IPL Data Analysis(Sports Case Study).ipynb

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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "656004d8-55fd-4d77-b351-4baf6a8d28b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "deliveries_data = pd.read_csv(r'D:\\ipl_resources/IPL Ball-by-Ball 2008-2020.csv')\n",
    "match_data = pd.read_csv(r'D:\\ipl_resources/IPL Matches 2008-2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "238206d4-5b77-4754-abaa-858bc31139fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>method</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335982</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>RE Koertzen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335983</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MR Benson</td>\n",
       "      <td>SL Shastri</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id        city        date player_of_match  \\\n",
       "0  335982   Bangalore  2008-04-18     BB McCullum   \n",
       "1  335983  Chandigarh  2008-04-19      MEK Hussey   \n",
       "\n",
       "                                        venue  neutral_venue  \\\n",
       "0                       M Chinnaswamy Stadium              0   \n",
       "1  Punjab Cricket Association Stadium, Mohali              0   \n",
       "\n",
       "                         team1                  team2  \\\n",
       "0  Royal Challengers Bangalore  Kolkata Knight Riders   \n",
       "1              Kings XI Punjab    Chennai Super Kings   \n",
       "\n",
       "                   toss_winner toss_decision                 winner result  \\\n",
       "0  Royal Challengers Bangalore         field  Kolkata Knight Riders   runs   \n",
       "1          Chennai Super Kings           bat    Chennai Super Kings   runs   \n",
       "\n",
       "   result_margin eliminator method    umpire1      umpire2  \n",
       "0          140.0          N    NaN  Asad Rauf  RE Koertzen  \n",
       "1           33.0          N    NaN  MR Benson   SL Shastri  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e22af3c6-0aea-4abd-8051-34e17c6a2433",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "816"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data.shape\n",
    "match_data.shape[0]        # Total Matches Played"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "787ddb20-f7b8-4f78-8e22-f910b776539c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(193468, 18)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff552514-67aa-455d-8268-1a9d3d0c6629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "type(match_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ca9c54c-bc53-405b-9bef-58be8ca41161",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bangalore', 'Chandigarh', 'Delhi', 'Mumbai', 'Kolkata', 'Jaipur',\n",
       "       'Hyderabad', 'Chennai', 'Cape Town', 'Port Elizabeth', 'Durban',\n",
       "       'Centurion', 'East London', 'Johannesburg', 'Kimberley',\n",
       "       'Bloemfontein', 'Ahmedabad', 'Cuttack', 'Nagpur', 'Dharamsala',\n",
       "       'Kochi', 'Indore', 'Visakhapatnam', 'Pune', 'Raipur', 'Ranchi',\n",
       "       'Abu Dhabi', nan, 'Rajkot', 'Kanpur', 'Bengaluru', 'Dubai',\n",
       "       'Sharjah'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data['city'].unique()                 # Venues Played At"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "364d2953-b0dc-4e77-bfbe-8a18ca45fd3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Royal Challengers Bangalore', 'Kings XI Punjab',\n",
       "       'Delhi Daredevils', 'Mumbai Indians', 'Kolkata Knight Riders',\n",
       "       'Rajasthan Royals', 'Deccan Chargers', 'Chennai Super Kings',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Sunrisers Hyderabad',\n",
       "       'Gujarat Lions', 'Rising Pune Supergiants',\n",
       "       'Rising Pune Supergiant', 'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data['team1'].unique()               # Total teams participated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d83b6552-ff1c-4c35-82cc-cef793b0d1db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mumbai Indians'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data['toss_winner'].value_counts()\n",
    "match_data['toss_winner'].value_counts().index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "735a0605-e019-477b-8591-7cae2007edc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AB de Villiers'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data['player_of_match'].value_counts().index[0]                   # Most Player of the match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e86b642a-5f06-4214-8d5a-21e1b4342dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Particular batsman analysis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f4c0223a-ffd4-4dce-828d-a48804850349",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>fielder</th>\n",
       "      <th>extras_type</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335982</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>RT Ponting</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>AA Noffke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335982</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>RT Ponting</td>\n",
       "      <td>AA Noffke</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335982</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>RT Ponting</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335982</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>RT Ponting</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335982</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>RT Ponting</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193463</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193464</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193465</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wides</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193466</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193467</th>\n",
       "      <td>1237181</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>193468 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  inning  over  ball      batsman  non_striker  \\\n",
       "0        335982       1     6     5   RT Ponting  BB McCullum   \n",
       "1        335982       1     6     6  BB McCullum   RT Ponting   \n",
       "2        335982       1     7     1  BB McCullum   RT Ponting   \n",
       "3        335982       1     7     2  BB McCullum   RT Ponting   \n",
       "4        335982       1     7     3   RT Ponting  BB McCullum   \n",
       "...         ...     ...   ...   ...          ...          ...   \n",
       "193463  1237181       1    12     5      RR Pant      SS Iyer   \n",
       "193464  1237181       1    12     6      RR Pant      SS Iyer   \n",
       "193465  1237181       1    13     1      RR Pant      SS Iyer   \n",
       "193466  1237181       1    13     2      RR Pant      SS Iyer   \n",
       "193467  1237181       1    13     3      SS Iyer      RR Pant   \n",
       "\n",
       "                 bowler  batsman_runs  extra_runs  total_runs  non_boundary  \\\n",
       "0             AA Noffke             1           0           1             0   \n",
       "1             AA Noffke             1           0           1             0   \n",
       "2                Z Khan             0           0           0             0   \n",
       "3                Z Khan             1           0           1             0   \n",
       "4                Z Khan             1           0           1             0   \n",
       "...                 ...           ...         ...         ...           ...   \n",
       "193463  NM Coulter-Nile             0           0           0             0   \n",
       "193464  NM Coulter-Nile             1           0           1             0   \n",
       "193465        KH Pandya             0           1           1             0   \n",
       "193466        KH Pandya             1           0           1             0   \n",
       "193467        KH Pandya             1           0           1             0   \n",
       "\n",
       "        is_wicket dismissal_kind player_dismissed fielder extras_type  \\\n",
       "0               0            NaN              NaN     NaN         NaN   \n",
       "1               0            NaN              NaN     NaN         NaN   \n",
       "2               0            NaN              NaN     NaN         NaN   \n",
       "3               0            NaN              NaN     NaN         NaN   \n",
       "4               0            NaN              NaN     NaN         NaN   \n",
       "...           ...            ...              ...     ...         ...   \n",
       "193463          0            NaN              NaN     NaN         NaN   \n",
       "193464          0            NaN              NaN     NaN         NaN   \n",
       "193465          0            NaN              NaN     NaN       wides   \n",
       "193466          0            NaN              NaN     NaN         NaN   \n",
       "193467          0            NaN              NaN     NaN         NaN   \n",
       "\n",
       "                 batting_team                 bowling_team  \n",
       "0       Kolkata Knight Riders  Royal Challengers Bangalore  \n",
       "1       Kolkata Knight Riders  Royal Challengers Bangalore  \n",
       "2       Kolkata Knight Riders  Royal Challengers Bangalore  \n",
       "3       Kolkata Knight Riders  Royal Challengers Bangalore  \n",
       "4       Kolkata Knight Riders  Royal Challengers Bangalore  \n",
       "...                       ...                          ...  \n",
       "193463         Delhi Capitals               Mumbai Indians  \n",
       "193464         Delhi Capitals               Mumbai Indians  \n",
       "193465         Delhi Capitals               Mumbai Indians  \n",
       "193466         Delhi Capitals               Mumbai Indians  \n",
       "193467         Delhi Capitals               Mumbai Indians  \n",
       "\n",
       "[193468 rows x 18 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ee273a34-d263-4618-932e-1913c04aee5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RT Ponting', 'BB McCullum', 'DJ Hussey', 'Mohammad Hafeez',\n",
       "       'SC Ganguly', 'CL White', 'MV Boucher', 'B Akhil', 'AA Noffke',\n",
       "       'P Kumar', 'Z Khan', 'SB Joshi', 'W Jaffer', 'JH Kallis',\n",
       "       'R Dravid', 'V Kohli', 'K Goel', 'MEK Hussey', 'JR Hopes',\n",
       "       'SK Raina', 'JDP Oram', 'S Badrinath', 'PA Patel', 'ML Hayden',\n",
       "       'MS Dhoni', 'KC Sangakkara', 'Yuvraj Singh', 'SM Katich',\n",
       "       'IK Pathan', 'T Kohli', 'YK Pathan', 'S Dhawan', 'G Gambhir',\n",
       "       'SK Warne', 'D Salunkhe', 'SK Trivedi', 'V Sehwag', 'SR Watson',\n",
       "       'M Kaif', 'DS Lehmann', 'RA Jadeja', 'M Rawat', 'L Ronchi',\n",
       "       'ST Jayasuriya', 'DJ Thornely', 'RV Uthappa', 'PR Shah',\n",
       "       'AM Nayar', 'SM Pollock', 'Harbhajan Singh', 'S Chanderpaul',\n",
       "       'LRPL Taylor', 'WP Saha', 'AS Yadav', 'A Symonds', 'SB Bangar',\n",
       "       'WPUJC Vaas', 'RP Singh', 'AC Gilchrist', 'Y Venugopal Rao',\n",
       "       'VVS Laxman', 'RG Sharma', 'SB Styris', 'LR Shukla', 'PP Chawla',\n",
       "       'Kamran Akmal', 'DPMD Jayawardene', 'S Sohal', 'B Lee', 'WA Mota',\n",
       "       'Shahid Afridi', 'DJ Bravo', 'A Nehra', 'MA Khote', 'GC Smith',\n",
       "       'Pankaj Singh', 'S Sreesanth', 'VRV Singh', 'SS Tiwary',\n",
       "       'RR Sarwan', 'DS Kulkarni', 'R Vinay Kumar', 'M Kartik',\n",
       "       'I Sharma', 'AB Agarkar', 'AM Rahane', 'TM Srivastava',\n",
       "       'Shoaib Malik', 'MK Tiwary', 'KD Karthik', 'R Bhatia',\n",
       "       'MF Maharoof', 'VY Mahesh', 'B Chipli', 'DW Steyn', 'DB Das',\n",
       "       'MK Pandey', 'HH Gibbs', 'DNT Zoysa', 'D Kalyankrishna',\n",
       "       'SE Marsh', 'SA Asnodkar', 'BJ Hodge', 'Sohail Tanvir',\n",
       "       'Salman Butt', 'Umar Gul', 'AB Dinda', 'AB de Villiers',\n",
       "       'SP Fleming', 'S Vidyut', 'JA Morkel', 'LPC Silva', 'DB Ravi Teja',\n",
       "       'Misbah-ul-Haq', 'PJ Sangwan', 'YV Takawale', 'RR Raje',\n",
       "       'Mohammad Asif', 'GD McGrath', 'Joginder Sharma', 'MS Gony',\n",
       "       'M Muralitharan', 'M Ntini', 'DT Patil', 'A Kumble', 'S Anirudha',\n",
       "       'MM Patel', 'CK Kapugedera', 'A Chopra', 'T Taibu', 'J Arunkumar',\n",
       "       'PP Ojha', 'SP Goswami', 'SR Tendulkar', 'U Kaul', 'Niraj Patel',\n",
       "       'AD Mascarenhas', 'TM Dilshan', 'A Mishra', 'LA Pomersbach',\n",
       "       'Iqbal Abdulla', 'Younis Khan', 'PM Sarvesh Kumar',\n",
       "       'DP Vijaykumar', 'Shoaib Akhtar', 'Abdur Razzak', 'H Das',\n",
       "       'DR Smith', 'SD Chitnis', 'CRD Fernando', 'VS Yeligati',\n",
       "       'L Balaji', 'A Mukund', 'RR Powar', 'JP Duminy', 'A Flintoff',\n",
       "       'T Thushara', 'KP Pietersen', 'JD Ryder', 'T Henderson',\n",
       "       'Kamran Khan', 'RS Bopara', 'MC Henriques', 'CH Gayle',\n",
       "       'R Bishnoi', 'KV Sharma', 'FH Edwards', 'AS Raut', 'PC Valthaty',\n",
       "       'RJ Quiney', 'Yashpal Singh', 'M Manhas', 'AA Bilakhia',\n",
       "       'AN Ghosh', 'BAW Mendis', 'DL Vettori', 'MN van Wyk',\n",
       "       'RE van der Merwe', 'TL Suman', 'Shoaib Ahmed', 'GR Napier',\n",
       "       'KP Appanna', 'LA Carseldine', 'NV Ojha', 'SM Harwood', 'M Vijay',\n",
       "       'DA Warner', 'SB Jakati', 'RJ Harris', 'D du Preez', 'M Morkel',\n",
       "       'AD Mathews', 'SL Malinga', 'C Nanda', 'J Botha',\n",
       "       'Mashrafe Mortaza', 'A Singh', 'GJ Bailey', 'AB McDonald',\n",
       "       'Y Nagar', 'SS Shaikh', 'R Ashwin', 'Mohammad Ashraful',\n",
       "       'Jaskaran Singh', 'Anirudh Singh', 'CA Pujara', 'OA Shah',\n",
       "       'AP Tare', 'AT Rayudu', 'R Sathish', 'R McLaren',\n",
       "       'AA Jhunjhunwala', 'P Dogra', 'A Uniyal', 'MS Bisla', 'YA Abdulla',\n",
       "       'EJG Morgan', 'JM Kemp', 'S Tyagi', 'RS Gavaskar', 'SE Bond',\n",
       "       'S Ladda', 'DP Nannes', 'KA Pollard', 'DR Martyn', 'S Narwal',\n",
       "       'MJ Lumb', 'Bipul Sharma', 'AB Barath', 'FY Fazal', 'AC Voges',\n",
       "       'MD Mishra', 'UT Yadav', 'J Theron', 'SJ Srivastava', 'R Sharma',\n",
       "       'Mandeep Singh', 'KM Jadhav', 'SW Tait', 'KB Arun Karthik',\n",
       "       'KAJ Roach', 'PD Collingwood', 'CK Langeveldt', 'VS Malik',\n",
       "       'A Mithun', 'AP Dole', 'AN Ahmed', 'RS Sodhi', 'DE Bollinger',\n",
       "       'S Sriram', 'B Sumanth', 'C Madan', 'AG Paunikar', 'MR Marsh',\n",
       "       'AJ Finch', 'STR Binny', 'Harmeet Singh', 'IR Jaggi',\n",
       "       'DT Christian', 'MA Agarwal', 'RV Gomez', 'AUK Pathan',\n",
       "       'UBT Chand', 'DJ Jacobs', 'NJ Rimmington', 'Sunny Singh',\n",
       "       'AL Menaria', 'WD Parnell', 'JJ van der Wath', 'R Ninan',\n",
       "       'S Aravind', 'MS Wade', 'TD Paine', 'JEC Franklin', 'SB Wagh',\n",
       "       'AC Thomas', 'Shakib Al Hasan', 'DH Yagnik', 'S Randiv',\n",
       "       'BJ Haddin', 'NLTC Perera', 'NL McCullum', 'JE Taylor',\n",
       "       'J Syed Mohammad', 'RN ten Doeschate', 'TR Birt', 'AG Murtaza',\n",
       "       'Harpreet Singh', 'M Klinger', 'AC Blizzard', 'I Malhotra',\n",
       "       'L Ablish', 'CA Ingram', 'S Nadeem', 'P Parameswaran',\n",
       "       'CJ Ferguson', 'VR Aaron', 'AA Chavan', 'ND Doshi', 'B Kumar',\n",
       "       'Y Gnaneswara Rao', 'S Rana', 'BA Bhatt', 'F du Plessis',\n",
       "       'RE Levi', 'GJ Maxwell', 'SPD Smith', 'SA Yadav', 'MN Samuels',\n",
       "       'KK Cooper', 'JP Faulkner', 'HV Patel', 'DAJ Bracewell',\n",
       "       'DJ Harris', 'Ankit Sharma', 'SP Narine', 'GB Hogg', 'RR Bhatkal',\n",
       "       'CJ McKay', 'N Saini', 'DA Miller', 'Azhar Mahmood', 'P Negi',\n",
       "       'RJ Peterson', 'KMDN Kulasekara', 'A Ashish Reddy',\n",
       "       'V Pratap Singh', 'BB Samantray', 'MJ Clarke', 'Gurkeerat Singh',\n",
       "       'AP Majumdar', 'PA Reddy', 'K Upadhyay', 'P Awana', 'AD Russell',\n",
       "       'A Chandila', 'CA Lynn', 'Sunny Gupta', 'MC Juneja', 'KK Nair',\n",
       "       'GH Vihari', 'R Shukla', 'MDKJ Perera', 'AS Rajpoot', 'B Laughlin',\n",
       "       'M Vohra', 'JD Unadkat', 'Mohammed Shami', 'BMAJ Mendis',\n",
       "       'R Rampaul', 'CH Morris', 'SV Samson', 'SMSM Senanayake',\n",
       "       'BJ Rohrer', 'KL Rahul', 'Q de Kock', 'R Dhawan', 'MG Johnson',\n",
       "       'LJ Wright', 'IC Pandey', 'CM Gautam', 'X Thalaivan Sargunam',\n",
       "       'DJG Sammy', 'KW Richardson', 'MM Sharma', 'UA Birla',\n",
       "       'Parvez Rasool', 'Sandeep Sharma', 'S Kaul', 'Sachin Baby',\n",
       "       'PV Tambe', 'NM Coulter-Nile', 'CJ Anderson', 'NJ Maddinson',\n",
       "       'AR Patel', 'JJ Bumrah', 'JDS Neesham', 'TG Southee', 'MA Starc',\n",
       "       'BR Dunk', 'RR Rossouw', 'Shivam Sharma', 'YS Chahal',\n",
       "       'LMP Simmons', 'VH Zol', 'Imran Tahir', 'BCJ Cutting',\n",
       "       'BE Hendricks', 'S Gopal', 'M de Lange', 'R Tewatia', 'JO Holder',\n",
       "       'Karanveer Singh', 'SS Iyer', 'DJ Hooda', 'Anureet Singh',\n",
       "       'KS Williamson', 'SA Abbott', 'J Suchith', 'RG More', 'D Wiese',\n",
       "       'HH Pandya', 'SN Khan', 'MJ McClenaghan', 'DJ Muthuswami',\n",
       "       'PJ Cummins', 'SN Thakur', 'JC Buttler', 'CR Brathwaite',\n",
       "       'MP Stoinis', 'Ishan Kishan', 'C Munro', 'P Sahu', 'KH Pandya',\n",
       "       'AD Nath', 'MJ Guptill', 'KJ Abbott', 'TM Head', 'M Ashwin',\n",
       "       'NS Naik', 'RR Pant', 'SW Billings', 'KC Cariappa',\n",
       "       'PSP Handscomb', 'Swapnil Singh', 'J Yadav', 'UT Khawaja',\n",
       "       'HM Amla', 'F Behardien', 'A Zampa', 'BB Sran', 'N Rana',\n",
       "       'S Kaushik', 'ER Dwivedi', 'CJ Jordan', 'A Choudhary', 'TS Mills',\n",
       "       'BA Stokes', 'JJ Roy', 'Vishnu Vinod', 'Basil Thampi', 'CR Woakes',\n",
       "       'RA Tripathi', 'DL Chahar', 'V Shankar', 'Rashid Khan',\n",
       "       'LH Ferguson', 'RD Chahar', 'C de Grandhomme', 'AF Milne',\n",
       "       'S Badree', 'Mohammad Nabi', 'AJ Tye', 'K Rabada', 'Kuldeep Yadav',\n",
       "       'Washington Sundar', 'DM Bravo', 'AR Bawne', 'SP Jackson',\n",
       "       'Ankit Soni', 'TA Boult', 'E Lewis', 'MA Wood', 'RK Singh',\n",
       "       'DJM Short', 'K Gowtham', 'TK Curran', 'M Markande', 'B Stanlake',\n",
       "       'Mujeeb Ur Rahman', 'A Dananjaya', 'Shivam Mavi', 'Shubman Gill',\n",
       "       'Mohammed Siraj', 'H Klaasen', 'RK Bhui', 'JC Archer', 'PP Shaw',\n",
       "       'LE Plunkett', 'Mustafizur Rahman', 'AD Hales', 'MK Lomror',\n",
       "       'DR Shorey', 'MM Ali', 'M Prasidh Krishna', 'P Chopra',\n",
       "       'JPR Scantlebury-Searles', 'Abhishek Sharma', 'IS Sodhi',\n",
       "       'SO Hetmyer', 'S Dube', 'NA Saini', 'JM Bairstow', 'Rasikh Salam',\n",
       "       'KMA Paul', 'N Pooran', 'P Ray Barman', 'S Lamichhane',\n",
       "       'SM Curran', 'GC Viljoen', 'Avesh Khan', 'HF Gurney', 'SD Lad',\n",
       "       'AS Joseph', 'MJ Santner', 'R Parag', 'JL Denly', 'LS Livingstone',\n",
       "       'KK Ahmed', 'AJ Turner', 'SE Rutherford', 'Harpreet Brar',\n",
       "       'Y Prithvi Raj', 'P Simran Singh', 'JL Pattinson', 'A Nortje',\n",
       "       'T Banton', 'D Padikkal', 'YBK Jaiswal', 'RD Gaikwad',\n",
       "       'TU Deshpande', 'Abdul Samad', 'PK Garg', 'JR Philippe',\n",
       "       'Kartik Tyagi', 'CV Varun', 'KL Nagarkoti', 'I Udana',\n",
       "       'Ravi Bishnoi', 'Shahbaz Ahmed', 'AT Carey', 'N Jagadeesan',\n",
       "       'T Natarajan', 'P Dubey', 'SS Cottrell', 'Arshdeep Singh',\n",
       "       'DR Sams'], dtype=object)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries_data['batsman'].unique()             # finding the list of all batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "1925e2ca-6cfd-41c2-be10-7dd4ff2c75d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'inning', 'over', 'ball', 'batsman', 'non_striker', 'bowler',\n",
       "       'batsman_runs', 'extra_runs', 'total_runs', 'non_boundary', 'is_wicket',\n",
       "       'dismissal_kind', 'player_dismissed', 'fielder', 'extras_type',\n",
       "       'batting_team', 'bowling_team'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = deliveries_data['batsman']=='V Kohli'         # Analysis the data for Virat Kohli (V Kohli)\n",
    "df_kohli = deliveries_data[filt]\n",
    "df_kohli.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "0f52a89a-37ce-4b2a-b170-a5be4128bd74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dismissal_kind\n",
       "caught               105\n",
       "bowled                30\n",
       "run out               15\n",
       "lbw                    7\n",
       "stumped                3\n",
       "caught and bowled      3\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kohli['dismissal_kind'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a2251144-9279-4b2b-8494-6babc176f127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 4, 6, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kohli['batsman_runs'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7b4a8d28-245d-4a1b-814a-7a74b5671ea0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1919"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = len(df_kohli[df_kohli['batsman_runs']==1])           # Total runs scored by taking singles ie 1's\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "46f68592-dcee-4754-99c1-5005ab1b6219",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = len(df_kohli[df_kohli['batsman_runs']==2])*2             # Total runs scored by taking doubles ie 2's\n",
    "c = len(df_kohli[df_kohli['batsman_runs']==3])*3             # Total runs scored by taking triples ie 3's\n",
    "d = len(df_kohli[df_kohli['batsman_runs']==4])*4             # Total runs scored by hitting boundaries ie 4's\n",
    "e = len(df_kohli[df_kohli['batsman_runs']==6])*6             # Total runs scored by hitting sixes ie 6's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "36aafb17-5ff4-4e21-8cf9-195725dc6b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import init_notebook_mode, plot , iplot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5f7c8415-6ba9-4af0-8d3f-511397aeafac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
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        "plotlyServerURL": "https://plot.ly"
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       "data": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "values = [a , b , c , d , e]\n",
    "run_cat = [1, 2, 3 ,4, 6]\n",
    "\n",
    "trace = go.Pie(labels= run_cat , values= values , hole=0.3)\n",
    "data = [trace]\n",
    "fig = go.Figure(data = data)\n",
    "\n",
    "fig.update_layout(width=600,height=600)\n",
    "\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "bea81ce3-3ffd-4cc6-b464-ccd7c97c9498",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Toss decision across seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c05a59fa-f298-4ed1-b487-9ddab32ae972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'city', 'date', 'player_of_match', 'venue', 'neutral_venue',\n",
       "       'team1', 'team2', 'toss_winner', 'toss_decision', 'winner', 'result',\n",
       "       'result_margin', 'eliminator', 'method', 'umpire1', 'umpire2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "b21fe686-01fd-49ad-b4e5-2f25652e0313",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_data['Season'] = pd.to_datetime(match_data['date']).dt.year  # extracting the season from \"date\" feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "cefdd811-fb81-4aec-b2e0-fa205723af76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>method</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335982</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2008-04-18</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>RE Koertzen</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335983</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2008-04-19</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>bat</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MR Benson</td>\n",
       "      <td>SL Shastri</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id        city        date player_of_match  \\\n",
       "0  335982   Bangalore  2008-04-18     BB McCullum   \n",
       "1  335983  Chandigarh  2008-04-19      MEK Hussey   \n",
       "\n",
       "                                        venue  neutral_venue  \\\n",
       "0                       M Chinnaswamy Stadium              0   \n",
       "1  Punjab Cricket Association Stadium, Mohali              0   \n",
       "\n",
       "                         team1                  team2  \\\n",
       "0  Royal Challengers Bangalore  Kolkata Knight Riders   \n",
       "1              Kings XI Punjab    Chennai Super Kings   \n",
       "\n",
       "                   toss_winner toss_decision                 winner result  \\\n",
       "0  Royal Challengers Bangalore         field  Kolkata Knight Riders   runs   \n",
       "1          Chennai Super Kings           bat    Chennai Super Kings   runs   \n",
       "\n",
       "   result_margin eliminator method    umpire1      umpire2  Season  \n",
       "0          140.0          N    NaN  Asad Rauf  RE Koertzen    2008  \n",
       "1           33.0          N    NaN  MR Benson   SL Shastri    2008  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "9a7af307-a8b0-41d9-8c01-bace4982b119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Season  toss_decision\n",
       "2008    bat              26\n",
       "        field            32\n",
       "2009    bat              35\n",
       "        field            22\n",
       "2010    bat              39\n",
       "        field            21\n",
       "2011    bat              25\n",
       "        field            48\n",
       "2012    bat              37\n",
       "        field            37\n",
       "2013    bat              45\n",
       "        field            31\n",
       "2014    bat              19\n",
       "        field            41\n",
       "2015    bat              25\n",
       "        field            34\n",
       "2016    bat              11\n",
       "        field            49\n",
       "2017    bat              11\n",
       "        field            48\n",
       "2018    bat              10\n",
       "        field            50\n",
       "2019    bat              10\n",
       "        field            50\n",
       "2020    bat              27\n",
       "        field            33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data.groupby(['Season' , 'toss_decision']).size()     # the output is a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "56d872da-0b16-458e-bf46-82614c8468da",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>bat</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>field</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>bat</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>field</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>bat</td>\n",
       "      <td>39</td>\n",
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       "      <th>5</th>\n",
       "      <td>2010</td>\n",
       "      <td>field</td>\n",
       "      <td>21</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2011</td>\n",
       "      <td>bat</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>field</td>\n",
       "      <td>48</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012</td>\n",
       "      <td>bat</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012</td>\n",
       "      <td>field</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013</td>\n",
       "      <td>bat</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013</td>\n",
       "      <td>field</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014</td>\n",
       "      <td>bat</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014</td>\n",
       "      <td>field</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>bat</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>field</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2016</td>\n",
       "      <td>bat</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2016</td>\n",
       "      <td>field</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017</td>\n",
       "      <td>bat</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017</td>\n",
       "      <td>field</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2018</td>\n",
       "      <td>bat</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2018</td>\n",
       "      <td>field</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019</td>\n",
       "      <td>bat</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019</td>\n",
       "      <td>field</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020</td>\n",
       "      <td>bat</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020</td>\n",
       "      <td>field</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Season toss_decision  count\n",
       "0     2008           bat     26\n",
       "1     2008         field     32\n",
       "2     2009           bat     35\n",
       "3     2009         field     22\n",
       "4     2010           bat     39\n",
       "5     2010         field     21\n",
       "6     2011           bat     25\n",
       "7     2011         field     48\n",
       "8     2012           bat     37\n",
       "9     2012         field     37\n",
       "10    2013           bat     45\n",
       "11    2013         field     31\n",
       "12    2014           bat     19\n",
       "13    2014         field     41\n",
       "14    2015           bat     25\n",
       "15    2015         field     34\n",
       "16    2016           bat     11\n",
       "17    2016         field     49\n",
       "18    2017           bat     11\n",
       "19    2017         field     48\n",
       "20    2018           bat     10\n",
       "21    2018         field     50\n",
       "22    2019           bat     10\n",
       "23    2019         field     50\n",
       "24    2020           bat     27\n",
       "25    2020         field     33"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "season_toss_count_df = match_data.groupby(['Season' , 'toss_decision']).size().reset_index().rename(columns={0:'count'})\n",
    "\n",
    "## converting the above Series into Dataframe\n",
    "season_toss_count_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "a1d04e15-d3bb-4a90-836c-923ce4b317eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Season', ylabel='count'>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.barplot(x='Season' , y='count' , hue = 'toss_decision' , data = season_toss_count_df)\n",
    "\n",
    "# plotting barplot of \"Season\" vs \"count\" for bat & field both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "fec116f5-82d2-42ea-8db8-1c337931ca54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysing if winning a toss implies winning game ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "1261f303-33c5-4f03-aff3-ca48c8475fca",
   "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>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>winner</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>811</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>814</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>815</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>816 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           team1                        team2  \\\n",
       "0    Royal Challengers Bangalore        Kolkata Knight Riders   \n",
       "1                Kings XI Punjab          Chennai Super Kings   \n",
       "2               Delhi Daredevils             Rajasthan Royals   \n",
       "3                 Mumbai Indians  Royal Challengers Bangalore   \n",
       "4          Kolkata Knight Riders              Deccan Chargers   \n",
       "..                           ...                          ...   \n",
       "811  Royal Challengers Bangalore               Mumbai Indians   \n",
       "812               Mumbai Indians               Delhi Capitals   \n",
       "813  Royal Challengers Bangalore          Sunrisers Hyderabad   \n",
       "814               Delhi Capitals          Sunrisers Hyderabad   \n",
       "815               Delhi Capitals               Mumbai Indians   \n",
       "\n",
       "                     toss_winner                       winner  \n",
       "0    Royal Challengers Bangalore        Kolkata Knight Riders  \n",
       "1            Chennai Super Kings          Chennai Super Kings  \n",
       "2               Rajasthan Royals             Delhi Daredevils  \n",
       "3                 Mumbai Indians  Royal Challengers Bangalore  \n",
       "4                Deccan Chargers        Kolkata Knight Riders  \n",
       "..                           ...                          ...  \n",
       "811               Mumbai Indians  Royal Challengers Bangalore  \n",
       "812               Delhi Capitals               Mumbai Indians  \n",
       "813          Sunrisers Hyderabad          Sunrisers Hyderabad  \n",
       "814               Delhi Capitals               Delhi Capitals  \n",
       "815               Delhi Capitals               Mumbai Indians  \n",
       "\n",
       "[816 rows x 4 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_data[['team1', 'team2' , 'toss_winner' , 'winner']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "c87c5d82-ce7f-4626-8c3a-ec30385fb855",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_data['toss_win_game_win'] = np.where(match_data['toss_winner']==match_data['winner'] , 'Yes' , 'No')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5ae1d051-3d89-44dc-b73f-28ef147e9097",
   "metadata": {},
   "outputs": [
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       "0       No\n",
       "1      Yes\n",
       "2       No\n",
       "3       No\n",
       "4       No\n",
       "      ... \n",
       "811     No\n",
       "812     No\n",
       "813    Yes\n",
       "814    Yes\n",
       "815     No\n",
       "Name: toss_win_game_win, Length: 816, dtype: object"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 149,
   "id": "5505e144-cce3-4923-961d-13b6a1e23c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toss_win_game_win\n",
       "Yes    418\n",
       "No     398\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "match_data['toss_win_game_win'].value_counts()"
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  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "33920ac8-e37d-4c94-985a-d69c5b2d6f46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Yes', 'No'], dtype='object', name='toss_win_game_win')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   "cell_type": "code",
   "execution_count": 151,
   "id": "874e123d-ca26-4be4-9ad2-1f7bd812eabd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
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            "linecolor": "white",
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           "bgcolor": "#E5ECF6",
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",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = match_data['toss_win_game_win'].value_counts().index\n",
    "values = match_data['toss_win_game_win'].value_counts().values\n",
    "\n",
    "trace = go.Pie(labels= labels , values= values , hole=0.3)\n",
    "data = [trace]\n",
    "fig = go.Figure(data = data)\n",
    "\n",
    "fig.update_traces(hoverinfo='label+percent' , textinfo='label+percent')\n",
    "fig.update_layout(width=600,height=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "00152500-de1b-4387-926d-d223d0685a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Analysis for the number of times team have won the tournament"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "bce04ec9-cde8-44ce-8bfe-c498c5c38199",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Chennai Super Kings'"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2018 = match_data[match_data['Season']==2018]\n",
    "df_2018['winner'].tail(1).values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "e83e6957-7b73-450b-bfc5-76c71cb7fd22",
   "metadata": {},
   "outputs": [],
   "source": [
    "winners_team ={}\n",
    "\n",
    "for year in sorted(match_data['Season'].unique()):\n",
    "    current_yr_df = match_data[match_data['Season']==year]\n",
    "    winners_team[year] = current_yr_df['winner'].tail(1).values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "346665e7-e47f-4f99-8565-b170e834a260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2008: 'Rajasthan Royals',\n",
       " 2009: 'Deccan Chargers',\n",
       " 2010: 'Chennai Super Kings',\n",
       " 2011: 'Chennai Super Kings',\n",
       " 2012: 'Kolkata Knight Riders',\n",
       " 2013: 'Mumbai Indians',\n",
       " 2014: 'Kolkata Knight Riders',\n",
       " 2015: 'Mumbai Indians',\n",
       " 2016: 'Sunrisers Hyderabad',\n",
       " 2017: 'Mumbai Indians',\n",
       " 2018: 'Chennai Super Kings',\n",
       " 2019: 'Mumbai Indians',\n",
       " 2020: 'Mumbai Indians'}"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "winners_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "7662c69c-f22d-450a-aa0e-cc2e3b15b1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Mumbai Indians': 5,\n",
       "         'Chennai Super Kings': 3,\n",
       "         'Kolkata Knight Riders': 2,\n",
       "         'Rajasthan Royals': 1,\n",
       "         'Deccan Chargers': 1,\n",
       "         'Sunrisers Hyderabad': 1})"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter(winners_team.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "00010343-a7f9-4dde-a7bf-872237342934",
   "metadata": {},
   "outputs": [],
   "source": [
    "#        ***         Mumbai Indians have won the tournament most           ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "67e23766-126e-4f04-8d96-281f3d88f36b",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Analysing for the team with most wins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "c3df8948-51fa-40c5-a900-eb4ce004d9e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chennai Super Kings            178\n",
       "Deccan Chargers                 75\n",
       "Delhi Capitals                  33\n",
       "Delhi Daredevils               161\n",
       "Gujarat Lions                   30\n",
       "Kings XI Punjab                190\n",
       "Kochi Tuskers Kerala            14\n",
       "Kolkata Knight Riders          192\n",
       "Mumbai Indians                 203\n",
       "Pune Warriors                   46\n",
       "Rajasthan Royals               161\n",
       "Rising Pune Supergiant          16\n",
       "Rising Pune Supergiants         14\n",
       "Royal Challengers Bangalore    195\n",
       "Sunrisers Hyderabad            124\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches_played = match_data['team1'].value_counts() + match_data['team2'].value_counts()\n",
    "matches_played                   # Output is in Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "d0a1ffb4-d26f-4276-bf9b-c1f2067174ca",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_name</th>\n",
       "      <th>Matches_played</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>178</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>75</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Kochi Tuskers Kerala</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Pune Warriors</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      team_name  Matches_played\n",
       "0           Chennai Super Kings             178\n",
       "1               Deccan Chargers              75\n",
       "2                Delhi Capitals              33\n",
       "3              Delhi Daredevils             161\n",
       "4                 Gujarat Lions              30\n",
       "5               Kings XI Punjab             190\n",
       "6          Kochi Tuskers Kerala              14\n",
       "7         Kolkata Knight Riders             192\n",
       "8                Mumbai Indians             203\n",
       "9                 Pune Warriors              46\n",
       "10             Rajasthan Royals             161\n",
       "11       Rising Pune Supergiant              16\n",
       "12      Rising Pune Supergiants              14\n",
       "13  Royal Challengers Bangalore             195\n",
       "14          Sunrisers Hyderabad             124"
      ]
     },
     "execution_count": 215,
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   ],
   "source": [
    "matches_played_df = matches_played.to_frame().reset_index()\n",
    "matches_played_df.columns = ['team_name' , 'Matches_played']\n",
    "matches_played_df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 217,
   "id": "42579bc2-a1d3-445c-977a-e335be97662a",
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       "      <td>Mumbai Indians</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>66</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>13</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Pune Warriors</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kochi Tuskers Kerala</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      team_name  Wins\n",
       "0                Mumbai Indians   120\n",
       "1           Chennai Super Kings   106\n",
       "2         Kolkata Knight Riders    99\n",
       "3   Royal Challengers Bangalore    91\n",
       "4               Kings XI Punjab    88\n",
       "5              Rajasthan Royals    81\n",
       "6              Delhi Daredevils    67\n",
       "7           Sunrisers Hyderabad    66\n",
       "8               Deccan Chargers    29\n",
       "9                Delhi Capitals    19\n",
       "10                Gujarat Lions    13\n",
       "11                Pune Warriors    12\n",
       "12       Rising Pune Supergiant    10\n",
       "13         Kochi Tuskers Kerala     6\n",
       "14      Rising Pune Supergiants     5"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wins = pd.DataFrame(match_data['winner'].value_counts()).reset_index()   # total wins for each team\n",
    "wins.columns = ['team_name' , 'Wins']\n",
    "wins"
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   "execution_count": 219,
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       "                      team_name  Matches_played  Wins\n",
       "0           Chennai Super Kings             178   106\n",
       "1               Deccan Chargers              75    29\n",
       "2                Delhi Capitals              33    19\n",
       "3              Delhi Daredevils             161    67\n",
       "4                 Gujarat Lions              30    13\n",
       "5               Kings XI Punjab             190    88\n",
       "6          Kochi Tuskers Kerala              14     6\n",
       "7         Kolkata Knight Riders             192    99\n",
       "8                Mumbai Indians             203   120\n",
       "9                 Pune Warriors              46    12\n",
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       "11       Rising Pune Supergiant              16    10\n",
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       "13  Royal Challengers Bangalore             195    91\n",
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    "played_vs_wins = matches_played_df.merge(wins, on='team_name' , how='inner' )\n",
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8z/3/u263sTstVfPvyVOm2uTJU93fJLJcM+REd8qqfnPru/42tzaE5YCZjwCZW+iRZ7TmzZrO0wClRigup7+xBSYsV6QeCEAAAhDIKgEEkKxaln5BIFkE3nqv2l54IfycQEsvXbBddqhNVmcz0Jo8CyD1LcIfetyFNmhALyeClEaA6Pqrb77feu2/k2n+qr/ffu9TThhp0bzpHAd3FLuH5sMSQAYed5DdPP5R23bz9ezTL76zFdov7f7X22qjMnO7h05LLQ0q8PrQZsnFnOih3213P2G7dN3Izh02xp6cMLFO9Cjuz59TplmPPoNs7122cCetFu/I+PHnSbPtztDf3n7/U9t3161C8/jMCyChkZpLRQggUROmfghAAALZJlCwCA8xUeUJ+CGAJMAINAECOSDw6sRqu+fe8BNVKgnmIQeWeWRIDjiH1cU8CyClB2uIabHAML8tMMXll1+uTdkCiO5z5sXXWds2i7tIkLF3PTHXXCOlbSwVQOrrg+cb8zp4RBEhF4wY68QbCSuekKKDRxyHQVfPFi0Slr959SCABCSKABIQIMUhAIGABMJf6VKDqqoKRrRvQNOUU7xQsHfer7ZXXm1UztUVXbPUUgXbftuaRJwQiwBSkem4GAIQ8EkAAcQnuJiK5V0AKc5DKRNI9Pjk829cNEV9Akjp1hhvW0klAoh3rXfKael95nYPbV2pTwAp7UO5AkjxdhqvjLelp3gLjLf9R0JJWD8EkIAkEUACAqQ4BCDgn0DB7Iuvqmz6jPBFkFatzNq01uka/ptHyfII5GHAjgBSni9wFQQgEIxAHt6nwQglq3TeBZD+A0fa0IFH1+XFmFcEiCdMeFtk/EaAeDk4PE8oFkCUF6R4G06UESBjbn8k9MSr5Xo3Aki5pOZyHQJIQIAUhwAEAhFgYhkIXyIK52HAjp8mwtVoBAQyTyAP79MsGTHPAkhp/oxSgUNJR4u3iZSKEfq7t1WkkgiQ+QkgxaJM8T1Urr68JIrk8HKAFOcpUQ4Q/bzcIKrLEz1Kc4DoOv29+Kc8KPpFcZwuAkjAtwgCSECAFIcABAIRYGIZCF8iCudhwI6fJsLVaAQEMk8gD+/TLBkxzwKI7Divk1A8gUT5MrxtIJdcdbvd/fAE5wLLLN3a/a8iSMISQJRcVYJDffeQAFLcXu9gEE/M+OrbH117ik+BmZsAUt8hJF4flQRVUSjeCTilB5CE4f8IIAEpIoAEBEhxCEAgEAEmloHwJaJwHgbs+GkiXI1GQCDzBPLwPs2SEfMkgGTJbmnvCwJIQAsigAQESHEIQCAQASaWgfAlonAeBuz4aSJcjUZAIPME8vA+zZIR4xJAamoL9sZ7M6y2trxEZ40aFazTqk2scaPyrs+SjbLYFwSQgFZFAAkIkOIQgEAgAkwsA+FLROE8DNjx00S4Go2AQOYJ5OF9miUjxiWAZIkhfamcAAJI5cxmK4EAEhAgxSEAgUAEmFgGwpeIwnkYsOOniXA1GgGBzBPIw/s0S0ZEAMmSNdPTFwSQgLZCAAkIkOIQgEAgAkwsA+FLROE8DNjx00S4Go2AQOYJ5OF9miUjIoBkyZrp6QsCSEBb1SeATJ1WZVOmVJkVAlZeWrxg1qJVwZouGHbFIbeT6iAAgQYjwMSywVBHdqM8DNjx08jch4ohAIEiAnl4n2bJ4AggWbJmevqCABLQVvUJIN/9YDb+zkYmISTMX7NmZrvvVmttl6oNs1rqggAEUkyAiWWKjfffpudhwI6fpt9P6QEE0kAgD+/TNNih3DYigJRLiuvCJIAAEpBmfQLIt9+Z3XhTI5syNVwBpHlzswO611rbNgggAc1GcQhkhgATy/SbMg8Ddvw0/X5KDyCQBgJ5eJ+mwQ7lthEBpFxSXBcmAQSQgDQRQAICpDgEIBCIABPLQPgSUTgPA3b8NBGuRiMgkHkCeXifZsmIcQkgOgb3tzfftKqa6WXhLDRuYgt1XJtjcMuilfyLEEAC2ggBJCBAikMAAoEIMLEMhC8RhfMwYMdPE+FqNAICmSeQh/dplowYlwAyfWatTR7Y26o/fLMsnLWrrGUtB46wBRpXl3V93BcNGHy1a8KgU3rF3ZRE3h8BJKBZEEACAqQ4BCAQiAATy0D4ElE4DwN2/DQRrkYjIJB5Anl4n2bJiHkQQKZMnWa9B1xq3/7wi40ZPsBaL76IM+FPv0yyHn0Guf+/+N/nZt9KRI1Krs2SP5XbFwSQcknN5ToEkIAAKQ4BCAQiwMQyEL5EFM7DgB0/TYSr0QgIZJ5AHt6nWTJingSQ3ydPsR22Wt967rejM+HosQ/Yg0+8ZJP/nIoA0sBOjQASEDgCSECAFIcABAIRYGIZCF8iCudhwI6fJsLVaAQEMk8gD+/TLBkxTwLIdlv83R5+6hW74LTDnQlPOneU6d+uu+2hOgFEosjQUePqTDxi0LG25Uad7cnnJ7ooEu+3fufVTX/TT//+0sT33P+/63Ybu20vigCZPGWqTZ481f1toZbN7ZohJ9qaq3Zw13lRKV65/ofvM5swU18biv3OK99j766ufe988LkNHHK9XT64r4twUT8++fybui04as/dD0+YrY2eCDTh5besZctm9vizr7m/e32O0s8RQALSRQAJCJDiEIBAIAJMLAPhS0ThPAzY8dNEuBqNgEDmCeThfZolI+ZJAJFY8OkX3znzrdB+aff/638vGDHWCSAtmje1q2++33rtv5M1b9bUiQi33/tUnThSuq3FEyHaLLlYndBw291P2C5dN7Jzh42xJydMrBM9VJeEhmLRxCunrThHnTLMBh53kGtbsZAhYePt9z+1fXfdag63U3tW7NDOCSeecOOJF6V/m9u9S8tJ6PF4eFuFovB3BJCAVBFAAgKkOAQgEIgAE8tA+BJROA8Ddvw0Ea5GIyCQeQJ5eJ9myYh5E0A6rrq8ExtatWjmIkHe/uCzuU74JT70HzjShg482kVulAogpX8v9ovSayUsjLn9ESeAfPbl97PVq3KeYCFBZsCgq2eLFpmbv3l1qh/nDLvRVmzfzn746Vc7rW8PO3nQVXZ49262/HJtXITKxl061UWYFIsc9zzyfJ0wI9GnNJIkKl9HAAlIFgEkIECKQwACgQgwsQyELxGF8zBgx08T4Wo0AgKZJ5CH92mWjJg3AUTbRYqjI0ojHiQAHHrchaZ8IfoVb12pTwApjtaoRAApvodXztsGU7wNZ9m2S841P4knVpx1/EF20x2P2mHdd7Gzh95gxx+5r118xW1O4FFUiwQQb6uM7lUsciCApPRpRgBJqeFoNgQyQoCJZfoNmYcBO36afj+lBxBIA4E8vE/TYIdy25hHAaSYTbEA8uPPk5z4MWhAr7q8GlFFgMxNOCm127xOk9EWHEV6KPKjZYtmLsLDu171KBeJt02HCJByn4iUXIcAkhJD0cw5CPz2u9mv/6myQiFcOFVVZosuWrBFFgq3XmqrnwATy/R7Rh4G7Phpyv20YPb9T2ZTplSF3pHGjc2WbVcwfTv4QSAogTy8T4MySlJ5BJD/5byQAFIseEgcKd6OUpzHQ9tFSnOA6L+9HCLKAeKJEPrf4i0w+m9FZRTnDvFyfSzZelFXTpEq+s3vOF39vTjXiJestTSpqpfLxIsI8e5d2ie2wCTp6ZxHWxBAUmIomjkHgW+/M7vxpkY2ZWq4o87mzc0O6F5rbdvUQr0BCDCxbADIEd8iDwN2/DRiJ2qA6vPgpw2AkVtETAA/jRhwyNUjgMye9LP4tJRllm7taHs5QJSstEefQfbVtz+adwrMn1Om1f2bri0+BWZuAkixeOKdAuNttVGZ4u0x3n1Upr5fsbCia4oTqnonznhCyrxOgVFuEnKAhPxwRVkdAkiUdKk7SgIIIFHSbbi6mVg2HOuo7pSHATt+GpX3NFy9efDThqPJnaIigJ9GRTaaevMggERDjlqDEMhVEtTSM4k9cMVqm/dvczsPuVQJQwAJ4n6UjZMAAkic9MO7NxPL8FjGVVMeBuz4aVzeFd598+Cn4dGiprgI4Kdxkfd337gEkJragk168w2rrplRVsMLjZvYQh3XtsaNwo2aLuvmXBQ6gVwIIN5+JNHzQoOKSc5rf1Npdt7SaxFAQvdJKmwgAgggDQQ64tswsYwYcANUn4cBO37aAI4U8S3y4KcRI6T6BiCAnzYA5BBvEZcAEmIXqCqFBHIhgHh2mVcEiK5RttrSX/FRRfpbqSCCAJJCr6fJjgACSDYcgYll+u2YhwE7foqfzo1Ah/YFO+TAmvQDogeJIJCH92kiQIfUCASQkEBSTUUEEED+m+HWS8wiet72l/qO7lF22uIMvQggFfkbFyeIAAJIgowRoClMLAPAS0jRPAzY8dOEOFuAZuTBTwPgoWhCCOCnCTFEmc1AACkTFJeFSgABpASnBA7vDGbl+9AxQT327lp3HFCpADJp8vQ5DPL5V7V27Q3VkZyucciBBeuwTKg+QGU5JYCfpt/wtYWCXX51lX3+Rfh7UnfvVmMbdamyKs6mjNRRdAr1hJcKdtc91aHfRyvrR/UqWHXMNsRPQzdtg1eYBz9tcKjcMHQC+GnoSCOvcJGWC0R+D24AgVICCCD1+IS37WW/3bZyAsjGXTpZz/12dFeWCiB/Tps5Rw2ffF5j11xfFYkAcuhBZiu2D3+yw6ORPwL4afptPrO2YMOvLEQigOyxa61tukG1VVfzvonSU2oLZs+8UGt33h0+ZwkgfY6ossYx2xA/jdKDGqbuPPhpw5DkLlESwE+jpBtN3S2aNo6mYmqFwDwIIIDMQwCR6EEOEJ6frBJgC0w2LMvWgvTbMQ8h2/gpfjo3AuQASb9vJKkHeXifJol30LawBSYoQcr7IZB7AeSnXybZ2Lset2N67llvhAenwPhxK8qkgQACSBqsNP82MrGcP6OkX5GHATt+mnQvnH/78uCn86fAFUkngJ8m3UKztw8BJF32ykprcyGAFB+D6xluxKBjXV4PL9HpSxPfq7Op9zfvH3R6zNBR49x/Ki+I/t68WVP33yRBzcqjkL9+IIBkw+ZMLNNvxzwM2PFT/HRuBIgASb9vJKkHeXifJol30LYggAQlSHk/BHIhgPgBU24ZBJBySXFd0ggggCTNIv7aw8TSH7cklcrDgB0/TZLH+WtLHvzUHxlKJYkAfpoka8y/LQgg82fEFeETQAAJyBQBJCBAisdGAAEkNvSh3piJZag4Y6ksDwN2/DQW1wr1pnnw01CBUVksBPDTWLD7vikCiG90FAxAAAEkADwVRQAJCJDisRFAAIkNfag3ZmIZKs5YKsvDgB0/jcW1Qr1pHvw0VGBUFgsB/DQW7L5vigDiGx0FAxBAAAkADwEkIDyKx0oAASRW/KHdnIllaChjqygPA3b8NDb3Cu3GefDT0GBRUWwE8NPY0Pu6MQKIL2wUCkgAASQgQCJAAgKkeGwEEEBiQx/qjZlYhoozlsryMGDHT2NxrVBvmgc/DRUYlcVCAD+NBbvvmyKA+EZHwQAEEEACwFNRBJCAACkeGwEEkNjQh3pjJpah4oylsjwM2PHTWFwr1JvmwU9DBUZlsRDAT2PB7vumCCC+0VEwAAEEkADwEEACwqN4rAQQQGLFH9rNmViGhjK2ivIwYMdPY3Ov0G6cBz8NDRYVxUYAP40Nva8bI4D4wkahgAQQQAICJAIkIECKx0YAASQ29KHemIllqDhjqSwPA3b8NBbXCvWmefDTUIFRWSwE8NNYsPu+KQKIb3QUDEAAASQAPBVFAAkIkOKxEUAAiQ19qDdmYhkqzlgqy8OAHT+NxbVCvWke/DRUYFQWCwH8NBbsvm+KAOIbHQUDEEAACQAPASQgPIrHSgABJFb8od2ciWVoKGOrKA8Ddvw0NvcK7cZ58NPQYFFRbATw09jQ+7oxAogvbBQKSAABJCBAIkACAqR4bAQQQGJDH+qNmViGijOWyvIwYMdPY3GtUG+aBz8NFRiVxUIAP40Fu++bIoD4RkfBAAQQQALAU1HmZY9eAAAgAElEQVQEkIAAKR4bAQSQ2NCHemMmlqHijKWyPAzY8dNYXCvUm+bBT0MFRmWxEMBPY8Hu+6YIIL7RUTAAAQSQAPAQQALCo3isBBBAYsUf2s2ZWIaGMraK8jBgx09jc6/QbpwHPw0NFhXFRgA/jQ29rxsjgPjCRqGABBBAAgIkAiQgQIrHRgABJDb0od6YiWWoOGOpLA8Ddvw0FtcK9aZ58NNQgVFZLARy4adVVZGwLRQKFk3Nc28uAkgkpqTS+RBAAAnoIgggAQFSPDYCCCCxoQ/1xkwsQ8UZS2V5GLDjp7G4Vqg3zYOfhgqMymIhkHU/LZjZI481sp9/CV+qWKtTjXVaQ3douB8CSMOx5k7/I4AAEtAbEEACAqR4bAQQQGJDH+qNmViGijOWyrI+YBdU/DQW1wr1pnnw01CBUVksBPLgp1l6nyKAxPKY5P6mCCABXQABJCBAisdGAAEkNvSh3jhLA6FQwaSoMgbs/o21z14zrePqBbNIQsK1Ehr+Kqv/3sZbMg9+Gi9h7h4GgTz4aZa++wggYXg9dVRKAAGkUmIl1yOABARI8dgIIIDEhj7UG2dpIBQqmBRVxoDdv7H67/ahLfXYSKua+qf/SuopWWjeyqbvc6QV2q0Qar1priwPfppm+9D2WQTy4KdZ+u4jgPDkxkEAASQgdQSQgAApHhsBBJDY0Id64ywNhEIFk6LKGLD7N1bfXd6zdjefbFV//u6/kvoEkFaL2LQ+g62w7Eqh1pvmyvLgp2m2D21HAAnqA7vuUmPrrlMbUURd/a1DAAlqNcr7IYAA4odaURkEkIAAKR4bAQSQ2NCHemMEkFBxxlJZHiaWUfkpAkjDuWwe/LThaHKnqAjkwU+jep8igETlldSbNAIIIAEtggASECDFYyOAABIb+lBvnKWBUKhgUlQZA3b/xkIA8c+u0pJ58NNKmXB98gjkwU+z9N0nAiR5z1AeWoQAEtDKCCABAVI8NgIIILGhD/XGWRoIhQomRZUxYPdvLAQQ/+wqLZkHP62UCdcnj0Ae/DRL330EkOQ9Q3loEQJIQCsjgAQESPHYCCCAxIY+1BtnaSAUKpgUVcaA3b+xEED8s6u0ZB78tFImXJ88Annw0yx99xFAkvcM5aFFCCABrYwAEhAgxWMjgAASG/pQb5ylgVCoYFJUGQN2/8ZCAPHPrtKSefDTSplwffII5MFPs/TdRwBJ3jOUhxYhgAS0MgJIQIAUj40AAkhs6EO9cZYGQqGCSVFlDNj9GwsBxD+7SkvmwU8rZcL1ySOQBz/N0ncfASR5z1AeWoQAEtDKCCABAVI8NgIIILGhD/XGWRoIhQomRZUxYPdvLAQQ/+wqLZkHP62UCdcnj0Ae/DRL330EkOQ9Q3loUa4EkNFjH7BPPv/GBp3Sazbb/vTLJOvRZ5B99e2P7t9HDDrWttyoc901Kjd01Dj33+t3Xt39vXmzpu6/G1IAWaiVWZ8dP7DmNZPC983GTWzmKmtZVfg1U2NCCSCAJNQwFTYrSwOhCruemcsZsPs3JQKIf3aVlsyDn1bKhOuTRyAPfpql7z4CSPKeoTy0KBcCyJPPT7TeAy519tx1u41nE0CmTJ3m/rZxl07Wc78d7Z0PPrf+A0fa0IFH25qrdjCVvWDEWBszfIC1XnwRGzD4alePJ6I0pACyxBJmfTreby3uGBG6b9au1NGm9Rkcer1UmFwCCCDJtU0lLcvSQKiSfmfpWgbs/q2JAOKfXaUl8+CnlTLh+uQRyIOfZum7jwCSvGcoDy3KhQDiGbK+CBAJHgOHXG+XD+7rBI5SQUSCx4od2jlxRL9SQaShBZDea9xrLW8bGrpv1q6ylk3rNyT0eqkwuQQQQJJrm0palqWBUCX9ztK1DNj9WxMBxD+7SkvmwU8rZcL1ySOQBz/N0ncfASR5z1AeWpR7AaRU0JDRvSiP0/r2mC06RH8rjRBBAMnDY5LNPiKAZMOuWRoIZcMilfeCAXvlzLwSCCD+2VVaMg9+WikTrk8egTz4aZa++wggyXuG8tAiBJDnJ9qY2x+ZLa9HqQDSY++udTlBSgWQ6TNq5vCTDz+tsauuM5syNdyMGtoCc0zHe6352GgiQBY87VJr0qg6D35PH80sKj9t3tzssIPNVlkeX4ra0WbU1Nollxfs8y/Cfdeo3XvuWmtbbtzIqqvDrztqLg1Xf3A2tQWzJybU2Pi7wm91h/YF63tUdTTv9YJZVZndn+WntZH4ab9u71vbm06yqj9/DxVgodUiVnXcBdZ85dVDrbfhKyuEcsv/+WmZRq/grvLTfkdVReOnFbSDS9NPIA9+mrXv/gJNGqXf8ehB6ggggJTk+JAFK4kA+em3v+Yw+tffFOy6G6sjEUD6rHmvtbg1GgFk5vFDrarcEW3qXJ0GlxKIyk8lgBx0QK0t2xbmURMoFAp21bXVkUwsd+tWY13W1SQ3/AlP1Fwarv7gk0vV8NIrVXb3veELhp061do/1n7LqqbP+Z0KzGihRc2WW6GsxNmFgkXmp327vWftbjo5EgFkxrGDrar9yoFRxVtBOM/vLD+1SPxUAshhh9TyronXUTJx9zz4ada++60XXjATvkcn0kUg9wIIOUBmOSw5QNL14IbRWrbAhEEx/jqyFAobP814WhBVyPYG69faHu/2s+oP3wy9Y3917281G21fdhhIVH7KFpjQTTvXCqPyUwkghxw4ZzRtw/WMO2WJQB78NKr36a671Ni669SW/V4Pw2/YAhMGReqolEDuBZC0nQJDEtRKXZzr50YAASQbvpGlgVA2LFJ5L6IasCOAVG4Lr4S2wOhktMKyK/mvJGMlo/JTBJCMOUrM3cmDn2bpu48AEvMDk9Pb50IAKT4G17PziEHH1uX1+OmXSdajzyD76tsf3Z+L/6b/1ukxQ0eNc39bv/Pqs+ULIQlqTp+cDHQbASQDRjSzLA2EsmGRynsR1YAdAaRyWyCAzJ1ZVH6KAOLfTyk5J4E8+GmWvvsIIDzFcRDIhQASJVgEkCjpUneUBBBAoqTbcHVnaSDUcNSSdaeoBuwIIP7tTARIPieW/j2GkkkhENX7NElCXZa++wggSXly8tUOBJCA9kYACQiQ4rERQACJDX2oN87SQChUMCmqLKoBOwKIfydAAEEA8e89lIyTQFTv01kCSG1EXassoXaWvvsIIBG5FNXOkwACSEAHQQAJCJDisRFAAIkNfag3ztJAKFQwKaosqgE7Aoh/J0AAQQDx7z2UjJNAlO/TXSddZtVffRJ692ZutbvVdN607OSjWfruI4CE7k5UWAYBBJAyIM3rEgSQgAApHhsBBJDY0Id646gGQnvuXmtrd9LJDOEcozl7p1VnZSteoUJLWGVRDtg5BcafsRFAEED8eQ6l4ibA+9S/BTgFxj87SqaLAAJIQHshgAQESPHYCCCAxIY+1BtHJYD03f1DW/r5m6zqrymhtrfQtLnN2Km71bZbIdR601wZA3b/1uMYXP/sKi0ZlZ8mKbdCpUy4PnkEovLTPETUIYAkz59pUTQEEEACckUACQiQ4rERQACJDX2oN45MANnlPWt388lW9efvobaXlfU5cTJg9+9iCCD+2VVaMio/RQCp1BJcPy8CUfkpAkg0fscWmGi4Uuu8CSCABPQQBJCAACkeGwEEkNjQh3pjBJBQccZSGQN2/9gRQPyzq7RkVH6KAFKpJbgeAaSRff5F+NtTiQDh2coLAQSQgJZGAAkIkOKxEUAAiQ19qDdGAAkVZyyVRTWxzMOKJQJIw7lsVH6KANJwNszDnaLy0zy8TxFA8vCE0EcRQAAJ6AcIIAEBUjw2AgggsaEP9cYIIKHijKUyBuz+sSOA+GdXacmo/HSF5Qt2YPcaqwp/QXvWqRoFEi5Xaus0Xx+VnyKAROMVbIGJhiu1zpsAAkhAD0EACQiQ4rERQACJDX2oN0YACRVnLJUxYPePHQHEP7tKS0bpp7v9fLFVffFRpU2a7/Uzt9vXav62ednHi863Qi5IPIEo/TTrp2oRAZJ496aBIRFAAAkIEgEkIECKx0YAASQ29KHeGAEkVJyxVMaA3T92BBD/7CotmQc/rZQJ1yePQB78NKrvPgJI8vyZFkVDAAEkIFcEkIAAKR4bAQSQ2NCHeuOoBkJMLEM10zwrY8DunzV+6p9dpSXz4KeVMuH65BHIg59G9d1HAEmeP9OiaAgggATkigASECDFYyOAABIb+lBvHNVAiIllqGZCALkhmlML8FP8dF4E/ure32o22p4tMA3nJrHfCQHEvwkQQPyzo2S6CCCABLRXLgWQQsHeea/aXn2tUUB6cxZfcsmCbb9tRMnQQm9tuitEAEm3/bzWI4Ck344M2P3bEAHEP7tKS+bBTytlwvXJI5AHP43qu48Akjx/pkXREEAACcg1lwKImUX1geE4vIAOWUFxBJAKYCX40qgGQkwsG87oUb1P83BqAX6KnxIB0nA+kIY78T71byUEEP/sKJkuAgggAe2FABIQYElxBJBwec6rNgSQhmMd5Z0QQKKk2zB1M2D3zxkBxD+7SkvmwU8rZcL1ySOQBz+N6ruPAJI8f6ZF0RBAAAnIFQEkIEAEkHABVlAbAkgFsBJ8aVQDISaWDWd0Buz+WeOn/tlVWjIPflopE65PHoE8+GlU330EkOT5My2KhgACSECuCCABASKAhAuwgtoQQCqAleBLoxoIMbFsOKMzYPfPGj/1z67Sknnw00qZcH3yCOTBT6P67iOAJM+faVE0BBBAAnJFAAkIEAEkXIAV1BaVALLQQma9d3jfWtRMqqA1ZV7auInNXGVtqyrz8jxcFtVAiIllw3kPA3b/rPFT/+wqLZkHP62UCdcnj0Ae/DSq7/7u3Wqs89q1DXpqUtvFmyXPiWhR5gkggAQ0MQJIQIAIIOECrKC2qASQJZYw693xAWs5fkQFrSnv0tqVOtq03oPKuzgnV0U1EGJi2XAOxIDdP2v81D+7SkvmwU8rZcL1ySOQBz+N6rvfZ/fPbemfX7GqmTNDNWyhcWMrrLme1Sy57Bz1IoCEiprKyiSAAFImqLldhgASECACSLgAK6gtUgFkjXut5W1DK2hNeZfWrrKWTes3pLyLc3JVVAMhJpYN50AM2P2zxk/9s6u0ZB78tFImXJ88Annw0yx99xFAkvcM5aFFCCABrYwAEhAgAki4ACuoDQGkAlgJvjRLA6EEY460aQzY/eNFAPHPrtKSefDTSplwffII5MFPs/TdRwBJ3jOUhxYhgAS0MgJIQIAIIOECrKA2BJAKYCX40iwNhBKMOdKmMWD3jxcBxD+7SkvmwU8rZcL1ySOQBz/N0ncfASR5z1AeWoQAEtDKCCABASKAhAuwgtoQQCqAleBLszQQSjDmSJvGgN0/XgQQ/+wqLZkHP62UCdcnj0Ae/DRL330EkOQ9Q3loEQJIQCsjgAQEiAASLsAKakMAqQBWgi/N0kAowZgjbRoDdv94EUD8s6u0ZB78tFImXJ88Annw0yx99xFAkvcM5aFFCCABrYwAEhAgAki4ACuoDQGkAlgJvjRLA6EEY460aQzY/eNFAPHPrtKSefDTSplwffII5MFPs/TdRwBJ3jOUhxYhgJjZgMFX290PT5jN3v0P38d67rej+7fRYx+woaPGuf9//c6r24hBx1rzZk3dfyOAhPuYdGhfsEMOrAm3UmqrlwACSDYcI0sDoWxYpPJeMGCvnJlXAgHEP7tKS+bBTytlwvXJI5AHP83Sdx8BJHnPUB5ahADyXwFExh50Sq85bP7k8xPtghFjbczwAdZ68UWcWFJ8LQJIuI8JAki4POdVGwJIw7GO8k5ZGghFySnJdTNg928dBBD/7CotmQc/rZQJ1yePQB78NEvffQSQ5D1DeWgRAsh8BBAJHit2aFcXDVIqiCCAhPuYIICEyxMBpOF4xnWnLA2E4mIY930ZsPu3AAKIf3aVlsyDn1bKhOuTRyAPfpql7z4CSPKeoTy0CAGkni0w3vaXKVOnWe8Bl9rGXTrVCSDvfPC59R840oYOPNrWXLWD/fCfaXP4ydffFOyGmxrZlKlVofrQEkuY9V7jXmt529BQ61VltausZdOPG2rltLhgZq/8u8ruvrc69HZIADn04Nqy2hH6zXNWYdb9NA/m1LN4zXXV9vkX5Ty5lRGJcmI5/ZjBZsutXFmDMnp1lO/TDdavtT3e7WfVH74ZOr3p3ftb7SY7WFXV/H0PPw0df4NXmAc/bXCo3DB0Annw06y9T5dadFZKAX4QaEgCCCAltCVwHHrchTZoQC+X70MCSI+9u9qWG3V2V5YKIDNrauew1wefzLRR11okAsgxHe+15mOjEUCanX6ZNWk8f1GjtmD22LM1Nv6u8F1VAshxR1eX1Y7w756vGrPup3mw5oyZtTZkZG0kAkj/bu/b0jedZFV//h4qykKrRazq+Aus5Sqrh1pvWiuL8n0apQBSe+Bx1rJrN6uunr8Agp+m1Tv/1+48+Gn6rUQP8uCnWXufNm40/3kHng2BsAkggNRD1Nv2st9uW803AoQtMOG6JFtgwuU5r9rIAdJwrKO8U5ZCYaPklOS6Cdn2b50oI5Wm9RlshWVX8t+4jJXMg59mzGS57E4e/DRL3322wOTyMY290wgg8xBAdAoMOUDq99GoPjAIIA33TkAAaTjWUd4pSwOhKDklue6o3qdRRoD81b2/1Wy0vVkZW2DEPhd+WiaLSn2xqmBWMAW+x/vLg5/GS5i7h0EgD36apfcpAkgYXk8dlRLIvQDy0y+TbOxdj9sxPfd07Eq3uHAKDAJIpQ9VWq5HAEmLpebdziwNhLJhkcp7wYC9cmZeiaREgPz0c5X9e2K1zZjhvy/1lWzSxOxvnWttySUQQPyQrVSo83MPyiSLAO9T//aI432KAOLfXpT0TyD3AoiX6PSlie/VURwx6Ni6nB/6x9FjH7Cho8a5vysviP7evNmspD1sgfHvfPWVJAIkXJ7zqg0BpOFYR3knBJAo6TZM3QzY/XOOY8BeX2u//a7KbrypOvTcX82bmx3Qvdbatpkz35h/av5K5sFP/ZGhVJII5MFPs/TdRwBJ0tOTn7bkXgAJamoEkKAEZy+PABIuTwSQhuMZ152yNBCKi2Hc92XA7t8CCCD+2VVaMg9+WikTrk8egTz4aZa++wggyXuG8tAiBJCAVkYACQiwpDgCSLg8EUAajmdcd8rSQCguhnHflwG7fwsggPhnV2nJPPhppUy4PnkE8uCnWfruI4Ak7xnKQ4sQQAJaGQEkIEAEkHABVlAbW2AqgJXgS7M0EEow5kibxoDdP14EEP/sKi2ZBz+tlAnXJ49AHvw0S999BJDkPUN5aBECSEArI4AEBIgAEi7ACmpDAKkAVoIvzdJAKMGYI20aA3b/eBFA/LOrtGQe/LRSJlyfPAJ58NMsffcRQJL3DOWhRQggAa2MABIQIAJIuAArqA0BpAJYCb40SwOhBGOOtGkM2P3jRQDxz67Sknnw00qZcH3yCOTBT7P03UcASd4zlIcWIYAEtDICSECACCDhAqygNgSQCmAl+NIsDYQSjDnSpjFg948XAcQ/u0pL5sFPK2XC9ckjkAc/zdJ3HwEkec9QHlqEABLQygggAQEmWQCpqgq3c15thUI09VZYKwJIhcASenmWBkIJRRx5sxiw+0eMAOKfXaUl8+CnlTLh+uQRyIOfZum7jwCSvGcoDy1CAAloZQSQgAATKoBIonju+WqbNCl8EWS1VWpt5ZXiF0EQQML13bhqy9JAKC6Gcd+XAbt/CyCA+GdXack8+GmlTLg+eQTy4KdZ+u4jgCTvGcpDixBAAloZASQgwIQKIGpWVB+YXXepsXXXqTWLKsKkTJPkQgCpqrIqi0ZsSkggT2R+mpSJZZnunOrLGLD7N19S/PTb76rsxpuqbcrUcEXz5s3NDuhea23b1PqHFFLJPPhpSKioJkYCefDTqMancbxPEUBifFhyfGsEkIDGRwAJCLCk+ArLF+ygvf6IRhuorrbCAk3LbnBUHxgEkLJNMMeFtausZdP6DSm7go8/Nfvq6+qyry/3wpYtzdbrXGNVMYtYUQp1cQyEyuWftesYsPu3aFL8FAHEvw03WL/W9ni3n1V/+Kb/SuZS8q/u/a1mo+1jX3AIvWNUOFcCvE/9O0cc71MEEP/2oqR/Aggg/tm5kgggAQGWFNdAaLdfhlrVlx+FW7GZzdx2H6v522ZlD4QQQPyZYIklzHqvca+1vG2ovwrmUapSASSqgVCH9gU75MCa0Pvnp8Ko/DSOgZCf/mehTFR+mqSJZdb9FAHE/5OYJD/13wtKJoUA71P/lojju48A4t9elPRPAAHEPzsEkHvDX1lP0kAoqgE7ESD+HzoEkDnZReWncQyE/HtGuksyYPdvv6T4KQKIfxsm6bvvvxeUTAoB3qf+LRHH+xQBxL+9KOmfAAKIf3YIIAggvrwHAcQXNlcIAQQBxL/3JLckA3b/toljwF5faxFA/NsQAcQ/O0rOSYD3qX+viON9igDi316U9E8AAcQ/OwQQBBBf3oMA4gsbAshcsBEB4t+fklKSAbt/S8QxYEcA8W+v+koigITLM++18T717wFxvE8RQPzbi5L+CSCA+GeHAIIA4st7EEB8YUMAQQDx7zgJL8mA3b+B4hiwI4D4txcCSLjsqG1OArxP/XtFHO9TBBD/9qKkfwIIIP7ZIYAggPjyHgQQX9gQQBBA/DtOwksyYPdvoDgG7Agg/u2FABIuO2pDAAnTB+J4nyKAhGlB6iqXAAJIuaTmch2nwAQEWFI8SaGwUW0t2H+PybbaMpPLPo2mEsKFpi3MFizvqN9vvzO78aZGNmVqVSW3mO+1nAIzX0ShXhCVn8YxEAoVTIoqQwDxb6yk+Ck5QPzbMEnfff+9oGRSCPA+9W+JON6nCCD+7UVJ/wQQQPyzcyURQAICzKEA0nf3D23phy61qqmTQ4VXaN7Kpu93jBXarVBWvQggZWGq9yKOwfXPrtBqEZvWZ7AVll3JfyUZK8mA3b9B4xiw19daBBD/NkQA8c+OknMS4H3q3yvieJ8igPi3FyX9E0AA8c8OAYQtML68p2+3963dTSdZ1Z+/+yo/t0KVTiwRQPzjRwDxz65SP/V/p/SUZMDu31ZxDNgRQPzbq76SSRJApk2vtnBjImf1uLrKrEmT2nDBUVu9BHif+neMON6nCCD+7UVJ/wQQQPyzQwBBAPHlPQggvrC5QhyDOye7rG+BqdJspODfZ+ZVMqJqK24sA/aKkdUViGPAjgDi315JF0Duvq/avvs+fAlkkw1rreMatZFsfa3EGrxPK6E1+7VJEuqy9N1HAPHvk5T0TwABxD87BBAEEF/egwDiC1viBJCeB9X478g8ShYKlc34szQQqg/LL/+pss8+rbLakBdPqxsVrMPyZkssGr8MggDi/1FCAPHPrtKS+GmlxP53fVKSn/M+9W9DBBD/7OYV+YkA4p8rJf0TQADxzw4BBAHEl/cggPjCligBRAOh3f8YYVVffeK/M3MpOX3z3ay28yZlrxRmXQAht4J/F2PA7p9dpVu1ovLTVq3Mjtz9G1uocbg5o0SmUN3ICku3L/tdgwDi35+SIoBE5afNm5sd0L3W2rYJWan2gRw/9QHtv0XiEJQRQPzbi5L+CSCA+GeHAIIA4st7EEB8YUucALLHu/2s+sM3/XdmLiX/6t7fajbavuxJCQKIPxMwYPfHzSuFn87OL6qJpTtVa60HrcW9VwczWD2la1dYw/46fGDZ9TKxLBvVHBfuuVuNrb2WxIHwt9dU0qqo/JT3aSVWmPPaPL9PEUCC+Q6l/RFAAPHHra4Up8AEBFhSPBcrliRB9e00SckBkgs/3eU9a3fzyQlI1ltlN95UHfpxzQzYfT+GrmCeB+z1kYtqYpmHY8Vz8T7d81Nr89o4q5o2JdiDV1K6sGAzm7ntPlarSJ4yflH5Ke/TMuDP45I8v08RQIL5DqX9EUAA8ccNAWRitd1DBIgv7yECxBc2VwgBZE52RID48ycG7P64eaXyPGBHAAnmO6WlcyGAJGbhA0HZr/fmwk9jWPhAAPHrkZQLQgABpAx6o8c+YENHjXNXrt95dRsx6Fhr3qyp+28iQMoAWMElufjAJGYgZHbjTY1CX1lnxbICh6/nUiaWs0OJasWyRQuzw/f71RZtMSOYweorXVVlhVaLlR3tztYC/yaIY886Aoh/e9VXku++f55JyVWDoOzfhiqZ5+8+Akgw36G0PwIIIPPh9uTzE+2CEWNtzPAB1nrxRWzA4Fn7cAed0gsBhAgQX08dESC+sLlCRIDMyY4IEH/+5IS6zo9a80dv8lfBPErVtl/Vph94Ytn1IoCUjWqOCxFA/LPjfdqA79PELHxEEwGy0EJmR3f71FoW/vDvkHMpWWjcxGrbr1J2vbxPy0aViPcpAoh/e1HSPwEEkPmwk+CxYod21nO/Hd2VpYIIESD+na++kqwE+edZ+UoQESB+aefCT2MIha3PHlFFgBCp5Nf7Z5XL84olfhrMd0pL5+J9mnEBxL1P17zPWt4+LFzn0MLHyp1s2rEXlV0vAkjZqBBA/KOiZMoJIIDMw4BTpk6z3gMutY27dKoTQN754HPrP3CkDR14tK25age2wIT8ADAQ8g8UAWROdgyE/PsTK+v+2bGyPic7IpX8+RNCnT9uXqnECHV5EEDWuNda3jY0mMHqKc37NNvvUyJAQn9kqLAMAgggZQggPfbualtu1NldWSqA1Ff8/U9m2H0PF+yvv8qwQAWXKMRwr2WetgWfvbOCUuVdWlh2RWvVs58t0Lh6vgVqC2ZPvzjDnn+xMN9rK71gjdULttkXI6zqq08qLTr/67fewxbZfGurrp7/MXTTZ9bamHEz7fsf5l9tpVfs3eVTW/zJ0VY17c9Ki87z+kLzVtZ4r5628CrlhYrmwU+feWmGTXgBP/XjaHt3+cQWf/xIaZYAACAASURBVPLa+P300xl230PRvE/3XOZpa8r71I97mCXmfYqf+jOgGd/92cnl4rvP+9Tv42L5GJ8m433q20gUhECZBBBAyhBA5hUBUiZnLoMABCAAAQhAAAIQgAAEIAABCEAgRgIIIPOBP78cIDHajltDAAIQgAAEIAABCEAAAhCAAAQgUCYBBJD5gJrfKTBlcuYyCEAAAhCAAAQgAAEIQAACEIAABGIkgABSBvzRYx+woaPGuSvX77y6jRh0rDVv1rSMklwCAQhAAAIQgAAEIAABCEAAAhCAQBIIIIAkwQq0AQIQgAAEIAABCEAAAhCAAAQgAIFICSCARIqXyv0SmPbXdLv93qdsm83Ws6WXXMxvNYku98t/frf7H3vB9thxM2vZolmi2+q3cY89+2/XN0VOVVXN//Qdv/ehXHQEJv022R5+6mXbpetGmY18m/DK27ZAk8b293VWiw5kjDXPmFlj4+9/2rbedF1bYrGFY2xJdLfmfRod24aqme9+Q5GO9j5Z/+7nwU+j9RBqh0D8BBBA4rdBxS3Qy1cfmFdf/8AWWKCJbb9lF1t7jRWtUaP5H2Fb8c1iKvDgEy/ZhZePdZPmPXbYzA7dfydruuACMbUm/NsWCgW75pb77aY7HrUFF2hivbrv7PqZJRt+8/3P1nvAMPtj8hRbeYVl7bS+PaxdmyXChxljjT/9Msnuf/xF+/zL763Dcm1sp603sNaLLxJji8K/tbYATnj5rcxu/fvux1/tqJOH2lEH7WbbbrZe+AATUOOjz7xql19/l11+fv9MCsre+3TY1f+y/ofvYz332zEB1MNtQh7ep8Xf/Z232dCOOKBbpkTX0u/+QfvuYHvvsoU1adwoXGeJsbZiP12hfVsbcEx367BsmxhbFP6ts+6nGrOde+kYUz8bN2pke+28hR3Tc4/MLtSF7yHUmAYCCCBpsFJRG7//6Vfre8YIq62ttX27beX+cts9T7iJ5cDjDraFF2qRsh7N2Vyt5B1+4hA7cO/tbMuNO9slV91u7370hV1y1tHWpnU2okE+/uwbO+b0y+ycE3ta2zZL2KDLbrImjRvbWccfZK1aNk+9DTXQu2DkWJNAMOiUXqYJ2GXX3GGnHtvDNt9w7dT3Tx14+oU37NTzr7GOqy1vGqx/+OlXdvfDE2y/3ba2nv/cKROD2i+/+cGOOGmoE682Wq9jJuxW3An56fBrx9tHn35tF51xZKZEVq+fiuA5esAw69Z1I9t311nfjKz99D7tP3CkEyHXXmOlzAkgpe9TRQ5eeeM9NuTMo6zT6itkwpz1ffe//OZHGzrwqEx8E2Wk+r77iy+ykJ3at0cmvhf1ffdHXneXXXja4fhpip7Scfc8aeMffNYuH9zP+eWtdz/hIrKHnnW0dVx1+RT1hKZCYO4EEEBS5B2K/Djh7CusadMF7KzjD65bGVF481Vj7rHX3/kkE4OF0hXnmppau/rm++yjz7628089PPUDhZk1NXb20Buc553R/0CnsMu251xyoy3VelHrc8geqd8u8s4Hn7sJydCBR9uaq3ZwfX3trQ9dHy89p48t126pFD15czZVwoBEukP/ubPtseOmdfb67fc/beCQ62zhVi1TP6iVnw667Gb77ffJ7rnT7633PrGxdz1uz770lptQ9ztsH2vWNL2RWe999IUTlCXSrbvWKvbzr7/ZfY+9YOMfeNb++mu6XXj6ES66Ls2/68c9ZI8+/aqNHNTXTSQ/+eJbZ8PHnnnVOnda2X1LFl24VWq76L1PtbVn8p9T3TYmL5JH/y2xYLMN10l15Et971NFu/w66Xc7+4RDUmu74oaXfvf1Tex7xnDbauO/2T7dtkx9H+v77ut9I4H5uMP3sQ3XWzP1fazPT2+583F77uU37ZKzerto17T/su6nso/6+M4Hn7nvn8anErYeevJlu2z0eLvygv7Wfpl0j9/S7oO0PxwCCCDhcGyQWjTpGHTZGBt14XFzTCC1ynf2JTe48N8pU/+yN9792HbfYVP38krTr3iFRBMS76fIlz6nKmLiEFttpeXS1KU52vr8q2/bucPGuA9JsRDw1vuf2bmX3GgjB/dN9T59T6hbeYVlZhNz9BE9edBVtsYqHVx0T1p/6ocidn6d9Ee9gpxWMrXi3v+wfaxL5/TmlPj3mx/a8Wdf7nLUvP3+Z/bGu584MUCCj6JBvMn0LeMfs6MP3t0WWyRdk2gJxyefN8pt0Vp1xeXsoadedi65w5ZdbNftN7EOy7RJ/ZY0CXWHnTDEttq4s/306yQnXCkcfe+dt3CRWFnIB6L36cVX3GYjB/ez4aPvsG03X8+23KizvfL6+3b+iFvs6+9+sk26dEqteO69TxddpJWd2f8g55N6BykyctLvkzMhgOi7f+jxF9mRB3RzYoe2vmpMc+Qpl9ju22+SCQFEfnrcwMvtwtOPtE3X7+TeNZ6Qfka/A50Aom+H7Hp4j11s2bZLpuoT6flpTW2tDTnzaCeMexF273/8ZZ0AoijJ3//40/bbfetUjk+z7qdyOi0M9B5wqYtk3W+3rdzzqO/lMy+8YZtvtHbq7JaqB4nGNhgBBJAGQx38RlqNnTJ1mhMB6ksoqRWGQsHcoF4KbdoiCepbIfGo/fbHn9bn1Eut98F7pHpSKfvpw7Jxl05zhGl/9e2PdvxZV9gFpx2e6j2z88o3oFXLGTNm2glH/SP4AxFTDf/57Q/r2f9C633IHm5iWd/v0y+/cwPYtO7t9gaz2s8tke6PP6fYZeccYy2az378t7Y4vfDvd6zr5n9P3fYRTUhOPGeUbbjeGvbmu5/aSUfvZ1tt8reYvCr823rh6E89/7qttfoK9uGnX9uVF/bPzDZCEdP7VMJ4183Xs9122NROGXS17bnTZk6se+bFN2yvnTa3a299wG0VWX3l9uFDboAa9T6V4NqoutqUU2HPnTa3Dz750sbe+bgNGXiUrbnK8rbggk1S9/x56Lzv/vc//mp/TplmtYWC7bLtRvb4c/9245xLBh6d+i0w3nd/sUUXcs9h26UWt802WNv+dd9TttbqK9ZFCypiSfndtthwHVtk4ZYN4F3h3cLb5rrqSsvZ629/5PJGKELp8Wdfs2Fn93ZbYPROeuu9T+33yVOcKJmmXx78tNgeL772rp0y6CrbcesNrG+vvVM7lkmTj9HWhiWAANKwvAPdbcDgq23FDu3mub/5iQkT7aqb7nXhzosvulCg+zV0Ya04n37haDfRWmn5drPd/vV3PnYv4xGD+tqK7ds2dNNCu9+9jzxvN9z+sIviKbXPA4+/ZNfccp+NHnpiakPS55VvQCvtCvfddbuNU72ip0m/+jHw+IOt02rLu7D748663NZecyU7YK+udYnCtGIy+c8pqbSl/FQTxysu6G/ffv+znXfpTc5niyMGNJDVap6izpo3WzC0Z6QhKvL8VNEC3ffc1k4850pnv9LIJE1cFFG38EItUzcA1PtU3wxNPnQS0zGnXWaDBxw2WwSdthcqiqBJk8a2UApzD8lP73roORt+3jHObbSd6ePPv3GTr/1338ZOu+AaK41Eawj/Cusexe9T9enx516zG8Y9ZK1aNrPDuu/ibCkBSJMUCT9p/BV/95dp29pte1lhubZukaBzx5XcVl9xuGbs/W47TBoTvhd/9yUCHH3KMNtms3Wtc8eV3XtHQrnE5ituuNv6HbZ36sZupd99bXtRAs0D9u5qHVdbwW0/U7+V9F32TKOvluOn6qPep/otslDLVG1l1vjs8hvutsO771Invmm75LGnD3dRnwfvu4PrzwuvvuPeQ1rEysKWpjS+M2lzOAQQQMLh2CC1aF/eBx9/aYMG9Ko3BM0LGV2pQzvbapPO1nnNlVOziqBJhpKCLrpwSzv7hJ6z5RXwQtW1+qycGT/98pvd9dCzNmXKX+7FvPxySzcI/6A3+eGn/1iv4y9yR1H2Pnj32cLrPdtts+m6dsg/drBPv/jW7npogjVvvqBbxUzLySISd7Ql4vLz+80hVGkQqFwuoy463hZu1dytDEnY0kqYVoPScgLOX9NnWL8zR9gmXdayf+6+tXMLiSJX3HiPPf7sv92+WR37qxUxPbNXnt8/Nc+h+iJfPOzEi63Hnl3d0bfafqYBu3INePlcvAiRdTqunMqEk3fc/4yNf+AZJxRrpVWi8edffe9ygeind87oW+539tPfa2pqXK6MTddfK+hroEHKy0cl6qzYoa2LBNT79ahTLrGD9t3ebQ/RT4Nb5en5z6Q/rLq62v6+zqqmUPziJMyKSps5syaR71i9T91Ws8P3dluy1GfZcYuNOjthsjgSrU3rRe3Sa+6wVVZY1nbcev0GsUEYN9H79MkJE234ucfUGwWhd6oSFHp+rHtqpTotW1+97/6G665Z9x5RpOsSiy3kBB7vJ5FZHG6563EnFugdm5aE6PLTI08eakceuKvLTeN9P7RF2ctV40VrSXD18oKF4T8NVYf89OnnX687KUyTZAlWxVGDXqJiibDed6Sh2hf0PuX4qfK5aIvvux9+7oQBRYCmyU+9rbubdulkh/XoVif4e+/Ray85yfXLi7jLakLtoL5C+fQQQABJj63cRESreN26buxOmiidMN529xPuSE79beLbH9vDT73sTm9Iy9GOOo5y5HV32ov/fseOPHA327hLRxcSO3z0ePvsq+/sisH97J3//7gMHn6zHbD3dtaqRXM3+Ntpmw3cym1924KSZt5PPv+m7nQUHbu5zpor2Y8/T3JHjkngGXrmUS779m13P2kH/2MHmzFjht161xN2zKF71tlRk8877n/adthqg8TlXZjVtmds9H9X67S6rqS99z36gl1/20NucNdx1Q52zOnD3erlxn/v6Aa2f/w51WWK9yZfmoxOfPsjNwFP4mBe0Q86AeasEw52q3j6KfpqxLXjXS6CZgsu4CZn/9h1K9cH/bTaXl1dlQo/lU8u1Kq5C6v3Bn/7dtuyzgc1KLrm5vud0KVIJv237K6tBqXbZJL2DKo98lMlrFXSYf2efelNF/Ey4ry+rv2aWGoAr4mntgB98fUPLjpNAkFpdFoS+6c2aauWTpZS9Id+Z1x0rS2zdGs3sfRWbLfYaB075B+zjoyVOKnVS20F0rs0DSKXtkbKXqXviOITRfT8KTmjIgiVlDkt9vP89K+/ZtR7ups3YdHxlN4JTfo3bRU97oh9XVLfNPz0HBZv4dG34okJr9UlYJQoMHXadLeirp+eU00ylZjZW4H2Vq8lSCcxd4aexZYtmtdNKrUVVL++vfZy/1vsn1rQ0Qr786+8beuts6ppUUTv4ST/9K4o9lMJpyecc6VddPoRzh7e9hFFf+j9onHdLXc+Zj//+rvtvM0GbntM0sdv8/JTb+u53kU69ld++cATL7lE0xJ8km4/z7c0x+h35khbaolF7fgj93URn0pcr23LWnh98PGX7M4Hn3WJ7L2xmr6d06fPtK03zc720SQ/a7QtPAIIIOGxbJCaFCZ58nlXucGtJl5KPHjgPtu7UPvep15mZ/Y/sG7goxeVJiZpy7792f/nT9DKqwZB+nDoiFEN2rVyoggKrZZcfMaRbjuQPrSnXTDaLjztiLrJTIMYIsBNvH2wV910n738+ntuAtl9z64uLPTrb39yyeC0X11igUJHdXrK1Tffb0POPNKFjxav+unYYw16VUeSBhBasRt375Pu6DS1r8s6q1u/w/Zye9h1PK5W30/ve4AL3S5YwU46d5TttM2GLqeGF/Hj5bGR3ec2CQhghsBFtUf21MHXWJfOqzsRbuT1d9lu223sjhqVGPncK2+7AaA3+NG/vTTxfbvg1MPclgP9nnx+on33w6+2T7ctEin0eJC0KqttLhqwy55HnXyJHbr/Tk4Q8SbT3/7wsxN5lKzxtGN7uNM40vJTNITeqzpyUxOto04ZZvvsskWdeOUl8N1+yy51ERRp6ZvXTh1tKJ/VquQjT71qN49/dLbIASVh1mliykGk90yxyKXvzG33PGkb/G2NVOQnKj6poUmTJi4vlgTXQ/+5U11CP/V/603+lsgJczm+pcUC5RoqPhlN/Vb+Be9IZyUz1ATUE8HKqTfua/S9u+iK2+zywX3t/Y++tH4DR1qLZk3dJFqLO/o2Xj/uQRt2dh+rrqpyp8PJtk8+/7p1WWe1VERLSuR54IkX3dhM7xad7udF0+m5G3HdnU6YFAtFTijXiyJedK0iDbUNeLst/h63qeZ6fwmTynV21IG7uuSuxYmKWy++sDsFT2MEbWnSiVTrdFypLs+E3sVpSKxd7KcSyM+46Dobcd4swVw/iQn6pmgMMLOm1r746nv3TUx6pKu3iKXt2IpqWXetVe38Uw9zfdICrMYAxYJr8VggsQ5JwyBQDwEEkBS6hT6CeuFKIFhy8UVdSO+Fl9/qEsJ54ZPeSRVVVdU24Jj9XS81kdRKSRr3s6v9UpoVRaBoCGVK18Bur503t6Gjxtm5J/VM7UC22AUVSqptTkoCd+noO2y9tVd120NG3/KAXXzmkS4cXZEFEoQkFmjlaMD5V9uws3onMky99PHSB7X3gGHW97C97YnnJrqVvGN67um2NOmjuvO2G7rBklbILjv3GDfo06D+zXc/cRO3pO051TOnrTx3PzLBWrVo5raJaPAnGylawFuFVXTT0adc4lZV1E9NNhWWr2z4n331vVvlS/JPQo0G7doion3cxZMsHbOq/dEa6Mk+Oi7vyjH32FUXHp8aUVJ2VAJNRexIwDn9wmtdXgkvzL74uMr11lnNHev4wqvvOvvqNJXiFT5dq2NlkzbQ1UkMF11xqxOP9f70okE8vys+rnLqf7ck9tirqxO50hRBoZM1lKNH0Y961urLi1U8IZPInLafl1Szx95d6wQ59Vv79c896VC3xcA7AUiinSZeXj6NpPdVOYdOGXy1ndznny7609tqqD7f99iLdvEVt9rhPbq5LTN61nSimiKX9P5Jy1ZRRRAOvWqci0h69Y0PZoumk1Cp6AFFn0m4uu62B+33P6a4iacE5udefsuWXGKRRCf1lVg18OLrbe01V7Sdtt5gtm0T3hYgLeBJkJQQonevIl71Pk1LYm3PTzXmlk1cVNKAw+oWonTi1vBr73C5s7SoN2rMvc7eaYiQrO8dIb+c8MrbdvGZR9VFMmls5kVjKVr52NNHuFNjspRMPOnvS9rnnwACiH92iSmpyeGZF1/nwtI89VkrP1pV12reSssvY9eOfcBu/NfDdSspJxz5D9PAKElRA/MDqkmYJlf6yOj30sT33OBnjZXbu/C8b7//xe0Bf/O9T63rZuu5/e5pWvlSn/RBUb4BCRwa7OhozsGX3ew+Ktoyc80t95smMlr10690ZXN+DOP+uwY3Co3VioImlxIGLhhxizt2TclfF27Vwg3itT9aoeveoF75F9ZaY8W4m1/W/eWnY25/pG4/tHcUoAbrGizpNAf5sSahGgDq2Mc0/OSPP2gb3unD7Yx+Bzh7yD6lkWdakdXKl5JvJjEcfW6sZafa2oKbVPU9c4SdfcLBtvLyy7hVV01CnpzwuoveOfuSG53IpS1BL732nmPgrdB6QtfJvfdP5GlV3jasa2990L7/8RcXrq1vgAbzOnL08O7dnKBeHEnQqFGjVL1nNKGSXXRco/qrfD0ScfRO0c87OWa37Tdx7xiJO9qCeMlZR6cmr4SX40URdToytXHjRm5V3dtioH4q0u7Hn//j+v7Mi2/ayxPfs0vP7WMdV10+8a+b6Yr6mzHTzhl6g4sa7LX/zk5QrE/MKo16SXzn/ttAb0FK4zQt5Oyw1azcNFqk0jdStjzr+IOsadMF3VZYRTJdMmqcbb7ROi7HVNJ/em9qa6vygSj6sXjbxNsffGb9zxxpB+27gxvbTJ32l+vf+x99karE2vLTmtqCOwlN0a56h0gMlw0VudR5zZXcEfEaEyhyV4s4SdzSW44v6Tn78NOv6kSe0pwu6t85w250EZQ65UfvV23Pkx/zg0ASCSCAJNEqFbZJ+RKU+d5bRS7dQqCV24uvvM1GDznR7X9WaJ4+uso2rhwUafl9/d1PLmnfKX32r8u7oHA9DXLdRGzApfaP3bay7bboYvc9+rw989KbLqdG2zZLpKWLLino+cNvdh9KT8zS/udCodYJPOq/l0SsNA9DGjqplSFtG5EfKsRXg1ovc7pWzTVQeuTpV93q+wILNJltUJ8Wse7lie/bucNudDZUaHZ9q+elESHaY6tJTBr6qJwgzZrO2pOuSdb0GTNdlJkGdp7Yo61cEiWnTZvuTunQCliakt2O+dcjdvt9T7sTYj7+7Ou6oxy12vXwU6/UDeY9ceTXSX/YcYfvY8OvHe9y+px53EGJPjVG34BjTxtuq628nDuGU9tBvOM4v/vhZydCeqJjGt8z3rvQi5TYb/et6/LXFL9jvO0xr7z+vnsXKcJOolAaFgck1J0/4hZ7+oU3/psHpdqUqFDfDUVknTX0hrqQfPmpBNdpf82oiwhNw/dC2yGUzFdigY5yfuWND+yYQ/ao25pWOgnTYtDgEbe4SC5tEUlD7gV93xdo0ti9I7VFbf89tnGCgIRWfRe8LU7FUUtLLr6Iy9GThmgCjc/0jdA3Q6eGLdu2tf2t0youCbPeMzp1REJkac4hjWPV/zScMKb3jI5Ur6quctvq7n30+dmOcJZ4oGS4+k7qWdR2LUW5DjzuIPtbp1k5xJL+k0iuxQ/5nCI8nnjuNReFpJwuP/z8n9m2xyhfSv+zRroIPEVq8YNAEgkggCTRKgHbVPyhbNm8qfU57TIX7qzJ9RE9utl2W3Zxq+5KtqWPrRJSaaCQtJDt+jBogHDGhde6rSGaUOl/dUrDgEFXm47Q04kHmkTqI6OPTtullkhV1n+1+55HnrdLr/mXdd3877b+31a39f5/D6ZWgc4acr0LfVUf9YHRNot55V3wBlZJs6sG7gp5nTxlqjvhZvWVl7M1VungjgHUMZYn9/6nC4XVIF4DJA2AtKKghKkSv7yjWMVKfUzaAEntUoTH0Ktud6vNEgEO3Ge72XxTwoG3ZW3GjBq3B/zzr793pwNpxTZpNqvvWZToeOr5o+28k3vWiXXFYk/Lls1c6HObJRerGxS2a7OEm2CmYWKiCcn4B56tO4mpRfNm7ojO3bbfdLZ3iifCSohOU6JNtfuxZ/9tE9/6qE6cUj6e4kgC7z3jJfPVZEbPqY7M1Xs3DT8JOIoU1GBcp+IolHvg8QfVuz1G+/rPvOg6G3ZOn9Qcty5RWZOp5dou6aLJ6kteq3+T7yrnQloizjzf8gTyux58zuXE8PKblCbW1Hdfk2YlGlfEq/KjXHZOn9QsgHgTTOU50er5ux99YQMvvs6uvKC/m3QWn76hMd75I8ba4FN62fLLtUnNKrsWNy6//i4XmbzqisvOdvpWqdAqoVJbnvR9War1Yonb/lr67vO2Jz3z4hvWudPKsyWvVQLq9su0cePty665w0WEXHj6kS4h/Ni7nnDikAS7pEdL6Pn64JMvXUTZo0+/YiMG9bW2Sy3uhH9ty/fEOm8Lvvtm/jfRbxq+FbQxXwQQQDJo73H/H4qnrQR6oRZvOdCgVfu873l4ghsoDB7Qy4Wo64g2DXC33LhzKs4u18RR+UA++/L7WQLO1GluNfPck3u6kHXvpw/Sb39Mdvkznn35LZcgrec/d3KJRZP+U2JJZYL/c8pU22/3beyV19+zi6+4zZ0wovbPL++ChAENeDuutrybeCftJ9u888FnLqRZob+rrLCMiybwRAGFliobufKcKKmoBvCDLrvJbVE458RDnMj14adfuxMPFBGT1FUUrVBefOWtdt7JvVyiWv3qiwjRgEFHHysUv327NnZq3x6JjiCoz5+KI88k5Cgvkd4/3qBIf5eIp1Xaqy8+ITX5Qby+KvG0JiEn9f6nO2a1+Of13YskkH+WbjPUaqZVVSXarqWRA3rPaKVvjx03Mw3sn3/1HTfg1XYS/V9athgqJP31dz6x8Q88bRqUK0Lnzz+nznFSkwbxh/S7wOWU0DOplU4Jsp7oKpsnNceL548PPvGSSyypI0g9kUrv2StvvNvlVVp80YXtwSdetDH/etSJx/o+SHBOevRZcSSB+ipfVQSMttppO+Ub737sBHFFisgvtV1U9vS+F0n7BtbXHm9LqBarZsycafvturUdffBuTrTzIiNVTu8hJdJWP7X9Ys8dN7M+PfdM9LtF7dYzpa3LSqqt508ilraNSBwoTqbpRUh2WLaNG6tK+NI3RePUpPtpqV3VR0UxKeeJbKgjngced7A72Uk+LT9WDjQt7pUeta7IQm1VVKRFkvqt8div//ndiYsa4xx/9hVuPqF26udtjdU2UkWZz22boRYN9G5Ny3ckDe8Q2lg+AQSQ8lml8koN/PoPvNyOOKDbbAkZn3nhddtjp83txtsfdmGJa6+xojt2VNsQLjrjiNTshZZRSo9c8wylMO8jTxrqwi11Us5LE9+1G8c9bINPPSwV+6C9fnh71rtuvp4TA8rJuyABpVmzBV2WfO/EkSQ7cKkoUHzSjTeIl3j36hvvu60l+ilyaYEmTVz4cBoiJtRmb6KsrTEKDX3trY9MExZFwCh0VivwioI59tA9nd+m6aeB3IUjx7o8A9oOc+TJl9ipx3av22bnVqbPudK9hw7aZ/s0dc21VYN3iXSK6CkVqEpzE+hdqmOSLzr9SJeQUgPd80fc7CK3zjv50MQ+kzo5rLq62nbdbmP3nul1/MUuKexPP09ykTubrN+pbiVWvixR8q+/prscDYooTPpPJxjJNzX50Aqz7KYJmFZeS3P1aDuM+id7azuXyiU9x4v4a7JoVlUnCnunNGnyuMPW67vJp8TW0/sd4LYYXHT5rbbpBmul4pnUZFJ2KP4mKqJFK9AP/P8RnUq6+e83PrAN1l3TTawkVqVJACn+5uvkEC1alW6XLN7CJb9VAOWOhQAAIABJREFURJZO59C7Ni3fDC+qR+1X4uziPC767+LFENlborlOWdHW2DTllfLGKUedcokp8W3/w/dxJxlpvKL352+/T3bHyarP+mboyFkdJa93qfe9KT7cIInv18tG32HVVdVOqJNIUxqZJT9WrronJ0x0z+QqKyzrTgRs26a1nXr+1dZptRVcAlx+EGhoAgggDU08hvsptHDoqNtdKJpWtJo0buRaUbp/1luh1UsqTWFreuFqUDdzpnIR/C+8vr5jAjXZ/PKbH+2w7ju7/d5pEAf0AVTeAUX0NGu64HzzLmiSpdWUIw7slpojO4vz2GiSUnzSjXzVG8Tvv8e2bvuBQoDl0yMH9U18JIGXeFKDg+Iw3+9//NXldFF+Ak2yNPlUxI5YSORZYbmlTckqdQSpl8AxhtdH2bcs3pKkyA8Jr4NOObRuwFosaml187izLnerX8oHk5ZjcyUon33JDfbmu5+6kzV0SspSSyw6RySBd9rRiUfv5yYlEuuqqhQAUl2XP6VssDFc6A2+tXe/X6+9nHB14N7buSMti3+Kinnq+dftihvutn/usY27xlupLPb7GLowz1vqHalVy+KFgfois4qPK5XQWhrqnbR+1dee4gmzoub0btU3XtsJlUzz3Q+/cFtodMqYIkdLf8q/kYTTt/Rd6H3qpe47qHxYiiDU+1/f/8NPHFInAMjvHnv2VTcm0Kkb2mKpE0kefPIl1+8Tj9ovFTlePDvo9BAt8ihqSe+V4tPEdI1yo2mrqE4gk9iatt93P/zitml7ibXrew4VESNx4PLB/RL/vS/mL9FfRxrrpDh9073IQe8o+e9+/MUJHTpqVkn7tfVZEVn6ZqTp5K3iPpdGERYvDizUqoWNu+cJW2KxRWy5dkum7ojutD1btHfeBBBAcuIh2hc7ZNQ4lx3/xKP+YXvuvPkcCSY1kFAujVX/uzqtl/cnn3/rEjkmfYVdIsFlo8e7hItSz5Vc6sLLx9pB++xgm67fqc7K3kkP2t+vQdPKKyxjJxy5r63YoV0qPGF+eRc0mUxrVnzPAJooa4vTeaf0mu24Ne/o1dra2tn2Q9dnOJ0EUV1dlYg9tTePf8wmvPKW2xMsQcNL+laaVX1WqOwol+dFicX07B16/EVue1CHZZe2nbfZwDqtvkKiQmHn9tBITD1v2Bj7ZdLvLtlyswUXsGPPGO4iBZTjRMc9v/bmh+5IPa1GKymzTjlSlFMafpqI6FnstPqKNv7+p2eLJFD75cPainj54L5OZFWOFx2fK4EgDT/Z7+GnXnY5lrS9QBNkrVR6Ce0kcEkE6rBcG+uwTBuXWPu4gSPt9H4HukmYyisfisor8iDpv9LE4RJxJHYVn0iVxgmJ3inKh7He2qu5iBAvGaNWXJWLQRNrJXxVItUhZx45x/tSpx2JgbdNJm47Shweef2dNn36TJO4qJxCGqdou+Saq3SwQ/ffyYk1xREEOoVk5PV32RXn95uVBHbwVbbp+mvZwfvOWnW+9Jo73Kq0hPUk/uSbVii4JNmlSZY1ntFWH0VIKDeKvnu65sV/v2sbrLuGHXHArqnY8usl1vaiBTTm1AlpXl4XbZ3UNgl9F7WtSduBP/3yW/dN1Yl5SdxCoZwu8su2bRav2/Li+Ze2ND0x4TUbdnYfa9K4sb3x7id2ziU3uAjsEYOOdSfiKGpCkaFpSiKqiLKTzrvK1lpjBRdRVhx9pkS33q++KOYkPnu0KdsEEECybd+59q54/6wGEfrpI3raBaPdqokm0hrEX3bteDdh096+NAghGixMm/aX1Ra0lWDWwG1uYaG6Vvvbh1x5mx1/5D9SM/kqNmrpwF0D2uITHOpzAC/8VBEwSq6W9MRbpR9L+aWiJYqP1Svup7d6rdVb7d9X0l8v6imOx102uuP+p93ztNsOm9geO2zmBEWtah038HIXbbX9Vuu75887uWHppZZw+TI0IDpg764uUkL7+pVLZKnWi8ban/kx1OS4UDBbqFVzZyclL5QNNRn2Ehgqguf0C651+/cl6ig8WOUUoVb6U5SBBolJHOTWF0lQGrI+r1NUNEAcPPxm233HTd1qfJL2eRfbQX1Q9JxWMTWw19YY5VuS8CzxQ5FZDzz+oovAk29LMNERrF7Oovn5TNx/11Y0fQdm5ciYlasnK6fFFLOV8PifSX/URXhqYURbC7p13chtryz9SfQ5a+j1NvzcYxO98q5tIDo9TafEKPG73j+jLjzOjWMU6aqol+OO2Nd943WtRJGNu3Q0fSP0XvbGPOq/BJW//prhtkkl6adnTWKNtvys2L6ta1pxFK+STWtRR9uZteX3uZffsrsfes5O73+AE4eS+m4pZqxvwOU33O3yfXjPYXFSfy9vj44N1ml/+q6+/f5ndtYJB7tE/0n6aRzy0y+/uX6ULiBqu9a4e5+0S8/u4/xM71BtY9JYQMma03zyVnGuq9JthrJP6dYeMdJ1itzaY8dNU7GVMkl+Rlv8E0AA8c8u1SW1OqmVEu319j76WqVcp+PKTnH2tiFsuVFnFzZ62z1P2GKLLGQXnX6EWwlMw08J/DQIGHLGUXWDGQ1udMqKBg6bb7i2dem8utszrQRxUuPrCwFOcl+L8y7ITsUnONQ34NHg7+TzrrL3P/7Sba3QCt9m669lpx7bI3EDPo+7jm/848+p7gg5fSxLQ4BL7SMmp1842k7p090NkH6d9LtLlOoJfUmy59sffGZX3nCPvf/xFy68ue9he7uVk+JBX3HS3mdfestOOu9K23qTdV3ERBIT+ipXy613Pe7EDmX6l7ihfBjK/L/S8su4Ix5btWjmorVGXHunnXtSzzm2VhTbSBnzFUWjpI5JO/Gn1Je8PBJeRnwN2I85/TK371vJfuu7XkkBFS2jKDQlWE2iTTUxGXndXXZsrz3d+/ONdz52Yoj250vEOfHcK913QQlvFTlxxElD6rYa/nP3bVJx6k/xVo9SEau+HC8Dh1zvJs7eZDRJ75W5tcVLWKiVdG87kxcVWV+Up1aqlZzRs7VsO+TKcdZtu40Tm3haOQlatWjuthQoma1EZAkeWtzpfcju7oQVfRv1vj3shIvt9z/+tHNPOtSduKZ/V5SMxGj1OQlbf+Zmy9JcC4ro0XfdE5lVzi0UXHOHXXPxCU4MStuvONeLopYVobzYogu5SBDvtD+NDzTOU2JtTzRJej/VLyXwVaL7Dddd07QdVgsben/O75vh9U0n4yn/0oILLuByxSTtV9/igNqoZ+vMi69zC1gasyqhr7598s+xdz5me++ype23W/qS3SaNP+2ZPwEEkPkzysUVxYqzJsale521Ctt7wKVOmS/dB55UQFp5v3n8o3b1zfc5YUOT4Fff+MCFdmtQ/vLE99zKurJU64OkcHwlALxw5C22zWbruRXZpP+K8y6U7r0sbbtWbo885RIXEaMBhI4ilSCkbRlKUHXp2b0TeWRgaQhw8XFrpX30joDU1ibv9BsdM6cs61rdVTh/En+aYGrgrWOqF1ig8Ty3+MhX73pogl1/24MuYZoiKPTT5OSjz762tddYKdYta16mf03qJVwp34f23WuV+ZXXP7Drxz1oF5x6uJswK6pAq9JXnt9/rseqSqC8+ub76w3RT5ot5ZvK6yJRR1Fz5W5Hk9/qaPI7HnjGbRk5pueeiYt48SbKOglGJxtdeNoRTuAqzSXlRU4MOqWXXXfbg7bGKu2tW9dZQntafsV5F+o7LUY5XXqfOswO+ceObjuFvo9VVpWKo4Hfev8zGzDoKpckWwsAElyLT7nxbOSdXrHB39ZwUQdeQvWJb39ol5zVZ7atpUm0q7bLKHrlvJMOdd81TbxOv+haG3ZWb7fK7I15+h+xjzuaVCH6G6235nwjKJPSV0XVKWJQuT8WXbilS57dY6/t6uxSnHDb205S2nZdM/nPKU4kSuJP735t8dH3Qu9Infp2zkn/O+1P4uu8BGb1KUnbYUsZ61v+3kdf2rnDbnQJs8vZwixhQXmolHtJgo9OQVQ0npJrJ3GRp7jPpUd0672pqCVtR9MCgcaoekbP6HegtV9mqSS6JG3KEAEEkAwZ029XFLImRVYDOb2ESge0qlcTKyWN81a8lNTpi2++d+H83ikdfu8fdTmF1k2eMtUaN6p2E0slnVQeAv2UQEzh3Kf36+FCD7XyrlBETc6UqEnRMLo26TlQ1JfiExxKmXphh8q3oG0HxdtevL9pBfTM/gdGbQ7f9Wuf8Ihrx7v8NXNbddWEWpNO7aP1xA5vpcwlqzx4d9/3b6iCioxQWLYy3nt20iDuljsfsz8mT7VtN1vX1lx1ebvnkQkuV4pWKgu1BfvX/U/bY8/8e7ZyDdXm+u5Tmulf17jjt4ffbB98/KXbLqH3yq13PzFPAURh+MVJGlXvR5994wb9rRdfJM4u1ntvbz+7tqP1PvUy90zp5Jt5/TSZOeKkoe70HwmZSnosPkr4mLSf3qfjH3zGJT6VLfTekMAhUfXbH36x3gOG2QlH/cO9T/XTNRJCXp74vh114K6pyGPjia7K31JfGLf8VpF0epcuvdTiLpJJfrzx3zu67ZRJjOIp9iPZ8POvv3fRD1oAqC9aUJMTbac86eh/uoSFpwy62totPevYS4lbWrHWL6lHWcrvJK6Of+BZdyS1vv/nj7zFCSCa8GtbjMRGLzdBaURFGraMeDbVRLpUAClnG4XGO8eePsJ232ETO+rA3RI9ltMiQakAMj+BOWnbYef2Li/3myERst/AkdZIC3WnHVF3lO7dDz/nFgl0gmPHVWc/oj1J34/6fFL/pjHBmf0PclHZimxRhKh+iuBq1KhRIiNcksSVtvgjgADij1tmS3knwSgRo1bQvWOtlFFdP21D0Mv3g0++ciHsjzz9iktsmJbz2XUyzNMvvmHnnNjTVurQziVU8yIKlDvES3q387YbunBSnX4gdXrPnTZLtc1/+Ok/1uv4i+yEo/ard+VO4cD6CGugm9aftm1pNUGJJosTbqk/2uutpGNeKHeS+6jJiU7faNZ0AddMb2AuG3Xd/O/20JMvu3wZmoAss/SSLonvuZeOsX/d97TbZrLzNhsmuXtuQqxJ/jW33GdTp013eVoULTG33ydffOvC13VcqSZbQ0eNc5Pu/oft43xZ+/2TKFBqa4v6eeQB3ea5/94bpMumCoFWvhrlZlCUwVprrJhoW0q4OeeSMXb2iYdYm9aLuuSTEut0YkVx3h1N0JSYUcko115zJXcCSZx5ecqFqtVWnTyirVcXn3GkE8a9lXWJb14ovurTvyuEW1uELjunT100nfz3lvGPOfFV0ZVp+Wn1XZGgg045zIaPvsOtTisPmHL6eMcCa2uJFk8keHmLCknrn56lK8fcY59/9YMd03MP26RLJxf1+NJr7862GKBnVd9/2VqTMIlZ8tP6omOS1ke155GnX3VHbSv/x+ortXc5bbrvue0c30Kv7V6Cyq5b/N1qamrs1ruecEcjayEsiT9PSL3zgWfdsamtF1/UBg+/yY3l5iYw17cdVmOApEaCzu+bITFWQusVF/Sfow/625PPv26XnNXbfvjpV/edTdrR5N4R3Z07ruSeQS3mLNduKbdFTVsKJazKlnpnSmD++ddZ+cQk0p7Wt0dqnsUkPj+0aU4CCCB4xWwENIh79OlX7e/rrFq3ulq8taJdm9ZOldXkTKvTGgDruEuFuP9fe+cd6GP9/v+L7JEks0hGNpFQiULKSEJJZe+ZY++9994zGZEZopCsjMxEMrIJpSGK8Ps+Lp/7/bu9ex8nFee+j+v1z+fT8T7n/Xo9X/f9Gs/reT2vfLkz69/i81xS2Gi8JsljfB+v2ijTPvw0sEmM699CL2Bu0zsn8k7kGWIA4sfPjSh6p/6TZGTvpuFu/myYfikNHGouiAat3/yVqj+CjV0dU0Y2WL81LsaoA7q0qB4oo+cc7PDFSJ82lV48MYPj4I6MGzM1UpyiQqNMYs3m/eTy5SuS9KHEqpJ4KldmJT3wF+k1fLr+zMsmoreaB6T5bXqN03Qm1iGHIOKy6UXj1/DGgmKgQdshSsg5xtOQIVt2fqN7Sab0adSgmkvmOxWKS5Fnc/vi8XSUBPhhUK6S8qvJHkos/TrUlfVbdsvo9xZJquQPSqWyxdRYE78bnsVyJW+Q5ry/X2z9WslLP72TrKfs76T2oPwg3QdCJFXyJJoSs333fq3e5GXyI9QDhu8V6bxUD3EqxEHQUbGDZxIzWKT6vYa9L9euXZfurWp42kCUtAEUcYUK5NSqPouWb9AgDwq5YLWnGw/OO5B1lBJGxYvXC5F4/ttrhqJ79x9R76jc2TNq2WZKGqN2fOap7Ho2w4souAWnwzKXPL/g5RB4vliA/tdJnlGeW9Kz33qt6F+6jjoE7yKCWG4/Py+O0SGzULBCsEKeswdSjal8yUKaqp0zSzq9V1DND9Jnx+4D0rNtrZBz7cUxWp+8j4ARIN6fo0jtoTtn7+1yxbS0ISx1rJgxdZNsVvd1mfbhJ2rKiGqChY3o9bETZ7REYqiNKVIH5Ppy5J/fHjquioHTZ8//xVyTiPsNWXoFz+c7R4Spw6gP6tJAa6+Hakib/VgamLE4Tv+dm1cNSO+dMXIoqNtygJYEpNShn9K3GINTnvrSH5c14pw86YNaIo8cWVRaHAh5L0lPeyRVUj0Mc3kOjrDfyuwwoucnMv6dCOWEmUtk3tI1muLTr2N9KZgv+02XEcaECR4+L5C2yIL9RBoEH9JR3IVKQYwM/G/3O3lOUQpy4YcEwYgS9QTlO+9PGF+JAyLSkAZUjQmPAEEBxaHXaykIkAHk65N3j4rlyLHT6veCcTGkwHtzlispwEULJRZRSwi6hcvXS7O6b3jewNc93yiswjqP0FLHGBLWqVxGrly5opeUuu+UEUqRk3KBaS+VVUijZZ69NmehnmF957qPkUzpUmvZXEgpUg65ZDlkAL+HmfOXO78JqAa9OkbIAYgoVI+c0VDnQsL1aVcnXAUZZugN2g5WTzdHucN7C4HZpXk1LWXtpUaZ5p5Dp+l+R1og5qHj3v9IRvRqGm6QjXRYPjOqT7NAShqG2tPmfBIySOKl8YbqS6hASKjPMW4qxhEc8XJ6Ouv8stWbVakEyYryanjPd/U8g09YsiSJ1Q+NtRPyq/vg91Tdwl7Cu+h1Q3SvP0/WPxEjQOwpuCUCbnNGNtqBY2fLmL7N9BCInwSRZyK04wa01E0Gt/wPF69WQydKrPqlcYkigtmpWVUlbYKrOYSSa7OA4z6+ZuNOHWbB/DmkYL6cnpR2O7JtzLJIWQovZcCvpYFJjWjbe7zOQ9tGbwV8BohAdx/ynpZ+JBoWN05sX6ZvEf35YOFnMn/ZOpWFElGnzOMD9yfQaI9j+nqrCwgqIKoEDene2NMeBfjyYEJJytJrJZ6TyuWLq5nh6688L1SlcjfGxCV0YJcGehD2qplfeOsg+c9Ub3AO6aF8CPgZ0T3m2usXTC6X+Jewbzye7hFVR7BfYELNM0wFkT37j8jo3mHhHs6p3nXw8EmBzPQygU70ctKspTKiZ1Pd6xwyDoUk+whquogiscytF8fIXGGwCYkFyaFpWUdOyoDRN9IqKNHtlI4njY1x4iniXKi9mJLmfgcJblAdBSKnSa3yqkpCEeKQck56CEQdpLnXx8g+N2LyfJm7ZI0kiB8n4KcQar1wUu7wlCJ1Ge8PUrt4lilljSKWin9em0MCGVyWV63bJqkfTqaVe3JkDu13QTosKb9nzv2kKZYvvZBPLl78XZp1HSnp0qTU1Cavpk6Gt1eEUtgFf5Zx128zSCqUKiwvFs7riz2Dc/QvF36TBPHj6TqD8oyULM6pBCghPliP1Oi1bS2Zu/hzJcCo2mgkiF9uWN7spxEg3pwXT/Zqx9cHpOfQ9zUXmuhzsGzUOTS4jcU8OZAQnXKb3vHPEUVhuZB0HjBF9nx7WCpXeFESJUygppRUvQAfr6X+MCZqzSPzJae5YbWySmJRQi1WzBhRojSwU30EGS+EB2QHhwbMGMkf5XIcUfqWH55XSlhzoSQSRu6sQ0reKlc9lNLAq2MlZ59qTaWKPR1Qc7TrPV4PdG4CJDwvBq+OK7hfznpJdRxk9zRSm/qOnCFDuzcJkFT8DNd/L0rTb4U1RqHNSY9sWEn9FH786Vdp0GawRt3D84twzGB5Xx0TVa/Op1MqHjKS9ZQ1h7UHJRZ+GRGZUHJppXxw3lyZtCqZ19Nj8FF6f+4nmhLaqEY5VX44jbWXgAheRI8+nELaN63syUBAqGcJ9QdkVs+2tQN9DmWu6ecxhiKNu7asLtu+2i9TZy+T33+/LIkfSCh92teRmDFiKNFAuqzfCGVnnE46LBV+INPXbtwpV69dk/x5sqoyhn3zkzVfqmLQ8dry6jrjfsdI4cHPDa+WUOsF40ZxlitrelWjMX8Yo3rV8yQU5ktXbtLgB1UbIUQgSPhvzKaTJE6kJdapzIXSnGcW89Q8OTJ6ffqsfx5EwAgQD06KV7vEhWPSzKXq6P/UE5lVhgYR4MhGQ7nlu8fCQoaxKBFeyidCongxohmRG7wTQUHe7NQyZ5z8HGPY6fNWBEzwvBbhcyK0HHqOnTwrQ7s3Uln3rUoDx44VU1D/kDrjh/QC5uHsDz/LkeOn9SLCIYBxR5S+xfPtN2klvgSkFFDC+VamksFKA6+uMeH1CwIkU4Y0mq7mNNRmmDPeSgbNs8BFFbLPa2sNF2DmBSILzxqeUQjK55/OFSBEHLk+xnCUK/VTA3sUcqgEqTZC42Dev1P9kIf3UGaw/A7RehRe2TM/5jmSgCAAKXX4J6AErPL6S0LJ0b9D9jA20kmbdhyusm5UTF6u4ICHFCQI5TZffiFfgLBDcp/18bRStGAeuS7XNTUGTx7HB8ZPzyx9hYRzqjdxsYKAjCpjdIjwh1MmDRj4OqVw708QX0uWR6Ra8vp8hkqH5T398+o1rSbCfhAREevVMTqBt1NnfpAW9Sqq3wepZ6gDUc2hhoQ4IIXJKWhAYARPML80xtiqx1idK9ILnYpazv7AXKKwoxjDus271T/k7P/MUjFXDfZ/88u4rZ93HwEjQO4+5r7/RvKD8fggHxgPECJ17kNDKEduJz98w5bdkitbejU0wvAPea0XSwaS1sNCmuj++H+ZLyKblNPlIBg8VmfTqVC6sDr+t+o+RqWaXMS92BxjrVuVBmZu67QcqIde5g4DSj8QIW683UqJ8NK3IPD8Jq3ksEA+O5dEnkeIxeAWSmng/gwRIy6oPANelOPTV0xskWlTlpoW0Zic8XEYrtNqgOTMkl5a1K/oOXM/9zyEysF353OTD91lwBQ1AORQi0Ec76RD7OCwf+7HX9RZ32tkD9VUWvccI41rlA/XX4CLJkQXCgpy1yFlKWH68y8X1PivXInnNLXNi411f+XarVIgT1bte0TlORmDs/Y+my+HpnehWPNyZRwIOyqmZX38UX2+nPQz1g0UBJRhh6SCrMIAnVQDKj2wD5LK5pdGOixKTsw19x04ppfK8MZIOXbGSwqUF0tyB2POMzd70Wp5uUi+kKoACFk83dz+EaScbN7xjTSv+4ZQFtrrjflA0RPefsa7iXLXqbjFeBgzKWtUl/F6c1KvJ85cou9gqwZvSvnShTW9lTOrU5nK8Q4jcMC+Cfl1/ucLShh4bX8Ixpy+ks5FOiQlcccPaCmnz/wY8DpzztOOChRVDMbUBOpInWQdtmYIRISAESARIWT/Hi4C5MWSPoFhHVE+h5kNdYnicF+7xQDp1KyKRoZYxKfOWaYl9QZ3ayQcJPzSiLpTijRUtRHGwNiiRRPpN2qWyvTCcyn3ynhvVRo4xn3RdW7PnDuvsvU1G3fJ5u17VR6L0SbNy1V/HIwjSt8iv9iv0koiIxwOIOtCRT9uVR2H30XJRPUYykN6tbF+QDw+9UQWufDbRflk9ZabKhiE6rdzOILwggAZNnGuHnC9WrLbSSnEXLpu5TKaB/1up+GaC827x6GeMrKj+4TJ739ckXa9x2nJSsp0YxIHKUtagpNO49W5DNUvJzJN6tN9992nJEK5ks/JS8/nU4NRrx/Y3WOCPG/cYZh0CqtyyzLGpF5MnbNcvXx4Rv3WWFe+PXRMUwqYHy5crbqPlWQP3SgPTES6Vov+6gWTNnVKKV2sQMCbyctjdafDRjRGiD0qVmRI+7AUKZhbcmfL6GnjyVvhHkoZEZzWTPoTJIiXibpbjTFUanNw8I7y46SXeM0D5Vbj4gzWZ8QM9Tlz0q+pLkb5eCclD6Vop36TJEGCuOqBElw1zWtqZfd4nb2cMycKO2c/IPCDcT9ployHswzldBkzCnNrhsCtEDACxJ6Pf40Al0cO411bVNea3qEaXgw1m/XTwzpu5TTHaDR+vLiByO6/7sxd+AMcivApuFVJVXfp4PAwuQtd/VtfcavSwMHjYM4oDxgtWnQldvxS9edW6VsQB24Cz5FWsoEiMfVzu5WnAnP3/txPlTQpX+pGyU6vt6Mnzsi7HYdpJLp3u9p6QQ6vUeWpx5BpasLJO3jo6CmV53drWV0rdHixEWWf8sEyWbluq6ZxIel1UkYcaXfzehWV6KCSA8om5o7UBCLwpOShlPFbQ+mCGSwqpmlzP1HDO0wp3c1Ply/6irdAeMQNfkyUtCQyW6JIfh0ml2mIPspa+4HwgVTG4wV5/ctF8gsXLoxTR/RsIimTP6RGqaQfVnm9uJLkVKYg6u5FxWd478utxsiagmpwycqNUqlsUdm++4CqlvCvCc/jxsvvZbCpL31lfOu27Jb+Hetp1x3DbcxhSbvwE0lA/yEcVf3xP9LOSavg3yDtSF3DRBSS/IVnc/vCRJS+4wtGqvKrLz2r8+Su3uioJvk5492195B07DvxJh8flKRhXUZKzTdLytN5s3nuMWVdHDV1oZosu8lip5wuSkH2PkeRRTW8UIFY1tjYsWN6LpXSc4DfIx1eUVO2AAAgAElEQVQyAuQemWgvDPOTz7+Uzv0nKVtb9uWCeshDRXJf9GgSK1ZML3Txb/UB74HJsz6WUb2bhrxsOJvPxm17Vf5b4MmsWoLVDwc/d2lgUp1C5QNT6hL3bQ6+wVV/uJCRRtKywZu6IXuthUrfQkbqlJF1pypx2aaMHqkFxZ57Ui9kfkr9cdLOSCFwy32dOYnI6Ndrc+f0BzJr+Web5cMln8vgrg1DGvVxoCMKj0LCOQA6KQeVXy/+l2oyXhvr5ctXZMLMpVqVgrQmzHu5TPJ+EdVrVOO1wBoKAd2w7WBN8fG6cWgonIk8E8XD3wVynEvKzq8PBMqPug/0VDvy6+XLGbtD/O8/dDxAbjk/o9xnjUolJdvjaT3rkeWew937vpMxUxfJNweOaBoeOft41bCPUDFmZO+wm/Y91lqMUnlv3caMEEIQz15MIwlvjKHSfiEhSSOhXKcX979brXMQICMmzZdRfcL0khk8PsZFFSf83/YdPCqXLv2h7yipzH5prC2ozCB0UHkEB3iigokoc+E2YGYvpJIKKs9SxQpohZgvtn4tH326QQZ2aqCKHoguzufDezbRszhkJr4+XjZmJjWLcuScsWcuWKVpTKiyw1OTO+fy7JnTSd3KN5Prfnl+rZ//LQJGgPy3eNpfC0KAiOa169fV0IjGAb51z7FSreLLqgZxS9k44PshouBI1VloKaEXvEkE5+1PmLFEzQDH9WvhK3ks4+DAMKp3mG4yNKLOjdoNUYIja8a00rDdEI2WEAH8eNVG3YjKvvSs52X4TvoWz2abnmM1ncctrSSXlAgtG2Whp3PJqnXbtcoPElO/OKpzsWzedZSmpuEXgV+N874FG/0i+8XA97ujp6RQgVyaDuOHdzG8BZcDHUQdUTyIAWTBpK1R3WB032aqMPBLI6edMsA9W9eSVCke0sNpx/6TZEjXRpI2dQqhMgCKGIfk4iIK0eeX1BHGg1dEr7a1VI3EHkG0j8Ms1Y6cA31Ely9wwhvFyyQlc8N7GdZ5hHRvVTPgIQUZ2aLbaHmjzAty/fo1VWY9nTe7StX9kG5AhJbLY73KZQSvmlDpWI6M/bP123WeHk+XWsLqVJBHU6eQoePnyoWLl6T9u+941ofIPcZg1SDR5mB1ZHhk35IVX0i5koU895wyP5hKoybImvFROfPDT1pdA8NJIudu41DGyrmGfRISxKveUcFzABlARa0NX36tJXT3HTwm75QvriR5cEDAryaiGLl3HjBZyX/2P4JY6dM+rM/cirVfyoeLP9dntXSxp6Vh9dcEAh1VZeuGb+l6FB556aX90gnu/PknKebvKNmIJyFrilvx4u6z31MOvYR/VOmLESBRZSY9Og4MxTiMO8ZMdJM8PaTouFXny51ZF2AimG0ava3/7YfGgYCNlFJjGC5x6CYyyf8GO4wfO3lGmnUZpSXm/OJ14uQD4+qfI3M6CavzunqbdBk4WUv+UuZw3pLPVQFCNC92rFjSb9RMjSQ0qFZWqyF4OXrgPGOhpJWOediaTbuEEqVNapZXk7uO/SbpXJd+8emQj2hEsvfIeK6Zs03b90jvYdP1EOSYDrsPOdevXZMmHYdrmbmXCj8lyz/fohcUDhZ+uHwF48p60qDNIKlZqZRcvnJFBo2do74hMWPGVHk66SNExCDucmZNHxnTclvfyWEVIhKZb8v6bwq+PH1GzlAChKgWJBfeJhzgeR/xDnn+mdxSpUJxJUj81oLXy2BvglCXLwjN9n3G61rlVSND9kHSBzmIk/ZCGVIujg4xQMpd4xrllKSE0MJ8k8ulX/YM5zmDSGWMRJPdnkQ8myjqqBpH1ZvZi1bJQw8+oAoCotB+UEg6Y4S045KJ7N5JcYUMaN1jrPTtUDekKsJ5biG3uLBVq1hCzW+9tsbynJL602/kTN3bGV8oU1+8w1hru7eq4YuULfc66FRvmrt0ja6j+GJEZCLqt3WU/kK4Nmw7RDqEVVHCh0blKdRZw3o0UUNUd/ovykO/eEkxh0PGz5V1m3dp9SyeWc7YoapPBasMnbnknUQFRADMy8S5H589P/TZCBA/zJKP+3jwyEkJ6zRCypYoKFVff1mjyj//+ptKnnH15wAQHMFkUbp27bovItAcAKgxz0Wk0mvF5L05y2XH7v03lXqcMX+lrNm4Q4Z0a6wHH1QS5Ic75S+9OL3OgQefE/4/44IYePPVotK0dnndWJ1SgUQNHBILxQQbCpLmvu3r6Tj91iBFIATwisDIb/z0xVptA7KrQunn5YVnntDI0YlT5yRrprR6cGeDRQ3ToOqrqqDwWuP5nDF/hXy6Zqv0aVtbug1+T4kAIs4cgBizoyDgYAFBiULLi/nAt8LWyek+ceps4B2EBPrlwm+SIH48vWy45cF+Mp/Ed2jMtEVy+Nj30qRmOcmfJ6umbvEsEvUrXvgpqVGpxE0pQbyXe/YdVl8Jr5YdD55P1EiN2g+RGm+W1Cjm37l8+aVMN8/nt4eOS8+h06R+lVf1/QoVcV276SsZPmmuGqSijOSdhGBNdH8CfYZV1dV9rLxZtojnzIt53zD/dq/9wWaazpyH93OvrZ/B/UFtdODwCU2NpIUisYJ/B4KyScdhqvxByQUZluSB+zWY4DUShL5DhHBeIRXm3Y7D1ePNIYzdSlDS7hxCgUuoXxSEvItUfoF8i8hElPXz0JGTsmDZeokXL7amkXgxXSv4mWOMkB2U6EatnCl9GqGcdYkX8qlKNzj9F+ISAjOYvPTy+8hZlPU/w2OPhCRRHeJx9NSFUr/qq/J2uWIBYtapZIVpKqWDrd1bCBgBcm/Nd6SMls2yTc9x8sfly1KxTBG9SH697zuVol+4cEmla5hSOf4LHBTa95mg0S8/OTkTteOggJGfs5gG59FyOGSzXbBsrWzctkcPFRzyvdQgOrSkYabHwo1kBZuGBpNYjIdI7qwFq7SaQ7pH/WHs50RNkKMT2UqdKpmSW5AHK9du0wjD2Gkf6aWFSMOWHd9ozjukHuMN5bXhtbllHCh1iF5yqWrQdrB0DKsSMAa9Uc1hjPoxvPBMbl+ZM3IRQZmUO8fjWn0iuHHh4hKCaaFjPsnPFixfJ2WKPxtI9fLSnIXXF55BygMTyVu+eosSchgTOxeyIePnCHnSeXNllm8PHtO1lHn2g0kqvjvnf7ogGdM9HOHlK6IS7F6eSyT57BmUiXXMwZ0US8iRyuWLy6yFK2X4pPlKKBAY6NG6ppw8fU7n3C+mt6w3jlrQrQpxew+gIvzmwFGtaOUQy16eO3ffIkobcFIO+R1SSlD+cHGr13qQlpf1MtHM2HZ8fVDqVymjSg+HZHaq/p05e14DBkTRn30qu5DiRBnhfh3q+mKtYU5uZSJa480SqsD7YOFnqi67cuWKnmsgFPxgdsuZc+eeg5oCyrqK0mF4jyZK/Lg93vzuJRXeWuEQjxDOqF/wusGHEPKSsusQWW6Ful/WHOvnv0fACJB/j6H9hb+BgHsRTvxAAmXQEz+QUNr1Gi84NjvS32BvAqIQROHZXP3gSeBOgwg+KFy/jtP/BT0UEFmBCEKKP7BzA3ksTcq/gaI3PhJc9QcWvWX30ToOtyEaqoIVa7fKzPkrJVOG1NIprOpNByI2Y3I2vSY9dOYN40lKWToHdp5N5MurN+yQod0aa9lZUqGadxsl+w4clTF9m/siiuCO0HLhICKELN2JaFFutnlX5rO+lneE3PKbOWN4b8qU2cuUgHRM8Pic+xIWqoywN966v/aC94e5xJSQ/z9++hKdM8aAyoWUra4tqqkyRC/OQ6dJlgxpVPXjlxbR5QvjzGAylveWRpUKr1dT4RIJkeFWAxKFnbVwlb6TmBWi+iFCSSoMah9UP1yeIWOJvnu9DDlrJIRyvSplAp4nzA+BEZRmbRq9pWRl5wFTtEIHvhMoCXhOMcb1+hw6F2j8apjHUI3nmIo5/TrWl+fy3yg3DnGHEpZ90csESPB43KoBlBG8f/OWrpGOTatIyaIFhLRZUoFKFXtaTZz92NwqwfM//arlnDnbQF6h+Axeb/0yRvodN05sHYt7jHi8sc+fOfeTdG5eTYeDp9u0D6kSF1vP524PMb+MNxTxiJKyQ79J8v3ZH9VjakSvpvq/lJL3g6rHL9j7oZ9GgPhhlqJoH7kgN2g7RLo0rxa4OHKY6ztyhgzt3kQ3Gg6DwybNk7rvvCLpHk0pubJm8AURwpS5DwqQPXiDnDrzg0ZQnsyZSY1gh074UDcX1AR+KvPofiSJQv928ZJ6RjiHVRzH8SLgEuLkvieMH0/NU2ler0CC6mPE5PmqhHn6yWzyYqEndc6I2L1bq0LgEBtM2PnhsO6eOyfd5dWXC0rJIvkF02LK4ZH3jZrg8NHTvjZndI+VUtysN1Qvyp87i/5TsAEch8K5S9YomYfvi18aKWdcsLgUJ3sosZpsUuoXgguShDF/sXWPIN3XeT12WsfntwNfsGTb7cUA8YOakEs0EfZHH0kh/TvVu8m0mPf6jz+uKHnpxeb2O8FviOf1jVeev6l0PL5akAeozfCDId3k2IkzkjZNCt+YUQYHB6bOXq6RWSLTBAh4V/FAYa31asnqv/v8ONWnHkx8v6Y/YU5MmsiHi1dLzizpPZsCE974hk2cqyQrZpM8r5iiU/0Ho3DePTyzULhCzoXnl/V3sYuMz7lNRFEJov4gwPHKi8/I0IlzJW+uTJp2NnHGUhnQuX7AqDky+vpPv5N9HuVZhdKFVQnpXldZRzAQJ+UHxSB49B81S54rkFPVrn5qEI9dB05VMtld4Y9yx/VaD5TyJQtpKtBX33wnKCYhmh3jbT+N0/r6zxAwAuSf4Wa/9R8ggPS8XptB6k7NQkSKjNuAyfFVQIaPKeMHi1ZJksSJAlHO/6ALd/RPcNg++f0Pki5NSpm96DNZtX6b+oDEjBFDJYndB0/V/G6qHUSPHl3liHmfyKxRLz81IujLVm+WXm3rKGk1Z/Fq6Tdyllb+iRZN1BT16rVrWt2BtCZasOGYV8eLRJ3DAQqWk6d/kC4Dp2j5Y+fi6JY+YyjmxzxhLsOknJ38/pzgv1D46VwalYwTJ7ZWyIkq5owQrhBYXVpUV0O44EsY6Xj4uIADpAGkZYd3K6vCwusNIm7se4vkqSeySPbMaZUIqPtOGSWWiXgRXf/20DEZ2Lmhjr1+28EareZQD0npB3Udc+C+fLnLdGO63HXgFIkePVqgKgC57JQrJ+8bFQxjxE8E0oR1yItlSrkgL/50gzSuWV7TllSd1btpgMQh171tr/EyoFN9PdAHlyH3+nPq9M994Ury4P2qhGhco3yAWObd5BnGe4n9n9Q2LmJEo/3W3NUnGBemlMUKPSm5s2fUMp74f/As//Lrb2oO68XnMjzMWVNbdh+jxCvV0SCU+46YoZ4MEwe1kkdSJr3pV5lHzFMdTzFSSFrUf9PTBrisGQRz6rzziu4LnHUwFa9UtohWUQlunBlQ21FFDyIW3zA8m7ymdA2uhOfs85xN2Qc5c1OFimDBnm+PaLql3wgfUpS/P3v+L0QcZ9ZFn2xQYuSBRAnksw3bZc5Hq31Zvtpv66GX+msEiJdm4x7sC5cvLpVEDshJJPI8uneYRuhClXf0Q85sqGlcunKTzP7os0DqBBHbJh2GaWSWSAmRZ1zlUUngJ0GEAUWFU37Wy48Ghxo2D+rKd2j6jjLuzmEWkuu9D5erlJIylxx4glU+Xh6bu2/HT53VqAmlGm8odn4PEHbItf2cJ8zBnNSB++67L1CyOiJzRqS0SGpJ7cqdPcNNFR+8OKdONRUuxVysiGIuX71ZhnVvopdJd3oMlxBypkk/oHx18qSJdUg86xNmLJb8ubOqVN9rDSKEtBAMFlGCOKWdObjvPXBEU5oWLlsnMxaslNzZMsqe/Yf1UI+PCKWC/dTcEm78dzr1nywjejYJVOVARVC/zWB5+7ViqqAIZebo5fFiII4XD1FJLifBRpukljhlyBkf/x49WjRfkFluE1HIkGBimbWWseGRhQKEC8vKdTcCCH4iQbh81W8zSEk49j6IDtRZeL7w36xJXMR6D58uCeLF0TkkVYgKVX5QE7LetO89QddPTIshGZ29hOBOcCMIQvW8kb3DAqXJUcFy0faqn4ZWFxk+XUlTp+IP5AHlqkOlTBLsmvfxWhnVO0zJLdLZOB8N6tpQfdW81gg0jpyyQBpWKyuYgkP48NzidzJqygL1NmO/JA2WFMv1W3b7UiXp4M4+wD2CinCcvWmc3QgUdGtZQ/+btZexs3+607q9NnfWn3+HgBEg/w4/++3/CAE2lE79J+rBgEUpVIqEI2FvWb+iXkA5LLg9Nf6jrtyRP8Nluc+IGXqII6UCozcuVciYify4S+fy2dUbdsoLzz6hBpw0P+Sz00+iH407DNPqPqRUcIhzXyyvXbvmmzJroR4EZNod+k7Ugw2qlsdSp1QjQg4Mt8oTRvUTI8Z9vjjUOvN4K3NGSj6jDmHcXLSJ+HVuXlXLBHu94Z+w8+uDMmHmEs1R55DDIc9d1Ygx4IWCefOQbo1U8QNJixfRxq175NHUKTxdnhSCNazzSEmdMqnUqFRS1xrWkOBxQn4cPnZK4sWLI3v2HZEUyR70jd8S5JtINCWiiOBBWGGm7Vwc3esO755fVGfO++Mm7DCXZC+AGLhRvvJBJQXcZcgHjPlAln22SWq9VVrKlyrkizLkjNUxJibdBZ8B9vWeQ6bJD+d/1kpO51GKth4ov/12SdN9UGZ1blbNkwRkqLWPc4tTgYp/J5JOI/hBMAAjRhQUKEIcTymMN53LmR/WU1RJFy5eUm+3LBnTSNbHQ1fU4D3FRJzqRk6qAV5orXqM1XeX+fdac0gqiBqUZPnzZJG8OTOFa/AKgYDJP4QJChB+n7Vp2MR5MqZvM88b+0MG4HvC86lkwOETSi6XKf6MvPR8Pt+qJBmLo/p0V73j56T8sEfy3kFIDho7W5rVfUPHHExE8jc++fxLrYjkp9LdXnuvvNAfI0C8MAvWh5sQ4ACEnJkNskX9ioEFCEnhlA+WqSSY6PTAMbNVLkpUhdzEdyoU92Q5OffgIDf27j8qPYa8J/073pAxhyrzyCUtonx2rz42VLfp1G+SSgvTP/qwbN6+Vw8DHG6QA+OrAWnAgb5511F6CAQHv6QbcGnkIsnBtVGN1/SgGlGeMJcVlBKUCfZDi8ickcgfsnXc1JHIfrX3kEZxnWi1H8YISUCaFkoWjPy4XOGP4RxaUaAdP3lWerWrLZcvX5Eh4+dq5SbSD0JVmPHamHm/HBNizAnfeq3oTYahjJPmRMQgd4gGxooZU3q3q60SaL+0zdu/kd7D31eFAGqJ4MowrDEQeqgH8CeCUCDq7Icxshcg5aaMY+23SwfULMGEHXNFCsKQcXOEHPdBXRr4pgoHxDKG4Nkzp5Pjp86oae+w7o0lZfIkfzG5Xbd5tySMH1efVT8Rds67tPjTL2Tpqo1K7nQdMOUmE3g+M+79j1SJh8cG+0yKZEk8f65hT+TSDyGHp0Sm9KlDLh2sufVbD5JnnsouTWu/ruPiQom6IF/uzJ5WEULUEcDC76zSa8XCnROCAY3aDZWab5XSNBku0Jxp13yxUwo/k8vzPj0EHzEtpjKKY87LHPFOTpv7ScBEPDyVpNf3DPYGKk45qk4n3Y5zHOeYL3fuk74d6uozzHo6eOxs2b3vO60aQxoUac4EwTjruH1FvD5u699fETACxJ4KzyHgGBdh0kf0ssQL+WXP/iPSXi+c5VSm17TTcPWcwFGdyxqu4yWKFJCSRfN7bjyhOuRUi+GAw8EcqS+MMs0hgCLKZ/fyQDkQkVJBzj4XTDbT02fPS8O2g5XUckiDbbu+lQGdG+imwhziF1K8cF4vDy3QN7fkPKI8YcgSpNzhVeBw8nG96sXgNmd0pNuTP/hY5i29QWZRoQJZOxWdnIu1LybxfwRA+z4TpWebmgGJc7DZJmMJds3/8/8i1aTt+aW5DUMdKTd95/KJBHp0nzBViTCvu785LH3b1/HF+Jy0H6TmGBejjuBnubKm16oNkFeoYVD7YHgHMUSKEAd6Sl1zQeHQP/b9RdIxrGogBcxr80qFhvsT3qgg5q58c+TYaflg0WpJlSKJRmghdVp0HaUpBX4yoWQf3777gI7P8cYI9R76nbDjkjVo3Gxp/25lTR/pEFZFfXloPJucbYo8m0dVlARBnsuX01fzGNF7Q3oBBrcER6hS5Yey3BGNKfjfIcqp1gTx7BA9t/s3IvPzmIK26zVO4saNLflyZ9EAo5r5th8mnZtVDSh13CrJ1KmSRWaX//F3OybFpDqR6kPqOX4tPKeQdRB6KNNnzF+hVbjYL57Ln1MJSmv+RsAIEH/PX5TrvdtXofAzT6gEfeuufZIoYQL1XsA8jEPDtevX5cjx79XIj8jmgmXrVbLO4uynFlzmkb6zEEeUz+6XMUISIH1Agh7s6cLYO/adpCkGObKkU/YdaWKRgnn8MrxAP2+VJ8yHMPel6g8bJ40oEYTBC8/mVmUIh97smR/TikBebG5zxrUbd+oFBX+amQtWybQPP1EjX0iQqNCCvRY48AQTQFyYUcBkTPeIkJKXPu3Dnh+623fB3VnS1qj8w8WZss8YMjPeUJ4gYDNvyedS9LknPaGegEju0HeCHlK58DuRVhz9UQpC9LjLyqJKo82Yv1K+3PmNRvrWb94tHyz6TJUjELVe915wlyGPGTOmkspUFUmaJJHM/mi1pEr+kJw5d15a1KuoEdxtX+2XpSs3qqTbT/4ZznuIMsnxsmHu/E7YMQZUqygJON9UrvBSwPwVaT1BA1IlWF8pE1y9YglflckNXggd49NyJZ8LEMyomlAZpH0kRaAKDusQZzg/VfpzxkrfR01dqNUKnTWGCzTBLcbNHLKuMG5UMNkeT+v5dUbTI4+f1hRtyOR+o2bdUiVJQADF4Zc79kneJzKpYiJO7Fie3hdRfyxdtUmr3GBU++pLBdXHBgK9Xa/xQuUmZ0/gs1TgWrf5Kxk/oKUGYq35GwEjQPw9f1Gu90h3SZGg9F+oyICj9qhTuYzmqi//n1EhLt0YbxZ/Pq/0GDxND7OYOPmxRZTPzqbCIXjPvsOSPm0qHavXD+1cVEZMmiflSxfWQxA115ExU6lhxKT50qN1TV8f8m6VJ8yhB18UyDlklcE5puR9E2nBwBCiiMsYEn0vRlQ4GJAv++efpIu8o9FaopSPp0sdZSIipEv0GzlThvZoHKi+ESpNjYP9qnXbZOCYD7SSgVu5BLl36dJlKfqcd8k81lKk50S9KItIpRiM/Xq3qxPuwRUVDOZwo/o080z+M9FKqhhxqciZNZ28N+cTqVGphLxe+nklcjDTRLqMAoSGlJ2f4WNTokg+rdDVbfB7uuc0r1fRc9UabrWHsVeQForhIopJ1iFUMFSQIcWCRnWxjdv26oW6wJNZpV6VVz0zd7caG2Q4l8pG1V+76bJxO4Sd1/d/5o+KIm+Xf1ErcJEqiTKEtcS9b3DWYb2hesoff1zWVCi/qM9QtaCGiB07lhKsjnEo723rHmNkeM8b5DljhwAa3rOJp1NhQj1TzjrzXL4cwtmU1B6as15OGtxa1XVu5ZaflJKorm6lkkR91rzraFX9VizzgmzYslu9e/p1rKuVDr3Y2PNQAlLhxkl5cfrpqJRId8342COBfaNJx2FSqWxRJdxDGcd7cZzWp/ARMALEng5fIeC4jlMlBed7GgvZ4hVfyMvP57shW2szSA/wDyRKqIuxnwzhGM+t8tlJkyG6ycUrb67MetCFAKFeu1+kpIxvyuyPpW/7utpnvF3w0BjTp1kgeuKrh9LV2VB5wjyT5IwO7FRfyY9PPt+ilxMOfY6yoF7VMlrukct310FTb6pm4TUsuDjjh7Fu8y51tVf1S/s6akwcFRoHG1KSnGi5u3rI4/8jsFCAUC4YeTBmnGPeW6j+E/gWORdsKjk4a5QXcXEqURGdhAT5/hwpakPUCZ/yucHNGRfGcKSSeKlxyYLM2fk/GTOKMkhh1B8oBnq2rR24lLjJLMeUudDTubTU5adrtmqZa68e2oMxxxSVigavvvSsvF2umF6a3TJ11taZC1ZqpaNYsWLIhBlLNIJJZSMnUu2lebxVX/4JYeeHsUH0fLjkc7l48Q9VCzyWJqV229k38ELhWYZopmEWikSfCyal170e/KDPkFYQrKfO/CBdmlfTwM2E6Uv03UTtcuHi7zelx/I7zDdpal4rHxveM+UYTyd/KLGm+aKow9eG8s34SO07cEyJILd3BGdXUp1DVZPx8rMbrJKERHaXlWUPZa2ZOnu5jOvfPFxT3MgcI3sG3kqYDwc/Y6EqU7n3jROnz6l669yPP6mCl/Rmqsr4wVMqMjH32ncbAeK1GbH+RIgAhkQtu42RyhWKS9mXnw1sHmyY7lxvUmSmz1shDaq++pfDHkQKERfHiTzCL71LH4gon/2z9dulY79JmjuLIzkHBGrOZ8mQJlx/ibvU9b/9NWyelP3bd+CoqgiQvZK/HxUIkFAgcEEeMPoDiR8vriSIH0e6NK+uZZ5p7k2V/27ZfYzm15Jz6/XGZYtUngyPPeKLiPLt4Ml7SISO/3VXD5k062MtnfvWa8XU3BfCisMPaw9eNihiqD5CucfBXRt6/mBLWgzqCZRHePVwKBzStVHIyzHj+vTzL2Vkr6ZKXHJB43K9Ys2XkjtHRvUx8jpxEGyOGio9JtRzAlG5ZMUXUq5kIc9dyI6eOKPGmTyPvJNlSzynZr4oy9zVxRgXnlPNuoxSwtJvKWu3S9jdzvvuxc+ikBg9dYFUKP28SvQhU6lSQXCHUqQYjSdIEFfNfDGidhMhPAe8i17ylCKlAkUECqXvjp5SA0nIHNQtpMfSZzx7QplQ+0Utwfo5d8kaLZXOeJ7MmUnftSSJE2nVNKrjON4RDomQLk1KaVj9NS8+guH2ycV4dNAAACAASURBVK2SvD9BfE3hLfvycwEPPqfaittY3E8DdO4Sj6RMqvP1w0+/6j6Jipd1s37bwZIzSzpp1aCSElicX3fsPiA929bynQean+blv+6rESD/NaL29+4KAqSAEHVdunKTKjzI08NdnYWIA3pE0S0k6n2Gz9BybDjNe6FFlM+ePOmDEtZ5hF64vjlwVDhQUKrsi6171E/COfQSUSLC4uXIEBskmygHBaLtbRq9FWXrra/d9JWavtWvWkbzvR15rFtZgLLHHa2N6Pn1wvMaVfvA89iqxxg96EAKLFy+TqOURHdw96/0WlH1m6ARga/dYoB0DKusxr7MaZ2WA1VRkStbenkqV2bPXZiD583J9aY0d+7sGUKSNs64SCM5++NPwjOdNnUKTTNBCeOXyBc53L/+XzlV1sqzP/ysUWeMtZ30mFDPtBPNfH/up0pwVatYQg0qnffYK+8Bh/aRkxfIm2WLaPpcqLQtvE/WbNyhaiVy9jH2O/fjL1K6WAH1YfLyngHOt0PYeWVe/mk/SI9p2nGEPJg4ofRsU0uefjLbTfPDc7lr7yHp2HeiPsNOCp5DFLWo96ZWVvF6IwDSqvuYQFU8+utUIqEaVZaMj/rWx8bBnvWSFOBRfcIC6VycQSmJTFlrCJJ+I2dIsUJ5lczyenOrJFl3SH+p/XapgArUrWRlLaJyE2c9zrgQCuyfXiLnQuGNOof0QnxNvj/7o7z6ckG9Z3y8arMqmJMlSayqOryVSOlG7TO4ayPzBvH6w+vqnxEgPpos62r4CITK9Q7v047r87P5cnjOt+BW+ez0Gxls3XfKqESd+uzISr89dEwGdm4oBfNl10jK7m++02gsBk4c2K1FPgKY3rnnIlhZgPeNE62lAgDl9sij9YuZWOQj/N/2gOjdmPcWaaQSOTOHcNrIyfPl8407pXurmpIh7cMycsp8NWPu076uxLgvuuZ4Y0DJAW/Nxl0alceMkyinX5sTzVu9YYdGvTDEHdOvWcAfxU/jckyZ8U+AMF+8YoOSAbcy6+MyRu43c54qxUOaN57kgfsD5o1eHD/PJAaMXJyddCa3+gVyDmUTh3zWG5Q8T2TP4IuKFX+HsPPinNxOn1h/UHpe+fNPfe5SJntQfx0CiIsWpZxLFSsgFUoVli+2fi0ffbpBBnZqENJs/Ha+NzI+C9Ezbc4NI21SQZwqeMkeekBNwdk73T42GHKG1X3DV6pDyEjOa73a1laISQni/aS6CMoeyJAmHYapsi5hgvhSt/IrmtLtdZKAsbA/UDls+WdbVMGDHwhqCdQvkB0oBz9c/Ln0alNL7k8YX8t5M842jd/yhVoCVQ/eO/h+OMQy6ZL48LAX8j5yNscrBAUIqU6oslGAUpHMmncRMALEu3NjPbsNBELleof363wWzwnUHzg5OwcqvBmI2JKCEJmRsPDy2dloOHwneyhxwBWfvu89cEQvY8dPnlWFSPbM6eTCxUvqTdAprKpWyrHmLQSCIyTky6Lq4SK9esN2lQnXeLOkziEXsIFdGthm6oEp5HD+8aqNMu3DTzUqxDs5rn8LJUiC/Vsc13iqqqDU8mtjXJRxploTudIc1DFKDSZ1uJRxMMTsLzLXz7+LM5eOfiNn/cUAz/37DlHJzxx5PpdTfDea133D08bNTql15sIhsTioMw72DfYK8t+psAIR0rbXeDUOZ/+zFjkIME8QGlRHwzDcSXmhN5wLIAKoOEUq1oq1X+rFkt8pXexpTaNgryBIQmoJKSZ+aKRRkpZVr0oZrRyCLwOVcEb2DlOSI5SPzZYd+9Srxy++Z6vWb5ch4+ZIywaV9P2at3SNkM6M4SvvokOGULaacwCpsA2qlg2klHh1HiFy8KKhYhhBm6mzlwn+GBguUykFZUTtFv2VxMJgGlKH9ROFJcb3XjR6jwhrVOer1m/TPRAVIPPHf6Mkz5Q+jaY6MbeoIjGHD6tTwRdV4iIad1T8dyNAouKs2pjCRYDUmQZtBunlMs0jybUKycq1W9UwFT+RYs/llVTJk2i5Ly+WDHSMtlKnTCo1KpWU5EkT64Xjz6vXdOHFEJWoCYfeU9//oJJDokYpkj2oFxY/RBTuhceXSkdcjDEv5NBKigwbKtFaDMUozTa8RxO9cBJd+eXXi76+REfFOeUCTQSo6usvBaKUT2TPeJOqjIo5rCN+JUA4uCJPJ6WOdYX1pEHbwVrSGdNeGgdaIn77Dh6TaNFEy7CiPCBFxsuNiyOmp0iz8SIitTC4McfNu4ySfh3rB0qVoqSgBDLkMmVmIcVQCpEy5dX1FVO/YANGvLSadR6pKT2VyhbRywpldb2W2uPlZ+hO9I1niYtlcEoS7xkmxR3CqkiOzI/pV2/avlf9pUij4MIVfAa4E/27E38TdcSYaYtk7/6jcvrMD9Ky/ptqtBxcfpzv5gx0K7PmO9G/f/s33Sm/h499L+d//lUVAqRNhqp+g6cP5zZHMfJvv/9O/T4mtiggIFWpXMeZs0nH4dKgWllNK0TdM+ej1api4hzz5c59Uq7Ec/LRp18IFVYuX7kiI6cs0Pn2S1lZ3s1WPcbK/QniSdM6r9+kRILoYu5Iw0ftMnvRKnnowQc0HZbP+83s9k49N175u0aAeGUmrB93HAE2IVJExk9fLLFixlTygEM9FWXckQQuNY3bD9ULaZ4cGe94v273C4gEkZc4c/5KKVm0gLr/c1DnIORETfibHNSJVEJ8cJBgzOTU+iVf/3Zx8ePng1NhIK7YYIkAsVlidhsnTmy5cuWKbZ4enmCnkhHlSIl+0YiENWo3RNo2fscXufjhwcthPWaMGAEvk079JwnmcJSWdYz8MJRGIUIqCdVY8MsY1KWBbyK0ocbupEqSSsieADFeqEAu+XDxasmZJX0gBcaJupN2GKp6jhceW/aMk9//IBguLly+XlKnSqr5+hjZEn2u+84rnq5Y5AUMI7sPnF/Y49ds3ClNapXXaDPvYokX8ilZEOwpMX3ep0qmp0uTyjdR6JOnz8mMBSvVuJ79L5SPDarInkPfDyh4Sfc6ePikZM2U1hdpMV/tPaQqHwJYjg8RaZaQITTOBJDOubJlUHKddWj77gOaQsvPvEZQQl516DdJTpw6q4FDDNxrvlVK+wlBQpCRMrMY2RKA7Dtihq6ljLnfqFmy7LPNegbnnFr25YKe98xijhyzW9J7EsaPq2qX+6JH1/LqwdXf8BTr3H+SrNm0S8oUf1aa1Cx30xj5W3/8cSVgjB/Z68y99P1GgNxLs32PjxWpZdNOI6Rry+pammzU1IVqbuiuBONUh4EsiBUzhmcjeu6pDM4ndf6NzQd2fXSfMFWJwMDv/uaw9G1fR8iBtxb5CHCBpLLGU09k0qoNG7ftUUKLaGy3/5XQIy3Ga4eeyEfOWz3gUD551sd6Kan1dik1mOw+5D2JGzu2p70i/gmKXKp4Tvt1rHfDb2jgFJX4YqJKOW4iec27jpL271aWx9M98k++whO/406V5PJJ1LlYoSc1bcS5iHB4PXD4hGTJ8Kgv9gqA/eTzL2XUlAWB1B+/RJs98VBEcieQ2+/cc1Avjaw5KARRCvJz9+WLUvO9h7+vCgJMbgeO+UAaVi8bMG+O5GH8ra+HWKzVor/uh6RpERxw0oAypntEL9pdBk6Vw8dOSbZMj8muvQelcIFc0rhmeV/sl05aGoQWF2jSSGgQWT2GTNOz6XdHT0vXQVPkiWwZ1Q/m+MkzOqeOJ9XfAvIufYgUSHyw3CoHx5uvYpkXlNxw0vGoXvj1vu+k84DJ0q1VTSUR8AaBBCENz48NJQ8KkPCqv0FkDZs4T8k6d3AAkm/XnoO6n5pn392deSNA7i7e9m2RhIATqUya5AF1cj6EuWHXUVq60l0OkM1n0Ng5KmFDIeKHxsK6fPUW9fpwbz4QI2FdRqrio1NYFU25YENyNlo/jO1e6iPRL+SjlcoW1cPCnv1HpMuAyXoQ4rm15m0EeA+HjJ8rsxau1I4yh1Q3imqyV3LU+4+epRLmHV8f0MsYngMoXgaNna3pIJTgHNq9sZIC73YcoSkWRQrm8fYEunr3/dnzUr/NIKlf9VW9NJIKhGcGpoVOBSA+HlEZXS6mREXjxonlmbFz8SJ1gvSs3367pBFnDu0oQqz5BwEIEKpUcRkOTqPgvWzdY6y+o6TSoDqA+Hq3dnlfGE86ykguygQDeIdeKvyULF21Uf//4C4NZfZHq5V0xguEKmqsvy26jZbK5Yt72pvHecKctDTWlNkffSZvli16w1B01sdKHjNvdVr0l7y5MknrRm+pug4jatJn/XRZZl9o03Ocpv089URmqVKhuPpl4GcTnDLqmPye/fFngTShwuOtDKq98rYSvOLZw8fG8VDizuGosFAG8lw+lial+t1goEqFpuCy7F4Zz73SDyNA7pWZvsfHGezk/POvv2lJS6SW5HHT2EAbtx+m5eSQlLKo+aGkbPDUMg5k6JTixNyOSjHXr1/TlB4/bCb38qPqSEQ5wBLxqfRqUY3c+cFU8l6et3tt7Fzso0ePptFo0gqpQEHpZudyzb+TEw5B0KHvRC3lSS60X0ogMp8c2BPEjxeIJlOykub4uQT7E7DHcCm9fl0C5YQh1IeOnyvDe74rVLXwUmOufvrlghJ0Fnn00szcXl/Y47lgOga2kAfI8WcuWCVTPvhYL8tUToFM8KpHTfCI2f869Z8sI3o2UdN3Un6/3LFP01zKFH9GSZEGbYeohxbVnB5Pn1rqVymjlbsypU8tb5R5QVD8YrKJSsZrjTlq12u8PJIqqaZhc/Gft3StEq0VyzyvijqUdigKUJwxfsZHFaq+I2fIoC4NlfxCTeAHbzfWGlLwTpw6J7lzZFSj9wnTl9xUFtjxm2L+CB6gjOB3+nWo68tUStb+PsNnSPfWNVURSur98VNn9R0kwIovH5W4WH8JytoZ7+6/pUaA3H3M7Rs9gAALMm7pWR9Pq3mWTjQPdp3IpUpLJ81TIoFczWyPp1WDUT8sUpiEUamhesUSSoJ8f+6870zDPPCIRGoXILEwtsU4y5oh4FUEiHL1Hj5djhw7Le2avC3pHk0VWCOddQjC4Okns8muvYfUcDpbpnRy4vQZKVeikJImfmlctIhAD+7aSNMj3ZWbiORhlhorVgxJliSxXr66tKguY95b+JeccL+M1/rpPwS4YHFu6fBuZT3DoM7i4uX2BvPDqCBQISDD8ysjMIXCBW8JPoOiiXUI1SvV/SD0IEga13hN8uXO4snKVATYYseOGW5QCgKABmkMyUVlnEWfbJBXXnxaTZtZj0hrBgNUvX5RGjJux0jbraZzSCHG0b5pZR37kPFzpGSRAp71VrrVu0SlyT3fHlZ1JPcG9kpU56iVmFOqq+G/hEk6RDQeMB2aVjaPvru4QBkBchfBtq/yFgLuBer0WUiCwWrMxEJE/imSNiIJqCcw9Xs6b3Zp2/htX+SXElGgMkOMGPdppIDo5JCujXx14fDW02K9MQQMgVAIcFn5dM2X6ql04beLMq5fC60aw0UMg0K3hw3EM9ViMMaj7DhSYEzjHGKZf0dZgY+I18hm0ggGjZut0dcffvwlULnpsTQp1FuKFBLGQ4Rv++79EtZ5pKZXUurSL5cTe8L9jQCXrJ5DpskPP/2il+TLl6/oJatP+7+WrfbzSB0vEEzgSxTJr0PhZ/sPHdfLMuaa+BORcsC7iOkm760X1SDhzcPaTV/J1DnLNN3HMemHaI0Vi0pNMdTzhXGR2qymqm+WvKkCmZfnF2NQt8eeoygkWMe6iaoFVQTjRGkYai9YsnKj7Pz64F9MRb0ybgym67ceJAXz59S0H1RNFC4Y1SdMUyIZJ5VyUJvz7PYa9r7EiR1bAwnW7g4CRoDcHZztWzyIAIsO+ewsthjBOYd1uhpcTo5/o1QpclK3Z4gHhxXoEpvK4eOn1Zwwd/YMdgj38mRZ3wyBKISAQyBTdcox7GM9ItLF4Q9CgMM8+dCtGlQKVMlBOQFxO7BzA728eK0h0yY1rd/ImQHp8rrNu2XohA/Vq8eJWJPK5ibUvTYO60/URYD3bNnqzULK1g8//qwlSSlbTRWOqNRYK1CBPPNUdiVRqdaEYpLIetdBUzV9Js3DyfVySSrFg4kSyvav90vC+PE8WU0leG4gs1BAbNy6RxpWK6vKgUT3J1Cj0WByGUUMjWpcEMykkfhFsUy/x077SNI8nEzJLBQizbqO1EAkSolQDXUh5AJplQePnJBab5VWzxCvpXj99PMFWbziC9m554Cs2/SVlgtmjKG8owgifLDoMxnWvYkqmK5dv+6LYKuf1xQjQPw8e9b3/wSBi5f+kBGT5kn50oWV3AhVUhY2fvikuSqvxOCPFAV+jw3JKnT8J9Ngf8QQMASiCALItaNHix7wr+ES0rbXOJWqU2GLMrrN6r4u0z78RIoWfFJKv/h0oKQuB3fy4r2mAHGmxl25CXNipOqUgqQ6A82p7sAewYGXKPzKtds0AkgZXUo+eu2gHkUeOxvGPYYAqSEfLvlclq3aJPWrlpX8ebKENNcklRli9YlsGfTs9utvl2Rot0bqqeG0YFWCF6BkLUF5Nm3uJ3L16nXp0bqGnDz9Q0B95pTddsiSRcvXKyG099sjkveJzDcplvlbjJGUC681xtiqx1jdL0jbHjZhrpLl3VrW+EtXGQcEEGqf/p3qy/mfL6h6IskD93u24hrP3OoNO6VIwdxKUqHeoYw8ChBnz2AM8ePFVY8p7hute47RvRECM2WyB702ZVGiP0aARIlptEH8VwiEKinL4oVcDbPUt8u9KBNnLNFDL9EG8hZ7tK4pz+XP+V91wf6OIWAIGAJRCoEvvvxaBo6drSoJ0lsOHTkpfUfOlFPf/yDjBrTUAx7EM2UCMYjjM35pS1du0ioOQ7s1lkT3xxenugOSey4bVHZCZYgp7Gfrt+vly6/Gfn6ZE+vnvYnAx6s2ycwFKzWK7vgLoRbA8B41AdF3LtB4o/QbNVMaVntNKpQurMRA007DJXvmx5R89WqD6KDsOIbKbpIYs9ThE+fKiF5N1Q8EwhkDThQvg6h0mPZh+fbQcWncfqia4efJkdFzQ0Q1R7nmVeu2qfoPFWDa1Cn+0k/UhY3aD9VKOc/lz6H/zhzXazVIurSopiSXlxvqj94jpkuX5tW14AIN1VLHfhP1uc3w2MP6M+4dC5atVyNjsKAqkLX/FgEjQP5bPO2v+RyBUCVl3XI1ci0xFRvSrZHKK8nzQ4ZZ4MmsUvedV3zpVu3zKbPuGwKGgMcRQP3Qc+j7WpYThYdDKjs50EiFiYohZX+l+DNqgBhNovnCs4hLCZcPynImShhffv3tohpQc+HCi2De0jXSsWkVwa/gulzX/aJUsacD0T+PT511zxDwDQJUYRKJdtMF/0Y1lW0ypFvjgOGom6Tkwsn6EzdubIkeLZrEjBnDs+MNVp/RUadEd8F8OeWt14oG+k6Qbsfu/aqSwAsO5QHqO7f3hmcHGk7HGD/p6cdOnpFzP/4sxZ7LK9XeeEk+37hT1YTj+rdQdaHX28nT5+TdTiMk+UOJ1dNl6659aoCKWfiM+Svk1wuX5MVCT0q2TI/Jok/Wy9pNuzT9PqqlsUX2PBkBEtkzYN/vaQTcpQ4L5MmqzuJvvPK8HtJpRBMGj5sj1Dp3m/15elDWOUPAEDAE7iICHFwnzVyqVQueeiKzXLnyp1ZsQO1BpPaDhau05OPgrg31QDhi0nyZtXCVqiZa1H/TFxJgLiLrN3+l1WEoe8ve0KjdEGla53VZtW67VgRoUrO8LFi2VvPbSfvBrDp+vDhCKo01Q8AQ+O8RgAi4evWqphzQHAPVJ7JnVJLSOePVq1pGXnjmRkqCnxpkMqRqncplJEfmx7TrqCTwrEPtQZoM6rpBY+foeps8aWI/De+mvjKOHkOmqZLwwQfu18o4Hy7+XF56Pp/Ueae0J32jwgPbKZt+6dJlLe+cNEkiLYsLsVO88FOqUsLbhfl6JGUyNUd1SpfzNx+4P4GmiULmDR4/R3q3rW37yG0+2UaA3CZg9vF7CwFkg4s/3SCNa5aXn37+VVp2H6OO6imS3sjJw4irba/xGtlEtrdy3TbZsGW35H0ikxR77slwS5zdWyjaaA0BQ8AQuBGtPHbijJYgxwMEIgAz1Ebth0nnZlXlyZyPB2CCNJk5f4WWfhzWvfFN+fpexpIxYmLH4dW9XyDx7jtihu4ZEwe1kgTx4kr9toPlpeefkldefEYPtOYN4uWZtb75EQGIVTwlKBeLOShmkxOmL9FqHKTaBSslvj97Xs93fnkXScPuNWy6/HbxkirN4sSJpRdpjKappHLp9z+kcfthmm7hVBzBNyNF0sSe9VkK9ZxBmDfuMEzTzd3mqCi0123+Snq2raUKiV17DkrvETNUTcjaGid2LF88tuwX9VqTxlM9QGS5U2MgybsNnqpqEMb06CMppHurGpo2hEFurbdK+Wo+vTApRoB4YRasD75AAJlkvTaDpHKF4lKySH758+q1m6rFrFi7VUZMnq/lyJBiwsIP7NIgQJb4YpDWSUPAEDAE7jACjuEgJQ7xAiGK2TGsiqxYs1VGv7dIUiV/UCqVLSbP5suu5oX5c2eRciUL3eFe/bd/notJ+94TNKebPYELlRPBw0gb1cuMBSsld7aMsmf/YaF6x7AeTTSH35ohYAj8ewSoKHJ/wngaTR80bo78/MsFuX5dpFNYFVXxQr5SJrhri+p6iVTlRNdRWs67eb2K8vLz+TxNhEASX7lyRYEaPmm+qiHixI4pcePElgkDW2qaNgTB/I/XytDujTVFe/2W3dKp3yRJkCCumqSytnrVcNr9BOANwl7QutFbShg7BBWGodM+XK5p6ZA+YIIpNYrDQ0dP+YY8Z79o12u8XPrjshJXyZM+GLhf1KvyqpJafIZnN3asWFpKfsTkeZIsSeIAmffv35h76y8YAXJvzbeN9h8i4NQpp2xuq+5j5PfLV7QGO07VY/o2V4k2uaZLV22S4T2aaL155N6//HpRXZ2tGQKGgCFgCNyMAKqIdr3H6QXk8uU/VbZNyghr63tzlsv23fvl2rXrGr31urldqLlFzoxC8MLFS1KhVGHJkjGNZH087V9UL+wv3x09qXL8Tz7/UtWEZV8uqPuINUPAELh9BFhbKJVat/IrWlmERnoaCgHHF8OtlHCTAEeOf68qNXwY8PPxKkGAmmXk5AUyrEdjJTscE3+UD6g9wivHDRG7a+8h6dh3ojSqUS5gxnn7KN/d3/ju6Cklso6fPKPVUVIlf0g69J0g+fNklRb1KsquvQfV0DZnlnS6djLfzCVKCa/OoRtBAgEfLPxM5i9bJ+d+/ElTWqg8efrMj9Kp/+RAaWd+B5+s+m0Gy9uvFQuk5N/d2fD/txkB4v85tBHcBQSmz1shLL6wz9RhP3rijLTsPloqly8eWHzYfJA8w0J3bVFN4sSJrew8/23NEDAEDAFDIHwEkPZOmrVURvRsqr4Yjr8SJDPlZP1qAAe58fW+79TjhCoUj6d7JKB6cY+LXG72lHfKF5cz587L6g07pHe72pIl46P22BgChsA/QACPHVQDV/78UyPlmNajhOAdJL2g66CpeqnkjNZv5Ez55uAxKVowj5qJnj57XtMLhnRt5FkzZqeiTf/Rs9Qw88Sps0qecuFHBUK5WAgA/On4t+6D35NfLlyUUsUKKCGLqf9Hn26QgZ0aeNr81T31jJm5mzBjsZ7DXyyUV/0/SGWiIleubOll6859UuDJbPLQg4nUUwM8INLx3WBv8UNzTLUhsxjbt4eOSZ92dQJEzpTZyxSH/h3r+SbNx2u4GwHitRmx/ngSAaSU5N+xiSDFXv3FDs3zxmTq+MmzsnHbHnm7XDHNt+w2+D01+TNTVE9OpXXKEDAEPIgA6gcqwRD1alitrJz/6VfpPXy6UE7WKQ3owW7fdpeIQHceMFkvYkRtnUap4D4jZ8io3mGaBoMSZOW6rdKjVU3fXE5uGwz7BUPgDiMAAXn4+GmNoufOniEQkCItJHr06FKm+DNKSOLB8G6tClqRg8slLVO61NKrXW25fPmKvNtxhFQqW0SKFMxzh3t8+38e36GdXx/Uyz0lt0kPuXjpDxkxaZ6UL11Y15OW3UZrOVzOryvWfqnpMpAJpYs9LQ2rv6Zmmj2GTlMjasfj7vZ7Ejm/AclRt9VAaf/uO5Inx+OaTsgY8UZh/8BbCgPVPiNmqlnoY2lS+Cowibqc1KUBnRtIzBj3heubFTno+/dbjQDx79xZz+8yAmwWe749ovmkmTKkDpicUtKqScfhUqlsUZUt79l/RLoMmKxO1ebuf5cnyb7OEDAEfIsAEuCVa7cpobxm406p8vpLUc7cjaj0gcMndP9wN3LXIXwwSeUSQvTyjz+u+CZi6duHzjp+TyMAUdCk4zCpWOYFVRPQUE1guMllmSoq+Gh0H/KeGhWjoOCc16RmOd9coiEIGrYdIh3CqgQMNjdt3ysDRn+gvkOsNZSX3bLjGyVPSBdpWf9NTR3yQ+oIyo6wziMl2+NppdbbpTQ46XhLobKDwMIIlhLHpMagfClfspAWN4BQ8Hpz1OUoA3NmSa/7x+PpU2tlGL8qI72AuREgXpgF64PvEXAc/nd8fUDllpVeLSoNq5f1xebhe/BtAIaAIRClEIAMWLl2q1B6nDK5UbmhfJm1YKWWsaSNm7ZINnz5daBEcFQeu43NEPACAnMWr9bS25SpfvrJrGoomuj++GpG+f2589KkwzD1cqNqFWrgZl1H6v93qpGcPvujLF+9RUkUL1YdIXgH2QGp3KRWecmUPo106j9JSryQT71CSM8b9/5HuuZQGQfvJVKH+nesrwQQbfGnX2glrjw5Mnphyv7ShxOnz0mf4dNly859kkBTKEX9M1APYjiNom74/9Kd+Cxz6ihGPDmgoE45SibMYBcuXy+jeje9SUHohzF4VPGuNwAADCZJREFUrY9GgHhtRqw/vkaACCbVYe5PEM/X47DOGwKGgCFgCNx5BIjuNesySnJRyvDtUmqsTbS2W8sagcvHne+FfYMhcO8iAEFw6MhJWbBsvfr1QGiMH9BSzZjdPhqoBfhsr2HvS/x4cZUU4TI9fe6nSpR4Oe2ZC/TOPQdVwUyVQpQQGPbzc1IPKRtLZRwaipFG7YZIq4aVNKVm9zc30mOCS5V79YkZNnGuJIwfT6q/WSKkEezxU2e1+o8f11jm69SZH+SRlEm9Cr9v+mUEiG+myjpqCBgChoAhYAgYAlENgQu/XRIO7R+v2qSqwWefyiGdm1f1ZDQ5qmFv4zEE3AiQosYFuWC+HFoWt0W30TeZEVM6t1H7YdKtZXXJnvkxGfveIhk1daGqRd4u96Kny+Y644QAwSQVg2XUEShASLtzDPtJ+cGEE5UBCrV6rQdJvLixNV3GTxdvyCoIrDPnfpLOzatpugs/c1cDYu3lMxu37pECT2YVSs5S1dFa1EfACJCoP8c2QkPAEDAEDAFDwBDwOAKYGaIixGvAD7n3HofTumcI/CsEICWjR4seSGf+8+pVcZfORe2Ldwbmo7/8elHLXWO66RcFMOk8EDz1qpRRo1BacOlcp1pO6WIF1Di1ae3XpWTR/P8K17v1y6ylQyfMlTfKvCDpH02lXwupRbl1ChikSPagmqeivqv6xsuybvNXsnDZOunYrIr6idgafLdmKnK+xwiQyMHdvtUQMAQMAUPAEDAEDAFDwBAwBHyAgLt0LhWcqCyCt8bI3mGSImlirbyCUsKvF2dHMeGUzr169apWj3kie0b1O4GgpX1/9kfZu/+oZEqfWh59JLlvxhtMYI2askC+OXBU+neqH1DbUR0I0mTCgJZRqvqYD16vu95FI0DuOuT2hYaAIWAIGAKGgCFgCBgChoAh4AcErlz5U1r3HCc5s6aTam+8LD/9fEG9Myiji5FoVGgQH6gjerSuqekxpMJMnbNczUQxR6VBArXqPkby5c4i+787rh4hVCNx0me8jAPpS10HTVXvj8SJEkjTTiOkcoWX5Ln8ObTbmG+j6GFMtd4qpYa3E2YukSLP5lGVCBVyrEUdBIwAiTpzaSMxBAwBQ8AQMAQMAUPAEDAEDIH/GAFIEIkWTb0k8M2YPOtj9clIGIVM71GxxI0TSy79/oeWji1fqvBNKS8QItt2fSsDOjdQdHsOmaYVc8LqvP4fo31n/xzpMcEEyKdrvpQJ05fIqD5hSvjgD/LZ+u0yY8FKnfN+HetJiqT/3x/k2Mkz8uefV+WxNCnvbGftr98RBIwAuSOw2h81BAwBQ8AQMAQMAUPAEDAEDIGohsDSlZtk9kefydBujZUAiGqN6lSNOwyT1195XkoWyR9Iczl5+pzUbztYihfKq6W7f//9D039SZ40se8goDRunxHT1f8jS4ZHZeCYD+Sd8i9qNRwIkku/X1Y/JtqkWUtlz7eHtdJP7Fgx5fc/Lt+UHuS7wVuHxQgQewgMAUPAEDAEDAFDwBAwBAwBQ8AQ+BsIkC4xcvJ8WbBsnRqf5smR8W/8lr8+snf/EWnXe7xkypBGGlYrK4ePfa/pIpQJDus8UvJkzygt6lf0jQeIgz4kzv7vTkihAjkFFcei5Rvk8407NS1mSLdGsvPrgxLWZaTEjxtH8A2pVLaopgRNmf2xDIHwShhfgtUi/ppZ6y0IGAFiz4EhYAgYAoaAIWAIGAKGgCFgCBgCt4EAREj0aNGirD8ExqikeRw4fEJa9xgrXVtWl9zZM8pX33wnXQZMljF9m0nSJA/cBmKR/1GIHcaC0uPtcsVk38Fj0r7PBOnTro6SPWGdR0jBfDnlrdeKqhJk8YqNMmD0LKlbuYyaweL/0qTjMKlcobi8WChv5A/IevCPEDAC5B/BZr9kCBgChoAhYAgYAoaAIWAIGAKGQNRHYMuOb6T7kGmSJlUy+emXC/JwioekR5ta6o/ht4a/x4jJ82XukjWSIH4c6dysmhR+OpdcvXZN2vUaL+keTSW13y6txBZ+L+Pe/0hG9mqq3iATZy6VHbv331Q9xm/jt/6aAsSeAUPAEDAEDAFDwBAwBAwBQ8AQMAQMgVsggOJl38Gj8vvvlyVXtgy+JD8imuCDR05qpRvK/ubMkk627NwnTWqUU8UIlWTe7ThcuraoLjmypJNP12yV8dMX659EHYIixKrFRISwN/7dFCDemAfrhSFgCBgChoAhYAgYAoaAIWAIGAKGQCQiQOoPKpcFH6+TbV99q2qPGDHuk26DpmrJ39YNK2mJ4A8WrpJ2TSpLqhRJZNiEuZIrW3qpXrGE73xRIhHqSPtqI0AiDXr7YkPAEDAEDAFDwBAwBAwBQ8AQMAQMAa8hcPXqNbl85U8tDbx117fSddBUGdGziUSPHl3qtR4k7Zq8Lc/kza7dRjnSuf9kNVJ96MFEXhuK9ScIASNA7JEwBAwBQ8AQMAQMAUPAEDAEDAFDwBAwBEIgMP/jtUp8vPrSs7L40y9k8YoNWhUmTuxY+un93x2Xjn0nyvCe70riBxJKj8HT5NFHkkv1N0sYnh5EwAgQD06KdckQMAQMAUPAEDAEDAFDwBAwBAwBQ8BbCMxe9Jls/epbrRwTLVo0IWVm+KR5cvzkWenVrraW0q3fZpCSIw8kSigVy7wg5UsVCpAl3hrNvdkbI0DuzXm3URsChoAhYAgYAoaAIWAIGAKGgCFgCNwGApihNusySprWriAFnswqy1dvkUFjZmv6S/q0qSSs80gp8mxuqfhqETly/HuZPm+FNKj6qjyQKMFtfIt99E4iYATInUTX/rYhYAgYAoaAIWAIGAKGgCFgCBgChkCUQeDg4RPSecAU2bnngJbN7fBuZXnqiczy0ScbZNbCVVo21yE8UIgcOnJSFixbL/HixZYKpQpL0iQPRBks/DgQI0D8OGvWZ0PAEDAEDAFDwBAwBAwBQ8AQMAQMAU8g8MP5X6RhuyFS551XVAFCg/y4UTHmM/UDuXLlisxasEqa1CqvZXOtRQ4CRoBEDu72rYaAIWAIGAKGgCFgCBgChoAhYAgYAlEAAdQfazftkp5ta0vMGPfpiA58d0JqtegvWTI+Kp2aVZWUyR7U0rrjpy+RgZ3ra1lda3cfASNA7j7m9o2GgCFgCBgChoAhYAgYAoaAIWAIGAJRGAHUH/sOHJVXXnxGhk6cK3lzZZKC+XLIxBlLZUDn+pIoYfwoPHrvDs0IEO/OjfXMEDAEDAFDwBAwBAwBQ8AQMAQMAUPAhwhMnLlUrl69qmkxV69ek2WrN0vvYdOlUtki0rD6az4cUdToshEgUWMebRSGgCFgCBgChoAhYAgYAoaAIWAIGAIeQWDH1wekz/Dp0q9jPUnzcHLt1aXfL8v169cs/SUS58gIkEgE377aEDAEDAFDwBAwBAwBQ8AQMAQMAUMg6iGACeqiTzbI0AkfSvHCT0n+PFkkb85MkjBBvKg3WB+NyAgQH02WddUQMAQMAUPAEDAEDAFDwBAwBAwBQ8A/CPz08wVZuW6b/HbxklR6rVjAJNU/I4haPTUCJGrNp43GEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEQiBgBIg9FoaAIWAIGAKGgCFgCBgChoAhYAgYAoaAIRDlETACJMpPsQ3QEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDAEjACxZ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQMASiPAJGgET5KbYBGgKGgCFgCBgChoAhYAgYAoaAIWAIGAKGgBEg9gwYAoaAIWAIGAKGgCFgCBgChoAhYAgYAoZAlEfACJAoP8U2QEPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPAEDACxJ4BQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQ8AQiPIIGAES5afYBmgIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAJGgNgzYAgYAoaAIWAIGAKGgCFgCBgChoAhYAgYAlEeASNAovwU2wANAUPAEDAEDAFDwBAwBAwBQ8AQMAQMAUPACBB7BgwBQ8AQMAQMAUPAEDAEDAFDwBAwBAwBQyDKI2AESJSfYhugIWAIGAKGgCFgCBgChoAhYAgYAoaAIWAIGAFiz4AhYAgYAoaAIWAIGAKGgCFgCBgChoAhYAhEeQT+H2arNaPltzLdAAAAAElFTkSuQmCC",
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   "source": [
    "trace1 = go.Bar(\n",
    "    x = played_vs_wins['team_name'] , \n",
    "    y = played_vs_wins['Matches_played'] , \n",
    "    name = 'Total Matches'\n",
    ")\n",
    "\n",
    "\n",
    "trace2 = go.Bar(\n",
    "    x = played_vs_wins['team_name'] , \n",
    "    y = played_vs_wins['Wins'] , \n",
    "    name = 'Matches won'\n",
    ")\n",
    "\n",
    "\n",
    "iplot([trace1 , trace2])                                  # Plotting 2 different cloumns of the same df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "4d4393ab-dc90-4aec-b019-bd5ebf5cb594",
   "metadata": {},
   "outputs": [],
   "source": [
    "###Mumbai Indians & Chennai Super Kings have more than 100 winning matches "
   ]
  },
  {
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   "execution_count": null,
   "id": "72bf40d5-5a12-4419-96c9-837e48cc2efd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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