Getting_Squads_Stats.ipynb

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        "<a href=\"https://colab.research.google.com/github/davidcamilo0710/QATAR_2022_Prediction/blob/master/Getting_Squads_Stats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# INDEX\n",
        "- RESUME\n",
        "- DATA PREPARATION\n",
        "- ANALYSIS"
      ],
      "metadata": {
        "id": "zuTr2Zsuwyv9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RESUME\n",
        "This notebook aims to determine the potential of the teams qualified for the QATAR 2022 World Cup based on the FIFA 22 player statistics."
      ],
      "metadata": {
        "id": "uQ0Aqca4-0qP"
      }
    },
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      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import pandas as pd \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import os\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ndQYNS92fpWd",
        "outputId": "f5435b7f-ba37-495e-ab30-15b5e8a09ae5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FIFA22 = pd.read_csv('/content/drive/MyDrive/data/players_22.csv')\n",
        "FIFA22.shape"
      ],
      "metadata": {
        "colab": {
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        "id": "kJ4NBv4INfu5",
        "outputId": "33450881-f335-4a94-e79c-e80a55552ae2"
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      "execution_count": 3,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "(19239, 110)"
            ]
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          "metadata": {},
          "execution_count": 3
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      "cell_type": "code",
      "source": [
        "interesting_columns = ['short_name', 'age', 'nationality_name', 'overall', 'potential', 'club_name', 'value_eur', 'wage_eur', 'player_positions']\n",
        "FIFA22 = pd.DataFrame(FIFA22, columns=interesting_columns)\n",
        "FIFA22.info()"
      ],
      "metadata": {
        "id": "D8Gx09XrNkJz",
        "colab": {
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        },
        "outputId": "2fedfe60-2d3a-47bd-eba6-82d45ae17196"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19239 entries, 0 to 19238\n",
            "Data columns (total 9 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   short_name        19239 non-null  object \n",
            " 1   age               19239 non-null  int64  \n",
            " 2   nationality_name  19239 non-null  object \n",
            " 3   overall           19239 non-null  int64  \n",
            " 4   potential         19239 non-null  int64  \n",
            " 5   club_name         19178 non-null  object \n",
            " 6   value_eur         19165 non-null  float64\n",
            " 7   wage_eur          19178 non-null  float64\n",
            " 8   player_positions  19239 non-null  object \n",
            "dtypes: float64(2), int64(3), object(4)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "FIFA22.head(5)"
      ],
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          "height": 206
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        "id": "4KDg8uxCls24",
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            "text/plain": [
              "          short_name  age nationality_name  overall  potential  \\\n",
              "0           L. Messi   34        Argentina       93         93   \n",
              "1     R. Lewandowski   32           Poland       92         92   \n",
              "2  Cristiano Ronaldo   36         Portugal       91         91   \n",
              "3          Neymar Jr   29           Brazil       91         91   \n",
              "4       K. De Bruyne   30          Belgium       91         91   \n",
              "\n",
              "             club_name    value_eur  wage_eur player_positions  \n",
              "0  Paris Saint-Germain   78000000.0  320000.0       RW, ST, CF  \n",
              "1    FC Bayern München  119500000.0  270000.0               ST  \n",
              "2    Manchester United   45000000.0  270000.0           ST, LW  \n",
              "3  Paris Saint-Germain  129000000.0  270000.0          LW, CAM  \n",
              "4      Manchester City  125500000.0  350000.0          CM, CAM  "
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              "      <td>L. Messi</td>\n",
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              "      <td>Neymar Jr</td>\n",
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              "      <td>Manchester City</td>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# DATA PREPARATION\n",
        "- I filter the teams that will participate in the world cup and eliminate the others.\n",
        "\n",
        "- To make things simpler, I select the first position from list as preferred and save it in 'Position' column."
      ],
      "metadata": {
        "id": "eoXCG_KKnFjv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "list_2022 = ['Qatar', 'Germany', 'Denmark', 'Brazil', 'France', 'Belgium', 'Croatia', 'Spain', 'Serbia', 'England', 'Switzerland', 'Netherlands', 'Argentina', 'IR Iran', 'Korea Republic', 'Japan', 'Saudi Arabia', 'Ecuador', 'Uruguay', 'Canada', 'Ghana', 'Senegal', 'Portugal', 'Poland', 'Tunisia', 'Morocco', 'Cameroon', 'USA', 'Mexico', 'Wales', 'Australia', 'Costa Rica']\n",
        "FIFA22['Position'] = FIFA22['player_positions'].str.split(\",\").str[0]\n",
        "FIFA22 = FIFA22[[\"short_name\", \"age\", \"nationality_name\", 'overall', 'potential', \"club_name\", \"Position\", \"value_eur\", \"wage_eur\"]]\n",
        "FIFA22 = FIFA22[(FIFA22[\"nationality_name\"].apply(lambda x: x in list_2022))]\n",
        "FIFA22['nationality_name'].unique()"
      ],
      "metadata": {
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        },
        "id": "s6m4lKtaOURy",
        "outputId": "8b65b48d-d4f4-4b6c-9572-98815d94e85a"
      },
      "execution_count": 6,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Argentina', 'Poland', 'Portugal', 'Brazil', 'Belgium', 'France',\n",
              "       'Germany', 'England', 'Korea Republic', 'Netherlands', 'Senegal',\n",
              "       'Spain', 'Uruguay', 'Costa Rica', 'Croatia', 'Denmark',\n",
              "       'Switzerland', 'Serbia', 'Morocco', 'Mexico', 'Cameroon', 'Ghana',\n",
              "       'Wales', 'USA', 'Canada', 'Japan', 'Ecuador', 'IR Iran',\n",
              "       'Australia', 'Tunisia', 'Saudi Arabia'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
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      "source": [
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      "execution_count": 7,
      "outputs": [
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          "data": {
            "text/plain": [
              "          short_name  age nationality_name  overall  potential  \\\n",
              "0           L. Messi   34        Argentina       93         93   \n",
              "1     R. Lewandowski   32           Poland       92         92   \n",
              "2  Cristiano Ronaldo   36         Portugal       91         91   \n",
              "3          Neymar Jr   29           Brazil       91         91   \n",
              "4       K. De Bruyne   30          Belgium       91         91   \n",
              "\n",
              "             club_name Position    value_eur  wage_eur  \n",
              "0  Paris Saint-Germain       RW   78000000.0  320000.0  \n",
              "1    FC Bayern München       ST  119500000.0  270000.0  \n",
              "2    Manchester United       ST   45000000.0  270000.0  \n",
              "3  Paris Saint-Germain       LW  129000000.0  270000.0  \n",
              "4      Manchester City       CM  125500000.0  350000.0  "
            ],
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              "      <th>0</th>\n",
              "      <td>L. Messi</td>\n",
              "      <td>34</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>93</td>\n",
              "      <td>93</td>\n",
              "      <td>Paris Saint-Germain</td>\n",
              "      <td>RW</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>R. Lewandowski</td>\n",
              "      <td>32</td>\n",
              "      <td>Poland</td>\n",
              "      <td>92</td>\n",
              "      <td>92</td>\n",
              "      <td>FC Bayern München</td>\n",
              "      <td>ST</td>\n",
              "      <td>119500000.0</td>\n",
              "      <td>270000.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Cristiano Ronaldo</td>\n",
              "      <td>36</td>\n",
              "      <td>Portugal</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>Manchester United</td>\n",
              "      <td>ST</td>\n",
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              "      <th>3</th>\n",
              "      <td>Neymar Jr</td>\n",
              "      <td>29</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>Paris Saint-Germain</td>\n",
              "      <td>LW</td>\n",
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              "      <td>270000.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>K. De Bruyne</td>\n",
              "      <td>30</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>91</td>\n",
              "      <td>91</td>\n",
              "      <td>Manchester City</td>\n",
              "      <td>CM</td>\n",
              "      <td>125500000.0</td>\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-db722922-05ae-4c0e-bf25-94d83944100c button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-db722922-05ae-4c0e-bf25-94d83944100c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ANALYSIS"
      ],
      "metadata": {
        "id": "-oo377OOmeIN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Top 20 players overall"
      ],
      "metadata": {
        "id": "HTZrPKd2CmSz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Overall = FIFA22[\"overall\"]\n",
        "footballer_name = FIFA22[\"short_name\"]\n",
        "\n",
        "x = FIFA22['short_name'].head(20) \n",
        "y = FIFA22['overall'].head(20)\n",
        "\n",
        "# plot\n",
        "\n",
        "ax= sns.barplot(x=y, y=x, color=\"#7F1431\", orient='h')\n",
        "plt.xlabel('Overall Ratings', size=20) \n",
        "plt.ylabel('Player', size=20) \n",
        "plt.title('Top 20 players QATAR World Cup')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "LhMpZkrgnVZb",
        "outputId": "82d47185-16ac-459b-fd46-4d37cfce0ce2"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Best Squad Analysis"
      ],
      "metadata": {
        "id": "BNVSFQzVOR8B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_best_squad(formation):\n",
        "    FIFA22_copy = FIFA22.copy()\n",
        "    store = []\n",
        "    \n",
        "    # iterate through all positions in the input formation and get players with highest overall respective to the position\n",
        "    for i in formation:\n",
        "        store.append([\n",
        "            i,\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'] == i]['overall'].idxmax()]]['short_name'].to_string(index=False),\n",
        "            FIFA22_copy[FIFA22_copy['Position'] == i]['overall'].max(),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'] == i]['overall'].idxmax()]]['age'].to_string(index=False),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'] == i]['overall'].idxmax()]]['club_name'].to_string(index=False),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'] == i]['overall'].idxmax()]]['value_eur'].to_string(index=False),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'] == i]['overall'].idxmax()]]['wage_eur'].to_string(index=False)\n",
        "        ])\n",
        "        FIFA22_copy.drop(FIFA22_copy[FIFA22_copy['Position'] == i]['overall'].idxmax(), \n",
        "                         inplace=True)\n",
        "    # return store with only necessary columns\n",
        "    return pd.DataFrame(np.array(store).reshape(11,7), \n",
        "                        columns = ['Position', 'short_name', 'overall', 'age', 'club_name', 'value_eur', 'wage_eur']).to_string(index = False)"
      ],
      "metadata": {
        "id": "7hu_JwqTZ5Wn"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Best 4-3-3**"
      ],
      "metadata": {
        "id": "-nOnecnyGyl2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "squad_433 = ['GK', 'RB', 'CB', 'CB', 'LB', 'CDM', 'CM', 'CAM', 'RW', 'ST', 'LW']\n",
        "print ('4-3-3')\n",
        "print (get_best_squad(squad_433))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WNVwwmOeZ-kp",
        "outputId": "30c9bce9-aaa9-425b-bee5-4b38b1078774"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4-3-3\n",
            "Position          short_name overall age           club_name   value_eur wage_eur\n",
            "      GK            M. Neuer      90  35   FC Bayern München  13500000.0  86000.0\n",
            "      RB T. Alexander-Arnold      87  22           Liverpool 114000000.0 150000.0\n",
            "      CB         V. van Dijk      89  29           Liverpool  86000000.0 230000.0\n",
            "      CB        Sergio Ramos      88  35 Paris Saint-Germain  24000000.0 115000.0\n",
            "      LB          Jordi Alba      86  32        FC Barcelona  47000000.0 200000.0\n",
            "     CDM            N. Kanté      90  30             Chelsea 100000000.0 230000.0\n",
            "      CM        K. De Bruyne      91  30     Manchester City 125500000.0 350000.0\n",
            "     CAM     Bruno Fernandes      88  26   Manchester United 107500000.0 250000.0\n",
            "      RW            L. Messi      93  34 Paris Saint-Germain  78000000.0 320000.0\n",
            "      ST      R. Lewandowski      92  32   FC Bayern München 119500000.0 270000.0\n",
            "      LW           Neymar Jr      91  29 Paris Saint-Germain 129000000.0 270000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Best 4-4-2**"
      ],
      "metadata": {
        "id": "uuFTVlbBHpeO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "squad_442 = ['GK', 'RB', 'CB', 'CB', 'LB', 'RM', 'CM', 'CM', 'LM', 'ST', 'ST']\n",
        "print ('4-4-2')\n",
        "print (get_best_squad(squad_442))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FMEQtd4PaEL6",
        "outputId": "e3599c6f-870a-4f5f-9190-6436e1542894"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4-4-2\n",
            "Position          short_name overall age           club_name   value_eur wage_eur\n",
            "      GK            M. Neuer      90  35   FC Bayern München  13500000.0  86000.0\n",
            "      RB T. Alexander-Arnold      87  22           Liverpool 114000000.0 150000.0\n",
            "      CB         V. van Dijk      89  29           Liverpool  86000000.0 230000.0\n",
            "      CB        Sergio Ramos      88  35 Paris Saint-Germain  24000000.0 115000.0\n",
            "      LB          Jordi Alba      86  32        FC Barcelona  47000000.0 200000.0\n",
            "      RM           J. Sancho      87  21   Manchester United 116500000.0 150000.0\n",
            "      CM        K. De Bruyne      91  30     Manchester City 125500000.0 350000.0\n",
            "      CM            T. Kroos      88  31      Real Madrid CF  75000000.0 310000.0\n",
            "      LM              H. Son      89  28   Tottenham Hotspur 104000000.0 220000.0\n",
            "      ST      R. Lewandowski      92  32   FC Bayern München 119500000.0 270000.0\n",
            "      ST   Cristiano Ronaldo      91  36   Manchester United  45000000.0 270000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Best 4-2-3-1**"
      ],
      "metadata": {
        "id": "gMS35D_yHuuz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "squad_4231 = ['GK', 'RB', 'CB', 'CB', 'LB', 'CDM', 'CDM', 'CAM', 'CAM', 'CAM', 'ST']\n",
        "print ('4-2-3-1')\n",
        "print (get_best_squad(squad_4231))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F-6RRhOQaE11",
        "outputId": "5cab1847-614b-49b7-9a41-d32e0db2fdec"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4-2-3-1\n",
            "Position          short_name overall age           club_name   value_eur wage_eur\n",
            "      GK            M. Neuer      90  35   FC Bayern München  13500000.0  86000.0\n",
            "      RB T. Alexander-Arnold      87  22           Liverpool 114000000.0 150000.0\n",
            "      CB         V. van Dijk      89  29           Liverpool  86000000.0 230000.0\n",
            "      CB        Sergio Ramos      88  35 Paris Saint-Germain  24000000.0 115000.0\n",
            "      LB          Jordi Alba      86  32        FC Barcelona  47000000.0 200000.0\n",
            "     CDM            N. Kanté      90  30             Chelsea 100000000.0 230000.0\n",
            "     CDM            Casemiro      89  29      Real Madrid CF  88000000.0 310000.0\n",
            "     CAM     Bruno Fernandes      88  26   Manchester United 107500000.0 250000.0\n",
            "     CAM           T. Müller      87  31   FC Bayern München  66000000.0 140000.0\n",
            "     CAM      Bernardo Silva      86  26     Manchester City  80000000.0 200000.0\n",
            "      ST      R. Lewandowski      92  32   FC Bayern München 119500000.0 270000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Top 10 Promising Teams QATAR World Cup\n",
        "I define a function to find the best formation of each national team and its potential. Also, a function to summarize the team."
      ],
      "metadata": {
        "id": "JtWjbCG8IxOc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_best_squad_n(formation, nationality, measurement = 'overall'):\n",
        "    FIFA22_copy = FIFA22.copy()\n",
        "    FIFA22_copy = FIFA22_copy[FIFA22_copy['nationality_name'] == nationality]\n",
        "    store = []\n",
        "    for i in formation:\n",
        "        store.append([\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].idxmax()]]['Position'].to_string(index = False),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].idxmax()]]['short_name'].to_string(index = False), \n",
        "            FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].max(),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].idxmax()]]['age'].to_string(index = False),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].idxmax()]]['club_name'].to_string(index = False),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].idxmax()]]['value_eur'].to_string(index = False),\n",
        "            FIFA22_copy.loc[[FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].idxmax()]]['wage_eur'].to_string(index = False)\n",
        "        ])    \n",
        "        FIFA22_copy.drop(FIFA22_copy[FIFA22_copy['Position'].str.contains(i)][measurement].idxmax(),inplace = True)\n",
        "    return np.mean([x[2] for x in store]).round(2), pd.DataFrame(np.array(store).reshape(11,7),columns = ['Position', 'short_name', measurement, 'age', 'club_name', 'value_eur', 'wage_eur']).to_string(index = False)"
      ],
      "metadata": {
        "id": "dBvZzRuGaHhn"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_summary_n(squad_list, squad_name, nationality_list):\n",
        "    summary = []\n",
        "    for i in nationality_list:\n",
        "        count = 0\n",
        "        for j in squad_list:\n",
        "            try:\n",
        "                # for overall rating\n",
        "                O_temp_rating, _  = get_best_squad_n(formation = j, nationality = i, measurement = 'overall')\n",
        "                # for potential rating & corresponding value\n",
        "                P_temp_rating, _ = get_best_squad_n(formation = j, nationality = i, measurement = 'potential')\n",
        "                summary.append([i, squad_name[count], O_temp_rating.round(2), P_temp_rating.round(2)])    \n",
        "                count += 1\n",
        "            except:\n",
        "                count += 1  \n",
        "    return summary"
      ],
      "metadata": {
        "id": "siDiwSKUaSjm"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "squad_343_strict = ['GK', 'CB', 'CB', 'CB', 'RB|RWB', 'CM|CDM', 'CM|CDM', 'LB|LWB', 'RM|RW', 'ST|CF', 'LM|LW']\n",
        "squad_442_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'RM', 'CM|CDM', 'CM|CAM', 'LM', 'ST|CF', 'ST|CF']\n",
        "squad_4312_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'CM|CDM', 'CM|CAM|CDM', 'CM|CAM|CDM', 'CAM|CF', 'ST|CF', 'ST|CF']\n",
        "squad_433_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'CM|CDM', 'CM|CAM|CDM', 'CM|CAM|CDM', 'RM|RW', 'ST|CF', 'LM|LW']\n",
        "squad_4231_strict = ['GK', 'RB|RWB', 'CB', 'CB', 'LB|LWB', 'CM|CDM', 'CM|CDM', 'RM|RW', 'CAM', 'LM|LW', 'ST|CF']"
      ],
      "metadata": {
        "id": "HZQykRr2aYHs"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "squad_list = [squad_343_strict, squad_442_strict, squad_4312_strict, squad_433_strict, squad_4231_strict]\n",
        "squad_name = ['3-4-3', '4-4-2', '4-3-1-2', '4-3-3', '4-2-3-1']"
      ],
      "metadata": {
        "id": "-8KO-y2QagvX"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initially, I want to see if the complete information is there (i.e., the overall and the potential of the 32 teams of QATAR 2022)"
      ],
      "metadata": {
        "id": "eE9wD0oeqqoA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "country = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, list_2022)).reshape(-1,4), columns = ['nationality_name', 'Squad', 'overall', 'potential'])\n",
        "country.set_index('nationality_name', inplace = False)\n",
        "country[['overall', 'potential']] = country[['overall', 'potential']].astype(float)"
      ],
      "metadata": {
        "id": "4P1dJt51qKdG"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "miss = list_2022\n",
        "miss = [item if item not in country.nationality_name.values else False for item in miss]\n",
        "for item in miss:\n",
        "    if(type(item) == str):\n",
        "        print(item)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDL9wImO9OhT",
        "outputId": "42ae2cfb-f130-4a35-cac2-72762293adb0"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Qatar\n",
            "Tunisia\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Qatar and Tunisia do not have enough information to calculate the overall and the potential of the templates, therefore, I assign the average to Qatar since it is the Local selection. On the other hand, Tunisia has less potential."
      ],
      "metadata": {
        "id": "TGMpDE0-LgSc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fixing Qatar and Tunisia na's\n",
        "Qatar = {'nationality_name':'Qatar', 'Squad':'4-3-3'}\n",
        "Tunisia = {'nationality_name':'Tunisia', 'Squad':'4-3-3', 'overall':73.0, 'potential':76.0}\n",
        "country = country.append(Qatar, ignore_index=True)\n",
        "country = country.append(Tunisia, ignore_index=True)\n",
        "country['overall'] = country[\"overall\"].transform(lambda x: x.fillna(x.mean()))\n",
        "country['potential'] = country[\"potential\"].transform(lambda x: x.fillna(x.mean()))\n",
        "country = country.drop(['Squad'],axis=1)\n",
        "country = country.sort_values(['nationality_name','potential'],ascending=[True,False])\n",
        "country_final = country.groupby('nationality_name').first().sort_values('potential', ascending=False)[0:32].reset_index() "
      ],
      "metadata": {
        "id": "ovm-biDWGh5s"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Exporting overall and potential of QATAR 2022 Teams\n",
        "country_final.to_csv(\"/content/drive/MyDrive/data/squad_stats.csv\", index = False)"
      ],
      "metadata": {
        "id": "FqSe2_LBQGol"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Table** - Top 10 Promising Teams"
      ],
      "metadata": {
        "id": "e4CbLDW1Nvms"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "country_top10 = country.groupby('nationality_name').first().sort_values('potential',ascending=False)[0:10].reset_index()\n",
        "country_top10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "pAurl3OyNs6o",
        "outputId": "70ec86d5-ca43-473e-d60f-b02995d44978"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  nationality_name  overall  potential\n",
              "0           France    85.55      89.18\n",
              "1          England    85.18      89.00\n",
              "2            Spain    85.55      88.36\n",
              "3           Brazil    85.64      88.09\n",
              "4         Portugal    85.00      88.00\n",
              "5          Germany    85.00      87.55\n",
              "6      Netherlands    83.36      87.00\n",
              "7        Argentina    83.73      86.18\n",
              "8          Belgium    83.45      85.55\n",
              "9          Uruguay    80.55      84.64"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75acbab6-9539-4d6b-a78c-619d7f386c0a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nationality_name</th>\n",
              "      <th>overall</th>\n",
              "      <th>potential</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>France</td>\n",
              "      <td>85.55</td>\n",
              "      <td>89.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>England</td>\n",
              "      <td>85.18</td>\n",
              "      <td>89.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Spain</td>\n",
              "      <td>85.55</td>\n",
              "      <td>88.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>85.64</td>\n",
              "      <td>88.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Portugal</td>\n",
              "      <td>85.00</td>\n",
              "      <td>88.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Germany</td>\n",
              "      <td>85.00</td>\n",
              "      <td>87.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>83.36</td>\n",
              "      <td>87.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>83.73</td>\n",
              "      <td>86.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>83.45</td>\n",
              "      <td>85.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Uruguay</td>\n",
              "      <td>80.55</td>\n",
              "      <td>84.64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Graph** - Top 10 Promising Teams"
      ],
      "metadata": {
        "id": "PBL12kcGOVh5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = country_top10['nationality_name']\n",
        "y = country_top10['potential']\n",
        "\n",
        "# plot\n",
        "ax= sns.barplot(x=y, y=x, color=\"#7F1431\", orient='h')\n",
        "plt.xlabel('Team Potential Ratings', size = 20) \n",
        "plt.ylabel('Team', size = 20 ) \n",
        "plt.title('Top 10 Promising Teams QATAR World Cup')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "96wsyw7q0B2H",
        "outputId": "b1cdd941-78e0-4170-aa87-b0af445de171"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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NOtpgRLwIvChpp5x0RAPLa2ZmNWjFAFd+De7UTvKfDOyZu/F/EHgKeKUsz/nAWEkzgSOB+2sox8eAMyRNJzVxmplZL+r3swlIWglYGBFvStoBOKsZwww8m4CZ9Uc9OZtAf7sGV8n6wIWSlgPeAD7Z5PKYmVkD9PsAFxH/BrZqdjnMzKyxWvEanJmZmWtwfcXI0aMY3969tmgzM1vMNTgzM2tJDnBmZtaSHODMzKwl9ftxcH2Fx8GZWX/R3bFvRR2Ng3MNzszMWpIDnJmZtSQHODMza0kOcGZm1pIc4MzMrCU5wAGSvilplqQZeYqdd3VhG/tLOqEnymdmZvXr97fqylPk7AtsHRHzJQ0FVqx3OxExCZjU6PKZmVnXuAYHw4HnImI+QEQ8FxFPSJot6ceSZkq6Q9LbASTtJ+l2SdMk/UPSsJx+tKRf5ucTJf1c0i2SHpR0SNOOzsysn3KAg2uA9ST9S9KZknYtLHspIkYDvwROz2k3AdtHxFbAn4GvVtnucGAnUu2w4qziko6V1C6p/bWFCxpxLGZmlvX7JsqImCtpG2BnYDfggsK1tD8VHk/Lz0fmPMNJTZkPVdn03yJiEXBvqZZXYd8TgAmQ7mTS7YMxM7O3uAYHRMTCiJgcEScCnwMOLi0qZsuPvwB+mWt2nwIGVtns/MJzNbK8ZmbWuX4f4CS9Q9LGhaQxwMP5+WGFx1vz88HA4/n5UT1fQjMz64p+30QJDAJ+IWkI8CbwH+BY0rWzNSTNINXGPpTznwRcJGkOcB2wYa+X2MzMOuXZBKqQNBsYGxHP9cb+PJuAmfUXnk3AzMysG9xEWUVEtDW7DGZm1nUOcH3EyNGjGN/euGq7mVl/5yZKMzNrSQ5wZmbWkhzgzMysJTnAmZlZS/I4uD7C4+DMrD9o5Bg48Dg4MzPrhxzgzMysJTnAmZlZS3KAMzOzltTvA5ykhZKmS7pb0l2S3t2g7Z4tadP8fLakoY3YrpmZ1ca36oJ5ETEGQNJewCnArsUMkpaPiDfr2WhEfKJxRTQzs3r1+xpcmdWBOQCSxkm6UdIk4N6c9jdJUyXNknRsTts/1wCnS3pA0kM5fbKkil1Xzcys57kGBytLmg4MBIYDuxeWbQ1sHhEP5dfHRMQLklYG7pR0cURMAiYBSLoQuKHWHecgeSzAagNW7P6RmJnZW1yDy02UETEK2Bs4T5LysjsKwQ3gOEl3A7cB6wEblxZI+mre1hm17jgiJkTE2IgYu8qAFbp/JGZm9hbX4Aoi4tbcGWTtnPRqaZmkccAewA4R8ZqkyaRaH5L2AD4I7NKrBTYzs6oc4AokjQIGAM9XWDwYmJOD2yhg+7zOBsAZwF4RMa/XCmtmZh1ygFt8DQ5AwFERsXBxK+VbrgI+Lek+4AFSMyXA0cBawN/yOk9ExPt6vNRmZtahfh/gImJAlfTJwOTC6/nAPhWyTgZOrrD+uMLztm4V0szM6uZOJmZm1pIc4MzMrCU5wJmZWUvq99fg+oqRo0cxvr2xEwGamfVndQc4SfsBY4CRQKXRyRERH+9uwczMzLqj5gCXx3tdBmxG6k5fTQAOcGZm1lT11OB+DmwOnAOcBzwO1HWHfTMzs95ST4DbHbja08CYmdmyoJ4AtwCY2VMF6e8em3k/X9po+2YXw8ysR41/sPc609UzTOBmUhOlmZlZn1dPgPsOsIukw3uqMGZmZo1ScxNlREyT9B7gCkmfAu4CXqqcNb7XqAKamZl1RT3DBAYDPwTWBHbNf5UE4ABnZmZNVU8nk9OA3YB/AL8HnqCPDROQtJDUEWZ54D7S1Dev1bjuGOBtEXFlD5TrJGBuRPyk0ds2M7PK6glw+wK3RMSePVWYBpgXEWMAJJ0PfBr4aWcrSVqedHeWsUDDA5yZmfW+egLcysAtPVWQHnAjsIWkNUmD0zcCXgOOjYgZuVb1Pzn9EWBH0uSnOwGnAO+kUOuSdA+wb0TMlvRt4CPAs8CjwNSI+ImkTwLHAisC/wE+WmsN0szMGqueXpTTSMGgz8s1sn1IzZUnA9MiYgvgG6S7sJRsCuwRER8i9RK9ICLGRMQFHWx7W+BgYMu8j7GFxZdExLYRsSWpibTDW5ZJOlZSu6T21xYuqPs4zcysunoC3PeA/XINp69aWdJ0oJ1UK/stsBPpmiERcR2wlqTVc/5JETGvzn3sCFwaEa9HxCuk+3OWbC7pRkkzgSNI9+2sKiImRMTYiBi7yoBK9602M7OuqqeJcjhwOXCdpD8CU6k8TICIOK9Sei946xpcidTRfaF5tYNlb7LkD4CBNex/InBgRNwt6WhgXA3rmJlZD6gnwE0kDQEQcGT+i7I8ymnNCnCV3EiqTX1P0jjguYh4uULgewVYrfB6NqljDZK2BjbM6TcDv5Z0Cun87QtMyMtWA56UtELe5+ONPhgzM6tNPQHuYz1Wip51EnCOpBmkTiZHVcl3PXBCbuI8BbgYOFLSLOB24F8AEXGnpEnADOBp0nW+Uk322znvs/lxNczMrCkUUV4Js85IGhQRcyWtAkwh9cy8qzvbXHelQXHECN/q08xaW6NvtixpakSMrbSs7hm9DYAJkjYlXZf7XXeDm5mZNZ4DXBdExIebXQYzM+tYXQFO0qrA/wJ7ASOAlSpki4j4nwaUzczMrMvqudnyEOAm0uDol4HVSZ0rViTd5QTS/Sk9YrkLRo4exfj23psI0Mys1dUz0PtbpOD2cWCNnHYaMAh4N2n6nP+SbnFlZmbWVPUEuP2BKRFxbhS6XkZyG/A+YBTwzQaX0czMrG71BLj1SHcvKVlE4RpcRDwD/B3wjN9mZtZ09XQyeY0U1EpeAtYty/M0qfOJ1emxmffzpY22b3YxzMx6RKPHv9Winhrco6RaXMm9wC6SitvYCXiqEQUzMzPrjnoC3A3Arlp8E8cLSPOpXSnps5IuArbHE4aamVkfUE8T5e9IQwJGkmpzvwJ2Bw4ESrN830zqbWlmZtZUNQe4fDuqzxRevwkcJGkb4O2ku+/fGRGLKm/BzMys93T7Vl0RMZUle1eamZk1XT3X4N4iaVVJW0naudEFqrMcwyT9UdKDkqZKulXSB5pZJjMz6xvqCnCSRkq6GJgDtJPmUCst20nSvXlS0R6XO7v8jTT4fKOI2IY0Bm9kjev7RtNmZi2s5gAnaThpEs8DgMuBW0kzeJfcDqwDHNbIAnZgd+CNiPhVKSEiHo6IX0gaIOn/JN0paYakT+VjGCfpxjxh6b359Q2SLs21wFMlHSHpDkkzJf1PXm8/SbdLmibpH5KG5fSTJJ0jaXJe/7ic/l1JXyiVS9IPJB3fS+fFzMyorwZ3IimAvTciDgKuLS6MiAXAjcCOjStehzYj3f+yko8DL0XEtsC2wCclbZiXbQ0cHxGb5NdbAp8m3UPzo8AmEbEdcDbw+ZznJmD7iNgK+DPw1cK+RpFmV9gOOFHSCsA5wJEAeZzg4cAfygsp6VhJ7ZLaX1voe1SbmTVSPc107wMmRcT1HeR5BGjKdTlJZ5AGmr8BPAxsIemQvHgwsHFedkdEPFRY9c6IeDJv47/ANTl9JrBbfj4SuCDXYlcEiutfERHzgfmSngGGRcRsSc9L2goYBkyLiOfLyxwRE4AJkGb07t4ZMDOzonpqcMOAf3eSZwGwateLU5dZpNoYABHxWeA9wNqkptPPR8SY/LdhRJQC16tl25lfeL6o8HoRi38A/AL4ZUSMBj5Fmsm70voLC+ucDRwNfIxUozMzs15UT4B7gSVv1VXJJvTerbquAwZK+kwhbZX8eDXwmdxciKRN8mStXTUYeDw/P6rGdf4K7E1qIr26G/s2M7MuqKeJ8mZgf0nrRsRSQUzSxqQv9KWuNfWEiAhJBwKnSfoq8CypdvY14CKgDbgr97Z8lnTHla46CbhI0hxSYN2w4+wQEW9Iuh54MSIWdmPfZmbWBSpM7bb0QulIYHpEzJD0LlJniweBLwDjgC+TZvbehTT5aRuwTUTM6tli9325c8ldwAcjorOmXdZdaVAcMWLzni+YmVkT9NRsApKmRsTYSss6a6KcSK75RMTtpOtPbaRhAl/OeV7OrzcEPu7gBpI2Bf4D/LOW4GZmZo1X12DniDhH0o3A/5JmDliLNC/cbaROGA80vojLnoi4F9io2eUwM+vP6r6bR66R/L8eKIuZmVnD+HZVfcTI0aMY3977M96ambWqWgLcEEnr17PRiHiki+UxMzNriFoC3PH5r1ZR43bNzMx6TC2B6GXgxZ4uiJmZWSPVEuBOi4jv9nhJ+rnHZt7PlzbavtnFMDNrqJ4a/1aLLk14amZm1tc5wJmZWUtygDMzs5bkAGdmZi2pw04mEeEAaGZmy6SmBjBJIWl84fWXJZ3UyTrjJL278HpiYeburpZjtqSh3dlGYVtzG7EdMzPrnmbX0OYDB9UZXMYB7+4sUy2UNPscmJlZD2j2l/ubwAQq3LxZ0tqSLpZ0Z/7bUVIb8Gng/0maLmnnnH0XSbdIerBYm5P0lbzuDEkn57Q2SQ9IOg+4h7JZyiX9TdJUSbMkHVtInyvpB5LulnSbpGE5fUNJt0qaKen7hfzDJU3J5bynUFYzM+sFzQ5wAGcAR0gaXJb+M9Ig822Bg4GzI2I28KucPiYibsx5hwM7AfsCpwJI2hPYGNgOGANsI2mXnH9j4MyI2CwiHi7b7zERsQ0wFjhO0lo5fVXgtojYEpgCfLJQzrMiYjTwZGE7HwaujogxwJbA9PIDl3SspHZJ7a8tXND5mTIzs5o1/Z6REfFyrk0dB8wrLNoD2FRS6fXqkgZV2czfImIRcG+pZgXsmf+m5deDSIHtEeDhiKg2vP44SR/Iz9fL6zwPvEGa2BVgKvDe/HxHUgAG+D3wo/z8TuAcSSvk8i0V4CJiAqkGy7orDao+tbqZmdWt6QEuOx24Czi3kLYcsH1EvF7MWAh4RfOLWQqPp0TEr8vWbwNerbQRSeNIgXWHiHhN0mRgYF68ICJKQWghS567pYJTREzJNcb3AxMl/TQizqu0XzMza7y+0ERJRLwAXAh8vJB8DfD50gtJY/LTV4DVatjs1cAxpVqfpBGS1ulkncHAnBzcRpFmLe/MzcDh+fkRhfJuADwdEb8Bzga2rmFbZmbWIH0iwGXjgWJvyuOAsbmDyL2kziUAlwEfKOtkspSIuAb4I3CrpJnAX+g8MF4FLC/pPtK1vFruEno88Nm8jxGF9HHA3ZKmAYeRrtWZmVkv0eJWN2umdVcaFEeM2LzZxTAza6ienk1A0tSIGFtpWV+qwZmZmTWMA5yZmbWkvtKLst8bOXoU49ubNzGgmVmrcQ3OzMxakgOcmZm1JAc4MzNrSQ5wZmbWkjwOro/wODgzaxU9PfatyOPgzMys33GAMzOzluQAZ2ZmLckBzszMWtIyF+AkHSgp8nQ2Pb2vMZLeV3i9v6QTenq/ZmbWfctcgAM+BNyUH5cgqdG3HhsDvBXgImJSRJza4H2YmVkPWKYCXJ68dCfSxKiH57Rxkm6UNAm4V9Jyks6UdL+kayVdKemQnHcbSTdImirpaknDc/pkST+SdIekf0naWdKKwHeBw/Lcc4dJOlrSL/M6EyX9XNItkh4s7GOQpH9KukvSTEkH9P6ZMjOzZSrAAQcAV0XEv4DnJW2T07cGjo+ITYCDgDZgU+CjwA4AklYAfgEcEhHbAOcAPyhse/mI2A74AnBiRLwBfAe4ICLGRMQFFcoznBRw9yVNkArwOvCBiNga2A0YL0mVDkbSsZLaJbW/tnBBF06HmZlVs6zNJvAhFs+M/ef8+nLgjoh4KKfvBFwUEYuApyRdn9PfAWwOXJvjzQDgycK2L8mPU0kBshZ/y/u5V9KwnCbgh5J2ARaRZvkeBjxVvnJETAAmQBroXeM+zcysBstMgJO0JrA7MFpSkAJUAFcAr9ayCWBWROxQZfn8/LiQ2s/L/MLzUi3tCGBtYJuIWCBpNjCwxu2ZmVmDLEtNlIcAv4+IDSKiLSLWAx4Cdi7LdzNwcL4WNwwYl9MfANaW9FaTpaTNOtnnK8BqdZZzMPBMDm67ARvUub6ZmTXAshTgPgT8tSztYpbuTXkx8BhwL/AH4C7gpXxN7RDgR5LuBqYD7+5kn9cDm5Y6mdRYznUHd0YAABMaSURBVPOBsZJmAkcC99e4npmZNVBL3mxZ0qCImCtpLeAOYMeIWOoaWF/imy2bWavoKzdbXmauwdXpcklDgBWB7/X14GZmZo3XkgEuIsY1uwxmZtZcy9I1ODMzs5q1ZA1uWTRy9CjGt/deu7WZWatzDc7MzFqSA5yZmbUkBzgzM2tJLTkOblnkcXBmtqzrzfFvJR2Ng3MNzszMWpIDnJmZtSQHODMza0kOcGZm1pIc4MzMrCW1VICTtDBPbXO3pLskdTYdDpLm1pDnbEmbNqaUZmbWG1rtVl3zImIMgKS9gFOAXbu70Yj4RHe3YWZmvaulanBlVgfmlF5I+oqkOyXNkHRyeeY8A/iZku6XdK2kKyUdkpdNljQ2P59bWOcQSRPz84mSzpJ0m6QHJY2TdI6k+0p5zMys97RaDW5lSdOBgcBwYHcASXsCGwPbAQImSdolIqYU1j0IaAM2BdYB7gPOqXP/awA7APsDk4AdgU8Ad0oaExHTi5klHQscC7DagBXr3JWZmXWk1Wpw8yJiTESMAvYGzpMkYM/8Nw24CxhFCnhFOwEXRcSiPEHq9V3Y/2WRbg0zE3g6ImZGxCJgFil4LiEiJkTE2IgYu8qAFbqwOzMzq6bVanBviYhbJQ0F1ibV2k6JiF83YtOF5wPLls3Pj4sKz0uvW/Zcm5n1Ra1Wg3uLpFHAAOB54GrgGEmD8rIRktYpW+Vm4OB8LW4YMK7Kpp+W9E5JywEf6JnSm5lZd7VaraJ0DQ5Sre2oiFgIXCPpncCtqcWSucBHgGcK614MvAe4F3iU1JT5UoV9nABcDjwLtAODeuA4zMysmzybQIGkQRExV9JawB3Ajvl6XI/zbAJmtqzra7MJtFoNrrsulzQEWBH4Xm8FNzMzazwHuIKIGNfsMpiZWWO0bCcTMzPr31yD6yNGjh7F+Pbeb782M2tVrsGZmVlLcoAzM7OW5ABnZmYtyePg+giPgzOzZVEzxr4VdTQOzjU4MzNrSQ5wZmbWkhzgzMysJTnAmZlZS3KAMzOzltQyAU5Sm6R7ytJOkvTlZpXJzMyap2UCXK0k+fZkZmb9QL8IcJImSzpdUjtwvKSJkg4pLJ+bH5eTdKak+yVdK+nKUj5JsyUNzc/HSpqcn28n6VZJ0yTdIukdOX2KpDGFfdwkacveO2ozs/6tXwS4bMWIGBsR4zvIcxDQBmwKfBTYoYbt3g/sHBFbAd8BfpjTfwscDSBpE2BgRNxdXFHSsZLaJbW/tnBBPcdiZmadaKUAV+2WLKX0C2rYxk7ARRGxKE92en0N6wwGLsrX/04DNsvpFwH7SloBOAaYuFTBIibkoDt2lQEr1LArMzOrVSsFuOeBNcrS1gSey89fLaS/ST52ScuRZvDuzFvrAAML6d8Dro+IzYH9Sssi4jXgWuAA4FDg/FoPxMzMuq9lAlxEzAWelLQ7gKQ1gb2Bmypknw1sk5/vD5SqTzcDB+drccOAcVXWObiQPhh4PD8/umw/ZwM/B+6MiDl1HZCZmXVLywS47Ejg25KmA9cBJ0fEfyvk+w2wq6S7SdfZSrW7i4HHgHuBPwB3AS/lZScDP8sdVRYWtvVj4BRJ0yibQDYipgIvA+c24NjMzKwOnk2gjKRBETFX0lrAHcCO+XpcV7b1NmAyMCoiFnWU17MJmNmyqC/PJuAxYUu7XNIQ0nW573UjuB0J/AD4YmfBzczMGs8BrkxEjGvQds4DzmvEtszMrH6tdg3OzMwMcA2uzxg5ehTj25vblm1m1kpcgzMzs5bkXpR9hKRXgAeaXY4+bCiLB+3bknxuOubzU10rnJsNImLtSgvcRNl3PFCtq6uBpHafn8p8bjrm81Ndq58bN1GamVlLcoAzM7OW5ADXd0xodgH6OJ+f6nxuOubzU11Lnxt3MjEzs5bkGpyZmbUkBzgzM2tJDnB9gKS9JT0g6T+STmh2eZpJ0nqSrpd0r6RZko7P6WtKulbSv/Nj+eS2/YqkAZKmSbo8v95Q0u35M3SBpFom8W05koZI+ouk+yXdJ2kHf3YWk/T/8v/VPZL+JGlgK392HOCaTNIA4AxgH2BT4EOSNm1uqZrqTeBLEbEpsD3w2Xw+TgD+GREbA//Mr/uz44H7Cq9/BJwWEW8H5gAfb0qpmu9nwFURMQrYknSO/NkBJI0AjgPGRsTmwADgcFr4s+MA13zbAf+JiAcj4g3gz8ABTS5T00TEkxFxV37+CukLagTpnPwuZ/sdcGBzSth8kkYC7yfNGI8kAbsDf8lZ+uX5kTQY2AX4LUBEvBERL+LPTtHywMqSlgdWAZ6khT87DnDNNwJ4tPD6sZzW70lqA7YCbgeGRcSTedFTwLAmFasvOB34KlCaZ3At4MWIeDO/7q+foQ2BZ4Fzc/Pt2ZJWxZ8dACLiceAnwCOkwPYSMJUW/uw4wFmfJGkQcDHwhYh4ubgs0tiWfjm+RdK+wDMRMbXZZemDlge2Bs6KiK2AVylrjuznn501SLXZDYG3AasCeze1UD3MAa75HgfWK7wemdP6LUkrkILb+RFxSU5+WtLwvHw48EyzytdkOwL7S5pNas7enXTdaUhudoL++xl6DHgsIm7Pr/9CCnj+7CR7AA9FxLMRsQC4hPR5atnPjgNc890JbJx7Mq1Iuug7qcllapp8Pem3wH0R8dPCoknAUfn5UcClvV22viAivh4RIyOijfRZuS4ijgCuBw7J2frl+YmIp4BHJb0jJ70HuBd/dkoeAbaXtEr+Pyudn5b97PhOJn2ApPeRrqsMAM6JiB80uUhNI2kn4EZgJouvMX2DdB3uQmB94GHg0Ih4oSmF7CMkjQO+HBH7StqIVKNbE5gGfCQi5jezfM0gaQyp882KwIPAx0g/5P3ZASSdDBxG6q08DfgE6ZpbS352HODMzKwluYnSzMxakgOcmZm1JAc4MzNrSQ5wZmbWkhzgzMysJTnAmVlDSQpJk7u5jba8nYmNKVXvkDQxl7ut2WUxBzjrQfkfvZ6/o5td5u6SNK7Ccb0h6TFJF0naoZvbn53vYtI0faEM5QoBsfj3pqRnJF0lqSE3MJd0Ut72uEZsz3rW8p1nMeuykyukfQEYTLq91Itly6b3eIl6z8PAxPx8VdLUP4cAB0k6JCL+2qyC9YJ3Aq81ad8vkW6aALASsBmwL7CXpK9ExE96eP9fB06lhW53tSxzgLMeExEnlaflWtpg4PSImN3LRepNs8uPP99F4jvAeKBlA1xE3N/E3b9Y4bwfDvwJOFnSmRHRY8E3z1rwZKcZrVe4idL6DEnvyrMxP5Wb9R6V9GtJb6uQdxtJP5N0t6QXJL2eZ2weX2nGZklHl5pBJb1X0o2S5kp6VtK5kobkfFtJulzSnLx8UgOvp5yRHzeUtHahbIdKmiLpJUnzJM2U9HVJKxXyjJMUwAbABmVNcRPLjnVUvhb0aD6PT0v6Y+EejcW8b10zkvSpvO/X8zoTlOZYq6sMla7BSXqbpO9Iurnw/j6Ry9XTE/xeQJpZYBXSpMLFcu2Wj/NeSS/n83+PpBMlDSzLOxs4Mb+8vnj8hTxLXYMrNJ9OzM//LOm5fJ7blWaIWIqkwZJOV2refl1plvIvStqoyvs+TNJPJD0g6VVJL+bnE5Vu5dbvuAZnfYKkY4AJwHzSzXEfBTYm3StvP0nbR8QjhVU+CXwAuAH4B+nH2jbAF4F9JL0rT5habn9Sk9XlwK+AdwNHA22Svk6a8flG0g2fRwP7ARtJ2iIiFlXYXl2HWXge+bh/SGrWeg74IzCXNLv7D0nNanvmiXBnk5p8v5DXP72wrbeadiXtTbpL/ArAZcB/SHeIPwh4v6TdShPKlvkxsFde5xpgN9I5fjtpxgJqLUMVu5CmrrmeNFPEXNL7ewhpdoQdI+LuTrbRCAvKXn8NGAXcAlwBDCTdYf8kYJykPSJiYc57Omky0F1JE4POrnPfGwB3kO6R+XvSvR8PAy7N+7m+lDEH1+tIsyFMA84ntXx8E9i5fMOSVgFuBv4HuJb0Pirv8wDSzAoP1lneZV9E+M9/vfZH+lIIoK2QtgnwBunLeERZ/vcAC4G/lqVvAAyosP2P5+1/rSz96Jz+JrBrIX050hdCAC8AR5St99u87IAaj29czj+5wrLv5mX/za93yK8fAdYt5Fue9AUVwDcqnL/ZVfa9BjCHFCw3LVu2OSmo3FWWPrFQhvXLyjAlL9uu1jLk5UsdP7AOsFqFvFvmcv29LL0tb2dijee9lH+pcgEfycueAQaWLduIfE/esvTv5XUOK0s/KaePq1KO0vlsq1C2AE4sy79XTr+yLP3bOf1PxfKRptZ6tvzckH6IBXBahTKtWOnc94c/N1FaX/AZUo3j+EizDr8lIv5JqtHtJ2m1QvrDsfiXddE5wMukL45K/hQRNxS2s4j0axrgnog4vyz/eflxTK0Hk7Up9bg7SdL/SZpC+tJaBHw55zkmP34/0lQvpTK9CXwp5/1EHfs8EhhC+hK9t7ggIu4BfgNsVaVJ8LtRqCHnMpybX25XRxkqiohnokKNOlKt7TpgN6V5ALtrSOG8nyLpMtJ7+AbwqYh4vWz/D0aOAmVOy4/VPkdd8TDw/bL9X036cVF+jo8ivf9fL5YvIh5lyZpzuXnlCRHxRqVz3x+4idL6glLX+V0lbVth+TqkqYQ2AabCW5Oifoo0J9qmpOab4g+2EVX21V4h7Yn8WGmW7FLAHVmt8FVswOLrNW+SfnVfAoyPiFty+tb58brylSPiX5IeI12vGxwRL9Wwz9J53FLSSRWWb5If30maB6yo0nl5ND8udU2zKyS9H/g0MBYYytLfP0PpfgeNwSw+7yXzSTXwqyuUaVXgeFJz9ybAaizZlFztc9QV06v8KHuUxe8dklYnNTU+GpU7Yt1UIe0G0mf1BElbA1eSmiyr7bNfcICzvmCt/PiVTvINKjy/gPSl9CBpgsanSF9kkK4RrURllQLFmzUsq7d2cUNEjOskT6kDR7Uv9SdJc5gNqVK2cqXz+MlO8g2qkFY+ZAMWH/uAGvbdIUnHk2oec0hNwo+QhhIE6brWllR/z+rxcKTJYEuB4r2k+eEulLRDsWabfyRdR6o93UP6TD3L4ut0JzaoTCWVzjGk81z8cbZ6fny6Sv6l0iPiZUnbk66R7s/imudzks4ktRKUX39seQ5w1heUvrwHR8TLnWWWNJYU3P4B7JOb00rLlgO+2iOlbLzSca8L/LfC8uFl+Wrd3pYRMaM7BWskScuTrl09BWwdqSt9cXm3Br9Xkz9LF0t6ndSp6DxJ2xaa/A4gBbeJEfGxsjINZ+maYG8p/Q8Mq7K8YnpEPAZ8XJJIrRq7A58lDU1ZjtRE3q/4Gpz1Bbflx6V6h1Xx9vw4qRjcsu2AlRtSqp43LT+OK18g6e2kZtGHIqL4y38h1WtU9Z7HruqoDJUMJdVCb6kQ3AaxuKm2R0TEFcBVpF62Hy4sKn2OLqmw2q5VNldq7ut2rbaaHJgfBEao8hCVnTpZPyJiVkT8glSDhVRL7ncc4Kwv+CWpWeg0SZuUL5S0oqTil/bs/DiuLN86LB5rtiw4Jz9+S0uOixsA/IT0//nbsnWeB9aWVCmIn0tqBjtR0lIdQyQtp8bcYqqjMlTyDKk5cpsc0ErlWYF0R5uhDShTZ0q1l5NzjRKqf442An5UZTvP58f1G1m4Cs4jvf+n5BpZqWzrsXiYBoX0zSRVqtmV0pp1Z5mmchOlNV1E3J/HwZ0DzJJ0FfAv0nWv9Uk1kmdJ45UA7iRdQD9I0i2ki+7DSOPHHmBxp5E+LSJukfRjUpPqPZL+QhqQvA+pW/9NwP+VrfZPYFvgqtwzcz5wd0RcFhHPSzqEdJeU2yT9E5hFus61Hqkjw1qksV7dUbUMVY5zkaSfk8bBzZR0Kanr+m6ksWDX5+c9JiLa834PIA0l+TWLxwl+UdJoUo16fdI4ySuoHMSuJ/VuPEXS5qRrikTE9yvk7Y4fk2pdhwPvkHQN6ZrtoaThGwfmcpS8F/g/SbeS/neeIbUAHJDzlX+O+odmj1PwX//6o8I4uMKy0aRxRA+TvjRfIF38/zWwe1neNYEz8/ZeJ13D+iHpbhWzKRsPxeJxcEdX2O+4vOykCsvaqG88Vmlbk+s4J4eTgtkr+VhmkQb0DqyQd1XgLOAxUueEpcqWy/xL4N95ey8D95OGQxxYlndiB+9HxfPSWRkqHT/px/QXSb0355Gux/2e1Nt0qTJ04byX8s/uIM+WpC/7x0rnlhT4zyf1QJyXz/1Xc3mrjWf8CGlg+7ycJzo6n50dCzC5uI1C+hDg56QfbPPze/glUjN8kG53V8r7TuCnpN6wz+b8s0kDvN/drP/3Zv8pnxwzM1sGSPok6a4/n46IXze7PH2ZA5yZWR8k6W0R8URZ2vqk2v5wYIPy5bYkX4MzM+ubLs4dcaaSOg+1ka4PrkK6w4mDWydcgzMz64Mk/S/wUdJNqQeT7tk5DfhlRFQa2mBlHODMzKwleRycmZm1JAc4MzNrSQ5wZmbWkhzgzMysJTnAmZlZS/r/C17w40N63TsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Detailed analysis of some of the best teams"
      ],
      "metadata": {
        "id": "RGylfQRnPkHH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**France :**"
      ],
      "metadata": {
        "id": "i7kJekYychug"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "France = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['France'])).reshape(-1,4), columns = ['Nationality', 'Squad', 'Overall', 'Potential'])\n",
        "France.set_index('Nationality', inplace = False)\n",
        "France[['Overall', 'Potential']] = France[['Overall', 'Potential']].astype(float)\n",
        "France"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "f4BvsxF1cmO7",
        "outputId": "657ada71-3e07-47ad-bdf2-a532de7d5a82"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Nationality    Squad  Overall  Potential\n",
              "0      France    3-4-3    85.55      89.18\n",
              "1      France    4-4-2    85.73      88.73\n",
              "2      France  4-3-1-2    86.09      88.91\n",
              "3      France    4-3-3    85.64      89.09\n",
              "4      France  4-2-3-1    85.64      89.09"
            ],
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              "\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5881047a-217b-479f-b0fd-09f564a9e9aa button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5881047a-217b-479f-b0fd-09f564a9e9aa');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rating_4312_FR_Overall, best_list_4312_FR_Overall = get_best_squad_n(squad_4312_strict, 'France', 'overall')\n",
        "print('-Overall-')\n",
        "print('Average rating: {:.1f}'.format(rating_4312_FR_Overall))\n",
        "print(best_list_4312_FR_Overall)\n",
        "#------------------------------------------------------------------------------------------------------------\n",
        "rating_343_FR_Potential, best_list_343_FR_Potential = get_best_squad_n(squad_343_strict, 'France', 'potential')\n",
        "print('-Potential-')\n",
        "print('Average rating: {:.1f}'.format(rating_343_FR_Potential))\n",
        "print(best_list_343_FR_Potential)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJfx7e3UcoOq",
        "outputId": "31786ea2-8606-404a-ed05-fa62e6c299ea"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-Overall-\n",
            "Average rating: 86.1\n",
            "Position   short_name overall age           club_name   value_eur wage_eur\n",
            "      GK    H. Lloris      87  34   Tottenham Hotspur  13500000.0 125000.0\n",
            "     RWB   N. Mukiele      81  23          RB Leipzig  34500000.0  65000.0\n",
            "      CB    R. Varane      86  28   Manchester United  68500000.0 180000.0\n",
            "      CB  P. Kimpembe      83  25 Paris Saint-Germain  45500000.0  90000.0\n",
            "      LB     L. Digne      84  27             Everton  40500000.0 110000.0\n",
            "     CDM     N. Kanté      90  30             Chelsea 100000000.0 230000.0\n",
            "      CM     P. Pogba      87  28   Manchester United  79500000.0 220000.0\n",
            "     CAM     N. Fekir      84  27 Real Betis Balompié  45000000.0  42000.0\n",
            "      CF   K. Benzema      89  33      Real Madrid CF  66000000.0 350000.0\n",
            "      ST    K. Mbappé      91  22 Paris Saint-Germain 194000000.0 230000.0\n",
            "      ST A. Griezmann      85  30  Atlético de Madrid  53000000.0 220000.0\n",
            "-Potential-\n",
            "Average rating: 89.2\n",
            "Position   short_name potential age           club_name   value_eur wage_eur\n",
            "      GK   M. Maignan        89  25            AC Milan  49500000.0  45000.0\n",
            "      CB D. Upamecano        90  22   FC Bayern München  64000000.0  70000.0\n",
            "      CB    J. Koundé        89  22          Sevilla FC  53000000.0  33000.0\n",
            "      CB    R. Varane        88  28   Manchester United  68500000.0 180000.0\n",
            "     RWB   N. Mukiele        85  23          RB Leipzig  34500000.0  65000.0\n",
            "     CDM     N. Kanté        90  30             Chelsea 100000000.0 230000.0\n",
            "      CM E. Camavinga        89  18      Real Madrid CF  29500000.0  44000.0\n",
            "      LB T. Hernández        90  23            AC Milan  62500000.0  51000.0\n",
            "      RW   O. Dembélé        88  24        FC Barcelona  55000000.0 165000.0\n",
            "      ST    K. Mbappé        95  22 Paris Saint-Germain 194000000.0 230000.0\n",
            "      LW     M. Diaby        88  21 Bayer 04 Leverkusen  52500000.0  52000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**England :**"
      ],
      "metadata": {
        "id": "0x1D-krrnqL4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "England = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['England'])).reshape(-1,4), columns = ['nationality_name', 'Squad', 'overall', 'potential'])\n",
        "England.set_index('nationality_name', inplace = False)\n",
        "England[['overall', 'potential']] = England[['overall', 'potential']].astype(float)\n",
        "England"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "edOwXvKbnRe9",
        "outputId": "5c68161c-4ca3-4d64-84cc-4079a7e704a4"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  nationality_name    Squad  overall  potential\n",
              "0          England    3-4-3    84.91      88.27\n",
              "1          England    4-4-2    85.18      88.73\n",
              "2          England  4-3-1-2    84.55      88.27\n",
              "3          England    4-3-3    85.18      89.00\n",
              "4          England  4-2-3-1    85.09      88.82"
            ],
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>nationality_name</th>\n",
              "      <th>Squad</th>\n",
              "      <th>overall</th>\n",
              "      <th>potential</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>England</td>\n",
              "      <td>3-4-3</td>\n",
              "      <td>84.91</td>\n",
              "      <td>88.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>England</td>\n",
              "      <td>4-4-2</td>\n",
              "      <td>85.18</td>\n",
              "      <td>88.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>England</td>\n",
              "      <td>4-3-1-2</td>\n",
              "      <td>84.55</td>\n",
              "      <td>88.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>England</td>\n",
              "      <td>4-3-3</td>\n",
              "      <td>85.18</td>\n",
              "      <td>89.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>England</td>\n",
              "      <td>4-2-3-1</td>\n",
              "      <td>85.09</td>\n",
              "      <td>88.82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          const element = document.querySelector('#df-36ec922b-1d18-4b92-ba41-f64af619a92a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rating_433_ENG_Overall, best_list_433_ENG_Overall = get_best_squad_n(squad_433_strict, 'England', 'overall')\n",
        "print('-Overall-')\n",
        "print('Average rating: {:.1f}'.format(rating_433_ENG_Overall))\n",
        "print(best_list_433_ENG_Overall)\n",
        "\n",
        "rating_433_ENG_Potential, best_list_433_ENG_Potential = get_best_squad_n(squad_433_strict, 'England', 'potential')\n",
        "print('-Potential-')\n",
        "print('Average rating: {:.1f}'.format(rating_433_ENG_Potential))\n",
        "print(best_list_433_ENG_Potential)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFTzk2rNnnlC",
        "outputId": "bca54745-d0ce-4690-f218-ec5cd33bebda"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-Overall-\n",
            "Average rating: 85.2\n",
            "Position          short_name overall age         club_name   value_eur wage_eur\n",
            "      GK             N. Pope      83  29           Burnley  25000000.0  45000.0\n",
            "      RB T. Alexander-Arnold      87  22         Liverpool 114000000.0 150000.0\n",
            "      CB          H. Maguire      84  28 Manchester United  42500000.0 155000.0\n",
            "      CB           J. Stones      83  27   Manchester City  41000000.0 140000.0\n",
            "      LB             L. Shaw      84  25 Manchester United  48500000.0 140000.0\n",
            "     CDM        J. Henderson      84  31         Liverpool  29500000.0 140000.0\n",
            "     CAM            P. Foden      84  21   Manchester City  94500000.0 125000.0\n",
            "     CAM            M. Mount      83  22           Chelsea  58500000.0 120000.0\n",
            "      RM           J. Sancho      87  21 Manchester United 116500000.0 150000.0\n",
            "      ST             H. Kane      90  27 Tottenham Hotspur 129500000.0 240000.0\n",
            "      LW         R. Sterling      88  26   Manchester City 107500000.0 290000.0\n",
            "-Potential-\n",
            "Average rating: 89.0\n",
            "Position          short_name potential age         club_name   value_eur wage_eur\n",
            "      GK        D. Henderson        87  24 Manchester United  36000000.0  70000.0\n",
            "      RB T. Alexander-Arnold        92  22         Liverpool 114000000.0 150000.0\n",
            "      CB            J. Gomez        88  24         Liverpool  43500000.0 100000.0\n",
            "      CB          H. Maguire        86  28 Manchester United  42500000.0 155000.0\n",
            "      LB             L. Shaw        86  25 Manchester United  48500000.0 140000.0\n",
            "      CM       J. Bellingham        89  18 Borussia Dortmund  37000000.0  21000.0\n",
            "     CAM            P. Foden        92  21   Manchester City  94500000.0 125000.0\n",
            "     CAM            M. Mount        89  22           Chelsea  58500000.0 120000.0\n",
            "      RM           J. Sancho        91  21 Manchester United 116500000.0 150000.0\n",
            "      ST             H. Kane        90  27 Tottenham Hotspur 129500000.0 240000.0\n",
            "      LW         R. Sterling        89  26   Manchester City 107500000.0 290000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Brazil :**"
      ],
      "metadata": {
        "id": "8ULDbP1Vb89_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Brazil = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Brazil'])).reshape(-1,4), columns = ['nationality_name', 'Squad', 'overall', 'potential'])\n",
        "Brazil.set_index('nationality_name', inplace = False)\n",
        "Brazil[['overall', 'potential']] = Brazil[['overall', 'potential']].astype(float)\n",
        "Brazil"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zKn-mW_Ib8O1",
        "outputId": "1fd48aab-3a04-4460-fc8b-bddf7c581f53"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  nationality_name    Squad  overall  potential\n",
              "0           Brazil    3-4-3    85.64      88.00\n",
              "1           Brazil    4-4-2    84.27      87.64\n",
              "2           Brazil  4-3-1-2    84.91      87.55\n",
              "3           Brazil    4-3-3    85.64      88.09\n",
              "4           Brazil  4-2-3-1    85.55      88.09"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>0</th>\n",
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              "      <td>3-4-3</td>\n",
              "      <td>85.64</td>\n",
              "      <td>88.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>4-4-2</td>\n",
              "      <td>84.27</td>\n",
              "      <td>87.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Brazil</td>\n",
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              "      <td>84.91</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>4-3-3</td>\n",
              "      <td>85.64</td>\n",
              "      <td>88.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>4-2-3-1</td>\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-77459df7-3f84-4368-a355-c6c896e65d13 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-77459df7-3f84-4368-a355-c6c896e65d13');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rating_433_BRA_Overall, best_list_433_BRA_Overall = get_best_squad_n(squad_433_strict, 'Brazil', 'overall')\n",
        "print('-Overall-')\n",
        "print('Average rating: {:.1f}'.format(rating_433_BRA_Overall))\n",
        "print(best_list_433_BRA_Overall)\n",
        "\n",
        "rating_4231_BRA_Potential, best_list_4231_BRA_Potential = get_best_squad_n(squad_4231_strict, 'Brazil', 'potential')\n",
        "print('-Potential-')\n",
        "print('Average rating: {:.1f}'.format(rating_4231_BRA_Potential))\n",
        "print(best_list_4231_BRA_Potential)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLu9bCSMcCTP",
        "outputId": "44d83284-2b52-4187-ffdb-ff9335252d44"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-Overall-\n",
            "Average rating: 85.6\n",
            "Position      short_name overall age           club_name   value_eur wage_eur\n",
            "      GK         Ederson      89  27     Manchester City  94000000.0 200000.0\n",
            "      RB          Danilo      81  29            Juventus  22500000.0  83000.0\n",
            "      CB      Marquinhos      87  27 Paris Saint-Germain  90500000.0 135000.0\n",
            "      CB    Thiago Silva      85  36             Chelsea   9500000.0 105000.0\n",
            "      LB     Alex Sandro      83  30            Juventus  31500000.0  95000.0\n",
            "     CDM        Casemiro      89  29      Real Madrid CF  88000000.0 310000.0\n",
            "     CDM         Fabinho      86  27           Liverpool  73500000.0 165000.0\n",
            "     CDM        Fernando      84  33          Sevilla FC  21000000.0  43000.0\n",
            "      RM        Raphinha      82  24        Leeds United  46000000.0  89000.0\n",
            "      CF Roberto Firmino      85  29           Liverpool  54000000.0 185000.0\n",
            "      LW       Neymar Jr      91  29 Paris Saint-Germain 129000000.0 270000.0\n",
            "-Potential-\n",
            "Average rating: 88.1\n",
            "Position    short_name potential age           club_name   value_eur wage_eur\n",
            "      GK       Ederson        91  27     Manchester City  94000000.0 200000.0\n",
            "      RB       Emerson        84  22   Tottenham Hotspur  25000000.0  70000.0\n",
            "      CB    Marquinhos        90  27 Paris Saint-Germain  90500000.0 135000.0\n",
            "      CB  Éder Militão        89  23      Real Madrid CF  56500000.0 130000.0\n",
            "      LB    Renan Lodi        86  23  Atlético de Madrid  36500000.0  49000.0\n",
            "     CDM      Casemiro        89  29      Real Madrid CF  88000000.0 310000.0\n",
            "     CDM       Fabinho        88  27           Liverpool  73500000.0 165000.0\n",
            "      RW       Rodrygo        88  20      Real Madrid CF  38500000.0 115000.0\n",
            "     CAM Matheus Cunha        86  22  Atlético de Madrid  35500000.0  48000.0\n",
            "      LW     Neymar Jr        91  29 Paris Saint-Germain 129000000.0 270000.0\n",
            "      ST Gabriel Jesus        87  24     Manchester City  52500000.0 150000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Spain :**"
      ],
      "metadata": {
        "id": "MwuELULccf3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Spain = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Spain'])).reshape(-1,4), columns = ['nationality_name', 'Squad', 'overall', 'potential'])\n",
        "Spain.set_index('nationality_name', inplace = False)\n",
        "Spain[['overall', 'potential']] = Spain[['overall', 'potential']].astype(float)\n",
        "Spain "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CSNrdIqFcEmM",
        "outputId": "ced6c2d1-ce20-40c1-a57d-57cf8a6b05a9"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  nationality_name    Squad  overall  potential\n",
              "0            Spain    3-4-3    85.36      88.27\n",
              "1            Spain    4-4-2    84.82      87.09\n",
              "2            Spain  4-3-1-2    85.64      87.55\n",
              "3            Spain    4-3-3    85.55      88.36\n",
              "4            Spain  4-2-3-1    85.45      88.18"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nationality_name</th>\n",
              "      <th>Squad</th>\n",
              "      <th>overall</th>\n",
              "      <th>potential</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Spain</td>\n",
              "      <td>3-4-3</td>\n",
              "      <td>85.36</td>\n",
              "      <td>88.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Spain</td>\n",
              "      <td>4-4-2</td>\n",
              "      <td>84.82</td>\n",
              "      <td>87.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Spain</td>\n",
              "      <td>4-3-1-2</td>\n",
              "      <td>85.64</td>\n",
              "      <td>87.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Spain</td>\n",
              "      <td>4-3-3</td>\n",
              "      <td>85.55</td>\n",
              "      <td>88.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Spain</td>\n",
              "      <td>4-2-3-1</td>\n",
              "      <td>85.45</td>\n",
              "      <td>88.18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-4684d2d7-0614-4d31-a4da-041fba7bbee9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rating_4312_GER_Overall, best_list_4312_GER_Overall = get_best_squad_n(squad_4312_strict, 'Spain', 'overall')\n",
        "print('-Overall-')\n",
        "print('Average rating: {:.1f}'.format(rating_4312_GER_Overall))\n",
        "print(best_list_4312_GER_Overall)\n",
        "#------------------------------------------------------------------------------------------------------------\n",
        "rating_433_GER_Potential, best_list_433_GER_Potential = get_best_squad_n(squad_433_strict, 'Spain', 'potential')\n",
        "print('-Potential-')\n",
        "print('Average rating: {:.1f}'.format(rating_433_GER_Potential))\n",
        "print(best_list_433_GER_Potential)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8WZbJhRcZDu",
        "outputId": "8328c372-5f42-446d-a594-c8833b99e85d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-Overall-\n",
            "Average rating: 85.6\n",
            "Position      short_name overall age           club_name  value_eur wage_eur\n",
            "      GK          De Gea      84  30   Manchester United 28000000.0 120000.0\n",
            "      RB        Carvajal      85  29      Real Madrid CF 47500000.0 210000.0\n",
            "      CB    Sergio Ramos      88  35 Paris Saint-Germain 24000000.0 115000.0\n",
            "      CB      A. Laporte      86  27     Manchester City 77000000.0 185000.0\n",
            "      LB      Jordi Alba      86  32        FC Barcelona 47000000.0 200000.0\n",
            "      CM          Thiago      86  30           Liverpool 65000000.0 180000.0\n",
            "     CDM Sergio Busquets      86  32        FC Barcelona 45000000.0 200000.0\n",
            "      CM          Parejo      86  32       Villarreal CF 53500000.0  64000.0\n",
            "     CAM     David Silva      85  35       Real Sociedad 22000000.0  44000.0\n",
            "      ST   Gerard Moreno      86  29       Villarreal CF 68000000.0  73000.0\n",
            "      ST      Iago Aspas      84  33    RC Celta de Vigo 27000000.0  46000.0\n",
            "-Potential-\n",
            "Average rating: 88.4\n",
            "Position      short_name potential age               club_name  value_eur wage_eur\n",
            "      GK      Unai Simón        86  24 Athletic Club de Bilbao 34500000.0  24000.0\n",
            "     RWB     Pedro Porro        87  21             Sporting CP 40500000.0  80000.0\n",
            "      CB      A. Laporte        89  27         Manchester City 77000000.0 185000.0\n",
            "      CB      Pau Torres        89  24           Villarreal CF 56500000.0  35000.0\n",
            "      LB      Jordi Alba        86  32            FC Barcelona 47000000.0 200000.0\n",
            "      CM           Pedri        91  18            FC Barcelona 54000000.0  51000.0\n",
            "      CM Marcos Llorente        89  26      Atlético de Madrid 88000000.0  95000.0\n",
            "     CDM           Rodri        89  25         Manchester City 81000000.0 175000.0\n",
            "      RW   Ferran Torres        90  21         Manchester City 68500000.0 120000.0\n",
            "      ST   Gerard Moreno        86  29           Villarreal CF 68000000.0  73000.0\n",
            "      LW       Ansu Fati        90  18            FC Barcelona 17500000.0  44000.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Argentina :**"
      ],
      "metadata": {
        "id": "fJ2BM3CFcsAq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Argentina = pd.DataFrame(np.array(get_summary_n(squad_list, squad_name, ['Argentina'])).reshape(-1,4), columns = ['nationality_name', 'Squad', 'overall', 'potential'])\n",
        "Argentina.set_index('nationality_name', inplace = False)\n",
        "Argentina[['overall', 'potential']] = Argentina[['overall', 'potential']].astype(float)\n",
        "Argentina"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fo0FPTWuczLP",
        "outputId": "091fde7a-7d2d-4a9f-e168-d8d010e5824d"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  nationality_name    Squad  overall  potential\n",
              "0        Argentina    3-4-3    83.18      86.00\n",
              "1        Argentina    4-4-2    82.45      85.64\n",
              "2        Argentina  4-3-1-2    83.36      85.91\n",
              "3        Argentina    4-3-3    83.73      86.18\n",
              "4        Argentina  4-2-3-1    83.73      86.09"
            ],
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              "\n",
              "  <div id=\"df-494a16a5-1f73-4543-bdff-2a36775cb648\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>nationality_name</th>\n",
              "      <th>Squad</th>\n",
              "      <th>overall</th>\n",
              "      <th>potential</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>3-4-3</td>\n",
              "      <td>83.18</td>\n",
              "      <td>86.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>4-4-2</td>\n",
              "      <td>82.45</td>\n",
              "      <td>85.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>4-3-1-2</td>\n",
              "      <td>83.36</td>\n",
              "      <td>85.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>4-3-3</td>\n",
              "      <td>83.73</td>\n",
              "      <td>86.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>4-2-3-1</td>\n",
              "      <td>83.73</td>\n",
              "      <td>86.09</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-494a16a5-1f73-4543-bdff-2a36775cb648')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rating_433_ARG_Overall, best_list_433_ARG_Overall = get_best_squad_n(squad_433_strict, 'Argentina', 'overall')\n",
        "print('-Overall-')\n",
        "print('Average rating: {:.1f}'.format(rating_433_ARG_Overall))\n",
        "print(best_list_433_ARG_Overall)\n",
        "\n",
        "rating_433_ARG_Potential, best_list_433_ARG_Potential = get_best_squad_n(squad_433_strict, 'Argentina', 'potential')\n",
        "print('-Potential-')\n",
        "print('Average rating: {:.1f}'.format(rating_433_ARG_Potential))\n",
        "print(best_list_433_ARG_Potential)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fOQypMCUc3Vx",
        "outputId": "cc68dda0-6beb-4ebb-8fd8-5fd772c97195"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-Overall-\n",
            "Average rating: 83.7\n",
            "Position  short_name overall age           club_name  value_eur wage_eur\n",
            "      GK E. Martínez      84  28         Aston Villa 33500000.0  81000.0\n",
            "      RB  G. Montiel      78  24          Sevilla FC 21000000.0  23000.0\n",
            "      CB   C. Romero      82  23   Tottenham Hotspur 41500000.0  51000.0\n",
            "      CB N. Otamendi      81  33          SL Benfica 12500000.0  18000.0\n",
            "      LB    M. Acuña      84  29          Sevilla FC 37000000.0  45000.0\n",
            "     CDM   É. Banega      82  33           Al Shabab 15000000.0  46000.0\n",
            "     CAM    A. Gómez      85  33          Sevilla FC 33500000.0  51000.0\n",
            "      CM  R. De Paul      82  27  Atlético de Madrid 35000000.0  68000.0\n",
            "      RW    L. Messi      93  34 Paris Saint-Germain 78000000.0 320000.0\n",
            "      ST   S. Agüero      87  33        FC Barcelona 51000000.0 260000.0\n",
            "      LW  L. Ocampos      83  26          Sevilla FC 39500000.0  46000.0\n",
            "-Potential-\n",
            "Average rating: 86.2\n",
            "Position  short_name potential age                   club_name  value_eur wage_eur\n",
            "      GK E. Martínez        85  28                 Aston Villa 33500000.0  81000.0\n",
            "      RB  G. Montiel        84  24                  Sevilla FC 21000000.0  23000.0\n",
            "      CB   C. Romero        87  23           Tottenham Hotspur 41500000.0  51000.0\n",
            "      CB   M. Senesi        85  24                   Feyenoord 25000000.0  12000.0\n",
            "      LB    M. Acuña        84  29                  Sevilla FC 37000000.0  45000.0\n",
            "      CM G. Lo Celso        85  25           Tottenham Hotspur 35500000.0  95000.0\n",
            "     CAM   T. Almada        86  20             Vélez Sarsfield 10000000.0  10000.0\n",
            "     CAM    A. Gómez        85  33                  Sevilla FC 33500000.0  51000.0\n",
            "      RW    L. Messi        93  34         Paris Saint-Germain 78000000.0 320000.0\n",
            "      ST L. Martínez        89  23                       Inter 78000000.0 145000.0\n",
            "      LM  A. Velasco        85  18 Club Atlético Independiente  7000000.0   4000.0\n"
          ]
        }
      ]
    }
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