EDA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EDA.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6uhnAX1Cu52X"
      },
      "source": [
        "# **EDA**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dhNN7ll5vq5C"
      },
      "source": [
        "**Importing required libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umixA6bxMMwp"
      },
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt # visulaziation : plots , charts , bar graphs etc\n",
        "import seaborn as sns # visulaziation : plots , charts , bar graphs etc\n",
        "import warnings # to avoid warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "43wQ08gXMuwW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "outputId": "89d9d34a-94bb-441b-a447-dc1357475378"
      },
      "source": [
        "df = pd.read_csv('players_20.csv') # loading dataset\n",
        "df.head() # displaying first five rows"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sofifa_id</th>\n",
              "      <th>player_url</th>\n",
              "      <th>short_name</th>\n",
              "      <th>long_name</th>\n",
              "      <th>age</th>\n",
              "      <th>dob</th>\n",
              "      <th>height_cm</th>\n",
              "      <th>weight_kg</th>\n",
              "      <th>nationality</th>\n",
              "      <th>club</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",
              "      <th>player_tags</th>\n",
              "      <th>team_position</th>\n",
              "      <th>team_jersey_number</th>\n",
              "      <th>loaned_from</th>\n",
              "      <th>joined</th>\n",
              "      <th>contract_valid_until</th>\n",
              "      <th>nation_position</th>\n",
              "      <th>nation_jersey_number</th>\n",
              "      <th>pace</th>\n",
              "      <th>shooting</th>\n",
              "      <th>passing</th>\n",
              "      <th>dribbling</th>\n",
              "      <th>defending</th>\n",
              "      <th>physic</th>\n",
              "      <th>player_traits</th>\n",
              "      <th>attacking_crossing</th>\n",
              "      <th>attacking_finishing</th>\n",
              "      <th>attacking_heading_accuracy</th>\n",
              "      <th>attacking_short_passing</th>\n",
              "      <th>attacking_volleys</th>\n",
              "      <th>skill_dribbling</th>\n",
              "      <th>skill_curve</th>\n",
              "      <th>skill_fk_accuracy</th>\n",
              "      <th>skill_long_passing</th>\n",
              "      <th>skill_ball_control</th>\n",
              "      <th>movement_acceleration</th>\n",
              "      <th>movement_sprint_speed</th>\n",
              "      <th>movement_agility</th>\n",
              "      <th>movement_reactions</th>\n",
              "      <th>movement_balance</th>\n",
              "      <th>power_shot_power</th>\n",
              "      <th>power_jumping</th>\n",
              "      <th>power_stamina</th>\n",
              "      <th>power_strength</th>\n",
              "      <th>power_long_shots</th>\n",
              "      <th>mentality_aggression</th>\n",
              "      <th>mentality_interceptions</th>\n",
              "      <th>mentality_positioning</th>\n",
              "      <th>mentality_vision</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>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>24-06-1987</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",
              "      <td>#Dribbler, #Distance Shooter, #Crosser, #FK Sp...</td>\n",
              "      <td>RW</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>01-07-2004</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>87.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Beat Offside Trap, Argues with Officials, Earl...</td>\n",
              "      <td>88</td>\n",
              "      <td>95</td>\n",
              "      <td>70</td>\n",
              "      <td>92</td>\n",
              "      <td>88</td>\n",
              "      <td>97</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>91</td>\n",
              "      <td>84</td>\n",
              "      <td>93</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>86</td>\n",
              "      <td>68</td>\n",
              "      <td>75</td>\n",
              "      <td>68</td>\n",
              "      <td>94</td>\n",
              "      <td>48</td>\n",
              "      <td>40</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>75</td>\n",
              "      <td>96</td>\n",
              "      <td>33</td>\n",
              "      <td>37</td>\n",
              "      <td>26</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>8</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>05-02-1985</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",
              "      <td>#Speedster, #Dribbler, #Distance Shooter, #Acr...</td>\n",
              "      <td>LW</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10-07-2018</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>LS</td>\n",
              "      <td>7.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>Long Throw-in, Selfish, Argues with Officials,...</td>\n",
              "      <td>84</td>\n",
              "      <td>94</td>\n",
              "      <td>89</td>\n",
              "      <td>83</td>\n",
              "      <td>87</td>\n",
              "      <td>89</td>\n",
              "      <td>81</td>\n",
              "      <td>76</td>\n",
              "      <td>77</td>\n",
              "      <td>92</td>\n",
              "      <td>89</td>\n",
              "      <td>91</td>\n",
              "      <td>87</td>\n",
              "      <td>96</td>\n",
              "      <td>71</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>85</td>\n",
              "      <td>78</td>\n",
              "      <td>93</td>\n",
              "      <td>63</td>\n",
              "      <td>29</td>\n",
              "      <td>95</td>\n",
              "      <td>82</td>\n",
              "      <td>85</td>\n",
              "      <td>95</td>\n",
              "      <td>28</td>\n",
              "      <td>32</td>\n",
              "      <td>24</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>11</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>05-02-1992</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",
              "      <td>#Speedster, #Dribbler, #Playmaker  , #Crosser,...</td>\n",
              "      <td>CAM</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>03-08-2017</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>LW</td>\n",
              "      <td>10.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>Power Free-Kick, Injury Free, Selfish, Early C...</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>62</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>96</td>\n",
              "      <td>88</td>\n",
              "      <td>87</td>\n",
              "      <td>81</td>\n",
              "      <td>95</td>\n",
              "      <td>94</td>\n",
              "      <td>89</td>\n",
              "      <td>96</td>\n",
              "      <td>92</td>\n",
              "      <td>84</td>\n",
              "      <td>80</td>\n",
              "      <td>61</td>\n",
              "      <td>81</td>\n",
              "      <td>49</td>\n",
              "      <td>84</td>\n",
              "      <td>51</td>\n",
              "      <td>36</td>\n",
              "      <td>87</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>94</td>\n",
              "      <td>27</td>\n",
              "      <td>26</td>\n",
              "      <td>29</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>11</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>07-01-1993</td>\n",
              "      <td>188</td>\n",
              "      <td>87</td>\n",
              "      <td>Slovenia</td>\n",
              "      <td>Atlético Madrid</td>\n",
              "      <td>91</td>\n",
              "      <td>93</td>\n",
              "      <td>77500000</td>\n",
              "      <td>125000</td>\n",
              "      <td>GK</td>\n",
              "      <td>Right</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Medium/Medium</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Yes</td>\n",
              "      <td>164700000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>GK</td>\n",
              "      <td>13.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16-07-2014</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>GK</td>\n",
              "      <td>1.0</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>Flair, Acrobatic Clearance</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>43</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>43</td>\n",
              "      <td>60</td>\n",
              "      <td>67</td>\n",
              "      <td>88</td>\n",
              "      <td>49</td>\n",
              "      <td>59</td>\n",
              "      <td>78</td>\n",
              "      <td>41</td>\n",
              "      <td>78</td>\n",
              "      <td>12</td>\n",
              "      <td>34</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>65</td>\n",
              "      <td>11</td>\n",
              "      <td>68</td>\n",
              "      <td>27</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>87</td>\n",
              "      <td>92</td>\n",
              "      <td>78</td>\n",
              "      <td>90</td>\n",
              "      <td>89</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>07-01-1991</td>\n",
              "      <td>175</td>\n",
              "      <td>74</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Real Madrid</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>90000000</td>\n",
              "      <td>470000</td>\n",
              "      <td>LW, CF</td>\n",
              "      <td>Right</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>High/Medium</td>\n",
              "      <td>Normal</td>\n",
              "      <td>Yes</td>\n",
              "      <td>184500000.0</td>\n",
              "      <td>#Speedster, #Dribbler, #Acrobat</td>\n",
              "      <td>LW</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>01-07-2019</td>\n",
              "      <td>2024.0</td>\n",
              "      <td>LF</td>\n",
              "      <td>10.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Beat Offside Trap, Selfish, Finesse Shot, Spee...</td>\n",
              "      <td>81</td>\n",
              "      <td>84</td>\n",
              "      <td>61</td>\n",
              "      <td>89</td>\n",
              "      <td>83</td>\n",
              "      <td>95</td>\n",
              "      <td>83</td>\n",
              "      <td>79</td>\n",
              "      <td>83</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>88</td>\n",
              "      <td>95</td>\n",
              "      <td>90</td>\n",
              "      <td>94</td>\n",
              "      <td>82</td>\n",
              "      <td>56</td>\n",
              "      <td>84</td>\n",
              "      <td>63</td>\n",
              "      <td>80</td>\n",
              "      <td>54</td>\n",
              "      <td>41</td>\n",
              "      <td>87</td>\n",
              "      <td>89</td>\n",
              "      <td>88</td>\n",
              "      <td>91</td>\n",
              "      <td>34</td>\n",
              "      <td>27</td>\n",
              "      <td>22</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sofifa_id  ... goalkeeping_reflexes\n",
              "0     158023  ...                    8\n",
              "1      20801  ...                   11\n",
              "2     190871  ...                   11\n",
              "3     200389  ...                   89\n",
              "4     183277  ...                    8\n",
              "\n",
              "[5 rows x 72 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U36C02igqtTo"
      },
      "source": [
        "# replacing null values with 0 , handling missing data\n",
        "df = df.fillna(value= 0)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssYUwrXot_sW",
        "outputId": "2ed63b59-e385-4293-f782-c34b206336fe"
      },
      "source": [
        "df.shape # This dataset contains 18278 rows and 72 columns"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18278, 72)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyt88dBmuzAp",
        "outputId": "1df094f9-d2c7-4089-ed07-2cbe4c9b8e00"
      },
      "source": [
        "print(df.columns)\n",
        "# to display all the attributesof the dataset"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['sofifa_id', 'player_url', 'short_name', 'long_name', 'age', 'dob',\n",
            "       'height_cm', 'weight_kg', 'nationality', 'club', 'overall', 'potential',\n",
            "       'value_eur', 'wage_eur', 'player_positions', 'preferred_foot',\n",
            "       'international_reputation', 'weak_foot', 'skill_moves', 'work_rate',\n",
            "       'body_type', 'real_face', 'release_clause_eur', 'player_tags',\n",
            "       'team_position', 'team_jersey_number', 'loaned_from', 'joined',\n",
            "       'contract_valid_until', 'nation_position', 'nation_jersey_number',\n",
            "       'pace', 'shooting', 'passing', 'dribbling', 'defending', 'physic',\n",
            "       'player_traits', 'attacking_crossing', 'attacking_finishing',\n",
            "       'attacking_heading_accuracy', 'attacking_short_passing',\n",
            "       'attacking_volleys', 'skill_dribbling', 'skill_curve',\n",
            "       'skill_fk_accuracy', 'skill_long_passing', 'skill_ball_control',\n",
            "       'movement_acceleration', 'movement_sprint_speed', 'movement_agility',\n",
            "       'movement_reactions', 'movement_balance', 'power_shot_power',\n",
            "       'power_jumping', 'power_stamina', 'power_strength', 'power_long_shots',\n",
            "       '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'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "exjWig7vvHwE",
        "outputId": "31031abb-7e66-487a-9fa5-a8e8c9334e95"
      },
      "source": [
        "df.info() #To display data type for each column in the dataset"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 18278 entries, 0 to 18277\n",
            "Data columns (total 72 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   sofifa_id                   18278 non-null  int64  \n",
            " 1   player_url                  18278 non-null  object \n",
            " 2   short_name                  18278 non-null  object \n",
            " 3   long_name                   18278 non-null  object \n",
            " 4   age                         18278 non-null  int64  \n",
            " 5   dob                         18278 non-null  object \n",
            " 6   height_cm                   18278 non-null  int64  \n",
            " 7   weight_kg                   18278 non-null  int64  \n",
            " 8   nationality                 18278 non-null  object \n",
            " 9   club                        18278 non-null  object \n",
            " 10  overall                     18278 non-null  int64  \n",
            " 11  potential                   18278 non-null  int64  \n",
            " 12  value_eur                   18278 non-null  int64  \n",
            " 13  wage_eur                    18278 non-null  int64  \n",
            " 14  player_positions            18278 non-null  object \n",
            " 15  preferred_foot              18278 non-null  object \n",
            " 16  international_reputation    18278 non-null  int64  \n",
            " 17  weak_foot                   18278 non-null  int64  \n",
            " 18  skill_moves                 18278 non-null  int64  \n",
            " 19  work_rate                   18278 non-null  object \n",
            " 20  body_type                   18278 non-null  object \n",
            " 21  real_face                   18278 non-null  object \n",
            " 22  release_clause_eur          18278 non-null  float64\n",
            " 23  player_tags                 18278 non-null  object \n",
            " 24  team_position               18278 non-null  object \n",
            " 25  team_jersey_number          18278 non-null  float64\n",
            " 26  loaned_from                 18278 non-null  object \n",
            " 27  joined                      18278 non-null  object \n",
            " 28  contract_valid_until        18278 non-null  float64\n",
            " 29  nation_position             18278 non-null  object \n",
            " 30  nation_jersey_number        18278 non-null  float64\n",
            " 31  pace                        18278 non-null  float64\n",
            " 32  shooting                    18278 non-null  float64\n",
            " 33  passing                     18278 non-null  float64\n",
            " 34  dribbling                   18278 non-null  float64\n",
            " 35  defending                   18278 non-null  float64\n",
            " 36  physic                      18278 non-null  float64\n",
            " 37  player_traits               18278 non-null  object \n",
            " 38  attacking_crossing          18278 non-null  int64  \n",
            " 39  attacking_finishing         18278 non-null  int64  \n",
            " 40  attacking_heading_accuracy  18278 non-null  int64  \n",
            " 41  attacking_short_passing     18278 non-null  int64  \n",
            " 42  attacking_volleys           18278 non-null  int64  \n",
            " 43  skill_dribbling             18278 non-null  int64  \n",
            " 44  skill_curve                 18278 non-null  int64  \n",
            " 45  skill_fk_accuracy           18278 non-null  int64  \n",
            " 46  skill_long_passing          18278 non-null  int64  \n",
            " 47  skill_ball_control          18278 non-null  int64  \n",
            " 48  movement_acceleration       18278 non-null  int64  \n",
            " 49  movement_sprint_speed       18278 non-null  int64  \n",
            " 50  movement_agility            18278 non-null  int64  \n",
            " 51  movement_reactions          18278 non-null  int64  \n",
            " 52  movement_balance            18278 non-null  int64  \n",
            " 53  power_shot_power            18278 non-null  int64  \n",
            " 54  power_jumping               18278 non-null  int64  \n",
            " 55  power_stamina               18278 non-null  int64  \n",
            " 56  power_strength              18278 non-null  int64  \n",
            " 57  power_long_shots            18278 non-null  int64  \n",
            " 58  mentality_aggression        18278 non-null  int64  \n",
            " 59  mentality_interceptions     18278 non-null  int64  \n",
            " 60  mentality_positioning       18278 non-null  int64  \n",
            " 61  mentality_vision            18278 non-null  int64  \n",
            " 62  mentality_penalties         18278 non-null  int64  \n",
            " 63  mentality_composure         18278 non-null  int64  \n",
            " 64  defending_marking           18278 non-null  int64  \n",
            " 65  defending_standing_tackle   18278 non-null  int64  \n",
            " 66  defending_sliding_tackle    18278 non-null  int64  \n",
            " 67  goalkeeping_diving          18278 non-null  int64  \n",
            " 68  goalkeeping_handling        18278 non-null  int64  \n",
            " 69  goalkeeping_kicking         18278 non-null  int64  \n",
            " 70  goalkeeping_positioning     18278 non-null  int64  \n",
            " 71  goalkeeping_reflexes        18278 non-null  int64  \n",
            "dtypes: float64(10), int64(45), object(17)\n",
            "memory usage: 10.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "XnEZskqsvRsk",
        "outputId": "c2cc9002-eaba-4591-ba4d-3fa6a75d4eca"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sofifa_id</th>\n",
              "      <th>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>skill_moves</th>\n",
              "      <th>release_clause_eur</th>\n",
              "      <th>team_jersey_number</th>\n",
              "      <th>contract_valid_until</th>\n",
              "      <th>nation_jersey_number</th>\n",
              "      <th>pace</th>\n",
              "      <th>shooting</th>\n",
              "      <th>passing</th>\n",
              "      <th>dribbling</th>\n",
              "      <th>defending</th>\n",
              "      <th>physic</th>\n",
              "      <th>attacking_crossing</th>\n",
              "      <th>attacking_finishing</th>\n",
              "      <th>attacking_heading_accuracy</th>\n",
              "      <th>attacking_short_passing</th>\n",
              "      <th>attacking_volleys</th>\n",
              "      <th>skill_dribbling</th>\n",
              "      <th>skill_curve</th>\n",
              "      <th>skill_fk_accuracy</th>\n",
              "      <th>skill_long_passing</th>\n",
              "      <th>skill_ball_control</th>\n",
              "      <th>movement_acceleration</th>\n",
              "      <th>movement_sprint_speed</th>\n",
              "      <th>movement_agility</th>\n",
              "      <th>movement_reactions</th>\n",
              "      <th>movement_balance</th>\n",
              "      <th>power_shot_power</th>\n",
              "      <th>power_jumping</th>\n",
              "      <th>power_stamina</th>\n",
              "      <th>power_strength</th>\n",
              "      <th>power_long_shots</th>\n",
              "      <th>mentality_aggression</th>\n",
              "      <th>mentality_interceptions</th>\n",
              "      <th>mentality_positioning</th>\n",
              "      <th>mentality_vision</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>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>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",
              "      <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",
              "      <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",
              "      <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>2.368038</td>\n",
              "      <td>4.404059e+06</td>\n",
              "      <td>19.839917</td>\n",
              "      <td>1994.576267</td>\n",
              "      <td>0.746307</td>\n",
              "      <td>60.159645</td>\n",
              "      <td>46.472754</td>\n",
              "      <td>50.858464</td>\n",
              "      <td>55.566145</td>\n",
              "      <td>45.810920</td>\n",
              "      <td>57.650016</td>\n",
              "      <td>49.718405</td>\n",
              "      <td>45.590218</td>\n",
              "      <td>52.221468</td>\n",
              "      <td>58.748003</td>\n",
              "      <td>42.809388</td>\n",
              "      <td>55.596509</td>\n",
              "      <td>47.325692</td>\n",
              "      <td>42.708885</td>\n",
              "      <td>52.768848</td>\n",
              "      <td>58.461976</td>\n",
              "      <td>64.299923</td>\n",
              "      <td>64.415746</td>\n",
              "      <td>63.504924</td>\n",
              "      <td>61.752544</td>\n",
              "      <td>63.856439</td>\n",
              "      <td>58.181694</td>\n",
              "      <td>64.928165</td>\n",
              "      <td>62.889485</td>\n",
              "      <td>65.234763</td>\n",
              "      <td>46.812945</td>\n",
              "      <td>55.742149</td>\n",
              "      <td>46.380239</td>\n",
              "      <td>50.072163</td>\n",
              "      <td>53.609749</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>0.765038</td>\n",
              "      <td>1.070065e+07</td>\n",
              "      <td>16.652277</td>\n",
              "      <td>230.080966</td>\n",
              "      <td>3.358197</td>\n",
              "      <td>23.814336</td>\n",
              "      <td>21.110257</td>\n",
              "      <td>20.506407</td>\n",
              "      <td>21.933090</td>\n",
              "      <td>22.419982</td>\n",
              "      <td>22.389483</td>\n",
              "      <td>18.325403</td>\n",
              "      <td>19.594609</td>\n",
              "      <td>17.428429</td>\n",
              "      <td>14.679653</td>\n",
              "      <td>17.701815</td>\n",
              "      <td>18.929118</td>\n",
              "      <td>18.422991</td>\n",
              "      <td>17.433612</td>\n",
              "      <td>15.246454</td>\n",
              "      <td>16.741878</td>\n",
              "      <td>15.042232</td>\n",
              "      <td>14.847763</td>\n",
              "      <td>14.808380</td>\n",
              "      <td>9.135613</td>\n",
              "      <td>14.201559</td>\n",
              "      <td>13.326083</td>\n",
              "      <td>11.934059</td>\n",
              "      <td>16.017954</td>\n",
              "      <td>12.534811</td>\n",
              "      <td>19.322343</td>\n",
              "      <td>17.318157</td>\n",
              "      <td>20.775812</td>\n",
              "      <td>19.594022</td>\n",
              "      <td>13.955626</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>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.000000</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>2.000000</td>\n",
              "      <td>4.350000e+05</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>34.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>44.000000</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>2.000000</td>\n",
              "      <td>1.100000e+06</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>59.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>58.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>55.000000</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>3.000000</td>\n",
              "      <td>3.200000e+06</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>2022.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>75.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>68.000000</td>\n",
              "      <td>73.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>64.000000</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>5.000000</td>\n",
              "      <td>1.958000e+08</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>2026.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>96.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>92.000000</td>\n",
              "      <td>95.000000</td>\n",
              "      <td>94.000000</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",
              "</div>"
            ],
            "text/plain": [
              "           sofifa_id  ...  goalkeeping_reflexes\n",
              "count   18278.000000  ...          18278.000000\n",
              "mean   219738.864482  ...             16.709924\n",
              "std     27960.200461  ...             18.038125\n",
              "min       768.000000  ...              1.000000\n",
              "25%    204445.500000  ...              8.000000\n",
              "50%    226165.000000  ...             11.000000\n",
              "75%    240795.750000  ...             14.000000\n",
              "max    252905.000000  ...             92.000000\n",
              "\n",
              "[8 rows x 55 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SndVjBM0321x"
      },
      "source": [
        "We can make the following observations:\n",
        "\n",
        "* outliers in columns like age, value_eur, wage_eur, international_reputation, etc\n",
        "* All features have different value scales."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qodmCkoTwV3x"
      },
      "source": [
        "### **Cleaning data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "QHD5kLmFwX4C",
        "outputId": "62c960b5-73eb-4aa0-8ebe-048e0bdcb940"
      },
      "source": [
        "# Retaining only usefull columns that will have large impact on analysis\n",
        "columns_to_be_removed = ['dob','sofifa_id','player_url','long_name','body_type','real_face','nation_position','loaned_from','nation_jersey_number']\n",
        "df=df.drop(columns_to_be_removed,axis=1)\n",
        "df.head()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>release_clause_eur</th>\n",
              "      <th>player_tags</th>\n",
              "      <th>team_position</th>\n",
              "      <th>team_jersey_number</th>\n",
              "      <th>joined</th>\n",
              "      <th>contract_valid_until</th>\n",
              "      <th>pace</th>\n",
              "      <th>shooting</th>\n",
              "      <th>passing</th>\n",
              "      <th>dribbling</th>\n",
              "      <th>defending</th>\n",
              "      <th>physic</th>\n",
              "      <th>player_traits</th>\n",
              "      <th>attacking_crossing</th>\n",
              "      <th>attacking_finishing</th>\n",
              "      <th>attacking_heading_accuracy</th>\n",
              "      <th>attacking_short_passing</th>\n",
              "      <th>attacking_volleys</th>\n",
              "      <th>skill_dribbling</th>\n",
              "      <th>skill_curve</th>\n",
              "      <th>skill_fk_accuracy</th>\n",
              "      <th>skill_long_passing</th>\n",
              "      <th>skill_ball_control</th>\n",
              "      <th>movement_acceleration</th>\n",
              "      <th>movement_sprint_speed</th>\n",
              "      <th>movement_agility</th>\n",
              "      <th>movement_reactions</th>\n",
              "      <th>movement_balance</th>\n",
              "      <th>power_shot_power</th>\n",
              "      <th>power_jumping</th>\n",
              "      <th>power_stamina</th>\n",
              "      <th>power_strength</th>\n",
              "      <th>power_long_shots</th>\n",
              "      <th>mentality_aggression</th>\n",
              "      <th>mentality_interceptions</th>\n",
              "      <th>mentality_positioning</th>\n",
              "      <th>mentality_vision</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>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>195800000.0</td>\n",
              "      <td>#Dribbler, #Distance Shooter, #Crosser, #FK Sp...</td>\n",
              "      <td>RW</td>\n",
              "      <td>10.0</td>\n",
              "      <td>01-07-2004</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Beat Offside Trap, Argues with Officials, Earl...</td>\n",
              "      <td>88</td>\n",
              "      <td>95</td>\n",
              "      <td>70</td>\n",
              "      <td>92</td>\n",
              "      <td>88</td>\n",
              "      <td>97</td>\n",
              "      <td>93</td>\n",
              "      <td>94</td>\n",
              "      <td>92</td>\n",
              "      <td>96</td>\n",
              "      <td>91</td>\n",
              "      <td>84</td>\n",
              "      <td>93</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>86</td>\n",
              "      <td>68</td>\n",
              "      <td>75</td>\n",
              "      <td>68</td>\n",
              "      <td>94</td>\n",
              "      <td>48</td>\n",
              "      <td>40</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>75</td>\n",
              "      <td>96</td>\n",
              "      <td>33</td>\n",
              "      <td>37</td>\n",
              "      <td>26</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>8</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>96500000.0</td>\n",
              "      <td>#Speedster, #Dribbler, #Distance Shooter, #Acr...</td>\n",
              "      <td>LW</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10-07-2018</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>93.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>78.0</td>\n",
              "      <td>Long Throw-in, Selfish, Argues with Officials,...</td>\n",
              "      <td>84</td>\n",
              "      <td>94</td>\n",
              "      <td>89</td>\n",
              "      <td>83</td>\n",
              "      <td>87</td>\n",
              "      <td>89</td>\n",
              "      <td>81</td>\n",
              "      <td>76</td>\n",
              "      <td>77</td>\n",
              "      <td>92</td>\n",
              "      <td>89</td>\n",
              "      <td>91</td>\n",
              "      <td>87</td>\n",
              "      <td>96</td>\n",
              "      <td>71</td>\n",
              "      <td>95</td>\n",
              "      <td>95</td>\n",
              "      <td>85</td>\n",
              "      <td>78</td>\n",
              "      <td>93</td>\n",
              "      <td>63</td>\n",
              "      <td>29</td>\n",
              "      <td>95</td>\n",
              "      <td>82</td>\n",
              "      <td>85</td>\n",
              "      <td>95</td>\n",
              "      <td>28</td>\n",
              "      <td>32</td>\n",
              "      <td>24</td>\n",
              "      <td>7</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>14</td>\n",
              "      <td>11</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>195200000.0</td>\n",
              "      <td>#Speedster, #Dribbler, #Playmaker  , #Crosser,...</td>\n",
              "      <td>CAM</td>\n",
              "      <td>10.0</td>\n",
              "      <td>03-08-2017</td>\n",
              "      <td>2022.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>95.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>58.0</td>\n",
              "      <td>Power Free-Kick, Injury Free, Selfish, Early C...</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>62</td>\n",
              "      <td>87</td>\n",
              "      <td>87</td>\n",
              "      <td>96</td>\n",
              "      <td>88</td>\n",
              "      <td>87</td>\n",
              "      <td>81</td>\n",
              "      <td>95</td>\n",
              "      <td>94</td>\n",
              "      <td>89</td>\n",
              "      <td>96</td>\n",
              "      <td>92</td>\n",
              "      <td>84</td>\n",
              "      <td>80</td>\n",
              "      <td>61</td>\n",
              "      <td>81</td>\n",
              "      <td>49</td>\n",
              "      <td>84</td>\n",
              "      <td>51</td>\n",
              "      <td>36</td>\n",
              "      <td>87</td>\n",
              "      <td>90</td>\n",
              "      <td>90</td>\n",
              "      <td>94</td>\n",
              "      <td>27</td>\n",
              "      <td>26</td>\n",
              "      <td>29</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>J. Oblak</td>\n",
              "      <td>26</td>\n",
              "      <td>188</td>\n",
              "      <td>87</td>\n",
              "      <td>Slovenia</td>\n",
              "      <td>Atlético Madrid</td>\n",
              "      <td>91</td>\n",
              "      <td>93</td>\n",
              "      <td>77500000</td>\n",
              "      <td>125000</td>\n",
              "      <td>GK</td>\n",
              "      <td>Right</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>Medium/Medium</td>\n",
              "      <td>164700000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>GK</td>\n",
              "      <td>13.0</td>\n",
              "      <td>16-07-2014</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Flair, Acrobatic Clearance</td>\n",
              "      <td>13</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>43</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>43</td>\n",
              "      <td>60</td>\n",
              "      <td>67</td>\n",
              "      <td>88</td>\n",
              "      <td>49</td>\n",
              "      <td>59</td>\n",
              "      <td>78</td>\n",
              "      <td>41</td>\n",
              "      <td>78</td>\n",
              "      <td>12</td>\n",
              "      <td>34</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>65</td>\n",
              "      <td>11</td>\n",
              "      <td>68</td>\n",
              "      <td>27</td>\n",
              "      <td>12</td>\n",
              "      <td>18</td>\n",
              "      <td>87</td>\n",
              "      <td>92</td>\n",
              "      <td>78</td>\n",
              "      <td>90</td>\n",
              "      <td>89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E. Hazard</td>\n",
              "      <td>28</td>\n",
              "      <td>175</td>\n",
              "      <td>74</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>Real Madrid</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>90000000</td>\n",
              "      <td>470000</td>\n",
              "      <td>LW, CF</td>\n",
              "      <td>Right</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>High/Medium</td>\n",
              "      <td>184500000.0</td>\n",
              "      <td>#Speedster, #Dribbler, #Acrobat</td>\n",
              "      <td>LW</td>\n",
              "      <td>7.0</td>\n",
              "      <td>01-07-2019</td>\n",
              "      <td>2024.0</td>\n",
              "      <td>91.0</td>\n",
              "      <td>83.0</td>\n",
              "      <td>86.0</td>\n",
              "      <td>94.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>Beat Offside Trap, Selfish, Finesse Shot, Spee...</td>\n",
              "      <td>81</td>\n",
              "      <td>84</td>\n",
              "      <td>61</td>\n",
              "      <td>89</td>\n",
              "      <td>83</td>\n",
              "      <td>95</td>\n",
              "      <td>83</td>\n",
              "      <td>79</td>\n",
              "      <td>83</td>\n",
              "      <td>94</td>\n",
              "      <td>94</td>\n",
              "      <td>88</td>\n",
              "      <td>95</td>\n",
              "      <td>90</td>\n",
              "      <td>94</td>\n",
              "      <td>82</td>\n",
              "      <td>56</td>\n",
              "      <td>84</td>\n",
              "      <td>63</td>\n",
              "      <td>80</td>\n",
              "      <td>54</td>\n",
              "      <td>41</td>\n",
              "      <td>87</td>\n",
              "      <td>89</td>\n",
              "      <td>88</td>\n",
              "      <td>91</td>\n",
              "      <td>34</td>\n",
              "      <td>27</td>\n",
              "      <td>22</td>\n",
              "      <td>11</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          short_name  age  ...  goalkeeping_positioning  goalkeeping_reflexes\n",
              "0           L. Messi   32  ...                       14                     8\n",
              "1  Cristiano Ronaldo   34  ...                       14                    11\n",
              "2          Neymar Jr   27  ...                       15                    11\n",
              "3           J. Oblak   26  ...                       90                    89\n",
              "4          E. Hazard   28  ...                        8                     8\n",
              "\n",
              "[5 rows x 63 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XdJiwzJhxNkD"
      },
      "source": [
        "## **Data visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 845
        },
        "id": "vB3NTE424Omp",
        "outputId": "363094d6-8f78-44cf-e879-6e50d0e35a7e"
      },
      "source": [
        "numeric_features = ['age','height_cm','weight_kg','overall','potential','value_eur',\n",
        "                 'wage_eur','international_reputation','weak_foot',\n",
        "                 'skill_moves','release_clause_eur']\n",
        "\n",
        "# histograms of numerical features\n",
        "df[numeric_features].hist(bins='auto', figsize=(14, 10))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd555160b10>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd5551b6110>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd55518b610>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd55513cc90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd5550f9350>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd5550ad9d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd55506f090>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd555026710>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd5550307d0>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7fd554fe6ed0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd554f52ad0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7fd554f15190>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1HHMDYN4gwd"
      },
      "source": [
        "So, we can see that\n",
        "\n",
        "* max players are from the age group 20-25.\n",
        "* maximum and patterns of other features can be seen in the histograms too.\n",
        "* frequency plots of value_eur and wage_eur is highly skewed.\n",
        "* international_reputation, skill_moves and weak_foot have categorical values.\n",
        "* all other features have discrete values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "_Ch76Nt6PY7M",
        "outputId": "a50caa72-3f58-4078-e0a6-f52ec7abb4f4"
      },
      "source": [
        "# histogram\n",
        "# Effect of age on overall rating\n",
        "\n",
        "plt.figure(dpi=125)\n",
        "plt.hist(df.age, color='green', edgecolor = 'black', alpha = 0.7)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Distribution of Age')\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 750x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Ft-vWU8ZPWC"
      },
      "source": [
        "\n",
        "* No definite pattern is visible since the plot is vastly spread.\n",
        "* But we can see that as mostly for players having age >=15 years, the overall rating of a player decreases as age increases. \n",
        "* Players in the age of range 20-25 years have highest overall rating.Players in their 30's and 40's have the lowest overall rating.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "cEsw2JQiZY_v",
        "outputId": "d739d39e-4b9c-4c95-d2cb-64d1f0f3f606"
      },
      "source": [
        "# histogram\n",
        "# Preferred foot while playing\n",
        "\n",
        "plt.figure(dpi=125)\n",
        "plt.hist(df.preferred_foot, color='blue', edgecolor = 'black', alpha = 0.7)\n",
        "plt.xlabel('Preferred Foot Players')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Count of Preferred Foot')\n",
        "plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 750x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "DpYVqcnAxwlU",
        "outputId": "e417c0e7-1c5a-4e90-dcde-6ea79da19d6e"
      },
      "source": [
        "figsize = ()\n",
        "label = df['preferred_foot'].value_counts().index\n",
        "name = df['preferred_foot'].value_counts()\n",
        "explode = [0,0.3]\n",
        "plt.pie(name,labels = label,shadow = True,autopct='%1.1f%%',explode =explode, radius = 2)\n",
        "plt.title('Perecentage of the preferred foot')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpWUjgX3b1Y4"
      },
      "source": [
        "So we can see that majority of the players prefer right foot for playing."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "8HjHUA-9b82g",
        "outputId": "3b4eb92d-74f9-4e09-b392-eae1703cf1cc"
      },
      "source": [
        "# histogram\n",
        "# Countries with players having highest potential\n",
        "\n",
        "plt.figure(dpi=125)\n",
        "ax = plt.subplot(1,1,1)\n",
        "df.groupby('nationality')['potential'].mean()[df.groupby('nationality')['potential'].mean()>75].plot.bar(ax=ax)\n",
        "\n",
        "plt.xlabel('country')\n",
        "plt.ylabel('potential_mean')\n",
        "plt.title('Country with highest potential')\n",
        "plt.show()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 750x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hE7TyfpSdknY"
      },
      "source": [
        "The countries having players with highest potential are : Central African Rep , Portugal , Russia , Spain"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "wrzw3wddc2ye",
        "outputId": "d0b0a4a2-92a2-4741-a1fe-a5c9013afabf"
      },
      "source": [
        "# box plot\n",
        "\n",
        "plt.figure(dpi=125)\n",
        "sns.boxplot(y='age',x='international_reputation',data=df)\n",
        "plt.title('Age vs International reputation')\n",
        "plt.ylabel('Age')\n",
        "plt.xlabel('International reputation')\n",
        "plt.show()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 750x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rz8h9S_oc3Cs"
      },
      "source": [
        "* Only a little population of players have high reputations of 5 and 4\n",
        "* As age increases the international_reputation of players increases \n",
        "* Except few outlires i.e there are some exception among the players who are old but have reputation of 1 and relatively young players who have high reputation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 878
        },
        "id": "wl_pdusCdy1l",
        "outputId": "ca193ec5-d279-4129-a712-177982d67bed"
      },
      "source": [
        "# box plot\n",
        "\n",
        "plt.figure(dpi=125,figsize=(8,8))\n",
        "sns.boxplot(y='wage_eur',x='international_reputation',data=df)\n",
        "plt.title('Wage v Reputation')\n",
        "plt.ylabel('Wage in Euros')\n",
        "plt.xlabel('international_reputation')\n",
        "plt.show()"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qiHiOeTcfNvf"
      },
      "source": [
        "\n",
        "* Players having high reputation have high wages \n",
        "* We can also say that wages of players increases as the international reputation of players increases .\n",
        "* There are few outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "EbzMMWOMc3c0",
        "outputId": "346f3abd-8c35-4dec-82c7-89e80ff328a8"
      },
      "source": [
        "# box plot\n",
        "\n",
        "plt.figure(dpi=125, figsize=(20,10))\n",
        "sns.boxplot(y='overall',x='work_rate',data=df)\n",
        "plt.title('Work rate vs Overall score')\n",
        "plt.ylabel('overall score')\n",
        "plt.xlabel('Work rate')\n",
        "plt.show()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2500x1250 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-JMZrTueUwP"
      },
      "source": [
        "* Players with high and medium -high work rate have high overall score\n",
        "* PLyers with low work rate have comparatively low overall score\n",
        "* Few outliers are also there i.e exceptional players who have low work rate but high overall score or high work rate but poor score."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "ogTDP0HCyLwu",
        "outputId": "16b92ae7-a042-4a5d-d4ce-6c97b553d76c"
      },
      "source": [
        "# Displaying top 10 countries with most number of players\n",
        "top = df['nationality'].value_counts().head(10)\n",
        "fig,ax = plt.subplots(figsize = (10,8))\n",
        "ar = top.values\n",
        "ra = top.index\n",
        "ax.bar(ra,ar, color = 'green')\n",
        "ax.set_title(('Top 10 Countries with most number of players'))\n",
        "plt.show()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nbj43xDL0RDf"
      },
      "source": [
        "So, we see that England has the highest number of player participating."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "1npDTwNgeTsD",
        "outputId": "16624a9f-dd36-4cd8-be95-4de45f1d0868"
      },
      "source": [
        "# scatter plot\n",
        "sns.lmplot(data=df, x=\"age\", y=\"pace\")"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fd5527bb350>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vbpy6tEMc3mv"
      },
      "source": [
        "From seeing the line we can say that as the age increases the pace of the players decreases , except few outliers."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "8RCE6oA8fVHO",
        "outputId": "ea3ace87-ca5b-4402-857e-a4054fb0978b"
      },
      "source": [
        "# Scatter Plot (colored by Age) year 2020 - Overall Rating vs Value in Euros\n",
        "\n",
        "sns.lmplot(x = \"overall\", y = \"value_eur\", data = df, fit_reg=False, hue='age')"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fd55266cc50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 408.75x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvDiCdQBiLO4"
      },
      "source": [
        "Players in their mid 20's and 30's have high overall rating and earn more in comparison to others. Except few outliers , the over rating and salary decreases as age increases."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "NGiEfTQMi91u",
        "outputId": "ccda450d-7172-48f9-bfc6-a7e06a65c650"
      },
      "source": [
        "# scatter plot\n",
        "sns.lmplot(x='skill_ball_control', y='skill_dribbling', data=df, col='preferred_foot')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7fd55363fb90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HdloidxPmq4"
      },
      "source": [
        "\n",
        "* There are more players who prefers right foot for playing\n",
        "* skill_ball_control and skill_dribbling are linearly dependent on each other."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "AJMGHw50kCZ9",
        "outputId": "99f19b41-e735-495e-fea2-5c84fbdcc6ff"
      },
      "source": [
        "# qq plot\n",
        "import statsmodels.api as sm\n",
        "fig = sm.qqplot(df.height_cm)\n",
        "plt.show()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5FFXmNZzKdv"
      },
      "source": [
        "height values of the players are normally distributed which means that majority of the players have relatively the  same height , with some few outliers ."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "go6bOfi210UF",
        "outputId": "207cf5b8-63b7-4d45-b9ce-3bdb202e4d65"
      },
      "source": [
        "# qq plot\n",
        "fig = sm.qqplot(df.weight_kg)\n",
        "plt.show()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYnJ3vJn2c5H"
      },
      "source": [
        "weight values of the players are normally distributed which means that majority of the players have relatively the  same weight , with some few outliers ."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "etkPzICx1BAY",
        "outputId": "0dd44aa4-014e-4146-adba-bcf71ec964f0"
      },
      "source": [
        "# qq plot\n",
        "fig = sm.qqplot(df.physic)\n",
        "plt.show()"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhLbiAJv13m8"
      },
      "source": [
        "physic values of the players are normally distributed which means that majority of the players have relatively the  physic value i.e majority of the players are fit , with some few outliers ( which are actually the missing values that were replaced by 0 )"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "7ju7AtMF0_Yd",
        "outputId": "68aa6877-a4bb-4aad-c2da-db29170ebe86"
      },
      "source": [
        "# Comparison of Potential of Top 10 FIFA countries\n",
        "\n",
        "plt.figure(1 , figsize = (12,6))\n",
        "sns.barplot(x = 'nationality', y = 'potential', data = df.head(10), palette='PuBuGn_d')\n",
        "plt.title('Comparison of Potential of Top 10 FIFA countries')\n",
        "plt.show()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "mHdZ5EQD1P2Z",
        "outputId": "a8a03b7f-5928-4782-96f3-52c25fed8e55"
      },
      "source": [
        "# comparison of potential of top 10 players\n",
        "\n",
        "plt.figure(1 , figsize = (12,6))\n",
        "sns.barplot(x = 'short_name', y = 'potential', data = df.head(10),palette='PuBuGn_d')\n",
        "plt.title('Comparison of Potential of Top 10 FIFA players')\n",
        "plt.xticks(rotation = 50)\n",
        "plt.show()"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "qE4E6mFv5cfp",
        "outputId": "1f79eb2a-663f-4a32-c881-19841cb9009d"
      },
      "source": [
        "# height distribution and mean height\n",
        "\n",
        "plt.figure(figsize=(14,5))\n",
        "plt.title('Height Distribution FIFA 20')\n",
        "sns.distplot(df['height_cm'], kde=True, bins=20)\n",
        "plt.axvline(x=np.mean(df['height_cm']),c='orange',label='Mean Height of All Players')\n",
        "plt.legend()"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd540e51c50>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MmjXryPZzzz2XZ555hmeeeQYz4/vvv6d3797k5OSQnJxMUFAQU6ZMqdEH/88991zuu+8+rrzySqKjo9m6dSuhoaFHekMABg8ezJQpU7j22mvJyspi1qxZTJo06bjnjYmJOWav0NChQ3nzzTe57777mDVrFo0bNyY2NvaY58rNzeWbb74hIyPjSDH26quvMnXq1GoVSMeTk5NDfHw8DRo0YO3atXz33XdH9g0cOJCMjAyWLFnC8uXLARg1ahTjxo3jzjvvJCkpiT179pCXl0fr1q2POm9+fj4HDhxgzJgxDB48mLZt29ZIXgjwSRrMbCBwwDm38hj7bzazRWa2KCsr6zSnExEREZFAkpycfNRzRIfdd999FBUVkZqaSrdu3bjvvvsAuO2225gyZQo9e/Zk7dq1REVF1ViW0aNHM2nSJM444wx69OjBhAkTyMvLO6rN+PHjSU5OpmvXrlx11VX06dOHuLi44573wgsv5P333690koYHH3yQxYsXk5qayuTJk5kyZcpxz/X+++8zcuTIo3rLxo0bx4cffsihQ4eqeceVO++88yguLqZLly5MnjyZQYMGHbX/sssuY/DgwcTHxwPQtWtXHn74YUaPHk1qairnnHMO27dv/9F58/LyGDt2LKmpqQwZMoQ///nPNZIXwE5mRo4qndjsDOBB59y5vs+/BXDOPVquzae+NvPMLATYASQeHmJnZk8CWc65E84X2K9fP3d4nnYREZFqmTm87M+zZ9XoafUMktQHa9asoUuXLl7HqLXy8/OJjo4mOzubAQMGMHfuXJo2bep1rNNm7Nix3HnnnYwaNcov56/s+9PMFjvn+h3rGH8OsVsIdDCzNsBWYCJQsc9wBnAtMA+YAHxZrjgKAi4DhiIiIiIiUgeNHTuWffv2UVhYyH333VdviqN9+/YxYMAAevbs6bfi6GT5rUDyPVN0B/ApEAy84pxbZWYPAYucczOAl4HXzSwN2ENZEXXYMCDj8CQPIiIiIiJ1TflnpeqThg0bHjVrYCDxZw8SzrlPgE8qbLu/3PsC4NJjHDsLGFTZPhEREREREX8I6EkaRERERCTw+euZdpFTcbLflyqQREREROSkRUREkJ2drSJJAopzjuzs7KPWtqoqvw6xExEREZG6LTk5mczMTLTkigSaiIgIkpOTq32cCiQREREROWmhoaG0adPG6xgiNUZD7ERERERERHxUIImIiIiIiPioQBIREREREfFRgSQiIiIiIuKjAklERERERMRHBZKIiIiIiIiPCiQREREREREfFUgiIiIiIiI+KpBERERERER8VCCJiIiIiIj4qEASERERERHxUYEkIiIiIiLiowJJRERERETERwWSiIiIiIiIjwokERERERERHxVIIiIiIiIiPiqQREREREREfFQgiYiIiIiI+KhAEhERERER8VGBJCIiIiIi4qMCSURERERExEcFkoiIiIiIiI8KJBERERERER8VSCIiIiIiIj5+LZDM7DwzW2dmaWY2uZL94WY2zbd/vpmllNuXambzzGyVma0wswh/ZhUREREREfFbgWRmwcBzwPlAV+AKM+taodmNwF7nXHvgSeAx37EhwBvArc65bsBwoMhfWUVERERERMC/PUgDgDTn3CbnXCHwNjCuQptxwBTf++nAKDMzYDSw3Dm3DMA5l+2cK/FjVhEREREREb8WSC2AjHKfM33bKm3jnCsGcoAEoCPgzOxTM1tiZv/lx5wiIiIiIiIAhHgd4BhCgCFAf+AA8IWZLXbOfVG+kZndDNwM0KpVq9MeUkRERERE6hZ/9iBtBVqW+5zs21ZpG99zR3FANmW9TV8753Y75w4AnwB9Kl7AOfeic66fc65fYmKiH25BRERERETqE38WSAuBDmbWxszCgInAjAptZgDX+t5PAL50zjngU6CHmTXwFU5nAav9mFVERERERMR/Q+ycc8VmdgdlxU4w8IpzbpWZPQQscs7NAF4GXjezNGAPZUUUzrm9ZvZnyoosB3zinPvYX1lFRERERETAz88gOec+oWx4XPlt95d7XwBceoxj36Bsqm8REREREZHTwq8LxYqIiIiIiNQmgTqLnYiI1IC35m/x9PqTBmqGURERqV3UgyQiIiIiIuKjAklERERERMRHBZKIiIiIiIiPnkESEZFapaTUMeXbdFZvzyUpJpykmHBaxDdgeKdEQoP1/34iInJqVCCJiEitsSu3gDvfWcrctGwaR4ex70ARxaUOgLGpzfjLxN4EB5nHKUVEpDZTgSQiIrXCrHW7+M07y9hfWMxj43twWb+WOAd7DxTy9sIMHv90HVFhIfzP+B6YqUgSEZGTowJJREQC3j8XZXD39OV0ahLD25MG0aFJDABmkBAdzu0j2lNQVMIzX6YRHRHCvRd0UZEkIiInRQWSiIgEtG/TdvPb91YwtENjXrqmHxGhwZW2+/U5HckrKOblOZuJiwzlF6M6nOakIiJSF+hpVhERCVgbduZxyxuLaZsYxXNX9jlmcQRgZtw/tisX92rOUzPXs25H3mlMKiIidYUKJBERCUhZeYe4/h8LCQ8J5pXr+hMbEXrCY4KCjAcu7EZ0eAiPfLLmNKQUEZG6RgWSiIgEnIOFJdz02iJ25x/i5Wv7kRzfoMrHxkeF8fORHZi9Pouv12f5MaWIiNRFKpBERCSglJY6fv3OUpZn7uOpy3vTs2XDap/jmjNb07JRJI98soYS3zTgIiIiVaECSUREAspjn67l3yt38N9junBe96YndY7wkGDuOa8za3fk8e6SzBpOKCIidZlmsRMRkYDx1vwt/G32Jq4a1Iobh7Q5pXNd0KMZf2+5mf/9bB1jU5vRIMzbH3nOOXbmHSIj+wAHikooKCrhYGEJRSWllDhHqSvrPSt1vlcpZdtLHSXO4Rw0jY2gbWIUbROjiYs88TNZIiJSfSqQREQkIHy6agf3/WslZ3VM5MELu53yOkZmxr0XdGHCX+fx6tx0bh/RvoaSVs+GnXms2JrD+p155BYUH9kebEZEaBBhIUEEmRFkRnCQEWRlk00c/hwSbIRZEA5YvT2XxVv2AtC8YQSX9E6mecNIT+5LRKSuUoEkIiKee3XuZh76aDWpyQ15dlJvQoJrZgR4v5RGjOiUyN+/2cR1Z6YQFX76fuztyivgzfk/sGpbLhGhQbRPjKZjkxjaJkYTHR5CaLBVuwgsdY4dOQVszMpnTtpuXpi9kfO6NeWMdgkEaWFcEZEaoQJJREQ8U1LqePjj1bw6N53RXZvwl4m9iQw79lpHJ+OOkR0Y/8K3vDV/Cz8d1rZGz10Z5xzTF2fy8Mdr2H+omHO7NmFIh0SCg069gAkyo3nDSJo3jKRPq3jeW5LJxyu2s2FXHhP6tiT6NBaAIiJ1lSZpEBERT+zIKeDm1xbx6tx0bhjchheu6lvjxRFA39bxDG6fwN++3kRBUUmNn7+80lLHb99bwd3Tl9OxSTQ/H9mBszol1UhxVFFUeAhXDWrNRT2bsylrP6/NS6e4pLTGryMiUt+oQBIRkdPqYGEJf5m5gRFPzOKbtN38/qJu3H9hV78UEYf9fGQHducfYtrCDL9dwznHAzNW8fbCDG4f0Y5pN59BYky4364HZc9ZDWqbwOX9W5K59yAfr9ju1+uJiNQH6osXEZHTwjnHjGXb+J9/r2V7TgEX9GjG5PM707JR1ReBPVkD2zSif0o8f529kYkDWhIeUrM9Vc45Hv54Da9/9wO3DGvLXaM7nfIkE9XRrXkcQzs05psNu2nVqAG9W8WftmuLiNQ16kESERG/W7JlL5e88C2/fHspCdFhvHPLGTx3ZZ/TUhxBWU/Lz0d2YHtOAe8u3lrj5//fz9bz8pzNXHdmCpPP73xai6PDRndtSpvGUXywdCs7cgpO+/VFROoKFUgiIuI3uQVF3DltKZc8/y2Zew/y+IRUZtw+hAFtGp32LEM7NKZnchzPz0qjsLjmntV5Z1EGz36VxhUDWvLAhV09KY4AgoOMif1bEhEazJvzf6jRexQRqU9UIImIiF/s2V/IJc9/y4fLtnH7iHbMums4l/ZrSZAfnzU6HjPjznM6krn3IK/NS6+Rcy7N2Me9769kSPvG/GFcd8+Ko8NiIkK5vF9LsvcXMicty9MsIiK1lQokERGpcZt37+f5WWnszj/E6zcO5O5zO5/WNYiOZXinJM7qmMhfvthAdv6hUzpXVt4hbn19MUmx4TxzRc2t3XSq2iZG0615LF+v382uPA21ExGprsD411xEROqMZRn7eGXOZhqEhfDBbYM5o12C15GOct/YLhwoLOHJmetP+hyFxaXc/uYS9h0s5G9X9yU+KqwGE56687o1pbi0lCc/3+B1FBGRWsevBZKZnWdm68wszcwmV7I/3Mym+fbPN7MU3/YUMztoZkt9r7/6M6eIiNSMzL0HmL4kk1YJDfjZWe1IaRzldaQfaZ8Uw9WDWvPW/C2s25FX7eOdc/z+w1UsSN/DY+NT6dY8zg8pT01CdDiD2iYwbeEW1u+s/j2KiNRnfiuQzCwYeA44H+gKXGFmXSs0uxHY65xrDzwJPFZu30bnXC/f61Z/5RQRkZpxsLCEqQu2EBMewpUDWvll0dea8stRHYiJCOUPH63GOVetY1/8ehNvzt/CrWe1Y1yvFn5KeOpGdkoiKjyERz9Z43UUEZFaxZ89SAOANOfcJudcIfA2MK5Cm3HAFN/76cAo8/oJVxERqbZS5/jn4gxyDxZzxYBWNAiA542OJz4qjDvP7sCctN18umpnlY/7aPk2Hv33WsamNuO/zu3kx4SnrkF4CD8f2Z6v1mUxZ8Nur+OIiNQa/iyQWgDllyzP9G2rtI1zrhjIAQ4PVm9jZt+b2WwzG+rHnCIicoq+2bCbtTvyOL9H09O2ttGpunJQazo3jeHX7ywlr6D4hO0Xpe/h1+8so39KPE9c2tOz2fiq45ozUkiOj+TxT9dWu6dMRKS+CtRJGrYDrZxzvYFfA2+ZWWzFRmZ2s5ktMrNFWVmazlRExAsZew7w+eod9GgRxxltA2tChuMJDQ7itRsG0CQ2grU7co9bJM1en8VNry0iuWEkL17dj4jQwB0+WF5EaDC3j2jPsswc5qSpF0lEpCr8WSBtBVqW+5zs21ZpGzMLAeKAbOfcIedcNoBzbjGwEehY8QLOuRedc/2cc/0SExP9cAsiInI8pc7x4fJtRIWH8JPeLTxfB6i6kmIjeOunAwkJDmLtjlxWZOYctT+3oIh7pi/n2lcWkBAVxqvX9w+4GetO5JI+LWgaG8GzX6Z5HUVEpFbw5yDxhUAHM2tDWSE0EZhUoc0M4FpgHjAB+NI558wsEdjjnCsxs7ZAB2CTH7OKiMhJWJaxj8y9B5nQJ7nW9KpU1CwukkbNYlm1LZfLX5xH12axJMdH0jQukn8t3crO3AJ+NrwdvxzVoVbeY3hIMDcPa8tDH61mUfoe+qU08jqSiEhA81sPku+ZojuAT4E1wDvOuVVm9pCZXeRr9jKQYGZplA2lOzwV+DBguZktpWzyhludc3v8lVVERKrvUHEJn67aQXJ8JL1aNfQ6zikJDwmia7NYLkxtTnCQsTB9Ly99s4mYiBDeu20w95zXuVYWR4ddMaAVCVFhPPuVepFERE7Er9MMOec+AT6psO3+cu8LgEsrOe5d4F1/ZhMRkVPz9foscgvKZq0LqmVD6yoTERrEYxNSj3wuLiklOMhq3bDBykSGBXPDkDY8/uk6Vm7NoXuLwFu7SUQkUATqJA0iIhLA9h4o5JsNu0lNjqN1QuAtBlsTQoKD6kRxdNjVZ7QmJiKE59SLJCJyXCqQRESk2v6zcgdmcF63pl5HkSqKjQjlujNT+PfKHWzYmed1HBGRgKUCSUREqmXr3oOs2JrDkPaJNGxQu2Z0q++uH9yGyNBgnp+10esoIiIBSwWSiIhUy+drdhAZGszQDo29jiLV1CgqjCsHtmLGsm1syT7gdRwRkYCkAklERKps8+79rN+Zz1kdE2v1rG712U+HtSXYjBdmqxdJRKQyKpBERKRKnHN8vnoHMeEhDGqb4HUcOUlNYiO4tF8y7y7OZEdOgddxREQCjl+n+RYRkbpjw6580rMPcFHP5oSFVO3/196av8XPqY5v0iDe5h0AACAASURBVMBWnl4/UN16VjveXpjBi19v4v4Lu3odR0QkoKgHSURETqis92gn8Q1C6ZcS73UcOUUtGzVgXK/mvLXgB7LzD3kdR0QkoKhAEhGRE1q5LZet+w4yqnMTQoL0o6MuuG14ew4Vl/LK3M1eRxERCSj6KSciIsdVXFLKf1ZuJykmnF6tGnodR2pI+6RoxnRvxj/mprMrT88iiYgcpgJJRESO69uN2ew9UMQFqc0IMvM6jtSgu87tRGFJKU9+vt7rKCIiAUMFkoiIHFNeQRFfrdtF56YxdEiK8TqO1LA2jaO45owUpi3MYM32XK/jiIgEBBVIIiJyTDPX7KSopJQx3Zt5HUX85BcjOxAbGcofP16Dc87rOCIinlOBJCIildq27yCL0vdyRtsEGseEex1H/CSuQSi/HNWBOWm7mbUuy+s4IiKeU4EkIiI/UuocH6/YTmRYMCM7N/E6jvjZVYNa07ZxFA9/vJqiklKv44iIeEoFkoiI/MjX67PYvHs/53ZtSmRYsNdxxM9Cg4P47ZgubMzaz0vfbPI6joiIp1QgiYjIUdJ25fP56p2kJsdpUdh65OwuSVzQoxn/+9l6FqXv8TqOiIhnVCCJiMgROQeLmLZwC4kx4fykdwtM03rXG2bGo+N7kBwfyR1vfc+e/YVeRxIR8YQKJBERAaC4tJSpC7ZQVOqYNLAV4SEaWlffxEaE8tykPuzZX8id05ZSWqpZ7USk/lGBJCIi7D9UzFvzt7BlzwHG90kmKSbC60jike4t4rjvwq7MXp/FC7M3eh1HROS0C/E6gIiIeGv9zjzeXZzJgaISxqY2o0eLOK8jiceuGtiKBZv38Pin6wgLDuKmoW003FJE6g0VSCIi9VBeQRHb9hWwensuC9P3kBQTznWDU2gWF+l1NAkAZsbjE1IpLXX88ZM1bM8p4N4LuhAUVPUi6a35W/yY8MQmDWzl6fVFpPZSgSQiUoes2pbDC7M2sn5nHvkFxew5UEhhcSlhIUGEhwQTERrEgcIS8gqKATBgcLsERndrSmiwRl3L/4sIDeaZK3rTJDaCV+ZuZmduAf97WU8iQvVsmojUbSqQRETqgBWZOfzliw3MXLOTmIgQzmyXQExEKNv2HSQ0OIjCklIOFZVyqLiEsOAgmjeMpFnDCJrHReoXXjmmoCDj/gu70rxhBA9/vIb1O/N4+OLuDGyb4HU0ERG/UYEkIlLLPT8rjT/9Zx1xkaH8+pyOXDc4hdiIUMD7YU5SN9w0tC3tkqK59/2VXP7id0zom8xvz+9MQnS419FERGpclcZTmNl7ZnaBmWn8hYhIAPnnogz+9J91jE1txpx7RvCLUR2OFEciNWlEpyRm/vosfja8HR98v5VRf57N2wu2aCpwEalzqlrwPA9MAjaY2f+YWSc/ZhIRkSr4at0uJr+3giHtG/Pny3oRo8JI/CwyLJh7zuvMJ78cSscmMUx+bwWX/m0ea3fkeh1NRKTGVKlAcs7NdM5dCfQB0oGZZvatmV1vZvqJLCJymi3L2Mftby6hU5MYXriqD2Eh6uCX06djkxim3TyIJy7tyebd+7ng6Tn8ZeYGnFNvkojUflX+iWpmCcB1wE3A98BfKCuYPj/OMeeZ2TozSzOzyZXsDzezab79880spcL+VmaWb2Z3VTWniEhdl3OwiJteW0SjqDD+cUN/9RyJJ8yMCX2T+eLXZ3FRz+Y8OXM9d05byqHiEq+jiYickipN0mBm7wOdgNeBC51z2327ppnZomMcEww8B5wDZAILzWyGc251uWY3Anudc+3NbCLwGHB5uf1/Bv5dnRsSEanrnv5iA7vzD/HhHUNIionwOo7Uc/FRYfz5sp60T4rm8U/XsS2ngBev7ut1LBGRk1bVHqSXnHNdnXOPHi6OzCwcwDnX7xjHDADSnHObnHOFwNvAuAptxgFTfO+nA6PMt1S3mV0MbAZWVfluRETquLRd+Uz5Np2J/VvRvUWc13FEgLLepNtHtOfpK3qzdMs+Lnn+W3ILiryOJSJyUqpaID1cybZ5JzimBZBR7nOmb1ulbZxzxUAOkGBm0cA9wO+rmE9EpM5zzvGHj1YTGRbMXaM7eh1H5Ecu6tmcN386kO05BUxdsIUSzXAnIrXQcQskM2tqZn2BSDPrbWZ9fK/hQAM/5noQeNI5l3+CfDeb2SIzW5SVleXHOCIi3vtq3S5mr8/il6M6aP0ZCVj9Uxrx2IRUfsg+wL9Xbj/xASIiAeZEzyCdS9nEDMmUPQ90WB7wuxMcuxVoWe5zsm9bZW0yzSwEiAOygYHABDP7E9AQKDWzAufcs+UPds69CLwI0K9fP/03lYjUWYXFpfzhozW0TYzimjNSvI4jclwX9WzOtAVbmLsxm5bxDejZsqHXkUREquy4BZJzbgowxczGO+ferea5FwIdzKwNZYXQRMrWUipvBnAtZcP1JgBfurI5QocebmBmDwL5FYsjEZH65PXvfmDz7v28en1/TekttcJ53Zuxdd9B3vs+kyaxETSN04QiIlI7nGiI3VW+tylm9uuKr+Md63um6A7gU2AN8I5zbpWZPWRmF/mavUzZM0dpwK+BH00FLiJS3x0qLuFvszdyRtsERnRK8jqOSJUEBxlXDGhFRGgwUxduobi01OtIIiJVcqIhdlG+P6NP5uTOuU+ATypsu7/c+wLg0hOc48GTubaISF3x/pKt7Mo7xP9e1tPrKCLVEhMRyk96t+C1eT8wb2M2Qzskeh1JROSETjTE7m++PzWbnIiIB0pKHX/7ehPdW8QypH1jr+OIVFvnprF0bhrDF2t30bNlQ2K1sLGIBLgqDWQ3sz+ZWayZhZrZF2aWVW74nYiI+Mlnq3awefd+fnZWe3zLxInUOhf0aEZJqeM/K3d4HUVE5ISq+qTvaOdcLjAWSAfaA3f7K5SIiJSte/TC7I2kJDTgvO5NvY4jctISosMZ1qExSzP2sXn3fq/jiIgcV1ULpMND8S4A/umcy/FTHhER8fl2YzbLM3O4eVg7goPUeyS121kdk2gYGcqHy7ZpAVkRCWhVLZA+MrO1QF/gCzNLBAr8F0tERP46eyOJMeFc0qeF11FETllYSBBjejRjR24Bi37Y43UcEZFjqlKB5JybDJwJ9HPOFQH7gXH+DCYiUp+t3JrDNxt2c8PgNkSEBnsdR6RGdGseS+tGDfhq7S6KSjTtt4gEpuqsNtgZuNzMrqFsUdfR/okkIiKvzNlMg7BgJg1s5XUUkRpjZpzTtQm5BcXM36xeJBEJTCdaBwkAM3sdaAcsBUp8mx3wmp9yiYjUW7tyC/hw+TYmDWhFXKSmRJa6pW1iNO0To5m9bhf9U+IJD1EPqYgElioVSEA/oKtzTk9Vioj42WvzfqC41HH94DZeRxHxi3O6NuGF2RuZtzGb4Z2SvI4jInKUqg6xWwlojlkRET8rKCrhzfk/cHaXJqQ0jvI6johftGzUgC5NY/h6QxYHC0tOfICIyGlU1QKpMbDazD41sxmHX/4MJiJSH723ZCt7DxRx4xD1HknddnbXJhQUlfJNWpbXUUREjlLVIXYP+jOEiIiULQz7ytzNdGsey8A2jbyOI+JXzeIi6dEijm/TsjmzXWOiw6v6K4mIiH9VdZrv2UA6EOp7vxBY4sdcIiL1zuz1WaTtyufGIW0w08KwUved3aUJRSWlzF63y+soIiJHVKlAMrOfAtOBv/k2tQA+8FcoEZH66OU5m0mMCWdsanOvo4icFokx4fRpFc/8zXvIOVjkdRwREaDqzyDdDgwGcgGccxsATTsjIlJDyi8MGxZSnSXqRGq3kZ2TcA6+Ui+SiASIqv4UPuScKzz8wcxCKFsHSUREasALszcSEx7ClYO0MKzUL/FRYfRLiWdR+h727C888QEiIn5W1SciZ5vZ74BIMzsHuA340H+xRETqj/Td+/n3iu3cPKwdsRFaGLYmvTV/S5Xajco9BMAXVWwvNWtEpyQW/7CXL9bs5NJ+Lb2OIyL1XFV7kCYDWcAK4BbgE+Bef4USEalPXvxmEyHBQdwwOMXrKCKeiI0M5Yy2CSzN2Meu3AKv44hIPVfVWexKKZuU4Tbn3ATn3EvOOQ2xExE5RbvyCpi+OJPxfZJJio3wOo6IZ4Z1TCQsJIj/rNrhdRQRqeeOWyBZmQfNbDewDlhnZllmdv/piSciUre9Ojed4pJSbhnW1usoIp6KCg9hZOck1u7IY92OPK/jiEg9dqIepDspm72uv3OukXOuETAQGGxmd/o9nYhIHZZXUMQb3/3A+T2akdI4yus4Ip47o10CjaPD+Gj5NopLS72OIyL11IkmabgaOMc5t/vwBufcJjO7CvgMeNKf4UREarvjTRLwxZqd5BUUk5IQVeXJBETqspCgIMamNucf36bzbVo2wzomeh1JROqhExVIoeWLo8Occ1lmpqmWREROUv6hYr5J20335rG0aBjpdRypo2pj4d2xSQxdmsbw5bpd9GrZkNhI/bohIqfXiYbYHW9BAi1WICJykmav20VRcSlnd23idRSRgDOmRzNKSp0mbBART5yoB6mnmeVWst0ATbckInIS9h4o5LvNe+jbOp6kGP1TKlJRQnQ4Q9s3Ztb6LFKT4+jcNNbrSCJSjxy3B8k5F+yci63kFeOcU5+3iMhJ+HLNLgwY2TnJ6ygiAWtE5ySaxkYwfXEmuQVFXscRkXqkqgvFiohIDdiZW8CSLXsZ1DaBhg3CvI4jErBCg4OY2L8lRSWl/HNRBqVaflFEThO/Fkhmdp6ZrTOzNDObXMn+cDOb5ts/38xSfNsHmNlS32uZmf3EnzlFRE6Xz1bvJCwkiLM0O5fICSXFRnBhanM2Zu3n6/VZXscRkXrCbwWSmQUDzwHnA12BK8ysa4VmNwJ7nXPtKZsy/DHf9pVAP+dcL+A84G9mdqLnpUREAtq6HXms2Z7LWR0TiQrXP2kiVdG3dTw9WsQxc81OtmTv9zqOiNQD/uxBGgCkOec2OecKgbeBcRXajAOm+N5PB0aZmTnnDjjnin3bIwD1q4tIrVZUUsqHy7fRODqcIe0bex1HpNYwM37SuwVxkaG8Pn8Lu/MPeR1JROo4fxZILYCMcp8zfdsqbeMriHKABAAzG2hmq4AVwK3lCiYRkVpn9vos9uwv5KKezQkJ1uOfItURERrMtWem4JzjlbmbyTmoSRtExH8C9qe0c26+c64b0B/4rZn9aC5cM7vZzBaZ2aKsLI1NFpHAtDv/ELN90xW3T4r2Oo5IrZQUE8H1Z7bhYGEJr8zdzP5D+n9TEfEPfxZIW4GW5T4n+7ZV2sb3jFEckF2+gXNuDZAPdK94Aefci865fs65fomJeuBZRAKPc44Pl20jJMgY06OZ13FEarUW8ZFcfUZr9u4vZMq8dAqKSryOJCJ1kD8LpIVABzNrY2ZhwERgRoU2M4Brfe8nAF8655zvmBAAM2sNdAbS/ZhVRMQvlm/NYcOufM7p2oTYCC0fJ3Kq2jaO5ooBrdi27yB/n7OJfPUkiUgN81uB5Htm6A7gU2AN8I5zbpWZPWRmF/mavQwkmFka8Gvg8FTgQ4BlZrYUeB+4zTm3219ZRUT8YVdeATOWbiM5PpKBbRK8jiNSZ3RpFsvVg1qzK/cQL329Sc8kiUiN8us8s865T4BPKmy7v9z7AuDSSo57HXjdn9lERPzJOcfv3ltBUUkpE/omExxkXkcSqVM6NY3l+sFteG1eOn/7eiM3DG5D4+hwr2OJSB0QsJM0iIjUZu8u2crMNbsY3a0pSTE/mmNGRGpAm8ZR3DSkLYXFpbz49Sa25xz0OpKI1AEqkEREati2fQf5/YxVDEhpxJntNLROxJ9axEdy89C2BBm89M0mLSYrIqdMBZKISA1yznHPu8spcY7HL00lyDS0TsTfkmIjuGVYO6LCQnh57mbSduV7HUlEajEVSCIiNejN+Vv4ZsNufjumC60ToryOI1JvxEeFcfOwtiREhTNlXjpfrdvldSQRqaX8OkmDiEggeGv+ltNynT37C3n6iw20T4om6DReV0TKxESE8tOhbXl5ziZue2MJb/10IL1bxXsdS0RqGfUgiYjUgFLnmL44EzO4pHcLTEPrRDwRGRbMtWemkBQbzg3/WKjhdiJSbSqQRERqwLyN2aRn72dsanMaNgjzOo5IvRYTEcprNwwgOMi49pUF7Mgp8DqSiNQiKpBERE5RVt4hPl21g85NY+jTqqHXcUQEaJ0QxT+uH8C+A4Vc9+oCDhQWex1JRGoJFUgiIqegpNQxfXEGocFBXKyhdSIBpXuLOJ6/qi/rduZx7/srcc55HUlEagEVSCIip+CbDVlk7D3IRb2aExsR6nUcEangrI6J/GJkB977fitvL8zwOo6I1AIqkERETtKOnAK+WLOL7s1jSW0R53UcETmGX4zqwNAOjXlgxipWbs3xOo6IBDgVSCIiJ6G4tJR/Ls4gIiyYi3ppaJ1IIAsOMp66vBeNGoRx+1tLyC0o8jqSiAQwFUgiIidh1rostucU8JNezYkO15JyIoEuITqcZyf1Zuveg9z3wUqv44hIAFOBJCJSTZl7DzBr3S56t2xI1+YaWidSW/RLacQdI9vzr6Xb+M/KHV7HEZEApQJJRKQaikpKmb44k+jwEMamNvc6johU0+0j2tO1WSz3frCCPfsLvY4jIgFIBZKISDXMXLOTXXmHuKRPMpFhwV7HEZFqCg0O4s+X9yTnYBH3/0tD7UTkx1QgiYhU0Q/Z+5mzYTf9U+Lp2CTG6zgicpI6N43lV2d35KPl2/l4+Xav44hIgFGBJCJSBYXFZUPrGjYIZUz3Zl7HEZFTdMuwtqQmx3HvByvIzj/kdRwRCSAqkEREquA/q3aQvb+QS/okEx6qoXUitV1IcBBPXNqT/EPFPPTRaq/jiEgAUYEkInIC63bk8d2mbM5sl0C7xGiv44hIDenYJIbbhpfNavfV2l1exxGRAKECSUTkOPIPFfPukkyaxIZzbremXscRkRp224h2dEiK5r/fX0H+oWKv44hIAFCBJCJyDM453luSSUFRCZf3a0VosP7JFKlrwkOC+Z/xqWzPLeCJT9d5HUdEAoB+2ouIHMOC9D2s3ZHHud2a0jQuwus4IuInfVvHc82g1kyZl87iH/Z6HUdEPKYCSUSkEjtyC/hkxXY6JEVzRrsEr+OIiJ/dfV5nmsVGMPnd5RwqLvE6joh4SAWSiEgFBw4V8/q8dCJCgxnfN5kgM68jiYifRYeH8Mef9GDDrnxemLXR6zgi4iEVSCIi5ZSUOt5auIW8gmKuGtia2IhQryOJyGkyonMS43o157mv0li/M8/rOCLiERVIIiLlfLJiO5uy9nNx7xa0bNTA6zgicprdP7Yr0eEh3PPuckpKnddxRMQDKpBERHwWbt7DvE3ZDGnfmD6t4r2OIyIeSIgO5/4Lu/L9ln288d0PXscREQ/4tUAys/PMbJ2ZpZnZ5Er2h5vZNN/++WaW4tt+jpktNrMVvj9H+jOniMjC9D18sHQrHZtEc153rXckUp9d3KsFwzom8qf/rCVjzwGv44jIaea3AsnMgoHngPOBrsAVZta1QrMbgb3OufbAk8Bjvu27gQudcz2Aa4HX/ZVTRGRO2m7e/34rHZpEM2lAa03KIFLPmRmP/KQ7ZsZv3lmmoXYi9Yw/e5AGAGnOuU3OuULgbWBchTbjgCm+99OBUWZmzrnvnXPbfNtXAZFmFu7HrCJSDznn+GLNTj5ZsZ1uzWO5alBrwkI08lhEIDm+AQ9e1I0F6Xt48etNXscRkdPIn78JtAAyyn3O9G2rtI1zrhjIASouODIeWOKcO+SnnCJSD+UfKmbqwgy+WLuLPq3imdi/FSFBKo5E5P+N79OCMT2a8ufP17Fya47XcUTkNAno3wbMrBtlw+5uOcb+m81skZktysrKOr3hRKRWcs6xLHMfT81cz5rtuYzu2oRL+rQgOEjD6kTkaGbGHy/uQXyDMO6ctpSCIi0gK1If+LNA2gq0LPc52bet0jZmFgLEAdm+z8nA+8A1zrlKV2xzzr3onOvnnOuXmJhYw/FFpK7ZsucAr837gWkLM2gUFcYdI9ozvFOSnjkSkWOKjwrjiUt7smFXPv/z77VexxGR0yDEj+deCHQwszaUFUITgUkV2sygbBKGecAE4EvnnDOzhsDHwGTn3Fw/ZhSROq6k1LFqWw5zNuzmhz0HiAgN4vzuTTmzXWP1GolIlQzrmMgNg9vwytzN9Gkdz0U9m3sdSUT8yG8FknOu2MzuAD4FgoFXnHOrzOwhYJFzbgbwMvC6maUBeygrogDuANoD95vZ/b5to51zu/yVV0Tqll25BbyzKIOpCzLYuu8g8Q1CGZvajL6t4wkPCfY6nojUMpPP78zyzH3cM305nZrE0KlpjNeRRMRPzLm6MXVlv3793KJFi7yOISIe27Azj6dmbuDTVTsoLnUMbp9Aq0ZRdG0Wqx4jOaZRWy8H4IsW0zxOIjVl0sBWNX7OXbkFXPDMHKLDQ/jXHYOJjQit8WuIiP+Z2WLnXL9j7Q/oSRpERKpqe85B/mv6Ms596mu+Xp/FDUPa8NVdw3nzpkH0aBGn4khETllSbATPTepDxp4D/OadZZRqfSSROsmfzyCJiPidc46/zt7EUzPX4xxcP7gNd4xoT3xUmNfRRKQOGtCmEb8b04WHPlrNkzPX85vRnbyOJCI1TAWSiNRaBwqLufufy/l4xXbO796U343pQstGDbyOJSJ13PWDU1i3I49nvkyjRcNIJg6o+eF8IuIdFUgiUitl7DnAT19bxPqdefz3mC7cNLQNpum6ReQ0MDMe/kl3tucW8N8frKRpXATDOyV5HUtEaoieQRKRWmf1tlzGPTeXbfsO8ur1A/jpsLYqjkTktAoNDuL5K/vQqUkMt7+5hJVbc7yOJCI1RAWSiNQqGXsOcN2rCwgPCeJfdwzhrI5aJFpEvBEdHsKr1/cnLjKU6/+xkPTd+72OJCI1QEPsRKTW2Lu/kGtfXUBBUQnTf3YmbRpHeR1JRALUW/O3nLZrXdqvJS99s4mLn5/LLcPaERcZ6pdpxkXk9FAPkojUCgcLS7hhykIy9x7k5ev607GJFmkUkcDQJDaC685M4WBhCa/M2Uz+oWKvI4nIKVCBJCIBzznHr6Z9z7KMfTw9sTf9Uxp5HUlE5CjJ8Q245owU9h4o5B/fbia3oMjrSCJyklQgiUjAe3nOZj5dtZPfjenCed2beh1HRKRSbRpHceXAVuzIKeDGfyzkYGGJ15FE5CSoQBKRgLY0Yx+P/Wcto7s24cYhbbyOIyJyXJ2axnJZv5Ys+mEvt76xmMLiUq8jiUg1qUASkYCVc7CIO95aQlJMBI9P6KmpvEWkVkhNbsijP+nB7PVZ3DltKSWlzutIIlINmsVORAKSc47/mr6MHTkF/PPWM4hrEOp1JBGRKps4oBX5h4p5+OM1RIUH89j4VP0nj0gtoQJJRALSG9/9wKerdvLfY7rQu1W813FERKrtpqFtyT1YxNNfphETEcq9F3RRkSRSC6hAEpGAk7Yrj4c/XsOwjoncNFTPHYlI7XXnOR3JLSjm5TmbiYkI4Vdnd/Q6koicgAokEQkohcWl/GraUhqEBfPEBA1JEZHa7f/au/P4qsp73+Of3x4yDyQkISEQZlAmUWQQBdE6tWrxWD1YbbUeq621Pa2v1nPbc8/x9va2r2vtPW1Pj9rWqQ6ts/VIi20VUQSVQSiDTBIIQyAmBEhCQqa993P/2IsQImGQ7Oxk5/t+vfJaaz3rWWv/Ak/WXr+1nvUsM+Peq8ZS3xzilwu2kJkS1IAzIj2cEiQR6VF+ueAjPtxdx2++NJmCrJR4hyMictp8PuO+aydQ3xTi//x5A1kpAa4/d3C8wxKRTihBEpEeY3nZfn69aCtzzx2s9x2JSK/2zLKdnyibMaI/pXvr+R8vr2Xd7lrOKMyK2effOK0kZvsWSXQa5ltEeoTaQ63c/fxqSnLTuPfqsfEOR0SkywX8Pm6aWkJRdirPLt/Jrv2H4h2SiByD7iCJSMwd60pqe845/rBsJxW1jXxt1gheXb2nmyITEeleyUE/N583hN++s40n39/O12aNID8zOd5hiUg7uoMkInG3tGw/GyrquHxcIYNz0+IdjohITGWmBLl1xlDMjN+9V0ZdY2u8QxKRdpQgiUhc7alp5LV1FYwZkMn5I/PiHY6ISLfon5HMV84byqGWME+8t52m1nC8QxIRjxIkEYmb5tYwzy7fSXqSn+smD8KnIb1FpA8pzknlpmklVB1s4umlOwiFI/EOSURQgiQicRJxjpdWlbO/oYW5U0pIT9YjkSLS94wqyOS6yYMoq27ghZXlRJyLd0gifZ4SJBGJiwUbKlm/p47Pji9kWF56vMMREYmbSYNz+Oz4Qj7cXcuf11bglCSJxJUu2YpIt1u18wBvf7SXKUNz9NyRiAgwc1Q+9U0hFpdWkxzwcfk4vQtOJF6UIIlIt9pe3cArf9/N8Lx0Pn9WMabnjkREALhifCHN4QiLPtpLcsDH7DEF8Q5JpE+KaRc7M7vCzDabWamZff8Y65PN7Hlv/TIzG+qV9zezt8ys3sweiGWMItJ9Kuua+P2yHfRLDXLjtBL8PiVHIiKHmRmfP2sgZw/ux+sbKnm3tDreIYn0STG7g2RmfuBB4FKgHFhhZvOccxvaVbsNOOCcG2lmNwA/BeYCTcC/A+O9HxHp5aoONvHYkjL8ZtwyYyhpSbqBLSLSkc+Ma88ZREs4wvx1FZjBjBHqiizSnWJ5B2kqUOqc2+acawGeA+Z0qDMHeNKbfwn4jJmZc67BObeEaKIkIr1cdX0zjy0pwwG3XTCMvAy9NV5EpDN+nzF3ymDGFmXx57UVLNxUpYEbRLpRLBOkYmBXu+Vyr+yYdZxzIaAW6B/DmESkm+3Y18Cji7cRjji+esEwCrJS4h2SiEiPF/D5+OLUxdll/gAAFVFJREFUEs4e3I8FGyv5y4cfK0kS6Sa9uo+Lmd0B3AFQUlIS52hEpKN15bXc+sQKQhHHbRcMY4CSIxGRk+b3GV+YPIiUoJ8lpdUcaglzzaSBBPx6S4tILMXyL2w3MLjd8iCv7Jh1zCwAZAP7TvYDnHMPO+fOdc6dm5+ff5rhikhXemtzFXMffp/kgI87Zg6nKDs13iGJiPQ6PjOumljExWcUsGrnAR5dUkZdU2u8wxJJaLFMkFYAo8xsmJklATcA8zrUmQfc4s1fByx0un8s0us9v2InX33yA4blpfPKN2aoW52IyGkwMy45cwBfnFpCRW0jD75Vys59DfEOSyRhxayLnXMuZGbfBP4G+IHHnXPrzexHwAfOuXnAY8DTZlYK7CeaRAFgZtuBLCDJzK4BLuswAp6I9DChcISf/nUTjywuY9bofB666Rwyknt1T14RkR5jQnE2+RnJ/H7ZDh5ZXMbl4wYwY2QePr1PTqRLxfTMxTn3GvBah7J72803Add3su3QWMYmIl3rQEML33x2Fe+W7uPm84bw71eNJah+8iIiXaowO4W7Zo/kpZW7eO3Dj1lfUcd15wyiv0YHFekyOnsRkdO2YU8dVz+whBVlB7j/uon8aM54JUciIjGSmuTnS9OHcN3kQVTWNfGrhVt4t7SacERPKYh0BfV9EZHT8qc1e7jnpTVkpwZ5/mvTObskJ94hiYgkPDPjnJIcRuRn8N9/3838dRWs3HGAKycWMSI/I97hifRqSpBE5FMJRxz3/20Tv120jXOH5PDQl86hIFODMYiIdKfs1CA3nzeE9Xvq+MuHFTy2pIyxRVnMGNGfoXnp8Q5PpFdSgiTSBzyzbGeX7u9QS4jnV+xiS1U9U4flctXEIhZsqOrSzxARkZNjZowvzmZMYSbvllbz9ua9XPLzRdw4rYR//swo8vR8ksgpUYIkIqek/MAhnlm+k4ONIf5hUjFThuXGOyQREQGCfh+zxxQweUgOuw4c4g/LdvLyynJunzWcr84crlFFRU6SnqIWkZPinGPptn389p1t4OCOWcOVHImI9ECZKUF+fM0E3rh7FheOyeeXC7Yw+2dv8dT722kJReIdnkiPpwRJRE6oORTmxZXlzFuzhxH56XzzopEMzk2Ld1giInIcw/MzeOimybzyjRmMyM/g3lfXc+kvFjF/bQXOacQ7kc4oQRKR46qqa+LXb29lza4aLh07gJvPG0qaummIiPQaZ5fk8Nwd0/ndrVNIDfq565lV3PjIMjZ/fDDeoYn0SDrLEZFOrSmv4ZVVuwn6jVvPH8bIAg0dKyLSG5kZF40pYNaofJ5ZvpP/eH0zn/vVYr48fQjfvWw0mSnBeIco0mMoQRKRT2huDTN/XQUf7DjAkNw0bphaQnaqvjxFRHo7v8/48vQhXDWhiP94YzNPvb+d19d/zP/9wkQuHJ0f7/BEegR1sRORo2yvbuBXC7ewcscBZo3K56szhys5EhFJMDnpSfz4mgm8fOcM0pID3PL4cu55cQ21ja3xDk0k7pQgiQgALaEIf/mwgkcWbwPg9pnDuWJ8IX6fxTkyERGJlbNLcvjzty7gG7NH8Me/7+ayXyzizY2V8Q5LJK7UxU6kj3POsaGijvlrK6hpbGXK0Bw+N76I5KA/3qGJiEg3SAn6+ZcrzuCK8YXc8+JabnvyA649u5h7rx5Lv7SkeIcn0u2UIIn0YXsPNjN/3R4+qqynMCuF288dzLC89HiHJSIicTBxUD/mfet8HlhYykNvb2VxaTU/uWY8l40rjHdoIt1KCZJIH7T3YDNvba5iza4akgI+rpxQxPTh/dWdTkSkj0sO+PnuZWO4fFwh97y0ljueXsmcSQP54dXjyEnX3STpG5QgifQh5QcO8d7WfazZVUPAb8wclccFo/LJ0HuNRESknfHF2bx61/k89HYpDyws5d3San58zXiuGF8U79BEYk5nRSIJrrElzMod+1m6bT+7axpJ8vu4YFQeM5UYiYgkrGeW7eyS/RRkpnDn7BG8vLKcr/9+FROKs7n6rIEn/P64cVpJl3y+SDzo7EgkAbWGIyzZUs28NXt4ff3HNLSEKchM5uqJRZxdkkOKBmAQEZGTVJSdyp2zR7Loo728tamKbXvr+fykYiYUZ8c7NJGYUIIkkiAamkMs3rKXNzZUsXBTJQcOtZKdGuTqswaSmRJkaP80zPSMkYiInDq/z7j4jALGFmXx8qpynl2+k9WFmXxuQhH9M5LjHZ5Il1KCJNKLfVzbxIKNlSzYWMl7pftoCUfISglw8RkFXDVxILNG55MU8HVZVwsREenbCrNT+PqFI3i3tJqFm6v45ZtbOH9EHheNydfrISRhKEES6UUOv7NowYYqFmysZN3uWgCG9E/jy+cN4ZIzB3Du0ByCfr0DWkREYsPvM2aNzmdSST9eX1/JO1v2smrnAS4cnc/UYbn6DpJeTwmSSA/XHAqzdNt+Fmyo5M2NleypbcIMzh7cj3+5YgyXnjmAkQUZ6j4nIiLdKislyHWTBzFtWC5/Xf8x89dV8M5He5k1Op9rzynW867SaylBEumB9tQ0sqS0mrc3V7Fo814aWsKkBv3MHJXHdy4ZzUVnFJCfqT7fIiISf4Nz07h95nC2VdezcGMV89dV8N7Wav5xymC+NG0Ig3PT4h2iyClRgiTSA9Q1tbJ06z6WlFazpLSabXsbAMjPTObzk4q5dGwBM0bk6WqciIj0WMPzMhg+M4Oy6gbKDxzi0cVlPPzONj5zRgHXTR7E7DEF+h6TXkEJkkgcVNY1sXpXDWt21bB02z7WlNcSjjhSg36mDc/lxqklXDAqjzEDMtV1TkREepVheen8zyvPZE9NI88s28lzK3ayYGMVmSkBPju+kCsnDmT68FySA0qWpGdSgiQSQ40tYbbva+CJd7fzcV0TH9c2UVHbSF1TCACfQXG/VGaNymNEQQYluWkEfNGHW1ftqGHVjpp4hi8iIvKpDeyXyvcuH8N3LhnFu1v38erq3cxfW8ELH5STluTn/JF5XHxGAReMzGNQTqouCEqPoQRJ5DS1hCLsrmlke3UD26obKKuup6y6gbK9DeypbWqr57PoG8mH52dQ3C+VwTmpFPVL1Wg/IiKS0AJ+HxeOzufC0fk0/UOY97ZWs3BTFW9t2ssbGyoBKMpOYeqwXKYMzWXioGzGFGbqDpPEjRIkkZPQ0Bxix75D7NzfwPZ9h9rmd+w7xJ6aRiLuSN2slADD8zOYPrw/w/LSGZqXzpbKevIyk9ruDomIiCSyE71/b2xRNmcWZlF1sDl6UbG6gTc3VvHq6j1A9KLigKwUCrNSyM9MJj8zmbyMZPqlBk/4vqUbp5V02e8hfVNMEyQzuwL4T8APPOqcu6/D+mTgKWAysA+Y65zb7q37AXAbEAb+2Tn3t1jGKontRAfqcMRxsKmVusZW9jW0sK+hhf3ez76GFhqaQ0fVT0vyk5ueRP/0JEYVZNI/PYn+GUnkZSSTluQ/qpvAwaYQhdkpMfm9REREeiszY0BWCgOyUpg+vD/OOQ4camV3TSN7vJ+te+v5+66ju5snB3xkpQTJSg140yBZKQFSk/ykBP2s2L6fzJQAmSnR8vSkAD6fuu/JyYtZgmRmfuBB4FKgHFhhZvOccxvaVbsNOOCcG2lmNwA/Beaa2VjgBmAcMBBYYGajnXPhWMUriaepNUzNoVZqGlvYVl1PY0uYxpYwDc0haptC1DW2UtfUSm1jK/VNIdrdBMKArNQguelJnFmYGU2GMpLbkiKNwiMiItK1zIzc9CRy05OYUJzdVt7cGmZvfTPV9S1t393RaYht1Q0cbGo9qifHU+/v6LBfSAv6SU3yfoJ+UpMCnyhLa1vXfj7Qti7Fm7afT03ykxLwKwFLMLG8gzQVKHXObQMws+eAOUD7BGkO8ENv/iXgAYteep8DPOecawbKzKzU29/7MYw3IUUijrBzhCMO52ibj0QcERddF4ngTb2ytikdlqPTiKNtH0ftO+La7efIPg+XO2+fx/usSMTRGnGEIxFCEUc47AhFHKFIhHDEEQpHtwlFHC2hCI2t0aSnsTXcNt/UGqa+OURzKNLpv0tKMHr1KTs1yICslLYrUdmpQXLTkshJT9KzQSIiIj1ActDPoJw0BuUc+31KEedoaA7R1BqhqTXMjJH9OehdCD3YFOJgUyuHWsIcag3T1BI+ar7qYFPbBdRD3nnE8c4fOpMS9JES9JMc8JEciE6TAr4jy8Ej852VH7VN276O3iYleKRu0O/DZ4bPR3Tacd7QwBefUiwTpGJgV7vlcmBaZ3WccyEzqwX6e+VLO2xbHLtQu9acB5Zw4FArjmji4LyrGs45HNHltnUcXn9k+RN1vXna6h97P3RYDre/nNLL+Mz7A/dZ27zfd+QP/vBykneASPL7SAn4yEwOEAz4SPb7vCtAgaOuBKV5ZUkBJT8iIiKJwGdGZkqQTK83+679jW3r0pMDpCef2uluxDlawxFaQhFaw46WcITWUOToaYf1LaEIoUiE0OELu+Hohd66xhCtkZa2i7ytXnmobRq9iBwrdvh8yo5OnHy+o5Mo8+p6W7Vta+32E11zrHVHkrC2eh3qTxiUzYM3nhOT3zEWevUgDWZ2B3CHt1hvZpvjGY90uzygOt5BSNypHUgXtoEhXbMbiQcdCwTUDnqkxcBDN3XrR56oHRz3YB/LBGk3MLjd8iCv7Fh1ys0sAGQTHazhZLbFOfcw8HAXxiy9iJl94Jw7N95xSHypHYjagIDagUSpHQicfjuIZT+jFcAoMxtmZklEB12Y16HOPOAWb/46YKFzznnlN5hZspkNA0YBy2MYq4iIiIiISOzuIHnPFH0T+BvRYb4fd86tN7MfAR845+YBjwFPe4Mw7CeaROHVe4HogA4h4C6NYCciIiIiIrEW02eQnHOvAa91KLu33XwTcH0n2/4E+Eks45NeT90rBdQORG1AotQOBNQOJOq02oG5GI6cISIiIiIi0ptorGMRERERERGPEiTpkczscTOrMrMPO5R/y8w2mdl6M7u/XfkPzKzUzDab2eXdH7HEwrHagZk9b2arvZ/tZra63Tq1gwTUSTuYZGZLvXbwgZlN9crNzH7ltYO1ZtZ7Xrwhx9VJOzjLzN43s3Vm9iczy2q3TseDBGNmg83sLTPb4J0HfNsrzzWzN8xsizfN8cp1PEhAx2kH13vLETM7t8M2p3Q8UIIkPdUTwBXtC8zsImAOcJZzbhzw/7zysUQH+BjnbfOQmfm7NVqJlSfo0A6cc3Odc5Occ5OAl4E/gtpBgnuCDu0AuB/43147uNdbBvgs0ZFPRxF9T96vuylGib0n+GQ7eBT4vnNuAvAKcA/oeJDAQsB3nXNjgenAXd7/9feBN51zo4A3vWXQ8SBRddYOPgSuBd5pX/nTHA+UIEmP5Jx7h+jIhu3dCdznnGv26lR55XOA55xzzc65MqAUmNptwUrMdNIOgOiVQeAfgWe9IrWDBNVJO3DA4bsF2cAeb34O8JSLWgr0M7Oi7olUYqmTdjCaIydDbwBf8OZ1PEhAzrkK59wqb/4gsBEoJvr//aRX7UngGm9ex4ME1Fk7cM5tdM5tPsYmp3w8UIIkvcloYKaZLTOzRWY2xSsvBna1q1fulUlimwlUOue2eMtqB33Ld4CfmdkuoneTf+CVqx30LeuJnvxAdFTcwy+ZVztIcGY2FDgbWAYMcM5VeKs+BgZ482oHCa5DO+jMKbcDJUjSmwSAXKK3U+8BXvDuIkjf9EWO3D2SvudO4G7n3GDgbqLv1ZO+55+Ab5jZSiATaIlzPNINzCyDaBfr7zjn6tqvc9HhmTVEcx9wvHZwupQgSW9SDvzRu1W+HIgAecBujlw1BBjklUmCMrMA0X7Gz7crVjvoW27Be/4MeJEj3SXUDvoQ59wm59xlzrnJRC+YbPVWqR0kKDMLEj0p/oNz7vAxoPJw1zlvergLvtpBguqkHXTmlNuBEiTpTf4buAjAzEYDSUA1MA+4wcySzWwY0Ycxl8ctSukOlwCbnHPl7crUDvqWPcCF3vzFwOGulvOAm73Rq6YDte263kiCMbMCb+oD/g34jbdKx4ME5PUaeQzY6Jz7ebtV84heNMGbvtquXMeDBHOcdtCZUz4eBE4/TJGuZ2bPArOBPDMrB/4X8DjwuDfEawtwi3crfb2ZvQBsIDqyyV3OuXB8IpeudKx24Jx7jOhoNEd1r3POqR0kqE6OB7cD/+ndTWwiOkIVwGvA54g+hHsIuLXbA5aY6KQdZJjZXV6VPwK/Ax0PEtj5wJeBdXbkFQ//CtxHtNv9bcAOogP4gI4HiaqzdpAM/BeQD8w3s9XOucs/zfHAoueXIiIiIiIioi52IiIiIiIiHiVIIiIiIiIiHiVIIiIiIiIiHiVIIiIiIiIiHiVIIiIiIiIiHiVIIiIiIiIiHiVIIiISF2Y21Huv2cnW/7qZ3XyCOl8xswc6WfevpxqjiIj0PUqQRESkV3DO/cY599Rp7EIJkoiInJASJBERiSe/mT1iZuvN7HUzSzWzEWb2VzNbaWaLzewMADP7oZl9z5ufYmZrzWy1mf2sw52ogd72W8zsfq/+fUCqV/8PnQVjZjd7+11jZk97ZU+Y2a/NbKmZbTOz2Wb2uJltNLMnYvYvIyIicaEESURE4mkU8KBzbhxQA3wBeBj4lnNuMvA94KFjbPc74GvOuUlAuMO6ScBcYAIw18wGO+e+DzQ65yY55246ViBmNg74N+Bi59xZwLfbrc4BzgPuBuYBvwDGARPMbNKn+L1FRKSHCsQ7ABER6dPKnHOrvfmVwFBgBvCimR2uk9x+AzPrB2Q65973ip4BrmpX5U3nXK1XdwMwBNh1ErFcDLzonKsGcM7tb7fuT845Z2brgErn3Dpv/+u9mFd33JmIiPROSpBERCSemtvNh4EBQI13Z6ir9tkV33WH9xnpsP9IF+1fRER6CHWxExGRnqQOKDOz6wEs6qz2FZxzNcBBM5vmFd1wkvtuNbPgcdYvBK43s/7eZ+eeWugiIpIIlCCJiEhPcxNwm5mtAdYDc45R5zbgETNbDaQDtSex34eBtZ0N0uCcWw/8BFjkffbPP03wIiLSu5lzLt4xiIiInBIzy3DO1Xvz3weKnHPfPsFmIiIiJ6R+0yIi0htdaWY/IPo9tgP4SnzDERGRRKE7SCIi0qd4zxi9eYxVn3HO7evueEREpGdRgiQiIiIiIuLRIA0iIiIiIiIeJUgiIiIiIiIeJUgiIiIiIiIeJUgiIiIiIiIeJUgiIiIiIiKe/w+lbFi9IWZPNgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "5feharlw5tOk",
        "outputId": "be83d70b-748b-4253-8b84-eca70a68bb10"
      },
      "source": [
        "# Weight distribution and mean weight\n",
        "\n",
        "plt.figure(figsize=(14,5))\n",
        "plt.title('Weight Distribution FIFA 20')\n",
        "sns.distplot(df['weight_kg'], kde=True, bins=20)\n",
        "plt.axvline(x=np.mean(df['weight_kg']),c='orange',label='Mean Weight of All Players')\n",
        "plt.legend()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fd540db7290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "RhY-h69B532u",
        "outputId": "07067b26-d02c-457d-a6df-2becb5f1416c"
      },
      "source": [
        "# Weight vs Pace\n",
        "\n",
        "plt.figure()\n",
        "x=df.head(20)['weight_kg']\n",
        "y=df.head(20)['pace']\n",
        "\n",
        "sns.regplot(x,y)\n",
        "plt.title('Weight v Pace')\n",
        "plt.xlabel('Weight')\n",
        "plt.ylabel('Pace')\n",
        "plt.show()"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FqGXH2W86E79"
      },
      "source": [
        "We observe:\n",
        "\n",
        "* Pace tends to decrease with increase in weight."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "RHW2TtSe6GqI",
        "outputId": "896e5459-64ab-42d0-c767-ea4f431f4cca"
      },
      "source": [
        "# Height vs weight\n",
        "\n",
        "plt.figure()\n",
        "x=df.head(20)['height_cm']\n",
        "y=df.head(20)['weight_kg']\n",
        "\n",
        "sns.regplot(x,y)\n",
        "plt.title('Height v Weight')\n",
        "plt.xlabel('Height')\n",
        "plt.ylabel('Weight')\n",
        "plt.show()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvO-gVxy6RDa"
      },
      "source": [
        "We observe:\n",
        "\n",
        "* Height and weight are linearly dependant"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HS6g4yJ31kGx"
      },
      "source": [
        "## **Data Anaylsis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6gOblf0i1nWk"
      },
      "source": [
        "Average player height in FIFA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJCHvud11lUY",
        "outputId": "a851ac18-b71d-4e69-ea24-d7cfc79207ae"
      },
      "source": [
        "height = df['height_cm'].mean()\n",
        "print(str(round(height,2)) + \" is the average height in fifa\")"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "181.36 is the average height in fifa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iBwZb4Bq1y23"
      },
      "source": [
        "Average player weight in FIFA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6hRhNcK-10uz",
        "outputId": "1ff3a2bd-d974-43b4-a087-6ef35369bf80"
      },
      "source": [
        "weight = df['weight_kg'].mean()\n",
        "print(str(round(weight,2)) + \" is the average weight in fifa\")"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "75.28 is the average weight in fifa\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8eP6spnU2ChI"
      },
      "source": [
        "10 Oldest players"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "bQUS-hqH2DFj",
        "outputId": "f83792ce-c376-42b5-ffa0-1d1cfb84d5d8"
      },
      "source": [
        "df.sort_values(by = 'age' , ascending = False)[['short_name','club','nationality','overall', 'age' ]].head(10)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>club</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13003</th>\n",
              "      <td>H. Sulaimani</td>\n",
              "      <td>Al Ahli</td>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>63</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11832</th>\n",
              "      <td>C. Muñoz</td>\n",
              "      <td>CD Universidad de Concepción</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>64</td>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>G. Buffon</td>\n",
              "      <td>Juventus</td>\n",
              "      <td>Italy</td>\n",
              "      <td>83</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3706</th>\n",
              "      <td>C. Lucchetti</td>\n",
              "      <td>Atlético Tucumán</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>72</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10665</th>\n",
              "      <td>F. Kippe</td>\n",
              "      <td>Lillestrøm SK</td>\n",
              "      <td>Norway</td>\n",
              "      <td>65</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>868</th>\n",
              "      <td>Hilton</td>\n",
              "      <td>Montpellier HSC</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>78</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2965</th>\n",
              "      <td>Cifuentes</td>\n",
              "      <td>Cádiz CF</td>\n",
              "      <td>Spain</td>\n",
              "      <td>73</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15812</th>\n",
              "      <td>M. Gurski</td>\n",
              "      <td>SpVgg Unterhaching</td>\n",
              "      <td>Germany</td>\n",
              "      <td>59</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8359</th>\n",
              "      <td>F. Cubero</td>\n",
              "      <td>Vélez Sarsfield</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>67</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2963</th>\n",
              "      <td>D. Dainelli</td>\n",
              "      <td>Livorno</td>\n",
              "      <td>Italy</td>\n",
              "      <td>73</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         short_name                          club   nationality  overall  age\n",
              "13003  H. Sulaimani                       Al Ahli  Saudi Arabia       63   42\n",
              "11832      C. Muñoz  CD Universidad de Concepción     Argentina       64   42\n",
              "200       G. Buffon                      Juventus         Italy       83   41\n",
              "3706   C. Lucchetti              Atlético Tucumán     Argentina       72   41\n",
              "10665      F. Kippe                 Lillestrøm SK        Norway       65   41\n",
              "868          Hilton               Montpellier HSC        Brazil       78   41\n",
              "2965      Cifuentes                      Cádiz CF         Spain       73   40\n",
              "15812     M. Gurski            SpVgg Unterhaching       Germany       59   40\n",
              "8359      F. Cubero               Vélez Sarsfield     Argentina       67   40\n",
              "2963    D. Dainelli                       Livorno         Italy       73   40"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASYmQrNf2MMi"
      },
      "source": [
        "10 youngest players"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "BYDLKE1l2QsB",
        "outputId": "9e5d7484-64d7-421c-c6d9-f9fbf18b4375"
      },
      "source": [
        "df.sort_values(by = 'age' , ascending = True)[['short_name','club','nationality','overall', 'age' ]].head(10)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18171</th>\n",
              "      <td>J. Starbuck</td>\n",
              "      <td>Grimsby Town</td>\n",
              "      <td>England</td>\n",
              "      <td>49</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4764</th>\n",
              "      <td>A. Hložek</td>\n",
              "      <td>Sparta Praha</td>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>70</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18243</th>\n",
              "      <td>E. Sartorius</td>\n",
              "      <td>Lincoln City</td>\n",
              "      <td>England</td>\n",
              "      <td>48</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17827</th>\n",
              "      <td>D. Burns</td>\n",
              "      <td>St. Patrick's Athletic</td>\n",
              "      <td>Northern Ireland</td>\n",
              "      <td>52</td>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>17614</th>\n",
              "      <td>D. Obbekjær</td>\n",
              "      <td>Odense Boldklub</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>53</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17782</th>\n",
              "      <td>S. Anderson</td>\n",
              "      <td>Colorado Rapids</td>\n",
              "      <td>United States</td>\n",
              "      <td>52</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18242</th>\n",
              "      <td>R. Wikberg</td>\n",
              "      <td>Östersunds FK</td>\n",
              "      <td>Sweden</td>\n",
              "      <td>48</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18031</th>\n",
              "      <td>O. Stefánsson</td>\n",
              "      <td>IFK Norrköping</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>51</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14626</th>\n",
              "      <td>A. Velasco</td>\n",
              "      <td>Independiente</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>60</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12160</th>\n",
              "      <td>S. Esposito</td>\n",
              "      <td>Inter</td>\n",
              "      <td>Italy</td>\n",
              "      <td>63</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          short_name                    club       nationality  overall  age\n",
              "18171    J. Starbuck            Grimsby Town           England       49   16\n",
              "4764       A. Hložek            Sparta Praha    Czech Republic       70   16\n",
              "18243   E. Sartorius            Lincoln City           England       48   16\n",
              "17827       D. Burns  St. Patrick's Athletic  Northern Ireland       52   16\n",
              "17614    D. Obbekjær         Odense Boldklub           Denmark       53   16\n",
              "17782    S. Anderson         Colorado Rapids     United States       52   16\n",
              "18242     R. Wikberg           Östersunds FK            Sweden       48   16\n",
              "18031  O. Stefánsson          IFK Norrköping           Iceland       51   16\n",
              "14626     A. Velasco           Independiente         Argentina       60   16\n",
              "12160    S. Esposito                   Inter             Italy       63   16"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvpzbuyn2XBr"
      },
      "source": [
        "Top 5 Best ball controllers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "_AQSketj2aGt",
        "outputId": "792e90c1-a092-4944-8fda-94edaf2cd90f"
      },
      "source": [
        "df.sort_values(by = 'skill_ball_control' , ascending = False)[['short_name','club','nationality','overall', 'age','skill_ball_control']].head()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "<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>nationality</th>\n",
              "      <th>overall</th>\n",
              "      <th>age</th>\n",
              "      <th>skill_ball_control</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>L. Messi</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>94</td>\n",
              "      <td>32</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Neymar Jr</td>\n",
              "      <td>Paris Saint-Germain</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>92</td>\n",
              "      <td>27</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E. Hazard</td>\n",
              "      <td>Real Madrid</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>91</td>\n",
              "      <td>28</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Isco</td>\n",
              "      <td>Real Madrid</td>\n",
              "      <td>Spain</td>\n",
              "      <td>86</td>\n",
              "      <td>27</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>L. Insigne</td>\n",
              "      <td>Napoli</td>\n",
              "      <td>Italy</td>\n",
              "      <td>87</td>\n",
              "      <td>28</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    short_name                 club  ... age  skill_ball_control\n",
              "0     L. Messi         FC Barcelona  ...  32                  96\n",
              "2    Neymar Jr  Paris Saint-Germain  ...  27                  95\n",
              "4    E. Hazard          Real Madrid  ...  28                  94\n",
              "62        Isco          Real Madrid  ...  27                  93\n",
              "54  L. Insigne               Napoli  ...  28                  93\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AzrZlIu2g9q"
      },
      "source": [
        "Top 5 Best defenders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "IYp7-vIa2jsT",
        "outputId": "0f93ca4a-b3ce-438c-f3ca-56cf54e379f0"
      },
      "source": [
        "df.sort_values(by = 'defending' , ascending = False)[['short_name','club','nationality','overall', 'age','defending']].head()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>short_name</th>\n",
              "      <th>club</th>\n",
              "      <th>nationality</th>\n",
              "      <th>overall</th>\n",
              "      <th>age</th>\n",
              "      <th>defending</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>G. Chiellini</td>\n",
              "      <td>Juventus</td>\n",
              "      <td>Italy</td>\n",
              "      <td>89</td>\n",
              "      <td>34</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>V. van Dijk</td>\n",
              "      <td>Liverpool</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>90</td>\n",
              "      <td>27</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>D. Godín</td>\n",
              "      <td>Inter</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>88</td>\n",
              "      <td>33</td>\n",
              "      <td>89.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>K. Koulibaly</td>\n",
              "      <td>Napoli</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>89</td>\n",
              "      <td>28</td>\n",
              "      <td>89.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>M. Hummels</td>\n",
              "      <td>Borussia Dortmund</td>\n",
              "      <td>Germany</td>\n",
              "      <td>87</td>\n",
              "      <td>30</td>\n",
              "      <td>89.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      short_name               club  nationality  overall  age  defending\n",
              "16  G. Chiellini           Juventus        Italy       89   34       90.0\n",
              "7    V. van Dijk          Liverpool  Netherlands       90   27       90.0\n",
              "35      D. Godín              Inter      Uruguay       88   33       89.0\n",
              "11  K. Koulibaly             Napoli      Senegal       89   28       89.0\n",
              "49    M. Hummels  Borussia Dortmund      Germany       87   30       89.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "41PHnpJP2pd6"
      },
      "source": [
        "Top 5 best shooters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "f4IyLePq2rLi",
        "outputId": "85598b5a-d202-4e22-daf9-73bd25a08d12"
      },
      "source": [
        "df.sort_values(by = 'shooting' , ascending = False)[['short_name','club','nationality','overall', 'age','shooting']].head()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\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>club</th>\n",
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              "      <th>overall</th>\n",
              "      <th>age</th>\n",
              "      <th>shooting</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>Juventus</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>93</td>\n",
              "      <td>34</td>\n",
              "      <td>93.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>L. Messi</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>94</td>\n",
              "      <td>32</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>H. Kane</td>\n",
              "      <td>Tottenham Hotspur</td>\n",
              "      <td>England</td>\n",
              "      <td>89</td>\n",
              "      <td>25</td>\n",
              "      <td>91.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>S. Agüero</td>\n",
              "      <td>Manchester City</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>89</td>\n",
              "      <td>31</td>\n",
              "      <td>90.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>L. Suárez</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>89</td>\n",
              "      <td>32</td>\n",
              "      <td>89.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           short_name               club nationality  overall  age  shooting\n",
              "1   Cristiano Ronaldo           Juventus    Portugal       93   34      93.0\n",
              "0            L. Messi       FC Barcelona   Argentina       94   32      92.0\n",
              "12            H. Kane  Tottenham Hotspur     England       89   25      91.0\n",
              "17          S. Agüero    Manchester City   Argentina       89   31      90.0\n",
              "19          L. Suárez       FC Barcelona     Uruguay       89   32      89.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7gAgCkhs2t_-"
      },
      "source": [
        "Top 5 best dribblers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "7dMPLOIO2x3G",
        "outputId": "1f20ada4-5581-4684-ec55-1bcae898cdf8"
      },
      "source": [
        "df.sort_values(by = 'dribbling' , ascending = False)[['short_name','club','nationality','overall', 'age','dribbling']].head()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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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>club</th>\n",
              "      <th>nationality</th>\n",
              "      <th>overall</th>\n",
              "      <th>age</th>\n",
              "      <th>dribbling</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>L. Messi</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>94</td>\n",
              "      <td>32</td>\n",
              "      <td>96.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Neymar Jr</td>\n",
              "      <td>Paris Saint-Germain</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>92</td>\n",
              "      <td>27</td>\n",
              "      <td>95.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E. Hazard</td>\n",
              "      <td>Real Madrid</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>91</td>\n",
              "      <td>28</td>\n",
              "      <td>94.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Bernardo Silva</td>\n",
              "      <td>Manchester City</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>87</td>\n",
              "      <td>24</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>Douglas Costa</td>\n",
              "      <td>Juventus</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>84</td>\n",
              "      <td>28</td>\n",
              "      <td>91.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         short_name                 club nationality  overall  age  dribbling\n",
              "0          L. Messi         FC Barcelona   Argentina       94   32       96.0\n",
              "2         Neymar Jr  Paris Saint-Germain      Brazil       92   27       95.0\n",
              "4         E. Hazard          Real Madrid     Belgium       91   28       94.0\n",
              "41   Bernardo Silva      Manchester City    Portugal       87   24       92.0\n",
              "150   Douglas Costa             Juventus      Brazil       84   28       91.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lz7Z_bWk220J"
      },
      "source": [
        "Top 5 quickest players"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "Nefy2D4c25QK",
        "outputId": "896733c4-d4c6-4acf-ac3c-df387f95e87a"
      },
      "source": [
        "df.sort_values(by = 'movement_acceleration' , ascending = False)[['short_name','club','nationality','overall', 'age','movement_acceleration']].head()"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "    }\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>club</th>\n",
              "      <th>nationality</th>\n",
              "      <th>overall</th>\n",
              "      <th>age</th>\n",
              "      <th>movement_acceleration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2167</th>\n",
              "      <td>Adama Traoré</td>\n",
              "      <td>Wolverhampton Wanderers</td>\n",
              "      <td>Spain</td>\n",
              "      <td>74</td>\n",
              "      <td>23</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>K. Mbappé</td>\n",
              "      <td>Paris Saint-Germain</td>\n",
              "      <td>France</td>\n",
              "      <td>89</td>\n",
              "      <td>20</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>R. Sterling</td>\n",
              "      <td>Manchester City</td>\n",
              "      <td>England</td>\n",
              "      <td>88</td>\n",
              "      <td>24</td>\n",
              "      <td>96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>754</th>\n",
              "      <td>I. Sarr</td>\n",
              "      <td>Watford</td>\n",
              "      <td>Senegal</td>\n",
              "      <td>78</td>\n",
              "      <td>21</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>Lucas Moura</td>\n",
              "      <td>Tottenham Hotspur</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>83</td>\n",
              "      <td>26</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        short_name                     club  ... age  movement_acceleration\n",
              "2167  Adama Traoré  Wolverhampton Wanderers  ...  23                     97\n",
              "10       K. Mbappé      Paris Saint-Germain  ...  20                     96\n",
              "26     R. Sterling          Manchester City  ...  24                     96\n",
              "754        I. Sarr                  Watford  ...  21                     95\n",
              "197    Lucas Moura        Tottenham Hotspur  ...  26                     95\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t64ubxcz295I"
      },
      "source": [
        "Top 5 highest earners"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "id": "uHTtodBa2_3P",
        "outputId": "a840a52b-fdb6-44be-a816-1e6dd228b330"
      },
      "source": [
        "df.sort_values(by = 'wage_eur' , ascending = False)[['short_name','club','nationality','overall', 'age','wage_eur']].head()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    .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>club</th>\n",
              "      <th>nationality</th>\n",
              "      <th>overall</th>\n",
              "      <th>age</th>\n",
              "      <th>wage_eur</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>L. Messi</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>94</td>\n",
              "      <td>32</td>\n",
              "      <td>565000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E. Hazard</td>\n",
              "      <td>Real Madrid</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>91</td>\n",
              "      <td>28</td>\n",
              "      <td>470000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>Juventus</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>93</td>\n",
              "      <td>34</td>\n",
              "      <td>405000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>K. De Bruyne</td>\n",
              "      <td>Manchester City</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>91</td>\n",
              "      <td>28</td>\n",
              "      <td>370000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>A. Griezmann</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>France</td>\n",
              "      <td>89</td>\n",
              "      <td>28</td>\n",
              "      <td>370000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           short_name             club nationality  overall  age  wage_eur\n",
              "0            L. Messi     FC Barcelona   Argentina       94   32    565000\n",
              "4           E. Hazard      Real Madrid     Belgium       91   28    470000\n",
              "1   Cristiano Ronaldo         Juventus    Portugal       93   34    405000\n",
              "5        K. De Bruyne  Manchester City     Belgium       91   28    370000\n",
              "22       A. Griezmann     FC Barcelona      France       89   28    370000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    }
  ]
}