABxtNfdqSM+a1alKyLv3rN6+H/+gUArnd2T59FHktvt7MnJwKD7+n+dBxcbGsmnvMTy8fOJ+H8oWZ9Ovk3D81zDju/cfmblsIwHBoeTInpUSBfOx/qfxlCmm/9VnyfGVaVb/OQrl12aW/c3FxYVZs2aRPXv2r04cXLBgwRefT0i13PWSXea/6Nk7L32HkC74uh/TdwjpQsybR/oOIX2IzBibVqU2haWjvkNIN0yLpO7OoK8qNtLZufLfOqmzc6WWJPcM3L59m6ioKCDuFsWJ3alQ7mAohBBCZCxJbgwsXrxYdXOis2fPplY8QgghhN5ltjkDSZ61V6FCBQIC4paaFSxYkMDAtFtPL4QQQqQlpVJ3R0aQ5MaAhYWFahWBh4cHsbGZbBGmEEII8R+V5GGCjh07UrduXXLlyoVCoaBSpUoYGia8//bLly91FqAQQgiR1jLbMEGSGwNr1qyhQ4cOvHjxguHDh9O/f39y5kx41zIhhBAiI5PtiL+gWbNmALi6ujJixAhpDAghhBD/AVptOrR+/fqvZxJCCCEyqMx2bwKtGgPv37/np59+4tSpU/j5+WlMJpQ5A0IIITKyWBkm+Lp+/fpx7tw5evTooZpQKIQQQoiMSavGwJEjRzh06BA1a9b8emYhhBAig5EJhElgaWmJlZXV1zMKIYQQGVBmW1qo1X2DZ82axdSpU/nw4YOu4xFCCCH0LrPtQKhVz8D8+fNxc3PD3t6eAgUKYGxsrPb8rVu3dBKcEEIIIVKfVo2Bdu3a6TgMIYQQIv3IbMMEWjUGpk2bpus4hBBCiHRDlhYmg6urK48fP0ahUFCyZEkqVKigq7iEEEIIkUa0agz4+fnRpUsXzp49i4WFBUqlktDQUOrXr8/27duxtbXVdZxCCCFEmslsSwu1Wk0wbNgwwsLCePjwIUFBQQQHB/PgwQPCwsIYPny4rmMUQggh0pSsJkiCo0ePcvLkSUqUKKFKK1myJMuXL6dJkyY6C04IIYQQqU+rxkBsbKzGckIAY2NjjfsUCCGEEBlNZptAqNUwQYMGDRgxYgRv375VpXl5eTFq1CgaNmyos+CEEEIIfVAqFTo7MgKtGgPLli3j3bt3FChQgEKFClG4cGGcnJx49+4dS5cu1XWMQgghhEhFWg0T5M2bl1u3bnHixAmePHmCUqmkZMmSNGrUSNfxCSGEEGkuo0z805Vk9QycPn2akiVLEhYWBkDjxo0ZNmwYw4cPp3LlypQqVYoLFy6kSqBCCCFEWolVKnR2ZATJ6hlYtGgR/fv3x8zMTOM5c3NzBg4cyIIFC6hdu3ayA8lhaJrsMv9FETFR+g4hXYh580jfIaQLhnlK6juEdCFqjww/AhiUrabvENKPIql7+owy1q8ryeoZuHv3Ls2aNUv0+SZNmuDq6prioIQQQgiRdpLVM+Dr65vgkkLVyYyM8Pf3T3FQQgghhD5llO59XUlWz4CjoyP3799P9Pl79+6RK1euFAclhBBC6JNSh0dGkKzGQIsWLZg6dSqfPn3SeO7jx49MmzaNVq1a6Sw4IYQQQqS+ZA0TTJ48mT179lC0aFGGDh1KsWLFUCgUPH78mOXLlxMTE8OkSZNSK1YhhBAiTWS2YYJkNQbs7e25fPkyP/zwAxMmTED590JMhUJB06ZNWbFiBfb29qkSqBBCCJFWMttqgmRvOpQ/f34OHz5McHAwL168QKlUUqRIESwtLVMjPiGEEEKkMq12IASwtLSkcuXKuoxFCCGESBcy2y33tLo3gRBCCPFfpkShsyO5VqxYgZOTE1myZMHZ2TnJO/teunQJIyMjypcvn+xrSmNACCGESCd27NjByJEjmTRpErdv36Z27do0b94cT0/PL5YLDQ2lZ8+eWt85WKvGwOvXr3nz5o3q8fXr1xk5ciRr1qzRKgghhBAiPYlV6u6IiIggLCxM7YiIiEjwugsWLOD777+nX79+lChRgkWLFpE3b15Wrlz5xXgHDhxIt27dqF69ulb11aox0K1bN86cOQOAj48PjRs35vr160ycOJGZM2dqFYgQQgiRXsSi0Nkxb948zM3N1Y558+ZpXDMyMhJXV1eaNGmilt6kSRMuX76caKzr16/Hzc2NadOmaV1frRoDDx48oEqVKgDs3LmT0qVLc/nyZbZu3cqGDRu0DkYIIYRID3Q5Z2DChAmEhoaqHRMmTNC4ZkBAADExMRpL9O3t7fHx8UkwzufPnzN+/Hi2bNmCkZHWawK0W00QFRWFqWncXQZPnjxJmzZtAChevDje3t5aByOEEEL815iamqo+M5NCoVCfdKhUKjXSAGJiYujWrRszZsygaNGiKYpRq8ZAqVKlWLVqFS1btuTEiRPMmjULgLdv32JtbZ2igIQQQgh908fSQhsbGwwNDTV6Afz8/BLc0O/du3fcvHmT27dvM3ToUABiY2NRKpUYGRlx/PhxGjRokKRrazVM8PPPP7N69Wrq1atH165dKVeuHAD79+9XDR8IIYQQGZU+lhaamJjg7OzMiRMn1NJPnDhBjRo1NPKbmZlx//597ty5ozoGDRpEsWLFuHPnDlWrVk3ytbXqGahXrx4BAQGEhYWp7Tw4YMAAsmXLps0phRBCiEzPxcWFHj16UKlSJapXr86aNWvw9PRk0KBBAEyYMAEvLy82bdqEgYEBpUuXVitvZ2dHlixZNNK/RuvZBoaGhhpbEBcoUEDb0wkhhBDphr52IOzcuTOBgYHMnDkTb29vSpcuzeHDh8mfPz8A3t7eX91zQBsK5T93G0oGX19fxowZw6lTp/Dz8yP+KWJiYpIdSKO8TZNd5r/oSuBTfYeQLgReXqbvENIFwzwl9R1CuhC1Z6m+Q0gXDMpW03cI6UaWap1T9fyH7bvo7FwtfLfr7FypRauegd69e+Pp6cmUKVPIlStXgrMchRBCCJExaNUYuHjxIhcuXNBq/2MhhBAivdPmngIZmVaNgbx582oMDQghhBD/FbGZqy2g3dLCRYsWMX78eDw8PHQcjhBCCCHSWpJ7BiwtLdXmBrx//55ChQqRLVs2jI2N1fIGBQXpLkIhhBAijcXKMEHCFi1alIphCCGEEOlHZhsIT3JjoFevXqkZh8616dmKTgM7YW1nhcezV6yYsYoH1x8kmr9stTIMmjKQAkXzE+gbyI5Vf3Jw8yHV8/N3/kK56uU0yl07dY1JvaemSh1S08RJI+nbtysWFubcuHEHl1FTePz4eaL5S5QowuQpLlSoUIb8+fMwbuxMli9fl4YRp8yOY5fYcOAsASFhFMrjwLhebalYomCi+bcfu8j2o5d46x+Eg40l/ds3onXdSqrnX7z2YcXOozx2f8Nb/2DG9mxL95Z10qAmaePmnfus37qLR09e4B8YxOJ5U2hYR3MHtIxs551XbLzxkoD3ERSyzsGY+iWpmMcqwbxTj97lwEMvjfSC1jnY3fvzz/3dpyiWXXzK6Re+hH2KwtE8K6PqlqB2QbtUq0dK7Th1nQ2HLxIQGk6h3LaM+645FYsVSDT/oct32XD4Ip6+QeTIakqNMkUY3bUpFjniNpyLio5h7cHzHLh4B7+QdxRwsGbkt02oWbZIGtVIN/S1z4C+aDVnwNDQED8/P430wMBADA0NUxxUStVrXZcfpg1i69JtDGo+mPvXHzBv02zsctsmmN8hrz1zNs7m/vUHDGo+mK3LtjNkxg/Ubl5LlWf6gFl0qthFdXzfcAAx0TGcO3QhraqlMy4ugxg27HtcXKZSp3YbfH39OXBwMzlyZE+0TNZsWfFw92TqlJ/x8dH82adnRy/f5peNf9G/fUN2/ORCxeJODJ73P7wDghPMv/P4ZZZsO8ygTk3YM38cP3Rqytx1ezjr+lCV51NEJHnsrRnetSU2FjnTqipp5uPHTxQrXJCJLoP1HUqqOPbkLb+eecT3VQuzrUctKuSxYuieG3iHfUww/9j6JTkxqKHqODqgPuZZjGlc1EGVJyomlkG7rvM27CO/tq7A3r51mdK4DHY5sqRVtZLt6LX7/LLlCP1b12XHzB+oWCw/g+dvxjswJMH8t569YvKaPbSr48zuuUP5dWhnHrp7MX3tX6o8y3afYteZm4zv0ZK9c4fSqX5lRi3ZxuNXchO79EyrxkBiKwkiIiIwMTFJUUC60LF/B47uOMaR7UfxfPGalTNW4ffWn9Y9WiWYv1X3Vvh5+bFyxio8X7zmyPajHN1xnE4DO6ryvAt5R7B/sOpwrl2RTx8/cf7g+bSqls4MGdqXX39Zzv6/jvHo0TMG9B9N1qxZ+bZz20TL3HK9x6RJ89i16wAREZFpGG3K/XHoPO0bVKFDw2oUzGPPuN7tcLC2YOfxhO8PfvDCTb5pVJ1mNSqQx96a5jUr0L5+Fdb/dVqVp3ThfLh0b03zmhUwMdb+tqHpVe3qlRk+oBeN69XUdyipYrOrO+3K5KVD2bwUtM7B2PolcciZhT/vvkowf05TY2yym6qORz6hhH2Kok3pvKo8+x68JuxTFAvaOlPe0YrcZlmpkMeKYnZmaVWtZPvj6GXa16lIh3rOFMxty7jvWuBgZcbOUzcSzH//xWty21jwXZNq5LG1pGLR/HxTvxKPPD73mhy6fJd+retQu1xR8thZ8W3DKtQoU5hNRy6lVbV0Ilah0NmRESTrr9iSJUuAuNsr/v777+TIkUP1XExMDOfPn6d48eK6jTCZjIyNKFqmCNtX7FBLdz3vSslKCe/mVtK5BK7nXdXSbp6/SfMuTTE0MiQmWnNHxeZdmnJ2/zk+fYzQXfBpoECBvDg42HHq1OcejcjISC5evEa1qs6sW7tVj9HpXlR0NI9fvqFvW/U7d1UvV4y7zzwSLBMZFaPxAW9qYsyDF6+Jio7B2Ej/vV9Ce1ExsTz2DaNPlUJq6dXy23L3bUiSzrHvwRuq5rcht1lWVdo5Nz/K5rbgp1MPOevmi2VWE5qXyE3vyoUwNEh/HwhR0dE89vCmb8vaaunVSxfm7ouEt7stVyQfS3ef4sLdZ9QqW4SgsPecvPGQ2uU+3z43Mipa8/1jbMyd57rfQjc1yZyBL1i4cCEQ1zOwatUqtSEBExMTChQowKpVq756noiICCIi1D9EY5WxGCi06qhQY25lhqGRIcH+IWrpwQEhWNlaJljGytaS4IB4+f1DMDI2wtzKnCA/9dURxcoXw6m4E7+NXZjieNOavX3cUImvn79aup+fP/ny5tFHSKkqOOw9MbGxWJvnUEu3Ns9BQMi7BMvUKFeMvaev0aByaUo45eHRyzfsO3ud6JgYQt69x9Yy/X7TE18X/DGSGKUSq2zq95e3zm5CoMfXG/f+4Z+45O7P3Jbl1dK9Qj5wI+wjzUvkZmmHyngGv+enUw+JjlUysHr6Gy8PfvchkfdGdgJCwxMsU75IPuYN+oZxK3YSGRVNdEws9SoUZ3z3lqo8NcoU5o+jl3EuVoC8dpZce/SSs7efEBOb2UbhM5ZkNQbc3d0BqF+/Pnv27NG4UVFSzZs3jxkzZqilOeUsSEHzwlqdLyHxhzIUCgVf3CdJI3/C5wFo3rkp7k/ceXon/d9HoHPntixZOlf1uGOHvnH/Sej1+Q+3heNvma1Ufv4ZxzegY2MCQsLoMXkJSiVYmeegTd3KbNh/BoN0+A1PaCf+z1+pJEmLyfY/fENOUyPqF1a/v3wsSqyymTClcRkMDRSUtDfHP/wTm266p8vGwD8SfB0SeXO4efnx8+bDDGxbjxqlC+Mf+o6F248ze+MBZnzfDoBx37Vg5vq/aDd+CQqFgjx2lrStXYG/LtxO5ZroVmZrumg12HnmzJkUXXTChAm4uLiopbUr2TGR3MkTGhRGTHQMVnbqDRULa3OCE5kwFuQfjGW8XgMLGwuio6IJCw5TSzfNYkr9NvXYMH+TTuJNbYcOneTGjTuqx6amcXM67O3t8PH53Dtga2uDn29AWoeX6izNsmNoYKDRCxAUFo61ecIT/7KYGDPzhy5M6d+JoNB32FiasfvkVbJnNcUyZ+KTLEXGYJnVBEOFgsD36r0AQR8iscpumkipOEqlkr8evKFlSUeMDdV7Mm2yZ8HIQKE2JOBknYOA9xFExcRq5Nc3y5zZ/n5vqPcCBIW9x9os4d/ztQcvUL5IPnq3iJtcXRQHspqa0GfOWoZ2bIitRU6szLKzaEQ3IiKjCAn/iJ1lThbtPEFuG4vUrpJOZbYdCLWe+fTmzRv279+Pp6cnkZHqE8oWLFjwxbKmpqaYmqq/6XQxRAAQHRXNs/vPca5dkUtHP08Qc65dkcvHryRY5pHrY6o3qqqWVqmOM8/uPdOYL1C3dR2MTYw5teeUTuJNbeHh7wkPf6+W5uPjR4MGtbh7N252vLGxMbVqVWXKlJ/0EWKqMjYyokTBPFy994yGVcqo0q/ee0a9SqW+UtYQe2sLIG5FQp2KJTEwSF9/0EXyGRsaUMLejKuvAmhQ5PNqgKuvAqhX+MtLAF3fBPE65APtyuTVeK58bkuOPHlLrFKJwd/frD2D32OT3TTdNQTg7/dGgVxcfehGw3/Np7r60I16FRKe+/UpMgrDeO+Bfxo/8XtRTU2MsbcyJio6hlM3H9Gkypffb0K/tGoMnDp1ijZt2uDk5MTTp08pXbo0Hh4eKJVKKlasqOsYk233//bw46KxPLv3jEeuj2n5XQvsHO048Pe+Ad//2AcbBxt+HvUrAAc3H6Rt7zYMmjqAw1uPUNK5BM06N2XuUM0Px+ZdmnHp+GXCEhlvzgiWL1vHmLFDeOHmgdsLd8aOHcLHjx/ZuePz8qD//W8+b9/6Mm3aL0Bcg6FEibiuThMTY3Lntqds2ZKEh7/n5cuEZ2CnFz1a1mHSsm2ULJSHckUKsPvUVbwDgunUuDoAi7cewi8olDlDuwHg8dafB26elCmcj7D3H/nj4DlevPZh1uCuqnNGRUfj9sb37//H4BccyhMPL7JlMSWfg03aV1LHPnz4iOebt6rHXm99efLMDXOznORySL9r5pOqu7MTk4/cpaS9OWVzW7Lnnic+7z7yTbm4e8YvufAEv/AIZjdX31tk3/3XlMllQWEbzV6lTuXysf22B7+cfkTXCvnxDPnA2mtudK1QIC2qpJUezWowafUeSjo5Uq5wXnafuYl3YCidGlQGYPHOE/gFhzHn75VVdcsXY+b6v9h56jo1yhTGP+Qdv249QumCjtj9PZfmnttr/ILfUTyfA37BYazcd4ZYpVLVm5BRyA6ESTBhwgRGjx7NzJkzyZkzJ7t378bOzo7vvvuOZs2a6TrGZDt74BxmljnpPuI7rOys8Hj6iom9JuPnFbc+3sreCjvHz3sO+Lz2ZVKvyfwwdSBterYm0DeI5dNWcuHIRbXzOjo5UqZKacZ1m5Cm9dG1BQtWkSVrFhYtmqXadKhN6x5qPQh58joSG/u5pZ8rlz1Xrh5WPR45aiAjRw3k/PmrNG+mu/t+p4ZmNSoQ+u4Da3afwD84jMJ5c7F8fD9y28ZtMBMQEobPv9ZVx8bGsungWV699cfI0JDKpQqxadYwHO0+b0jjFxRG5x8/94BtPHCWjQfOUqlkIdZOy/hr8x88eU7fYT+qHv+ydA0AbZs3Ys7k0foKS2eaFs9N6Kco1lx9QcD7CApb52Bph8qq1QEB7yPwibfnwLuIKE4992Fs/YRXJTmYZWXFN1WYf/Yx3266iF2OLHSrWIDelQslmD89aFa1DKHhH1nz11n8Q95R2NGO5S7dVV36AaHv8AkKVeVvW7sC7z9FsO3kNeZvP0bObFmoXMKJkd82UeWJjIpm+e5TvPEPJpupCbXKFmHOgI6YZc8a//Lp2n93BlXCFEotbj+YM2dO7ty5Q6FChbC0tOTixYuUKlWKu3fv0rZtW61uYNQob9Nkl/kvuhKY/iclpoXAy8v0HUK6YJgn4Q+ezCZqz1J9h5AuGJStpu8Q0o0s1Tqn6vk35+6us3N1f7tZZ+dKLVoNZGXPnl21NDB37ty4ubmpngsI+O9NQhNCCJG5xCp0d2QEWg0TVKtWjUuXLlGyZElatmzJ6NGjuX//Pnv27KFaNWm5CiGEyNhkaWESLFiwgPDwuOUo06dPJzw8nB07dlC4cGHVxkRCCCFERpXZ5gxo1RgoWPDz3d6yZcvGihUrdBaQEEIIIdKW1otfQ0JC+P3335kwYQJBQXHb9d66dQsvL83bfAohhBAZicwZSIJ79+7RqFEjzM3N8fDwoH///lhZWbF3715evXrFpk0ZY3c+IYQQIiGZbc6AVj0DLi4u9O7dm+fPn5Mly+d7dTdv3pzz5zPeLX2FEEKIzEyrnoEbN26wevVqjXRHR0d8fHxSHJQQQgihT5mtZ0CrxkCWLFkICwvTSH/69Cm2trYJlBBCCCEyDmUGGevXFa2GCdq2bcvMmTOJiooC4m536enpyfjx4+nYUTd3HxRCCCFE2tCqMfDbb7/h7++PnZ0dHz9+pG7duhQuXJicOXMyZ84cXccohBBCpKlYHR4ZgVbDBGZmZly8eJEzZ87g6upKbGwsFStWpFGjRrqOTwghhEhzGeVDXFeS3RiIjY1lw4YN7NmzBw8PDxQKBU5OTjg4OKBUKlEoMtlAixBCCJHBJWuYQKlU0qZNG/r164eXlxdlypShVKlSvHr1it69e9O+ffvUilMIIYRIM0odHhlBsnoGNmzYwPnz5zl16hT169dXe+706dO0a9eOTZs20bNnT50GKYQQQqSljLJzoK4kq2dg27ZtTJw4UaMhANCgQQPGjx/Pli1bdBacEEIIoQ+ZbQJhshoD9+7do1mzZok+37x5c+7evZvioIQQQgiRdpI1TBAUFIS9vX2iz9vb2xMcHJzioIQQQgh9yijf6HUlWY2BmJgYjIwSL2JoaEh0dHSKgxJCCCH0KaNM/NOVZDUGlEolvXv3xtTUNMHnIyIidBKUEEIIIdJOshoDvXr1+moeWUkghBAio8tsqwmS1RhYv359asUhhBBCpBuZbc6AVvcmEEIIIcR/R5J7BhK6ZXFizMzMtApGCCGESA9kAmEiLCwsknzfgZiYGK0DEkIIIfQtNpM1B5LcGDhz5ozq/x4eHowfP57evXtTvXp1AK5cucLGjRuZN2+eVoEERoVrVe6/xsI0u75DSB8iP+o7gnQhas9SfYeQLhh3GKbvENKFyNXT9R1C+lGts74j+E9JcmOgbt26qv/PnDmTBQsW0LVrV1VamzZtKFOmDGvWrEnSqgMhhBAivZIJhElw5coVKlWqpJFeqVIlrl+/nuKghBBCCH3KbHct1KoxkDdvXlatWqWRvnr1avLmzZvioIQQQgh9ymw3KkrWPgP/WLhwIR07duTYsWNUq1YNgKtXr+Lm5sbu3bt1GqAQQgghUpdWPQMtWrTg2bNntGnThqCgIAIDA2nbti3Pnj2jRYsWuo5RCCGESFOxCt0dGYFWPQMQN1Qwd+5cXcYihBBCpAuZbWmh1jsQXrhwge7du1OjRg28vLwA+OOPP7h48aLOghNCCCFE6tOqMbB7926aNm1K1qxZuXXrlupuhe/evZPeAiGEEBmerCZIgtmzZ7Nq1Sr+97//YWxsrEqvUaMGt27d0llwQgghhD5kttUEWjUGnj59Sp06dTTSzczMCAkJSWlMQgghhEhDWjUGcuXKxYsXLzTSL168SMGCBVMclBBCCKFPsSh1dmQEWjUGBg4cyIgRI7h27RoKhYK3b9+yZcsWxowZw+DBg3UdoxBCCJGm9DlnYMWKFTg5OZElSxacnZ25cOFConn37NlD48aNsbW1xczMjOrVq3Ps2LFkX1OrxsC4ceNo164d9evXJzw8nDp16tCvXz8GDhzI0KFDtTmlEEIIkent2LGDkSNHMmnSJG7fvk3t2rVp3rw5np6eCeY/f/48jRs35vDhw7i6ulK/fn1at27N7du3k3VdhVKpTFbDJSYmhosXL1KmTBmyZMnCo0ePiI2NpWTJkuTIkSNZF/+3Cg41tS77X+IXEaLvENIFt2PT9R1CuhB757K+Q0gX5K6FceSuhZ9ln7QpVc8/pkDXr2dKot88tiU5b9WqValYsSIrV65UpZUoUYJ27dol+a7ApUqVonPnzkydOjXJ1032pkOGhoY0bdqUx48fY2VlleANi4QQQoiMTJdj/REREaol+P8wNTXF1NRULS0yMhJXV1fGjx+vlt6kSRMuX07aF4PY2FjevXuHlZVVsmLUapigTJkyvHz5UpuiQgghRLqnyzkD8+bNw9zcXO1I6Ft+QEAAMTEx2Nvbq6Xb29vj4+OTpLjnz5/P+/fv+fbbb5NVX622I54zZw5jxoxh1qxZODs7kz17drXnzczMtDmtEEII8Z8zYcIEXFxc1NLi9wr8m0KhfkMDpVKpkZaQbdu2MX36dP766y/s7OySFaNWjYFmzZoB0KZNG7UA/wk4JiZGm9MKIYQQ6YIuNwtKaEggITY2NhgaGmr0Avj5+Wn0FsS3Y8cOvv/+e/78808aNWqU7Bi1agycOXNGm2JCCCFEhqDUw/4AJiYmODs7c+LECdq3b69KP3HiBG3btk203LZt2+jbty/btm2jZcuWWl1bq8ZA3bp1tbqYEEIIIRLn4uJCjx49qFSpEtWrV2fNmjV4enoyaNAgIG7IwcvLi02b4lZTbNu2jZ49e7J48WKqVaum6lXImjUr5ubmSb6u3LVQCCGEiEdf9ybo3LkzixYtYubMmZQvX57z589z+PBh8ufPD4C3t7fangOrV68mOjqaIUOGkCtXLtUxYsSIZF1Xq56B3bt306NHD7777rsE71p4+PBhbU4rhBBCpAv63EZ48ODBie7mu2HDBrXHZ8+e1ck15a6FQgghRCanVc+A3LVQCCHEf1nGuL2Q7shdC4UQQoh4MttdC7XqGfjnroXr1q1T3bXwypUrjBkzJll7IetSp97t6TW4GzZ21rg9dee3qUu4fe1uovmdq5fHZfowChVzwt83gI3Lt7Jr0z7V8wWLOTF4bD9KlCtG7ry5+HXKYrb+b6fGeWwdbBgxeTA1G1TDNIspni9fM8NlHo/vPU2NaiaJy4+D+a5XJ8wtzLjteo9JY2fz7InbF8u0aN2YsROHkd8pL6/cX/Pz7MUcPXRK7Zyjxw9RK+PnG0CF4nErS4yMjBg3eTgNGtcmf/48hIWFc/HcFebOWIivj7/uK6kDO05eY8OhCwSEhlPI0Y5x3VtQsViBRPNvP3GV7Sev8dY/GAdrC/q3rUvrWhXSLmAd2XnnFRtvvCTgfQSFrHMwpn5JKuZJeOvSqUfvcuChl0Z6Qesc7O79uXfw3acoll18yukXvoR9isLRPCuj6pagdsHkbXyS3ty8c5/1W3fx6MkL/AODWDxvCg3r1NB3WDpl5NwQ42otUOQwJ9bfi8gTW4h9/SzBvCat+mNcrrZGeqz/Gz6umQiAwsYRk7odMHAogIGFLRHHtxB9I/l30RNpS6vGwLhx4wgNDaV+/fp8+vSJOnXqYGpqypgxY/Ry18ImbRsyduYI5o2fz50b9+jYox3Ltv5Gxzrd8fHy1cifO18ulm75jT2bDzB56EzKVy7LhJ9GExwYwqlDZwHIktWUN55vOXHgNKNnDk/wujnNc7LhwCpuXLrF0O9GExQQTN78jrwLDU/N6n7R4BHfM2BwL0YNmcRLNw9GjBnItj2/U6dKS96Hf0iwjHPlcqxc9xu/zl3KkYOnaN6qIavWz6d98x7cdr2vyvfk8XO6tOunevzvzaWyZstCmbIlWPzrKh49eIq5hRkz5o5n/dZltGjQOfUqrKWjV+/zy+bDTOrdmvJF8rHrzA0G/7qJvT8NJ5eNhUb+nSevsWTnCaZ+347SBR257/aGmev2kTNbVupVLJ72FdDSsSdv+fXMIyY0LE15R0t23/Nk6J4b7O5dh1xmWTXyj61fkuG1P9cvJjaWzpsu0riogyotKiaWQbuuY5XNhF9bV8AuZ1Z8wz6SzUSrPy/pysePnyhWuCDtWjRh1KTZ+g5H5wxLVMWk8XdEHt1IzOvnGFesT5YuY/i4egLKsECN/JEnNhN15l9figwMyNpvDtGPb6iSFMYmxAb7E/34OiaNv0uLaqQKXW46lBFo/W6dM2cOkyZN0tldC1Oi+8DO7Nt2kL1bDwDw29TFVK9XhU692rN07iqN/N/0bIf3G19+m7oYAPfnryhZrjg9f+iqagw8uvOER3eeADB88g8JXrfP0O/w8fJj+si5qjTv10nbPzq19BvUgyUL1nDk4EkARv4wkTvPztP+m5Zs3vBnomXOn73CsoW/A7Bs4e9Uq1GZfj/0ZEi/sap8MdEx+PsFJHiOd2HhdO3QXy1t8o9zOXx6B7nz5OLtG29dVE9n/jhyifZ1nelQL+5GW+O6t+Ty/RfsPHWdEZ2baOQ/eOkO3zSoTLNqZQDIY2fFfbfXrD90PkM1Bja7utOuTF46lM0LxH3YX/Hw58+7r9Q+9P+R09SYnP/aOO3Mcx/CPkXRpnReVdq+B68J+xTFhq7VMTaMG3nMnUDDIiOqXb0ytatX1ncYqca4ajOi75wj+s45ACJPbMGwYBmMKjYg6mwCfy8iPqKM+Kh6aFi0ImTNRvTd86q0WG93Yr3d4x7UT97++OmJPjYd0iet5gxs3LiR9+/fky1bNipVqkSVKlX01hAwMjaiRNliXDl7XS396rnrlKtcOsEy5ZxLc/Wcev7LZ69RolxxjIwMk3ztuk1r8ejuE3753yxOPTjIthPraf9d6+RXQkfy5c+DvYMt505fUqVFRkZx9dJNKlVJvDvbuUp5zp9WvyPWudOXqFSlvFqaU8F8uD46w5U7x1ix9lfy5c/zxXjMzHIQGxtLWGhY8iuTiqKio3ns8ZbqZQqrpVcvXZi7zxO+Z3hkdAwmxuptZ1NjYx64eREVnTG2346KieWxbxjV89uopVfLb8vdtyFJOse+B2+omt9G7cP+nJsfZXNb8NOphzRceZJvNpxn7bUXxMRmrj+mGY6BIQa5ChDj/kAtOeblfQzzFEnSKYzK1yXW/WGCvQgZnb72GdAXrRoDY8aMwc7Oji5dunDw4EGio6OTVT4iIoKwsDC1I1ap3UtmaWWBkZERQf5BaumB/sFY21onWMbazopA/2C1tCD/IIyNjbCwskjytR3z5aZTr3Z4vnzD4C6j2LVpH+Nmj6JVp2bJrocu2NnH/ZEP8Fd/Y/r7BWJrZ5NQEQBs7Wzwj1/GX73Mbdd7jPhhIt99M4BxI6Zha2fDX8e2YGmZ8A5XpqYmTJg2ir27DhH+7r22VUoVwe8+EBMbi7WZegPW2jw7AYkM8dQoU5i9Z2/yyN0LpVLJw5de7DvvSnRMDCGJDL+kN8EfI4lRKrHKpr5HunV2EwLfRyRS6jP/8E9ccvenfZm8auleIR84+cyHGKWSpR0q069aYf646c7v1zQnGYv0Q5EtJwoDQ5ThoWrpyvdhKHJ8fec6RQ5zDAuVJervXgWRsWnVGPD29mbHjh0YGhrSpUsXcuXKxeDBg5N8v+WEbufo+/6NNqGoKJXq30IUCs20eAXUH/99w6UvlonHwMCAJ/efsWzeap4+eM7uP/5i75b9dOrV/uuFdaB9p5Y8e31DdRgZxX1z1XwtFF+t19devzMnL3L4wAmePHrOhXNX6dk5bkOMTl3baZzLyMiIFWt/w8DAgIljZmlTtTQR/yZgSqVm2j8GtKtPzXJF6TFjNc69pzFi0Wba1K4IgEES7iaWniRY7ySU2//wDTlNjahfWP2GKbEoscpmwpTGZShpb06z4rn5vmohdt1NuJdFpHMKNP8+JsCobG349IGYp66pH5MeKHX4LyPQas6AkZERrVq1olWrVnz48IG9e/eydetW6tevT548eXBz+/LM9YRu51i7SFNtQiE4KITo6Gis7dR7AaxsLAkKCEqwTKBfENZ2Vhr5o6KiCQ0OTbBMQgL8Ann5zEMtzf25Bw1b1kvyOVLi+JEz3L75eYKfiWncBlC2djb4+X4e27extdLoLfg3f78A7OL1HNjYWH+xzMcPH3ny6BlOhfKppRsZGbFq/Xzy5c/Dt236pLteAQDLnNkwNDDQ6AUICnuv0Vvwjywmxszs34EpfdoSFBaOjUVOdp++QfYspljmzJYWYaeYZVYTDBUKjV6AoA+RWGX/8h3VlEolfz14Q8uSjqp5Af+wyZ4FIwMFhgafmxRO1jkIeB9BVEysRn6RPig/vEMZG6PRC6DIZoby/deH9ozK1SH6/iWIzRjDZMmVUbr3dSXF79Js2bLRtGlTmjdvTpEiRfDw8PhqGVNTU8zMzNQOA4V2oURHRfP43lOq1VWf5FOtbmXu3niQYJm7rg808levV4XHd58QnYzx3zvX75E/3odhvoL58H6TNpMI34d/wMPdU3U8e+KGr48/dep/XvpkbGxMtZqVuHn9dqLncb1+h9r1q6ul1WlQg5vX7yRaxsTEmCJFC+Lr87nR8U9DwKlQfjq3+57gZDSs0pKxkRElCuTm6gP1buyrD15Qrki+REr9U9YQeytzDA0MOHr1PnUqFMPAIGN82BkbGlDC3oyrr9QngV59FUC53BZfLOv6JojXIR9oF2+IAKB8bkteh3wg9l/fJj2D32OT3VQaAulZbAyx3h4YOqnPrTJ0Kk3Mm+dfLGqQrzgGVg5E3ZUhgv8Krd+pHz58YMuWLbRo0YLcuXOzcOFC2rVrx4MHCX8Ap6bNq3fQvltr2nZtiVOR/IyeMRwHR3t2bdoLwLCJg5i1dLIq/65N+8iVx4HR04fhVCQ/bbu2pF3XVmxauU2Vx8jYiKKlilC0VBGMjY2xy2VL0VJFyFvA8fN11+ygjHMp+g7vSd4CjjRr35iOPdqwY/2etKt8PL+v+oNhLv1p1rIhxUoUZuGKOXz88Im9uw6p8ixeOZfxU0eqHq9dvZm69WsweMT3FCrixOAR31O7bjV+X7lJlWfKzDFUq1GJvPkcqeBchjUbF5EjZw7+3L4PAENDQ9ZsXEi5CqUYNuBHDA0NsbWzwdbORm3L6vSiR/Oa7Dnryt5zrrz08uPXzYfxDgylU8O4RuLiHceZtGqXKr+HdwAHL93hlU8A993eMG7ZDl54+TKsU2N9VUEr3Z2d2Hv/Nfvuv+ZlYDi/nXmEz7uPfFMu7iYoSy48YfIRzf059t1/TZlcFhS2yanxXKdy+Qj9GMkvpx/xKiicCy/9WHvNjc7l86d6fVLbhw8fefLMjSfP4no7vd768uSZG94+fnqOTDeirh3FqHxdjMrVQWGdG5NG3VCYWxN96zQAxvU6YdJ6gEY54/J1ifF6gdJfcw8KDAwxsM+HgX0+MDRCkdMSA/t8KCwz1p4TsUqlzo6MQKthgq5du3LgwAGyZctGp06dOHv2LDVq6G8jjuN/ncLc0owBLn2wsbPmxZOXDPtuDN5v4vYYsLG3xsHx8zjnW09vhn03htEzhvNtnw74+wbwy+RFqmWFELeZ0I5TG1SPew3uRq/B3bh5+Rb9OwwD4pYfju47gWETBzHApTdent78OmUxR/YcT5N6J2TF4rVkyWLK3N+mqDYd6taxv9oeA7nz5CL2XzO9b16/w+DvxzJu0jDGThzGK3dPfug7Rm2PgVyO9iz//VesrC0JDAji1s17tG7SDa/XcUsGc+W2p2mLBgCcuKDeGPqmVW+uXLpBetKsWhlCwz+wZt8Z/EPeUTiPPcvH9CC3jSUAASHv8AkMUeWPjY1l05FLvPIOwMjQgMolCrJp6gAcbS31VAPtNC2em9BPUay5+oKA9xEUts7B0g6VVasDAt5H4BP2Ua3Mu4goTj33YWz9kgme08EsKyu+qcL8s4/5dtNF7HJkoVvFAvSuXCjV65PaHjx5Tt9hP6oe/7J0DQBtmzdizuTR+gpLZ2IeXyMyWw6Ma7XFJIcFsf5v+LR9vmp1gCKHBQbm8SZim2bFsHglIo9vSfCcipyWZO33eU8Gk+otoHoLYl495tPmealWF13LGB/huqNQJmfG3N+6devGd999R9OmTVWT1lKqgkNNnZwno/OLCNF3COmC27Hp+g4hXYi9k7RJuf91xn83wDO7yNXT9R1CupF90qavZ0qB7vk76Oxcm1/pr7c4qbT6JN+6dauu4xBCCCHSjYxyTwFd0XrOwLlz52jdujWFCxemSJEitGnThgsXLugyNiGEEEIvMtvSQq0aA5s3b6ZRo0Zky5aN4cOHM3ToULJmzUrDhg2l10AIIYTIYLQaJpgzZw6//PILo0aNUqWNGDGCBQsWMGvWLLp166azAIUQQoi0JvsMJMHLly9p3VpzD/42bdrg7u6e4qCEEEIIfYpFqbMjI9CqMZA3b15OnTqlkX7q1Cny5tXclEQIIYTISDLbnAGthglGjx7N8OHDuXPnDjVq1EChUHDx4kU2bNjA4sWLdR2jEEIIIVKRVo2BH374AQcHB+bPn8/OnTsBKFGiBDt27KBt27Y6DVAIIYRIa5ltzoDWOwa1b9+e9u3T5u58QgghRFrSYj++DE2rOQM3btzg2rVrGunXrl3j5s2bKQ5KCCGEEGlHq8bAkCFDeP36tUa6l5cXQ4YMSXFQQgghhD5lttUEWg0TPHr0iIoVK2qkV6hQgUePHqU4KCGEEEKfMtucAa16BkxNTfH19dVI9/b21tmNi4QQQgiRNrRqDDRu3JgJEyYQGhqqSgsJCWHixIk0bpyx7u8uhBBCxCf7DCTB/PnzqVOnDvnz56dChQoA3LlzB3t7e/744w+dBiiEEEKktYwy1q8rWjUGHB0duXfvHlu2bOHu3btkzZqVPn360LVrV4yNjXUdoxBCCCFSkdYD/NmzZ2fAgAG6jEUIIYRIF2SfgST6448/qFWrFrlz5+bVq1cALFy4kL/++ktnwQkhhBD6EKvDIyPQqjGwcuVKXFxcaN68OcHBwcTExABgaWnJokWLdBmfEEIIkeYy2wRCrRoDS5cu5X//+x+TJk1SW0pYqVIl7t+/r7PghBBCCJH6tJoz4O7urlpF8G+mpqa8f/8+xUEJIYQQ+pTZVhNo1TPg5OTEnTt3NNKPHDlCiRIlUhqTEEIIoVdKpVJnR0agVc/A2LFjGTJkCJ8+fUKpVHL9+nW2bdvG3LlzWbt2ra5jFEIIIUQq0qox0KdPH6Kjoxk3bhwfPnygW7duODo6snTpUmrXrq3rGIUQQog0JcMESdS/f39evXqFn58fPj4+XL9+ndu3b1O4cGFdxieEEEKkucy2miBZPQMhISEMGTKE48ePY2xszPjx4xk6dCgzZszgt99+o2TJkqxbt06rQLIZmmpV7r8mPOqTvkNIFxSWjvoOIV0wKFtN3yGkC5Grp+s7hHTBZOB0fYcg/qOS1RiYOHEi58+fp1evXhw9epRRo0Zx9OhRPn36xOHDh6lbt25qxSmEEEKkmdgMMvFPV5LVGDh06BDr16+nUaNGDB48mMKFC1O0aFHZaEgIIcR/SuZqCiRzzsDbt28pWbIkAAULFiRLliz069cvVQITQgghRNpIVs9AbGys2l0JDQ0NyZ49u86DEkIIIfQps60mSFZjQKlU0rt3b0xN4yb7ffr0iUGDBmk0CPbs2aO7CIUQQog0Jo2BL+jVq5fa4+7du+s0GCGEECI9yCg7B+pKshoD69evT604hBBCCKEnWu1AKIQQQvyXyTCBEEIIkclllJ0DdUXr7YiFEEII8d8gPQNCCCFEPJltAqHWPQMvXrzg2LFjfPz4Ech8L5wQQoj/rliUOjsygmQ3BgIDA2nUqBFFixalRYsWeHt7A9CvXz9Gjx6t8wCFEEIIkbqS3RgYNWoURkZGeHp6ki1bNlV6586dOXr0qE6DE0IIIfRBqVTq7MgIkj1n4Pjx4xw7dow8efKopRcpUoRXr17pLDAhhBBCXzJK976uJLtn4P3792o9Av8ICAhQbVMshBBCCO2sWLECJycnsmTJgrOzMxcuXPhi/nPnzuHs7EyWLFkoWLAgq1atSvY1k90YqFOnDps2bVI9VigUxMbG8uuvv1K/fv1kByCEEEKkN0od/kuOHTt2MHLkSCZNmsTt27epXbs2zZs3x9PTM8H87u7utGjRgtq1a3P79m0mTpzI8OHD2b17d7Kuq1Amc0Dj0aNH1KtXD2dnZ06fPk2bNm14+PAhQUFBXLp0iUKFCiUrgH/UdGygVbn/mgehMtQC4Hf7D32HkC4oA1/rO4R0IebUIX2HkC6YDJyu7xDSDWObgql6/tL21XR2LlfPc0RERKilmZqaJtibXrVqVSpWrMjKlStVaSVKlKBdu3bMmzdPI/+PP/7I/v37efz4sSpt0KBB3L17lytXriQ5xmT3DJQsWZJ79+5RpUoVGjduzPv37+nQoQO3b9/WuiEghBBCpCe67BmYN28e5ubmakdCH+yRkZG4urrSpEkTtfQmTZpw+fLlBOO8cuWKRv6mTZty8+ZNoqKiklxfrTYdcnBwYMaMGdoUFUIIITKVCRMm4OLiopaWUK9AQEAAMTEx2Nvbq6Xb29vj4+OT4Ll9fHwSzB8dHU1AQAC5cuVKUozJbgw4OTnRvXt3unfvTrFixZJbXAghhEj3YnW4JDCxIYHEKBQKtcdKpVIj7Wv5E0r/kmQPEwwbNoyjR49SokQJnJ2dWbRokWrjISGEEOK/QB8TCG1sbDA0NNToBfDz89P49v8PBweHBPMbGRlhbW2d5GsnuzHg4uLCjRs3ePLkCa1atWLlypXky5ePJk2aqK0yEEIIIUTSmZiY4OzszIkTJ9TST5w4QY0aNRIsU716dY38x48fp1KlShgbGyf52lrfm6Bo0aLMmDGDp0+fcuHCBfz9/enTp4+2pxNCCCHSjVilUmdHcri4uPD777+zbt06Hj9+zKhRo/D09GTQoEFA3PyDnj17qvIPGjSIV69e4eLiwuPHj1m3bh1r165lzJgxybpuiu5aeP36dbZu3cqOHTsIDQ3lm2++ScnpUqR9rzZ0G9QZaztr3J95sGTacu5ev59o/vLVyjJs2mCcihYgwDeArSt3sO+PAwnmbdimPjNXTuH80YtM+H6qKr2vSy++H91LLW+gXxBtKujvdQAYP3E4vft0wcLCnJs37zDGZTpPHj//Ypk2bZsyacoonJzy4e7uyawZCzh44LjqeUNDQyZMGsG337bBzt4WXx8/tmzZza8/L1fbbrNosULMmDmOmrWqYmCg4Mnj5/TuOYw3b/Q7lLT90Gk27DlCQFAIhfI5Mq5/N5xLF008/8FTbDt4ird+ATjYWtP/21a0aVhT9fxfJy8yZdFajXI39qzB1CTprXF92HHqOhsOXyQgNJxCuW0Z911zKhYrkGj+Q5fvsuHwRTx9g8iR1ZQaZYowumtTLHLEbT4WFR3D2oPnOXDxDn4h7yjgYM3Ib5tQs2yRNKqRdoycG2JcrQWKHObE+nsReWILsa+fJZjXpFV/jMvV1kiP9X/DxzUTAVDYOGJStwMGDgUwsLAl4vgWom8cS9U6pKWbd+6zfusuHj15gX9gEIvnTaFhnYS/rf4XJHd/AF3p3LkzgYGBzJw5E29vb0qXLs3hw4fJnz8/AN7e3mp7Djg5OXH48GFGjRrF8uXLyZ07N0uWLKFjx47Jum6yGwPPnj1jy5YtbN26FQ8PD+rXr89PP/1Ehw4dyJkzZ3JPpxMN29RjxPQhzJ+4mHs3HtCuR2t+2/wT3ev1wfetn0b+XHkd+O2PeRzYepiZw+ZStnJpRs8dQUhgCGcPq+/0ZO9oz9Cpg7hz9V6C1375xJ0RXT63wGJjYnVbuWQaOWoAQ4b2ZfCgcbx44cHYcUPYt38jlSo0Jjz8fYJlKlepwPqNS5gzayEHDhyndesmbNi0hKaNO+N68y4Ao1wG0vf7rgwaMJYnj59ToWIZlq/8mbCwcFat2ACAk1M+jh3fwR+b/mTenMWEhb2jaLFCfIqITKvqJ+jo+Wv88r+tTPqhBxVKFuHPI2cZPH0B+1bMIZed5pjajsOnWbxxF9OG9aZ0USfuP3VnxrINmOXITr2q5VX5cmTLyv7V6suD0ntD4Oi1+/yy5QiTeraifNF87Dpzg8HzN7N33lByWVto5L/17BWT1+xhTLfm1K1QDL/gMGZvOMD0tX+xaERXAJbtPsWhy3eZ1rctTrlsuHz/BaOWbGPjlP6UyJ+0mcxpzbBEVUwaf0fk0Y3EvH6OccX6ZOkyho+rJ6AMC9TIH3liM1Fndn5OMDAga785RD++oUpSGJsQG+xP9OPrmDT+Li2qkaY+fvxEscIFadeiCaMmzdZ3OP9pgwcPZvDgwQk+t2HDBo20unXrcuvWrRRdM9mNgeLFi1OpUiWGDBlCly5dcHBwSFEAutC5fycObj/CgW2HAVg8bTlV6laifc82rPrpd4387Xq0xtfLj8XTlgPw6oUnxcsVpeugb9UaAwYGBkxbNpG1v22gXNUy5DDLoXGumJgYgvyDU6lmyffDkD7M/3UFB/bHfasfNGAsz19eo9O3bVi/bluCZQYP6cOZ05dYMD9uC8sF81dRs1ZVBg/pw/d9RgJxDYbDB09y/NhZADw9vfimU2sqVCitOs+UaaM5fvwsU6f8rErz8ND/pjmb9h2nfeM6dGxaF4AfB3Tj8q0H7Dx8mhG9O2nkP3j6Mt80r0ezOlUByONgx72nbqzffVitMaBQgI2leZrUQVf+OHqZ9nUq0qGeMwDjvmvB5fsv2HnqBiO+bayR//6L1+S2seC7JnEbsOSxteSb+pXYcPiiKs+hy3fp17oOtcvF9bR827AKlx+8YNORS8wbpN9essQYV21G9J1zRN85B0DkiS0YFiyDUcUGRJ39U7NAxEeUER9VDw2LVoSs2Yi+e16VFuvtTqy3e9yD+t+mavz6ULt6ZWpXr6zvMNKMLlcTZATJnjPw5MkTrl+/zsiRI9NFQ8DI2IhiZYty/dxNtfTr525SulKpBMuUdi6lkf/a2ZsUL1sMQyNDVVqfUT0ICQzl4PYjiV4/j5Mjf7nu5M8rW5ixYjK58+nvm1CBAnlxcLDj9KnPf6gjIyO5dPEaVapWTLRc5SoVOH1KvUfk1MnzamWuXrlJnXo1KFS4AAClSxenWvVKHD8e98dUoVDQpGk9XrzwYM++9bxwv86pM7tp2UrzAyYtRUVF8/iFBzUqqP8uVK9QijtP3BIsExkVjWm8iTdZTIy5/+wlUdHRqrQPHyNo2mcMjXq5MHTGIh67pe/dI6Oio3ns4U310uqbg1UvXZi7LxLe6rRckXz4Bodx4e4zlEolgaHhnLzxUPXBD3Gvl4mx+vcKU2Nj7jxP+Jx6Z2CIQa4CxLg/UEuOeXkfwzxJG9owKl+XWPeHCfYiiP8GfW1HrC/J7hkoWjTxcdakioiI0NiaMVYZi4Ei+fMZLazMMTIyJChA/dt5cEAw1nZWCZaxsrMkOF7+oIBgjIyNsLAyJ9AviDKVStGqawt6N+6f6LUf3X7M7BE/4fnyDVa2lvQa3p1Vfy2le4O+hAWHJbsuKWVnbwuAn1+AWrq/fyB58+ZOtJy9vY1GGT+/AOztbVSPFy5YjZlZTm7eOkFMTAyGhobMmjGf3X/GzbOwtbUmZ84cjHIZyOyZC5g25RcaNa7D5q0raNXiOy5dvK6raiZLcNg7YmJjsbY0U0u3tjQn4NaDBMvUqFiaPcfP06B6RUoUys+jFx7sPXmR6OgYQsLCsbWyoECeXMwa9T1F8uch/MMntuw/Qa9xc/lzyQzyO+q/kZyQ4Hcf4l4Lc/UeLmvz7ASEhidYpnyRfMwb9A3jVuwkMiqa6JhY6lUozvjuLVV5apQpzB9HL+NcrAB57Sy59uglZ28/ISZWv0NmiVFky4nCwBBleKhauvJ9GIocX+/pUeQwx7BQWSL2rfxqXiEyiiQ1BqysrHj27Bk2NjZYWlp+cSODoKCgr55v3rx5GjsY5slRgHxmTkkJJ0Eat1hQKL54H+n4zyn+lZ4te1amLp3Iz2PnE/qFD/WrZz5/wL184s6Dm4/YeXkzzTs1YceaXcmuQ3J1+rYNi5Z8Hrv79pt+QEJ1U/C1Hq/4TysU6mU6ftOKb7u0o1/fUTx+/IwyZUry08+T8fb2Y9vWPRgYxDXkDh86yYrl6wG4f/8xVapWpO/33fTWGPiHgoQ28Ug478AubQgMDqX76NkolUqsLcxo27Am63cfUdWzXPFClCv++Rt2hZKF6TxiOtsOnmL8wPQ9Xhy/3kpl4puTuHn58fPmwwxsW48apQvjH/qOhduPM3vjAWZ83w6IG2qYuf4v2o1fgkKhII+dJW1rV+CvC7dTuSY6poCvvlEAo7K14dMHYp66pn5MQm+UyvTZmE0tSWoMLFy4UDU5cOHChcna1SghCW3N2LR4G63OFRIUSnR0DNa26r0AltYWiY7lB/kFYxU/v40l0VHRhAaHUbBYAXLny8XPG+aonjcwiKvzuVcn6FanF16v3mqc99PHT7x88pK8Tnm0qktyHTl8SjXBD8DE1AQAe3tbfH39Vek2tlYa3/z/zdc3AHs7G7U0W1trtTIzZ49n4YJV7N51EIBHD5+RN58jLmMGsW3rHgIDg4mKiuLJkxdq53n21I1q1StpX8kUsjTLiaGBAQHB6t8Cg0LCsLZI+FtgFlMTZo78nilDexEYEoatpQW7jp0le9YsWCYwbwTi5peUKuLEq7e+Oq+DrljmzBb3WoSo9wIEhb3H2ix7gmXWHrxA+SL56N2iFgBFcSCrqQl95qxlaMeG2FrkxMosO4tGdCMiMoqQ8I/YWeZk0c4T5LaxSO0qaUX54R3K2BiNXgBFNjOU77/eo2dUrg7R9y9BbExqhSjSgdgM0r2vK0lqDPTq9Xn5XO/evVN80YS2ZtRmiAAgOiqap/eeUbmOM+ePfh4rr1zHmYvHEr6xwwPXh9RsXF0trUrdSjy595SY6BhevfCke4O+as8PGNeXbDmysWjqsgRXKAAYmxiTv0h+7l5LfEmjLoWHv9dYIeDj40f9BrW4d+9RXEzGxtSsVZXpU39J9Dw3rt+mfoNaqm/0AA0a1ub6tc+zU7NlzYIyXrdvbEyM6ucWFRXFLdf7FCmi3rtTqIgTr197aVdBHTA2NqJE4QJcufOQhjWcVelX7zyi/r8mAyZY1sgIB5u4RuPR89epU6WcqmcgPqVSyVN3T4rkT5uGoDaMjYwoUSAXVx+60bBSSVX61Ydu1KtQPMEynyKjMIxXZ8O/G8bxe6BMTYyxtzImKjqGUzcf0aRKwnN29C42hlhvDwydSqt9uzd0Kk30sy/PyDbIVxwDKwc+3T2X2lEKPUvmDX0zvGTPGTA0NMTb2xs7Ozu19MDAQOzs7IiJSfvW8o7//cmUxRN4cvcpD1wf0bZ7K+wd7dn7974Bg8b3wyaXDbNH/ATAvj8O0LFPO4ZN+4H9Ww5R2rkkrbo0Z/qQuC73yIgo3J96qF0jPCzu29S/04dMGcSlE5fx9fLD0saCXiN6kD1HNg7/eRx9Wbl8PS5jfsDNzQM3Nw9Gj/mBjx8/8ufO/ao8q9b8hvdbH2ZM/y2uzIoNHDm2jZGjBnDo0ElatmxEvfo1aNq4s6rMkSOnGT12MK9fv+XJ4+eULVeKIcP6snnT5+GQJYv/x/qNi7l86QYXzl+lYeM6NG/egJbNu6XdC5CAnu2aMHHB/yhVuADlShRm19FzePsH0qlFfQAWb/gT38AQ5o6Omx/i4eXDg2cvKVO0IGHhH/hj3zFevHrD7FH9VOdcuXUfZYsVIr+jPeEfPrF1/wmevnzNxEE99FLHpOrRrAaTVu+hpJMj5QrnZfeZm3gHhtKpQdws8cU7T+AXHMacgXFrlOuWL8bM9X+x89R1apQpjH/IO37deoTSBR2x+3sexj231/gFv6N4Pgf8gsNYue8MsUqlqjchPYq6dhTTtgOJ9XYn5s0LjCvUQ2FuTfSt0wAY1+uEIqclkQfWqJUzLl+XGK8XKP0TaOAaGGJg6xj3f0MjFDktMbDPhzLyE8rghL9AZCQfPnzE883nHlGvt748eeaGuVlOcjnYfaGkyAiS3RhIrLUUERGBiYlJigPSxqn9ZzGzNKPPqJ5Y21nx8qkHY3pMwNcrrsvW2t4K+9yff1m9X/swpscEhk8fQodebQnwDWTR1GUaewx8jV0uG2Ysn4y5lTkhgaE8vPWIAa2Hqq6rD4sWriFL1izMXzhDtelQ+7a91XoQ8uTNRey/vuVfv3aLvr1HMHmqC5OmjMLd3ZM+vYarDUGMGzODSVNGMX/hTGxtrfHx9mX9uu38PG+pKs/BA8cZNWIKLqN/4Odfp/L8+Ut6fDeEq1f0O7barE5VQt69Z/X2/fgHhVI4vyPLp48i999DI/7Bofj4f54VHhsby6a9x/Dw8sHI0JDKZYuz6ddJOP5rQuW79x+ZuWwjAcGh5MielRIF87H+p/GUKZa691hPqWZVyxAa/pE1f53FP+QdhR3tWO7SXdWlHxD6Dp+gz0MqbWtX4P2nCLadvMb87cfImS0LlUs4MfLbz7dMjYyKZvnuU7zxDyabqQm1yhZhzoCOmGXPmtbVS7KYx9eIzJYD41ptMclhQaz/Gz5tn69aHaDIYYGBebw9KEyzYli8EpHHtyR4TkVOS7L2+zyHx6R6C6jegphXj/m0WfN2tRnNgyfP6TvsR9XjX5bGNZTaNm/EnMmj9RVWqslswwQKZRL7QpYsWQLAqFGjmDVrFjlyfB47jYmJ4fz583h4eHD7tnaThmo6NtCq3H/Ng9D0vTwtrfjd/kPfIaQLykD979OQHsScOqTvENIFk4HT9R1CumFsk7oNb0dL3Q1zeQU/1Nm5UkuSewYWLlwIxPUMrFq1CkPDz+vxTUxMKFCgAKtWrdJ9hEIIIYRIVUluDLi7x+2sVb9+ffbs2YOlpWWqBSWEEELoU2bbgTDZcwbOnDmTGnEIIYQQ6UZG2TlQV5K9nu+bb77hp59+0kj/9ddf6dRJc593IYQQ/2/vzqOiuu44gH8nMAwzbAooIlKIG0stBsEF1CJVRIwQ1CgiOWLrSRQVNCbo0URJtNVqgkvV1MQI1FOMpkHNwVQqBdxiNYql8cgIggtaMRrrAp7jgvPtH5RXhiWCGUUyvw+HP+a9d9/73fvezPvNnXtnhHi+tToZOHDgAF5++eVGy0eNGoWDBw82UUIIIYRoX0ia7L89aPXHBNXV1U1OIVSr1bhz59l/H78QQghhauY2tbDVPQN9+vTBjh07Gi3fvn07fH19myghhBBCiOdZq3sGFi9ejPHjx6O8vBy/+lXtdwPk5eVh27Zt+OKLp//jPEIIIcTT1l66902l1clAVFQUdu/ejeXLl+OLL76AVqtF3759kZ+fD3t7+8fvQAghhHjOydTCFnj55ZeVQYS3bt1CZmYm5s6di3/9619t8tsEQgghhCmZW8/Ak/1UIID8/Hy89tpr6Nq1KzZs2IDRo0fjxIkTpoxNCCGEEM9Aq3oGLl++jIyMDKSlpeHu3buYOHEiHj58iKysLBk8KIQQ4idDZhM0Y/To0fD19UVxcTHWr1+PK1euYP369Y8vKIQQQrQz8j0Dzdi3bx+SkpKQkJCAXr16Pc2YhBBCCPEMtbhn4NChQ6iqqkJgYCAGDhyIDRs24Pr1608zNiGEEKJNGEiT/bcHLU4GgoKCsHnzZlRWVmL69OnYvn073NzcYDAYkJubi6qqqqcZpxBCCPHM0IR/7UGrZxPodDr85je/weHDh3Hq1Cm89dZb+P3vf4/OnTsjKirqacQohBBCiKfoiacWAoCXlxdWrVqFy5cv47PPPjNVTEIIIUSbMrePCZ7oS4casrCwQHR0NKKjo02xOyGEEKJNtZdZAKbyo3oGhBBCCNH+maRnQAghhPgpaS8D/0xFkgEhhBCiAXP7mECSASGEEKIBc0sGZMyAEEIIYeakZ0AIIYRowLz6BQBQkCTv3bvHlJQU3rt3r61DaVPSDrWkHWpJO9SSdqgl7fDTpSLN7IORZty5cwcODg64ffs27O3t2zqcNiPtUEvaoZa0Qy1ph1rSDj9dMmZACCGEMHOSDAghhBBmTpIBIYQQwsxJMvA/Go0GKSkp0Gg0bR1Km5J2qCXtUEvaoZa0Qy1ph58uGUAohBBCmDnpGRBCCCHMnCQDQgghhJmTZEAIIYQwc5IMCCGEEGZOkgFhtkjijTfegKOjI1QqFYqKijBs2DDMnTu3rUNTZGRkoEOHDm0dxmO1lzjbu/3790OlUuHWrVttGoec758es0gGpk6diujo6Ccq2x4v+h9T3+fF1atXkZiYiO7du0Oj0cDd3R2RkZHIy8sz2TFycnKQkZGBPXv2oLKyEn369MHOnTuxbNkykx3jWZk6dSpUKhVUKhXUajVcXFwQFhaGtLQ0GAwGkx7L09MTa9euNek+m1O/XpaWlvjZz36GhIQE3Lx585kc/6fkyJEjsLCwwKhRo1pVrqnzHRMTg9LSUhNGJ9qaWSQDon25cOECAgICkJ+fj1WrVuHUqVPIyclBaGgoZs2aZbLjlJeXw9XVFcHBwejSpQssLS3h6OgIOzu7Zss8ePDAZMc3tVGjRqGyshIXLlzA3r17ERoaijlz5mDMmDGoqalp6/CeWP16ffrpp8jOzsbMmTPbOqwnRrJNzkdaWhoSExNx+PBhVFRU/Kh9abVadO7c2USRiedCW/5K0rMSHx/PV155pcl1qamp7NOnD3U6Hbt168aEhARWVVWRJAsKCojaX7JU/lNSUp5d4E/oh+q7f/9+9u/fn1ZWVuzSpQsXLFjAhw8fKutDQkKYmJjI5ORkduzYkS4uLo3qrNfrOXjwYGo0Gvr4+DA3N5cAuGvXLpPEHxERQTc3N1ZXVzdad/PmTZLkxYsXGRUVRRsbG9rZ2XHChAm8evWqsl1KSgr79u3LrVu30sPDg/b29oyJieGdO3dI1rZR/fPq4eGh1H/OnDnKfjw8PLhs2TLGx8fT3t6eU6ZMYXp6Oh0cHJidnc3evXtTq9Vy/PjxrK6uZkZGBj08PNihQwfOnj2bNTU1yr7u37/P5ORkdu3alTqdjgMGDGBBQYFR/dLT0+nu7k6tVsvo6Gh++OGHdHBweGybNXfO8/LyCICbN28mSd66dYuvv/46O3XqRDs7O4aGhrKoqEjZvqysjFFRUezcuTNtbGwYGBjI3NxcZX1ISEij50Rd3A4ODszJyaG3tzdtbGwYHh7OK1euKGULCgrYv39/6nQ6Ojg4MDg4mBcuXGh1vebNm0dHR0flcVpaGr29vanRaOjl5cWNGzcabX/p0iXGxMSwY8eO1Ol0DAgI4NGjR5X1H330Ebt37061Ws3evXtz69atyrpJkyYxJibGaH8PHjygk5MT09LSSJIGg4ErV67kiy++SGtra/r5+fEvf/mLUb0BMCcnhwEBAVSr1czPz39sOZL86quv2KtXL1pbW3PYsGFMT08nAOV50FLV1dW0s7PjmTNnGBMTw/fff99o/ZdffsmAgABqNBo6OTlx7NixJB9/vknyzJkzBEC9Xm+0z9TUVHp4eNBgMJAkT58+zYiICNrY2LBz58587bXXeP369VbVQzw9Zp8MrFmzhvn5+Tx37hzz8vLo5eXFhIQEkrUv3mvXrqW9vT0rKytZWVmpJArPs+bqe/nyZep0Os6cOZN6vZ67du2is7Oz0c0+JCSE9vb2fO+991haWso//elPVKlU3LdvH0ny0aNH9PLyYlhYGIuKinjo0CEOGDDAZMnAjRs3qFKpuHz58ma3MRgM9Pf355AhQ3jixAkePXqU/fr1Y0hIiLJNSkoKbW1tOW7cOJ46dYoHDx5kly5duGjRIpK1N8WlS5eyW7durKys5LVr15T6N0wG7O3t+cEHH/Ds2bM8e/Ys09PTqVarGRYWxpMnT/LAgQN0cnLiyJEjOXHiRJ4+fZrZ2dm0srLi9u3blX1NnjyZwcHBPHjwIMvKyvjBBx9Qo9GwtLSUJHn06FGqVCquWLGCJSUlXLduHTt06PCjkgGS7Nu3LyMiImgwGDh48GBGRkby+PHjLC0t5VtvvUUnJyfeuHGDJFlUVMRNmzbx22+/ZWlpKd955x1aW1vz4sWLyvnp1q0bly5dqjwnSCptMmLECB4/fpyFhYX08fHh5MmTSZIPHz6kg4MD3377bZaVlbG4uJgZGRnKfltar/Lycvr6+tLFxYUk+cknn9DV1ZVZWVk8d+4cs7Ky6OjoyIyMDJJkVVUVu3fvzqFDh/LQoUM8e/Ysd+zYwSNHjpAkd+7cSbVazY0bN7KkpISpqam0sLBgfn4+STI7O5tardboeZ+dnU1ra2vevn2bJLlo0SJ6e3szJyeH5eXlTE9Pp0aj4f79+0n+Pxnw8/Pjvn37WFZWxu+///6x5SoqKqjRaDhnzhyeOXOGf/7zn+ni4vJEycCWLVsYGBioxO/p6ancpPfs2UMLCwsuWbKExcXFLCoq4u9+97vHnu/612VAQADfffddo2MGBARw4cKFJMkrV67Q2dmZCxcupF6v58mTJxkWFsbQ0NBW1UM8PWafDDT0+eef08nJSXnc8KJvD5qr76JFi+jl5aW8CJDkxo0baWtry0ePHpGsvRkOGTLEqFz//v25YMECkuTevXtpaWmpvCiQNGnPwLFjxwiAO3fubHabffv20cLCghUVFcqy06dPEwC/+eYbkrXJgE6nU3oCSDI5OZkDBw5UHq9Zs0bpEajTVDIQHR1ttE3du7OysjJl2fTp06nT6YxuGuHh4Zw+fTrJ2nfcKpWK//73v432NXz4cOUFMzY2lqNGjTJaHxMT86OTgZiYGPr4+DAvL4/29vaNfou+R48e/Pjjj5vdt6+vL9evX6889vDw4Jo1a4y2aapNNm7cqNy0b9y4QQDKja6l4uPjaWFhQRsbG1pbWyvvTlevXk2SdHd357Zt24zKLFu2jEFBQSTJjz/+mHZ2dkqy01BwcDBff/11o2UTJkzg6NGjSdb2Ajg7Oxv1FsTGxnLChAkka99xW1tbK8lFnWnTpjE2Npbk/5OB3bt3K+tbUm7hwoX08fExer4uWLDgiZKB4OBgrl27lmRtYubs7Kz0+AQFBTEuLq7Zss2d7/rX5erVq9m9e3flcUlJCQHw9OnTJMnFixdz5MiRRvu4dOkSAbCkpKRVdRFPh9mPGSgoKEBYWBjc3NxgZ2eHKVOm4MaNG7h7925bh2Zyer0eQUFBUKlUyrLBgwejuroaly9fVpb5+fkZlXN1dcW1a9cAACUlJXB3d0eXLl2U9QMGDDBZjPzft2PXj7EhvV4Pd3d3uLu7K8t8fX3RoUMH6PV6ZZmnp6fR5//169EagYGBjZbpdDr06NFDeezi4gJPT0/Y2toaLas73smTJ0ESvXv3hq2trfJ/4MABlJeXK/UKCgoyOk7Dx0+CJFQqFQoLC1FdXQ0nJyejGM6fP6/EcPfuXcyfP19pT1tbW5w5c6ZFnzE3bJP67e3o6IipU6ciPDwckZGRWLduHSorK1sUf2hoKIqKinDs2DEkJiYiPDwciYmJuH79Oi5duoRp06YZ1ee3v/2tUp+ioiL4+/vD0dGxyX3r9XoMHjzYaNngwYOV60itVmPChAnIzMxU2ufLL79EXFwcAKC4uBj37t1DWFiYUQxbt25VYqhT/zpqSTm9Xo9BgwYZPRee5HooKSnBN998g0mTJgEALC0tERMTg7S0NKWNhg8f3ur91jdp0iRcvHgRR48eBQBkZmbipZdegq+vLwCgsLAQBQUFRnX19vYGgEbtJNqGZVsH0JYuXryI0aNHY8aMGVi2bBkcHR1x+PBhTJs2DQ8fPmzr8Eyu7qbQcBlgfPNVq9VG26hUKmVEelP7MKVevXpBpVJBr9c3OyOiuRgaLv+herSGjY1No2VN7fuHjmcwGGBhYYHCwkJYWFgYbVeXQPAp/UyIXq/Hiy++CIPBAFdXV+zfv7/RNnUzZpKTk/G3v/0NH374IXr27AmtVotXX321RQMnm6p//Tqlp6cjKSkJOTk52LFjB959913k5uZi0KBBP7hfGxsb9OzZEwDwhz/8AaGhoXj//fcxe/ZsAMDmzZsxcOBAozJ1bazVah8bd1PPifrL4uLiEBISgmvXriE3NxfW1taIiIgAAOX8fvXVV3BzczPaT8Mf86l/HbWknKmuhy1btqCmpsboOCShVqtx8+bNFrXR47i6uiI0NBTbtm3DoEGD8Nlnn2H69OnKeoPBgMjISKxcubLJsqLtmXUycOLECdTU1CA1NRUvvFDbSfL5558bbWNlZYVHjx61RXgm5+vri6ysLKMXuyNHjsDOzq7RC1JzvL29UVFRge+++w4uLi4AgOPHj5ssRkdHR4SHh2Pjxo1ISkpqdCO+desWfH19UVFRgUuXLim9A8XFxbh9+zZ8fHxMFosp+fv749GjR7h27RqGDh3a5Da+vr7KO6s6DR+3Vn5+Pk6dOoU333wT3bp1w9WrV2FpaQlPT88mtz906BCmTp2KsWPHAgCqq6tx4cIFo21+zHPC398f/v7+WLhwIYKCgpSbR2ukpKQgIiICCQkJcHNzw7lz55R36g35+fnh008/xX/+858mewd8fHxw+PBhTJkyRVl25MgRo+soODgY7u7u2LFjB/bu3YsJEybAysoKQO0502g0qKioQEhISIvr0JJyvr6+2L17t9Gy1l4PNTU12Lp1K1JTUzFy5EijdePHj0dmZib8/PyQl5eHX//6103uo6XnOy4uDgsWLEBsbCzKy8uVnggA6NevH7KysuDp6QlLS7O+7Ty3zOas3L59G0VFRUbLOnXqhJqaGqxfvx6RkZH4+uuvsWnTJqNtPD09UV1djby8PPTt2xc6nQ46ne4ZRv5kmqrvG2+8gbVr1yIxMRGzZ89GSUkJUlJSMG/ePCUZepywsDD06NED8fHxWLVqFaqqqvDOO+8A+OGu/db46KOPEBwcjAEDBmDp0qXw8/NDTU0NcnNz8cc//hHFxcXw8/NDXFwc1q5di5qaGsycORMhISFNduk/D3r37o24uDhMmTIFqamp8Pf3x/fff4/8/Hz84he/wOjRo5GUlITg4GCsWrUK0dHR2LdvH3Jyclp8jPv37+Pq1at49OgRvvvuO+Tk5GDFihUYM2YMpkyZghdeeAFBQUGIjo7GypUr4eXlhStXruCvf/0roqOjERgYiJ49e2Lnzp2IjIyESqXC4sWLG/WmeHp64uDBg5g0aRI0Gg2cnZ0fG9v58+fxySefICoqCl27dkVJSQlKS0uNbsItNWzYMPz85z/H8uXL8d577yEpKQn29vaIiIjA/fv3ceLECdy8eRPz5s1DbGwsli9fjujoaKxYsQKurq745z//ia5duyIoKAjJycmYOHEi+vXrh+HDhyM7Oxs7d+7E3//+d+V4KpUKkydPxqZNm1BaWoqCggJlnZ2dHd5++228+eabMBgMGDJkCO7cuYMjR47A1tYW8fHxTdahJeVmzJiB1NRUzJs3D9OnT0dhYSEyMjJa1VZ79uzBzZs3MW3aNDg4OBite/XVV7FlyxasWbMGw4cPR48ePTBp0iTU1NRg7969mD9/PoCWn+9x48YhISEBCQkJCA0NNXqDMWvWLGzevBmxsbFITk6Gs7MzysrKsH37dmzevLlRb5loA20xUOFZaziNrO4/Pj6eq1evpqurK7VaLcPDw7l169ZGA3RmzJhBJyendjW1sLn6tmRqYf0BdCT5yiuvMD4+XnlcN7XQysqK3t7ezM7OVqZOmcqVK1c4a9Ysenh40MrKim5uboyKilKm4rV0amF9DQcMtnQA4eMGTzV3vIaD+h48eMAlS5bQ09OTarWaXbp04dixY/ntt98q22zZsoXdunWjVqtlZGRkq6YW1p1nS0tLdurUiSNGjGBaWpoyOJQk79y5w8TERHbt2pVqtZru7u6Mi4tTBmOeP3+eoaGh1Gq1dHd354YNGxq1yT/+8Q/6+flRo9E0OdWszq5du5T1V69eZXR0NF1dXWllZUUPDw8uWbLEKLbm6tXUwMjMzExaWVmxoqKCmZmZfOmll2hlZcWOHTvyl7/8pdEA1AsXLnD8+PG0t7enTqdjYGAgjx07pqz/oamFdeoGqNafKlfHYDBw3bp19PLyolqtZqdOnRgeHs4DBw6Q/P8AwoaD/h5Xjqwd+d+zZ09qNBoOHTqUaWlprRpAOGbMGGUwZEOFhYUEwMLCQmZlZSlt6OzszHHjxinbtfR8k7WDLwEo0y7rKy0t5dixY9mhQwdqtVp6e3tz7ty5jdpTtA0V+ZQ+qBRm4+uvv8aQIUNQVlZmNIBMCCFE+yDJgGi1Xbt2wdbWFr169UJZWRnmzJmDjh074vDhw20dmhBCiCdgNmMGhOlUVVVh/vz5uHTpEpydnTFixAikpqa2dVhCCCGekPQMCCGEEGbO7L90SAghhDB3kgwIIYQQZk6SASGEEMLMSTIghBBCmDlJBoQQQggzJ8mAEEIIYeYkGRBCCCHMnCQDQgghhJn7L09bFR8JTPw0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data1.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Covid-e tutulmush shexslerin (Confirmed sutunu) diger sutunlardan asilliqi ne qederdir? \n",
    "\n",
    "# Death = 91 faiz\n",
    "# Recovered = 89 faiz\n",
    "# Avtive = 95 faiz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Europe                   14852\n",
       "Western Pacific          10340\n",
       "Africa                    9024\n",
       "Americas                  8648\n",
       "Eastern Mediterranean     4136\n",
       "South-East Asia           1880\n",
       "Name: WHO Region, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Region sutunu uzre deyerlerin sayi ne qederdir? value_counts ile gosterin\n",
    "data1[\"WHO Region\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='WHO Region', ylabel='count'>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Region sutunu uzre deyerlerin sayi ne qederdir? countplot ile vizuallashdirin4\n",
    "plt.figure(figsize=(16,9))\n",
    "sns.countplot(x=data1[\"WHO Region\"], order = data1[\"WHO Region\"].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48875</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>865</td>\n",
       "      <td>14</td>\n",
       "      <td>734</td>\n",
       "      <td>117</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48876</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>1691</td>\n",
       "      <td>483</td>\n",
       "      <td>833</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48877</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>354</td>\n",
       "      <td>7</td>\n",
       "      <td>328</td>\n",
       "      <td>19</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48878</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>7235</td>\n",
       "      <td>60</td>\n",
       "      <td>6028</td>\n",
       "      <td>1147</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48879</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>505</td>\n",
       "      <td>12</td>\n",
       "      <td>128</td>\n",
       "      <td>365</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48880 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country/Region        Lat       Long       Date  Confirmed  Deaths  \\\n",
       "0         Azerbaijan  33.939110  67.709953 2020-01-22          0       0   \n",
       "1         Azerbaijan  41.153300  20.168300 2020-01-22          0       0   \n",
       "2         Azerbaijan  28.033900   1.659600 2020-01-22          0       0   \n",
       "3         Azerbaijan  42.506300   1.521800 2020-01-22          0       0   \n",
       "4         Azerbaijan -11.202700  17.873900 2020-01-22          0       0   \n",
       "...              ...        ...        ...        ...        ...     ...   \n",
       "48875     Azerbaijan   0.186400   6.613100 2020-07-27        865      14   \n",
       "48876     Azerbaijan  15.552727  48.516388 2020-07-27       1691     483   \n",
       "48877     Azerbaijan -11.645500  43.333300 2020-07-27        354       7   \n",
       "48878     Azerbaijan  38.861000  71.276100 2020-07-27       7235      60   \n",
       "48879     Azerbaijan -29.610000  28.233600 2020-07-27        505      12   \n",
       "\n",
       "       Recovered  Active             WHO Region  \n",
       "0              0       0  Eastern Mediterranean  \n",
       "1              0       0                 Europe  \n",
       "2              0       0                 Africa  \n",
       "3              0       0                 Europe  \n",
       "4              0       0                 Africa  \n",
       "...          ...     ...                    ...  \n",
       "48875        734     117                 Africa  \n",
       "48876        833     375  Eastern Mediterranean  \n",
       "48877        328      19                 Africa  \n",
       "48878       6028    1147                 Europe  \n",
       "48879        128     365                 Africa  \n",
       "\n",
       "[48880 rows x 9 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Esas datadan yalniz Azerbaycan uzre neticeleri chixarib yeni dataya menimsedin\n",
    "data1az = data1[data1['Country/Region'] == 'Azerbaijan']\n",
    "data1az"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19642</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>41.20438</td>\n",
       "      <td>74.766098</td>\n",
       "      <td>2020-04-06</td>\n",
       "      <td>216</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>179</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10592</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-4.67960</td>\n",
       "      <td>55.492000</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15246</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>52.13260</td>\n",
       "      <td>5.291300</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>2994</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td>2888</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23487</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>35.12640</td>\n",
       "      <td>33.429900</td>\n",
       "      <td>2020-04-21</td>\n",
       "      <td>784</td>\n",
       "      <td>12</td>\n",
       "      <td>98</td>\n",
       "      <td>674</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48288</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>43.94240</td>\n",
       "      <td>12.457800</td>\n",
       "      <td>2020-07-25</td>\n",
       "      <td>699</td>\n",
       "      <td>42</td>\n",
       "      <td>657</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country/Region       Lat       Long       Date  Confirmed  Deaths  \\\n",
       "19642     Azerbaijan  41.20438  74.766098 2020-04-06        216       4   \n",
       "10592     Azerbaijan  -4.67960  55.492000 2020-03-02          0       0   \n",
       "15246     Azerbaijan  52.13260   5.291300 2020-03-20       2994     106   \n",
       "23487     Azerbaijan  35.12640  33.429900 2020-04-21        784      12   \n",
       "48288     Azerbaijan  43.94240  12.457800 2020-07-25        699      42   \n",
       "\n",
       "       Recovered  Active WHO Region  \n",
       "19642         33     179     Europe  \n",
       "10592          0       0     Africa  \n",
       "15246          0    2888     Europe  \n",
       "23487         98     674     Europe  \n",
       "48288        657       0     Europe  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bu data-dan 5 ferqli tarix uchun ( .sample(5) metodu ile sechilecek ) Date ve Deaths sutunu arasinda barplot qurun\n",
    "sample1az = data1az.sample(5)\n",
    "sample1az"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample1az.plot.bar(x = 'Date', y = 'Deaths')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Date</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41860</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>31836</td>\n",
       "      <td>774</td>\n",
       "      <td>15651</td>\n",
       "      <td>15411</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41861</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>2580</td>\n",
       "      <td>65</td>\n",
       "      <td>1516</td>\n",
       "      <td>999</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41862</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>14272</td>\n",
       "      <td>920</td>\n",
       "      <td>10040</td>\n",
       "      <td>3312</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41863</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>855</td>\n",
       "      <td>52</td>\n",
       "      <td>799</td>\n",
       "      <td>4</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41864</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>2020-07-01</td>\n",
       "      <td>291</td>\n",
       "      <td>15</td>\n",
       "      <td>97</td>\n",
       "      <td>179</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48875</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>0.186400</td>\n",
       "      <td>6.613100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>865</td>\n",
       "      <td>14</td>\n",
       "      <td>734</td>\n",
       "      <td>117</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48876</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>1691</td>\n",
       "      <td>483</td>\n",
       "      <td>833</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48877</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>354</td>\n",
       "      <td>7</td>\n",
       "      <td>328</td>\n",
       "      <td>19</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48878</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>38.861000</td>\n",
       "      <td>71.276100</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>7235</td>\n",
       "      <td>60</td>\n",
       "      <td>6028</td>\n",
       "      <td>1147</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48879</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>-29.610000</td>\n",
       "      <td>28.233600</td>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>505</td>\n",
       "      <td>12</td>\n",
       "      <td>128</td>\n",
       "      <td>365</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7020 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country/Region        Lat       Long       Date  Confirmed  Deaths  \\\n",
       "41860     Azerbaijan  33.939110  67.709953 2020-07-01      31836     774   \n",
       "41861     Azerbaijan  41.153300  20.168300 2020-07-01       2580      65   \n",
       "41862     Azerbaijan  28.033900   1.659600 2020-07-01      14272     920   \n",
       "41863     Azerbaijan  42.506300   1.521800 2020-07-01        855      52   \n",
       "41864     Azerbaijan -11.202700  17.873900 2020-07-01        291      15   \n",
       "...              ...        ...        ...        ...        ...     ...   \n",
       "48875     Azerbaijan   0.186400   6.613100 2020-07-27        865      14   \n",
       "48876     Azerbaijan  15.552727  48.516388 2020-07-27       1691     483   \n",
       "48877     Azerbaijan -11.645500  43.333300 2020-07-27        354       7   \n",
       "48878     Azerbaijan  38.861000  71.276100 2020-07-27       7235      60   \n",
       "48879     Azerbaijan -29.610000  28.233600 2020-07-27        505      12   \n",
       "\n",
       "       Recovered  Active             WHO Region  \n",
       "41860      15651   15411  Eastern Mediterranean  \n",
       "41861       1516     999                 Europe  \n",
       "41862      10040    3312                 Africa  \n",
       "41863        799       4                 Europe  \n",
       "41864         97     179                 Africa  \n",
       "...          ...     ...                    ...  \n",
       "48875        734     117                 Africa  \n",
       "48876        833     375  Eastern Mediterranean  \n",
       "48877        328      19                 Africa  \n",
       "48878       6028    1147                 Europe  \n",
       "48879        128     365                 Africa  \n",
       "\n",
       "[7020 rows x 9 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Azerbaycan uchun yalniz iyul ayina olan neticeleri ayri data-ya menimsedin\n",
    "data1az_july = data1az[(data1az['Date'] >= '2020-07-01') &  (data1az['Date'] <= '2020-07-31']\n",
    "data1az_july"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>Confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>362021697</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Confirmed\n",
       "Country/Region           \n",
       "Azerbaijan      362021697"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "9# Iyul ayinda Confirmed Deaths Recovered ve Active sutunlari uzre neticelerin cemine baxin (groupby metodu)\n",
    "data1az_july_groupby = data1az_july.groupby('Country/Region').agg({'Confirmed':sum})\n",
    "data1az_july_groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EEID</th>\n",
       "      <th>Full Name</th>\n",
       "      <th>Job Title</th>\n",
       "      <th>Department</th>\n",
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       "      <th>Bonus %</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E02387</td>\n",
       "      <td>Emily Davis</td>\n",
       "      <td>Sr. Manger</td>\n",
       "      <td>IT</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>Female</td>\n",
       "      <td>Black</td>\n",
       "      <td>55</td>\n",
       "      <td>2016-04-08</td>\n",
       "      <td>141604</td>\n",
       "      <td>0.15</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E04105</td>\n",
       "      <td>Theodore Dinh</td>\n",
       "      <td>Technical Architect</td>\n",
       "      <td>IT</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>59</td>\n",
       "      <td>1997-11-29</td>\n",
       "      <td>99975</td>\n",
       "      <td>0.00</td>\n",
       "      <td>China</td>\n",
       "      <td>Chongqing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>E02572</td>\n",
       "      <td>Luna Sanders</td>\n",
       "      <td>Director</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>50</td>\n",
       "      <td>2006-10-26</td>\n",
       "      <td>163099</td>\n",
       "      <td>0.20</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E02832</td>\n",
       "      <td>Penelope Jordan</td>\n",
       "      <td>Computer Systems Manager</td>\n",
       "      <td>IT</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>26</td>\n",
       "      <td>2019-09-27</td>\n",
       "      <td>84913</td>\n",
       "      <td>0.07</td>\n",
       "      <td>United States</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E01639</td>\n",
       "      <td>Austin Vo</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>55</td>\n",
       "      <td>1995-11-20</td>\n",
       "      <td>95409</td>\n",
       "      <td>0.00</td>\n",
       "      <td>United States</td>\n",
       "      <td>Phoenix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>E03094</td>\n",
       "      <td>Wesley Young</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Male</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>33</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>98427</td>\n",
       "      <td>0.00</td>\n",
       "      <td>United States</td>\n",
       "      <td>Columbus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>E01909</td>\n",
       "      <td>Lillian Khan</td>\n",
       "      <td>Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>44</td>\n",
       "      <td>2010-05-31</td>\n",
       "      <td>47387</td>\n",
       "      <td>0.00</td>\n",
       "      <td>China</td>\n",
       "      <td>Chengdu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>E04398</td>\n",
       "      <td>Oliver Yang</td>\n",
       "      <td>Director</td>\n",
       "      <td>Marketing</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Male</td>\n",
       "      <td>Asian</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>176710</td>\n",
       "      <td>0.15</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>E02521</td>\n",
       "      <td>Lily Nguyen</td>\n",
       "      <td>Sr. Analyst</td>\n",
       "      <td>Finance</td>\n",
       "      <td>Speciality Products</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>33</td>\n",
       "      <td>2012-01-28</td>\n",
       "      <td>95960</td>\n",
       "      <td>0.00</td>\n",
       "      <td>China</td>\n",
       "      <td>Chengdu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>E03545</td>\n",
       "      <td>Sofia Cheng</td>\n",
       "      <td>Vice President</td>\n",
       "      <td>Accounting</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>63</td>\n",
       "      <td>2020-07-26</td>\n",
       "      <td>216195</td>\n",
       "      <td>0.31</td>\n",
       "      <td>United States</td>\n",
       "      <td>Miami</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       EEID        Full Name                 Job Title  Department  \\\n",
       "0    E02387      Emily Davis                Sr. Manger          IT   \n",
       "1    E04105    Theodore Dinh       Technical Architect          IT   \n",
       "2    E02572     Luna Sanders                  Director     Finance   \n",
       "3    E02832  Penelope Jordan  Computer Systems Manager          IT   \n",
       "4    E01639        Austin Vo               Sr. Analyst     Finance   \n",
       "..      ...              ...                       ...         ...   \n",
       "995  E03094     Wesley Young               Sr. Analyst   Marketing   \n",
       "996  E01909     Lillian Khan                   Analyst     Finance   \n",
       "997  E04398      Oliver Yang                  Director   Marketing   \n",
       "998  E02521      Lily Nguyen               Sr. Analyst     Finance   \n",
       "999  E03545      Sofia Cheng            Vice President  Accounting   \n",
       "\n",
       "              Business Unit  Gender  Ethnicity  Age  Hire Date  Annual Salary  \\\n",
       "0    Research & Development  Female      Black   55 2016-04-08         141604   \n",
       "1             Manufacturing    Male      Asian   59 1997-11-29          99975   \n",
       "2       Speciality Products  Female  Caucasian   50 2006-10-26         163099   \n",
       "3             Manufacturing  Female  Caucasian   26 2019-09-27          84913   \n",
       "4             Manufacturing    Male      Asian   55 1995-11-20          95409   \n",
       "..                      ...     ...        ...  ...        ...            ...   \n",
       "995     Speciality Products    Male  Caucasian   33 2016-09-18          98427   \n",
       "996     Speciality Products  Female      Asian   44 2010-05-31          47387   \n",
       "997     Speciality Products    Male      Asian   31 2019-06-10         176710   \n",
       "998     Speciality Products  Female      Asian   33 2012-01-28          95960   \n",
       "999               Corporate  Female      Asian   63 2020-07-26         216195   \n",
       "\n",
       "     Bonus %        Country       City  \n",
       "0       0.15  United States    Seattle  \n",
       "1       0.00          China  Chongqing  \n",
       "2       0.20  United States    Chicago  \n",
       "3       0.07  United States    Chicago  \n",
       "4       0.00  United States    Phoenix  \n",
       "..       ...            ...        ...  \n",
       "995     0.00  United States   Columbus  \n",
       "996     0.00          China    Chengdu  \n",
       "997     0.15  United States      Miami  \n",
       "998     0.00          China    Chengdu  \n",
       "999     0.31  United States      Miami  \n",
       "\n",
       "[1000 rows x 13 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Employee data-sini oxuyun\n",
    "data2 = pd.read_excel(r'C:\\Users\\nijat\\Desktop\\PYTHON - KURS\\Ders 11 - practice\\Employee.xlsx')\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nijat\\AppData\\Local\\Temp\\ipykernel_17736\\2000786006.py:2: FutureWarning:\n",
      "\n",
      "Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "\n"
     ]
    },
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       "      <td>Everly Walker</td>\n",
       "      <td>Director</td>\n",
       "      <td>IT</td>\n",
       "      <td>Manufacturing</td>\n",
       "      <td>Female</td>\n",
       "      <td>Asian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-11-29 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>121</td>\n",
       "      <td>241</td>\n",
       "      <td>269</td>\n",
       "      <td>518</td>\n",
       "      <td>404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>643</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992-01-09 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-12-26 00:00:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.382000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113217.365000</td>\n",
       "      <td>0.088660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.246981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53545.985644</td>\n",
       "      <td>0.117856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40063.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71430.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96557.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>150782.250000</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>258498.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EEID      Full Name Job Title Department  Business Unit  Gender  \\\n",
       "count     1000           1000      1000       1000           1000    1000   \n",
       "unique     911            991        33          7              4       2   \n",
       "top     E03349  Everly Walker  Director         IT  Manufacturing  Female   \n",
       "freq         3              2       121        241            269     518   \n",
       "first      NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "last       NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "mean       NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "std        NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "min        NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "25%        NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "50%        NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "75%        NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "max        NaN            NaN       NaN        NaN            NaN     NaN   \n",
       "\n",
       "       Ethnicity          Age            Hire Date  Annual Salary  \\\n",
       "count       1000  1000.000000                 1000    1000.000000   \n",
       "unique         4          NaN                  949            NaN   \n",
       "top        Asian          NaN  2010-11-29 00:00:00            NaN   \n",
       "freq         404          NaN                    3            NaN   \n",
       "first        NaN          NaN  1992-01-09 00:00:00            NaN   \n",
       "last         NaN          NaN  2021-12-26 00:00:00            NaN   \n",
       "mean         NaN    44.382000                  NaN  113217.365000   \n",
       "std          NaN    11.246981                  NaN   53545.985644   \n",
       "min          NaN    25.000000                  NaN   40063.000000   \n",
       "25%          NaN    35.000000                  NaN   71430.250000   \n",
       "50%          NaN    45.000000                  NaN   96557.000000   \n",
       "75%          NaN    54.000000                  NaN  150782.250000   \n",
       "max          NaN    65.000000                  NaN  258498.000000   \n",
       "\n",
       "            Bonus %        Country     City  \n",
       "count   1000.000000           1000     1000  \n",
       "unique          NaN              3       13  \n",
       "top             NaN  United States  Seattle  \n",
       "freq            NaN            643      118  \n",
       "first           NaN            NaN      NaN  \n",
       "last            NaN            NaN      NaN  \n",
       "mean       0.088660            NaN      NaN  \n",
       "std        0.117856            NaN      NaN  \n",
       "min        0.000000            NaN      NaN  \n",
       "25%        0.000000            NaN      NaN  \n",
       "50%        0.000000            NaN      NaN  \n",
       "75%        0.150000            NaN      NaN  \n",
       "max        0.400000            NaN      NaN  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistika\n",
    "data2.describe(include = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# numeric columnlar uchun outlier boxplot ile gosterin\n",
    "plt.figure(figsize=(16,9))\n",
    "sns.boxplot(data=data2['Age'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.boxplot(data=data2['Annual Salary'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "sns.boxplot(data=data2['Bonus %'],orient='h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IT                 241\n",
       "Engineering        158\n",
       "Sales              140\n",
       "Human Resources    125\n",
       "Finance            120\n",
       "Marketing          120\n",
       "Accounting          96\n",
       "Name: Department, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Departamentleri hem value counts hem de countplot ile gosterin\n",
    "data2['Department'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Department', ylabel='count'>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "\n",
    "sns.countplot(x=data2[\"Department\"], order = data2[\"Department\"].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EEID', 'Full Name', 'Job Title', 'Department', 'Business Unit',\n",
       "       'Gender', 'Ethnicity', 'Age', 'Hire Date', 'Annual Salary', 'Bonus %',\n",
       "       'Country', 'City'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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>Age</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Department</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accounting</th>\n",
       "      <td>25</td>\n",
       "      <td>123146.947917</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Engineering</th>\n",
       "      <td>25</td>\n",
       "      <td>109035.208861</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Finance</th>\n",
       "      <td>25</td>\n",
       "      <td>122802.891667</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Human Resources</th>\n",
       "      <td>25</td>\n",
       "      <td>118058.440000</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IT</th>\n",
       "      <td>25</td>\n",
       "      <td>97790.452282</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Marketing</th>\n",
       "      <td>25</td>\n",
       "      <td>129663.033333</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>25</td>\n",
       "      <td>111049.857143</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Age  Annual Salary  Bonus %\n",
       "Department                                  \n",
       "Accounting        25  123146.947917     0.40\n",
       "Engineering       25  109035.208861     0.40\n",
       "Finance           25  122802.891667     0.39\n",
       "Human Resources   25  118058.440000     0.39\n",
       "IT                25   97790.452282     0.39\n",
       "Marketing         25  129663.033333     0.40\n",
       "Sales             25  111049.857143     0.38"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Her departament uchun maashin ortalamasi, yashin minimumu ve bonuslarin maximumun gosterin \n",
    "data2.groupby(['Department']).agg({'Age' : min, 'Annual Salary' : 'mean', 'Bonus %': max})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>Age</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>43.654676</td>\n",
       "      <td>258426</td>\n",
       "      <td>0.086763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>45.389908</td>\n",
       "      <td>257194</td>\n",
       "      <td>0.084954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>44.197512</td>\n",
       "      <td>258498</td>\n",
       "      <td>0.090327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Age  Annual Salary   Bonus %\n",
       "Country                                          \n",
       "Brazil         43.654676         258426  0.086763\n",
       "China          45.389908         257194  0.084954\n",
       "United States  44.197512         258498  0.090327"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Olkeler uzre maashin maksimumu, yashin ortalamasi ve bonuslarin ortalamasin gosterin \n",
    "\n",
    "data2.groupby(['Country']).agg({'Age' : 'mean', 'Annual Salary' : 'max', 'Bonus %': 'mean'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Age</th>\n",
       "      <th>Annual Salary</th>\n",
       "      <th>Bonus %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.023497</td>\n",
       "      <td>-0.015555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Annual Salary</th>\n",
       "      <td>-0.023497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.925692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bonus %</th>\n",
       "      <td>-0.015555</td>\n",
       "      <td>0.925692</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age  Annual Salary   Bonus %\n",
       "Age            1.000000      -0.023497 -0.015555\n",
       "Annual Salary -0.023497       1.000000  0.925692\n",
       "Bonus %       -0.015555       0.925692  1.000000"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sutunlar uzre korrelasiyaya baxin . Hansi sutunlar bir birinden chox asilidir. Biznes mentiqi varmi?\n",
    "\n",
    "data2.corr()\n",
    "\n",
    "# annual salary ve bonus elaqelidirler\n",
    "# maas artdiqca bonus artir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "City\n",
       "Austin            116000.767677\n",
       "Beijing           108979.890909\n",
       "Chengdu           110387.369565\n",
       "Chicago           112293.413043\n",
       "Chongqing         115434.953846\n",
       "Columbus          116108.380531\n",
       "Manaus            115079.500000\n",
       "Miami             112765.250000\n",
       "Phoenix           107438.834862\n",
       "Rio de Janerio    108275.931818\n",
       "Sao Paulo         113503.245283\n",
       "Seattle           114532.415254\n",
       "Shanghai          119972.019231\n",
       "Name: Annual Salary, dtype: float64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# barplot ile sheherler uchun orta maashi plot edin, en yuksek orta maash hansi sheherdedir qeyd edin.\n",
    "\n",
    "data2gr = data2.groupby('City')\n",
    "data2gr['Annual Salary'].mean()\n",
    "\n",
    "#Shanghai          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='City'>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g7OyMOXPmoKysTGojhEBISAg0Gg3MzMzQuXNnnDp1Suv3FBUVYdy4cbC1tYWFhQX69OmDy5cva7XJzc1FYGAg1Go11Go1AgMDkZeXp9UmIyMDvXv3hoWFBWxtbTF+/HgUFxdX98smIiKiWqjag9AXX3yBb7/9FmFhYUhNTcXChQvx5ZdfIjQ0VGqzcOFCLFmyBGFhYTh8+DAcHBzQtWtX3L59W2ozceJEREVFITIyEvHx8bhz5w569eqF0tJSqU1AQACSk5MRHR2N6OhoJCcnIzAwUDpfWlqKnj17oqCgAPHx8YiMjMSWLVsQHBxc3S+biIiIaiGj6v6Fhw4dQt++fdGzZ08AQKNGjbBp0yYcOXIEwIPeoGXLlmHmzJkYMGAAAGDt2rWwt7fHxo0bMXLkSOTn52PNmjVYv349fH19AQARERFwdHTE7t270a1bN6SmpiI6OhoJCQnw9PQEAKxatQpeXl5IS0uDi4sLYmJicPr0aWRmZkKj0QAAFi9ejKFDh2Lu3LmwsrKq7pdPREREtUi19wi99tpr2LNnD86ePQsAOH78OOLj49GjRw8AwIULF5CdnQ0/Pz/pZ0xMTNCpUyccPHgQAJCUlISSkhKtNhqNBm5ublKbQ4cOQa1WSyEIADp06AC1Wq3Vxs3NTQpBANCtWzcUFRUhKSmpul86ERER1TLV3iM0ffp05Ofn4+WXX4ahoSFKS0sxd+5cvPfeewCA7OxsAIC9vb3Wz9nb2+PSpUtSG2NjY1hbW1dqU/7z2dnZsLOzq3R9Ozs7rTYVr2NtbQ1jY2OpTUVFRUUoKiqSnt+6deupXzsRERHVLtXeI/Tjjz8iIiICGzduxNGjR7F27VosWrQIa9eu1WqnUqm0ngshKh2rqGKbqtr/kzYPmz9/vjT4Wq1Ww9HR8bE1ERERUe1V7UFo6tSp+OSTT/Duu+/C3d0dgYGBmDRpEubPnw8AcHBwAIBKPTI5OTlS742DgwOKi4uRm5v72DbXrl2rdP3r169rtal4ndzcXJSUlFTqKSo3Y8YM5OfnS4/MzMxn/SsgIiKiWqLag9Ddu3dhYKD9aw0NDaXp840bN4aDgwNiY2Ol88XFxYiLi0PHjh0BAB4eHqhTp45Wm6ysLKSkpEhtvLy8kJ+fj8TERKnNn3/+ifz8fK02KSkpyMrKktrExMTAxMQEHh4eVdZvYmICKysrrQcRERE9n6p9jFDv3r0xd+5cNGzYEK+88gqOHTuGJUuWYPjw4QAe3KqaOHEi5s2bh2bNmqFZs2aYN28ezM3NERAQAABQq9UYMWIEgoODUb9+fdjY2GDKlClwd3eXZpG5urrC398fQUFB+O677wAAH330EXr16gUXFxcAgJ+fH1q0aIHAwEB8+eWXuHnzJqZMmYKgoCAGHCIiIqr+IBQaGopZs2ZhzJgxyMnJgUajwciRI/Hpp59KbaZNm4bCwkKMGTMGubm58PT0RExMDCwtLaU2S5cuhZGREQYOHIjCwkL4+PggPDwchoaGUpsNGzZg/Pjx0uyyPn36ICwsTDpvaGiIX3/9FWPGjIG3tzfMzMwQEBCARYsWVffLJiIiolqo2oOQpaUlli1bhmXLlj2yjUqlQkhICEJCQh7ZxtTUFKGhoVoLMVZkY2ODiIiIx9bTsGFD7Ny580llExERkQJxrzEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlKsGglCV65cwfvvv4/69evD3Nwcr776KpKSkqTzQgiEhIRAo9HAzMwMnTt3xqlTp7R+R1FREcaNGwdbW1tYWFigT58+uHz5slab3NxcBAYGQq1WQ61WIzAwEHl5eVptMjIy0Lt3b1hYWMDW1hbjx49HcXFxTbxsIiIiqmWqPQjl5ubC29sbderUwX//+1+cPn0aixcvRr169aQ2CxcuxJIlSxAWFobDhw/DwcEBXbt2xe3bt6U2EydORFRUFCIjIxEfH487d+6gV69eKC0tldoEBAQgOTkZ0dHRiI6ORnJyMgIDA6XzpaWl6NmzJwoKChAfH4/IyEhs2bIFwcHB1f2yiYiIqBYyqu5f+MUXX8DR0RE//PCDdKxRo0bSn4UQWLZsGWbOnIkBAwYAANauXQt7e3ts3LgRI0eORH5+PtasWYP169fD19cXABAREQFHR0fs3r0b3bp1Q2pqKqKjo5GQkABPT08AwKpVq+Dl5YW0tDS4uLggJiYGp0+fRmZmJjQaDQBg8eLFGDp0KObOnQsrK6vqfvlERERUi1R7j9COHTvQtm1bvPPOO7Czs0Pr1q2xatUq6fyFCxeQnZ0NPz8/6ZiJiQk6deqEgwcPAgCSkpJQUlKi1Uaj0cDNzU1qc+jQIajVaikEAUCHDh2gVqu12ri5uUkhCAC6deuGoqIirVt1DysqKsKtW7e0HkRERPR8qvYg9Ndff2HFihVo1qwZfvvtN4waNQrjx4/HunXrAADZ2dkAAHt7e62fs7e3l85lZ2fD2NgY1tbWj21jZ2dX6fp2dnZabSpex9raGsbGxlKbiubPny+NOVKr1XB0dHzWvwIiIiKqJao9CJWVlaFNmzaYN28eWrdujZEjRyIoKAgrVqzQaqdSqbSeCyEqHauoYpuq2v+TNg+bMWMG8vPzpUdmZuZjayIiIqLaq9qD0IsvvogWLVpoHXN1dUVGRgYAwMHBAQAq9cjk5ORIvTcODg4oLi5Gbm7uY9tcu3at0vWvX7+u1abidXJzc1FSUlKpp6iciYkJrKystB5ERET0fKr2IOTt7Y20tDStY2fPnoWTkxMAoHHjxnBwcEBsbKx0vri4GHFxcejYsSMAwMPDA3Xq1NFqk5WVhZSUFKmNl5cX8vPzkZiYKLX5888/kZ+fr9UmJSUFWVlZUpuYmBiYmJjAw8Ojml85ERER1TbVPmts0qRJ6NixI+bNm4eBAwciMTERK1euxMqVKwE8uFU1ceJEzJs3D82aNUOzZs0wb948mJubIyAgAACgVqsxYsQIBAcHo379+rCxscGUKVPg7u4uzSJzdXWFv78/goKC8N133wEAPvroI/Tq1QsuLi4AAD8/P7Ro0QKBgYH48ssvcfPmTUyZMgVBQUHs6SEiIqLqD0Lt2rVDVFQUZsyYgTlz5qBx48ZYtmwZBg8eLLWZNm0aCgsLMWbMGOTm5sLT0xMxMTGwtLSU2ixduhRGRkYYOHAgCgsL4ePjg/DwcBgaGkptNmzYgPHjx0uzy/r06YOwsDDpvKGhIX799VeMGTMG3t7eMDMzQ0BAABYtWlTdL5uIiIhqoWoPQgDQq1cv9OrV65HnVSoVQkJCEBIS8sg2pqamCA0NRWho6CPb2NjYICIi4rG1NGzYEDt37nxizURERKQ83GuMiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIiBSLQYiIiIgUq8aD0Pz586FSqTBx4kTpmBACISEh0Gg0MDMzQ+fOnXHq1CmtnysqKsK4ceNga2sLCwsL9OnTB5cvX9Zqk5ubi8DAQKjVaqjVagQGBiIvL0+rTUZGBnr37g0LCwvY2tpi/PjxKC4urqmXS0RERLVIjQahw4cPY+XKlWjZsqXW8YULF2LJkiUICwvD4cOH4eDggK5du+L27dtSm4kTJyIqKgqRkZGIj4/HnTt30KtXL5SWlkptAgICkJycjOjoaERHRyM5ORmBgYHS+dLSUvTs2RMFBQWIj49HZGQktmzZguDg4Jp82URERFRL1FgQunPnDgYPHoxVq1bB2tpaOi6EwLJlyzBz5kwMGDAAbm5uWLt2Le7evYuNGzcCAPLz87FmzRosXrwYvr6+aN26NSIiInDy5Ens3r0bAJCamoro6GisXr0aXl5e8PLywqpVq7Bz506kpaUBAGJiYnD69GlERESgdevW8PX1xeLFi7Fq1SrcunWrpl46ERER1RI1FoTGjh2Lnj17wtfXV+v4hQsXkJ2dDT8/P+mYiYkJOnXqhIMHDwIAkpKSUFJSotVGo9HAzc1NanPo0CGo1Wp4enpKbTp06AC1Wq3Vxs3NDRqNRmrTrVs3FBUVISkpqcq6i4qKcOvWLa0HERERPZ+MauKXRkZGIikpCUeOHKl0Ljs7GwBgb2+vddze3h6XLl2S2hgbG2v1JJW3Kf/57Oxs2NnZVfr9dnZ2Wm0qXsfa2hrGxsZSm4rmz5+Pzz777GleJhEREdVy1d4jlJmZiQkTJmDDhg0wNTV9ZDuVSqX1XAhR6VhFFdtU1f6ftHnYjBkzkJ+fLz0yMzMfWxMRERHVXtUehJKSkpCTkwMPDw8YGRnByMgIcXFxWL58OYyMjKQemoo9Mjk5OdI5BwcHFBcXIzc397Ftrl27Vun6169f12pT8Tq5ubkoKSmp1FNUzsTEBFZWVloPIiIiej5VexDy8fHByZMnkZycLD3atm2LwYMHIzk5Gc7OznBwcEBsbKz0M8XFxYiLi0PHjh0BAB4eHqhTp45Wm6ysLKSkpEhtvLy8kJ+fj8TERKnNn3/+ifz8fK02KSkpyMrKktrExMTAxMQEHh4e1f3SiYiIqJap9jFClpaWcHNz0zpmYWGB+vXrS8cnTpyIefPmoVmzZmjWrBnmzZsHc3NzBAQEAADUajVGjBiB4OBg1K9fHzY2NpgyZQrc3d2lwdeurq7w9/dHUFAQvvvuOwDARx99hF69esHFxQUA4OfnhxYtWiAwMBBffvklbt68iSlTpiAoKIg9PURERFQzg6WfZNq0aSgsLMSYMWOQm5sLT09PxMTEwNLSUmqzdOlSGBkZYeDAgSgsLISPjw/Cw8NhaGgotdmwYQPGjx8vzS7r06cPwsLCpPOGhob49ddfMWbMGHh7e8PMzAwBAQFYtGiR7l4sERER6S2dBKHff/9d67lKpUJISAhCQkIe+TOmpqYIDQ1FaGjoI9vY2NggIiLisddu2LAhdu7c+SzlEhERkUJwrzEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUiwGISIiIlIsBiEiIiJSLAYhIiIiUqxqD0Lz589Hu3btYGlpCTs7O/Tr1w9paWlabYQQCAkJgUajgZmZGTp37oxTp05ptSkqKsK4ceNga2sLCwsL9OnTB5cvX9Zqk5ubi8DAQKjVaqjVagQGBiIvL0+rTUZGBnr37g0LCwvY2tpi/PjxKC4uru6XTURERLVQtQehuLg4jB07FgkJCYiNjcX9+/fh5+eHgoICqc3ChQuxZMkShIWF4fDhw3BwcEDXrl1x+/Ztqc3EiRMRFRWFyMhIxMfH486dO+jVqxdKS0ulNgEBAUhOTkZ0dDSio6ORnJyMwMBA6XxpaSl69uyJgoICxMfHIzIyElu2bEFwcHB1v2wiIiKqhYyq+xdGR0drPf/hhx9gZ2eHpKQkvPHGGxBCYNmyZZg5cyYGDBgAAFi7di3s7e2xceNGjBw5Evn5+VizZg3Wr18PX19fAEBERAQcHR2xe/dudOvWDampqYiOjkZCQgI8PT0BAKtWrYKXlxfS0tLg4uKCmJgYnD59GpmZmdBoNACAxYsXY+jQoZg7dy6srKyq++UTERFRLVLjY4Ty8/MBADY2NgCACxcuIDs7G35+flIbExMTdOrUCQcPHgQAJCUloaSkRKuNRqOBm5ub1ObQoUNQq9VSCAKADh06QK1Wa7Vxc3OTQhAAdOvWDUVFRUhKSqqhV0xERES1RbX3CD1MCIHJkyfjtddeg5ubGwAgOzsbAGBvb6/V1t7eHpcuXZLaGBsbw9raulKb8p/Pzs6GnZ1dpWva2dlptal4HWtraxgbG0ttKioqKkJRUZH0/NatW0/9eomIiKh2qdEeoY8//hgnTpzApk2bKp1TqVRaz4UQlY5VVLFNVe3/SZuHzZ8/Xxp8rVar4ejo+NiaiIiIqPaqsSA0btw47NixA/v27cNLL70kHXdwcACASj0yOTk5Uu+Ng4MDiouLkZub+9g2165dq3Td69eva7WpeJ3c3FyUlJRU6ikqN2PGDOTn50uPzMzMZ3nZREREVItUexASQuDjjz/G1q1bsXfvXjRu3FjrfOPGjeHg4IDY2FjpWHFxMeLi4tCxY0cAgIeHB+rUqaPVJisrCykpKVIbLy8v5OfnIzExUWrz559/Ij8/X6tNSkoKsrKypDYxMTEwMTGBh4dHlfWbmJjAyspK60FERETPp2ofIzR27Fhs3LgR27dvh6WlpdQjo1arYWZmBpVKhYkTJ2LevHlo1qwZmjVrhnnz5sHc3BwBAQFS2xEjRiA4OBj169eHjY0NpkyZAnd3d2kWmaurK/z9/REUFITvvvsOAPDRRx+hV69ecHFxAQD4+fmhRYsWCAwMxJdffombN29iypQpCAoKYsAhIiKi6g9CK1asAAB07txZ6/gPP/yAoUOHAgCmTZuGwsJCjBkzBrm5ufD09ERMTAwsLS2l9kuXLoWRkREGDhyIwsJC+Pj4IDw8HIaGhlKbDRs2YPz48dLssj59+iAsLEw6b2hoiF9//RVjxoyBt7c3zMzMEBAQgEWLFlX3yyYiIqJaqNqDkBDiiW1UKhVCQkIQEhLyyDampqYIDQ1FaGjoI9vY2NggIiLisddq2LAhdu7c+cSaiIiISHm41xgREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESkWgxAREREpFoMQERERKRaDEBERESmWIoLQN998g8aNG8PU1BQeHh74448/5C6JiIiI9MBzH4R+/PFHTJw4ETNnzsSxY8fw+uuvo3v37sjIyJC7NCIiIpLZcx+ElixZghEjRuDDDz+Eq6srli1bBkdHR6xYsULu0oiIiEhmRnIXUJOKi4uRlJSETz75ROu4n58fDh48WOXPFBUVoaioSHqen58PALh169YTr1dWdPf/UK22p7ne02Jdz4Z1PRsl1AXob22s69mwrmdTm+sqbyOEeHxD8Ry7cuWKACAOHDigdXzu3LmiefPmVf7M7NmzBQA++OCDDz744OM5eGRmZj42KzzXPULlVCqV1nMhRKVj5WbMmIHJkydLz8vKynDz5k3Ur1//kT/ztG7dugVHR0dkZmbCysrq//S7qhPrenb6Whvrejas69mwrmenr7UpoS4hBG7fvg2NRvPYds91ELK1tYWhoSGys7O1jufk5MDe3r7KnzExMYGJiYnWsXr16lVrXVZWVnr1P1451vXs9LU21vVsWNezYV3PTl9re97rUqvVT2zzXA+WNjY2hoeHB2JjY7WOx8bGomPHjjJVRURERPriue4RAoDJkycjMDAQbdu2hZeXF1auXImMjAyMGjVK7tKIiIhIZs99EBo0aBBu3LiBOXPmICsrC25ubti1axecnJx0XouJiQlmz55d6dab3FjXs9PX2ljXs2Fdz4Z1PTt9rY11/Y9KiCfNKyMiIiJ6Pj3XY4SIiIiIHodBiIiIiBSLQYiIiIgUi0GIiIiIFItBiIiIqAYIIZ68zxXJjkGI6BldvnwZV65ckbsMvVdYWIi7d/+3yeKlS5ewbNkyxMTEyFgVPU+SkpIQERGBDRs24OjRo3KXI1m3bh3c3d1hZmYGMzMztGzZEuvXr5e7LHqE534dIbnl5eUhMTEROTk5KCsr0zr3wQcfyFQVPauysjJ8/vnnWLx4Me7cuQMAsLS0RHBwMGbOnAkDA3m+U/Tv37/KPfBUKhVMTU3RtGlTBAQEwMXFRee19e3bFwMGDMCoUaOQl5cHT09P1KlTB3///TeWLFmC0aNH67ymqpSWluLkyZNwcnKCtbW13OXopVOnTuGVV16p8lx0dDT8/f11Wk9OTg7effdd/P7776hXrx6EEMjPz0eXLl0QGRmJF154Qaf1PGzJkiWYNWsWPv74Y3h7e0MIgQMHDmDUqFH4+++/MWnSJNlqAx58Jv38889IT0/H1KlTYWNjg6NHj8Le3h4NGjTQWR0DBgxAeHg4rKysMGDAgMe23bp1a43WwiBUg3755RcMHjwYBQUFsLS01PrAUqlUsgehEydOVHm8/EO0YcOGOl9sy8DA4LGb25aWluqwmv+ZOXMm1qxZgwULFmi9uYWEhODevXuYO3euLHWp1Wps27YN9erVg4eHB4QQOHbsGPLy8uDn54cff/wRX3zxBfbs2QNvb2+d1nb06FEsXboUAPDzzz/D3t4ex44dw5YtW/Dpp5/KFoQmTpwId3d3jBgxAqWlpejUqRMOHjwIc3Nz7Ny5E507d9ZpPZMnT8Z//vMfWFhYaG34XJUlS5boqCptbdu2xcKFCzFu3DjpWFFREYKDg7FmzRoUFhbqtJ5x48bh1q1bOHXqFFxdXQEAp0+fxpAhQzB+/Hhs2rRJp/U8LDQ0FCtWrNB6f+/bty9eeeUVhISEyBqETpw4AV9fX6jValy8eBFBQUGwsbFBVFQULl26hHXr1umsFrVaLb3XP81+YDXqsXvT0/9Js2bNxIQJE0RBQYHcpVRJpVIJAwODRz5MTEzEBx98IAoLC3VW07Zt27QeP/30k/jXv/4lGjRoIFavXq2zOip68cUXxfbt2ysd37Ztm9BoNDJU9MD06dPF6NGjRWlpqXSstLRUfPzxx2LGjBmirKxMfPTRR8Lb21vntZmZmYlLly4JIYR45513REhIiBBCiIyMDGFmZqbzeso1aNBAHD58WAghRFRUlNBoNCItLU3MnDlTdOzYUef1dO7cWeTm5kp/ftSjS5cuOq+t3JYtW0T9+vWFv7+/yMrKEseOHROurq7C1dVVJCUl6bweKysrkZiYWOn4n3/+KdRqtc7reZiJiYk4d+5cpeNnz54VJiYmMlT0Pz4+PmLq1KlCCCHq1q0r0tPThRBCHDhwQDg5OclYmbwYhGqQubm59D+aPtq2bZtwcXERq1evFidOnBDHjx8Xq1evFq6uriIyMlJERESIl156SQQHB8tdqtiwYYPo06ePbNc3MTERaWlplY6fOXNGmJqaylDRA7a2tlXWlZaWJurXry+EEOLEiROyfDi4u7uLr776SmRkZAgrKytx8OBBIYQQR44cEfb29jqvp5yJiYnIzMwUQggRFBQkJkyYIIQQ4q+//hKWlpay1aXvrly5Inx9fUX9+vWFqampGD16tLh7964stdStW1ccO3as0vGjR4/K/t/wlVdeEXPnzq10/D//+Y9wc3OToaL/sbKyEufPnxdCaAehixcvyh7S5MRbYzWoW7duOHLkCJydneUupUpz587FV199hW7duknHWrZsiZdeegmzZs1CYmIiLCwsEBwcjEWLFslYKeDp6YmgoCDZrt+qVSuEhYVh+fLlWsfDwsLQqlUrmaoC7t+/jzNnzqB58+Zax8+cOSPdRjQ1NX3s7caa8umnnyIgIACTJk2Cj48PvLy8AAAxMTFo3bq1zuspZ29vj9OnT+PFF19EdHQ0vvnmGwDA3bt3YWhoKFtd+q60tBTFxcUoLS1FaWkpHBwcZNun6s0338SECROwadMmaDQaAMCVK1ek/9fk9Nlnn2HQoEHYv38/vL29oVKpEB8fjz179mDz5s2y1mZqaopbt25VOp6WlibruCrgwe3zzZs3IyMjA8XFxVrnanogPINQDerZsyemTp2K06dPw93dHXXq1NE636dPH5kqe6B8gGhFTk5OOHnyJADg1VdfRVZWlq5L01JYWIjQ0FC89NJLstWwcOFC9OzZE7t374aXlxdUKhUOHjyIzMxM7Nq1S7a6AgMDMWLECPzrX/9Cu3btoFKpkJiYiHnz5kljFOLi4h450LUmvf3223jttdeQlZWlFRZ9fHzQv39/nddTbtiwYRg4cCBefPFFqFQqdO3aFQDw559/4uWXX5atLgC4d+8eQkNDsW/fvionWMg1MyoyMhKjR4/G66+/jrNnzyI5ORnDhg3Db7/9hvXr1+v8y15YWBj69u2LRo0awdHRESqVChkZGXB3d0dERIROa6norbfewp9//omlS5di27ZtEEKgRYsWSExMlPULAPBgrNKcOXOkQFb+9/bJJ5/grbfekq2u5cuXY+bMmRgyZAi2b9+OYcOGIT09HYcPH8bYsWNr/PrcdLUGPW4mkUqlkm3gb7nWrVujVatWWLlyJYyNjQEAJSUlCAoKwvHjx3Hs2DEcOHAA77//Pi5cuKCTmqytrbV6L4QQuH37NszNzRERESFreLx69Sq+/vprnDlzRnpzGzNmjPSNVA6lpaVYsGABwsLCcO3aNQAPejzGjRuH6dOnw9DQEBkZGTAwMJA1SOqbn3/+GZmZmXjnnXekv5e1a9eiXr166Nu3r2x1BQQEIDY2Fm+//Tbs7e0r9eTNnj1blrosLCywaNEirQHuubm5GDlyJKKjo6vsZdCF2NhYrX+Pvr6+stRRW9y6dQs9evTAqVOncPv2bWg0GmRnZ8PLywu7du2ChYWFLHW9/PLLmD17Nt577z1YWlri+PHjcHZ2xqeffoqbN28iLCysRq/PIKRgBw8eRJ8+fWBgYICWLVtCpVLhxIkTKC0txc6dO9GhQwesX78e2dnZmDp1qk5qCg8P13rzNzAwwAsvvABPT09ObX6C8g8jKysrmSt5oEuXLo+9Jbd3714dVlM7qNVq7Nq1S+cz/J4kLS3tkUswrF+/HoGBgTquSL88SxDUh3+fe/fuxdGjR1FWVoY2bdrIHiDNzc2RmpoKJycn2NnZITY2Fq1atcK5c+fQoUMH3Lhxo0avz1tjCtaxY0dcvHgREREROHv2LIQQePvttxEQEABLS0sA0Pkb3NChQ3V6vaelj0sNVKQPb7APe/XVV7Wel5SUIDk5GSkpKRgyZIg8RQGYM2fOY89/+umnOqqksgYNGkj/9vTJ49ah0tV7xPLly/HRRx/B1NS00li9isaPH6+TmsrVq1fviePwhBB6cScAeDDG6s0335S7DImDgwNu3LgBJycnODk5ISEhAa1atcKFCxd0sjI3e4SqmT7/Y9VXjwoZVWnZsmUNVvJoD69vVP5P5uE3vjp16mDQoEH47rvvYGpqqrO6rl27hilTpmDPnj3Iycmp9KahD2+6FYWEhODOnTuyDcCvOE6jpKQEFy5cgJGREZo0aSLrCsX//e9/sXz5cnz77bdVjt/TJX1b36hx48Y4cuQI6tevj8aNGz+ynUqlwl9//VXj9TwsLi7uqdt26tSpBiup7EmfQw+T6zPpww8/hKOjI2bPno1vv/0WkydPhre3N44cOYIBAwZgzZo1NXp9BqFqps//WKty9uxZ/P7771UOzNTVN+PykFFVwKhIrg/27du3Y/r06Zg6dSrat28PIQQOHz6MxYsXY/bs2bh//z4++eQTDBo0SKcf8N27d0dGRgY+/vhjafDvw+Qc7/Io58+fR/v27XHz5k25S5HcunULQ4cORf/+/WW9zXP9+nUMHDgQ+/fvh7m5eaUJFrr8O+vSpQuioqJQr149dOnS5ZHtVCoVb3Pqscd9Dj1Mzs+ksrIylJWVwcjowU2qzZs3Iz4+Hk2bNsWoUaOkMaw1hUFIwVatWoXRo0fD1tYWDg4OlVa+1tU340uXLkl/PnbsGKZMmYKpU6dK060PHTqExYsXY+HChejXr59Oaqqoffv2+M9//qO11AAA/Pbbb9JSA9u2bUNwcDDS09N1VpelpSX++OOPSreh9Nn69esxffp0XL16Ve5StKSkpKBXr164ePGibDX4+voiIyMDI0aMqHKwtJy3FPVFSUkJXFxcsHPnTrRo0ULucirZv3//Y8+/8cYbOqqEnhbHCNWgOXPmYMqUKTA3N9c6XlhYiC+//FLWsQgA8Pnnn2Pu3LmYPn26rHU8fAvgnXfewfLly9GjRw/pWMuWLeHo6IhZs2bJFoT0dakBR0dHvd3duuL+QUIIZGVl4ciRI5g1a5ZMVT1aXl4e8vPzZa3h4MGDOHTokKxrU+m7OnXqoKioSJa1sZ5GVVu0PFyrnLer9fkzSc59OdkjVIMMDQ2RlZUFOzs7reM3btyAnZ2d7OM3rKyskJycrFcLPpqZmeHo0aPS/kHlUlNT0aZNG53vaVROH5caAB4sTrh48WJ89913aNSokc6u+zSGDRum9bx8BuCbb74JPz8/maqqPGaiPKCtX78eb7zxhqz7VLVp0wbffPMNOnToIFsNVSkoKMCCBQuksWgVP6h0fUtlwYIFOHPmDFavXi3dTtEXFcN0SUkJjh07hlmzZmHu3LmyLvior59JT9qXs6ZvCTMI1SADAwNcu3at0oqde/fuxaBBg3D9+nWZKntgxIgRaNeuHUaNGiVrHQ9r06YNXF1dsWbNGmnQcVFREYYPH47U1FTZBrLq41IDwIN1l+7evYv79+/LPqaktqg4ZuLhgDZjxgxZZ23FxMTgs88+w9y5c6tchFWumYHvvfce4uLiEBgYWOVYtAkTJui0nv79+2PPnj2oW7cu3N3dK61/U9O7lf8T+/fvx6RJk5CUlCRbDfr6mdS8eXP06NED8+bNq9RbpQsMQjWgfFHA/Px8WFlZVeoWvXPnDkaNGoWvv/5axiqB+fPnY8mSJejZs2eVb7pyzCBITExE7969UVZWJt0eOH78OFQqFXbu3In27dvrvKZyd+7c0Vpq4OWXX9ZaakAOa9eufex5fRhTcuTIEaSmpkKlUsHV1RUeHh5yl6S3yhdhrRg05J56Xa9ePfz66696s75Rxd7Gin744QcdVfL0UlNT0a5dO9y5c0fn19b3zyQLCwucPHlStrsTDEI1YO3atRBCYPjw4Vi2bBnUarV0ztjYGI0aNZIGAstJX2e13b17FxEREVorxgYEBMi26in9M5cvX8Z7772HAwcOoF69egAejAPo2LEjNm3aBEdHR3kL1ENPmoat66nX5Ro3boxdu3ZVumVNlVVcDqT81uuCBQtQUlKCAwcO6Lwmff9MGjBgAN59910MHDhQluszCNWguLg4eHt76909bPrnTp8+XeWmgLrc+uPWrVvSLZInrWgr5yKLfn5+uHXrFtauXSstyJeWlobhw4fDwsICMTExstV2+PBh/PTTT1X+t9TH2ypyi4iIwPbt27F27VpZbl1U5f79+/j999+Rnp4u9cxevXoVVlZWqFu3rmx1VVwOpFyHDh3w/fffy7qfnT59Ju3YsUP68/Xr1zFnzhwMGzZMln05GYRq0NGjR1GnTh24u7sDeLAWzQ8//IAWLVogJCSkxtdGqI0e/sfxsPIVnJs2bfrU62JUp7/++gv9+/fHyZMnq1zzSJe3LB4e8PjwQo8Pk/tWCvBg4PvBgwcrLWB49OhReHt7yzbwPTIyEh988AH8/PwQGxsLPz8/nDt3DtnZ2ejfv79e3Fa5e/dulSFNrgVFW7dujfT0dAgh0KhRo0ofVLoeu3fp0iX4+/sjIyMDRUVFOHv2LJydnTFx4kTcu3cP3377rU7rqVjbw8rHoOlyodVH0afB0o/bi/Nhungfkz8WPsdGjhyJTz75BO7u7vjrr78waNAgDBgwAD/99BPu3r2LZcuW6bwmfVsttqJ+/fpV+W2q/JhKpcJrr72Gbdu26XTvsQkTJqBx48bYvXs3nJ2dkZiYiBs3biA4OFjnKyTv3bsXNjY2AIB9+/bp9NrPomHDhigpKal0/P79+2jQoIEMFT0wb948LF26FGPHjoWlpSW++uorNG7cGCNHjsSLL74oW13Ag2/Gw4YNw3//+98qz8sVbOVatuJRJkyYgLZt2+L48eOoX7++dLx///748MMPZawMsq8I/jiP6vcoKirS+RfzijMP5cQgVIPOnj0rLXT3008/oVOnTti4cSMOHDiAd999V5YgdOzYMenD6dixY49sJ9caHbGxsZg5cybmzp0rDYxOTEzEv//9b8yaNQtqtRojR47ElClTanzZ9YcdOnQIe/fuxQsvvAADAwMYGBjgtddew/z58zF+/PjH/l1Wt4fHicg1ZuRpLFy4EOPGjcPXX38NDw8PqFQqHDlyBBMmTJBtew0ASE9PR8+ePQEAJiYmKCgogEqlwqRJk/Dmm2/is88+k622iRMnIjc3FwkJCdLKzteuXcPnn3+OxYsXy1aXXLveP0p8fDwOHDhQ6cPbyckJV65ckamq/ykoKEBcXFyVvXpyTEIpXzJCpVJh9erVWrcOS0tLsX//fllv2cmNQagGCSGk1Lt792706tULwINF8P7++29Zanq4B0EfexMmTJiAlStXomPHjtIxHx8fmJqa4qOPPsKpU6ewbNkyDB8+XKd1lZaWSm8etra2uHr1KlxcXODk5IS0tDSd1vIwfdunrXx2SrmCggJ4enpKYxLu378PIyMjDB8+XLZeBhsbG9y+fRvAg01OU1JS4O7ujry8PNy9e1eWmsrt3bsX27dvR7t27WBgYAAnJyd07doVVlZWmD9/vhTg5JCXl4eff/4Z6enpmDp1KmxsbHD06FHY29vrvIevrKysyt6xy5cvy75p7bFjx9CjRw/cvXsXBQUFsLGxwd9//w1zc3PY2dnJEoSWLl0K4MFn0rfffgtDQ0PpXPlgaTlvJz5qP7SHh0S88cYbWnVXJwahGtS2bVt8/vnn8PX1RVxcHFasWAEAuHDhAuzt7WWuTj+lp6dXOcDXyspKmsXWrFkznQdJNzc3nDhxAs7OzvD09MTChQthbGyMlStXyrog5auvvqpXu17L0cv5rF5//XXExsbC3d0dAwcOxIQJE7B3717ExsbKutgd8CA4lo/fsLGxwfXr19G8eXO4u7vLuhnsiRMn4OvrC7VajYsXLyIoKAg2NjaIiorCpUuXsG7dOp3W07VrVyxbtgwrV64E8OAD886dO5g9e7bWqvRymDRpEnr37o0VK1agXr16SEhIQJ06dfD+++/rfL2lcuWLvHbp0gVbt27V6bCCp7F06VJcv34dd+/ehbW1NYQQyMvLg7m5OerWrYucnBw4Oztj3759NTPbVFCNOX78uHBzcxNWVlYiJCREOv7xxx+L9957T5aa+vfvL/Lz86U/P+4RGBgoFixYIPLy8nRWn7e3t/D39xc5OTnSsZycHOHv7y9ef/11IYQQsbGxolmzZjqrSQghoqOjxZYtW4QQQqSnpwtXV1ehUqmEra2t2LNnj05reVhUVJRo0qSJ+Pbbb8Xx48fF8ePHxbfffiuaNWsmoqKixMWLF6UHPXDjxg1x5coVIYQQpaWl4osvvhC9e/cWkyZNEjdv3pS1trZt24ro6GghhBB9+/YVgYGB4vLly2LatGnC2dlZtrp8fHzE1KlThRBC1K1bV6SnpwshhDhw4IBwcnLSeT1XrlwRzZs3F66ursLIyEh06NBB1K9fX7i4uIhr167pvJ6HqdVqcebMGenPp0+fFkIIkZCQIFxcXOQsTW9t3LhRdO7cWZw/f146du7cOfHmm2+KyMhIkZmZKby9vcVbb71VI9fnrDEZ3Lt3D4aGhpVmXujCsGHDsHz5clhaWj5xUbKioiIcOnQI7u7uj5zNVd3S0tLQt29fXLhwAY6OjlCpVMjIyICzszO2b9+O5s2bY9u2bbh9+7asu4QDD1ZtrngrSNfat2+PkJCQSt+Cd+3ahVmzZsm6im25nJycKrdlkGsGlD7bsGEDSkpKMHToUBw7dgzdunXDjRs3YGxsjPDwcAwaNEiWutRqNY4ePYomTZrA0tISx48fh7OzMy5dugQXFxfcu3dP5zUVFhZi06ZNOHr0KMrKytCmTRsMHjwYZmZmOq/lYS+88AIOHDiA5s2bw8XFBcuXL0e3bt1w5swZtGnTRvbbr5cvX8aOHTuqHL8kxwQZAGjSpAm2bNlSafPoY8eO4a233sJff/2FgwcP4q233qqR/Rx5a0wGck6jfHhq8NNMEz59+jTatWtXkyVpcXFxQWpqKn777TetFZy7du0qTbeUY2xJfn4+SktLpdlawINbFzdv3oSRkZFs6/WcPHmyyuUEGjdujNOnT8tQ0f8kJSVhyJAhSE1NrXIWoJxT+8vKynD+/PkqA5qcu4MPHjxY+nPr1q1x8eJFnDlzBg0bNoStra1sdZmamla5ZlVaWlql7Rp0xczMDMOHD9f5eMEnad26NY4cOYLmzZujS5cu+PTTT/H3339j/fr10lIqctmzZw/69OmDxo0bIy0tDW5ubrh48SKEEGjTpo1sdWVlZeH+/fuVjt+/fx/Z2dkAAI1GI43tq27sEapBj1rjpZzcm64+jdLSUqSkpCh+N+zu3bujd+/eGDNmjNbxb7/9Fjt27MCuXbtkqUtf92YDHvT4NG3aFNOnT4e9vX2lfwtyTTNOSEhAQEAALl26pHcBTV999NFHuH79OjZv3gwbGxucOHEChoaG6NevH9544w1ZxoadPXsWv//+e5VhVs5d1I8cOYLbt2+jS5cuuH79OoYMGYL4+Hg0bdoUP/zwg6zvpe3bt4e/vz/mzJkj9ezZ2dlh8ODB8Pf3x+jRo2Wpq2fPnsjOzsbq1auldceOHTuGoKAgODg4YOfOnfjll1/wr3/9CydPnqz26zMI1aDt27drPS/fhXjt2rX47LPPMGLECJ3XNGDAAISHh8PKygoDBgx4bFu5Vtjds2fPI3e5/v7772WpycbGBgcOHKi0xcCZM2fg7e2NGzduyFKXPu/NZmlpiWPHjqFp06ay1VCVV199Fc2bN8dnn31W5QaiD28/oAv6vrYX8GAF8x49euDUqVO4ffs2NBoNsrOz4eXlhV27dul8+5tVq1Zh9OjRsLW1hYODQ6XdyuX6AiCEwPnz51FSUoLmzZvrxQrOD7O0tERycjKaNGkCa2trxMfH45VXXsHx48fRt29fXLx4UZa6srOzERgYiD179khDRu7fvw8fHx+sX78e9vb22LdvH0pKSuDn51ft19ev/0rPmb59+1Y69vbbb+OVV17Bjz/+KEsQUqvV0puGrt/wn8Znn32GOXPmoG3btlV+SMmlqKioyq7bkpIS2VZIBh58w7tw4YLW3myDBg3Si73ZfHx8cPz4cb0LQufOncPPP/+sN3U97dpecrKyskJ8fDz27t2rNSbH19dXlno+//xzzJ07F9OnT5fl+lW5ePEi+vbti5SUFAAPlknZunWrrLecKrKwsEBRURGAB7ea0tPT8corrwCAbEu6AICDgwNiY2Nx5swZrSER5VvzAA9mvNUU9gjJID09HS1btkRBQYHcpeidF198EQsXLpR9IHRFnTt3hru7O0JDQ7WOjx07FidOnMAff/whU2X66++//8aQIUPQvn17uLm56Xz/oEd58803MW3aNPj7+8tyffq/s7KyQnJysqxLV1Q0aNAgJCcnY/bs2TA1NcWXX36J0tJSJCYmyl2apF+/fujZsyeCgoIwbdo0REVFYejQodKU+t27d8tdoizYI6RjhYWFCA0NxUsvvSR3KQD0b+PC4uJircUU9cXcuXPh6+uL48ePS2vN7NmzB4cPH5Z181B93ZsNAA4ePIj4+Pgqt4uQcyzOuHHjEBwcjOzs7Co3eJRjNtvTDPhVqVQ6XU29ori4OCxatAipqalQqVRwdXXF1KlT8frrr+u8lnfeeQcxMTEYNWqUzq/9KH/88Qc2bdokrfbevn17ODk5obCwUPaZbOWWLFmCO3fuAABCQkJw584d/Pjjj2jatKm06KIcSktLER4e/sghEXv37q3R67NHqAZVnFothMDt27dhZmaGDRs2yPaNuJw+blw4ffp01K1bF7NmzdL5tZ8kOTkZX375JZKTk2FmZoaWLVtixowZaNasmWw1PWqna7n3ZgOARo0aoVevXpg1a5ZeLSBa1WaPD/99yRHQyleRbt269SP3gwKAqKgoHVb1PxERERg2bBgGDBgAb29vCCFw8OBBREVFITw8HAEBATqtZ/78+ViyZAl69uxZZZiVY/VmAwMDZGVlaf2/XrduXaSkpKBRo0Y6r6c2+fjjjxEeHo6ePXtWOSSipkMag1ANWrt2rdbz8l2IPT09cenSpUprJuhav379YGlpiTVr1qB+/frS2iBxcXH48MMPce7cOZ3XNGHCBKxbtw4tW7ZEy5YtK73ByTVYVF/t2bPnqfZm8/T01HlvwsMDM/VJxd3BK5JjNtuYMWMQGRmJhg0bYvjw4Xj//fe1lmqQm6urKz766CNMmjRJ6/iSJUuwatUqpKam6rSex/VyqlQqaRV6XTI0NER2drbWcgJWVlY4fvy4bL2yVdGnrVLK2draYt26dbKtCs4gpEP5+fnYsGED1qxZg+TkZNmn6dra2uLAgQNwcXHRWiTt4sWLaNGihSwLfz1uQJxKparxLtKH3bp1S1ofqKo1VB4m1zpCbm5ulfZmA4ADBw5Ie7Pt3r0bw4cPR0ZGhk5rGzJkCF5//XXZdwOvLYqKirB161Z8//33OHjwIHr27IkRI0bAz89P9kkDJiYmOHXqVKUB5ufPn4ebm5ssCyrqGwMDA63JKMCD0GFlZaXVC3nz5k05ygNQeauUtLQ0ODs7Y9asWbJslVJOo9Hg999/R/PmzWW5PscI6cDevXvx/fffY+vWrXBycsJbb72F1atXy12WXm5cqE8bwVpbWyMrKwt2dnaoV69elR9Gct5OAfR3bzYAaN68OWbMmIH4+Hi9uX3xsNOnT1e5uq5ct6xNTEzw3nvv4b333sOlS5cQHh6OMWPGoKSkBKdPn5ZlzF45R0dH7Nmzp1IQ2rNnT83s/VQLPc0CtXKbPHkyhg4dioULF2q9x3fv3l3ntzcfFhwcjK+++gphYWGyhH4GoRpy+fJlhIeH4/vvv0dBQQEGDhyIkpISbNmyBS1atJC7PAD6vXHh+fPnkZ6ejjfeeANmZmZS4NClvXv3Srcn9CmgPczDwwNTp07FunXrpC7569evY9q0adKK4OfOnZNlcP7q1atRt25dxMXFIS4uTuucSqWSLQj99ddf6N+/P06ePKk1vqr8/y+5e2qBB7WU11Zx4KgcgoODMX78eCQnJ6Njx45QqVSIj49HeHg4vvrqK1lq0retIoYMGaLzaz6rw4cP47vvvqt0vEGDBtIKznKIj4/Hvn378N///hevvPJKpS9NNb2mHYNQDejRowfi4+PRq1cvhIaGwt/fH4aGhrIMPn6cpUuXokuXLmjRogXu3buHgIAAnDt3DvXr18emTZtkqenGjRsYOHAg9u3bB5VKhXPnzsHZ2Rkffvgh6tWrh8WLF+uslvLZHxX/rE/WrFmDvn374qWXXqpybzYAuHPnjiyDz8t3vNY3EyZMQOPGjbF79244OzsjMTERN27cQHBwMBYtWiRbXQ/fGit//wgLC4O/v3+VA7x1afTo0XBwcMDixYuxefNmAA/GDf34449VrpdW0/R1qwh9p49bpQBAvXr10L9/f9muzzFCNcDIyAjjx4/H6NGjtWYU1alTB8ePH9ebHiFA/zYu/OCDD5CTk4PVq1fD1dVVGrcUExODSZMm4dSpU7LUBTy435+YmFjl9M4PPvhApqoe3J573N5spM3W1hZ79+5Fy5YtoVarkZiYCBcXF+zduxfBwcGyLGr48GDpYcOG4f3330f9+vV1Xkdtoa9bReg7fdwqRR8wCNWAQ4cO4fvvv8fmzZvx8ssvIzAwEIMGDYJGo9GrIHTjxg3pzTYjIwOrV69GYWEh+vTpI8vaIMCDFUZ/++03tGrVSmsA94ULF+Du7i6tgaFrv/zyCwYPHoyCggJYWlpWWtJfzgGQ+upJa+PItV2KtbU1kpKS4OzsjCZNmmD16tXo0qUL0tPT4e7uLsskAQMDAzRs2BCtW7d+7C1guba9KVdcXFzlF4GGDRvqtA593SpC31W1VUpWVha8vLzw3//+V/bV6OXCW2M1wMvLC15eXvjqq68QGRmJ77//HpMnT0ZZWRliY2Ph6Ogo22Bk4MGO5b1790ZmZiaaNWuGyMhI+Pv7o6CgAAYGBli6dCl+/vlnWXZ5LygogLm5eaXjf//9N0xMTHReT7ng4GAMHz4c8+bNq7I+Oenj3mwAkJubq/W8pKQEKSkpyMvLw5tvvilTVQ9m2p04cQLOzs7w9PTEwoULYWxsjJUrV8q2UvEHH3wg+8ywxzl37hyGDx+OgwcPah2Xa7KAvm4Voe+q2irFw8NDWiRWTj///DM2b95c5ZivGt87TpBOnDlzRkydOlU4ODgIU1NT0bt3b9lq8ff3F7169RJ//PGHGDlypGjQoIEYNmyYKC0tFaWlpWLMmDHC09NTltp69Ogh/v3vfwshhKhbt67466+/RGlpqXjnnXfEW2+9JUtNQghhbm4u0tPTZbv+o4SEhAgDAwPRvn170bdvX9GvXz+th74pLS0VI0eOFF988YVsNURHR4stW7YIIYRIT08Xrq6uQqVSCVtbW7F7927Z6tJnHTt2FG+88YbYtWuXOHbsmEhOTtZ66Frfvn3FypUrhRBCTJ06VTRt2lR8/vnnok2bNsLHx0fn9TxKWVmZKCsrk7sMkZCQIHbt2qV1LDw8XDg5OYkXXnhBBAUFiXv37slUnRBfffWVqFu3rhg7dqwwNjYWI0eOFL6+vkKtVot//etfNX59BiEdu3//voiKipI1CNWvX18cP35cCCHE7du3hUqlEocPH5bOp6amCrVaLUttp06dEi+88ILw9/cXxsbG4u233xaurq7C3t5enD9/XpaahBCif//+4scff5Tt+o/i4OAg1q1bJ3cZz+TMmTPCwcFB7jK03LhxQy8+sPSVubm5SE1NlbsMSXp6uvQeVlBQIEaPHi3c3d1F//79xcWLF2WuToi1a9cKNzc3YWJiIkxMTIS7u7us/079/f3FggULpOcnTpwQderUER9++KFYvHixcHBwELNnz5atPhcXF7Fx40YhxIMvwOVfOmfNmiXGjh1b49fnrTEdKx+YJsdtp3I3b96Eg4MDgAdLwFtYWGitYmttbY3bt2/LUluLFi1w4sQJrFixAoaGhigoKMCAAQMwduxYvPjiizqt5eF9vHr27ImpU6fi9OnTVa6JI9faM/q6N9vjpKen4/79+zq/7tPs5wXIeztRX7Vo0UKvbjk9fAvT3Nwc33zzjYzVaFuyZAlmzZqFjz/+WNqO5MCBAxg1ahT+/vvvSqtz60JycjL+85//SM8jIyPRvn17rFq1CsCDdaJmz56NkJAQndcGPBijWv4+ZmZmJn3+BAYGokOHDggLC6vR6zMIKVTF8Qj6ND7BwcEBn332mdxlVBlW58yZU+mYnAsqfvjhh9i4caNe7s02efJkredCCGRlZeHXX3+VZc2V8PDwp9rPix54eJr1F198gWnTpmHevHlVfhGQa2V1fRQaGooVK1ZozSTt27cvXnnlFYSEhMgShHJzc7X2QIuLi4O/v7/0vF27dsjMzNR5XeUcHBxw48YNODk5wcnJCQkJCWjVqhUuXLigk3+rDEIKNXToUGnw8b179zBq1ChpxkD5IES56Ms0dX1YyO5J7t27h5UrV2L37t16tzdbxWno5XvtLV68+Kl7Z6rTqFGjEBkZib/++ksv9/PSNxVXUxdC4M0336x0TJdfBCpuZF0VIyMjODg4oGvXrpg1axbq1aunk9rKZWVlVdlL27FjR2RlZem0lnL29va4cOECHB0dUVxcjKNHj2p92bx9+3al9w5devPNN/HLL7+gTZs2GDFiBCZNmoSff/4ZR44cwYABA2r8+pw+r0DDhg17qnZyLBmvb9PU9+7di48//hgJCQmVvvXm5+ejY8eO+Pbbb2VbbkCf9marDfR5Py99U3E18MfR1YKjFTeyrkpZWRlycnLwww8/oHXr1jpfHNbNzQ0BAQH417/+pXX8888/x48//oiTJ0/qtB4AGDlyJE6ePIkvvvgC27Ztw9q1a3H16lUYGxsDADZs2IBly5bh8OHDOq8NePDfrKysDEZGD/pmNm/ejPj4eDRt2hSjRo2S6qwpDEKkV5o3b44ePXrozTT1Pn36oEuXLo/szl6+fDn27duHqKgoHVdG/1fl+3mtW7dOL/bz0kd3797F1KlTsW3bNpSUlMDX1xfLly+Hra2t3KU90dGjR9G1a1fcuHFDp9fdsmULBg0aBF9fX3h7e0vbkezZswebN2+WZQXl69evY8CAAThw4ADq1q2LtWvXatXh4+ODDh06YO7cuTqvTR8wCJFesbCwwMmTJ2Vbz6UiJycnREdHw9XVtcrzZ86cgZ+fn853dq/K5cuXoVKp0KBBA9lqeNKCgA+r8bVBniAjIwPh4eEIDw9HcXExzpw5wyBUwdSpU/HNN99Iq81v3LgRnTt3xk8//SR3aU9UWFiIlStXYsKECTq/dlJSEpYuXYrU1FQIIdCiRQsEBwejdevWOq/lYfn5+ahbty4MDQ21jt+8eRN169at8Z6Xx5FzSASDEOmVAQMG4N1338XAgQPlLgXAg715UlJSKu26Xe78+fNwd3dHYWGhjit7oKysDJ9//jkWL14srbptaWmJ4OBgzJw5U+fbbDzLIPfZs2fXYCVVq2o/r2HDhunFfl76qEmTJpg7dy7effddAEBiYiK8vb1x7969Sh+mRP+U3EMiOFiaZKfP09QbNGiAkydPPjIInThxQufT+h82c+ZMrFmzBgsWLNCaqhsSEoJ79+7pvKtbjnDztCru5xUZGcn9vJ4gMzNTa/xb+/btYWRkhKtXr8LR0VHGymqHwsJClJSUaB3jDLvK5F65nz1CJLun/SYuxzT1cePG4ffff8fhw4dhamqqda6wsBDt27dHly5dsHz5cp3WVU6j0eDbb7+tFBC3b9+OMWPG4MqVK7LU9bCkpCSkpqZCpVKhRYsWst0eqC37eekTQ0NDZGdna+1MbmlpiRMnTqBx48YyVqa/7t69i2nTpmHz5s1Vjk+Sa6kNfSb3kAj2CJHs9Hma+r///W9s3boVzZs3x8cffwwXFxeoVCqkpqbi66+/RmlpKWbOnClbfTdv3sTLL79c6fjLL78s+0awOTk5ePfdd/H777+jXr16EEIgPz8fXbp0QWRkpNaHqy7o+35e+kgIobXUBlB5uQ1AvvBYXFyMCxcuoEmTJtKMI7lNnToV+/btwzfffIMPPvgAX3/9Na5cuYLvvvsOCxYskLs8vdStWzccOXJEtiDEHiHSC/o8Tf3SpUsYPXo0fvvtN2lxL5VKhW7duuGbb75Bo0aNdF5TOU9PT3h6elbqkRo3bhwOHz6MhIQEmSoDBg0ahPT0dKxfv14abH769GkMGTIETZs21fm0Znp2+rrUxt27dzFu3DhpOv3Zs2fh7OyM8ePHQ6PR4JNPPtFpPQ9r2LAh1q1bh86dO8PKygpHjx5F06ZNsX79emzatAm7du2SrTZ98vCQiOvXr2POnDkYNmyYLEMiGIRIL9SGaeq5ubk4f/48hBBo1qwZrK2tZaulXFxcHHr27ImGDRvCy8sLKpUKBw8eRGZmJnbt2iXb+kYAoFarsXv3brRr107reGJiIvz8/JCXlydPYVTrTZgwAQcOHMCyZcvg7++PEydOwNnZGTt27MDs2bMrLeapS3Xr1sWpU6fg5OSEl156CVu3bkX79u1x4cIFuLu7S5MalE6fhkRwmgTphePHj2st+V6Rn58fkpKSdFhRZdbW1mjXrh3at2+vFyEIeLCQ3dmzZ9G/f3/k5eXh5s2bGDBgANLS0mQNQcCDW55VrVZbp04dvb4dSvpv27ZtCAsLw2uvvaZ1u7NFixZIT0+XsbIH+6BdvHhRqmfz5s0AHsyM0vUq1/qsfBHFJz10MaZKP26qkuJdu3btsUu8GxkZ4fr16zqsqPbQaDR6uRDam2++iQkTJmDTpk3QaDQAgCtXrmDSpEnw8fGRuTqqza5fvw47O7tKxwsKCmQfBzZs2DAcP34cnTp1wowZM9CzZ0+Ehobi/v37sm55o4/+/PNP3Lx5E927d5eOrVu3DrNnz0ZBQQH69euH0NBQrTFqNYFBiPSCvk9T12f6sjdbRWFhYejbty8aNWoER0dHqFQqZGRkwN3dHREREbLVRbVfu3bt8Ouvv2LcuHEA/rdp9KpVq+Dl5SVnaVq397t06YLU1FQkJSWhSZMmaNWqlYyV6Z/Zs2ejS5cuUhA6efIkRowYgaFDh8LV1RVffvklNBoNQkJCarQOjhEivaDv09T1ldwLkT2N2NhYnDlzRlph19fXV+6SqJY7ePAg/P39MXjwYISHh2PkyJE4deoUDh06hLi4OHh4eMhdIj2FF198Eb/88gvatm0L4MG6aHFxcYiPjwcA/PTTT5g9ezZOnz5do3UwCJFeuHbtGtq0aQNDQ8NHTlM/evQo7O3t5S5Vr+jb3myAfs8ApOfHyZMnsWjRIiQlJaGsrAxt2rTB9OnT4e7uLks9+nKbpzYxNTXFuXPnpMU5X3vtNfj7++Pf//43AODixYtwd3fH7du3a7YQQaQnLl68KLp37y4MDAyESqUSKpVKGBgYiO7du4sLFy7IXZ5eMjc3F+np6XKXoaV3795iyZIljzz/1VdfiX79+umwIqKa5+/vLxYsWCA9P3HihDAyMhIffvihWLx4sXBwcBCzZ8+Wr0A91LBhQxEXFyeEEKKoqEiYmZmJ3bt3S+dPnDghrK2ta7wOjhEiveHk5IRdu3bp5TR1fSX3QmRVOX78OL744otHnvfz88OiRYt0WBE9D27duvXUbeXYxiI5ORn/+c9/pOeRkZHw9PTEqlWrAACOjo6YPXt2jY93qU38/f3xySef4IsvvsC2bdtgbm6u1VN84sQJNGnSpMbrYBAivVM+TZ2qps97swGcAUg1o169ek89I0yObSxyc3O1bt3HxcVpLQnSrl07ZGZm6rwuffb5559jwIAB6NSpE+rWrYu1a9fC2NhYOv/999/Dz8+vxutgECKqZfr161fp2Jw5cyodk2NvNoAzAKlm7Nu3T/rzxYsX8cknn2Do0KHSLLFDhw5h7dq1mD9/viz12dvb48KFC3B0dERxcTGOHj2Kzz77TDp/+/btx35BUKIXXngBf/zxB/Lz81G3bl0YGhpqnf/pp59Qt27dGq+Dg6WJqFpxBiDVNB8fH3z44Yd47733tI5v3LgRK1euxO+//67zmkaOHImTJ09Kt3nWrl2Lq1evSj0cGzZswLJly3D48GGd10aPxyBEVAvp88wszgCkmmZubo7jx4+jWbNmWsfPnj2LV199FXfv3tV5TdevX8eAAQNw4MAB6TZP//79pfM+Pj7o0KGDXi5+qnQMQkS1kL7vzabPG9VS7efi4oJevXph8eLFWseDg4Oxc+dOpKWlyVQZHnmb5+bNm6hbt67WGBjSDwxCRLWQk5MToqOjpV3dKzpz5gz8/PyQkZGh48q0cQYg1YRdu3bhrbfeQpMmTdChQwcAQEJCAtLT07Flyxb06NFD5gqpNmEQIqqFTE1NkZKS8sgByefPn4e7uzsKCwt1XBmRbly+fBkrVqxAamqqtGr5qFGjpMX5iJ4WZ40R1UKcmUVK99JLL3G8DVULA7kLIKJn16NHD3z66ae4d+9epXOFhYWYPXs2evXqJUNlRES1C2+NEdVCnJlFRFQ9GISIainOzCIi+r9jECKq5Tgzi4jon2MQIiKiWun69etIS0uDSqVC8+bN8cILL8hdEtVCHCxNRES1SkFBAYYPHw6NRoM33ngDr7/+OjQaDUaMGCHLqtJUuzEIERFRrTJ58mTExcVhx44dyMvLQ15eHrZv3464uDgEBwfLXR7VMrw1RkREtYqtrS1+/vlndO7cWev4vn37MHDgQFy/fl2ewqhWYo8QERHVKnfv3q1yaQg7OzveGqNnxh4hIiKqVXx8fFC/fn2sW7cOpqamAB4sJDpkyBDcvHkTu3fvlrlCqk0YhIiIqFZJSUmBv78/7t27h1atWkGlUiE5ORmmpqb47bff8Morr8hdItUiDEJERFTrFBYWIiIiAmfOnJE2XR08eDDMzMzkLo1qGQYhIiIiUizuPk9ERHpvx44d6N69O+rUqYMdO3Y8tm2fPn10VBU9D9gjREREes/AwADZ2dmws7ODgcGjJzyrVCqUlpbqsDKq7RiEiIiISLG4jhARET03rly5IncJVMswCBERUa2XnZ2NcePGoWnTpnKXQrUMgxAREdUKeXl5GDx4MF544QVoNBosX74cZWVl+PTTT+Hs7IyEhAR8//33cpdJtQzHCBERUa0wZswY/PLLLxg0aBCio6ORmpqKbt264d69e5g9ezY6deokd4lUCzEIERFRreDk5IQ1a9bA19cXf/31F5o2bYrx48dj2bJlcpdGtRiDEBER1Qp16tTBpUuXoNFoAADm5uZITEyEm5ubzJVRbcYxQkREVCuUlZWhTp060nNDQ0NYWFjIWBE9D7iyNBER1QpCCAwdOhQmJiYAgHv37mHUqFGVwtDWrVvlKI9qKQYhIiKqFYYMGaL1/P3335epEnqecIwQERERKRbHCBEREZFiMQgRERGRYjEIERERkWIxCBEREZFiMQgRkeKoVCps27ZN7jKISA8wCBHRc6d8J3JnZ2eYmJjA0dERvXv3xp49ewAAWVlZ6N69OwDg4sWLUKlUSE5OlrFiIpIL1xEioufKxYsX4e3tjXr16mHhwoVo2bIlSkpK8Ntvv2Hs2LE4c+YMHBwc5C6TiPQE1xEioudKjx49cOLECaSlpVVacTgvLw/16tWDSqVCVFQU+vXrB5VKpdWmU6dOmDNnDnx8fJCZmakVmoKDg3H48GHs379fJ6+FiGoeb40R0XPj5s2biI6OxtixY6vcg6pevXqVjiUmJgIAdu/ejaysLGzduhVvvPEGnJ2dsX79eqnd/fv3ERERgWHDhtVY/USkewxCRPTcOH/+PIQQePnll5/6Z1544QUAQP369eHg4AAbGxsAwIgRI/DDDz9I7X799VfcvXsXAwcOrN6iiUhWDEJE9Nwov9Nf8XbXPzF06FCcP38eCQkJAIDvv/8eAwcO5G7nRM8ZBiEiem40a9YMKpUKqamp/+ffZWdnh969e+OHH35ATk4Odu3aheHDh1dDlUSkTxiEiOi5YWNjg27duuHrr79GQUFBpfN5eXmVjhkbGwMASktLK5378MMPERkZie+++w5NmjSBt7d3tddMRPJiECKi58o333yD0tJStG/fHlu2bMG5c+eQmpqK5cuXw8vLq1J7Ozs7mJmZITo6GteuXUN+fr50rlu3blCr1fj88885SJroOcUgRETPlcaNG+Po0aPo0qULgoOD4ebmhq5du2LPnj1YsWJFpfZGRkZYvnw5vvvuO2g0GvTt21c6Z2BggKFDh6K0tBQffPCBLl8GEekI1xEiInqMoKAgXLt2DTt27JC7FCKqAVxZmoioCvn5+Th8+DA2bNiA7du3y10OEdUQBiEioir07dsXiYmJGDlyJLp27Sp3OURUQ3hrjIiIiBSLg6WJiIhIsRiEiIiISLEYhIiIiEixGISIiIhIsRiEiIiISLEYhIiIiEixGISIiIhIsRiEiIiISLEYhIiIiEix/h9eAEsE8YHrDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2gr['Annual Salary'].mean().plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='City', ylabel='Annual Salary'>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "\n",
    "sns.barplot( x=data2['City'],y=data2['Annual Salary'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}