AnshumanMishra_week2_AiMl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "cef03aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as pyplt\n",
    "%matplotlib inline\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "matplotlib.rcParams['figure.figsize'] = (10, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "da038ada",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv (\"C:/Users/ANSHUMAN/Downloads/fifa.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "0ec1ace4",
   "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>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value</th>\n",
       "      <th>Wage</th>\n",
       "      <th>Preferred Foot</th>\n",
       "      <th>International Reputation</th>\n",
       "      <th>Skill Moves</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Contract Valid Until</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>110500.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>2004</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.583333</td>\n",
       "      <td>159.0</td>\n",
       "      <td>226500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>77000.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>2018</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>183.0</td>\n",
       "      <td>127100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118500.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>2017</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>228100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>2011</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>168.0</td>\n",
       "      <td>138600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>2015</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>154.0</td>\n",
       "      <td>196400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>183277</td>\n",
       "      <td>E. Hazard</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>93000.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>LF</td>\n",
       "      <td>2012</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>163.0</td>\n",
       "      <td>172100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>177003</td>\n",
       "      <td>L. Modrić</td>\n",
       "      <td>32</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>67000.0</td>\n",
       "      <td>420.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>2012</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>146.0</td>\n",
       "      <td>137400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>176580</td>\n",
       "      <td>L. Suárez</td>\n",
       "      <td>31</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RS</td>\n",
       "      <td>2014</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>190.0</td>\n",
       "      <td>164000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>155862</td>\n",
       "      <td>Sergio Ramos</td>\n",
       "      <td>32</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>51000.0</td>\n",
       "      <td>380.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>2005</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>181.0</td>\n",
       "      <td>104600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>200389</td>\n",
       "      <td>J. Oblak</td>\n",
       "      <td>25</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>2014</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>192.0</td>\n",
       "      <td>144500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>188545</td>\n",
       "      <td>R. Lewandowski</td>\n",
       "      <td>29</td>\n",
       "      <td>Poland</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>FC Bayern München</td>\n",
       "      <td>77000.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>2014</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>176.0</td>\n",
       "      <td>127100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>182521</td>\n",
       "      <td>T. Kroos</td>\n",
       "      <td>28</td>\n",
       "      <td>Germany</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>76500.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>LCM</td>\n",
       "      <td>2014</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>168.0</td>\n",
       "      <td>156800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>182493</td>\n",
       "      <td>D. Godín</td>\n",
       "      <td>32</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CB</td>\n",
       "      <td>2010</td>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>172.0</td>\n",
       "      <td>90200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>168542</td>\n",
       "      <td>David Silva</td>\n",
       "      <td>32</td>\n",
       "      <td>Spain</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>60000.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>LCM</td>\n",
       "      <td>2010</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>148.0</td>\n",
       "      <td>111000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>215914</td>\n",
       "      <td>N. Kanté</td>\n",
       "      <td>27</td>\n",
       "      <td>France</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>63000.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>LDM</td>\n",
       "      <td>2016</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>159.0</td>\n",
       "      <td>121300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>211110</td>\n",
       "      <td>P. Dybala</td>\n",
       "      <td>24</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>89</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>LF</td>\n",
       "      <td>2015</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>165.0</td>\n",
       "      <td>153500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>202126</td>\n",
       "      <td>H. Kane</td>\n",
       "      <td>24</td>\n",
       "      <td>England</td>\n",
       "      <td>89</td>\n",
       "      <td>91</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>83500.0</td>\n",
       "      <td>205.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>2010</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>196.0</td>\n",
       "      <td>160700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>194765</td>\n",
       "      <td>A. Griezmann</td>\n",
       "      <td>27</td>\n",
       "      <td>France</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "      <td>Atlético Madrid</td>\n",
       "      <td>78000.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>CAM</td>\n",
       "      <td>2014</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>161.0</td>\n",
       "      <td>165800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>192448</td>\n",
       "      <td>M. ter Stegen</td>\n",
       "      <td>26</td>\n",
       "      <td>Germany</td>\n",
       "      <td>89</td>\n",
       "      <td>92</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>58000.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>2014</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>187.0</td>\n",
       "      <td>123300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>192119</td>\n",
       "      <td>T. Courtois</td>\n",
       "      <td>26</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>89</td>\n",
       "      <td>90</td>\n",
       "      <td>Real Madrid</td>\n",
       "      <td>53500.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>2018</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>212.0</td>\n",
       "      <td>113700.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID               Name  Age Nationality  Overall  Potential  \\\n",
       "0   158023           L. Messi   31   Argentina       94         94   \n",
       "1    20801  Cristiano Ronaldo   33    Portugal       94         94   \n",
       "2   190871          Neymar Jr   26      Brazil       92         93   \n",
       "3   193080             De Gea   27       Spain       91         93   \n",
       "4   192985       K. De Bruyne   27     Belgium       91         92   \n",
       "5   183277          E. Hazard   27     Belgium       91         91   \n",
       "6   177003          L. Modrić   32     Croatia       91         91   \n",
       "7   176580          L. Suárez   31     Uruguay       91         91   \n",
       "8   155862       Sergio Ramos   32       Spain       91         91   \n",
       "9   200389           J. Oblak   25    Slovenia       90         93   \n",
       "10  188545     R. Lewandowski   29      Poland       90         90   \n",
       "11  182521           T. Kroos   28     Germany       90         90   \n",
       "12  182493           D. Godín   32     Uruguay       90         90   \n",
       "13  168542        David Silva   32       Spain       90         90   \n",
       "14  215914           N. Kanté   27      France       89         90   \n",
       "15  211110          P. Dybala   24   Argentina       89         94   \n",
       "16  202126            H. Kane   24     England       89         91   \n",
       "17  194765       A. Griezmann   27      France       89         90   \n",
       "18  192448      M. ter Stegen   26     Germany       89         92   \n",
       "19  192119        T. Courtois   26     Belgium       89         90   \n",
       "\n",
       "                   Club     Value   Wage Preferred Foot  \\\n",
       "0          FC Barcelona  110500.0  565.0           Left   \n",
       "1              Juventus   77000.0  405.0          Right   \n",
       "2   Paris Saint-Germain  118500.0  290.0          Right   \n",
       "3     Manchester United   72000.0  260.0          Right   \n",
       "4       Manchester City  102000.0  355.0          Right   \n",
       "5               Chelsea   93000.0  340.0          Right   \n",
       "6           Real Madrid   67000.0  420.0          Right   \n",
       "7          FC Barcelona   80000.0  455.0          Right   \n",
       "8           Real Madrid   51000.0  380.0          Right   \n",
       "9       Atlético Madrid   68000.0   94.0          Right   \n",
       "10    FC Bayern München   77000.0  205.0          Right   \n",
       "11          Real Madrid   76500.0  355.0          Right   \n",
       "12      Atlético Madrid   44000.0  125.0          Right   \n",
       "13      Manchester City   60000.0  285.0           Left   \n",
       "14              Chelsea   63000.0  225.0          Right   \n",
       "15             Juventus   89000.0  205.0           Left   \n",
       "16    Tottenham Hotspur   83500.0  205.0          Right   \n",
       "17      Atlético Madrid   78000.0  145.0           Left   \n",
       "18         FC Barcelona   58000.0  240.0          Right   \n",
       "19          Real Madrid   53500.0  240.0           Left   \n",
       "\n",
       "    International Reputation  Skill Moves Position  Joined  \\\n",
       "0                        5.0          4.0       RF    2004   \n",
       "1                        5.0          5.0       ST    2018   \n",
       "2                        5.0          5.0       LW    2017   \n",
       "3                        4.0          1.0       GK    2011   \n",
       "4                        4.0          4.0      RCM    2015   \n",
       "5                        4.0          4.0       LF    2012   \n",
       "6                        4.0          4.0      RCM    2012   \n",
       "7                        5.0          3.0       RS    2014   \n",
       "8                        4.0          3.0      RCB    2005   \n",
       "9                        3.0          1.0       GK    2014   \n",
       "10                       4.0          4.0       ST    2014   \n",
       "11                       4.0          3.0      LCM    2014   \n",
       "12                       3.0          2.0       CB    2010   \n",
       "13                       4.0          4.0      LCM    2010   \n",
       "14                       3.0          2.0      LDM    2016   \n",
       "15                       3.0          4.0       LF    2015   \n",
       "16                       3.0          3.0       ST    2010   \n",
       "17                       4.0          4.0      CAM    2014   \n",
       "18                       3.0          1.0       GK    2014   \n",
       "19                       4.0          1.0       GK    2018   \n",
       "\n",
       "   Contract Valid Until    Height  Weight  Release Clause  \n",
       "0            2021-01-01  5.583333   159.0        226500.0  \n",
       "1            2022-01-01  6.166667   183.0        127100.0  \n",
       "2            2022-01-01  5.750000   150.0        228100.0  \n",
       "3            2020-01-01  6.333333   168.0        138600.0  \n",
       "4            2023-01-01  5.916667   154.0        196400.0  \n",
       "5            2020-01-01  5.666667   163.0        172100.0  \n",
       "6            2020-01-01  5.666667   146.0        137400.0  \n",
       "7            2021-01-01  6.000000   190.0        164000.0  \n",
       "8            2020-01-01  6.000000   181.0        104600.0  \n",
       "9            2021-01-01  6.166667   192.0        144500.0  \n",
       "10           2021-01-01  6.000000   176.0        127100.0  \n",
       "11           2022-01-01  6.000000   168.0        156800.0  \n",
       "12           2019-01-01  6.166667   172.0         90200.0  \n",
       "13           2020-01-01  5.666667   148.0        111000.0  \n",
       "14           2023-01-01  5.500000   159.0        121300.0  \n",
       "15           2022-01-01  5.833333   165.0        153500.0  \n",
       "16           2024-01-01  6.166667   196.0        160700.0  \n",
       "17           2023-01-01  5.750000   161.0        165800.0  \n",
       "18           2022-01-01  6.166667   187.0        123300.0  \n",
       "19           2024-01-01  6.500000   212.0        113700.0  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "1be939e5",
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Value</th>\n",
       "      <th>Wage</th>\n",
       "      <th>International Reputation</th>\n",
       "      <th>Skill Moves</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Release Clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18207.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "      <td>17955.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "      <td>18159.000000</td>\n",
       "      <td>18159.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "      <td>18207.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>214298.338606</td>\n",
       "      <td>25.122206</td>\n",
       "      <td>66.238699</td>\n",
       "      <td>71.307299</td>\n",
       "      <td>2444.530214</td>\n",
       "      <td>9.731312</td>\n",
       "      <td>1.113222</td>\n",
       "      <td>2.361308</td>\n",
       "      <td>2016.420607</td>\n",
       "      <td>5.946771</td>\n",
       "      <td>165.979129</td>\n",
       "      <td>4585.060971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29965.244204</td>\n",
       "      <td>4.669943</td>\n",
       "      <td>6.908930</td>\n",
       "      <td>6.136496</td>\n",
       "      <td>5626.715434</td>\n",
       "      <td>21.999290</td>\n",
       "      <td>0.394031</td>\n",
       "      <td>0.756164</td>\n",
       "      <td>2.018194</td>\n",
       "      <td>0.220514</td>\n",
       "      <td>15.572775</td>\n",
       "      <td>10630.414430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>200315.500000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>570.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>221759.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>236529.500000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>4585.060806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>246620.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>118500.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>228100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID           Age       Overall     Potential          Value  \\\n",
       "count   18207.000000  18207.000000  18207.000000  18207.000000   17955.000000   \n",
       "mean   214298.338606     25.122206     66.238699     71.307299    2444.530214   \n",
       "std     29965.244204      4.669943      6.908930      6.136496    5626.715434   \n",
       "min        16.000000     16.000000     46.000000     48.000000      10.000000   \n",
       "25%    200315.500000     21.000000     62.000000     67.000000     325.000000   \n",
       "50%    221759.000000     25.000000     66.000000     71.000000     700.000000   \n",
       "75%    236529.500000     28.000000     71.000000     75.000000    2100.000000   \n",
       "max    246620.000000     45.000000     94.000000     95.000000  118500.000000   \n",
       "\n",
       "               Wage  International Reputation   Skill Moves        Joined  \\\n",
       "count  18207.000000              18159.000000  18159.000000  18207.000000   \n",
       "mean       9.731312                  1.113222      2.361308   2016.420607   \n",
       "std       21.999290                  0.394031      0.756164      2.018194   \n",
       "min        0.000000                  1.000000      1.000000   1991.000000   \n",
       "25%        1.000000                  1.000000      2.000000   2016.000000   \n",
       "50%        3.000000                  1.000000      2.000000   2017.000000   \n",
       "75%        9.000000                  1.000000      3.000000   2018.000000   \n",
       "max      565.000000                  5.000000      5.000000   2018.000000   \n",
       "\n",
       "             Height        Weight  Release Clause  \n",
       "count  18207.000000  18207.000000    18207.000000  \n",
       "mean       5.946771    165.979129     4585.060971  \n",
       "std        0.220514     15.572775    10630.414430  \n",
       "min        5.083333    110.000000       13.000000  \n",
       "25%        5.750000    154.000000      570.000000  \n",
       "50%        5.916667    165.000000     1300.000000  \n",
       "75%        6.083333    176.000000     4585.060806  \n",
       "max        6.750000    243.000000   228100.000000  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "8983e4c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18207 entries, 0 to 18206\n",
      "Data columns (total 18 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   ID                        18207 non-null  int64  \n",
      " 1   Name                      18207 non-null  object \n",
      " 2   Age                       18207 non-null  int64  \n",
      " 3   Nationality               18207 non-null  object \n",
      " 4   Overall                   18207 non-null  int64  \n",
      " 5   Potential                 18207 non-null  int64  \n",
      " 6   Club                      17966 non-null  object \n",
      " 7   Value                     17955 non-null  float64\n",
      " 8   Wage                      18207 non-null  float64\n",
      " 9   Preferred Foot            18207 non-null  object \n",
      " 10  International Reputation  18159 non-null  float64\n",
      " 11  Skill Moves               18159 non-null  float64\n",
      " 12  Position                  18207 non-null  object \n",
      " 13  Joined                    18207 non-null  int64  \n",
      " 14  Contract Valid Until      17918 non-null  object \n",
      " 15  Height                    18207 non-null  float64\n",
      " 16  Weight                    18207 non-null  float64\n",
      " 17  Release Clause            18207 non-null  float64\n",
      "dtypes: float64(7), int64(5), object(6)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "9a0eba4b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(raw_data.isnull(),cbar=False,yticklabels=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "05200379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                            0\n",
       "Name                          0\n",
       "Age                           0\n",
       "Nationality                   0\n",
       "Overall                       0\n",
       "Potential                     0\n",
       "Club                        241\n",
       "Value                       252\n",
       "Wage                          0\n",
       "Preferred Foot                0\n",
       "International Reputation     48\n",
       "Skill Moves                  48\n",
       "Position                      0\n",
       "Joined                        0\n",
       "Contract Valid Until        289\n",
       "Height                        0\n",
       "Weight                        0\n",
       "Release Clause                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedfaeef",
   "metadata": {},
   "source": [
    "As we can see here the the missing values are of the columns club , value, International Reputation , Skill Moves and contract valid until.\n",
    "We can use the mean of the columns International Reputation,Value, Skill Moves to replace the null values.\n",
    "As for the column Club and contract valid until , there is no resonable values that can be used to replace the null values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "c1950a41",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data.rename(columns = {'Preferred Foot':'P_Foot', 'International Reputation':'I_Reputation','Release Clause':'R_clause','Contract Valid Until':'cvu','Skill Moves' : 'S_move' }, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "06bb5b22",
   "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>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value</th>\n",
       "      <th>Wage</th>\n",
       "      <th>P_Foot</th>\n",
       "      <th>I_Reputation</th>\n",
       "      <th>S_move</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>cvu</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>R_clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>110500.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>2004</td>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>5.583333</td>\n",
       "      <td>159.0</td>\n",
       "      <td>226500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>77000.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>2018</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>183.0</td>\n",
       "      <td>127100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118500.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>2017</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>228100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>2011</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>168.0</td>\n",
       "      <td>138600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>2015</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>154.0</td>\n",
       "      <td>196400.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID               Name  Age Nationality  Overall  Potential  \\\n",
       "0  158023           L. Messi   31   Argentina       94         94   \n",
       "1   20801  Cristiano Ronaldo   33    Portugal       94         94   \n",
       "2  190871          Neymar Jr   26      Brazil       92         93   \n",
       "3  193080             De Gea   27       Spain       91         93   \n",
       "4  192985       K. De Bruyne   27     Belgium       91         92   \n",
       "\n",
       "                  Club     Value   Wage P_Foot  I_Reputation  S_move Position  \\\n",
       "0         FC Barcelona  110500.0  565.0   Left           5.0     4.0       RF   \n",
       "1             Juventus   77000.0  405.0  Right           5.0     5.0       ST   \n",
       "2  Paris Saint-Germain  118500.0  290.0  Right           5.0     5.0       LW   \n",
       "3    Manchester United   72000.0  260.0  Right           4.0     1.0       GK   \n",
       "4      Manchester City  102000.0  355.0  Right           4.0     4.0      RCM   \n",
       "\n",
       "   Joined         cvu    Height  Weight  R_clause  \n",
       "0    2004  2021-01-01  5.583333   159.0  226500.0  \n",
       "1    2018  2022-01-01  6.166667   183.0  127100.0  \n",
       "2    2017  2022-01-01  5.750000   150.0  228100.0  \n",
       "3    2011  2020-01-01  6.333333   168.0  138600.0  \n",
       "4    2015  2023-01-01  5.916667   154.0  196400.0  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "90ea01e7",
   "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>ID</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Nationality</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Club</th>\n",
       "      <th>Value</th>\n",
       "      <th>Wage</th>\n",
       "      <th>P_Foot</th>\n",
       "      <th>I_Reputation</th>\n",
       "      <th>S_move</th>\n",
       "      <th>Position</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>R_clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158023</td>\n",
       "      <td>L. Messi</td>\n",
       "      <td>31</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>FC Barcelona</td>\n",
       "      <td>110500.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>Left</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RF</td>\n",
       "      <td>2004</td>\n",
       "      <td>5.583333</td>\n",
       "      <td>159.0</td>\n",
       "      <td>226500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20801</td>\n",
       "      <td>Cristiano Ronaldo</td>\n",
       "      <td>33</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>Juventus</td>\n",
       "      <td>77000.0</td>\n",
       "      <td>405.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>183.0</td>\n",
       "      <td>127100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190871</td>\n",
       "      <td>Neymar Jr</td>\n",
       "      <td>26</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>Paris Saint-Germain</td>\n",
       "      <td>118500.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>LW</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>150.0</td>\n",
       "      <td>228100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>193080</td>\n",
       "      <td>De Gea</td>\n",
       "      <td>27</td>\n",
       "      <td>Spain</td>\n",
       "      <td>91</td>\n",
       "      <td>93</td>\n",
       "      <td>Manchester United</td>\n",
       "      <td>72000.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>GK</td>\n",
       "      <td>2011</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>168.0</td>\n",
       "      <td>138600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>192985</td>\n",
       "      <td>K. De Bruyne</td>\n",
       "      <td>27</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>91</td>\n",
       "      <td>92</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>102000.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>RCM</td>\n",
       "      <td>2015</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>154.0</td>\n",
       "      <td>196400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18202</th>\n",
       "      <td>238813</td>\n",
       "      <td>J. Lundstram</td>\n",
       "      <td>19</td>\n",
       "      <td>England</td>\n",
       "      <td>47</td>\n",
       "      <td>65</td>\n",
       "      <td>Crewe Alexandra</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CM</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>134.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18203</th>\n",
       "      <td>243165</td>\n",
       "      <td>N. Christoffersson</td>\n",
       "      <td>19</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>47</td>\n",
       "      <td>63</td>\n",
       "      <td>Trelleborgs FF</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>2018</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>170.0</td>\n",
       "      <td>113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18204</th>\n",
       "      <td>241638</td>\n",
       "      <td>B. Worman</td>\n",
       "      <td>16</td>\n",
       "      <td>England</td>\n",
       "      <td>47</td>\n",
       "      <td>67</td>\n",
       "      <td>Cambridge United</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>ST</td>\n",
       "      <td>2017</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>148.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18205</th>\n",
       "      <td>246268</td>\n",
       "      <td>D. Walker-Rice</td>\n",
       "      <td>17</td>\n",
       "      <td>England</td>\n",
       "      <td>47</td>\n",
       "      <td>66</td>\n",
       "      <td>Tranmere Rovers</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>RW</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>154.0</td>\n",
       "      <td>143.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18206</th>\n",
       "      <td>246269</td>\n",
       "      <td>G. Nugent</td>\n",
       "      <td>16</td>\n",
       "      <td>England</td>\n",
       "      <td>46</td>\n",
       "      <td>66</td>\n",
       "      <td>Tranmere Rovers</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Right</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>CM</td>\n",
       "      <td>2018</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>176.0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18207 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ID                Name  Age Nationality  Overall  Potential  \\\n",
       "0      158023            L. Messi   31   Argentina       94         94   \n",
       "1       20801   Cristiano Ronaldo   33    Portugal       94         94   \n",
       "2      190871           Neymar Jr   26      Brazil       92         93   \n",
       "3      193080              De Gea   27       Spain       91         93   \n",
       "4      192985        K. De Bruyne   27     Belgium       91         92   \n",
       "...       ...                 ...  ...         ...      ...        ...   \n",
       "18202  238813        J. Lundstram   19     England       47         65   \n",
       "18203  243165  N. Christoffersson   19      Sweden       47         63   \n",
       "18204  241638           B. Worman   16     England       47         67   \n",
       "18205  246268      D. Walker-Rice   17     England       47         66   \n",
       "18206  246269           G. Nugent   16     England       46         66   \n",
       "\n",
       "                      Club     Value   Wage P_Foot  I_Reputation  S_move  \\\n",
       "0             FC Barcelona  110500.0  565.0   Left           5.0     4.0   \n",
       "1                 Juventus   77000.0  405.0  Right           5.0     5.0   \n",
       "2      Paris Saint-Germain  118500.0  290.0  Right           5.0     5.0   \n",
       "3        Manchester United   72000.0  260.0  Right           4.0     1.0   \n",
       "4          Manchester City  102000.0  355.0  Right           4.0     4.0   \n",
       "...                    ...       ...    ...    ...           ...     ...   \n",
       "18202      Crewe Alexandra      60.0    1.0  Right           1.0     2.0   \n",
       "18203       Trelleborgs FF      60.0    1.0  Right           1.0     2.0   \n",
       "18204     Cambridge United      60.0    1.0  Right           1.0     2.0   \n",
       "18205      Tranmere Rovers      60.0    1.0  Right           1.0     2.0   \n",
       "18206      Tranmere Rovers      60.0    1.0  Right           1.0     2.0   \n",
       "\n",
       "      Position  Joined    Height  Weight  R_clause  \n",
       "0           RF    2004  5.583333   159.0  226500.0  \n",
       "1           ST    2018  6.166667   183.0  127100.0  \n",
       "2           LW    2017  5.750000   150.0  228100.0  \n",
       "3           GK    2011  6.333333   168.0  138600.0  \n",
       "4          RCM    2015  5.916667   154.0  196400.0  \n",
       "...        ...     ...       ...     ...       ...  \n",
       "18202       CM    2017  5.750000   134.0     143.0  \n",
       "18203       ST    2018  6.250000   170.0     113.0  \n",
       "18204       ST    2017  5.666667   148.0     165.0  \n",
       "18205       RW    2018  5.833333   154.0     143.0  \n",
       "18206       CM    2018  5.833333   176.0     165.0  \n",
       "\n",
       "[18207 rows x 17 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.drop(['cvu'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "76acdbe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data['I_Reputation'] = raw_data['I_Reputation'].replace(np.nan , 1.113222 )\n",
    "raw_data['Value'] = raw_data['Value'].replace(np.nan , 2444.530214 )\n",
    "raw_data['S_move'] = raw_data['S_move'].replace(np.nan , 2.361308 )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "9e3933d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                0\n",
       "Name              0\n",
       "Age               0\n",
       "Nationality       0\n",
       "Overall           0\n",
       "Potential         0\n",
       "Club            241\n",
       "Value             0\n",
       "Wage              0\n",
       "P_Foot            0\n",
       "I_Reputation      0\n",
       "S_move            0\n",
       "Position          0\n",
       "Joined            0\n",
       "cvu             289\n",
       "Height            0\n",
       "Weight            0\n",
       "R_clause          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a2834c",
   "metadata": {},
   "source": [
    "Now that we have replaced the null values in the data, we shal now drop the rest of the null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "948cf2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "new=raw_data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "f5d95a10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID              0\n",
       "Name            0\n",
       "Age             0\n",
       "Nationality     0\n",
       "Overall         0\n",
       "Potential       0\n",
       "Club            0\n",
       "Value           0\n",
       "Wage            0\n",
       "P_Foot          0\n",
       "I_Reputation    0\n",
       "S_move          0\n",
       "Position        0\n",
       "Joined          0\n",
       "cvu             0\n",
       "Height          0\n",
       "Weight          0\n",
       "R_clause        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "af6f52db",
   "metadata": {
    "scrolled": true
   },
   "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>ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Overall</th>\n",
       "      <th>Potential</th>\n",
       "      <th>Value</th>\n",
       "      <th>Wage</th>\n",
       "      <th>I_Reputation</th>\n",
       "      <th>S_move</th>\n",
       "      <th>Joined</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>R_clause</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "      <td>17918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>214225.713472</td>\n",
       "      <td>25.105257</td>\n",
       "      <td>66.236801</td>\n",
       "      <td>71.329334</td>\n",
       "      <td>2450.129469</td>\n",
       "      <td>9.883748</td>\n",
       "      <td>1.113908</td>\n",
       "      <td>2.362875</td>\n",
       "      <td>2016.427391</td>\n",
       "      <td>5.946753</td>\n",
       "      <td>165.968858</td>\n",
       "      <td>4585.060974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30042.543245</td>\n",
       "      <td>4.675372</td>\n",
       "      <td>6.929243</td>\n",
       "      <td>6.144098</td>\n",
       "      <td>5631.477190</td>\n",
       "      <td>22.142769</td>\n",
       "      <td>0.395495</td>\n",
       "      <td>0.756309</td>\n",
       "      <td>2.033693</td>\n",
       "      <td>0.220812</td>\n",
       "      <td>15.604084</td>\n",
       "      <td>10715.805404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>5.083333</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>200235.250000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>325.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>563.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>221701.500000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>5.916667</td>\n",
       "      <td>165.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>236508.750000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2100.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>6.083333</td>\n",
       "      <td>176.000000</td>\n",
       "      <td>4585.060806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>246620.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>118500.000000</td>\n",
       "      <td>565.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>243.000000</td>\n",
       "      <td>228100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID           Age       Overall     Potential          Value  \\\n",
       "count   17918.000000  17918.000000  17918.000000  17918.000000   17918.000000   \n",
       "mean   214225.713472     25.105257     66.236801     71.329334    2450.129469   \n",
       "std     30042.543245      4.675372      6.929243      6.144098    5631.477190   \n",
       "min        16.000000     16.000000     46.000000     48.000000      10.000000   \n",
       "25%    200235.250000     21.000000     62.000000     67.000000     325.000000   \n",
       "50%    221701.500000     25.000000     66.000000     71.000000     700.000000   \n",
       "75%    236508.750000     28.000000     71.000000     75.000000    2100.000000   \n",
       "max    246620.000000     45.000000     94.000000     95.000000  118500.000000   \n",
       "\n",
       "               Wage  I_Reputation        S_move        Joined        Height  \\\n",
       "count  17918.000000  17918.000000  17918.000000  17918.000000  17918.000000   \n",
       "mean       9.883748      1.113908      2.362875   2016.427391      5.946753   \n",
       "std       22.142769      0.395495      0.756309      2.033693      0.220812   \n",
       "min        1.000000      1.000000      1.000000   1991.000000      5.083333   \n",
       "25%        1.000000      1.000000      2.000000   2016.000000      5.750000   \n",
       "50%        3.000000      1.000000      2.000000   2017.000000      5.916667   \n",
       "75%        9.000000      1.000000      3.000000   2018.000000      6.083333   \n",
       "max      565.000000      5.000000      5.000000   2018.000000      6.750000   \n",
       "\n",
       "             Weight       R_clause  \n",
       "count  17918.000000   17918.000000  \n",
       "mean     165.968858    4585.060974  \n",
       "std       15.604084   10715.805404  \n",
       "min      110.000000      13.000000  \n",
       "25%      154.000000     563.000000  \n",
       "50%      165.000000    1300.000000  \n",
       "75%      176.000000    4585.060806  \n",
       "max      243.000000  228100.000000  "
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27c4fcfa",
   "metadata": {},
   "source": [
    "Here we can see no major changes occur on the data as a whole after deleting the null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "de16e0b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 17918 entries, 0 to 18206\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   ID            17918 non-null  int64  \n",
      " 1   Name          17918 non-null  object \n",
      " 2   Age           17918 non-null  int64  \n",
      " 3   Nationality   17918 non-null  object \n",
      " 4   Overall       17918 non-null  int64  \n",
      " 5   Potential     17918 non-null  int64  \n",
      " 6   Club          17918 non-null  object \n",
      " 7   Value         17918 non-null  float64\n",
      " 8   Wage          17918 non-null  float64\n",
      " 9   P_Foot        17918 non-null  object \n",
      " 10  I_Reputation  17918 non-null  float64\n",
      " 11  S_move        17918 non-null  float64\n",
      " 12  Position      17918 non-null  object \n",
      " 13  Joined        17918 non-null  int64  \n",
      " 14  cvu           17918 non-null  object \n",
      " 15  Height        17918 non-null  float64\n",
      " 16  Weight        17918 non-null  float64\n",
      " 17  R_clause      17918 non-null  float64\n",
      "dtypes: float64(7), int64(5), object(6)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "new.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "51169870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(new.isnull(),cbar=False,yticklabels=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "b890a7a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "England        1657\n",
       "Germany        1195\n",
       "Spain          1071\n",
       "Argentina       935\n",
       "France          911\n",
       "Brazil          825\n",
       "Italy           699\n",
       "Colombia        616\n",
       "Japan           478\n",
       "Netherlands     452\n",
       "Name: Nationality, dtype: int64"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_countries = new.Nationality.value_counts().head(10)\n",
    "major_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "0f99edb3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANSHUMAN\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Nationality'>"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplt.figure(figsize=(12,6))\n",
    "pyplt.xticks(rotation = 75)\n",
    "\n",
    "sns.barplot(major_countries.index , major_countries )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43c30cf4",
   "metadata": {},
   "source": [
    "Here we can see that England has the most number of players i.e. over 1600.\n",
    "In the same way we can figure out the same for the different clubs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "393f9ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tottenham Hotspur          33\n",
       "Burnley                    33\n",
       "Real Madrid                33\n",
       "Chelsea                    33\n",
       "Fortuna Düsseldorf         33\n",
       "Frosinone                  33\n",
       "Borussia Dortmund          33\n",
       "Manchester United          33\n",
       "Empoli                     33\n",
       "Newcastle United           33\n",
       "Arsenal                    33\n",
       "FC Barcelona               33\n",
       "Wolverhampton Wanderers    33\n",
       "Liverpool                  33\n",
       "Rayo Vallecano             33\n",
       "Eintracht Frankfurt        33\n",
       "Valencia CF                33\n",
       "Southampton                33\n",
       "Manchester City            33\n",
       "RC Celta                   33\n",
       "TSG 1899 Hoffenheim        33\n",
       "AS Monaco                  33\n",
       "Atlético Madrid            33\n",
       "Cardiff City               33\n",
       "CD Leganés                 33\n",
       "Hertha BSC                 32\n",
       "Bournemouth                32\n",
       "VfL Wolfsburg              32\n",
       "1. FSV Mainz 05            32\n",
       "SV Werder Bremen           32\n",
       "Name: Club, dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_clubs = new.Club.value_counts().head(30)\n",
    "major_clubs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "7a5c99c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33    25\n",
       "32     5\n",
       "Name: Club, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_clubs.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "6511dc7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tottenham Hotspur          33\n",
       "Burnley                    33\n",
       "Real Madrid                33\n",
       "Chelsea                    33\n",
       "Fortuna Düsseldorf         33\n",
       "Frosinone                  33\n",
       "Borussia Dortmund          33\n",
       "Manchester United          33\n",
       "Empoli                     33\n",
       "Newcastle United           33\n",
       "Arsenal                    33\n",
       "FC Barcelona               33\n",
       "Wolverhampton Wanderers    33\n",
       "Liverpool                  33\n",
       "Rayo Vallecano             33\n",
       "Eintracht Frankfurt        33\n",
       "Valencia CF                33\n",
       "Southampton                33\n",
       "Manchester City            33\n",
       "RC Celta                   33\n",
       "TSG 1899 Hoffenheim        33\n",
       "AS Monaco                  33\n",
       "Atlético Madrid            33\n",
       "Cardiff City               33\n",
       "CD Leganés                 33\n",
       "Name: Club, dtype: int64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "major_clubs = new.Club.value_counts().head(25)\n",
    "major_clubs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b99f5f",
   "metadata": {},
   "source": [
    "These are the clubs that have the most number of players ie. 33."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "434bd846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Right    13756\n",
       "Left      4162\n",
       "Name: P_Foot, dtype: int64"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.P_Foot.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "d9bedabf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl8AAAFyCAYAAADLZb9oAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAc8klEQVR4nO3dfWxe9X3//5ft2Cn4Zm4mV5vaem1aLA2msNw0KlOSjUlVNqFqKS03tpR22kAdraiSFkgEhIAKDZFqaxLIY0SpthpsN1srdRtIk5aihJss67ySSBFpt1QlwEpnCFl9ucR2nOv7x3545FdGjAmfKw6Ph4TQdfxx8z4i5/jZc851ua5arVYDAEAR9bUeAADg3UR8AQAUJL4AAAoSXwAABYkvAICCFtR6gNk6depUpqe9MZPZaWio8/cFOOucW5itxsaG//Nr8ya+pqerOX78F7Ueg3mivf1Cf1+As865hdnq6Gj9P7/mtiMAQEHiCwCgIPEFAFCQ+AIAKEh8AQAUJL4AAAqaVXwdOHAg69evP23b3//93+eaa66Zeb1r165ceeWVufrqq/PYY48lSU6cOJEbb7wxPT09uf7663Ps2LEkydNPP52rrroq1157be6///6ztS8AAOe8M8bXjh07cvvtt2diYmJm2zPPPJO//du/TbX6Px80Nzo6moGBgQwPD2fnzp3p6+vL5ORkhoaG0tXVlcHBwaxbty79/f1Jkq1bt6a3tzdDQ0M5cOBADh069A7tHgDAueWMH7La2dmZ++67L7fcckuS5JVXXsnXv/713HrrrdmyZUuS5ODBg1m6dGmamprS1NSUzs7OHD58OCMjI7nuuuuSJGvWrEl/f38qlUomJyfT2dmZJFm1alX27duXSy655E3naGioS3v7hW9rZ3n3aGio9/cFOOucWzgbzhhfa9euzfPPP58kmZ6ezm233ZZbb701CxcunFlTqVTS2vq/n+Ta3NycSqVy2vbm5uaMjY2lUqmkpaXltLXPPffcGQf1Cfe8FT6FGngnOLcwW2/2Cfdv6dcLHTp0KM8++2zuvPPOTExM5D/+4z9yzz335OMf/3jGx8dn1o2Pj6e1tTUtLS0z28fHx9PW1nbattdvBwB4N3hL73ZcsmRJHnnkkQwMDKSvry8f/ehHc9ttt2XJkiUZGRnJxMRExsbGcuTIkXR1dWXZsmXZs2dPkmTv3r1Zvnx5Wlpa0tjYmKNHj6ZareaJJ57IihUr3pGdAwA415yVX6zd0dGR9evXp6enJ9VqNRs3bszChQvT3d2dTZs2pbu7O42Njent7U2S3HXXXbnpppsyPT2dVatW5dJLLz0bYwAAnPPqqq+9ZfEcNzU17T47s+a5DOCd4NzCbJ21Z74497S0XZALFvrP+Ebe7C/+u9WrEydT+fmrtR4D4F3NT+157oKFC/KhzY/UegzmiZ/ce0UqtR4C4F3OrxcCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKmlV8HThwIOvXr0+SPPPMM+np6cn69evzp3/6p3nppZeSJLt27cqVV16Zq6++Oo899liS5MSJE7nxxhvT09OT66+/PseOHUuSPP3007nqqqty7bXX5v77738n9gsA4Jx0xvjasWNHbr/99kxMTCRJ7rnnnmzZsiUDAwP5xCc+kR07dmR0dDQDAwMZHh7Ozp0709fXl8nJyQwNDaWrqyuDg4NZt25d+vv7kyRbt25Nb29vhoaGcuDAgRw6dOid3UsAgHPEgjMt6OzszH333ZdbbrklSdLX15f3ve99SZLp6eksXLgwBw8ezNKlS9PU1JSmpqZ0dnbm8OHDGRkZyXXXXZckWbNmTfr7+1OpVDI5OZnOzs4kyapVq7Jv375ccsklbzpHQ0Nd2tsvfFs7C8RxBG9DQ0O9Y4i37YzxtXbt2jz//PMzr18Lr3/7t3/LQw89lIcffjiPP/54WltbZ9Y0NzenUqmkUqnMbG9ubs7Y2FgqlUpaWlpOW/vcc8+dcdDp6WqOH//F7PfsXaKjo/XMi+B1HEcwd+3tFzqGmJU3+/l8xvh6I48++mj+4i/+Ig8++GAWLVqUlpaWjI+Pz3x9fHw8ra2tp20fHx9PW1vbG65ta2ubyxgAAPPOW36343e/+9089NBDGRgYyAc/+MEkyZIlSzIyMpKJiYmMjY3lyJEj6erqyrJly7Jnz54kyd69e7N8+fK0tLSksbExR48eTbVazRNPPJEVK1ac3b0CADhHvaUrX9PT07nnnnvy67/+67nxxhuTJB/72MfypS99KevXr09PT0+q1Wo2btyYhQsXpru7O5s2bUp3d3caGxvT29ubJLnrrrty0003ZXp6OqtWrcqll1569vcMAOAcVFetVqu1HmI2pqam3Wd/Ax0drfnQ5kdqPQbzxE/uvSKjo2O1HgPmLc98MVtv9syXD1kFAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgmYVXwcOHMj69euTJM8++2y6u7vT09OTrVu35tSpU0mSXbt25corr8zVV1+dxx57LEly4sSJ3Hjjjenp6cn111+fY8eOJUmefvrpXHXVVbn22mtz//33vxP7BQBwTjpjfO3YsSO33357JiYmkiTbtm3Lhg0bMjg4mGq1mt27d2d0dDQDAwMZHh7Ozp0709fXl8nJyQwNDaWrqyuDg4NZt25d+vv7kyRbt25Nb29vhoaGcuDAgRw6dOid3UsAgHPEGeOrs7Mz991338zrQ4cOZeXKlUmSNWvW5KmnnsrBgwezdOnSNDU1pbW1NZ2dnTl8+HBGRkayevXqmbX79u1LpVLJ5ORkOjs7U1dXl1WrVmXfvn3v0O4BAJxbFpxpwdq1a/P888/PvK5Wq6mrq0uSNDc3Z2xsLJVKJa2trTNrmpubU6lUTtv++rUtLS2nrX3uuefOOGhDQ13a2y+c/Z4Bb8hxBHPX0FDvGOJtO2N8/f/V1//vxbLx8fG0tbWlpaUl4+Pjp21vbW09bfubrW1razvjnzs9Xc3x4794q+Oe9zo6Ws+8CF7HcQRz195+oWOIWXmzn89v+d2OF198cfbv358k2bt3b1asWJElS5ZkZGQkExMTGRsby5EjR9LV1ZVly5Zlz549M2uXL1+elpaWNDY25ujRo6lWq3niiSeyYsWKOe4aAMD88pavfG3atClbtmxJX19fFi9enLVr16ahoSHr169PT09PqtVqNm7cmIULF6a7uzubNm1Kd3d3Ghsb09vbmyS56667ctNNN2V6ejqrVq3KpZdeetZ3DADgXFRXrVartR5iNqampl3qfQMdHa350OZHaj0G88RP7r0io6NjtR4D5i23HZmts3rbEQCAuRNfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKCgBXP5pqmpqWzevDkvvPBC6uvr89WvfjULFizI5s2bU1dXl4suuihbt25NfX19du3aleHh4SxYsCA33HBDLr/88pw4cSI333xzXn755TQ3N2f79u1ZtGjR2d43AIBzzpyufO3ZsycnT57M8PBwvvjFL+bP//zPs23btmzYsCGDg4OpVqvZvXt3RkdHMzAwkOHh4ezcuTN9fX2ZnJzM0NBQurq6Mjg4mHXr1qW/v/9s7xcAwDlpTvH14Q9/ONPT0zl16lQqlUoWLFiQQ4cOZeXKlUmSNWvW5KmnnsrBgwezdOnSNDU1pbW1NZ2dnTl8+HBGRkayevXqmbX79u07e3sEAHAOm9NtxwsvvDAvvPBC/vAP/zCvvPJKHnjggXz/+99PXV1dkqS5uTljY2OpVCppbW2d+b7m5uZUKpXTtr+29kwaGurS3n7hXMYFXsdxBHPX0FDvGOJtm1N8/dVf/VVWrVqVr3zlK/npT3+az33uc5mampr5+vj4eNra2tLS0pLx8fHTtre2tp62/bW1ZzI9Xc3x47+Yy7jntY6O1jMvgtdxHMHctbdf6BhiVt7s5/Ocbju2tbXNXLn6lV/5lZw8eTIXX3xx9u/fnyTZu3dvVqxYkSVLlmRkZCQTExMZGxvLkSNH0tXVlWXLlmXPnj0za5cvXz6XMQAA5p26arVafavfND4+nltvvTWjo6OZmprKZz/72fzWb/1WtmzZkqmpqSxevDh33313GhoasmvXrnzrW99KtVrN5z//+axduzavvvpqNm3alNHR0TQ2Nqa3tzcdHR1v+mdOTU37fxtvoKOjNR/a/Eitx2Ce+Mm9V2R09My3+YE35soXs/VmV77mFF+1IL7emPjirRBf8PaIL2brrN92BABgbsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChowVy/8S//8i/zve99L1NTU+nu7s7KlSuzefPm1NXV5aKLLsrWrVtTX1+fXbt2ZXh4OAsWLMgNN9yQyy+/PCdOnMjNN9+cl19+Oc3Nzdm+fXsWLVp0NvcLAOCcNKcrX/v3788PfvCDDA0NZWBgIC+++GK2bduWDRs2ZHBwMNVqNbt3787o6GgGBgYyPDycnTt3pq+vL5OTkxkaGkpXV1cGBwezbt269Pf3n+39AgA4J80pvp544ol0dXXli1/8Yv7sz/4sv/d7v5dDhw5l5cqVSZI1a9bkqaeeysGDB7N06dI0NTWltbU1nZ2dOXz4cEZGRrJ69eqZtfv27Tt7ewQAcA6b023HV155Jf/5n/+ZBx54IM8//3xuuOGGVKvV1NXVJUmam5szNjaWSqWS1tbWme9rbm5OpVI5bftra8+koaEu7e0XzmVc4HUcRzB3DQ31jiHetjnFV3t7exYvXpympqYsXrw4CxcuzIsvvjjz9fHx8bS1taWlpSXj4+OnbW9tbT1t+2trz2R6uprjx38xl3HPax0drWdeBK/jOIK5a2+/0DHErLzZz+c53XZcvnx5Hn/88VSr1fzsZz/Lq6++mssuuyz79+9PkuzduzcrVqzIkiVLMjIykomJiYyNjeXIkSPp6urKsmXLsmfPnpm1y5cvn8sYAADzzpyufF1++eX5/ve/n8985jOpVqu544478oEPfCBbtmxJX19fFi9enLVr16ahoSHr169PT09PqtVqNm7cmIULF6a7uzubNm1Kd3d3Ghsb09vbe7b3CwDgnFRXrVartR5iNqampl3qfQMdHa350OZHaj0G88RP7r0io6NnfsYSeGNuOzJbZ/22IwAAcyO+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAgsQXAEBB4gsAoCDxBQBQkPgCAChIfAEAFCS+AAAKEl8AAAWJLwCAghbUegAAzj0tbRfkgoV+RLyRjo7WWo9wznl14mQqP3+11mPMG44sAH7JBQsX5EObH6n1GMwTP7n3ilRqPcQ88rZuO7788sv53d/93Rw5ciTPPvtsuru709PTk61bt+bUqVNJkl27duXKK6/M1VdfncceeyxJcuLEidx4443p6enJ9ddfn2PHjr39PQEAmAfmHF9TU1O544478p73vCdJsm3btmzYsCGDg4OpVqvZvXt3RkdHMzAwkOHh4ezcuTN9fX2ZnJzM0NBQurq6Mjg4mHXr1qW/v/+s7RAAwLlszrcdt2/fnmuvvTYPPvhgkuTQoUNZuXJlkmTNmjV58sknU19fn6VLl6apqSlNTU3p7OzM4cOHMzIykuuuu25m7Wziq6GhLu3tF851XOD/4zgC3gnOLbM3p/j6zne+k0WLFmX16tUz8VWtVlNXV5ckaW5uztjYWCqVSlpb//fBxObm5lQqldO2v7b2TKanqzl+/BdzGfe85sFP3irHEbPh3MJb5dxyujc7huYUX9/+9rdTV1eXffv25ZlnnsmmTZtOe25rfHw8bW1taWlpyfj4+GnbW1tbT9v+2loAgHeDOT3z9fDDD+ehhx7KwMBAfvM3fzPbt2/PmjVrsn///iTJ3r17s2LFiixZsiQjIyOZmJjI2NhYjhw5kq6urixbtix79uyZWbt8+fKzt0cAAOews/ZRE5s2bcqWLVvS19eXxYsXZ+3atWloaMj69evT09OTarWajRs3ZuHChenu7s6mTZvS3d2dxsbG9Pb2nq0xAADOaXXVarVa6yFmY2pq2v3kN9DR0eqzeJi1n9x7RUZHz/yMJTi38FY4t/yyN3vmy68XAgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgILEFwBAQeILAKAg8QUAUJD4AgAoSHwBABQkvgAAChJfAAAFiS8AgIIWzOWbpqamcuutt+aFF17I5ORkbrjhhnz0ox/N5s2bU1dXl4suuihbt25NfX19du3aleHh4SxYsCA33HBDLr/88pw4cSI333xzXn755TQ3N2f79u1ZtGjR2d43AIBzzpyufP3d3/1d2tvbMzg4mB07duSrX/1qtm3blg0bNmRwcDDVajW7d+/O6OhoBgYGMjw8nJ07d6avry+Tk5MZGhpKV1dXBgcHs27duvT395/t/QIAOCfN6crXH/zBH2Tt2rUzrxsaGnLo0KGsXLkySbJmzZo8+eSTqa+vz9KlS9PU1JSmpqZ0dnbm8OHDGRkZyXXXXTezdjbx1dBQl/b2C+cyLvA6jiPgneDcMntziq/m5uYkSaVSyZe+9KVs2LAh27dvT11d3czXx8bGUqlU0traetr3VSqV07a/tvZMpqerOX78F3MZ97zW0dF65kXwOo4jZsO5hbfKueV0b3YMzfmB+5/+9Kf57Gc/mz/6oz/KJz/5ydTX/+//1Pj4eNra2tLS0pLx8fHTtre2tp62/bW1AADvBnOKr5deeil/8id/kptvvjmf+cxnkiQXX3xx9u/fnyTZu3dvVqxYkSVLlmRkZCQTExMZGxvLkSNH0tXVlWXLlmXPnj0za5cvX36WdgcA4Nw2p9uODzzwQH7+85+nv79/5nmt2267LXfffXf6+vqyePHirF27Ng0NDVm/fn16enpSrVazcePGLFy4MN3d3dm0aVO6u7vT2NiY3t7es7pTAADnqrpqtVqt9RCzMTU17X7yG+joaM2HNj9S6zGYJ35y7xUZHT3zM5bg3MJb4dzyy96RZ74AAHjrxBcAQEHiCwCgIPEFAFCQ+AIAKEh8AQAUJL4AAAoSXwAABYkvAICCxBcAQEHiCwCgIPEFAFCQ+AIAKEh8AQAUJL4AAAoSXwAABYkvAICCxBcAQEHiCwCgIPEFAFCQ+AIAKEh8AQAUJL4AAAoSXwAABYkvAICCxBcAQEHiCwCgIPEFAFCQ+AIAKEh8AQAUJL4AAAoSXwAABYkvAICCxBcAQEHiCwCgIPEFAFCQ+AIAKGhBrf7gU6dO5c4778wPf/jDNDU15e67785v/MZv1GocAIAianbl65/+6Z8yOTmZb33rW/nKV76Se++9t1ajAAAUU1etVqu1+IO3bduWJUuW5IorrkiSrF69Oo8//ngtRgEAKKZmV74qlUpaWlpmXjc0NOTkyZO1GgcAoIiaxVdLS0vGx8dnXp86dSoLFtTsETQAgCJqFl/Lli3L3r17kyRPP/10urq6ajUKAEAxNXvm67V3O/7oRz9KtVrN1772tXzkIx+pxSgAAMXULL4AAN6NfMgqAEBB4gsAoCDxBQBQkPgCAChIfHHeePHFF097/eMf/7hGkwDnk7/5m7857fU3v/nNGk3C+cK7HZn3fvSjH+VnP/tZvv71r+fmm29OkkxPT6evry/f/e53azwdMF/9wz/8Q773ve9l//79+fjHP57kf84t//7v/55HHnmkxtMxn/lIeea9n//853n00Ufz8ssvz5wQ6+rq0tPTU+PJgPls9erV6ejoyPHjx3PNNdckSerr6/PBD36wxpMx34kv5r0VK1ZkxYoVOXToUC655JJajwOcJ77whS/k4YcfzqOPPpqVK1fWehzOI+KL88ahQ4dyyy235MSJEzPbdu/eXcOJgPnsPe95Tz796U/n2WefzQ9/+MMkSbVaTV1dXYaHh2s8HfOZZ744b1x55ZW577770tHRMbOtqamphhMB89mpU6fyX//1X7njjjty55135vU/Lt///vfXcDLmO+925Lzx3ve+N+9///vT1NQ08w/AXNXX1+fXfu3X0t/fnyeffDLf/va38/zzz+eCCy6o9WjMc658Me/19fUlSX7wgx+kqakpF198cerq6pIkX/7yl2s5GnAeuO222/K+970vTz31VD7/+c9naGgoO3bsqPVYzGOe+WLe+/CHP3zavwHOpqNHj+aee+7JyMhIfv/3fz8PPvhgrUdinnPbkXnvU5/6VD71qU+lrq7utH8aGxvzr//6r7UeD5jnpqenc+zYsSRJpVJJfb0fnbw9rnxx3njkkUdy4sSJ/PZv/3YOHjyYiYmJLFiwIBdffHFuvfXWWo8HzFMbN25Md3d3RkdHc8011+SP//iPaz0S85z44rxx8uTJ/PVf/3Xq6+tz6tSpXH/99dm5c2euvfbaWo8GzGMf+9jH8o//+I85duxY2tvbc/XVV+eqq66q9VjMY66dct44fvx4Tp48meR/Quy///u/kySTk5O1HAs4TyxatCj19fXxPjXeLle+OG/09PTkk5/8ZC666KL8+Mc/znXXXZcHHnggq1evrvVowHnktXdTw1z5qAnOK6+88kqOHj2azs7OvPe978309HQaGhpqPRYwD335y1/+pdCqVqt58skns3///hpNxflAfDHv9ff35wtf+MIbnih7e3trNBUw3/3Lv/zL//k1v+uRt0N8Me8dPHgwL774Ytra2mbeAj46Opp77rknTz31VI2nA4DTeeaLee8b3/hGGhoa8tJLL+UTn/hEPvCBD+RrX/taPve5z9V6NAD4JeKLee/o0aP5zne+k8nJyXz6059OY2NjvvnNb+YjH/lIrUcDgF8ivpj3WlpakiRNTU05depUvvGNb6S9vb22QwHA/8HnfHFe+dVf/VXhBcA5zQP3zHu/8zu/k8suuyzVajX//M//nMsuu2zma97tCMC5Rnwx73k7OADzifgCACjIM18AAAWJLwCAgsQXAEBB4gsAoKD/Bys5RU3bPIdJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pFoot=new.P_Foot.value_counts()\n",
    "pFoot.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9742ac87",
   "metadata": {},
   "source": [
    "Here we can see that the majority of players prefer their right foot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "77ac9ea4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANSHUMAN\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x= new.Potential)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd941adf",
   "metadata": {},
   "source": [
    "We can see here that most of the players belong to the 65 to 80 range. With the mean potential of all the players is 71.32."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "a7d65d30",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANSHUMAN\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(x=new.Age)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cf71610",
   "metadata": {},
   "source": [
    "Here we can see most of the players age ranges from 20 to 27 , while the average age of all the players is 25."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "0b4196ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x202fb2f1820>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 485.875x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new.sort_values(by = 'Potential')\n",
    "sns.relplot(x=\"Age\", y=\"Potential\", hue= 'Nationality', data=new.head(100))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a2b9cbe",
   "metadata": {},
   "source": [
    "A general plot showing the nationality , potential , and age of the top 100 players ranked on potential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1352328b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e92eb0d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab8cf12c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a012566",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}