Advance_EDA_on_FIFADATASET.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "626b7b8e-c5ed-4464-b90e-183e54b3a5ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading required libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "39fa3771-e761-46bd-ba36-a539fc37f8ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "fifa = pd.read_csv('players_20.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b40a103-b136-47f2-979b-be667754be60",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sofifa_id</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</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>weak_foot</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>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>1.827800e+04</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "      <td>18278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>219738.864482</td>\n",
       "      <td>25.283291</td>\n",
       "      <td>181.362184</td>\n",
       "      <td>75.276343</td>\n",
       "      <td>66.244994</td>\n",
       "      <td>71.546887</td>\n",
       "      <td>2.484038e+06</td>\n",
       "      <td>9456.942773</td>\n",
       "      <td>1.103184</td>\n",
       "      <td>2.944250</td>\n",
       "      <td>...</td>\n",
       "      <td>48.383357</td>\n",
       "      <td>58.528778</td>\n",
       "      <td>46.848889</td>\n",
       "      <td>47.640333</td>\n",
       "      <td>45.606631</td>\n",
       "      <td>16.572765</td>\n",
       "      <td>16.354853</td>\n",
       "      <td>16.212934</td>\n",
       "      <td>16.368038</td>\n",
       "      <td>16.709924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27960.200461</td>\n",
       "      <td>4.656964</td>\n",
       "      <td>6.756961</td>\n",
       "      <td>7.047744</td>\n",
       "      <td>6.949953</td>\n",
       "      <td>6.139669</td>\n",
       "      <td>5.585481e+06</td>\n",
       "      <td>21351.714095</td>\n",
       "      <td>0.378861</td>\n",
       "      <td>0.664656</td>\n",
       "      <td>...</td>\n",
       "      <td>15.708099</td>\n",
       "      <td>11.880840</td>\n",
       "      <td>20.091287</td>\n",
       "      <td>21.585641</td>\n",
       "      <td>21.217734</td>\n",
       "      <td>17.738069</td>\n",
       "      <td>16.996925</td>\n",
       "      <td>16.613665</td>\n",
       "      <td>17.136497</td>\n",
       "      <td>18.038125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</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>204445.500000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>177.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>3.250000e+05</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>29.000000</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>226165.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>7.000000e+05</td>\n",
       "      <td>3000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>52.000000</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>240795.750000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2.100000e+06</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>64.000000</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>252905.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.055000e+08</td>\n",
       "      <td>565000.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>92.000000</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>92.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sofifa_id           age     height_cm     weight_kg       overall  \\\n",
       "count   18278.000000  18278.000000  18278.000000  18278.000000  18278.000000   \n",
       "mean   219738.864482     25.283291    181.362184     75.276343     66.244994   \n",
       "std     27960.200461      4.656964      6.756961      7.047744      6.949953   \n",
       "min       768.000000     16.000000    156.000000     50.000000     48.000000   \n",
       "25%    204445.500000     22.000000    177.000000     70.000000     62.000000   \n",
       "50%    226165.000000     25.000000    181.000000     75.000000     66.000000   \n",
       "75%    240795.750000     29.000000    186.000000     80.000000     71.000000   \n",
       "max    252905.000000     42.000000    205.000000    110.000000     94.000000   \n",
       "\n",
       "          potential     value_eur       wage_eur  international_reputation  \\\n",
       "count  18278.000000  1.827800e+04   18278.000000              18278.000000   \n",
       "mean      71.546887  2.484038e+06    9456.942773                  1.103184   \n",
       "std        6.139669  5.585481e+06   21351.714095                  0.378861   \n",
       "min       49.000000  0.000000e+00       0.000000                  1.000000   \n",
       "25%       67.000000  3.250000e+05    1000.000000                  1.000000   \n",
       "50%       71.000000  7.000000e+05    3000.000000                  1.000000   \n",
       "75%       75.000000  2.100000e+06    8000.000000                  1.000000   \n",
       "max       95.000000  1.055000e+08  565000.000000                  5.000000   \n",
       "\n",
       "          weak_foot  ...  mentality_penalties  mentality_composure  \\\n",
       "count  18278.000000  ...         18278.000000         18278.000000   \n",
       "mean       2.944250  ...            48.383357            58.528778   \n",
       "std        0.664656  ...            15.708099            11.880840   \n",
       "min        1.000000  ...             7.000000            12.000000   \n",
       "25%        3.000000  ...            39.000000            51.000000   \n",
       "50%        3.000000  ...            49.000000            60.000000   \n",
       "75%        3.000000  ...            60.000000            67.000000   \n",
       "max        5.000000  ...            92.000000            96.000000   \n",
       "\n",
       "       defending_marking  defending_standing_tackle  defending_sliding_tackle  \\\n",
       "count       18278.000000               18278.000000              18278.000000   \n",
       "mean           46.848889                  47.640333                 45.606631   \n",
       "std            20.091287                  21.585641                 21.217734   \n",
       "min             1.000000                   5.000000                  3.000000   \n",
       "25%            29.000000                  27.000000                 24.000000   \n",
       "50%            52.000000                  55.000000                 52.000000   \n",
       "75%            64.000000                  66.000000                 64.000000   \n",
       "max            94.000000                  92.000000                 90.000000   \n",
       "\n",
       "       goalkeeping_diving  goalkeeping_handling  goalkeeping_kicking  \\\n",
       "count        18278.000000          18278.000000         18278.000000   \n",
       "mean            16.572765             16.354853            16.212934   \n",
       "std             17.738069             16.996925            16.613665   \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             90.000000             92.000000            93.000000   \n",
       "\n",
       "       goalkeeping_positioning  goalkeeping_reflexes  \n",
       "count             18278.000000          18278.000000  \n",
       "mean                 16.368038             16.709924  \n",
       "std                  17.136497             18.038125  \n",
       "min                   1.000000              1.000000  \n",
       "25%                   8.000000              8.000000  \n",
       "50%                  11.000000             11.000000  \n",
       "75%                  14.000000             14.000000  \n",
       "max                  91.000000             92.000000  \n",
       "\n",
       "[8 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da7d329c-3ed5-4de0-8d07-7048f20287e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18278 entries, 0 to 18277\n",
      "Columns: 104 entries, sofifa_id to rb\n",
      "dtypes: float64(16), int64(45), object(43)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "fifa.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd65410a-4b2d-45c2-ae9d-121b331d28de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of rows in the dataset are: 18278\n",
      "The number of columns in the dataset are: 104\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of rows in the dataset are:\", fifa.shape[0])\n",
    "print(\"The number of columns in the dataset are:\", fifa.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3fd1774-db8f-4bbb-83c7-f10f2ba12ff1",
   "metadata": {},
   "outputs": [
    {
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       "<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</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>32</td>\n",
       "      <td>6/24/1987</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>...</td>\n",
       "      <td>68+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>68+2</td>\n",
       "      <td>63+2</td>\n",
       "      <td>52+2</td>\n",
       "      <td>52+2</td>\n",
       "      <td>52+2</td>\n",
       "      <td>63+2</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>34</td>\n",
       "      <td>2/5/1985</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>53+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>53+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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 Junior</td>\n",
       "      <td>27</td>\n",
       "      <td>2/5/1992</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>...</td>\n",
       "      <td>66+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>46+3</td>\n",
       "      <td>46+3</td>\n",
       "      <td>46+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200389</td>\n",
       "      <td>https://sofifa.com/player/200389/jan-oblak/20/...</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>Jan Oblak</td>\n",
       "      <td>26</td>\n",
       "      <td>1/7/1993</td>\n",
       "      <td>188</td>\n",
       "      <td>87</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183277</td>\n",
       "      <td>https://sofifa.com/player/183277/eden-hazard/2...</td>\n",
       "      <td>E. Hazard</td>\n",
       "      <td>Eden Hazard</td>\n",
       "      <td>28</td>\n",
       "      <td>1/7/1991</td>\n",
       "      <td>175</td>\n",
       "      <td>74</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>...</td>\n",
       "      <td>66+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>63+3</td>\n",
       "      <td>66+3</td>\n",
       "      <td>61+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>49+3</td>\n",
       "      <td>61+3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 104 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     190871  https://sofifa.com/player/190871/neymar-da-sil...   \n",
       "3     200389  https://sofifa.com/player/200389/jan-oblak/20/...   \n",
       "4     183277  https://sofifa.com/player/183277/eden-hazard/2...   \n",
       "\n",
       "          short_name                            long_name  age        dob  \\\n",
       "0           L. Messi       Lionel Andrés Messi Cuccittini   32  6/24/1987   \n",
       "1  Cristiano Ronaldo  Cristiano Ronaldo dos Santos Aveiro   34   2/5/1985   \n",
       "2          Neymar Jr        Neymar da Silva Santos Junior   27   2/5/1992   \n",
       "3           J. Oblak                            Jan Oblak   26   1/7/1993   \n",
       "4          E. Hazard                          Eden Hazard   28   1/7/1991   \n",
       "\n",
       "   height_cm  weight_kg nationality                 club  ...   lwb   ldm  \\\n",
       "0        170         72   Argentina         FC Barcelona  ...  68+2  66+2   \n",
       "1        187         83    Portugal             Juventus  ...  65+3  61+3   \n",
       "2        175         68      Brazil  Paris Saint-Germain  ...  66+3  61+3   \n",
       "3        188         87    Slovenia      Atlético Madrid  ...   NaN   NaN   \n",
       "4        175         74     Belgium          Real Madrid  ...  66+3  63+3   \n",
       "\n",
       "    cdm   rdm   rwb    lb   lcb    cb   rcb    rb  \n",
       "0  66+2  66+2  68+2  63+2  52+2  52+2  52+2  63+2  \n",
       "1  61+3  61+3  65+3  61+3  53+3  53+3  53+3  61+3  \n",
       "2  61+3  61+3  66+3  61+3  46+3  46+3  46+3  61+3  \n",
       "3   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "4  63+3  63+3  66+3  61+3  49+3  49+3  49+3  61+3  \n",
       "\n",
       "[5 rows x 104 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29a6bf39-4ef8-4050-957c-a7dc3798adc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's try to delete as many columns as we can, sofifa_id, player_url, long_name and dob won't be useful for performing lets delete these columns.\n",
    "fifa.drop([\"sofifa_id\", \"player_url\", \"long_name\", \"dob\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4064d170-970e-494f-bf62-b21e9fbdb0d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['short_name', 'age', 'height_cm', 'weight_kg', 'nationality', 'club',\n",
       "       'overall', 'potential', 'value_eur', 'wage_eur', 'player_positions',\n",
       "       'preferred_foot', 'international_reputation', 'weak_foot',\n",
       "       'skill_moves', 'work_rate', 'body_type', 'real_face',\n",
       "       'release_clause_eur', 'player_tags', 'team_position',\n",
       "       'team_jersey_number', 'loaned_from', 'joined', 'contract_valid_until',\n",
       "       'nation_position', 'nation_jersey_number', 'pace', 'shooting',\n",
       "       'passing', 'dribbling', 'defending', 'physic', 'gk_diving',\n",
       "       'gk_handling', 'gk_kicking', 'gk_reflexes', 'gk_speed',\n",
       "       'gk_positioning', 'player_traits', 'attacking_crossing',\n",
       "       'attacking_finishing', 'attacking_heading_accuracy',\n",
       "       'attacking_short_passing', 'attacking_volleys', 'skill_dribbling',\n",
       "       'skill_curve', 'skill_fk_accuracy', 'skill_long_passing',\n",
       "       'skill_ball_control', 'movement_acceleration', 'movement_sprint_speed',\n",
       "       'movement_agility', 'movement_reactions', 'movement_balance',\n",
       "       'power_shot_power', 'power_jumping', 'power_stamina', 'power_strength',\n",
       "       'power_long_shots', 'mentality_aggression', 'mentality_interceptions',\n",
       "       'mentality_positioning', 'mentality_vision', 'mentality_penalties',\n",
       "       'mentality_composure', 'defending_marking', 'defending_standing_tackle',\n",
       "       'defending_sliding_tackle', 'goalkeeping_diving',\n",
       "       'goalkeeping_handling', 'goalkeeping_kicking',\n",
       "       'goalkeeping_positioning', 'goalkeeping_reflexes', 'ls', 'st', 'rs',\n",
       "       'lw', 'lf', 'cf', 'rf', 'rw', 'lam', 'cam', 'ram', 'lm', 'lcm', 'cm',\n",
       "       'rcm', 'rm', 'lwb', 'ldm', 'cdm', 'rdm', 'rwb', 'lb', 'lcb', 'cb',\n",
       "       'rcb', 'rb'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98cf7422-60dd-4bcc-9246-6b4b143ea69b",
   "metadata": {},
   "outputs": [],
   "source": [
    "players_analyze = fifa[['short_name', 'age', 'height_cm', 'weight_kg', 'nationality', 'club',\n",
    "       'overall', 'potential', 'value_eur', 'wage_eur', 'player_positions',\n",
    "       'preferred_foot', 'international_reputation', 'weak_foot',\n",
    "       'skill_moves', 'work_rate', 'body_type', 'real_face',\n",
    "       'release_clause_eur']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ec154837-48a7-4ba9-bf4a-450110799b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import output_notebook\n",
    "from bokeh.io import show\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.transform import cumsum\n",
    "from bokeh.palettes import Spectral6\n",
    "from bokeh.models import ColumnDataSource\n",
    "from bokeh.layouts import gridplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a6d0bc8-2418-47ed-957c-b422030e7bd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "    <style>\n",
       "        .bk-notebook-logo {\n",
       "            display: block;\n",
       "            width: 20px;\n",
       "            height: 20px;\n",
       "            background-image: url(data:image/png;base64,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);\n",
       "        }\n",
       "    </style>\n",
       "    <div>\n",
       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-notebook-logo\"></a>\n",
       "        <span id=\"d2508d20-172f-4950-b05c-a63556f42f58\">Loading BokehJS ...</span>\n",
       "    </div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  const force = true;\n",
       "\n",
       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "const JS_MIME_TYPE = 'application/javascript';\n",
       "  const HTML_MIME_TYPE = 'text/html';\n",
       "  const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  const CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    const script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    function drop(id) {\n",
       "      const view = Bokeh.index.get_by_id(id)\n",
       "      if (view != null) {\n",
       "        view.model.document.clear()\n",
       "        Bokeh.index.delete(view)\n",
       "      }\n",
       "    }\n",
       "\n",
       "    const cell = handle.cell;\n",
       "\n",
       "    const id = cell.output_area._bokeh_element_id;\n",
       "    const server_id = cell.output_area._bokeh_server_id;\n",
       "\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null) {\n",
       "      drop(id)\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd_clean, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            const id = msg.content.text.trim()\n",
       "            drop(id)\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd_destroy);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    const output_area = handle.output_area;\n",
       "    const output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      const bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      const script_attrs = bk_div.children[0].attributes;\n",
       "      for (let i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "        toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      const toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    const events = require('base/js/events');\n",
       "    const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  const NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    const el = document.getElementById(\"d2508d20-172f-4950-b05c-a63556f42f58\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
       "        if (callback != null)\n",
       "          callback();\n",
       "      });\n",
       "    } finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(css_urls, js_urls, callback) {\n",
       "    if (css_urls == null) css_urls = [];\n",
       "    if (js_urls == null) js_urls = [];\n",
       "\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
       "\n",
       "    function on_load() {\n",
       "      root._bokeh_is_loading--;\n",
       "      if (root._bokeh_is_loading === 0) {\n",
       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
       "        run_callbacks()\n",
       "      }\n",
       "    }\n",
       "\n",
       "    function on_error(url) {\n",
       "      console.error(\"failed to load \" + url);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < css_urls.length; i++) {\n",
       "      const url = css_urls[i];\n",
       "      const element = document.createElement(\"link\");\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.rel = \"stylesheet\";\n",
       "      element.type = \"text/css\";\n",
       "      element.href = url;\n",
       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
       "      document.body.appendChild(element);\n",
       "    }\n",
       "\n",
       "    for (let i = 0; i < js_urls.length; i++) {\n",
       "      const url = js_urls[i];\n",
       "      const element = document.createElement('script');\n",
       "      element.onload = on_load;\n",
       "      element.onerror = on_error.bind(null, url);\n",
       "      element.async = false;\n",
       "      element.src = url;\n",
       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.head.appendChild(element);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  function inject_raw_css(css) {\n",
       "    const element = document.createElement(\"style\");\n",
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       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
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       "  } else {\n",
       "    load_libs(css_urls, js_urls, function() {\n",
       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
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Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    const el = document.getElementById(\"d2508d20-172f-4950-b05c-a63556f42f58\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error(url) {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (let i = 0; i < css_urls.length; i++) {\n      const url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (let i = 0; i < js_urls.length; i++) {\n      const url = js_urls[i];\n      const element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error.bind(null, url);\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.4.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.4.min.js\"];\n  const css_urls = [];\n\n  const inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {\n    }\n  ];\n\n  function run_inline_js() {\n    if (root.Bokeh !== undefined || force === true) {\n          for (let i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\nif (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      const cell = $(document.getElementById(\"d2508d20-172f-4950-b05c-a63556f42f58\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cdaa184d-f583-4628-a294-89ea6481daf5",
   "metadata": {},
   "outputs": [
    {
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       "0"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First let's check whether there are any null values for the 'Age' column.\n",
    "players_analyze['age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9185cbf9-d24a-4374-81ab-3898a6715eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "id": "p1001"
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     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of age is 0.38061976075931975\n"
     ]
    }
   ],
   "source": [
    "hist, edges = np.histogram(players_analyze['age'], density=True, bins = 20)\n",
    "Age = figure(\n",
    "    x_axis_label = 'Age of Players',\n",
    "    title = 'Distribution of Age of Players'\n",
    ")\n",
    "\n",
    "Age.quad(\n",
    "    bottom = 0,\n",
    "    top = hist,\n",
    "    left = edges[:-1],\n",
    "    right = edges[1:],\n",
    "    line_color = 'white'\n",
    ")\n",
    "\n",
    "\n",
    "show(Age)\n",
    "#Skewness measures the asymmetry of a distribution. It tells you whether the data is concentrated more on one side (left or right) of the mean.\n",
    "print(\"Skewness of age is\", players_analyze['age'].skew())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd666ac0-d0a6-4d2c-bf6b-553f19054fa5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The age of the youngest player is 16\n",
      "The age of the oldest player is 42\n"
     ]
    }
   ],
   "source": [
    "#We can see that most of the players are in between 20 to 30 years of age, also more and more players in between 15 to 20 years of age are playing in different clubs and representing their respective countries which is great. \n",
    "#Let's see more statistics related to age of the players.\n",
    "print(\"The age of the youngest player is\", \n",
    "      players_analyze['age'].min())\n",
    "print(\"The age of the oldest player is\", players_analyze['age'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "31a18172-cc51-4fc3-94f3-9921cf2734b8",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
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       "      <th>4764</th>\n",
       "      <td>A. Hložek</td>\n",
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       "      <td>Czech Republic</td>\n",
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       "      <td>49+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6630</th>\n",
       "      <td>Fábio Silva</td>\n",
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       "      <td>Portugal</td>\n",
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       "      <td>38+2</td>\n",
       "      <td>41+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12158</th>\n",
       "      <td>E. Millot</td>\n",
       "      <td>16</td>\n",
       "      <td>175</td>\n",
       "      <td>65</td>\n",
       "      <td>France</td>\n",
       "      <td>AS Monaco</td>\n",
       "      <td>63</td>\n",
       "      <td>86</td>\n",
       "      <td>800000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>58+2</td>\n",
       "      <td>56+2</td>\n",
       "      <td>56+2</td>\n",
       "      <td>56+2</td>\n",
       "      <td>58+2</td>\n",
       "      <td>56+2</td>\n",
       "      <td>51+2</td>\n",
       "      <td>51+2</td>\n",
       "      <td>51+2</td>\n",
       "      <td>56+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12160</th>\n",
       "      <td>S. Esposito</td>\n",
       "      <td>16</td>\n",
       "      <td>186</td>\n",
       "      <td>75</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Inter</td>\n",
       "      <td>63</td>\n",
       "      <td>85</td>\n",
       "      <td>825000</td>\n",
       "      <td>2000</td>\n",
       "      <td>...</td>\n",
       "      <td>41+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>41+2</td>\n",
       "      <td>39+2</td>\n",
       "      <td>37+2</td>\n",
       "      <td>37+2</td>\n",
       "      <td>37+2</td>\n",
       "      <td>39+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14626</th>\n",
       "      <td>A. Velasco</td>\n",
       "      <td>16</td>\n",
       "      <td>167</td>\n",
       "      <td>63</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Independiente</td>\n",
       "      <td>60</td>\n",
       "      <td>83</td>\n",
       "      <td>450000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>55+2</td>\n",
       "      <td>54+2</td>\n",
       "      <td>54+2</td>\n",
       "      <td>54+2</td>\n",
       "      <td>55+2</td>\n",
       "      <td>55+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>55+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17614</th>\n",
       "      <td>D. Obbekjær</td>\n",
       "      <td>16</td>\n",
       "      <td>193</td>\n",
       "      <td>86</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Odense Boldklub</td>\n",
       "      <td>53</td>\n",
       "      <td>71</td>\n",
       "      <td>110000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>44+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>44+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>51+2</td>\n",
       "      <td>51+2</td>\n",
       "      <td>51+2</td>\n",
       "      <td>46+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17782</th>\n",
       "      <td>S. Anderson</td>\n",
       "      <td>16</td>\n",
       "      <td>175</td>\n",
       "      <td>64</td>\n",
       "      <td>United States</td>\n",
       "      <td>Colorado Rapids</td>\n",
       "      <td>52</td>\n",
       "      <td>75</td>\n",
       "      <td>100000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>50+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>48+2</td>\n",
       "      <td>50+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17827</th>\n",
       "      <td>D. Burns</td>\n",
       "      <td>16</td>\n",
       "      <td>176</td>\n",
       "      <td>62</td>\n",
       "      <td>Northern Ireland</td>\n",
       "      <td>St. Patrick's Athletic</td>\n",
       "      <td>52</td>\n",
       "      <td>69</td>\n",
       "      <td>100000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>43+2</td>\n",
       "      <td>39+2</td>\n",
       "      <td>39+2</td>\n",
       "      <td>39+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>42+2</td>\n",
       "      <td>36+2</td>\n",
       "      <td>36+2</td>\n",
       "      <td>36+2</td>\n",
       "      <td>42+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18031</th>\n",
       "      <td>O. Stefánsson</td>\n",
       "      <td>16</td>\n",
       "      <td>190</td>\n",
       "      <td>75</td>\n",
       "      <td>Iceland</td>\n",
       "      <td>IFK Norrköping</td>\n",
       "      <td>51</td>\n",
       "      <td>64</td>\n",
       "      <td>60000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>43+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>45+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>46+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>50+2</td>\n",
       "      <td>46+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18171</th>\n",
       "      <td>J. Starbuck</td>\n",
       "      <td>16</td>\n",
       "      <td>174</td>\n",
       "      <td>69</td>\n",
       "      <td>England</td>\n",
       "      <td>Grimsby Town</td>\n",
       "      <td>49</td>\n",
       "      <td>72</td>\n",
       "      <td>60000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>44+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>44+2</td>\n",
       "      <td>43+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>40+2</td>\n",
       "      <td>43+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18242</th>\n",
       "      <td>R. Wikberg</td>\n",
       "      <td>16</td>\n",
       "      <td>186</td>\n",
       "      <td>72</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>Östersunds FK</td>\n",
       "      <td>48</td>\n",
       "      <td>67</td>\n",
       "      <td>50000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>39+2</td>\n",
       "      <td>41+2</td>\n",
       "      <td>41+2</td>\n",
       "      <td>41+2</td>\n",
       "      <td>39+2</td>\n",
       "      <td>42+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>47+2</td>\n",
       "      <td>42+2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18243</th>\n",
       "      <td>E. Sartorius</td>\n",
       "      <td>16</td>\n",
       "      <td>174</td>\n",
       "      <td>69</td>\n",
       "      <td>England</td>\n",
       "      <td>Lincoln City</td>\n",
       "      <td>48</td>\n",
       "      <td>67</td>\n",
       "      <td>60000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>33+2</td>\n",
       "      <td>30+2</td>\n",
       "      <td>30+2</td>\n",
       "      <td>30+2</td>\n",
       "      <td>33+2</td>\n",
       "      <td>32+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>27+2</td>\n",
       "      <td>32+2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          short_name  age  height_cm  weight_kg       nationality  \\\n",
       "4764       A. Hložek   16        185         80    Czech Republic   \n",
       "6630     Fábio Silva   16        185         75          Portugal   \n",
       "12158      E. Millot   16        175         65            France   \n",
       "12160    S. Esposito   16        186         75             Italy   \n",
       "14626     A. Velasco   16        167         63         Argentina   \n",
       "17614    D. Obbekjær   16        193         86           Denmark   \n",
       "17782    S. Anderson   16        175         64     United States   \n",
       "17827       D. Burns   16        176         62  Northern Ireland   \n",
       "18031  O. Stefánsson   16        190         75           Iceland   \n",
       "18171    J. Starbuck   16        174         69           England   \n",
       "18242     R. Wikberg   16        186         72            Sweden   \n",
       "18243   E. Sartorius   16        174         69           England   \n",
       "\n",
       "                         club  overall  potential  value_eur  wage_eur  ...  \\\n",
       "4764             Sparta Praha       70         86    3500000      1000  ...   \n",
       "6630                 FC Porto       68         85    1800000      1000  ...   \n",
       "12158               AS Monaco       63         86     800000      1000  ...   \n",
       "12160                   Inter       63         85     825000      2000  ...   \n",
       "14626           Independiente       60         83     450000      1000  ...   \n",
       "17614         Odense Boldklub       53         71     110000      1000  ...   \n",
       "17782         Colorado Rapids       52         75     100000      1000  ...   \n",
       "17827  St. Patrick's Athletic       52         69     100000      1000  ...   \n",
       "18031          IFK Norrköping       51         64      60000      1000  ...   \n",
       "18171            Grimsby Town       49         72      60000      1000  ...   \n",
       "18242           Östersunds FK       48         67      50000      1000  ...   \n",
       "18243            Lincoln City       48         67      60000      1000  ...   \n",
       "\n",
       "        lwb   ldm   cdm   rdm   rwb    lb   lcb    cb   rcb    rb  \n",
       "4764   52+2  50+2  50+2  50+2  52+2  49+2  46+2  46+2  46+2  49+2  \n",
       "6630   43+2  40+2  40+2  40+2  43+2  41+2  38+2  38+2  38+2  41+2  \n",
       "12158  58+2  56+2  56+2  56+2  58+2  56+2  51+2  51+2  51+2  56+2  \n",
       "12160  41+2  40+2  40+2  40+2  41+2  39+2  37+2  37+2  37+2  39+2  \n",
       "14626  55+2  54+2  54+2  54+2  55+2  55+2  50+2  50+2  50+2  55+2  \n",
       "17614  44+2  48+2  48+2  48+2  44+2  46+2  51+2  51+2  51+2  46+2  \n",
       "17782  50+2  48+2  48+2  48+2  50+2  50+2  48+2  48+2  48+2  50+2  \n",
       "17827  43+2  39+2  39+2  39+2  43+2  42+2  36+2  36+2  36+2  42+2  \n",
       "18031  43+2  45+2  45+2  45+2  43+2  46+2  50+2  50+2  50+2  46+2  \n",
       "18171  44+2  43+2  43+2  43+2  44+2  43+2  40+2  40+2  40+2  43+2  \n",
       "18242  39+2  41+2  41+2  41+2  39+2  42+2  47+2  47+2  47+2  42+2  \n",
       "18243  33+2  30+2  30+2  30+2  33+2  32+2  27+2  27+2  27+2  32+2  \n",
       "\n",
       "[12 rows x 100 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see the statistics of the youngest and the oldest player.\n",
    "fifa.loc[players_analyze['age'] == players_analyze['age'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d0850f93-fb64-4ba8-bbaf-a6a7e597b6e5",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</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>11832</th>\n",
       "      <td>C. Muñoz</td>\n",
       "      <td>42</td>\n",
       "      <td>177</td>\n",
       "      <td>73</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>CD Universidad de Concepción</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>50000</td>\n",
       "      <td>1000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13003</th>\n",
       "      <td>H. Sulaimani</td>\n",
       "      <td>42</td>\n",
       "      <td>173</td>\n",
       "      <td>70</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Al Ahli</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>...</td>\n",
       "      <td>62+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>62+2</td>\n",
       "      <td>62+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>66+2</td>\n",
       "      <td>62+2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         short_name  age  height_cm  weight_kg   nationality  \\\n",
       "11832      C. Muñoz   42        177         73     Argentina   \n",
       "13003  H. Sulaimani   42        173         70  Saudi Arabia   \n",
       "\n",
       "                               club  overall  potential  value_eur  wage_eur  \\\n",
       "11832  CD Universidad de Concepción       64         64      50000      1000   \n",
       "13003                       Al Ahli       63         63          0      5000   \n",
       "\n",
       "       ...   lwb   ldm   cdm   rdm   rwb    lb   lcb    cb   rcb    rb  \n",
       "11832  ...   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "13003  ...  62+2  66+2  66+2  66+2  62+2  62+2  66+2  66+2  66+2  62+2  \n",
       "\n",
       "[2 rows x 100 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa.loc[players_analyze['age'] == players_analyze['age'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "40914e27-dffa-452c-afed-8e0861be9deb",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>32</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  short_name  age  overall\n",
       "0   L. Messi   32       94"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see the age of the player who has the highest overall score and highest value_eur.\n",
    "fifa.loc[players_analyze['overall'] == players_analyze['overall'].max()][['short_name', 'age', 'overall']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb09d06a-ab89-4c80-8ef0-43caf77e10b2",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>value_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>27</td>\n",
       "      <td>105500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  short_name  age  value_eur\n",
       "2  Neymar Jr   27  105500000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fifa.loc[players_analyze['value_eur'] == players_analyze['value_eur'].max()][['short_name', 'age', 'value_eur']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aabc8595-64f6-4085-93d3-274794ae932c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Height in cm\n",
    "#Checking for null values\n",
    "players_analyze['height_cm'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48d6ee01-bc32-4848-b7fd-1e9d50b7c2c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"ef8ec76c-1748-4e5e-934b-dbe60c85cf52\" data-root-id=\"p1046\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"e7daa2fe-d441-4775-ae19-fd03b428ae66\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1046\",\"attributes\":{\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1047\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1048\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1056\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1057\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1049\",\"attributes\":{\"text\":\"Distribution of Height of Players\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1085\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1079\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1080\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1081\"},\"data\":{\"type\":\"map\",\"entries\":[[\"top\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"UYtvW/1EHT+Rhtw2y48hP4JWkxR41FA/F3LcuvlHYD9RBQHtrlOAP67eSk6Cn4s/HFimCbBgnD9DexxhZdegPyLcAYPGxa0/E8vThe8cqD+Lq2WDuJKhP6hG6s+aI7I/cAoBTKOloD8+ztxoKAumP6uB3NfWPZE/De5Ka1+VjD+AOkpu9mBxP1kQb3MUllU/NaiTBL7zNT/tRwHAF30kPw==\"},\"shape\":[20],\"dtype\":\"float64\",\"order\":\"little\"}],[\"left\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AAAAAACAY0BmZmZmZs5jQM3MzMzMHGRAMzMzMzNrZECamZmZmblkQAAAAAAACGVAZmZmZmZWZUDNzMzMzKRlQDMzMzMz82VAmpmZmZlBZkAAAAAAAJBmQGZmZmZm3mZAzczMzMwsZ0AzMzMzM3tnQJqZmZmZyWdAAAAAAAAYaEBmZmZmZmZoQM3MzMzMtGhAMzMzMzMDaUCamZmZmVFpQA==\"},\"shape\":[20],\"dtype\":\"float64\",\"order\":\"little\"}],[\"right\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"ZmZmZmbOY0DNzMzMzBxkQDMzMzMza2RAmpmZmZm5ZEAAAAAAAAhlQGZmZmZmVmVAzczMzMykZUAzMzMzM/NlQJqZmZmZQWZAAAAAAACQZkBmZmZmZt5mQM3MzMzMLGdAMzMzMzN7Z0CamZmZmclnQAAAAAAAGGhAZmZmZmZmaEDNzMzMzLRoQDMzMzMzA2lAmpmZmZlRaUAAAAAAAKBpQA==\"},\"shape\":[20],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1086\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1087\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p1082\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"value\",\"value\":0},\"top\":{\"type\":\"field\",\"field\":\"top\"},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p1083\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"value\",\"value\":0},\"top\":{\"type\":\"field\",\"field\":\"top\"},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p1084\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"value\",\"value\":0},\"top\":{\"type\":\"field\",\"field\":\"top\"},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1055\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1068\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1069\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1070\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1071\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1076\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1077\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1078\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1063\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1064\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1065\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1066\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1058\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1059\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1060\"},\"axis_label\":\"Height of Players\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1061\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1062\",\"attributes\":{\"axis\":{\"id\":\"p1058\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1067\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1063\"}}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"e7daa2fe-d441-4775-ae19-fd03b428ae66\",\"roots\":{\"p1046\":\"ef8ec76c-1748-4e5e-934b-dbe60c85cf52\"},\"root_ids\":[\"p1046\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1046"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of height is -0.03819688576323108\n"
     ]
    }
   ],
   "source": [
    "hist, edges = np.histogram(players_analyze['height_cm'], density=True, bins = 20)\n",
    "Height = figure(\n",
    "    x_axis_label = 'Height of Players',\n",
    "    title = 'Distribution of Height of Players',\n",
    ")\n",
    "\n",
    "Height.quad(\n",
    "    bottom = 0,\n",
    "    top = hist,\n",
    "    left = edges[:-1],\n",
    "    right = edges[1:],\n",
    "    line_color = 'white'\n",
    ")\n",
    "\n",
    "\n",
    "show(Height)\n",
    "print(\"Skewness of height is\", players_analyze['height_cm'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d19a1e0d-dfe9-4773-8619-ab72eef66a58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The height of the shortest player is 156 cm and his name is N. Barrios\n",
      "The height of the tallest player is 205 cm and his name is T. Holý\n"
     ]
    }
   ],
   "source": [
    "print(\"The height of the shortest player is {} cm and his name is {}\".format(players_analyze['height_cm'].min(),\n",
    "            list(fifa.loc[fifa['height_cm'] == players_analyze['height_cm'].min()]['short_name'])[0]))\n",
    "\n",
    "print(\"The height of the tallest player is {} cm and his name is {}\".format(players_analyze['height_cm'].max(), \n",
    "            list(fifa.loc[fifa['height_cm'] == players_analyze['height_cm'].max()]['short_name'])[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "21fac6ad-a890-4f89-89d4-b2e7a3cebb11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Weight in kg\n",
    "#Checking for null values\n",
    "players_analyze['weight_kg'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "99f352a7-c114-4381-bb4e-741f5fd216ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"f0bbfad4-1a96-4ab5-8848-717a08281efa\" data-root-id=\"p1091\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"e67b2325-d2f0-4c6f-91ff-67d02fb92a4f\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1091\",\"attributes\":{\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1092\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1093\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1101\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1102\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1094\",\"attributes\":{\"text\":\"Distribution of Weight of Players\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1130\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1124\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1125\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1126\"},\"data\":{\"type\":\"map\",\"entries\":[[\"top\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"8soBQCGvDD+4CwEQfrswPy8tAdLt0lI/jDWBwslYcz8Bi8FIELCIP3+MIY/3x5g/SJEhPXUUqT/qe5Gbo76nPxfYgd56ga0/EHwBiQfBpz+yQgFhIyukP5OrIXoouZo/ttLBem0rjT/GwwG7ZDx8PxenAadycWo/ZygBJHCGUj/XHgHIdO0xP3V+AWBG5yc/9jEBgGsfAz/2MQGAax8DPw==\"},\"shape\":[20],\"dtype\":\"float64\",\"order\":\"little\"}],[\"left\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AAAAAAAASUAAAAAAAIBKQAAAAAAAAExAAAAAAACATUAAAAAAAABPQAAAAAAAQFBAAAAAAAAAUUAAAAAAAMBRQAAAAAAAgFJAAAAAAABAU0AAAAAAAABUQAAAAAAAwFRAAAAAAACAVUAAAAAAAEBWQAAAAAAAAFdAAAAAAADAV0AAAAAAAIBYQAAAAAAAQFlAAAAAAAAAWkAAAAAAAMBaQA==\"},\"shape\":[20],\"dtype\":\"float64\",\"order\":\"little\"}],[\"right\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"AAAAAACASkAAAAAAAABMQAAAAAAAgE1AAAAAAAAAT0AAAAAAAEBQQAAAAAAAAFFAAAAAAADAUUAAAAAAAIBSQAAAAAAAQFNAAAAAAAAAVEAAAAAAAMBUQAAAAAAAgFVAAAAAAABAVkAAAAAAAABXQAAAAAAAwFdAAAAAAACAWEAAAAAAAEBZQAAAAAAAAFpAAAAAAADAWkAAAAAAAIBbQA==\"},\"shape\":[20],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1131\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1132\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p1127\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"value\",\"value\":0},\"top\":{\"type\":\"field\",\"field\":\"top\"},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p1128\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"value\",\"value\":0},\"top\":{\"type\":\"field\",\"field\":\"top\"},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Quad\",\"id\":\"p1129\",\"attributes\":{\"left\":{\"type\":\"field\",\"field\":\"left\"},\"right\":{\"type\":\"field\",\"field\":\"right\"},\"bottom\":{\"type\":\"value\",\"value\":0},\"top\":{\"type\":\"field\",\"field\":\"top\"},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"value\",\"value\":\"#1f77b4\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1100\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p1113\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p1114\",\"attributes\":{\"renderers\":\"auto\"}},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p1115\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p1116\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left\":{\"type\":\"number\",\"value\":\"nan\"},\"right\":{\"type\":\"number\",\"value\":\"nan\"},\"top\":{\"type\":\"number\",\"value\":\"nan\"},\"bottom\":{\"type\":\"number\",\"value\":\"nan\"},\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p1121\"},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p1122\"},{\"type\":\"object\",\"name\":\"HelpTool\",\"id\":\"p1123\"}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1108\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1109\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1110\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1111\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1103\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1104\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1105\"},\"axis_label\":\"Weight of Players\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1106\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1107\",\"attributes\":{\"axis\":{\"id\":\"p1103\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1112\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1108\"}}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"e67b2325-d2f0-4c6f-91ff-67d02fb92a4f\",\"roots\":{\"p1091\":\"f0bbfad4-1a96-4ab5-8848-717a08281efa\"},\"root_ids\":[\"p1091\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1091"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness of weight is 0.22080235007640786\n"
     ]
    }
   ],
   "source": [
    "hist, edges = np.histogram(players_analyze['weight_kg'], density=True, bins = 20)\n",
    "Weight = figure(\n",
    "    x_axis_label = 'Weight of Players',\n",
    "    title = 'Distribution of Weight of Players'\n",
    ")\n",
    "\n",
    "Weight.quad(\n",
    "    bottom = 0,\n",
    "    top = hist,\n",
    "    left = edges[:-1],\n",
    "    right = edges[1:],\n",
    "    line_color = 'white'\n",
    ")\n",
    "\n",
    "\n",
    "show(Weight)\n",
    "print(\"Skewness of weight is\", players_analyze['weight_kg'].skew())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16a0123d-6d05-4dbb-a273-3ffcb3d22579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The weight of the lightest player is 50 kg and his name is B. Al Mutairi\n",
      "The weight of the heaviest player is 110 kg and his name is A. Akinfenwa\n"
     ]
    }
   ],
   "source": [
    "print(\"The weight of the lightest player is {} kg and his name is {}\".format(players_analyze['weight_kg'].min(),\n",
    "            list(players_analyze.loc[players_analyze['weight_kg'] == players_analyze['weight_kg'].min()]['short_name'])[0]))\n",
    "\n",
    "print(\"The weight of the heaviest player is {} kg and his name is {}\".format(players_analyze['weight_kg'].max(),\n",
    "            list(players_analyze.loc[players_analyze['weight_kg'] == players_analyze['weight_kg'].max()]['short_name'])[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "38f8c0be-5da8-4684-8178-72ef8dbbc932",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10693</th>\n",
       "      <td>A. Akinfenwa</td>\n",
       "      <td>37</td>\n",
       "      <td>178</td>\n",
       "      <td>110</td>\n",
       "      <td>England</td>\n",
       "      <td>Wycombe Wanderers</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>190000</td>\n",
       "      <td>2000</td>\n",
       "      <td>ST</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Low/Low</td>\n",
       "      <td>Akinfenwa</td>\n",
       "      <td>Yes</td>\n",
       "      <td>333000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         short_name  age  height_cm  weight_kg nationality               club  \\\n",
       "10693  A. Akinfenwa   37        178        110     England  Wycombe Wanderers   \n",
       "\n",
       "       overall  potential  value_eur  wage_eur player_positions  \\\n",
       "10693       65         65     190000      2000               ST   \n",
       "\n",
       "      preferred_foot  international_reputation  weak_foot  skill_moves  \\\n",
       "10693          Right                         1          3            2   \n",
       "\n",
       "      work_rate  body_type real_face  release_clause_eur  \n",
       "10693   Low/Low  Akinfenwa       Yes            333000.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see statistics of both of these players\n",
    "players_analyze.loc[players_analyze['weight_kg'] == players_analyze['weight_kg'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "20177a24-20a8-4814-a27d-821bf02221c4",
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13070</th>\n",
       "      <td>B. Al Mutairi</td>\n",
       "      <td>29</td>\n",
       "      <td>168</td>\n",
       "      <td>50</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>Al Fayha</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>300000</td>\n",
       "      <td>4000</td>\n",
       "      <td>LWB</td>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>495000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          short_name  age  height_cm  weight_kg   nationality      club  \\\n",
       "13070  B. Al Mutairi   29        168         50  Saudi Arabia  Al Fayha   \n",
       "\n",
       "       overall  potential  value_eur  wage_eur player_positions  \\\n",
       "13070       63         63     300000      4000              LWB   \n",
       "\n",
       "      preferred_foot  international_reputation  weak_foot  skill_moves  \\\n",
       "13070           Left                         1          3            2   \n",
       "\n",
       "           work_rate body_type real_face  release_clause_eur  \n",
       "13070  Medium/Medium      Lean        No            495000.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze.loc[players_analyze['weight_kg'] == players_analyze['weight_kg'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a7ebf18a-ff16-462d-b58d-45b7e96f4a1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Nationality\n",
    "players_analyze['nationality'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e00cd6f-ed09-47f0-9499-1643e4ca5c5d",
   "metadata": {},
   "outputs": [
    {
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       "  function embed_document(root) {\n",
       "  const docs_json = {\"e3239174-4570-4413-97a1-54345d5dfb5b\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1139\",\"attributes\":{\"width\":1000,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1149\",\"attributes\":{\"factors\":[\"England\",\"Germany\",\"Spain\",\"France\",\"Argentina\",\"Brazil\",\"Italy\",\"Colombia\",\"Japan\",\"Netherlands\",\"China PR\",\"Chile\",\"Sweden\",\"Norway\",\"Republic of Ireland\",\"United States\",\"Denmark\",\"Portugal\",\"Mexico\",\"Poland\",\"Korea Republic\",\"Austria\",\"Saudi Arabia\",\"Turkey\",\"Romania\",\"Scotland\",\"Belgium\",\"Switzerland\",\"Australia\",\"Uruguay\",\"Serbia\",\"Ghana\",\"Senegal\",\"Croatia\",\"Nigeria\",\"Wales\",\"Ivory Coast\",\"Czech 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Count\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1169\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1136\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1137\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1138\"},\"data\":{\"type\":\"map\",\"entries\":[[\"Country\",[\"England\",\"Germany\",\"Spain\",\"France\",\"Argentina\",\"Brazil\",\"Italy\",\"Colombia\",\"Japan\",\"Netherlands\",\"China PR\",\"Chile\",\"Sweden\",\"Norway\",\"Republic of Ireland\",\"United States\",\"Denmark\",\"Portugal\",\"Mexico\",\"Poland\",\"Korea Republic\",\"Austria\",\"Saudi Arabia\",\"Turkey\",\"Romania\",\"Scotland\",\"Belgium\",\"Switzerland\",\"Australia\",\"Uruguay\",\"Serbia\",\"Ghana\",\"Senegal\",\"Croatia\",\"Nigeria\",\"Wales\",\"Ivory Coast\",\"Czech 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       "  const render_items = [{\"docid\":\"e3239174-4570-4413-97a1-54345d5dfb5b\",\"roots\":{\"p1139\":\"a2755405-94ad-44c1-8079-4dd7ffacca74\"},\"root_ids\":[\"p1139\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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       "    }, 10, root)\n",
       "  }\n",
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      "application/vnd.bokehjs_exec.v0+json": ""
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   ],
   "source": [
    "countries = list(players_analyze['nationality'].value_counts()[:40].index)\n",
    "count = list(players_analyze['nationality'].value_counts()[:40].values)\n",
    "source = ColumnDataSource(data = dict(Country = countries, counts = count, color = ['teal'] * 40))\n",
    "\n",
    "p = figure(x_range = countries, height = 600, width = 1000, title = \"Each Nationality Player Count\", tools = \"hover\", tooltips = \"@Country: @counts\")\n",
    "p.vbar(x = 'Country', top = 'counts', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p.xgrid.grid_line_color = None\n",
    "p.y_range.start = 0\n",
    "p.xaxis.major_label_orientation = \"vertical\"\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bffbb224-0d3a-417f-a4ad-715746ee95d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12155</th>\n",
       "      <td>G. Chatterjee</td>\n",
       "      <td>33</td>\n",
       "      <td>195</td>\n",
       "      <td>84</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>GK</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13102</th>\n",
       "      <td>P. Bhatt</td>\n",
       "      <td>34</td>\n",
       "      <td>182</td>\n",
       "      <td>78</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13103</th>\n",
       "      <td>B. Raj</td>\n",
       "      <td>31</td>\n",
       "      <td>175</td>\n",
       "      <td>69</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RB</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13970</th>\n",
       "      <td>A. Chakraborty</td>\n",
       "      <td>33</td>\n",
       "      <td>189</td>\n",
       "      <td>79</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>CB</td>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13971</th>\n",
       "      <td>H. Bhandari</td>\n",
       "      <td>30</td>\n",
       "      <td>178</td>\n",
       "      <td>74</td>\n",
       "      <td>India</td>\n",
       "      <td>India</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>RM, LM</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           short_name  age  height_cm  weight_kg nationality   club  overall  \\\n",
       "12155   G. Chatterjee   33        195         84       India  India       64   \n",
       "13102        P. Bhatt   34        182         78       India  India       63   \n",
       "13103          B. Raj   31        175         69       India  India       63   \n",
       "13970  A. Chakraborty   33        189         79       India  India       62   \n",
       "13971     H. Bhandari   30        178         74       India  India       62   \n",
       "\n",
       "       potential  value_eur  wage_eur player_positions preferred_foot  \\\n",
       "12155         64          0         0               GK          Right   \n",
       "13102         63          0         0               ST           Left   \n",
       "13103         63          0         0               RB          Right   \n",
       "13970         62          0         0               CB           Left   \n",
       "13971         62          0         0           RM, LM          Right   \n",
       "\n",
       "       international_reputation  weak_foot  skill_moves      work_rate  \\\n",
       "12155                         1          3            1  Medium/Medium   \n",
       "13102                         1          3            2    Medium/High   \n",
       "13103                         1          2            2  Medium/Medium   \n",
       "13970                         1          3            2    Medium/High   \n",
       "13971                         1          3            3  Medium/Medium   \n",
       "\n",
       "      body_type real_face  release_clause_eur  \n",
       "12155    Normal        No                 NaN  \n",
       "13102    Normal        No                 NaN  \n",
       "13103    Normal        No                 NaN  \n",
       "13970      Lean        No                 NaN  \n",
       "13971      Lean        No                 NaN  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check the number of players belonging to India.\n",
    "players_analyze.loc[players_analyze['nationality'] == 'India'].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0190b56d-9c61-4d52-a9fc-fd082953081a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"c424413e-1981-4a82-b689-9428ef837ccf\" data-root-id=\"p1175\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"0d5b769b-bece-4918-9829-750749ab969b\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1175\",\"attributes\":{\"height\":400,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1185\",\"attributes\":{\"factors\":[\"G. Chatterjee\",\"P. Bhatt\",\"B. Raj\",\"A. Chakraborty\",\"H. Bhandari\",\"A. Swaminathan\",\"D. Pillai\",\"A. Ginti\",\"A. Khurana\",\"A. Deshpande\",\"T. Atwal\",\"B. Sidhu\",\"R. Nadkarni\",\"V. Boral\",\"D. Sundaram\",\"R. Jayaraman\",\"D. Bajwa\",\"O. Patla\",\"P. Nagarajan\",\"T. Agarwal\",\"A. Varkay\",\"C. Palan\",\"D. Singhal\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1177\",\"attributes\":{\"start\":0}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1186\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1187\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1178\",\"attributes\":{\"text\":\"Overall Score of Each Indian Player \"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1205\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1172\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1173\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1174\"},\"data\":{\"type\":\"map\",\"entries\":[[\"Names\",[\"G. Chatterjee\",\"P. Bhatt\",\"B. Raj\",\"A. Chakraborty\",\"H. Bhandari\",\"A. Swaminathan\",\"D. Pillai\",\"A. Ginti\",\"A. Khurana\",\"A. Deshpande\",\"T. Atwal\",\"B. Sidhu\",\"R. Nadkarni\",\"V. Boral\",\"D. Sundaram\",\"R. Jayaraman\",\"D. Bajwa\",\"O. Patla\",\"P. Nagarajan\",\"T. Agarwal\",\"A. Varkay\",\"C. Palan\",\"D. Singhal\"]],[\"Score\",[64,63,63,62,62,62,61,60,60,60,60,60,60,59,59,59,59,58,58,58,58,58,57]],[\"color\",[\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\",\"salmon\"]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1206\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1207\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1202\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Names\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1203\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Names\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1204\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Names\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1184\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1198\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@Names: @Score\"}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1193\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1194\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1195\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1196\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1188\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1189\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1190\"},\"major_label_orientation\":\"vertical\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1191\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1192\",\"attributes\":{\"axis\":{\"id\":\"p1188\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1197\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1193\"}}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"0d5b769b-bece-4918-9829-750749ab969b\",\"roots\":{\"p1175\":\"c424413e-1981-4a82-b689-9428ef837ccf\"},\"root_ids\":[\"p1175\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1175"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's plot the overall score of each Indian Player\n",
    "names = players_analyze.loc[players_analyze['nationality'] == 'India']['short_name'].to_list()\n",
    "score = players_analyze.loc[players_analyze['nationality'] == 'India']['overall'].to_list()\n",
    "\n",
    "source = ColumnDataSource(data = dict(Names = names, Score = score, color = ['salmon'] * 23))\n",
    "\n",
    "p1 = figure(x_range = names, height = 400, width = 600, title = \"Overall Score of Each Indian Player \", \n",
    "            tools = \"hover\", tooltips = \"@Names: @Score\")\n",
    "\n",
    "p1.vbar(x = 'Names', top = 'Score', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p1.xgrid.grid_line_color = None\n",
    "p1.y_range.start = 0\n",
    "p1.xaxis.major_label_orientation = \"vertical\"\n",
    "show(p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fe2b6fd9-fdcb-47a0-b798-fe89edd4cd40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[63.25, 65.94, 69.95, 67.42, 69.12, 71.16, 67.66, 65.68, 63.69, 68.15, 59.49, 64.81, 63.79, 63.03, 61.01, 64.53, 63.68, 70.51, 65.81, 63.49]\n"
     ]
    }
   ],
   "source": [
    "#Let's see the average of players belonging to England, Germany and Spain\n",
    "def avg_score(country_name):\n",
    "    \"\"\"\n",
    "    A function which is used to return the average score of players belonging to country given as argument.\n",
    "    \"\"\"\n",
    "    \n",
    "    return np.mean(players_analyze.loc[players_analyze['nationality'] == country_name]['overall'])\n",
    "\n",
    "top_20_countries = countries[:20] # Taking the 20 country names(to which players belong) from the 'countries' variable defined above.\n",
    "avg_scores = []\n",
    "\n",
    "for i in top_20_countries:\n",
    "    avg_scores.append(round(avg_score(i), 2))\n",
    "    \n",
    "print(avg_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a6ec903-ead7-4feb-9fa3-dc091a62a9ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"b08cd613-e10b-4289-92d6-1ce227d40cfc\" data-root-id=\"p1211\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"fb76bcec-0763-48cb-8ae9-1e14b7c596fe\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1211\",\"attributes\":{\"height\":400,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1221\",\"attributes\":{\"factors\":[\"England\",\"Germany\",\"Spain\",\"France\",\"Argentina\",\"Brazil\",\"Italy\",\"Colombia\",\"Japan\",\"Netherlands\",\"China PR\",\"Chile\",\"Sweden\",\"Norway\",\"Republic of Ireland\",\"United States\",\"Denmark\",\"Portugal\",\"Mexico\",\"Poland\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1213\",\"attributes\":{\"start\":0}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1222\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1223\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1214\",\"attributes\":{\"text\":\"Average Score of Players Belonging to Different Countries \"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1241\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1208\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1209\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1210\"},\"data\":{\"type\":\"map\",\"entries\":[[\"Countries\",[\"England\",\"Germany\",\"Spain\",\"France\",\"Argentina\",\"Brazil\",\"Italy\",\"Colombia\",\"Japan\",\"Netherlands\",\"China PR\",\"Chile\",\"Sweden\",\"Norway\",\"Republic of Ireland\",\"United States\",\"Denmark\",\"Portugal\",\"Mexico\",\"Poland\"]],[\"Score\",[63.25,65.94,69.95,67.42,69.12,71.16,67.66,65.68,63.69,68.15,59.49,64.81,63.79,63.03,61.01,64.53,63.68,70.51,65.81,63.49]],[\"color\",[\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\",\"pink\"]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1242\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1243\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1238\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Countries\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1239\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Countries\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1240\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Countries\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1220\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1234\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@Countries: @Score\"}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1229\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1230\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1231\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1232\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1224\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1225\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1226\"},\"major_label_orientation\":\"vertical\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1227\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1228\",\"attributes\":{\"axis\":{\"id\":\"p1224\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1233\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1229\"}}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"fb76bcec-0763-48cb-8ae9-1e14b7c596fe\",\"roots\":{\"p1211\":\"b08cd613-e10b-4289-92d6-1ce227d40cfc\"},\"root_ids\":[\"p1211\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1211"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "source = ColumnDataSource(data = dict(Countries = top_20_countries, Score = avg_scores, color = ['pink'] * 20))\n",
    "\n",
    "p2 = figure(x_range = top_20_countries, height = 400, width = 600, title = \"Average Score of Players Belonging to Different Countries \",\n",
    "            tools = \"hover\", tooltips = \"@Countries: @Score\")\n",
    "\n",
    "p2.vbar(x = 'Countries', top = 'Score', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p2.xgrid.grid_line_color = None\n",
    "p2.y_range.start = 0\n",
    "\n",
    "p2.xaxis.major_label_orientation = \"vertical\"\n",
    "\n",
    "show(p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a7d4bef4-cf5a-4437-8558-18e1b2bf9681",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>32</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>95500000</td>\n",
       "      <td>565000</td>\n",
       "      <td>RW, CF, ST</td>\n",
       "      <td>Left</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Medium/Low</td>\n",
       "      <td>Messi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>195800000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  short_name  age  height_cm  weight_kg nationality          club  overall  \\\n",
       "0   L. Messi   32        170         72   Argentina  FC Barcelona       94   \n",
       "\n",
       "   potential  value_eur  wage_eur player_positions preferred_foot  \\\n",
       "0         94   95500000    565000       RW, CF, ST           Left   \n",
       "\n",
       "   international_reputation  weak_foot  skill_moves   work_rate body_type  \\\n",
       "0                         5          4            4  Medium/Low     Messi   \n",
       "\n",
       "  real_face  release_clause_eur  \n",
       "0       Yes         195800000.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see the highest rated player belongs to which country?\n",
    "players_analyze.loc[ players_analyze['overall'] ==  players_analyze['overall'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f2d043ab-d434-41f0-b119-c51fc96783d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Club\n",
    "players_analyze['club'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "25e30757-f116-4f2b-bda5-17de106717b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "clubs = list(players_analyze['club'].value_counts()[:50].index)\n",
    "count = list(players_analyze['club'].value_counts()[:50].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2a136209-4791-44e8-be0e-da4df9fa30a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "  const docs_json = {\"4a539fc4-b6bc-48eb-8b06-65ec2819f944\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1247\",\"attributes\":{\"width\":1000,\"height\":400,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1257\",\"attributes\":{\"factors\":[\"FC Barcelona\",\"Real Valladolid CF\",\"VfL Wolfsburg\",\"Crystal Palace\",\"Eintracht Frankfurt\",\"Hertha BSC\",\"Leicester City\",\"AS Monaco\",\"SC Paderborn 07\",\"Sevilla FC\",\"Athletic Club de Bilbao\",\"Deportivo Alav\\u00e9s\",\"Juventus\",\"Udinese\",\"Watford\",\"Bournemouth\",\"Lecce\",\"Borussia M\\u00f6nchengladbach\",\"FC Nantes\",\"Newcastle United\",\"Brighton & Hove Albion\",\"Aston Villa\",\"Norwich City\",\"Sheffield United\",\"Southampton\",\"Burnley\",\"1. FC Union Berlin\",\"Hellas Verona\",\"Fortuna D\\u00fcsseldorf\",\"Parma\",\"CD Legan\\u00e9s\",\"FC Augsburg\",\"Everton\",\"1. 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       "  const render_items = [{\"docid\":\"4a539fc4-b6bc-48eb-8b06-65ec2819f944\",\"roots\":{\"p1247\":\"d6cc6cc9-2893-4df3-84ad-31203ea636e6\"},\"root_ids\":[\"p1247\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1247"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "source = ColumnDataSource(data = dict(Clubs = clubs, count = count, color = ['lightskyblue'] * 50))\n",
    "\n",
    "p3 = figure(x_range = clubs, height = 400, width = 1000, title = \"Count of Players in Each Club\", \n",
    "            tools = \"hover\", tooltips = \"@Clubs: @count\")\n",
    "\n",
    "p3.vbar(x = 'Clubs', top = 'count', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p3.xgrid.grid_line_color = None\n",
    "p3.y_range.start = 0\n",
    "p3.xaxis.major_label_orientation = \"vertical\"\n",
    "show(p3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7c5d368b-ed10-4ef1-b9ad-1136b3dac05b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[78.36, 71.94, 73.15, 70.79, 73.55, 71.76, 74.36, 73.27, 66.58, 75.42, 73.73, 72.03, 80.06, 71.85, 75.0, 72.3, 66.27, 72.97, 69.52, 73.33, 72.39, 69.18, 69.33, 68.88, 72.24, 71.3, 68.79, 69.79, 69.76, 69.73, 70.73, 70.33, 75.12, 69.97, 75.94, 72.58, 73.88, 77.06, 76.85, 76.48]\n"
     ]
    }
   ],
   "source": [
    "#Let's see the average scores of clubs where are plotted above.\n",
    "def avg_score(club_name):\n",
    "    \"\"\"\n",
    "    A function which is used to return the average score of players belonging to club given as argument.\n",
    "    \"\"\"\n",
    "    \n",
    "    return np.mean(players_analyze.loc[players_analyze['club'] == club_name]['overall'])\n",
    "\n",
    "clubs_40 = clubs[:40] # Taking the 20 country names(to which players belong) from the 'countries' variable defined above.\n",
    "avg_scores_clubs = []\n",
    "\n",
    "for i in clubs_40:\n",
    "    avg_scores_clubs.append(round(avg_score(i), 2))\n",
    "    \n",
    "print(avg_scores_clubs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "829d14cf-c1ab-4709-8443-ec5c9391099b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"d8eac4a6-dbd8-4f07-8163-994a10c40914\" data-root-id=\"p1283\" style=\"display: contents;\"></div>\n"
      ]
     },
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    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"a67e0c2d-5fce-4d23-9d61-4acce540719c\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1283\",\"attributes\":{\"width\":1000,\"height\":400,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1293\",\"attributes\":{\"factors\":[\"FC Barcelona\",\"Real Valladolid CF\",\"VfL Wolfsburg\",\"Crystal Palace\",\"Eintracht Frankfurt\",\"Hertha BSC\",\"Leicester City\",\"AS Monaco\",\"SC Paderborn 07\",\"Sevilla FC\",\"Athletic Club de Bilbao\",\"Deportivo Alav\\u00e9s\",\"Juventus\",\"Udinese\",\"Watford\",\"Bournemouth\",\"Lecce\",\"Borussia M\\u00f6nchengladbach\",\"FC Nantes\",\"Newcastle United\",\"Brighton & Hove Albion\",\"Aston Villa\",\"Norwich City\",\"Sheffield United\",\"Southampton\",\"Burnley\",\"1. FC Union Berlin\",\"Hellas Verona\",\"Fortuna D\\u00fcsseldorf\",\"Parma\",\"CD Legan\\u00e9s\",\"FC Augsburg\",\"Everton\",\"1. FSV Mainz 05\",\"Lazio\",\"West Ham United\",\"RB Leipzig\",\"Chelsea\",\"Manchester United\",\"Tottenham Hotspur\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1285\",\"attributes\":{\"start\":0}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1294\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1295\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1286\",\"attributes\":{\"text\":\"Average Score of Players Belonging to Different Clubs \"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1313\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1280\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1281\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1282\"},\"data\":{\"type\":\"map\",\"entries\":[[\"Clubs\",[\"FC Barcelona\",\"Real Valladolid CF\",\"VfL Wolfsburg\",\"Crystal Palace\",\"Eintracht Frankfurt\",\"Hertha BSC\",\"Leicester City\",\"AS Monaco\",\"SC Paderborn 07\",\"Sevilla FC\",\"Athletic Club de Bilbao\",\"Deportivo Alav\\u00e9s\",\"Juventus\",\"Udinese\",\"Watford\",\"Bournemouth\",\"Lecce\",\"Borussia M\\u00f6nchengladbach\",\"FC Nantes\",\"Newcastle United\",\"Brighton & Hove Albion\",\"Aston Villa\",\"Norwich City\",\"Sheffield United\",\"Southampton\",\"Burnley\",\"1. FC Union Berlin\",\"Hellas Verona\",\"Fortuna D\\u00fcsseldorf\",\"Parma\",\"CD Legan\\u00e9s\",\"FC Augsburg\",\"Everton\",\"1. FSV Mainz 05\",\"Lazio\",\"West Ham United\",\"RB Leipzig\",\"Chelsea\",\"Manchester United\",\"Tottenham Hotspur\"]],[\"Score\",[78.36,71.94,73.15,70.79,73.55,71.76,74.36,73.27,66.58,75.42,73.73,72.03,80.06,71.85,75.0,72.3,66.27,72.97,69.52,73.33,72.39,69.18,69.33,68.88,72.24,71.3,68.79,69.79,69.76,69.73,70.73,70.33,75.12,69.97,75.94,72.58,73.88,77.06,76.85,76.48]],[\"color\",[\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\",\"tomato\"]]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1314\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1315\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1310\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Clubs\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1311\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Clubs\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1312\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"Clubs\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"Score\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1292\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1306\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@Clubs: @Score\"}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1301\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1302\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1303\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1304\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1296\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1297\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1298\"},\"major_label_orientation\":\"vertical\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1299\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1300\",\"attributes\":{\"axis\":{\"id\":\"p1296\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1305\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1301\"}}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"a67e0c2d-5fce-4d23-9d61-4acce540719c\",\"roots\":{\"p1283\":\"d8eac4a6-dbd8-4f07-8163-994a10c40914\"},\"root_ids\":[\"p1283\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1283"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "source = ColumnDataSource(data = dict(Clubs = clubs_40, Score = avg_scores_clubs, color = ['tomato'] * 40))\n",
    "\n",
    "p4 = figure(x_range = clubs_40, height = 400, width = 1000, title = \"Average Score of Players Belonging to Different Clubs \",\n",
    "            tools = \"hover\", tooltips = \"@Clubs: @Score\")\n",
    "\n",
    "p4.vbar(x = 'Clubs', top = 'Score', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p4.xgrid.grid_line_color = None\n",
    "p4.y_range.start = 0\n",
    "\n",
    "p4.xaxis.major_label_orientation = \"vertical\"\n",
    "\n",
    "show(p4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f56b509c-7d74-4a1e-8d9e-4b9c23338f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.36363636363636"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(players_analyze.loc[players_analyze['club'] == 'FC Barcelona']['overall'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "cf7f7822-ce2d-4f6f-8a36-ccfd245d0f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Overall Score\n",
    "players_analyze['overall'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ddafd50c-76b5-44c6-bcf8-29e9a54bc479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average overall score of all players is  66.24\n"
     ]
    }
   ],
   "source": [
    "print(\"The average overall score of all players is \", round(np.mean(players_analyze['overall']), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6f8631ab-e6b8-4868-90ce-2e2b01392edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The heighest rated player is L. Messi and his rating is 94\n"
     ]
    }
   ],
   "source": [
    "#Checking the highest and the worst rated player\n",
    "print(\"The heighest rated player is {} and his rating is {}\".format(players_analyze.loc[players_analyze['overall'] == \n",
    "                                                                                 players_analyze['overall'].max()]['short_name'][0],\n",
    "                                                                    players_analyze.loc[players_analyze['overall'] == \n",
    "                                                                                 players_analyze['overall'].max()]['overall'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "29f8c38c-02f4-49a5-afdf-788e87792a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>short_name</th>\n",
       "      <th>overall</th>\n",
       "      <th>nationality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18235</th>\n",
       "      <td>Tao Qianglong</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18236</th>\n",
       "      <td>B. McPherson</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18237</th>\n",
       "      <td>J. Hope</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18238</th>\n",
       "      <td>L. Singh</td>\n",
       "      <td>48</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18239</th>\n",
       "      <td>V. Storsve</td>\n",
       "      <td>48</td>\n",
       "      <td>Norway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18240</th>\n",
       "      <td>C. Painter</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18241</th>\n",
       "      <td>M. Peck</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18242</th>\n",
       "      <td>R. Wikberg</td>\n",
       "      <td>48</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18243</th>\n",
       "      <td>E. Sartorius</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18244</th>\n",
       "      <td>K. Barker</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18245</th>\n",
       "      <td>C. Pattison</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>H. Kyprianou</td>\n",
       "      <td>48</td>\n",
       "      <td>Cyprus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18247</th>\n",
       "      <td>Liu Guobo</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18248</th>\n",
       "      <td>T. Klysner</td>\n",
       "      <td>48</td>\n",
       "      <td>Denmark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18249</th>\n",
       "      <td>Shan Huanhuan</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18250</th>\n",
       "      <td>R. Roache</td>\n",
       "      <td>48</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18251</th>\n",
       "      <td>G. Sykes-Kenworthy</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18252</th>\n",
       "      <td>A. Aksungur</td>\n",
       "      <td>48</td>\n",
       "      <td>Turkey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18253</th>\n",
       "      <td>N. Morley</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18254</th>\n",
       "      <td>E. Jääskeläinen</td>\n",
       "      <td>48</td>\n",
       "      <td>Finland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18255</th>\n",
       "      <td>C. Heath</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18256</th>\n",
       "      <td>S. Cartwright</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18257</th>\n",
       "      <td>Song Min Kyu</td>\n",
       "      <td>48</td>\n",
       "      <td>Korea Republic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18258</th>\n",
       "      <td>L. Collins</td>\n",
       "      <td>48</td>\n",
       "      <td>Wales</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18259</th>\n",
       "      <td>H. Sveijer</td>\n",
       "      <td>48</td>\n",
       "      <td>Sweden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18260</th>\n",
       "      <td>A. De Angelis</td>\n",
       "      <td>48</td>\n",
       "      <td>Italy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18261</th>\n",
       "      <td>H. Shirai</td>\n",
       "      <td>48</td>\n",
       "      <td>Japan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18262</th>\n",
       "      <td>S. Callan</td>\n",
       "      <td>48</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18263</th>\n",
       "      <td>F. Nevarez</td>\n",
       "      <td>48</td>\n",
       "      <td>Mexico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18264</th>\n",
       "      <td>Zhang Yufeng</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18265</th>\n",
       "      <td>L. Offord</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18266</th>\n",
       "      <td>Wang Peng</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18267</th>\n",
       "      <td>J. Ryan</td>\n",
       "      <td>48</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18268</th>\n",
       "      <td>M. Gallagher</td>\n",
       "      <td>48</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18269</th>\n",
       "      <td>Huang Jiahui</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18270</th>\n",
       "      <td>M. Sagaf</td>\n",
       "      <td>48</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18271</th>\n",
       "      <td>E. Tweed</td>\n",
       "      <td>48</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18272</th>\n",
       "      <td>P. Martin</td>\n",
       "      <td>48</td>\n",
       "      <td>Republic of Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18273</th>\n",
       "      <td>Shao Shuai</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18274</th>\n",
       "      <td>Xiao Mingjie</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18275</th>\n",
       "      <td>Zhang Wei</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18276</th>\n",
       "      <td>Wang Haijian</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18277</th>\n",
       "      <td>Pan Ximing</td>\n",
       "      <td>48</td>\n",
       "      <td>China PR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               short_name  overall          nationality\n",
       "18235       Tao Qianglong       48             China PR\n",
       "18236        B. McPherson       48              England\n",
       "18237             J. Hope       48              England\n",
       "18238            L. Singh       48               Canada\n",
       "18239          V. Storsve       48               Norway\n",
       "18240          C. Painter       48              England\n",
       "18241             M. Peck       48              England\n",
       "18242          R. Wikberg       48               Sweden\n",
       "18243        E. Sartorius       48              England\n",
       "18244           K. Barker       48              England\n",
       "18245         C. Pattison       48              England\n",
       "18246        H. Kyprianou       48               Cyprus\n",
       "18247           Liu Guobo       48             China PR\n",
       "18248          T. Klysner       48              Denmark\n",
       "18249       Shan Huanhuan       48             China PR\n",
       "18250           R. Roache       48  Republic of Ireland\n",
       "18251  G. Sykes-Kenworthy       48              England\n",
       "18252         A. Aksungur       48               Turkey\n",
       "18253           N. Morley       48              England\n",
       "18254     E. Jääskeläinen       48              Finland\n",
       "18255            C. Heath       48              England\n",
       "18256       S. Cartwright       48              England\n",
       "18257        Song Min Kyu       48       Korea Republic\n",
       "18258          L. Collins       48                Wales\n",
       "18259          H. Sveijer       48               Sweden\n",
       "18260       A. De Angelis       48                Italy\n",
       "18261           H. Shirai       48                Japan\n",
       "18262           S. Callan       48  Republic of Ireland\n",
       "18263          F. Nevarez       48               Mexico\n",
       "18264        Zhang Yufeng       48             China PR\n",
       "18265           L. Offord       48              England\n",
       "18266           Wang Peng       48             China PR\n",
       "18267             J. Ryan       48  Republic of Ireland\n",
       "18268        M. Gallagher       48  Republic of Ireland\n",
       "18269        Huang Jiahui       48             China PR\n",
       "18270            M. Sagaf       48              England\n",
       "18271            E. Tweed       48  Republic of Ireland\n",
       "18272           P. Martin       48  Republic of Ireland\n",
       "18273          Shao Shuai       48             China PR\n",
       "18274        Xiao Mingjie       48             China PR\n",
       "18275           Zhang Wei       48             China PR\n",
       "18276        Wang Haijian       48             China PR\n",
       "18277          Pan Ximing       48             China PR"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze.loc[players_analyze['overall'] == players_analyze['overall'].min()][['short_name', 'overall', 'nationality']]"
   ]
  },
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   "id": "c192b301-9657-4c40-afac-2e35682da8d9",
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     "text": [
      "Skewness of overall score is 0.07089343291701235\n"
     ]
    }
   ],
   "source": [
    "#Distribution of overall score\n",
    "hist, edges = np.histogram(players_analyze['overall'], density=True, bins = 20)\n",
    "overall = figure(\n",
    "    x_axis_label = 'Overall ratings of Players',\n",
    "    title = 'Distribution of Overall Score of Players'\n",
    ")\n",
    "\n",
    "overall.quad(\n",
    "    bottom = 0,\n",
    "    top = hist,\n",
    "    left = edges[:-1],\n",
    "    right = edges[1:],\n",
    "    line_color = 'white'\n",
    ")\n",
    "\n",
    "\n",
    "show(Weight)\n",
    "print(\"Skewness of overall score is\", players_analyze['overall'].skew())\n"
   ]
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  {
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    "#Creating a function which will plot the overall score of each player belonging to a user-input club.\n",
    "def plot_overall_accto_club(club_name):\n",
    "    player_names = players_analyze.loc[players_analyze['club'] == club_name]['short_name'].to_list()\n",
    "    player_overall_scores = players_analyze.loc[players_analyze['club'] == club_name]['overall'].to_list()\n",
    "    \n",
    "    source = ColumnDataSource(data = dict(names = player_names, overall = player_overall_scores, color = ['cornflowerblue'] * len(player_overall_scores)))\n",
    "\n",
    "    p5 = figure(x_range = player_names, height = 400, width = 800, title = \"Overall Score of Players of \" + club_name,\n",
    "                tools = \"hover\", tooltips = \"@names: @overall\")\n",
    "\n",
    "    p5.vbar(x = 'names', top = 'overall', width = 0.9, source = source, color = 'color')\n",
    "    p5.xgrid.grid_line_color = None\n",
    "    p5.y_range.start = 0\n",
    "\n",
    "    p5.xaxis.major_label_orientation = \"vertical\"\n",
    "\n",
    "    show(p5)\n",
    "    \n",
    "    print(\"The average of overall rating of players belonging to {} is {}\".format(club_name, round(np.mean(players_analyze.loc[players_analyze['club'] == club_name]['overall']), 2)))\n"
   ]
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       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
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     "text": [
      "The average of overall rating of players belonging to FC Barcelona is 78.36\n"
     ]
    }
   ],
   "source": [
    "plot_overall_accto_club('FC Barcelona')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "39f1997a-6121-403a-808f-9f5b36c6c2cf",
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       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
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       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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     "text": [
      "The average of overall rating of players belonging to Juventus is 80.06\n"
     ]
    }
   ],
   "source": [
    "plot_overall_accto_club('Juventus')"
   ]
  },
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       "  }\n",
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       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
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     "text": [
      "The average of overall rating of players belonging to Manchester City is 77.0\n"
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    }
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   "source": [
    "plot_overall_accto_club('Manchester City')"
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   "execution_count": 49,
   "id": "e277ebc4-4152-4129-99a0-41f8b0380bdf",
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       "  const render_items = [{\"docid\":\"2a5a052a-6f26-494c-864d-d56876f26609\",\"roots\":{\"p1472\":\"d654a075-250f-4946-853f-22cad087742c\"},\"root_ids\":[\"p1472\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
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       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
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     "text": [
      "The average of overall rating of players belonging to Real Madrid is 80.12\n"
     ]
    }
   ],
   "source": [
    "plot_overall_accto_club('Real Madrid')"
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       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
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       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1508"
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average of overall rating of players belonging to Manchester United is 76.85\n"
     ]
    }
   ],
   "source": [
    "plot_overall_accto_club('Manchester United')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "321c18fe-cb19-4dd4-b203-665c5edeb27b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Potential\n",
    "players_analyze['potential'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a0e779b8-44b6-4658-bb2b-a6466e4701b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K. Mbappé</td>\n",
       "      <td>20</td>\n",
       "      <td>178</td>\n",
       "      <td>73</td>\n",
       "      <td>France</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>93500000</td>\n",
       "      <td>155000</td>\n",
       "      <td>ST, RW</td>\n",
       "      <td>Right</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>High/Low</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>191700000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   short_name  age  height_cm  weight_kg nationality                 club  \\\n",
       "10  K. Mbappé   20        178         73      France  Paris Saint-Germain   \n",
       "\n",
       "    overall  potential  value_eur  wage_eur player_positions preferred_foot  \\\n",
       "10       89         95   93500000    155000           ST, RW          Right   \n",
       "\n",
       "    international_reputation  weak_foot  skill_moves work_rate body_type  \\\n",
       "10                         3          4            5  High/Low    Normal   \n",
       "\n",
       "   real_face  release_clause_eur  \n",
       "10       Yes         191700000.0  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze.loc[players_analyze['potential'] == players_analyze['potential'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "552805d2-e8e2-4fa2-b839-3c2d683b1db3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18233</th>\n",
       "      <td>Zhou Ting</td>\n",
       "      <td>40</td>\n",
       "      <td>182</td>\n",
       "      <td>73</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Dalian YiFang FC</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>LB, RB</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Low/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18234</th>\n",
       "      <td>Zhang Junzhe</td>\n",
       "      <td>28</td>\n",
       "      <td>183</td>\n",
       "      <td>69</td>\n",
       "      <td>China PR</td>\n",
       "      <td>Hebei China Fortune FC</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>30000</td>\n",
       "      <td>2000</td>\n",
       "      <td>RB</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Low/Low</td>\n",
       "      <td>Lean</td>\n",
       "      <td>No</td>\n",
       "      <td>47000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         short_name  age  height_cm  weight_kg nationality  \\\n",
       "18233     Zhou Ting   40        182         73    China PR   \n",
       "18234  Zhang Junzhe   28        183         69    China PR   \n",
       "\n",
       "                         club  overall  potential  value_eur  wage_eur  \\\n",
       "18233        Dalian YiFang FC       49         49          0      1000   \n",
       "18234  Hebei China Fortune FC       49         49      30000      2000   \n",
       "\n",
       "      player_positions preferred_foot  international_reputation  weak_foot  \\\n",
       "18233           LB, RB          Right                         1          3   \n",
       "18234               RB          Right                         1          2   \n",
       "\n",
       "       skill_moves work_rate body_type real_face  release_clause_eur  \n",
       "18233            2  Low/High    Normal        No                 NaN  \n",
       "18234            2   Low/Low      Lean        No             47000.0  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze.loc[players_analyze['potential'] == players_analyze['potential'].min()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "136ad2b0-8388-4d80-988d-e7d5d5cbca8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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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>short_name</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>K. Mbappé</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>89</td>\n",
       "      <td>95</td>\n",
       "      <td>93500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>95500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>M. ter Stegen</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "      <td>67500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>M. de Ligt</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>85</td>\n",
       "      <td>93</td>\n",
       "      <td>50000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>58500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>João Félix</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>80</td>\n",
       "      <td>93</td>\n",
       "      <td>28000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>77500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>G. Donnarumma</td>\n",
       "      <td>Milan</td>\n",
       "      <td>85</td>\n",
       "      <td>92</td>\n",
       "      <td>41500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>J. Sancho</td>\n",
       "      <td>Borussia Dortmund</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>44500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>K. Havertz</td>\n",
       "      <td>Bayer 04 Leverkusen</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>46000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>Vinícius Jr.</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>79</td>\n",
       "      <td>92</td>\n",
       "      <td>22500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>L. Sané</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>61000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>P. Dybala</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>88</td>\n",
       "      <td>92</td>\n",
       "      <td>76500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>105500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>90000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>V. van Dijk</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>90</td>\n",
       "      <td>91</td>\n",
       "      <td>78000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Ederson</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>54500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>P. Pogba</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>72500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>F. de Jong</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>85</td>\n",
       "      <td>91</td>\n",
       "      <td>52000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Alisson</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>58000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            short_name                 club  overall  potential  value_eur\n",
       "10           K. Mbappé  Paris Saint-Germain       89         95   93500000\n",
       "0             L. Messi         FC Barcelona       94         94   95500000\n",
       "6        M. ter Stegen         FC Barcelona       90         93   67500000\n",
       "73          M. de Ligt             Juventus       85         93   50000000\n",
       "1    Cristiano Ronaldo             Juventus       93         93   58500000\n",
       "411         João Félix      Atlético Madrid       80         93   28000000\n",
       "3             J. Oblak      Atlético Madrid       91         93   77500000\n",
       "74       G. Donnarumma                Milan       85         92   41500000\n",
       "108          J. Sancho    Borussia Dortmund       84         92   44500000\n",
       "109         K. Havertz  Bayer 04 Leverkusen       84         92   46000000\n",
       "568       Vinícius Jr.          Real Madrid       79         92   22500000\n",
       "55             L. Sané      Manchester City       86         92   61000000\n",
       "23           P. Dybala             Juventus       88         92   76500000\n",
       "2            Neymar Jr  Paris Saint-Germain       92         92  105500000\n",
       "5         K. De Bruyne      Manchester City       91         91   90000000\n",
       "7          V. van Dijk            Liverpool       90         91   78000000\n",
       "25             Ederson      Manchester City       88         91   54500000\n",
       "24            P. Pogba    Manchester United       88         91   72500000\n",
       "75          F. de Jong         FC Barcelona       85         91   52000000\n",
       "13             Alisson            Liverpool       89         91   58000000"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#20 players who have the highest potential.\n",
    "players_analyze.sort_values(\"potential\", axis = 0, ascending = False)[['short_name', 'club', 'overall', 'potential', 'value_eur']].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "85a3ebd7-fa24-4f78-8112-4f27d95899e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearsons correlation for overall score and potential is 0.647\n",
      "Pearsons correlation for value_eur and potential is 0.581\n"
     ]
    }
   ],
   "source": [
    "#We can observe that all the high potential players are highly paid and their overall score is also very high\n",
    "\n",
    "from scipy.stats import pearsonr\n",
    "# Calculating correlation coefficient of Overall Score and Potential of players\n",
    "corr, _ = pearsonr(players_analyze['overall'], players_analyze['potential'])\n",
    "corr1, _ = pearsonr(players_analyze['value_eur'], players_analyze['potential'])\n",
    "print('Pearsons correlation for overall score and potential is %.3f' % corr)\n",
    "print('Pearsons correlation for value_eur and potential is %.3f' % corr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f754730c-7620-435d-bed8-d00fb53e95e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preferred Foot\n",
    "players_analyze['preferred_foot'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "888012e5-ef34-4489-bb8d-cd487f6658fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "preferred_foot\n",
       "Right    13960\n",
       "Left      4318\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze['preferred_foot'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "797e8e96-0ecd-42cc-bf65-c094dcd4345a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.7988438  1.48434151]\n",
      "[76.37597111 23.62402889]\n",
      "['Right', 'Left']\n",
      "[13960  4318]\n"
     ]
    }
   ],
   "source": [
    "from math import pi\n",
    "foot = players_analyze['preferred_foot'].value_counts().index.to_list()\n",
    "count = players_analyze['preferred_foot'].value_counts().values\n",
    "count1 = count / sum(count) * 100\n",
    "angle = count / sum(count) * 2 * pi\n",
    "print(angle)\n",
    "print(count1)\n",
    "print(foot)\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c57ab7ac-202f-4ba5-aa63-36bca57f5b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "source = ColumnDataSource(data = dict(foot = foot, count = count, color = ['skyblue', 'salmon'], angle = angle, percentage = count1))\n",
    "\n",
    "p6 = figure(x_range = foot,height = 300,width = 500, title = \"Count of left and right foot players \",\n",
    "                tools = \"hover\", tooltips = \"@foot: @count\")\n",
    "\n",
    "p6.vbar(x = 'foot', top = 'count', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p6.xgrid.grid_line_color = None\n",
    "p6.y_range.start = 0\n",
    "\n",
    "p6.xaxis.major_label_orientation = \"horizontal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0f88cbce-2616-4d7d-990f-4175dbceb5c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "p7 = figure(height = 300, width = 300, title=\"Pie Chart\",\n",
    "           tools = \"hover\", tooltips = \"@foot: @percentage\", x_range=(-0.5, 1.0))  ## Pie chart for right and left foot players\n",
    "\n",
    "p7.wedge(x = 0, y = 1, radius = 0.4,\n",
    "        start_angle = cumsum('angle', include_zero=True), end_angle = cumsum('angle'),\n",
    "        line_color = \"white\", fill_color = 'color', legend_field = 'foot', source = source)\n",
    "p7.legend.location = \"top_right\"\n",
    "p7.legend.label_text_font_size = '5pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a9256722-ff82-44be-b186-f35154e3b8a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"e8673c2b-6170-4803-b007-9ee61af12790\" data-root-id=\"p1615\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"2458a4bc-c649-4b08-a944-668715fed5a4\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p1615\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1614\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1600\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@foot: @percentage\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1567\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@foot: @count\"}}]}},\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1577\",\"attributes\":{\"width\":300,\"height\":300,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1587\",\"attributes\":{\"start\":-0.5}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1579\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1588\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1589\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1580\",\"attributes\":{\"text\":\"Pie Chart\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1609\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1541\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1542\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1543\"},\"data\":{\"type\":\"map\",\"entries\":[[\"foot\",[\"Right\",\"Left\"]],[\"count\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"iDYAAN4QAAA=\"},\"shape\":[2],\"dtype\":\"int32\",\"order\":\"little\"}],[\"color\",[\"skyblue\",\"salmon\"]],[\"angle\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"6ZKbGwQyE0C+aKLi3L/3Pw==\"},\"shape\":[2],\"dtype\":\"float64\",\"order\":\"little\"}],[\"percentage\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"gXEk6Q8YU0D8OW5bwJ83QA==\"},\"shape\":[2],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1610\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1611\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1606\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.4},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1601\",\"attributes\":{\"field\":\"angle\",\"include_zero\":true}}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1602\",\"attributes\":{\"field\":\"angle\"}}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1607\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.4},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1601\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1602\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1608\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.4},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1601\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1602\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1586\",\"attributes\":{\"tools\":[{\"id\":\"p1600\"}]}},\"toolbar_location\":null,\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1595\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1596\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1597\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1598\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1590\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1591\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1592\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1593\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1594\",\"attributes\":{\"axis\":{\"id\":\"p1590\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1599\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1595\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1612\",\"attributes\":{\"label_text_font_size\":\"5pt\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1613\",\"attributes\":{\"label\":{\"type\":\"field\",\"field\":\"foot\"},\"renderers\":[{\"id\":\"p1609\"}]}}]}}]}},0,0],[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1544\",\"attributes\":{\"width\":500,\"height\":300,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1554\",\"attributes\":{\"factors\":[\"Right\",\"Left\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1546\",\"attributes\":{\"start\":0}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1555\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1556\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1547\",\"attributes\":{\"text\":\"Count of left and right foot players \"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1574\",\"attributes\":{\"data_source\":{\"id\":\"p1541\"},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1575\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1576\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1571\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"foot\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1572\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"foot\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1573\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"foot\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1553\",\"attributes\":{\"tools\":[{\"id\":\"p1567\"}]}},\"toolbar_location\":null,\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1562\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1563\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1564\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1565\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1557\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1558\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1559\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1560\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1561\",\"attributes\":{\"axis\":{\"id\":\"p1557\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1566\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1562\"}}}]}},0,1]]}}]}};\n",
       "  const render_items = [{\"docid\":\"2458a4bc-c649-4b08-a944-668715fed5a4\",\"roots\":{\"p1615\":\"e8673c2b-6170-4803-b007-9ee61af12790\"},\"root_ids\":[\"p1615\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1615"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(gridplot([[p7, p6]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c84b3976-fc1c-4b09-b07d-336ccfdc0523",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average of overall scores of players who prefer Right foot is 66.11\n",
      "The average of overall scores of players who prefer Left foot is 66.68\n"
     ]
    }
   ],
   "source": [
    "print(\"The average of overall scores of players who prefer Right foot is\", round(players_analyze.loc[players_analyze['preferred_foot'] == 'Right']['overall'].mean(), 2))\n",
    "\n",
    "print(\"The average of overall scores of players who prefer Left foot is\", round(players_analyze.loc[players_analyze['preferred_foot'] == 'Left']['overall'].mean(), 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "0e361328-6962-47e9-8ea7-a1fea1f355bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "international_reputation\n",
       "1    16794\n",
       "2     1140\n",
       "3      292\n",
       "4       46\n",
       "5        6\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#International Reputation.\n",
    "players_analyze['international_reputation'].isnull().sum()\n",
    "players_analyze['international_reputation'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f81a41b8-07ee-4623-835d-697f7d80a6ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#There are five categories of international reputation attribute\n",
    "\n",
    "reputation = players_analyze['international_reputation'].value_counts().index.to_list()\n",
    "count = players_analyze['international_reputation'].value_counts().values\n",
    "count1 = count / sum(count) * 100\n",
    "angle = count / sum(count) * 2 * pi\n",
    "\n",
    "source = ColumnDataSource(data = dict(reputation = reputation, count = count, color = ['skyblue', 'salmon', 'brown', 'cyan', 'red'], angle = angle, percentage = count1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "b9ba082e-0c65-4f5c-8690-d71253569511",
   "metadata": {},
   "outputs": [],
   "source": [
    "p8 = figure(height = 400, width = 400, title=\"Pie Chart\",\n",
    "           tools = \"hover\", tooltips = \"@reputation: @percentage\", x_range=(-1.0, 1.0))  ## Pie chart for reputations\n",
    "\n",
    "p8.wedge(x = 0, y = 1, radius = 0.8,\n",
    "        start_angle = cumsum('angle', include_zero=True), end_angle = cumsum('angle'),\n",
    "        line_color = \"white\", fill_color = 'color', legend_field = 'reputation', source = source)\n",
    "\n",
    "p8.legend.location = \"top_right\"\n",
    "\n",
    "p8.legend.label_text_font_size = '5pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "2d94f759-a647-4410-96ab-24fb76a9f9f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.18809498e+01, 6.23700624e+00, 1.59754897e+00, 2.51668673e-01,\n",
       "       3.28263486e-02])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "29b9d156-6353-4c9a-90b7-c9618a42137a",
   "metadata": {},
   "outputs": [],
   "source": [
    "p9 = figure(height = 300, width = 500, title = \"International Reputation \",\n",
    "                tools = \"hover\", tooltips = \"@reputation: @count\")\n",
    "\n",
    "p9.vbar(x = 'reputation', top = 'count', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p9.xgrid.grid_line_color = None\n",
    "p9.y_range.start = 0\n",
    "\n",
    "p9.xaxis.major_label_orientation = \"horizontal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4dfae46d-0995-48a7-a4b8-23e855327c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"b894ee94-c79b-48ce-af8c-13b662356bbb\" data-root-id=\"p1693\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"a5be3a77-7b91-462c-88e1-e13fb8d2fa65\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p1693\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1692\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1646\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@reputation: @percentage\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1682\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@reputation: @count\"}}]}},\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1623\",\"attributes\":{\"width\":400,\"height\":400,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1633\",\"attributes\":{\"start\":-1.0}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1625\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1634\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1635\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1626\",\"attributes\":{\"text\":\"Pie Chart\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1655\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1620\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1621\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1622\"},\"data\":{\"type\":\"map\",\"entries\":[[\"reputation\",[1,2,3,4,5]],[\"count\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"mkEAAHQEAAAkAQAALgAAAAYAAAA=\"},\"shape\":[5],\"dtype\":\"int32\",\"order\":\"little\"}],[\"color\",[\"skyblue\",\"salmon\",\"brown\",\"cyan\",\"red\"]],[\"angle\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"+QzkgZoXF0B6qJgBmxTZP8Ivp/hNsrk/ab80pzsxkD+8xyDmdeVgPw==\"},\"shape\":[5],\"dtype\":\"float64\",\"order\":\"little\"}],[\"percentage\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"hv8qe2H4VkAsj1PDsfIYQPmYKYGPj/k/tQGx61Yb0D/qDIF7nc6gPw==\"},\"shape\":[5],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1656\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1657\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1652\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1647\",\"attributes\":{\"field\":\"angle\",\"include_zero\":true}}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1648\",\"attributes\":{\"field\":\"angle\"}}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1653\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1647\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1648\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1654\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1647\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1648\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1632\",\"attributes\":{\"tools\":[{\"id\":\"p1646\"}]}},\"toolbar_location\":null,\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1641\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1642\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1643\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1644\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1636\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1637\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1638\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1639\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1640\",\"attributes\":{\"axis\":{\"id\":\"p1636\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1645\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1641\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1658\",\"attributes\":{\"label_text_font_size\":\"5pt\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1659\",\"attributes\":{\"label\":{\"type\":\"field\",\"field\":\"reputation\"},\"renderers\":[{\"id\":\"p1655\"}]}}]}}]}},0,0],[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1660\",\"attributes\":{\"width\":500,\"height\":300,\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1661\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1662\",\"attributes\":{\"start\":0}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1670\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1671\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1663\",\"attributes\":{\"text\":\"International Reputation \"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1689\",\"attributes\":{\"data_source\":{\"id\":\"p1620\"},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1690\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1691\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1686\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"reputation\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1687\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"reputation\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1688\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"reputation\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1669\",\"attributes\":{\"tools\":[{\"id\":\"p1682\"}]}},\"toolbar_location\":null,\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1677\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1678\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1679\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1680\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1672\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1673\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1674\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1675\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1676\",\"attributes\":{\"axis\":{\"id\":\"p1672\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1681\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1677\"}}}]}},0,1]]}}]}};\n",
       "  const render_items = [{\"docid\":\"a5be3a77-7b91-462c-88e1-e13fb8d2fa65\",\"roots\":{\"p1693\":\"b894ee94-c79b-48ce-af8c-13b662356bbb\"},\"root_ids\":[\"p1693\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1693"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(gridplot([[p8, p9]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "6d1fce43-8fb6-44f0-8be9-0e7f15753908",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>M. Škriniar</td>\n",
       "      <td>24</td>\n",
       "      <td>187</td>\n",
       "      <td>80</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>Inter</td>\n",
       "      <td>86</td>\n",
       "      <td>90</td>\n",
       "      <td>52000000</td>\n",
       "      <td>89000</td>\n",
       "      <td>CB</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>No</td>\n",
       "      <td>92300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>A. Robertson</td>\n",
       "      <td>25</td>\n",
       "      <td>178</td>\n",
       "      <td>64</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>85</td>\n",
       "      <td>89</td>\n",
       "      <td>43500000</td>\n",
       "      <td>125000</td>\n",
       "      <td>LB</td>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>High/High</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>83700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>K. Havertz</td>\n",
       "      <td>20</td>\n",
       "      <td>188</td>\n",
       "      <td>83</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Bayer 04 Leverkusen</td>\n",
       "      <td>84</td>\n",
       "      <td>92</td>\n",
       "      <td>46000000</td>\n",
       "      <td>70000</td>\n",
       "      <td>CAM, RM</td>\n",
       "      <td>Left</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>87400000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Kepa</td>\n",
       "      <td>24</td>\n",
       "      <td>186</td>\n",
       "      <td>85</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>84</td>\n",
       "      <td>89</td>\n",
       "      <td>31000000</td>\n",
       "      <td>92000</td>\n",
       "      <td>GK</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>61200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>J. Pavlenka</td>\n",
       "      <td>27</td>\n",
       "      <td>196</td>\n",
       "      <td>81</td>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>SV Werder Bremen</td>\n",
       "      <td>84</td>\n",
       "      <td>86</td>\n",
       "      <td>26000000</td>\n",
       "      <td>37000</td>\n",
       "      <td>GK</td>\n",
       "      <td>Right</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>46200000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       short_name  age  height_cm  weight_kg     nationality  \\\n",
       "57    M. Škriniar   24        187         80        Slovakia   \n",
       "82   A. Robertson   25        178         64        Scotland   \n",
       "109    K. Havertz   20        188         83         Germany   \n",
       "111          Kepa   24        186         85           Spain   \n",
       "125   J. Pavlenka   27        196         81  Czech Republic   \n",
       "\n",
       "                    club  overall  potential  value_eur  wage_eur  \\\n",
       "57                 Inter       86         90   52000000     89000   \n",
       "82             Liverpool       85         89   43500000    125000   \n",
       "109  Bayer 04 Leverkusen       84         92   46000000     70000   \n",
       "111              Chelsea       84         89   31000000     92000   \n",
       "125     SV Werder Bremen       84         86   26000000     37000   \n",
       "\n",
       "    player_positions preferred_foot  international_reputation  weak_foot  \\\n",
       "57                CB          Right                         1          4   \n",
       "82                LB           Left                         1          2   \n",
       "109          CAM, RM           Left                         1          4   \n",
       "111               GK          Right                         1          4   \n",
       "125               GK          Right                         1          3   \n",
       "\n",
       "     skill_moves      work_rate body_type real_face  release_clause_eur  \n",
       "57             2    Medium/High    Normal        No          92300000.0  \n",
       "82             3      High/High      Lean       Yes          83700000.0  \n",
       "109            4  Medium/Medium      Lean       Yes          87400000.0  \n",
       "111            1  Medium/Medium      Lean       Yes          61200000.0  \n",
       "125            1  Medium/Medium      Lean       Yes          46200000.0  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see some of the players who has internal reputation of 1, 2 and 5\n",
    "players_analyze.loc[fifa['international_reputation'] == 1].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "98abd8d4-1f34-4efc-832d-145c15843b9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Ederson</td>\n",
       "      <td>25</td>\n",
       "      <td>188</td>\n",
       "      <td>86</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>88</td>\n",
       "      <td>91</td>\n",
       "      <td>54500000</td>\n",
       "      <td>185000</td>\n",
       "      <td>GK</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>104900000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>A. Laporte</td>\n",
       "      <td>25</td>\n",
       "      <td>189</td>\n",
       "      <td>85</td>\n",
       "      <td>France</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>87</td>\n",
       "      <td>90</td>\n",
       "      <td>56500000</td>\n",
       "      <td>195000</td>\n",
       "      <td>CB, LB</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>108800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Bernardo Silva</td>\n",
       "      <td>24</td>\n",
       "      <td>173</td>\n",
       "      <td>64</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>87</td>\n",
       "      <td>90</td>\n",
       "      <td>64000000</td>\n",
       "      <td>210000</td>\n",
       "      <td>RW, CAM, CM</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>High/High</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>123200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>L. Sané</td>\n",
       "      <td>23</td>\n",
       "      <td>183</td>\n",
       "      <td>75</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>86</td>\n",
       "      <td>92</td>\n",
       "      <td>61000000</td>\n",
       "      <td>195000</td>\n",
       "      <td>LW</td>\n",
       "      <td>Left</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>125100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Parejo</td>\n",
       "      <td>30</td>\n",
       "      <td>182</td>\n",
       "      <td>74</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Valencia CF</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>41000000</td>\n",
       "      <td>69000</td>\n",
       "      <td>CM</td>\n",
       "      <td>Right</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>86100000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        short_name  age  height_cm  weight_kg nationality             club  \\\n",
       "25         Ederson   25        188         86      Brazil  Manchester City   \n",
       "40      A. Laporte   25        189         85      France  Manchester City   \n",
       "41  Bernardo Silva   24        173         64    Portugal  Manchester City   \n",
       "55         L. Sané   23        183         75     Germany  Manchester City   \n",
       "69          Parejo   30        182         74       Spain      Valencia CF   \n",
       "\n",
       "    overall  potential  value_eur  wage_eur player_positions preferred_foot  \\\n",
       "25       88         91   54500000    185000               GK           Left   \n",
       "40       87         90   56500000    195000           CB, LB           Left   \n",
       "41       87         90   64000000    210000      RW, CAM, CM           Left   \n",
       "55       86         92   61000000    195000               LW           Left   \n",
       "69       86         86   41000000     69000               CM          Right   \n",
       "\n",
       "    international_reputation  weak_foot  skill_moves      work_rate body_type  \\\n",
       "25                         2          3            1  Medium/Medium    Normal   \n",
       "40                         2          3            2  Medium/Medium    Normal   \n",
       "41                         2          3            4      High/High      Lean   \n",
       "55                         2          3            4    High/Medium      Lean   \n",
       "69                         2          4            3  Medium/Medium      Lean   \n",
       "\n",
       "   real_face  release_clause_eur  \n",
       "25       Yes         104900000.0  \n",
       "40       Yes         108800000.0  \n",
       "41       Yes         123200000.0  \n",
       "55       Yes         125100000.0  \n",
       "69       Yes          86100000.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze.loc[fifa['international_reputation'] == 2].head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "73aff9b2-2d10-4652-9409-73a7c0c333e2",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>L. Messi</td>\n",
       "      <td>32</td>\n",
       "      <td>170</td>\n",
       "      <td>72</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>95500000</td>\n",
       "      <td>565000</td>\n",
       "      <td>RW, CF, ST</td>\n",
       "      <td>Left</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>Medium/Low</td>\n",
       "      <td>Messi</td>\n",
       "      <td>Yes</td>\n",
       "      <td>195800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>34</td>\n",
       "      <td>187</td>\n",
       "      <td>83</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>58500000</td>\n",
       "      <td>405000</td>\n",
       "      <td>ST, LW</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>High/Low</td>\n",
       "      <td>C. Ronaldo</td>\n",
       "      <td>Yes</td>\n",
       "      <td>96500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>27</td>\n",
       "      <td>175</td>\n",
       "      <td>68</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>105500000</td>\n",
       "      <td>290000</td>\n",
       "      <td>LW, CAM</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Neymar</td>\n",
       "      <td>Yes</td>\n",
       "      <td>195200000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>L. Suárez</td>\n",
       "      <td>32</td>\n",
       "      <td>182</td>\n",
       "      <td>86</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>53000000</td>\n",
       "      <td>355000</td>\n",
       "      <td>ST</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>High/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>108700000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>M. Neuer</td>\n",
       "      <td>33</td>\n",
       "      <td>193</td>\n",
       "      <td>92</td>\n",
       "      <td>Germany</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>32000000</td>\n",
       "      <td>155000</td>\n",
       "      <td>GK</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium/Medium</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>52800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Z. Ibrahimović</td>\n",
       "      <td>37</td>\n",
       "      <td>195</td>\n",
       "      <td>95</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>LA Galaxy</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>14000000</td>\n",
       "      <td>15000</td>\n",
       "      <td>ST</td>\n",
       "      <td>Right</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>Medium/Low</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>21000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           short_name  age  height_cm  weight_kg nationality  \\\n",
       "0            L. Messi   32        170         72   Argentina   \n",
       "1   Cristiano Ronaldo   34        187         83    Portugal   \n",
       "2           Neymar Jr   27        175         68      Brazil   \n",
       "19          L. Suárez   32        182         86     Uruguay   \n",
       "31           M. Neuer   33        193         92     Germany   \n",
       "96     Z. Ibrahimović   37        195         95      Sweden   \n",
       "\n",
       "                   club  overall  potential  value_eur  wage_eur  \\\n",
       "0          FC Barcelona       94         94   95500000    565000   \n",
       "1              Juventus       93         93   58500000    405000   \n",
       "2   Paris Saint-Germain       92         92  105500000    290000   \n",
       "19         FC Barcelona       89         89   53000000    355000   \n",
       "31    FC Bayern München       88         88   32000000    155000   \n",
       "96            LA Galaxy       85         85   14000000     15000   \n",
       "\n",
       "   player_positions preferred_foot  international_reputation  weak_foot  \\\n",
       "0        RW, CF, ST           Left                         5          4   \n",
       "1            ST, LW          Right                         5          4   \n",
       "2           LW, CAM          Right                         5          5   \n",
       "19               ST          Right                         5          4   \n",
       "31               GK          Right                         5          4   \n",
       "96               ST          Right                         5          4   \n",
       "\n",
       "    skill_moves      work_rate   body_type real_face  release_clause_eur  \n",
       "0             4     Medium/Low       Messi       Yes         195800000.0  \n",
       "1             5       High/Low  C. Ronaldo       Yes          96500000.0  \n",
       "2             5    High/Medium      Neymar       Yes         195200000.0  \n",
       "19            3    High/Medium      Normal       Yes         108700000.0  \n",
       "31            1  Medium/Medium      Normal       Yes          52800000.0  \n",
       "96            5     Medium/Low      Normal       Yes          21000000.0  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze.loc[fifa['international_reputation'] == 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c3cfa410-348d-4f3d-afc6-c2c243963e87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearsons correlation for overall score and international reputations of players is 0.476\n",
      "Pearsons correlation for value_eur and internationalreputations of players is 0.618\n"
     ]
    }
   ],
   "source": [
    "corr, _ = pearsonr(players_analyze['overall'], players_analyze['international_reputation'])\n",
    "corr1, _ = pearsonr(players_analyze['value_eur'], players_analyze['international_reputation'])\n",
    "print('Pearsons correlation for overall score and international reputations of players is %.3f' % corr)\n",
    "print('Pearsons correlation for value_eur and internationalreputations of players is %.3f' % corr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "186dba96-2562-4af0-aed6-daef3a2f6a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "skill_moves\n",
       "2    8617\n",
       "3    6536\n",
       "1    2036\n",
       "4    1040\n",
       "5      49\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Skill Moves\n",
    "players_analyze['skill_moves'].isnull().sum()\n",
    "players_analyze['skill_moves'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "85365d21-72db-499e-a74a-1f7013e55f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "no_of_skills = players_analyze['skill_moves'].value_counts().index.to_list()\n",
    "count = players_analyze['skill_moves'].value_counts().values\n",
    "count1 = count / sum(count) * 100\n",
    "angle = count / sum(count) * 2 * pi\n",
    "\n",
    "source = ColumnDataSource(data = dict(no_of_skills = no_of_skills, count = count, color = ['skyblue', 'salmon', 'turquoise', 'cyan', 'red'], angle = angle, percentage = count1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "b862e6b0-18fb-432a-83fc-a2e9bda4768d",
   "metadata": {},
   "outputs": [],
   "source": [
    "p10 = figure(height = 400,width = 400, title=\"Pie Chart\",\n",
    "           tools = \"hover\", tooltips = \"@no_of_skills: @percentage\", x_range=(-1.0, 1.0))  ## Pie chart for skill moves\n",
    "\n",
    "p10.wedge(x = 0, y = 1, radius = 0.8,\n",
    "        start_angle = cumsum('angle', include_zero=True), end_angle = cumsum('angle'),\n",
    "        line_color = \"white\", fill_color = 'color', legend_field = 'no_of_skills', source = source)\n",
    "\n",
    "p10.legend.location = \"top_right\"\n",
    "\n",
    "p10.legend.label_text_font_size = '5pt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e895dc6f-1778-44b1-890d-b7213ee0665b",
   "metadata": {},
   "outputs": [],
   "source": [
    "p11 = figure(height = 300, width = 500, title = \"Number of Skill Moves\",\n",
    "                tools = \"hover\", tooltips = \"@no_of_skills: @count\")\n",
    "\n",
    "p11.vbar(x = 'no_of_skills', top = 'count', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p11.xgrid.grid_line_color = None\n",
    "p11.y_range.start = 0\n",
    "\n",
    "p11.xaxis.major_label_orientation = \"horizontal\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "dfa6ffc6-b0e8-473a-b8fb-484ec01cc20a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"b84411ab-6e2d-49ef-8261-bb293f170c50\" data-root-id=\"p1771\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"9fc9acb2-059d-4f1d-b6dd-091bbc6d6458\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"GridPlot\",\"id\":\"p1771\",\"attributes\":{\"rows\":null,\"cols\":null,\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1770\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1724\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@no_of_skills: @percentage\"}},{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1760\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@no_of_skills: @count\"}}]}},\"children\":[[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1701\",\"attributes\":{\"width\":400,\"height\":400,\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1711\",\"attributes\":{\"start\":-1.0}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1703\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1712\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1713\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1704\",\"attributes\":{\"text\":\"Pie Chart\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1733\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1698\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1699\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1700\"},\"data\":{\"type\":\"map\",\"entries\":[[\"no_of_skills\",[2,3,1,4,5]],[\"count\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"qSEAAIgZAAD0BwAAEAQAADEAAAA=\"},\"shape\":[5],\"dtype\":\"int32\",\"order\":\"little\"}],[\"color\",[\"skyblue\",\"salmon\",\"turquoise\",\"cyan\",\"red\"]],[\"angle\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"H+T2j3yyB0B6NHoWb/kBQMDItPF8ZeY/eA73/GTh1j/ky5YFkz+RPw==\"},\"shape\":[5],\"dtype\":\"float64\",\"order\":\"little\"}],[\"percentage\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"J8nBHnKSR0ASnreHIeFBQHRkCr80RyZAJ2zpQXXCFkCEEmnDQCjRPw==\"},\"shape\":[5],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1734\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1735\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1730\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1725\",\"attributes\":{\"field\":\"angle\",\"include_zero\":true}}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1726\",\"attributes\":{\"field\":\"angle\"}}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1731\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1725\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1726\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1732\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1725\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1726\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1710\",\"attributes\":{\"tools\":[{\"id\":\"p1724\"}]}},\"toolbar_location\":null,\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1719\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1720\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1721\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1722\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1714\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1715\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1716\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1717\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1718\",\"attributes\":{\"axis\":{\"id\":\"p1714\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1723\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1719\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1736\",\"attributes\":{\"label_text_font_size\":\"5pt\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1737\",\"attributes\":{\"label\":{\"type\":\"field\",\"field\":\"no_of_skills\"},\"renderers\":[{\"id\":\"p1733\"}]}}]}}]}},0,0],[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1738\",\"attributes\":{\"width\":500,\"height\":300,\"x_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1739\"},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1740\",\"attributes\":{\"start\":0}},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1748\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1749\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1741\",\"attributes\":{\"text\":\"Number of Skill Moves\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1767\",\"attributes\":{\"data_source\":{\"id\":\"p1698\"},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1768\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1769\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1764\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"no_of_skills\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1765\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"no_of_skills\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"VBar\",\"id\":\"p1766\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"no_of_skills\"},\"width\":{\"type\":\"value\",\"value\":0.9},\"top\":{\"type\":\"field\",\"field\":\"count\"},\"line_color\":{\"type\":\"field\",\"field\":\"color\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_color\":{\"type\":\"field\",\"field\":\"color\"},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1747\",\"attributes\":{\"tools\":[{\"id\":\"p1760\"}]}},\"toolbar_location\":null,\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1755\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1756\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1757\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1758\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1750\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1751\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1752\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1753\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1754\",\"attributes\":{\"axis\":{\"id\":\"p1750\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1759\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1755\"}}}]}},0,1]]}}]}};\n",
       "  const render_items = [{\"docid\":\"9fc9acb2-059d-4f1d-b6dd-091bbc6d6458\",\"roots\":{\"p1771\":\"b84411ab-6e2d-49ef-8261-bb293f170c50\"},\"root_ids\":[\"p1771\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1771"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(gridplot([[p10, p11]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "46482bc6-fc28-4e2a-80fc-32fa80ca0f27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Work Rate¶\n",
    "players_analyze['work_rate'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "18511a1f-354e-4305-a8cd-bfc1a7c7d7e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "work_rate\n",
       "Medium/Medium    9875\n",
       "High/Medium      3216\n",
       "Medium/High      1718\n",
       "High/High         986\n",
       "Medium/Low        886\n",
       "High/Low          684\n",
       "Low/Medium        461\n",
       "Low/High          417\n",
       "Low/Low            35\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "players_analyze['work_rate'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "35129bd0-c70e-468b-a1fa-b40767c275d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "different_work_rates = players_analyze['work_rate'].value_counts().keys().to_list()\n",
    "count = players_analyze['work_rate'].value_counts().values\n",
    "count1 = count / sum(count) * 100\n",
    "angle = count / sum(count) * 2 * pi\n",
    "\n",
    "source = ColumnDataSource(data = dict(work_rates = different_work_rates, count = count, color = ['skyblue', 'salmon', 'turquoise', 'cyan', 'red', 'lightseagreen', 'teal', 'mediumaquamarine', 'yellowgreen'], angle = angle, percentage = count1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "bc01b820-9c79-4721-b384-cc9df408afa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"d884f9fa-4060-4807-9613-74b74a61d2a2\" data-root-id=\"p1779\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"6387b225-65da-4a56-abb6-3cfd41a62787\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1779\",\"attributes\":{\"x_range\":{\"type\":\"object\",\"name\":\"Range1d\",\"id\":\"p1789\",\"attributes\":{\"start\":-1.0}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1781\"},\"x_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1790\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1791\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1782\",\"attributes\":{\"text\":\"Pie Chart\"}},\"renderers\":[{\"type\":\"object\",\"name\":\"GlyphRenderer\",\"id\":\"p1811\",\"attributes\":{\"data_source\":{\"type\":\"object\",\"name\":\"ColumnDataSource\",\"id\":\"p1776\",\"attributes\":{\"selected\":{\"type\":\"object\",\"name\":\"Selection\",\"id\":\"p1777\",\"attributes\":{\"indices\":[],\"line_indices\":[]}},\"selection_policy\":{\"type\":\"object\",\"name\":\"UnionRenderers\",\"id\":\"p1778\"},\"data\":{\"type\":\"map\",\"entries\":[[\"work_rates\",[\"Medium/Medium\",\"High/Medium\",\"Medium/High\",\"High/High\",\"Medium/Low\",\"High/Low\",\"Low/Medium\",\"Low/High\",\"Low/Low\"]],[\"count\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"kyYAAJAMAAC2BgAA2gMAAHYDAACsAgAAzQEAAKEBAAAjAAAA\"},\"shape\":[9],\"dtype\":\"int32\",\"order\":\"little\"}],[\"color\",[\"skyblue\",\"salmon\",\"turquoise\",\"cyan\",\"red\",\"lightseagreen\",\"teal\",\"mediumaquamarine\",\"yellowgreen\"]],[\"angle\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"BcUqySIoC0AYUepsN7DxP2b/D4n75eI/bcDIskKx1T9rJieuDH7TP8Zj6gG6GM4/yK/wNM5IxD/lmOcHLlnCP0cjRZr2o4g/\"},\"shape\":[9],\"dtype\":\"float64\",\"order\":\"little\"}],[\"percentage\",{\"type\":\"ndarray\",\"array\":{\"type\":\"bytes\",\"data\":\"NyB23WoDS0CFGUvdTJgxQMeseCRuzCJAP1k6L+6TFUA7NtCtsWMTQAHf/YMI8A1A1kKaD2EtBEAGJB4EX0ACQCuIcamlgsg/\"},\"shape\":[9],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p1812\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p1813\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1808\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1803\",\"attributes\":{\"field\":\"angle\",\"include_zero\":true}}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"type\":\"object\",\"name\":\"CumSum\",\"id\":\"p1804\",\"attributes\":{\"field\":\"angle\"}}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1809\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1803\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1804\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Wedge\",\"id\":\"p1810\",\"attributes\":{\"x\":{\"type\":\"value\",\"value\":0},\"y\":{\"type\":\"value\",\"value\":1},\"radius\":{\"type\":\"value\",\"value\":0.8},\"start_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1803\"}},\"end_angle\":{\"type\":\"expr\",\"expr\":{\"id\":\"p1804\"}},\"line_color\":{\"type\":\"value\",\"value\":\"white\"},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"color\"},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p1788\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p1802\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":\"@work_rates: @percentage\"}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1797\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1798\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1799\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1800\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1792\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1793\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1794\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1795\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1796\",\"attributes\":{\"axis\":{\"id\":\"p1792\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1801\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1797\"}}},{\"type\":\"object\",\"name\":\"Legend\",\"id\":\"p1814\",\"attributes\":{\"label_text_font_size\":\"5pt\",\"items\":[{\"type\":\"object\",\"name\":\"LegendItem\",\"id\":\"p1815\",\"attributes\":{\"label\":{\"type\":\"field\",\"field\":\"work_rates\"},\"renderers\":[{\"id\":\"p1811\"}]}}]}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"6387b225-65da-4a56-abb6-3cfd41a62787\",\"roots\":{\"p1779\":\"d884f9fa-4060-4807-9613-74b74a61d2a2\"},\"root_ids\":[\"p1779\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1779"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p12 = figure(height = 600, width = 600, title=\"Pie Chart\",\n",
    "           tools = \"hover\", tooltips = \"@work_rates: @percentage\", x_range=(-1.0, 1.0))  ## Pie chart for work rates\n",
    "\n",
    "p12.wedge(x = 0, y = 1, radius = 0.8,\n",
    "        start_angle = cumsum('angle', include_zero=True), end_angle = cumsum('angle'),\n",
    "        line_color = \"white\", fill_color = 'color', legend_field = 'work_rates', source = source)\n",
    "\n",
    "p12.legend.location = \"top_right\"\n",
    "\n",
    "p12.legend.label_text_font_size = '5pt'\n",
    "\n",
    "show(p12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9553fb1d-4c98-4e49-a745-f912294de831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"d1ed1d05-a809-4ec9-a557-9fd6bd4f2b0c\" data-root-id=\"p1819\" style=\"display: contents;\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
       "  const docs_json = {\"b27fa6db-15aa-40ed-80b3-29851bbf4083\":{\"version\":\"3.3.4\",\"title\":\"Bokeh Application\",\"roots\":[{\"type\":\"object\",\"name\":\"Figure\",\"id\":\"p1819\",\"attributes\":{\"width\":500,\"height\":300,\"x_range\":{\"type\":\"object\",\"name\":\"FactorRange\",\"id\":\"p1829\",\"attributes\":{\"factors\":[\"Medium/Medium\",\"High/Medium\",\"Medium/High\",\"High/High\",\"Medium/Low\",\"High/Low\",\"Low/Medium\",\"Low/High\",\"Low/Low\"]}},\"y_range\":{\"type\":\"object\",\"name\":\"DataRange1d\",\"id\":\"p1821\",\"attributes\":{\"start\":0}},\"x_scale\":{\"type\":\"object\",\"name\":\"CategoricalScale\",\"id\":\"p1830\"},\"y_scale\":{\"type\":\"object\",\"name\":\"LinearScale\",\"id\":\"p1831\"},\"title\":{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p1822\",\"attributes\":{\"text\":\"Different Work 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@count\"}}]}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1837\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1838\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1839\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1840\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p1832\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p1833\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p1834\"},\"major_label_orientation\":\"vertical\",\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1835\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1836\",\"attributes\":{\"axis\":{\"id\":\"p1832\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1841\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1837\"}}}]}}]}};\n",
       "  const render_items = [{\"docid\":\"b27fa6db-15aa-40ed-80b3-29851bbf4083\",\"roots\":{\"p1819\":\"d1ed1d05-a809-4ec9-a557-9fd6bd4f2b0c\"},\"root_ids\":[\"p1819\"]}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    let attempts = 0;\n",
       "    const timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else {\n",
       "        attempts++;\n",
       "        if (attempts > 100) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1819"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p13 = figure(x_range = different_work_rates, height = 300, width = 500, title = \"Different Work Rates\",\n",
    "                tools = \"hover\", tooltips = \"@work_rates: @count\")\n",
    "\n",
    "p13.vbar(x = 'work_rates', top = 'count', width = 0.9, source = source, color = 'color')\n",
    "\n",
    "p13.xgrid.grid_line_color = None\n",
    "p13.y_range.start = 0\n",
    "\n",
    "p13.xaxis.major_label_orientation = \"vertical\"\n",
    "\n",
    "show(p13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "3560d93b-92fb-4285-9fd8-8f919b8cdbd0",
   "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>short_name</th>\n",
       "      <th>age</th>\n",
       "      <th>height_cm</th>\n",
       "      <th>weight_kg</th>\n",
       "      <th>nationality</th>\n",
       "      <th>club</th>\n",
       "      <th>overall</th>\n",
       "      <th>potential</th>\n",
       "      <th>value_eur</th>\n",
       "      <th>wage_eur</th>\n",
       "      <th>player_positions</th>\n",
       "      <th>preferred_foot</th>\n",
       "      <th>international_reputation</th>\n",
       "      <th>weak_foot</th>\n",
       "      <th>skill_moves</th>\n",
       "      <th>work_rate</th>\n",
       "      <th>body_type</th>\n",
       "      <th>real_face</th>\n",
       "      <th>release_clause_eur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>28</td>\n",
       "      <td>181</td>\n",
       "      <td>70</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>90000000</td>\n",
       "      <td>370000</td>\n",
       "      <td>CAM, CM</td>\n",
       "      <td>Right</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>High/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>166500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>33</td>\n",
       "      <td>172</td>\n",
       "      <td>66</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>45000000</td>\n",
       "      <td>340000</td>\n",
       "      <td>CM</td>\n",
       "      <td>Right</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>High/High</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>92300000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>H. Kane</td>\n",
       "      <td>25</td>\n",
       "      <td>188</td>\n",
       "      <td>89</td>\n",
       "      <td>England</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>83000000</td>\n",
       "      <td>220000</td>\n",
       "      <td>ST</td>\n",
       "      <td>Right</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>High/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>159800000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>A. Griezmann</td>\n",
       "      <td>28</td>\n",
       "      <td>176</td>\n",
       "      <td>73</td>\n",
       "      <td>France</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>89</td>\n",
       "      <td>89</td>\n",
       "      <td>69000000</td>\n",
       "      <td>370000</td>\n",
       "      <td>CF, ST, LW</td>\n",
       "      <td>Left</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>High/High</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Yes</td>\n",
       "      <td>141500000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>C. Eriksen</td>\n",
       "      <td>27</td>\n",
       "      <td>181</td>\n",
       "      <td>76</td>\n",
       "      <td>Denmark</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>88</td>\n",
       "      <td>89</td>\n",
       "      <td>68000000</td>\n",
       "      <td>205000</td>\n",
       "      <td>CAM, CM</td>\n",
       "      <td>Right</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>High/High</td>\n",
       "      <td>Lean</td>\n",
       "      <td>Yes</td>\n",
       "      <td>130900000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      short_name  age  height_cm  weight_kg nationality               club  \\\n",
       "5   K. De Bruyne   28        181         70     Belgium    Manchester City   \n",
       "8      L. Modrić   33        172         66     Croatia        Real Madrid   \n",
       "12       H. Kane   25        188         89     England  Tottenham Hotspur   \n",
       "22  A. Griezmann   28        176         73      France       FC Barcelona   \n",
       "27    C. Eriksen   27        181         76     Denmark  Tottenham Hotspur   \n",
       "\n",
       "    overall  potential  value_eur  wage_eur player_positions preferred_foot  \\\n",
       "5        91         91   90000000    370000          CAM, CM          Right   \n",
       "8        90         90   45000000    340000               CM          Right   \n",
       "12       89         91   83000000    220000               ST          Right   \n",
       "22       89         89   69000000    370000       CF, ST, LW           Left   \n",
       "27       88         89   68000000    205000          CAM, CM          Right   \n",
       "\n",
       "    international_reputation  weak_foot  skill_moves  work_rate body_type  \\\n",
       "5                          4          5            4  High/High    Normal   \n",
       "8                          4          4            4  High/High      Lean   \n",
       "12                         3          4            3  High/High    Normal   \n",
       "22                         4          3            4  High/High    Normal   \n",
       "27                         3          5            4  High/High      Lean   \n",
       "\n",
       "   real_face  release_clause_eur  \n",
       "5        Yes         166500000.0  \n",
       "8        Yes          92300000.0  \n",
       "12       Yes         159800000.0  \n",
       "22       Yes         141500000.0  \n",
       "27       Yes         130900000.0  "
      ]
     },
     "execution_count": 459,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see the players who have 'High/High' Work Rate\n",
    "players_analyze.loc[players_analyze['work_rate'] == 'High/High'].head(5)"
   ]
  }
 ],
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