FiFA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/head.webp\" width=\"300px\" height=\"300px\" align=\"left\"></img>\n",
    "<br>\n",
    "<br>\n",
    "<center>\n",
    "<h1>FIFA 21 DATA ANALYSIS</h1>\n",
    "<br>\n",
    "presented by  <a href=\"https://github.com/some-earth11\">SomeEarth</a>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required libraries\n",
    "\n",
    "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\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>sofifa_id</th>\n",
       "      <th>player_url</th>\n",
       "      <th>short_name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>age</th>\n",
       "      <th>dob</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club_name</th>\n",
       "      <th>...</th>\n",
       "      <th>lwb</th>\n",
       "      <th>ldm</th>\n",
       "      <th>cdm</th>\n",
       "      <th>rdm</th>\n",
       "      <th>rwb</th>\n",
       "      <th>lb</th>\n",
       "      <th>lcb</th>\n",
       "      <th>cb</th>\n",
       "      <th>rcb</th>\n",
       "      <th>rb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>https://sofifa.com/player/158023/lionel-messi/...</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>33</td>\n",
       "      <td>1987-06-24</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>...</td>\n",
       "      <td>66+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>62+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20801</td>\n",
       "      <td>https://sofifa.com/player/20801/c-ronaldo-dos-...</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>Cristiano Ronaldo dos Santos Aveiro</td>\n",
       "      <td>35</td>\n",
       "      <td>1985-02-05</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>...</td>\n",
       "      <td>65+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200389</td>\n",
       "      <td>https://sofifa.com/player/200389/jan-oblak/210002</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>1993-01-07</td>\n",
       "      <td>188</td>\n",
       "      <td>87</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>...</td>\n",
       "      <td>32+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>32+3</td>\n",
       "      <td>32+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>32+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>188545</td>\n",
       "      <td>https://sofifa.com/player/188545/robert-lewand...</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>31</td>\n",
       "      <td>1988-08-21</td>\n",
       "      <td>184</td>\n",
       "      <td>80</td>\n",
       "      <td>Poland</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>...</td>\n",
       "      <td>64+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>190871</td>\n",
       "      <td>https://sofifa.com/player/190871/neymar-da-sil...</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>28</td>\n",
       "      <td>1992-02-05</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>...</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>62+3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sofifa_id                                         player_url  \\\n",
       "0     158023  https://sofifa.com/player/158023/lionel-messi/...   \n",
       "1      20801  https://sofifa.com/player/20801/c-ronaldo-dos-...   \n",
       "2     200389  https://sofifa.com/player/200389/jan-oblak/210002   \n",
       "3     188545  https://sofifa.com/player/188545/robert-lewand...   \n",
       "4     190871  https://sofifa.com/player/190871/neymar-da-sil...   \n",
       "\n",
       "          short_name                            long_name  age         dob  \\\n",
       "0           L. Messi       Lionel Andrés Messi Cuccittini   33  1987-06-24   \n",
       "1  Cristiano Ronaldo  Cristiano Ronaldo dos Santos Aveiro   35  1985-02-05   \n",
       "2           J. Oblak                            Jan Oblak   27  1993-01-07   \n",
       "3     R. Lewandowski                   Robert Lewandowski   31  1988-08-21   \n",
       "4          Neymar Jr        Neymar da Silva Santos Júnior   28  1992-02-05   \n",
       "\n",
       "   height_cm  weight_kg nationality            club_name  ...   lwb   ldm  \\\n",
       "0        170         72   Argentina         FC Barcelona  ...  66+3  65+3   \n",
       "1        187         83    Portugal             Juventus  ...  65+3  61+3   \n",
       "2        188         87    Slovenia      Atlético Madrid  ...  32+3  36+3   \n",
       "3        184         80      Poland    FC Bayern München  ...  64+3  65+3   \n",
       "4        175         68      Brazil  Paris Saint-Germain  ...  67+3  62+3   \n",
       "\n",
       "    cdm   rdm   rwb    lb   lcb    cb   rcb    rb  \n",
       "0  65+3  65+3  66+3  62+3  52+3  52+3  52+3  62+3  \n",
       "1  61+3  61+3  65+3  61+3  54+3  54+3  54+3  61+3  \n",
       "2  36+3  36+3  32+3  32+3  33+3  33+3  33+3  32+3  \n",
       "3  65+3  65+3  64+3  61+3  60+3  60+3  60+3  61+3  \n",
       "4  62+3  62+3  67+3  62+3  49+3  49+3  49+3  62+3  \n",
       "\n",
       "[5 rows x 106 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#importing the dataset of FIFA21 players\n",
    "\n",
    "fifa=pd.read_csv('players_21.csv')\n",
    "fifa.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>1. Basic Stats and Info about the dataset</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18944 entries, 0 to 18943\n",
      "Columns: 106 entries, sofifa_id to rb\n",
      "dtypes: float64(18), int64(44), object(44)\n",
      "memory usage: 15.3+ MB\n"
     ]
    }
   ],
   "source": [
    "fifa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sofifa_id</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>league_rank</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>...</th>\n",
       "      <th>mentality_penalties</th>\n",
       "      <th>mentality_composure</th>\n",
       "      <th>defending_marking</th>\n",
       "      <th>defending_standing_tackle</th>\n",
       "      <th>defending_sliding_tackle</th>\n",
       "      <th>goalkeeping_diving</th>\n",
       "      <th>goalkeeping_handling</th>\n",
       "      <th>goalkeeping_kicking</th>\n",
       "      <th>goalkeeping_positioning</th>\n",
       "      <th>goalkeeping_reflexes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18719.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>1.894400e+04</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "      <td>18944.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>226242.402872</td>\n",
       "      <td>25.225823</td>\n",
       "      <td>181.190773</td>\n",
       "      <td>75.016892</td>\n",
       "      <td>1.357070</td>\n",
       "      <td>65.677787</td>\n",
       "      <td>71.086729</td>\n",
       "      <td>2.224813e+06</td>\n",
       "      <td>8675.852513</td>\n",
       "      <td>1.091850</td>\n",
       "      <td>...</td>\n",
       "      <td>48.050412</td>\n",
       "      <td>57.978674</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.581767</td>\n",
       "      <td>45.546505</td>\n",
       "      <td>16.446052</td>\n",
       "      <td>16.236486</td>\n",
       "      <td>16.103357</td>\n",
       "      <td>16.225982</td>\n",
       "      <td>16.551309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27171.091056</td>\n",
       "      <td>4.697354</td>\n",
       "      <td>6.825672</td>\n",
       "      <td>7.057140</td>\n",
       "      <td>0.739327</td>\n",
       "      <td>7.002278</td>\n",
       "      <td>6.109985</td>\n",
       "      <td>5.102486e+06</td>\n",
       "      <td>19654.774894</td>\n",
       "      <td>0.361841</td>\n",
       "      <td>...</td>\n",
       "      <td>15.671721</td>\n",
       "      <td>12.118390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.402461</td>\n",
       "      <td>20.953997</td>\n",
       "      <td>17.577332</td>\n",
       "      <td>16.845480</td>\n",
       "      <td>16.519399</td>\n",
       "      <td>17.017341</td>\n",
       "      <td>17.878121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>210030.500000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>3.000000e+05</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.750000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>232314.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>6.500000e+05</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>246760.250000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>1.800000e+06</td>\n",
       "      <td>7000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>258970.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.055000e+08</td>\n",
       "      <td>560000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sofifa_id           age     height_cm     weight_kg   league_rank  \\\n",
       "count   18944.000000  18944.000000  18944.000000  18944.000000  18719.000000   \n",
       "mean   226242.402872     25.225823    181.190773     75.016892      1.357070   \n",
       "std     27171.091056      4.697354      6.825672      7.057140      0.739327   \n",
       "min        41.000000     16.000000    155.000000     50.000000      1.000000   \n",
       "25%    210030.500000     21.000000    176.000000     70.000000      1.000000   \n",
       "50%    232314.500000     25.000000    181.000000     75.000000      1.000000   \n",
       "75%    246760.250000     29.000000    186.000000     80.000000      1.000000   \n",
       "max    258970.000000     53.000000    206.000000    110.000000      4.000000   \n",
       "\n",
       "            overall     potential     value_eur       wage_eur  \\\n",
       "count  18944.000000  18944.000000  1.894400e+04   18944.000000   \n",
       "mean      65.677787     71.086729  2.224813e+06    8675.852513   \n",
       "std        7.002278      6.109985  5.102486e+06   19654.774894   \n",
       "min       47.000000     47.000000  0.000000e+00       0.000000   \n",
       "25%       61.000000     67.000000  3.000000e+05    1000.000000   \n",
       "50%       66.000000     71.000000  6.500000e+05    3000.000000   \n",
       "75%       70.000000     75.000000  1.800000e+06    7000.000000   \n",
       "max       93.000000     95.000000  1.055000e+08  560000.000000   \n",
       "\n",
       "       international_reputation  ...  mentality_penalties  \\\n",
       "count              18944.000000  ...         18944.000000   \n",
       "mean                   1.091850  ...            48.050412   \n",
       "std                    0.361841  ...            15.671721   \n",
       "min                    1.000000  ...             6.000000   \n",
       "25%                    1.000000  ...            38.750000   \n",
       "50%                    1.000000  ...            49.000000   \n",
       "75%                    1.000000  ...            60.000000   \n",
       "max                    5.000000  ...            92.000000   \n",
       "\n",
       "       mentality_composure  defending_marking  defending_standing_tackle  \\\n",
       "count         18944.000000                0.0               18944.000000   \n",
       "mean             57.978674                NaN                  47.581767   \n",
       "std              12.118390                NaN                  21.402461   \n",
       "min              12.000000                NaN                   5.000000   \n",
       "25%              50.000000                NaN                  27.000000   \n",
       "50%              59.000000                NaN                  55.000000   \n",
       "75%              66.000000                NaN                  65.000000   \n",
       "max              96.000000                NaN                  93.000000   \n",
       "\n",
       "       defending_sliding_tackle  goalkeeping_diving  goalkeeping_handling  \\\n",
       "count              18944.000000        18944.000000          18944.000000   \n",
       "mean                  45.546505           16.446052             16.236486   \n",
       "std                   20.953997           17.577332             16.845480   \n",
       "min                    4.000000            1.000000              1.000000   \n",
       "25%                   24.000000            8.000000              8.000000   \n",
       "50%                   52.000000           11.000000             11.000000   \n",
       "75%                   63.000000           14.000000             14.000000   \n",
       "max                   90.000000           90.000000             92.000000   \n",
       "\n",
       "       goalkeeping_kicking  goalkeeping_positioning  goalkeeping_reflexes  \n",
       "count         18944.000000             18944.000000          18944.000000  \n",
       "mean             16.103357                16.225982             16.551309  \n",
       "std              16.519399                17.017341             17.878121  \n",
       "min               1.000000                 1.000000              1.000000  \n",
       "25%               8.000000                 8.000000              8.000000  \n",
       "50%              11.000000                11.000000             11.000000  \n",
       "75%              14.000000                14.000000             14.000000  \n",
       "max              93.000000                91.000000             90.000000  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sofifa_id\n",
      "player_url\n",
      "short_name\n",
      "long_name\n",
      "age\n",
      "dob\n",
      "height_cm\n",
      "weight_kg\n",
      "nationality\n",
      "club_name\n",
      "league_name\n",
      "league_rank\n",
      "overall\n",
      "potential\n",
      "value_eur\n",
      "wage_eur\n",
      "player_positions\n",
      "preferred_foot\n",
      "international_reputation\n",
      "weak_foot\n",
      "skill_moves\n",
      "work_rate\n",
      "body_type\n",
      "real_face\n",
      "release_clause_eur\n",
      "player_tags\n",
      "team_position\n",
      "team_jersey_number\n",
      "loaned_from\n",
      "joined\n",
      "contract_valid_until\n",
      "nation_position\n",
      "nation_jersey_number\n",
      "pace\n",
      "shooting\n",
      "passing\n",
      "dribbling\n",
      "defending\n",
      "physic\n",
      "gk_diving\n",
      "gk_handling\n",
      "gk_kicking\n",
      "gk_reflexes\n",
      "gk_speed\n",
      "gk_positioning\n",
      "player_traits\n",
      "attacking_crossing\n",
      "attacking_finishing\n",
      "attacking_heading_accuracy\n",
      "attacking_short_passing\n",
      "attacking_volleys\n",
      "skill_dribbling\n",
      "skill_curve\n",
      "skill_fk_accuracy\n",
      "skill_long_passing\n",
      "skill_ball_control\n",
      "movement_acceleration\n",
      "movement_sprint_speed\n",
      "movement_agility\n",
      "movement_reactions\n",
      "movement_balance\n",
      "power_shot_power\n",
      "power_jumping\n",
      "power_stamina\n",
      "power_strength\n",
      "power_long_shots\n",
      "mentality_aggression\n",
      "mentality_interceptions\n",
      "mentality_positioning\n",
      "mentality_vision\n",
      "mentality_penalties\n",
      "mentality_composure\n",
      "defending_marking\n",
      "defending_standing_tackle\n",
      "defending_sliding_tackle\n",
      "goalkeeping_diving\n",
      "goalkeeping_handling\n",
      "goalkeeping_kicking\n",
      "goalkeeping_positioning\n",
      "goalkeeping_reflexes\n",
      "ls\n",
      "st\n",
      "rs\n",
      "lw\n",
      "lf\n",
      "cf\n",
      "rf\n",
      "rw\n",
      "lam\n",
      "cam\n",
      "ram\n",
      "lm\n",
      "lcm\n",
      "cm\n",
      "rcm\n",
      "rm\n",
      "lwb\n",
      "ldm\n",
      "cdm\n",
      "rdm\n",
      "rwb\n",
      "lb\n",
      "lcb\n",
      "cb\n",
      "rcb\n",
      "rb\n"
     ]
    }
   ],
   "source": [
    "#printing all columns in the dataset for better understanding\n",
    "\n",
    "def col_data():\n",
    "    l=fifa.columns.tolist()\n",
    "    for i in l:\n",
    "        print(i)\n",
    "col_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>2. Data Cleaning and Pre-Processing</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>sofifa_id</th>\n",
       "      <th>player_url</th>\n",
       "      <th>short_name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>age</th>\n",
       "      <th>dob</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club_name</th>\n",
       "      <th>...</th>\n",
       "      <th>lwb</th>\n",
       "      <th>ldm</th>\n",
       "      <th>cdm</th>\n",
       "      <th>rdm</th>\n",
       "      <th>rwb</th>\n",
       "      <th>lb</th>\n",
       "      <th>lcb</th>\n",
       "      <th>cb</th>\n",
       "      <th>rcb</th>\n",
       "      <th>rb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>https://sofifa.com/player/158023/lionel-messi/...</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>33</td>\n",
       "      <td>1987-06-24</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>...</td>\n",
       "      <td>66+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>62+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20801</td>\n",
       "      <td>https://sofifa.com/player/20801/c-ronaldo-dos-...</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>Cristiano Ronaldo dos Santos Aveiro</td>\n",
       "      <td>35</td>\n",
       "      <td>1985-02-05</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>...</td>\n",
       "      <td>65+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>200389</td>\n",
       "      <td>https://sofifa.com/player/200389/jan-oblak/210002</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>1993-01-07</td>\n",
       "      <td>188</td>\n",
       "      <td>87</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>...</td>\n",
       "      <td>32+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>32+3</td>\n",
       "      <td>32+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>32+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>188545</td>\n",
       "      <td>https://sofifa.com/player/188545/robert-lewand...</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>31</td>\n",
       "      <td>1988-08-21</td>\n",
       "      <td>184</td>\n",
       "      <td>80</td>\n",
       "      <td>Poland</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>...</td>\n",
       "      <td>64+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>190871</td>\n",
       "      <td>https://sofifa.com/player/190871/neymar-da-sil...</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>28</td>\n",
       "      <td>1992-02-05</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>...</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>62+3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 106 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   sofifa_id                                         player_url  \\\n",
       "0     158023  https://sofifa.com/player/158023/lionel-messi/...   \n",
       "1      20801  https://sofifa.com/player/20801/c-ronaldo-dos-...   \n",
       "2     200389  https://sofifa.com/player/200389/jan-oblak/210002   \n",
       "3     188545  https://sofifa.com/player/188545/robert-lewand...   \n",
       "4     190871  https://sofifa.com/player/190871/neymar-da-sil...   \n",
       "\n",
       "          short_name                            long_name  age         dob  \\\n",
       "0           L. Messi       Lionel Andrés Messi Cuccittini   33  1987-06-24   \n",
       "1  Cristiano Ronaldo  Cristiano Ronaldo dos Santos Aveiro   35  1985-02-05   \n",
       "2           J. Oblak                            Jan Oblak   27  1993-01-07   \n",
       "3     R. Lewandowski                   Robert Lewandowski   31  1988-08-21   \n",
       "4          Neymar Jr        Neymar da Silva Santos Júnior   28  1992-02-05   \n",
       "\n",
       "   height_cm  weight_kg nationality            club_name  ...   lwb   ldm  \\\n",
       "0        170         72   Argentina         FC Barcelona  ...  66+3  65+3   \n",
       "1        187         83    Portugal             Juventus  ...  65+3  61+3   \n",
       "2        188         87    Slovenia      Atlético Madrid  ...  32+3  36+3   \n",
       "3        184         80      Poland    FC Bayern München  ...  64+3  65+3   \n",
       "4        175         68      Brazil  Paris Saint-Germain  ...  67+3  62+3   \n",
       "\n",
       "    cdm   rdm   rwb    lb   lcb    cb   rcb    rb  \n",
       "0  65+3  65+3  66+3  62+3  52+3  52+3  52+3  62+3  \n",
       "1  61+3  61+3  65+3  61+3  54+3  54+3  54+3  61+3  \n",
       "2  36+3  36+3  32+3  32+3  33+3  33+3  33+3  32+3  \n",
       "3  65+3  65+3  64+3  61+3  60+3  60+3  60+3  61+3  \n",
       "4  62+3  62+3  67+3  62+3  49+3  49+3  49+3  62+3  \n",
       "\n",
       "[5 rows x 106 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating a copy of the dataset\n",
    "\n",
    "data=fifa.copy()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping the columns which we do not require for EDA or algorithmic usage\n",
    "\n",
    "data.drop(['sofifa_id','player_url','body_type','dob','real_face'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>short_name</th>\n",
       "      <th>long_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club_name</th>\n",
       "      <th>league_name</th>\n",
       "      <th>league_rank</th>\n",
       "      <th>overall</th>\n",
       "      <th>...</th>\n",
       "      <th>lwb</th>\n",
       "      <th>ldm</th>\n",
       "      <th>cdm</th>\n",
       "      <th>rdm</th>\n",
       "      <th>rwb</th>\n",
       "      <th>lb</th>\n",
       "      <th>lcb</th>\n",
       "      <th>cb</th>\n",
       "      <th>rcb</th>\n",
       "      <th>rb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>33</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>Spain Primera Division</td>\n",
       "      <td>1.0</td>\n",
       "      <td>93</td>\n",
       "      <td>...</td>\n",
       "      <td>66+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>52+3</td>\n",
       "      <td>62+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>Cristiano Ronaldo dos Santos Aveiro</td>\n",
       "      <td>35</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Italian Serie A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92</td>\n",
       "      <td>...</td>\n",
       "      <td>65+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>54+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>188</td>\n",
       "      <td>87</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>Spain Primera Division</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>32+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>32+3</td>\n",
       "      <td>32+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>32+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>31</td>\n",
       "      <td>184</td>\n",
       "      <td>80</td>\n",
       "      <td>Poland</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>German 1. Bundesliga</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>64+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>65+3</td>\n",
       "      <td>64+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>60+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>28</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>62+3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          short_name                            long_name  age  height_cm  \\\n",
       "0           L. Messi       Lionel Andrés Messi Cuccittini   33        170   \n",
       "1  Cristiano Ronaldo  Cristiano Ronaldo dos Santos Aveiro   35        187   \n",
       "2           J. Oblak                            Jan Oblak   27        188   \n",
       "3     R. Lewandowski                   Robert Lewandowski   31        184   \n",
       "4          Neymar Jr        Neymar da Silva Santos Júnior   28        175   \n",
       "\n",
       "   weight_kg nationality            club_name             league_name  \\\n",
       "0         72   Argentina         FC Barcelona  Spain Primera Division   \n",
       "1         83    Portugal             Juventus         Italian Serie A   \n",
       "2         87    Slovenia      Atlético Madrid  Spain Primera Division   \n",
       "3         80      Poland    FC Bayern München    German 1. Bundesliga   \n",
       "4         68      Brazil  Paris Saint-Germain          French Ligue 1   \n",
       "\n",
       "   league_rank  overall  ...   lwb   ldm   cdm   rdm   rwb    lb   lcb    cb  \\\n",
       "0          1.0       93  ...  66+3  65+3  65+3  65+3  66+3  62+3  52+3  52+3   \n",
       "1          1.0       92  ...  65+3  61+3  61+3  61+3  65+3  61+3  54+3  54+3   \n",
       "2          1.0       91  ...  32+3  36+3  36+3  36+3  32+3  32+3  33+3  33+3   \n",
       "3          1.0       91  ...  64+3  65+3  65+3  65+3  64+3  61+3  60+3  60+3   \n",
       "4          1.0       91  ...  67+3  62+3  62+3  62+3  67+3  62+3  49+3  49+3   \n",
       "\n",
       "    rcb    rb  \n",
       "0  52+3  62+3  \n",
       "1  54+3  61+3  \n",
       "2  33+3  32+3  \n",
       "3  60+3  61+3  \n",
       "4  49+3  62+3  \n",
       "\n",
       "[5 rows x 101 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>3. Exploratory Data Analysis (EDA)</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.1 Individual Stats</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t\tTop 10 players in the World\n"
     ]
    },
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>Lionel Andrés Messi Cuccittini</td>\n",
       "      <td>33</td>\n",
       "      <td>170</td>\n",
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       "      <td>93</td>\n",
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       "      <th>1</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
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       "      <td>35</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Juventus</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>92</td>\n",
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       "      <td>65+3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>27</td>\n",
       "      <td>188</td>\n",
       "      <td>87</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Atlético Madrid</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
       "      <td>32+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
       "      <td>36+3</td>\n",
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       "      <td>32+3</td>\n",
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       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>32+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>Robert Lewandowski</td>\n",
       "      <td>31</td>\n",
       "      <td>184</td>\n",
       "      <td>80</td>\n",
       "      <td>Poland</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>German 1. Bundesliga</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>Neymar da Silva Santos Júnior</td>\n",
       "      <td>28</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91</td>\n",
       "      <td>...</td>\n",
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       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>67+3</td>\n",
       "      <td>62+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>62+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Kevin De Bruyne</td>\n",
       "      <td>29</td>\n",
       "      <td>181</td>\n",
       "      <td>70</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91</td>\n",
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       "      <td>79+3</td>\n",
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       "      <td>80+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>79+3</td>\n",
       "      <td>75+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>69+3</td>\n",
       "      <td>75+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>K. Mbappé</td>\n",
       "      <td>Kylian Mbappé Lottin</td>\n",
       "      <td>21</td>\n",
       "      <td>178</td>\n",
       "      <td>73</td>\n",
       "      <td>France</td>\n",
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       "      <td>55+3</td>\n",
       "      <td>55+3</td>\n",
       "      <td>55+3</td>\n",
       "      <td>63+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>M. ter Stegen</td>\n",
       "      <td>Marc-André ter Stegen</td>\n",
       "      <td>28</td>\n",
       "      <td>187</td>\n",
       "      <td>85</td>\n",
       "      <td>Germany</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>Spain Primera Division</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
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       "      <td>41+3</td>\n",
       "      <td>41+3</td>\n",
       "      <td>41+3</td>\n",
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       "      <td>31+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>31+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>V. van Dijk</td>\n",
       "      <td>Virgil van Dijk</td>\n",
       "      <td>28</td>\n",
       "      <td>193</td>\n",
       "      <td>92</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90</td>\n",
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       "      <td>85+3</td>\n",
       "      <td>85+3</td>\n",
       "      <td>85+3</td>\n",
       "      <td>80+3</td>\n",
       "      <td>82+3</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>89+2</td>\n",
       "      <td>82+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Alisson</td>\n",
       "      <td>Alisson Ramsés Becker</td>\n",
       "      <td>27</td>\n",
       "      <td>191</td>\n",
       "      <td>91</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>English Premier League</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>31+3</td>\n",
       "      <td>35+3</td>\n",
       "      <td>35+3</td>\n",
       "      <td>35+3</td>\n",
       "      <td>31+3</td>\n",
       "      <td>30+3</td>\n",
       "      <td>30+3</td>\n",
       "      <td>30+3</td>\n",
       "      <td>30+3</td>\n",
       "      <td>30+3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          short_name                            long_name  age  height_cm  \\\n",
       "0           L. Messi       Lionel Andrés Messi Cuccittini   33        170   \n",
       "1  Cristiano Ronaldo  Cristiano Ronaldo dos Santos Aveiro   35        187   \n",
       "2           J. Oblak                            Jan Oblak   27        188   \n",
       "3     R. Lewandowski                   Robert Lewandowski   31        184   \n",
       "4          Neymar Jr        Neymar da Silva Santos Júnior   28        175   \n",
       "5       K. De Bruyne                      Kevin De Bruyne   29        181   \n",
       "6          K. Mbappé                 Kylian Mbappé Lottin   21        178   \n",
       "7      M. ter Stegen                Marc-André ter Stegen   28        187   \n",
       "8        V. van Dijk                      Virgil van Dijk   28        193   \n",
       "9            Alisson                Alisson Ramsés Becker   27        191   \n",
       "\n",
       "   weight_kg  nationality            club_name             league_name  \\\n",
       "0         72    Argentina         FC Barcelona  Spain Primera Division   \n",
       "1         83     Portugal             Juventus         Italian Serie A   \n",
       "2         87     Slovenia      Atlético Madrid  Spain Primera Division   \n",
       "3         80       Poland    FC Bayern München    German 1. Bundesliga   \n",
       "4         68       Brazil  Paris Saint-Germain          French Ligue 1   \n",
       "5         70      Belgium      Manchester City  English Premier League   \n",
       "6         73       France  Paris Saint-Germain          French Ligue 1   \n",
       "7         85      Germany         FC Barcelona  Spain Primera Division   \n",
       "8         92  Netherlands            Liverpool  English Premier League   \n",
       "9         91       Brazil            Liverpool  English Premier League   \n",
       "\n",
       "   league_rank  overall  ...   lwb   ldm   cdm   rdm   rwb    lb   lcb    cb  \\\n",
       "0          1.0       93  ...  66+3  65+3  65+3  65+3  66+3  62+3  52+3  52+3   \n",
       "1          1.0       92  ...  65+3  61+3  61+3  61+3  65+3  61+3  54+3  54+3   \n",
       "2          1.0       91  ...  32+3  36+3  36+3  36+3  32+3  32+3  33+3  33+3   \n",
       "3          1.0       91  ...  64+3  65+3  65+3  65+3  64+3  61+3  60+3  60+3   \n",
       "4          1.0       91  ...  67+3  62+3  62+3  62+3  67+3  62+3  49+3  49+3   \n",
       "5          1.0       91  ...  79+3  80+3  80+3  80+3  79+3  75+3  69+3  69+3   \n",
       "6          1.0       90  ...  67+3  63+3  63+3  63+3  67+3  63+3  55+3  55+3   \n",
       "7          1.0       90  ...  33+3  41+3  41+3  41+3  33+3  31+3  33+3  33+3   \n",
       "8          1.0       90  ...  80+3  85+3  85+3  85+3  80+3  82+3  89+2  89+2   \n",
       "9          1.0       90  ...  31+3  35+3  35+3  35+3  31+3  30+3  30+3  30+3   \n",
       "\n",
       "    rcb    rb  \n",
       "0  52+3  62+3  \n",
       "1  54+3  61+3  \n",
       "2  33+3  32+3  \n",
       "3  60+3  61+3  \n",
       "4  49+3  62+3  \n",
       "5  69+3  75+3  \n",
       "6  55+3  63+3  \n",
       "7  33+3  31+3  \n",
       "8  89+2  82+3  \n",
       "9  30+3  30+3  \n",
       "\n",
       "[10 rows x 101 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the top 10 players in the game\n",
    "\n",
    "a=data[0:10]   #since the dataset is already sorted by rating\n",
    "print()\n",
    "print(\"\\t\\t\\t\\t\\tTop 10 players in the World\")\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><center>\n",
    "<img src=\"images/messi.png\" height=\"20%\" width=\"30%\"><br>\n",
    "The Highest Rated player, Lionel Messi\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t\t\t5 Oldest Players\n"
     ]
    },
    {
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       "      <th>short_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K. Miura</th>\n",
       "      <td>53</td>\n",
       "      <td>177</td>\n",
       "      <td>72</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Yokohama FC</td>\n",
       "      <td>Japanese J. League Division 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>33+2</td>\n",
       "      <td>37+2</td>\n",
       "      <td>37+2</td>\n",
       "      <td>37+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>30+2</td>\n",
       "      <td>32+2</td>\n",
       "      <td>32+2</td>\n",
       "      <td>32+2</td>\n",
       "      <td>30+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Butrón</th>\n",
       "      <td>43</td>\n",
       "      <td>184</td>\n",
       "      <td>82</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Alianza Lima</td>\n",
       "      <td>Peruvian Primera División</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>45000</td>\n",
       "      <td>...</td>\n",
       "      <td>25+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>25+2</td>\n",
       "      <td>25+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>25+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H. Sulaimani</th>\n",
       "      <td>43</td>\n",
       "      <td>173</td>\n",
       "      <td>70</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Al Ahli</td>\n",
       "      <td>Saudi Abdul L. Jameel League</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>62+2</td>\n",
       "      <td>66+</td>\n",
       "      <td>66+</td>\n",
       "      <td>66+</td>\n",
       "      <td>62+2</td>\n",
       "      <td>62+2</td>\n",
       "      <td>66+</td>\n",
       "      <td>66+</td>\n",
       "      <td>66+</td>\n",
       "      <td>62+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G. Buffon</th>\n",
       "      <td>42</td>\n",
       "      <td>192</td>\n",
       "      <td>92</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>Italian Serie A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>82</td>\n",
       "      <td>82</td>\n",
       "      <td>2200000</td>\n",
       "      <td>...</td>\n",
       "      <td>28+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>33+3</td>\n",
       "      <td>28+3</td>\n",
       "      <td>26+3</td>\n",
       "      <td>29+3</td>\n",
       "      <td>29+3</td>\n",
       "      <td>29+3</td>\n",
       "      <td>26+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C. Lucchetti</th>\n",
       "      <td>42</td>\n",
       "      <td>182</td>\n",
       "      <td>74</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Atlético Tucumán</td>\n",
       "      <td>Argentina Primera División</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>240000</td>\n",
       "      <td>...</td>\n",
       "      <td>30+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>30+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>31+2</td>\n",
       "      <td>31+2</td>\n",
       "      <td>31+2</td>\n",
       "      <td>29+2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  height_cm  weight_kg   nationality         club_name  \\\n",
       "short_name                                                                \n",
       "K. Miura       53        177         72         Japan       Yokohama FC   \n",
       "L. Butrón      43        184         82          Peru      Alianza Lima   \n",
       "H. Sulaimani   43        173         70  Saudi Arabia           Al Ahli   \n",
       "G. Buffon      42        192         92         Italy          Juventus   \n",
       "C. Lucchetti   42        182         74     Argentina  Atlético Tucumán   \n",
       "\n",
       "                                league_name  league_rank  overall  potential  \\\n",
       "short_name                                                                     \n",
       "K. Miura      Japanese J. League Division 1          1.0       59         59   \n",
       "L. Butrón         Peruvian Primera División          1.0       64         64   \n",
       "H. Sulaimani   Saudi Abdul L. Jameel League          1.0       64         64   \n",
       "G. Buffon                   Italian Serie A          1.0       82         82   \n",
       "C. Lucchetti     Argentina Primera División          1.0       71         71   \n",
       "\n",
       "              value_eur  ...   lwb   ldm   cdm   rdm   rwb    lb   lcb    cb  \\\n",
       "short_name               ...                                                   \n",
       "K. Miura              0  ...  33+2  37+2  37+2  37+2  33+2  30+2  32+2  32+2   \n",
       "L. Butrón         45000  ...  25+2  29+2  29+2  29+2  25+2  25+2  29+2  29+2   \n",
       "H. Sulaimani          0  ...  62+2   66+   66+   66+  62+2  62+2   66+   66+   \n",
       "G. Buffon       2200000  ...  28+3  33+3  33+3  33+3  28+3  26+3  29+3  29+3   \n",
       "C. Lucchetti     240000  ...  30+2  33+2  33+2  33+2  30+2  29+2  31+2  31+2   \n",
       "\n",
       "               rcb    rb  \n",
       "short_name                \n",
       "K. Miura      32+2  30+2  \n",
       "L. Butrón     29+2  25+2  \n",
       "H. Sulaimani   66+  62+2  \n",
       "G. Buffon     29+3  26+3  \n",
       "C. Lucchetti  31+2  29+2  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting by Age in descending order\n",
    "\n",
    "a=data.sort_values(by='age',ascending=False)[0:5]\n",
    "print()\n",
    "print(\"\\t\\t\\t\\t\\t\\t5 Oldest Players\")\n",
    "a.drop(['long_name'],axis=1,inplace=True)\n",
    "a=a.set_index('short_name')\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><center>\n",
    "<img src=\"images/kazuyoshi-miura-1488881754-8803.jpg\" height=\"20%\" width=\"30%\"><br>\n",
    "The Oldest Player, Kazuyoshi Miura\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t\t\t5 Youngest Players\n"
     ]
    },
    {
     "data": {
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       "      <th>age</th>\n",
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       "    <tr>\n",
       "      <th>M. Tanlongo</th>\n",
       "      <td>16</td>\n",
       "      <td>181</td>\n",
       "      <td>63</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Rosario Central</td>\n",
       "      <td>Argentina Primera División</td>\n",
       "      <td>1.0</td>\n",
       "      <td>58</td>\n",
       "      <td>73</td>\n",
       "      <td>250000</td>\n",
       "      <td>...</td>\n",
       "      <td>48+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>45+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Beyaz</th>\n",
       "      <td>16</td>\n",
       "      <td>171</td>\n",
       "      <td>62</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>Fenerbahçe SK</td>\n",
       "      <td>Turkish Süper Lig</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
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       "      <td>52+2</td>\n",
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       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>52+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>L. Gourna-Douath</th>\n",
       "      <td>16</td>\n",
       "      <td>185</td>\n",
       "      <td>70</td>\n",
       "      <td>France</td>\n",
       "      <td>AS Saint-Étienne</td>\n",
       "      <td>French Ligue 1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>59</td>\n",
       "      <td>79</td>\n",
       "      <td>300000</td>\n",
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       "      <td>16</td>\n",
       "      <td>189</td>\n",
       "      <td>75</td>\n",
       "      <td>Poland</td>\n",
       "      <td>Wisła Kraków</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>59</td>\n",
       "      <td>81</td>\n",
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       "      <td>59+2</td>\n",
       "      <td>59+2</td>\n",
       "      <td>55+2</td>\n",
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       "    <tr>\n",
       "      <th>R. Cherki</th>\n",
       "      <td>16</td>\n",
       "      <td>176</td>\n",
       "      <td>71</td>\n",
       "      <td>France</td>\n",
       "      <td>Olympique Lyonnais</td>\n",
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       "      <td>67</td>\n",
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      ],
      "text/plain": [
       "                  age  height_cm  weight_kg nationality           club_name  \\\n",
       "short_name                                                                    \n",
       "M. Tanlongo        16        181         63   Argentina     Rosario Central   \n",
       "O. Beyaz           16        171         62      Turkey       Fenerbahçe SK   \n",
       "L. Gourna-Douath   16        185         70      France    AS Saint-Étienne   \n",
       "D. Hoyo-Kowalski   16        189         75      Poland        Wisła Kraków   \n",
       "R. Cherki          16        176         71      France  Olympique Lyonnais   \n",
       "\n",
       "                                  league_name  league_rank  overall  \\\n",
       "short_name                                                            \n",
       "M. Tanlongo        Argentina Primera División          1.0       58   \n",
       "O. Beyaz                    Turkish Süper Lig          1.0       57   \n",
       "L. Gourna-Douath               French Ligue 1          1.0       59   \n",
       "D. Hoyo-Kowalski  Polish T-Mobile Ekstraklasa          1.0       59   \n",
       "R. Cherki                      French Ligue 1          1.0       67   \n",
       "\n",
       "                  potential  value_eur  ...   lwb   ldm   cdm   rdm   rwb  \\\n",
       "short_name                              ...                                 \n",
       "M. Tanlongo              73     250000  ...  48+2  45+2  45+2  45+2  48+2   \n",
       "O. Beyaz                 80     230000  ...  52+2  52+2  52+2  52+2  52+2   \n",
       "L. Gourna-Douath         79     300000  ...  59+2  59+2  59+2  59+2  59+2   \n",
       "D. Hoyo-Kowalski         81     325000  ...  52+2  53+2  53+2  53+2  52+2   \n",
       "R. Cherki                88    1700000  ...  47+2  45+2  45+2  45+2  47+2   \n",
       "\n",
       "                    lb   lcb    cb   rcb    rb  \n",
       "short_name                                      \n",
       "M. Tanlongo       45+2  40+2  40+2  40+2  45+2  \n",
       "O. Beyaz          52+2  48+2  48+2  48+2  52+2  \n",
       "L. Gourna-Douath  59+2  59+2  59+2  59+2  59+2  \n",
       "D. Hoyo-Kowalski  55+2  59+2  59+2  59+2  55+2  \n",
       "R. Cherki         42+2  35+2  35+2  35+2  42+2  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting by Age in ascending order\n",
    "\n",
    "a=data.sort_values(by='age',ascending=True)[:5]\n",
    "print()\n",
    "print(\"\\t\\t\\t\\t\\t\\t5 Youngest Players\")\n",
    "a.drop(['long_name'],axis=1,inplace=True)\n",
    "a=a.set_index('short_name')\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><center>\n",
    "<img src=\"images/mateo.jpg\" height=\"20%\" width=\"30%\"><br>\n",
    "The Youngest Player, Mateo Tanlongo\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t\t\t5 Tallest Players\n"
     ]
    },
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>T. Holý</th>\n",
       "      <td>28</td>\n",
       "      <td>206</td>\n",
       "      <td>102</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>Ipswich Town</td>\n",
       "      <td>English League One</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65</td>\n",
       "      <td>67</td>\n",
       "      <td>475000</td>\n",
       "      <td>...</td>\n",
       "      <td>23+2</td>\n",
       "      <td>25+2</td>\n",
       "      <td>25+2</td>\n",
       "      <td>25+2</td>\n",
       "      <td>23+2</td>\n",
       "      <td>23+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>23+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A. Ba</th>\n",
       "      <td>26</td>\n",
       "      <td>203</td>\n",
       "      <td>94</td>\n",
       "      <td>Mauritania</td>\n",
       "      <td>AJ Auxerre</td>\n",
       "      <td>French Ligue 2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>64</td>\n",
       "      <td>67</td>\n",
       "      <td>500000</td>\n",
       "      <td>...</td>\n",
       "      <td>55+2</td>\n",
       "      <td>57+2</td>\n",
       "      <td>57+2</td>\n",
       "      <td>57+2</td>\n",
       "      <td>55+2</td>\n",
       "      <td>57+2</td>\n",
       "      <td>64+2</td>\n",
       "      <td>64+2</td>\n",
       "      <td>64+2</td>\n",
       "      <td>57+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C. Pantilimon</th>\n",
       "      <td>33</td>\n",
       "      <td>203</td>\n",
       "      <td>96</td>\n",
       "      <td>Romania</td>\n",
       "      <td>Denizlispor</td>\n",
       "      <td>Turkish Süper Lig</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>1300000</td>\n",
       "      <td>...</td>\n",
       "      <td>28+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>31+2</td>\n",
       "      <td>31+2</td>\n",
       "      <td>31+2</td>\n",
       "      <td>28+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S. Maierhofer</th>\n",
       "      <td>37</td>\n",
       "      <td>202</td>\n",
       "      <td>98</td>\n",
       "      <td>Austria</td>\n",
       "      <td>FC Admira Wacker Mödling</td>\n",
       "      <td>Austrian Football Bundesliga</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>160000</td>\n",
       "      <td>...</td>\n",
       "      <td>44+2</td>\n",
       "      <td>52+2</td>\n",
       "      <td>52+2</td>\n",
       "      <td>52+2</td>\n",
       "      <td>44+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>58+2</td>\n",
       "      <td>58+2</td>\n",
       "      <td>58+2</td>\n",
       "      <td>46+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V. Milinković-Savić</th>\n",
       "      <td>23</td>\n",
       "      <td>202</td>\n",
       "      <td>92</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>Torino</td>\n",
       "      <td>Italian Serie A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>68</td>\n",
       "      <td>74</td>\n",
       "      <td>925000</td>\n",
       "      <td>...</td>\n",
       "      <td>27+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>26+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>26+2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     age  height_cm  weight_kg     nationality  \\\n",
       "short_name                                                       \n",
       "T. Holý               28        206        102  Czech Republic   \n",
       "A. Ba                 26        203         94      Mauritania   \n",
       "C. Pantilimon         33        203         96         Romania   \n",
       "S. Maierhofer         37        202         98         Austria   \n",
       "V. Milinković-Savić   23        202         92          Serbia   \n",
       "\n",
       "                                    club_name                   league_name  \\\n",
       "short_name                                                                    \n",
       "T. Holý                          Ipswich Town            English League One   \n",
       "A. Ba                              AJ Auxerre                French Ligue 2   \n",
       "C. Pantilimon                     Denizlispor             Turkish Süper Lig   \n",
       "S. Maierhofer        FC Admira Wacker Mödling  Austrian Football Bundesliga   \n",
       "V. Milinković-Savić                    Torino               Italian Serie A   \n",
       "\n",
       "                     league_rank  overall  potential  value_eur  ...   lwb  \\\n",
       "short_name                                                       ...         \n",
       "T. Holý                      3.0       65         67     475000  ...  23+2   \n",
       "A. Ba                        2.0       64         67     500000  ...  55+2   \n",
       "C. Pantilimon                1.0       71         71    1300000  ...  28+2   \n",
       "S. Maierhofer                1.0       64         64     160000  ...  44+2   \n",
       "V. Milinković-Savić          1.0       68         74     925000  ...  27+2   \n",
       "\n",
       "                      ldm   cdm   rdm   rwb    lb   lcb    cb   rcb    rb  \n",
       "short_name                                                                 \n",
       "T. Holý              25+2  25+2  25+2  23+2  23+2  27+2  27+2  27+2  23+2  \n",
       "A. Ba                57+2  57+2  57+2  55+2  57+2  64+2  64+2  64+2  57+2  \n",
       "C. Pantilimon        33+2  33+2  33+2  28+2  28+2  31+2  31+2  31+2  28+2  \n",
       "S. Maierhofer        52+2  52+2  52+2  44+2  46+2  58+2  58+2  58+2  46+2  \n",
       "V. Milinković-Savić  29+2  29+2  29+2  27+2  26+2  28+2  28+2  28+2  26+2  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting by Height in descending order\n",
    "\n",
    "a=data.sort_values(by='height_cm',ascending=False)[:5]\n",
    "print()\n",
    "print(\"\\t\\t\\t\\t\\t\\t5 Tallest Players\")\n",
    "a.drop(['long_name'],axis=1,inplace=True)\n",
    "a=a.set_index('short_name')\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><center>\n",
    "<img src=\"images/tomas.jpg\" height=\"20%\" width=\"30%\"><br>\n",
    "The Tallest Player, Tomas Holy\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t\t\t5 Heaviest Players\n"
     ]
    },
    {
     "data": {
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       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
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       "      <th>nationality</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A. Akinfenwa</th>\n",
       "      <td>38</td>\n",
       "      <td>178</td>\n",
       "      <td>110</td>\n",
       "      <td>England</td>\n",
       "      <td>Wycombe Wanderers</td>\n",
       "      <td>English League Championship</td>\n",
       "      <td>2.0</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>190000</td>\n",
       "      <td>...</td>\n",
       "      <td>45+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>44+2</td>\n",
       "      <td>49+2</td>\n",
       "      <td>49+2</td>\n",
       "      <td>49+2</td>\n",
       "      <td>44+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C. Seitz</th>\n",
       "      <td>33</td>\n",
       "      <td>191</td>\n",
       "      <td>107</td>\n",
       "      <td>United States</td>\n",
       "      <td>DC United</td>\n",
       "      <td>USA Major League Soccer</td>\n",
       "      <td>1.0</td>\n",
       "      <td>66</td>\n",
       "      <td>66</td>\n",
       "      <td>350000</td>\n",
       "      <td>...</td>\n",
       "      <td>27+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>26+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>26+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F. Farnolle</th>\n",
       "      <td>35</td>\n",
       "      <td>196</td>\n",
       "      <td>104</td>\n",
       "      <td>Benin</td>\n",
       "      <td>BB Erzurumspor</td>\n",
       "      <td>Turkish Süper Lig</td>\n",
       "      <td>1.0</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>475000</td>\n",
       "      <td>...</td>\n",
       "      <td>26+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>28+2</td>\n",
       "      <td>26+2</td>\n",
       "      <td>26+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>29+2</td>\n",
       "      <td>26+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O. Oularé</th>\n",
       "      <td>24</td>\n",
       "      <td>196</td>\n",
       "      <td>104</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Standard de Liège</td>\n",
       "      <td>Belgian Jupiler Pro League</td>\n",
       "      <td>1.0</td>\n",
       "      <td>71</td>\n",
       "      <td>75</td>\n",
       "      <td>3100000</td>\n",
       "      <td>...</td>\n",
       "      <td>45+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>43+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D. Corozo</th>\n",
       "      <td>32</td>\n",
       "      <td>191</td>\n",
       "      <td>104</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Delfín SC</td>\n",
       "      <td>Ecuadorian Serie A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>180000</td>\n",
       "      <td>...</td>\n",
       "      <td>19+2</td>\n",
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       "      <td>23+2</td>\n",
       "      <td>23+2</td>\n",
       "      <td>19+2</td>\n",
       "      <td>19+2</td>\n",
       "      <td>22+2</td>\n",
       "      <td>22+2</td>\n",
       "      <td>22+2</td>\n",
       "      <td>19+2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 99 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              age  height_cm  weight_kg    nationality          club_name  \\\n",
       "short_name                                                                  \n",
       "A. Akinfenwa   38        178        110        England  Wycombe Wanderers   \n",
       "C. Seitz       33        191        107  United States          DC United   \n",
       "F. Farnolle    35        196        104          Benin     BB Erzurumspor   \n",
       "O. Oularé      24        196        104        Belgium  Standard de Liège   \n",
       "D. Corozo      32        191        104        Ecuador          Delfín SC   \n",
       "\n",
       "                              league_name  league_rank  overall  potential  \\\n",
       "short_name                                                                   \n",
       "A. Akinfenwa  English League Championship          2.0       65         65   \n",
       "C. Seitz          USA Major League Soccer          1.0       66         66   \n",
       "F. Farnolle             Turkish Süper Lig          1.0       70         70   \n",
       "O. Oularé      Belgian Jupiler Pro League          1.0       71         75   \n",
       "D. Corozo              Ecuadorian Serie A          1.0       62         62   \n",
       "\n",
       "              value_eur  ...   lwb   ldm   cdm   rdm   rwb    lb   lcb    cb  \\\n",
       "short_name               ...                                                   \n",
       "A. Akinfenwa     190000  ...  45+2  50+2  50+2  50+2  45+2  44+2  49+2  49+2   \n",
       "C. Seitz         350000  ...  27+2  29+2  29+2  29+2  27+2  26+2  28+2  28+2   \n",
       "F. Farnolle      475000  ...  26+2  28+2  28+2  28+2  26+2  26+2  29+2  29+2   \n",
       "O. Oularé       3100000  ...  45+2  46+2  46+2  46+2  45+2  43+2  47+2  47+2   \n",
       "D. Corozo        180000  ...  19+2  23+2  23+2  23+2  19+2  19+2  22+2  22+2   \n",
       "\n",
       "               rcb    rb  \n",
       "short_name                \n",
       "A. Akinfenwa  49+2  44+2  \n",
       "C. Seitz      28+2  26+2  \n",
       "F. Farnolle   29+2  26+2  \n",
       "O. Oularé     47+2  43+2  \n",
       "D. Corozo     22+2  19+2  \n",
       "\n",
       "[5 rows x 99 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sorting by Weight in descending order\n",
    "\n",
    "a=data.sort_values(by='weight_kg',ascending=False)[:5]\n",
    "print()\n",
    "print(\"\\t\\t\\t\\t\\t\\t5 Heaviest Players\")\n",
    "a.drop(['long_name'],axis=1,inplace=True)\n",
    "a=a.set_index('short_name')\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><center>\n",
    "<img src=\"images/akinfenwa.jpg\" height=\"20%\" width=\"30%\"><br>\n",
    "The Heaviest Player, Adebayo Akinfenwa\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#setting players with a non-existing release clause as 0 to avoid errors while sorting for the same.\n",
    "\n",
    "data['release_clause_eur'] = data['release_clause_eur'].replace(np.nan, 0)\n",
    "data['release_clause_eur'] = data['release_clause_eur'].apply(np.int64) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t\t5 Players with the highest Release Clause\n",
      "\n",
      "K. Mbappé has a release clause worth $ 203100000\n",
      "\n",
      "Neymar Jr has a release clause worth $ 166500000\n",
      "\n",
      "K. De Bruyne has a release clause worth $ 161000000\n",
      "\n",
      "J. Oblak has a release clause worth $ 159400000\n",
      "\n",
      "M. ter Stegen has a release clause worth $ 147700000\n"
     ]
    }
   ],
   "source": [
    "#sorting by Release Clause Value in descending order\n",
    "\n",
    "a=data.sort_values(by='release_clause_eur',ascending=False)[:5]\n",
    "print()\n",
    "print(\"\\t\\t\\t\\t\\t5 Players with the highest Release Clause\")\n",
    "d1=a['short_name']\n",
    "d2=a['release_clause_eur']\n",
    "n=[]\n",
    "r=[]\n",
    "for i in d1:\n",
    "    n.append(i)\n",
    "for j in d2:\n",
    "    r.append(j)\n",
    "for x in range(5):\n",
    "    print()\n",
    "    print(n[x],'has a release clause worth $',r[x])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr><center>\n",
    "<img src=\"images/mbappe.jpeg\" height=\"20%\" width=\"30%\"><br>\n",
    "The Most Valuable Player, Kylian Mbappe\n",
    "</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\some_\\.conda\\envs\\test\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=125)\n",
    "sns.distplot(a=data['age'],kde=False,bins=20,color='red')\n",
    "plt.axvline(x=np.mean(data['age']),c='green',ls='--',label='Mean Age of All Players')\n",
    "plt.legend()\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Insight: The average age of players is just a little over 25</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\some_\\.conda\\envs\\test\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t 4496 players prefer their left foot.\n",
      "\t\t\t\t 14448 players prefer their right foot.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 750x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=125)\n",
    "sns.countplot('preferred_foot',data=data,palette='Blues')\n",
    "plt.xlabel('Preferred Foot')\n",
    "plt.ylabel('Count')\n",
    "Right,Left=data.preferred_foot.value_counts()\n",
    "print()\n",
    "print('\\t\\t\\t\\t',Left,\"players prefer their left foot.\")\n",
    "print('\\t\\t\\t\\t',Right,\"players prefer their right foot.\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Insight: As much as 3x players prefer their right foot than players who prefer left foot.</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\some_\\.conda\\envs\\test\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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Zys7igNbamfNMP3jk8Z2qakVdmvXB+INGRh3YWvvOPLO8IiMnLpI8dSXPvxqq6pnZtg/kD8712i2kPxFwqZMoc/iHJOf3jx84z2v1+pHHj6mqHedZ5r7pWsMnXTj4ifECVXXLbO0a49dJHjPf+rbWzkvXXcCMJy0yiHtBa+17C5QZPQZ8c4F9e6Y+bQnbeNJePB7gjjkkW1/THZPcdvgqLcpvkvxpa+3C2Sb2x9VXj4zac7ZyVXWVbNu/+P7j4e0syz0gW29u+8dVdaWlVHzChvgcm82TWmtnzzahfw3+PN1rknRXOTxskctdbVcceXz2Et53v5pjGXONW7DP8UUuFwCWRQANAAOqqstV1c2r6tnpQsOrjEx+dWtttLXTH448fmMfSs2ptfa/6fp8nfGQWYpdMPL4Nous9lo1un6fbK19bb7CfWjztjnmn8t3WmufWqDMZ9NdLp50Nya84TxlF+NB2XqztfMyzw3tkr5vim1bgt+tqq6+wjpMXFXtXFV7VdXbk7x0ZNJFWaVWmq21c9J1J5F0YeXvzFH049naH/aV0p3smMtoC+k3zdYCO11IPRMgv7e19qNF1PULI3W4Vro+j+dzcbouERYyegy4aX9zuLXs9fNN7IP8r42MWmg7rZaPzLQ6n8exI49vWFXj/aInXVA68xp9ubV27CxlttG6fuCP6f+9XJK7LTTPgIb4HBv3tdbaZxZY7rfTva9n7LOI5U7DaAv+C+YsdWnnjzye7SqA8XGLXfZCywWAZRFAA8Dk3KOq2uiQ7kffd5K8OF0/yzP+NyOXklfV+A3ARu9yP5/3jzy+8yzTvzTy+PCq+r1FLnctGl2/xW6f0Rbmt6iqhVp0LeZGTeen66t2xi6LrMtcRtfrk4tsGfz+dK3AZ9xphXVYqTfPsu+flS4UG+164zdJnrBAC9dFq6pdqupRVfX/quqwqjqqqj44OmTrzQKTbW9e+Ft9iDwa/M/aDUffvcBMi9vfZO4AePR9dqkW0vP4xsjj2y1Q9luttfluivfbculObCTdCbB3VdWN5ik/TSfM19p3xOiNN3cZqC5LteCxI9vWu9Kd7Bi3GvvOIAb8HBs3181tx40+/x3nLDVdo1dlLOWGtqNlLzPL9PGrPRa77IWWCwDLsqJLRgGAJftFutagLxu71PbGY+WOX+TyRvtyHV9Gkrwyyb1Gpn+yqr6Xrs/QTyb5zCIDn6nqu04Y7cN5Odtnu3StlWft/7Z36iKXO9qqb6UtSkdft0WtV2vtV1X1f9naR+dsr/1a88UkT1+o5eJiVNWV053U2T9di8/Fmq9rgjen6yLnMulOJt2070921BNGHn+gtfbTOZY12tL6z6rqwYus3x4jj6+2QNkFW1UnXXcEVfXadDeBTLr+cX9QVZ9Nd9PRY5N8brldokzYNN5/k7Jg3Vtr5431rDJb3Uf3nQdV1R6zlJnNTUYeL7TvDGWoz7Fx31i4SJKtVz8kyW5VtWW8H+01YHRf3mEJ842Wne29O97yfIdZxi1nuQCwLAJoAJicM5J8YWzchelagp6Y5HNJPjZHH6G7jDw+f6HLlkf8YuTxlaqqRrsEaK29r6r+Ll1YN3Pl08364elJUlXHJ/nvdJdLL/YGSKttl7H/fzFboVmMl7vyAuV/vcjljlpMX73z2WXk8WLXa6bsbv3jhdZraN9I19/rjJYuvDi9n3bM+M3Clquqdk3Xsvqmy5h9zrC6tfazqnpPtna/8fhse5XCDuluJDdj1q5Sqmq7bPuaLreP4oX68Z21/9s5PCfdvjLTD26l66ZhpquGi6rq00nemuS/WmtL6Qpgkqbx/puUSdX9qiOPd8/yuhiZVh/Qu4w8ntjn2Cx+Oc+0+crtkqUdY1fDOSOP5+t7ftzoyYtzZpk+Pm7HLC6AXmi5ALAsAmgAmJzjWmv3X+a8o8HYUoKM0TB7u3StN7eZv7X2kr4rgr9N1w/mFcaWsUc/PLuqnt9ae/kSnn+1jAeHi91G42H/UlrLrpZJvPbTXq+Xt9YOXaXnelO2hs+XJHlnknena215cpLzWmu/3Y5VdXSSeyxy2a/P1gB6v6r6h5EWkw/P1oDtlHRXEcxmx0ymm7uFlrHomwT22+PhVfWHSf4qyd7Z2u940h039u6HA6tq/9baR5dSWSZm/Pi8HNPqZnGwz7ExG+n4PxqI71RVO/V91y/kWiOPZwvkx4P2Xecot9TlAsCyCKABYG0YvUP9Um78s/PI4/NHg7dRrbXjkjy2v+nVXdL1M7p3/3fmR/nlk7ysqi5prb1yCXVYDWeN/b9QX84zdh77/8yVV2XiRtdtseuVbLtuZ06mKmtbVf1uui4kZjy6tfbfC8y2lG360XRdW9woXWDzwCRH9dNG+4U+dK5L+Vtr51bVRdnaf+rerbVjZiu72lprRyU5qqqulGSvdO//e6XrM3imNe51kry/qu7RWvvcdGq6qZ2ZrV1o7N9a+88p1mWpBv0cG7HY9/R4ufHPkbXgu2P/Xz9dv+0Lud7I4+8scrmL6bpkoeUCwLK4CSEArA0/H3m8fVVdf86S2xrtL/Pnc5bqtdZ+3Vr7ZGvtn1tr900XdDw527Z0ekFVTaIV3sT0LcLOHxl1w0XOOt6f6ILbaApG67So9aquI9nRsmtxvYZw35HHxywifE6Say924XPdjLCqbpytrahbkv9YYFGjr8dyugoZVGvtrNbae1trf9tau0O6cOpf0t1YMUkum+Sfp1bBze1nI4/X3L6zgFX5HMvWrocWMnqMPG+RLYtX27fH/v/dhWaoqstk277Cx5eRJN/PtjcUXHC5vdEug2ZbLgAsiwAaANaG47Ltj8U7L3K+u4w8/spSn7S1dk5r7XXZ2u1A0rUau8scsyxkm24BauyOWyv01ZHHy9k+Z6Tri3utGX3dFrtet8q2LQyX/NqvU6OB1hcXKlxVN8y2l5QvxpuSXNQ/fmDf5/SfZWsL4aNbaz9cYBmjLYfvvcTnX3WttZNaa89O8sKR0XtV1VrssmAIo8etafcpva72nTGr9Tl2x0Uud7TcV+csNUWttR8lOWlk1N0XMdvts21fzZ+cZbm/TvL5pSy3qq6VbW9meanlAsByCaABYA1orZ2f5Msjo/54oXmq6srpugiY8akVPP/R2fby5Gsuc1Hnjv2/lJsqLWR0/R7WdyeykH1HHh+7wI2tpmV0vW5ZVbdZxDyj63VmFndp9UZwmZHHi3kt91vqE7TWTkvy3v7fLenC59HlzHrzwTEfHnm8Tx/srAdHjjzekm1viLeRjR63JnnMWo7Rfecufbcz68Iqfo49qKouP1+BqtqSbU+sHruI5U7LUSOPH7GIz7bR4/835zkh9p6Rx/epqoU+18c/VwTQAEyMABoA1o43jTzep6oWaoX8j0l26B//OslhoxOX0vq4/8E7Gu6dvth5x5yZ5IKR/8e7wFiJN488vmaSp89XuKoelm1b4C3UbcK0fCTJT0b+f/F8havqekmeOjLq0Nbab+Yqv8GcMvJ4z/kKVtVuSZ6xzOd5/cjj52ZrNx5nJPmfRcx/WLbeBGyHJP8+4asBFm2Jzzveb+8Zk6zLGnbqyOMbTeu16n042/YB/Lp11hJ9op9jc9g5yd8tUObJSa478v9a7kv70JHHV0tywFwFq+q62faE2KFzFE2St2XrjRgvk+5GxHMtd6d0NyidcXhr7aK5ygPAUgmgAWDtOCzJ//WPK8m7qmr32QpW1V9k2xDy9a21n40Vu0FVfaqqHty3BpvPgdl6Se+vs+1l4IvWB6HHjYx6WlVN5PtGa+27Sd45Muqfq+qhs5XtQ4/RIOTrSd43iXpMWr/N/mVk1AOq6qWzbbe+Je37ksz00X1ukoMHr+TaMXozv7tV1eNmK9R3vfGhLO1GaKM+nK3dtewwMv7w1toFly6+rdbauUmePzLqj5IcXlXz3jytqq5UVU+tqrcvsb7z2beqDq+q2y/w3DsmOWhk1Bf7Fq2bwfHZ2nXEVZPMul+thv4qjWdlawv/O6e7KeS8rVeraseqelxVfWzoOi5g0p9jc3luVT1yjuX+fpKXjox6T2ttzfZn3Fr7YrZtBf2iqrrUCbaq2jnJW7P15oqnJvm3eZZ7UpJDRkY9rT8xO77cy6Q7wTvTxdH5SV60lHUAgIUs9GMUAFglrbXzqurx6cKv7dO1uvxKVf1Hko+ma118gySPzbY3Y/t+kmfPsdi798Mvqur96frNPSFddxs7Jtk9yaOzbWvSf2+tnbmCVXlrkjv1j/8syR9U1Tey7WXub2+tLSdke0qS30vXAnpLunDj3Un+O8nJ6VqPPTBdC7GZ7zkXJHncGm8l/O/pQsqZPl+fmWTv/rX/broQdM8kT8q23SL8TWvt/7J5HJPka9l6Q61Dq+p+6Voln5bu9b9Pkj9Nd0Ll+HRhyp3GFzSf1lqrqjdm2z6Rk8V1vzGzjNf2J0Jmwsw/TnL/qnpruu4AZlrdXiXJLZPcta/7ZbNt360rtSXJY5I8pqq+ly6Y/3K61uTnJtkl3Y3HHp9tb9q2aQKo1tqvqup9SfbpRx1aVc9J8sN0J+RmPK+1Nnh3N621D1TVC5P8Qz/q3kl+VFVHJPlEumPdxUmunOTm6ULq+6U7MXXa0PWbz0CfY+PekeSRSY6oqkelO/7/NMk1kjwk3f4+cwLvzCR/ufw12lZVvSHJnyxQ7A1V9brxka21HWYr3HtakrulO4btlORj/Tb7cJJzktw63XrMvEcvSfLERZwkOijJA9Ld0HL7JO/oj0FHprvS6ebpWovfemSeZ7XWfrrAcgFgSQTQALCGtNY+3v+gfmu6IGrHdC3EnjrHLN9Jct++xeV8rpYulF2oT9z3Jvn7xdd4Vv+eLgS4Z///tXLpG8F9bTkLbq39rKr2Ttdtxczl1X/UD7M5O8kfttaOm2P6mtAHnn+UrhXc3v3oO/TDXJ7VWnv9PNM3nH47PTbJp5NcKV0Ly8f0w7iT0/UBu9xt9KZ04c3M9+Uvtda+vsRl/GmSn6U7oZB0geFT+mEabtYPC3lua+3Igeuy1vx1uvfbzHFltm118GpVprX2/Ko6PcnL0gWHl0+3P/3patVhuQb8HJvx2nQnBh6b5KH9MJtzk/xBa+0nc0xfjsskWahLlC1Z4u/s1tqJVfWQdJ/BV+mf4y/6Ydxvkjy9tfbeWaaNL/eMqnpQuvD/eumC+cf2w2xe0lqbs1U1ACyXLjgAYI1prb0rXQvP96X7oTmbs9K1zrxDf5ntbE5L1+fjp7JtK77ZfDfJnyd5yGK6GJhP32/kfZPsn24dfpKuFepEtNa+k+Q2SV6VS9/0cMZF6fq/vFV/g8U1r7V2drrt9rRs29fxuE8n2bO19rJVqdga01r7ZpK7ZO6bil2U5Igkv9ta+94KnurUdOHxjEW3fp7RWruktfasdPX9QLZ28zBr8XQnZv4hySOW+lzz+ESSl6S7UeV8N25s6W46do/W2qZp/TyjtXZiuuPK36fbDj/LwsfNoet0cJI9krw92/atP5vvpOvKZ+9ha7U4E/wcm2v5f5Lu8+2XcxT5RJLbtdY+s5TlTlNf11sneVfmPlZ8MclerbXXLGG53+uX+x+Z+7P42+k+/xfqWxsAlqXW5s3gAYAkqaqrpgsUrpPu8upfJvlekmNba/OFWePL2SFduHLTdK2Rd0wX3p6a5KtruX/M+fTrtVeSG6VrNfarJD9OcnRr7VfTrNtK9DdBu2O68Onq6W4kdWqSTy01qNnIqup3svWy9bPTtXo+prW23Jtoji77Xklm+tM9L8muK92n+j6g756ur9WrpAvmzkzygyTHtdZ+MffcK1dVV04XCt44XVcuW9Jd3n9iuj6fXXa/RvXHuj3THetmuuE5K8mPkhy/ll+7SXyOVdWJ6bruSJJ7zpxY7G+ge690XVNcKd1x8tjW2g8mtwarr6qunu6z7brpWpH/NN1VGN9d4XKvmG57XS/da3FKuv3nqyurMQDMTwANAABj+hsBPqr/982ttcdPsz6wmc0VQAMA64MuOAAAYERV3SjJw0ZGvXZadQEAgPVOAA0AAL2q2iVdX6kzNxE7urX2xenVCAAA1rcl3Z0XAAA2mqp6YZJbJdk5ye3S9SWbJJckefa06gUAABuBABoAgM3u7knuMcv4A1trn1/tygAAwEYigAYAgK3OTvKVJK9qrf3PtCsDAADrXbXWpl0HAAAAAAA2IDchBAAAAABgEAJoAAAAAAAGoQ/oeVTVVZPcL8mJSS6Ybm0AAAAAAKZqhyS7JflQa+2Xi5lBAD2/+yU5fNqVAAAAAABYQ/ZN8tbFFBRAz+/EJDnssMNyi1vcYspVAQAAAACYnm9/+9t57GMfm/S56WIIoOd3QZLc4ha3yO1ud7tp1wUAAAAAYC1YdHfFbkIIAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADGLLtCsA69Udb//3067CRH3xyy+edhUAAAAA2GC0gAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQazqArqqdquqgqnpvVZ1SVa2qDl3EfPfqy7aquskqVBUAAAAAgDFrOoBOcrUkBya5fZIvLWaGqrpskn9Lcu6A9QIAAAAAYAFrPYA+Jcl1W2vXTvJHi5znGUmukuQNg9UKAAAAAIAFrekAurV2YWvt5MWWr6obJHlekmcnOWuwigEAAAAAsKA1HUAvw6uSHJfk0CnXAwAAAABg09sy7QpMSlU9KMmDktyptdaqaqnz75pk17HRu0+oegAAAAAAm86GCKCrasd0rZ/f2Fr78jIXc0C6Gx4CAAAAADABGyKATvLcJLv0f5frkCRHjY3bPcnhK1gmAAAAAMCmte4D6Kq6dpJnJjk4yS5VtUs/6Sr93+tX1SWttR/Nt5zW2ilJThlb9mQrCwAAAACwiaz7ADrJNZJcLsnf9cO4jyU5N8lOq1kpAAAAAIDNbiME0CckecQs4x/Zj//LJCetao0AAAAAAFj7AXRVPTVd/87b9aNuXVXP6x8f1Vo7Lsk7Z5nvVv3DD7bWfjB4RQEAAAAA2MaaD6DT9e98g5H/b9sPSdey+bhVrxEAAAAAAAta8wF0a223Zc53UJKDJlkXAAAAAAAWb7uFiwAAAAAAwNIJoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYxJoOoKtqp6o6qKreW1WnVFWrqkNnKXeHqjq4qo6rqrOr6tSq+lhV3WcK1QYAAAAAIGs8gE5ytSQHJrl9ki/NU+7ZSfZN8pkkz0jykiTXSPKRqnry0JUEAAAAAODStky7Ags4Jcl1W2snV9WWJBfNUe6VSfZtrV04M6KqXpvka0n+uare0Fq7ePDaAgAAAADwW2u6BXRr7cLW2smLKPfp0fC5H3d+kvcluXKSXQeqIgAAAAAAc1jTAfQEXDvJxUlOn3ZFAAAAAAA2m7XeBceyVdUtkjw0yVGttXMXUX7XXLql9O5D1A0AAAAAYDPYkAF0VV0pybuSnJfkrxc52wHpbngIAAAAAMAEbLgAuqp2TPLeJDdMcv/W2o8XOeshSY4aG7d7ksMnWD0AAAAAgE1jQwXQVXXZJO9OcpckD22tHbPYeVtrpyQ5ZWx5k60gAAAAAMAmsmEC6KrakuQdSe6bZN/W2vumXCUAAAAAgE1tQwTQVbVdksOSPCTJE1trb59ylQAAAAAANr01H0BX1VOT7JJku37Uravqef3jo1prxyV5WZJHJTkmyflV9dixxXyktXbaatQXAAAAAIDOmg+gkzwzyQ1G/r9tPyTJSUmOS3K7/v979MO4eyYRQAMAAAAArKI1H0C31nZbRJm9h68JAAAAAABLsd3CRQAAAAAAYOkE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMYk0H0FW1U1UdVFXvrapTqqpV1aFzlN2+qp5dVT+oqgv7v8+uqu1XudoAAAAAAGSNB9BJrpbkwCS3T/KlBcq+OsmLk3wyyVP6vy9O8qohKwgAAAAAwOy2TLsCCzglyXVbaydX1ZYkF81WqKr2SPKkJK9qrT2tH/3Gqjo7yV9W1etaa8evTpUBAAAAAEjWeAvo1tqFrbWTF1H00UkqycFj4w/uxz96sjUDAAAAAGAhazqAXoI7JDmttXbC6Mj+/5+l68IDAAAAAIBVtNa74FisayeZq6X0yUmus9ACqmrXJLuOjd59hfUCAAAAANi0NkoAffkkZ88x7YIkOy9iGQeku+EhAAAAAAATsFEC6POSXG6OaTskOX8RyzgkyVFj43ZPcvgK6gUAAAAAsGltlAD6p0luM8e06yT56kILaK2dkuSU0XFVtfKaAQAAAABsUhvlJoRfTnLNqrrh6Mj+/2v00wEAAAAAWEUbJYA+IklL8vSx8U/vxx+xyvUBAAAAANj01nwXHFX11CS7ZGtYfuuqel7/+KjW2nGtta9X1euT/FVVXTHJp5PsmeRPkxzSWjtutesNAAAAALDZrfkAOskzk9xg5P/b9kOSnJRkJlx+apIfJ3lCksf2056b5CWrU00AAAAAAEat+QC6tbbbIstdnORF/QAAAAAAwJRtlD6gAQAAAABYYwTQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxiy7QrAACwnh1+v6dOuwoTs++HXjPtKgAAABuMFtAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAICYSQFfVXlV19XmmX62q9prEcwEAAAAAsD5MqgX0J5Lcd57p9+7LAAAAAACwSUwqgK4Fpl82ySUTei4AAAAAANaBLcudsap2TrLLyKirVtX1Zyl65SR/nOSU5T4XAAAAAADrz7ID6CR/neT5/eOW5OB+mE0l+acVPBcAAAAAAOvMSgLoTyZ5Ubpw+TlJ3pPkG2NlWpJzk3yptfaxFTwXAAAAAADrzLID6NbaJ9LfWLCqrpPkda21z0+qYgAAAAAArG8raQH9W621P53EcgAAAAAA2DgmEkDPqKrdk9w4yVXSdc2xjdbaWyb5fAAAAAAArF0TCaCr6vpJ/ivJ3TNL8NxrSQTQAAAAAACbxKRaQL8uyR2S/E2SY5KcMaHlAgAAAACwTk0qgL5Hkpe31v51QssDAAAAAGCd225CyzknyakTWhYAAAAAABvApALotybZZ0LLWraqul5VvaGqflRV51fViVX1n1V1s2nXDQAAAABgs5lUFxz/keRNVfXuJP+e5MQkvxkv1Fr70YSe71Kq6qpJvpDksklem+SEJDdJ8uQkf1hVe7TWThrq+QEAAAAA2NakAujjkrR0NyL8w3nKbT+h55vNo5JcK8kfttbeOzOyqr6U5J1JHp7k4AGfHwAAAACAEZMKoP8xXQA9TTv3f08ZG//T/u95q1gXAAAAAIBNbyIBdGvtoEksZ4U+1v99dVU9M103IDdJ8ook30vy9inVCwAAAABgU5pUC+ipa619saoOSPKiJMeOTPpkkt9vrf1qvvmratcku46N3n2ytQQAAAAA2DwmEkBX1V6LKdda++Qknm8eP0/y5SQfSvKDJHskeVaS91TV/Vpr584z7wFJDhy4fgAbyn57HjbtKkzUf376sdOuAgAAAGwok2oBfXQW1wf0YDchrKqHJjkiye1aa8f3o4+qqi+mC6SfkuQl8yzikCRHjY3bPcnhk64rAAAAAMBmMKkA+p6zjNs+yY2T/EWSC5M8Z0LPNZenJfn+SPicJGmtfbiqzk6yV+YJoFtrp2TsBoZVNUQ9AQAAAAA2hUndhPCYOSZ9vKr+M8nnktw1yccn8XxzuNZsI6tLkbfLBurvGgAAAABgPdhu6Cdorf06XTcWBwz8VN9JctOquuvY+IcluUKSLw38/AAAAAAAjFitVsEtydUHfo5/SfKAJB+uqn9P8sN0NyF8YpJTk7xm4OcHAAAAAGDE4AF0Vd02ydOTHL9A0RVprX2mqu6Q5PlJHpXk2klOT/KOJM9rrZ065PMDAAAAALCtiQTQVXVCulbO466cZOckv0rymEk813xaa8clefjQzwMAAAAAwMIm1QL6mFw6gG5Jzkjy/SRva62dNaHnAgAAAABgHZhIAN1a238SywEAAAAAYOPYbtoVAAAAAABgY5rYTQiravskj0/ykCQ3TNcFxwlJjkxyaGvtN5N6LgAAAAAA1r6JtICuqp2SfDLJ65LsneSSdAH03klen+SYqrrCJJ4LAAAAAID1YVJdcByU5K5J/j7J1Vpre7TW9khytSTP7qcdNKHnAgAAAABgHZhUAP3wJG9qrb2ktXbBzMjW2oWttZcmOTTJIyf0XAAAAAAArAOTCqB3TfKleaZ/Mcm1JvRcAAAAAACsA5MKoE9Jcvt5pt8hyakTei4AAAAAANaBSQXQ/5Pk8VX1zKq63MzIqrpsVf1Nkv2TvHNCzwUAAAAAwDqwZULLOTDJ3ZO8JMnzq+qEfvwNk+yUrguOAyf0XAAAAAAArAMTaQHdWjs7yZ5JnpLk2CSX7Ydjkzw5yd1ba+dM4rkAAAAAAFgfJtUCOq21i5K8th8AAAAAANjkVtQCuqp2r6rdV1oGAAAAAICNZ9kBdFXdKck3k9x3gaL3SfKNqrrtcp8LAAAAAID1ZyUtoJ+U5PtJXrNAuX9L8r10fUEDAAAAALBJrCSAvkeSd7bW2nyF+unvSnKvFTwXAAAAAADrzEoC6Osk+eEiy57QlwcAAAAAYJNYSQB9QZLLL7Lsjkl+vYLnAgAAAABgnVlJAP2DJHsusuzd0vUXDQAAAADAJrGSAPqoJA+rqtvOV6if/ogk71nBcwEAAAAAsM6sJID+1yQ/S/KRqtq/qi47OrGqLltV+yf5cJJTk7xqBc8FAAAAAMA6s+wAurV2VpIHJjk7yX8kObOqvlpVx1TVV5Kc2Y8/O8kD+/IAAAAAAGwSW1Yyc2vt+KraI8kBSR6S5JZJdk7yqyRfSnJkkte31s5ZYT0BAAAAAFhnVhRAJ0kfLr+8HwAAAAAAIMnK+oAGAAAAAIA5CaABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEFsmXYFAGA9e8bdD552FSbm5cc+fdpVAAAAYIPRAhoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBbLgAuqp2rarXVtWPq+rCqvppVR1ZVdecdt0AAAAAADaTLdOuwCRV1U2TfDLJ+UnemOSkJFdPctckOyc5bXq1AwAAAADYXDZMAF1VleTwJD9Nco/W2jlTrhIAAAAAwKa2YQLoJPdMcsckD26tnVNVOyS5pLX26ynXCwAAAABgU9pIfUDfv/97dlV9Ol03HBdU1Wer6q5TrBcAAAAAwKa0kVpA36z/+84kn0ryyCTXTvL8JB+vqju11o6fa+aq2jXJrmOjdx+iogAAAAAAm8FGCqB36v9+vbX20JmRVfWVdDcm/Id0ofRcDkhy4HDVg43n3nd6xbSrMDEf+8LfLHmefe76XwPUZHqO/OyfTLsKALAuff0ZN592FSbqNi//7pLn+cI/7jb5ikzJnZ5/4rSrAAAbykYKoM/v/26TCLXWPlVV/5fkHgvMf0iSo8bG7Z7uxoYAAAAAACzRRgqgT+7/njbLtFOT3G6+mVtrpyQ5ZXRcVU2mZgAAAAAAm9BGugnhF/u/151l2nWT/GwV6wIAAAAAsOltpAD6PUnOS/KEqtp+ZmRVPSjJdZJ8cFoVAwAAAADYjDZMFxyttV9U1XOSHJzkE1X1jnTB818l+XmSF0yxegAAAAAAm85GagGd1tq/JvmTJDsleVmSA5K8L8ldWms/mWbdAAAAAAA2mw3TAnpGa+2wJIdNux4AAAAAAJvdhmoBDQAAAADA2iGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQW6ZdgfXqKle5xbSrMFGnn/7taVcBAAAAWKe+8dknTrsKE3Wru75+2lWADUMLaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAaxYQPoqrpXVbV+uMm06wMAAAAAsNlsyAC6qi6b5N+SnDvtugAAAAAAbFYbMoBO8owkV0nyhmlXBAAAAABgs9pwAXRV3SDJ85I8O8lZU64OAAAAAMCmteEC6CSvSnJckkOnXA8AAAAAgE1ty7QrMElV9aAkD0pyp9Zaq6qlzLtrkl3HRu8+weoBAAAAAGwqGyaArqod07V+fmNr7cvLWMQBSQ6cbK0AAAAAADavDRNAJ3lukl36v8txSJKjxsbtnuTwFdQJAAAAAGDT2hABdFVdO8kzkxycZJeq2qWfdJX+7/Wr6pLW2o/mWkZr7ZQkp4wtd/KVBQAAAADYJDZEAJ3kGkkul+Tv+mHcx5Kcm2Sn1awUAAAAAMBmtlEC6BOSPGKW8Y/sx/9lkpNWtUYAAAAAAJvchgigW2tnJXnn+PiqulX/8IOttR+sbq0AAAAAADa37aZdAQAAAAAANqYNHUC31g5qrZXWzwAAAAAAq29DB9AAAAAAAEyPABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQWyZdgVYv25600dMuwoT8/3v//e0qwCwLv2/e/zDtKswUc8+5p+mXQXWoZMeea9pV2GirvuOjy+p/PefcMeBajIdN33jF6ddBdahL/7b7tOuwkTd8SnfmXYV1p2vfvA+067CRN32/h+ddhWADUQLaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAYhgAYAAAAAYBACaAAAAAAABiGABgAAAABgEAJoAAAAAAAGIYAGAAAAAGAQAmgAAAAAAAaxYQLoqrpDVR1cVcdV1dlVdWpVfayq7jPtugEAAAAAbEYbJoBO8uwk+yb5TJJnJHlJkmsk+UhVPXmaFQMAAAAA2Iy2TLsCE/TKJPu21i6cGVFVr03ytST/XFVvaK1dPK3KAQAAAABsNhumBXRr7dOj4XM/7vwk70ty5SS7TqViAAAAAACb1IYJoOdx7SQXJzl92hUBAAAAANhMNlIXHJdSVbdI8tAkR7XWzl2g7K65dCvp3YeqGwAAAADARrdhA+iqulKSdyU5L8lfL2KWA5IcOGilAAA2mM/u88fTrsJE3fXIt027CgDr0pfe/rvTrsLE3OHRX5t2FWBdOumkd027ChN13es+bMnznH32dwaoyXRc8YqTa5e7IQPoqtoxyXuT3DDJ/VtrP17EbIckOWps3O5JDp9w9QAAAAAANoUNF0BX1WWTvDvJXZI8tLV2zGLma62dkuSUsWVNvoIAAAAAAJvEhgqgq2pLknckuW+SfVtr75tylQAAAAAANq0NE0BX1XZJDkvykCRPbK29fcpVAgAAAADY1DZMAJ3kZUkeleSYJOdX1WPHpn+ktXba6lcLAAAAAGBz2kgB9O36v/foh3H3TCKABgAAAABYJRsmgG6t7T3tOgAAAAAAsNV2064AAAAAAAAbkwAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABiEABoAAAAAgEEIoAEAAAAAGIQAGgAAAACAQQigAQAAAAAYhAAaAAAAAIBBCKABAAAAABjElmlXAAAAAADWu+9++8XTrsLE3PwWfz/tKrCBaAENAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMYkMF0FW1fVU9u6p+UFUX9n+fXVXbT7tuAAAAAACbzZZpV2DCXp3kyUnenOQzSe6W5MVJrpfkKVOsFwAAAADAprNhAuiq2iPJk5K8qrX2tH70G6vq7CR/WVWva60dP70aAgAAAABsLhupC45HJ6kkB4+NP7gf/+hVrg8AAAAAwKa2YVpAJ7lDktNaayeMjmytnVBVP0ty+/lmrqpdk+w6NvrWSfLtb3/7UuUvvviCFVV2rfnKV76y5HkuuOCMAWoyHctZ/3PPO3WAmkzPcrbB2eeeNEBNpmM563/muSdOviJTtJxt8MtzTpx8RaZoOdvgtHN+MkBNpmM563/y2acMUJPpWc42OOFXPx+gJtOxnPX/7pmnD1CT6bncco4DZ5w9QE2m52dL3AY//sW5A9VkOs5exj7wvdM21m+D3yxjG3znpxcOUJPp2LKc9f/JxtoHtl/ONjjhvAFqMh3bLWf9f/CrAWoyPW0Z2+AH3/3FADWZnl9fbunb4MQTTh6gJtNx7vnL+E502o8GqMn0/Oxny8iKzj1h4ULrxBWuMPtxfSQn3WGxy6rW2gSqNH1VdXySX7fWLhU0V9VXklymtbbHPPMflOTA4WoIAAAAALAh7Ntae+tiCm6kFtCXTzJX85MLkuy8wPyHJDlqbNyVktwiydf6Zay23ZMcnmTfJN+ZwvNP22Zf/8Q2SGyDzb7+iW2w2dc/sQ0S22Czr39iG2z29U9sg82+/oltkNgGm339E9tgs69/Yhsk098GOyTZLcmHFjvDRgqgz0tyuTmm7ZDk/Plmbq2dkmS264g/scJ6LVtVzTz8Tmtt6e3+17nNvv6JbZDYBpt9/RPbYLOvf2IbJLbBZl//xDbY7Ouf2Aabff0T2yCxDTb7+ie2wWZf/8Q2SNbMNvjMUgpvpJsQ/jTJdeaYdp0kG6cjHgAAAACAdWAjBdBfTnLNqrrh6Mj+/2v00wEAAAAAWCUbKYA+IklL8vSx8U/vxx+xyvUBAAAAANjUNkwf0K21r1fV65P8VVVdMcmnk+yZ5E+THNJaO26qFVyeU5K8ILP3Tb0ZbPb1T2yDxDbY7Ouf2Aabff0T2yCxDTb7+ie2wWZf/8Q22Ozrn9gGiW2w2dc/sQ02+/ontkGyDrdBtdamXYeJqaotSf42yROSXDfJSUnemOQlrbWLp1k3AAAAAIDNZkMF0AAAAAAArB0bqQ9oAAAAAADWEAE0AAAAAACDEEADAAAAADAIATQAAAAAAIMQQAMAAAAAMAgB9ICqapeqen5VfbmqzqqqC6vqR1X15qq68xzzXLuqflNVbZ4ye/fTW1X97Rxlnj5S5j6TXK9JGlmXJ8xTpqrqkVX1yao6taouqKofV9X/VtVTV7O+k1ZVO1fV31fV56vqzKr6db+OH6yqJ1fV5cfKH11VJ82ynF2r6lv9tvmD1VuDlauq3avqv6rqB339f15VX6qqV/br1RY5HD3tdVmKufb9qnpwP/6vZpnnwH7aO2aZdu9+2rOHrPdSjR2vWlVdUlVnVNVHquq+s5TfUlWPr6qPVdUvRt4TR1XVI6qqRsqe2C/zm3M899X6+VtVHTbkei7GYo53I2VvV1Vv6dfxgqo6u6q+UFX/UFVXHSl30CzvhV9W1eeq6k9Ht9daspj9oj/2/7yqvjLL/Nfv57u4qq44y/SfVNV3VmNdlmKh411fZuY1vckcy5iZvvcs49bVfjCuFvm9qap2W+Dz4GrTXI/ZrGDdLq6q06vq61V1SFXddY7lH92XP72qLjfL9C1V9bO+zLFDrusc9Vv0Z8FG3QYLWco26stft6r+vaq+U1Xn9+t9XFW9tqp2n8Y6LEYN/1l4pzmWdeRImS2TXKdJWeH7pFXVeVX1/ap6RVVdc1rrsVKL2Ueq6tBZttVpVfWJqtpnFas7UUtc94ur/+4wS5mv9WUu9btxrVjGMW85vxFmhouqyw/eUVW/u6orukzLfB+MDq9ZzfpOWq3sO+G6PRau4HPg3+dY3j4jZRb83B3amvzw3Qiqao8kH0hyrSTvSnJokvOS3DjJw5PsX1W/01r71tisf5Lk10nOSLJfks/P8zQX9OVfMsu0x/XTd1j+WqwZr0zytCSfSvKyJGcluWGSvZM8I8m6PLhW1S3T7SPXTXJkkrelW7drJLlHuvW6T5KHLbCc6yX5eJJrJ3lQa+2jw9V6sqrqLkk+keTMdO+RHyW5apJbJ/nzJO9Jt4+PemiSP0ryzCSnjYw/LRvDp5JckmSvJK8am7ZXkouT/N4s8+3V//3kcFVbkf9IcnSS7dO9fw9I8sGqut/MPltVV0pyVLp1+WS6Y9svklwzyQOTvCPJU5KMfsBekOSWVXX71tqXx57zMUl+k+QyA63TIKrqb5K8NMkp6Y4L301yuSR3TPLsJA9OMv4De+b9UEmuluTRSd6UZNckL1qVii/PvPtFdSHRH1bVlVprZ43Md49074UtSfZM8sGZCVV1w3TH1TesziosziKPd6es8GnW636w3O9N70nyzlkWd/awtV2aFa5bJblikluk+z7wxKp6Q5IntdYuGXuqC5NcOcmD+ucZ9cAkV+/LTNOCnwUjNuo2WMhiPi9vlOSLfZlDk3wryc5JbpnkUel+P6y5k3BLsczPwpnfRl8YW9ZV073+6+W30XLeJ0ly+XTb56+S3L+qbt1au3h1qjw1+6X73rxdus+6/ZK8u6r2ba29dao1G95F6b7rvnx0ZP+Zc5t0+/t6MNRvhO8l+af+8WWS3DTJE5M8qH9v/GDY1VpVM++DUd+dRkUmYQLfCTfCsXApnwMXJHlUVT29tfbrsWlrKxdsrRkmPKT7knxCktOT3GGW6dunC1RvOcu0byY5It1B9fQkl5ulzN5JWroDbUvyu2PTf6cff0T/9z7T3ibzbKuZdXnCHNOvmS5Aes8c068z7XVY5nrvlC58+OVs+0hf5iZJnjE27ugkJ438f6N+X/tVkr2mvV7L2A7vT3JOkuvOsY2uNMv4g/p95ibTrv8K133OfT/J15KcNjbuMknOTfcB3JLcbGz6J/rpl5n2ui1mPUeOU+8fGffOBY4H90nyqJH/T0zymSQ/SXLwLOW/NHIcPGytbouxMg/uy3wwyRVmmX7NJP888v+s74d0P9JPTvLNaa/3SvaLJE/v/3/gWLk3JPlsui/XLxqbtl8/z77TXs+xei3qeLfQMW5k+t7rfT8YqeeSvjcl2a1f3xdOu+6ruW7963loP/0FY9OOTnJSf0w8cpZ535kulDwxybFT2A6Les9v5G0w4W30b/24286xja427fVZ6nqOlVnuZ+E7kvw8Y9+Fkjw13Q/w9/Tltkx7O0xgH5jvffKyftodp71OA+4jh872Wia5SrrGXO8fso5rZN3fkeRrc7z+P03XqOWkoeo61LrOsb8v5zfCpY7z6U5OtiTPmvb6T3hfWJPHtGWu9yS/N627Y+EyPwdmcsF9xua5SrqT7kfMtsxpDLrgGMYT0+0Mz2qtfWl8YmvtN621f21jrZ+r6o7pWi4cluS/0rXgePA8z/PRdB8u4y1E90vXAuojy12BNeSG6c5of2K2ia21k1e3OhNzQLp1e8Zs+0iStNZ+0Fp7+WzTkqSqbpbuDPAuSX6/tbZWW77O58ZJftBau9TlYa21c9q2LR43k08muUZtewntHdKdzT04yfnZ2uI5VXXZJHdO8rnW2kWrWM9la619M13LhZskSVXdPl3LtsNaa2+cY56PttaOGBt9SZLDk/xxjVxSW1W3SHL7dMfS9eRF6U4oPaa1du74xNbaaa215y60kNbahem+uK2L/WHG+H6RrS369xorule6H1afmmPa6Lxrxaof79bRfrCs703rxMTWrX89/zzJ95P8Tc3S/UyStyR5YG3bPcFMi+A1dzyc5T2/UPkNtw0WMsc2unGSs1prX52l/IWttV+sVv0GstzPwreku/rj/mPjH5fkfemuMF13lvo+ydaradb6sX8IZ6T7nrwZ1v0tSW7TtxZNklTV9ulaRb81XSOudWeCvxFms5nfG+vFJL8TbpjXe4HPge+lO8E+ngs+uv+7mPfGqhBAD2OfdGcaDl/ifPul26k+2Fo7Psnx/bi5XJLuw+Ux/YdNqmq7dB86b0t3efJ6d0L/96FVtcs0KzJh+6RrifG25cxcVb+T5Jh0l1Lcu7X2uclVbVWdkGT3qrrbtCuyxhzT/x0N1vZK12L+6+kuLR2ddsckO47Mt+b17+crp1unpHtPJN3lRkv1lnRd1/z+yLj90rWC+uCsc6xBVXXjJLdK8u7W2ulLnP3K1fV5fbWqunlVvaBf1psnXtEBzbJffC1d10SjJ1yumeRm6cLnY5PcsapGLyvbK8kJrbWfrEKVl2I1jnfrdT/YJ8v73rTjyPrODJdfeLZVtU+Wt26z6k8yHpau1fzdZylyRLrvh48aGfeodN/53z6JOkzSLO/5BW20bbCQObbRCUmuVFXzdtO2Hq3ws/A76a5++u2P8Kq6ebrvSevu5MOMBd4no8fB61XVg5P8TZIvJzluFas5LVft1/3qVXWrJK9N14Ly0OlWa1V8JMmp2TZ0um+6rkg20v6+T/93qb8Rtoy8N65VVXum69rzV0n+ZxJ1XUOuOv59aNoVWoF9svLvhBvuWLiI70v/la57mSuPjHtcuq5MlvpZOhgB9DBumeS7rbVF97vUt2B8dJJ3jLRgPCxdnzXXmGfW/0zXN85Mh+T3SXKddIHMutdaOy3J69P1eXtSdTfnO7C6G66t5z7MfyfdPrJNP4RVdakf07PMe6V0l5lWukuwL3WDrnXkRekuo/l0VX21ql5dVfsusM9vBrO1+twr3aVkLZdu9blWW3yO2qnfp69R3Y0j3pHutZ85CXPL/u/Xl7rg/gz4V9J9yM6ciNs3ydva+urva9nbIN1JiZ/3w3eSPC/J81tr/zqhug1l3v2idf27fjrJHapqx36evdJdRvbpdO+Fyya5S9LdkDVdy4C1+F5YjePdet0Plvy9qfc32bq+M8M/TrhuK7XcdZvP8f3fm45PaK2dka67l8eNjH5ckv9dI61iF/osWKz1vA0Wspht9Ip0Xfq8s7qbEL6+uptzXX8aFZ6wlXwWJt1voD8cabiyX7of7B9YYb1W01LeJ6PHwR+n6yf3xHRdV433CbsRnZpu3X+WrY23/ry1duQ0K7VKfpOuMdq+/XffpDvWHd9aW+77ZxqG+o1w52x9b5ySrtHCjZLct7X245VXe02ZeR/8dqiqnaZbpWWbxHfCjXAsXOr3pZkT7I9Kfnu1/J2zxnJBAfQwdk53Zm0pHpTuZkSHjYw7PN1rtO9cM7XWvpGuhdjMmc/Hpevr8VKX5K1jT053KcbxSe6drp+3jyb5v/7M1nq0c2a/SdJTcukPj/Gg/XLp+vM5I+v8xnuttU8luWu61kq7peuj77AkJ1fVq6pqXd08blJaaz9P8u30wXL/pXLPdGFb+r83GPmhuVe6/u7Wckv4V6bbp09LV889k/xDtt5EdOf+71KPnTNmfnDunORe6W5Ct95af6xkG+yX7kTkfdOdzHxzkoOq6vkTqttQFtovkq5l/2XSHSuSbn//Zmvt9NbaD9P9qNhrZNrMPGvKKh3v1ut+sJzvTUn3Pem+Y8MhE6zXJCx33eYz8/1htu4nku54eOequmlV3STdfrdWjoeLec8vxnreBgtZcBu11r6frpup/0jXFduf949PrKq3zdE1yXqx0u8Db0v3++kRVVXpfke9fb10UdZbyvtk9Dj4oCR/m66Llv8dawm3Ud0vW9f/selOPr2+qv50qrVaPW9JdyP6e/fv+32yxgKnRRjqN8I3snXfuF+64+TPk3ygqm67wjqvNaPvg5nh/KnWaPkm8Z1wIxwLl/R9qbU2c6J1NBecOSG/ZqznFqRr2a8y9xfiueyX/sxV/0V5xlf7aa+cZ963JHlh3/Lrj7L2Wv+sSH/G6g1J3tBfWnu7JA9NF9a+o6pu01r73jTruAxz7SPvSHdCIUmelW27FJjxs3St2t6U5KNVdc9lXKK4ZvR9Oz26/5Fw03QnGZ6R5C/TdUmzofbnJfhkkgOqard0l9tcKVsD6M+ma/WwV1W9Lcndknxhwi3sJu0VSf43Xbcxv5fuNd6xb9GdbP2iccUkZy5j+W9Nd6OJh6e7ecN35upffQ0b3QZL9Zm27d28j6iqi5IcWFXvXMN95y60XyTbXhHw8Wzt/3nGsbl0AL0WW0BP+njXZhm3XveD5XxvSpIT26XvBL7WLHfd5jOzvLl+oH0g3f408yPkzCTvnXAdlmsx7/nFWM/bYCGL2kb9d98nJElV3SDJPdMdSx6dLnh4/CrWeZJW8lmY1tovqup/0732P0xy/ayfkw8zlvI+GT8Ovr+qPpkusPi7JM8eurJT9vGxq90O71//V1fVUX0os2G11r5eVcel29+vl66h0lunW6slG+o3wlnj3xGq6h3p7iHw2vRXz20Q4++D9WxS3wnX+7FwOd+X3pLkf/o88bFJjmit/br72bE2aAE9jG+l6+dxhwVLJqmqqyd5QLquNL4/Ntw+3c0FbjPPIt6a7sPmsHQ76ET6GVyLWmvntdaOba39TboAeods28ffevHNJDevqsuNjmyt/bi/icJHs7XT/EtprR2a5ElJbp3kI1V1pSEruxpa53uttdemu1zkrMzfB/pGN9oP9F5JzkvXzURaa2enuwxtryS/m+5M8Zpr8Tnm2/2+/b7W2t+lO4P7nKp6UD99Jhj73eUsvG81/qF0N/h8aNbfj81khdtgFh9K9zm/94SWN4SF9ouk68/z3HQnXHZJ1zfoaAD9qSR37VsQ75Xk5L5l9Jq1wPFu5kTSXH0Zz4xfbMuW9bAfLOl70zozxLrduv/7/dkm9i09j0j34+OxSf57vMuvKVrMe34x1vM2WMiSt1Fr7f/674Z3Sxe67ruOu6qbxGfhf6XrH/x5Sb7XWvv8Siu1ylb0PunX96x0V4RtRh9KcoV0n6+bwX+l++57QJKPtdZ+OuX6LNWgvxFGtdZ+la4hz52r6gorXR6DmNj3pnV+LFzO58D70/X3fEiSG2QN/h4WQA/jyHSB8GMWWf4x6S4vflKSR4wNj0rXCfucQVzfT/KH072xPtFaO2m5FV9nZr5MXmeqtVieI7PC8Ly19vokT0vXIvyD6/xyy230rRV+mPX52k7KeAD92bEz258amZas0Raf83hFuv65Xtp3MXJkP34lLbbekuRO6QK6wxYou+b0oek3kuwzoUvFZrp0WE/HhvH9Iv1+/9l0LVXule67y7Ej83wq3Wt+33T966+r98Isx7uZm+/uPscst+j/nrjIp1gP+8GRWdr3pvXkyExw3foTLfum64Li2HmKviXJDfthLV+Ofan3/EI24DZYyKK3UR+yfz1d3/jr8iZUE/osfG+6lpL3zBr8Ab4MS36fpLvSeS0f94e0Hj73JunwdL8r75T1faybMcRvhFEz+8d67SN5ozsyk/1OuFGOhQt+DrTWfp3u5Pu9kvywtfaZVazfogigh/H6dD8MX1pVtxufWFXbVdVfVtVMh/r7pTs7f0hr7Z1jwzvSXXK8UEuGF/TDgZNdlemq7o61t55j8kz/z99erfpM0OvThQyvmG0f6S14rURr7VXp+je6S7r+rNbVmdyquk9VbT/L+BulC1nW42s7EX3rhR+mC5jvnm1bfCbdj+6bp+ty4uIka+4DZj79j+RXpAvZHtla+3K6O1I/tqr2n22e6m4+Ot9Jm6PS9RH/l+v45iLPSdfdymEjN937rf5GFC9c5LL26f+um3sCjO8XI5OOSbJjuhuMnNha+8nItOPSXa73nHTHzTV5NcASjncfSde6+S+qu0HxaNlbpgvaP99a+9kin3qf/u9a3g+W+r1pPZnYuvX7wxvSdd/ystbaOXOVba19Id3lps9Nd8PONWme9/ysNuI2WMhs26iq7jFb67Cqumq6/q5/ma7vyPVqRZ+F/TZ7crrfRm8erJarZBnvk/umawG8lo/7g+i7uHpI/++mWP/W2inprgx+QZJ3T7k6KzbQb4SZcldPd6XIT9N1a8naM8nvTRvmWLiEz4F/TXcseNpq1Gup1uulWWtaa+3sqvrDdH22fL6q/jtdOHR+ulYYD0sXHN2qqvZIctsk/zLPIt+drouO+yd53xzP+flsbRG8Hv1BVV1rlvEXJnlJVX063Y0Hf5zubOXvpbvU6Pvp+kJeV/p95MHp+ij8fFUdmS5Q/FWSa6Rbvwck+UmSee/a2lp7af8j5B+THFVVD2qtrZebDhycZJeqek+61i4XJ7lZupMyl0136eRGN9e+//l0LTlnbqIyHkDP/D/T//OcP8LXsDeku5zouVV1RLqWDVdL8uaq2i9b+/C8Rrrj395J/mKuhbWuD+wXDFznSZjzNW+tvbeqnpnkpUm+V1VvTfK9dC0Bbp/uC8e3cun3xkOqauampFfJ1htwfCJdoLmebLNf9H2dzbRq3jNjrdlaa5dU1WfS7SPJ2m0BfXAWcbxrrZ1eVX+X5FVJvtR/h/hZuu8NT0zX//tcXyrX5X6wlO9N06vl8qxg3W5VVY/tH18x3UmKh6W72dQhSRY8EdVam++75Voy/lkwYzNtg4WMb6NnJblLfzz5arr9abd0Nx3aNcmTW2u/mVJdF2uIz8Lfaq0dMde0dWox75PLJbllus+Kc5L88+pWceLm+4484zFVNfNb6Rrp7oe0Z5JD2/q7R9Coxaz7b7XW1toNeFdqEr8Rrj7y3tguXX/wf5buxq1PX6Av3bVkSfvCejeh700b7Vg4Y67Pgd9qrX03XYOstam1ZhhoSHfjsAPT9dt6drow9Ufp7lJ9h77My9LdSOgu8yznGul+cL6z/3/vfp4nLPD8+/fl7jPtbTFPHWfWZa7hjenO6L43XYvh89P1hfvNdKH9Vaa9Ditc/52T/H2SL6Trn+iidHc6/XC6LlkuP1b+6CQnzbGsF/bb7INJLjftdVvk+t8v3Y/Ib6S7S+tFSU5O8s4kd51jnoP69bzJtOu/wnVfaN8/OF0w1frtcvlZlvG9fvpLp70+i1jPWY9XI6/nH/X/b0n3JfPj6VpwXZTuBq3vSXfSqUbmPTHJsYuoQ0ty2BraFnO+5iNlb5/uMsr/S/fZcXZ/nHhukivPsv1GhwvTtaZ9QbqbVUx9P5jAfnG5/vjfkjxxlvLP6af9bNrrNs86L+l4ly44/shI2Z+mu+fD78yzvdbVfjDLeiz4vakvt1u/ji+cdp0HXLeZ4TfpuhI4rt9/Zv2+mHm+H4yVW9Rxc4D1X/R7fqNugwlvo7um+57w1XQhzEXpTlR9IMkDp70ui1zPIT4L5/1umOTQvtyWaW+HCewD4++TmffKaekuwb7ltNdnyH1k5LUcHc5PdzP3pyfZftrrsQrrPu9+vNhj4hpY16F+I4xvu7P6ef9g2uu+2vvCehyy/O9N6/pYuJT3RRb5fXihZa7mUH2FAAAAAABgovQBDQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AAAAAwCAE0AAAAAAADEIADQAAAADAIATQAAAAAAAMQgANAAAAAMAgBNAAAAAAAAxCAA0AwIZUVSdW1aHTrsd6tJRtV1X7V1Wrqt2GrRUAAOuRABoAgHWjqvbuw86Z4ZKqOr2qPlxV9552/Satqg4aW9+Lq+qnVXVYVd14Fetxlb4ue6/WcwIAsDFsmXYFAABgGf4jydFJtk9ykyRPSvKhqrp/a+2j06zYQJ6Z5LQkOyS5Q5LHJ/n9qtqjtXbaAM93yyS/Gfn/KkkO7B8fPVb2sCTvTHLuAPUAAGCdE0ADALAefa61dtjMP1X1P0m+kuQZSdZNAF1Vl02S1tqvFyj6ntbaD/rHb6yq7yZ5RZL9k/zLpOvVWjtvCWUvTnLOpOsAAMDGoAsOAADWvdbaV5P8Msmc3VJU1WX7biQ+X1W/rKoLqupbVfX0qqqRck/pu7vYc5Zl3KGf9oyRcdtX1V9X1XH9Ms+oqndX1S3G5p3pK/mBVfXPVfWTJOena228VB/p//52favqvlV1TFWd0w/HzNYtSVXtU1Wf6et5blX9sKreUlVXHCnz2z6g+243vt9POnCkO5CZ6bP2Ad1vqw9U1VlVdX5VfbGqHjVLfU6sqmOrao+q+nhVnVdVp1bVC0dfFwAA1ictoAEAWPeq6qpJrpzke/MU2znJU5P8d5K3puti4n5JXpmui4nn9+XemuRlSfZL8umxZeyX5OJ03U7MeHuSfZL8V5J/T3LVJH+R5LNVdcfW2vfHlvEvSS5KcnCSluT0xa3lNm7S//1FklTVw5K8I8kPk7ywn/b4JB+uqoe11o7sy90ryf8k+WS/vhckuUGSB6XbPmfP8lzfTtcFyMuSvLufP/1zzaqq7prk40nOTNdS+1dJ9k3y9qq6Rmvt1WOz7Jrkw0mO6NfjAUmem+SEdN2tAACwTgmgAQBYj3aqqqulu6LvJkle3D9+2zzznJHkOq21C0fGvaaq3pzkr6vqha21X7fWzqiqI5M8sqqe1lo7P/ltdxl/nOR/Z/pdrqpHJHl4kke01t45s9C+dfC3kvxjP8+o7ZLcrbV2wRLW98r9+l4uXR/Qr0oXoL+jqrYk+dckpya5c2vtjL4Or09yfJJXV9X7+q4yHpwuZL5P//+M5831xK2106rqPekC6ONGuz6Zx8HpwvW7ttZO7Ovz2iSfTfL/quqwmXr2bpTk4a21d/X/v66qvpbkgAigAQDWNV1wAACwHr0yyc/T3Zjv00lun+Sfk7xmrhlaa7+ZCZ+raktVzYS6H0uyU5LdR4q/KcmV0rVsnvGgdK2bDx0Z98dJTklydFVdbWZIcmGSzyW5zyxVecMSw+ck+UK69T0pyZHpbr746Nba19Kt+3WSvG401G2tnZ7kkCTXTXK7fvSZSa6Q5A+G6t6iqq6Z5E5J3jYTPvf1uSDd63b5JPcdm+3UkfB5xjGZp0sVAADWBy2gAQBYj16R5H+TXJLkrCTfmmmpPJ+qemy6GxXukS7E/f/t3U2IlVUcBvDnL5QEEUoSQYtoE9WmRasQolpEQhElQZ8YLpKILBetKox2QS2ihQsRCSssiCJqlZSBKQYmQV9UYIaZpNGij4Vlp8W5k9fbnWn8uMo4vx8Md+bOee/5v7N8OPO8wxYNfb8lyd70yo2pU9Ur0isv3hlad0V6fcTBGfZc0Fr7e+itaasrZrAiyf70+o+fknw19JmXDV6/GHPdF0NrPk6vCLkjPcQ+VFVbk7yb5LXZ/P1mabbzDPtuzNpf0qtRAACYwwTQAADMRV+21rYczwWDuoxN6cH1i+mVFYfTTwc/m6H/DmytTT1k76mqumSwblmSda21w0MfuyC9p/jBGbZuIz+fSNC7vbX27Qlcd8wMrbWDVXVNkuuT3JTkhiQb0+9zaWvtwEnscdzzDDlymvYFAOA0E0ADADBf3JMeFt8yfCK5qqarediY/qC++9IrNc7JsfUbSfJNepj7YWvtz1M872ztGbxelWS0xuLKkTUZdD9vGXylqm5N8naSh5KsnWaP0cB4tvOM+s88AACc3XRAAwAwX0ydsv23+7iqzkuyetzi1treJO+n11+sSPJpa233yLJX0/ujnxj3GVV10UnOPBu7kvyQZFVVLRrae1H6Q/z2Jflk8N6SMddP3dOFM+zx++B18f8NM3hA484kd1XVpUPzLEzyWJI/Mgi/AQA4+zkBDQDAfPFWkuVJ3q2qN9P7hR9I8tsM12xID5mTZM2Y329OcluStVV1bZL3kvya5NIkN6d3Ht93CmafVmvtr6p6NMnrSXZW1Yb0kH1lej/18tbaVPi+vqouTg+Av09ywWDdkRztuh63x4Gq2pceKn+d5Ocke1prO6e5ZE16eL+jqtal/03uTa87WT38sEQAAM5uAmgAAOaF1trLVbU4ySNJXkjyY3rNxvb04HicN9Mfhnd+klfGfGarqruTfJAe5D6dHv7uT7ItyfpTexfjtdbeqKplSZ7M0RqNXUkeHunK3jSYc2WSJen3tivJqtbaR/+zzf1JnkvyfJKFSV5KP+k8bp4dVXVdkmeSPJ5eX/J5krtba5uP/w4BAJirqrXjqXMDAID5o6rOTa+32NZau/1MzwMAAHONDmgAAJjeneknhTec6UEAAGAucgIaAABGVNWNSS5Pr7M4lOTq1trfZ3YqAACYewTQAAAwoqq2JlmaZHeSla21z87sRAAAMDcJoAEAAAAAmAgd0AAAAAAATIQAGgAAAACAiRBAAwAAAAAwEQJoAAAAAAAmQgANAAAAAMBECKABAAAAAJgIATQAAAAAABMhgAYAAAAAYCIE0AAAAAAATIQAGgAAAACAiRBAAwAAAAAwEf8A7VdXbzaLqRgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1750x875 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7),dpi=125)\n",
    "sns.countplot('team_position',data=data.head(100),palette='CMRmap')\n",
    "plt.xlabel('Player Position')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Position of Players in the Top 100',fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Insight: Most players(15) in the top 100 play as a Goalkeeper</h5>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.2 Team Stats</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t\t\t\t10 Best Clubs in the World\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Overall_Avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Club</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FC Bayern München</th>\n",
       "      <td>81.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Madrid</th>\n",
       "      <td>79.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chelsea</th>\n",
       "      <td>79.212121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FC Barcelona</th>\n",
       "      <td>78.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Liverpool</th>\n",
       "      <td>78.575758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inter</th>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manchester United</th>\n",
       "      <td>77.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manchester City</th>\n",
       "      <td>77.484848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Juventus</th>\n",
       "      <td>77.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tottenham Hotspur</th>\n",
       "      <td>77.242424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Overall_Avg\n",
       "Club                          \n",
       "FC Bayern München    81.045455\n",
       "Real Madrid          79.250000\n",
       "Chelsea              79.212121\n",
       "FC Barcelona         78.909091\n",
       "Liverpool            78.575758\n",
       "Inter                78.000000\n",
       "Manchester United    77.818182\n",
       "Manchester City      77.484848\n",
       "Juventus             77.483871\n",
       "Tottenham Hotspur    77.242424"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g=data['club_name'].unique()                                                    #gets all unique club names in an array\n",
    "c_list=g.tolist()                                                               #converts the array into a list\n",
    "\n",
    "dict_oc={}                                                                     \n",
    "for i in c_list:\n",
    "    c_dt = data[data['club_name']==i]\n",
    "    a=np.mean(c_dt['overall'])                                                  #get us average overall ratings of a club\n",
    "    dict_oc.update({i:a})                                                       #stores it in a dictionary\n",
    "\n",
    "t=sorted(dict_oc.items(), key=lambda x: x[1],reverse=True)                      #sorting the dictionary\n",
    "t_ds=pd.DataFrame.from_dict(t)                                                  #converting the dict to a dataframe\n",
    "\n",
    "t_ds.rename(columns = {0:'Club',1:'Overall_Avg'}, inplace = True)               #renaming columns for better analysis\n",
    "\n",
    "print()\n",
    "print(\"\\t\\t\\t\\t10 Best Clubs in the World\")\n",
    "\n",
    "t_ds=t_ds.set_index('Club')                                                     \n",
    "t_ds.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Insight: The 2019/20 Sextuple winners Bayern Munich are the best Team in the World</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\some_\\.conda\\envs\\test\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7),dpi=150)\n",
    "sns.countplot('club_name',data=data.head(50),palette='CMRmap')\n",
    "plt.xlabel('Club')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Clubwise players in the Top 50',fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Insight: Real Madrid C.F and Liverpool FC equally have the most players in the Top 50, i.e 8 players</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2100x1050 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7),dpi=150)\n",
    "sns.countplot(x='nationality',data=data.head(50),palette='CMRmap')\n",
    "plt.xlabel('Nationality')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Country-wise players in the Top 50',fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Insight: Most players amongst the Top 50 belong to France(8), but if we consider the fact that many french players are migrants from various African nations, Brazil(6) produces the best players. </h5>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>3.3 Player vs Player Comparisons</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a function to plot radar graphs, I have used plotly , you may use matplotlib to do so as well.\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "def radar(p1,r1,p2,r2):\n",
    "    attributes = [\"Overall\",\"Potential\",\"Pace\",\"Shooting\",\"Passing\",\"Dribbling\",\"Defending\",\"Physical\"]\n",
    "\n",
    "    fig = go.Figure()\n",
    "\n",
    "    fig.add_trace(go.Scatterpolar(\n",
    "          r=r1,\n",
    "          theta=attributes,\n",
    "          fill='toself',\n",
    "          name=p1,\n",
    "    ))\n",
    "    fig.add_trace(go.Scatterpolar(\n",
    "          r=r2,\n",
    "          theta=attributes,\n",
    "          fill='toself',\n",
    "          name=p2,\n",
    "    ))\n",
    "\n",
    "    fig.update_layout(\n",
    "      polar=dict(\n",
    "        radialaxis=dict(\n",
    "          visible=True,\n",
    "          range=[0, 100]\n",
    "        )),\n",
    "      showlegend=True\n",
    "    )\n",
    "\n",
    "    fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>\n",
    "<h4>3.3.1  Battle of the GOATs</h4>\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Better not to comment on this one 💀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the list of values of various attributes in both players\n",
    "\n",
    "att=['short_name','overall','potential','pace','shooting','passing','dribbling','defending','physic']\n",
    "pdata = data[att]\n",
    "pdata=pdata.set_index('short_name')\n",
    "\n",
    "messi = pdata.iloc[0].tolist()\n",
    "messi += messi [:1]\n",
    "\n",
    "ronaldo = pdata.iloc[1].tolist()\n",
    "ronaldo += ronaldo[:1]"
   ]
  },
  {
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   "source": [
    "<h5>Insight: Lionel Messi is statistically superior than Cristiano Ronaldo as he betters the latter in 5 categories.</h5>"
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    "<font color='red'>\n",
    "<h4>3.3.2  Battle of the Playmakers</h4>\n",
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   "source": [
    "Dumb Man United fans have been all over twitter with KDB 🎩 vs Bruno Fernandes ever since the latter arrived at the club in January 2020, lets settle this, shall we? "
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    "kdb += kdb [:1]\n",
    "\n",
    "bruno = pdata.iloc[30].tolist()\n",
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   "source": [
    "<h5>Insight: Therefore, My favorite player is clear of the Portuguese fraud.</h5>"
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    "<font color='red'>\n",
    "<h4>3.3.3  Battle of the Defensive Rocks</h4>\n",
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   "metadata": {},
   "source": [
    "Since the 2018-19 season, Virgil Van Dijk has been goated as the 'Best Defender', the tag had been associated with Sergio Ramos for several years, is Van Dijk actually better than the Real Madrid Captain?🚧"
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   "execution_count": 96,
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   ],
   "source": [
    "vvd = pdata.iloc[8].tolist()\n",
    "vvd += vvd [:1]\n",
    "\n",
    "sr = pdata.iloc[14].tolist()\n",
    "sr += sr[:1]\n",
    "\n",
    "radar('Virgil Van Dijk',np.array(vvd),'Sergio Ramos',np.array(sr))"
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    "<h5>Insight: Senor Ramos and the Scouser are equal to the task.</h5>"
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    "<h4>3.3.4  Battle of the German Walls</h4>\n",
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    "There has been a lot of heat in the German Camp around the discussion on who's their No 1 in Goal, is it the evergreen ManWall Neuer or Barcelona's saviour Marc Andre Ter Stegen? 🥇"
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   ],
   "source": [
    "#A more accurate Goalkeeper cmparison can be done by making another radar chart model wherein the parameters are GK related.\n",
    "\n",
    "wall = pdata.iloc[16].tolist()\n",
    "wall += wall [:1]\n",
    "\n",
    "mats = pdata.iloc[7].tolist()\n",
    "mats += mats[:1]\n",
    "\n",
    "radar('Manuel Neuer',np.array(vvd),'Marc Andre ter Stegen',np.array(sr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h5>Insight: Although these parameters don't tell us who is the best GK, but we do get an idea on who's better on the ball</h5>"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<h2>4. Conclusion</h2>"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<h4>4.1 Issues I faced with the Dataset</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a> The dataset has a few columns which are not required (which i removed).<br>\n",
    "b> The dataset is fairly inconsistent with player names, such as for Messi, his short name is L.Messi whereas Ronaldo's short      name is Cristiano Ronaldo.<br>\n",
    "c> In the column for Release Clauses, the values are entered as NaN, and have to be set to zero in order for Analysis.<br>\n",
    "d> in the Player positions, a position 'SUB' is there, which does not make sense."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>4.2 Analysis and Overall Thoughts</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset offers plenty for Analysis and is quite fun to work with especially if you play FIFA or are a football fan.<br>\n",
    "<br>\n",
    "You should definetly try your hand on this, it provides good EDA practice and is beginner-friendly.<br><br>\n",
    "I hope you liked my Analysis of it, please <font color='red'>upvote</font> if so."
   ]
  }
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