Ayudantías/Ayudantía 3/solución_A2.ipynb

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        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
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        "df = pd.read_csv('data (1).csv')\n",
        "df.head()"
      ],
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              "       league  season                 date           team h_a result  pts  \\\n",
              "0  Bundesliga    2014  2014-08-22 19:30:00  Bayern Munich   h      w    3   \n",
              "1  Bundesliga    2014  2014-08-30 17:30:00  Bayern Munich   a      d    1   \n",
              "2  Bundesliga    2014  2014-09-13 14:30:00  Bayern Munich   h      w    3   \n",
              "3  Bundesliga    2014  2014-09-20 14:30:00  Bayern Munich   a      d    1   \n",
              "4  Bundesliga    2014  2014-09-23 19:00:00  Bayern Munich   h      w    3   \n",
              "\n",
              "   goals_scored  goals_missed  deep_passes  deep_passes_allowed      ppda  \\\n",
              "0             2             1            5                    4  9.625000   \n",
              "1             1             1           10                    1  4.756098   \n",
              "2             2             0           13                    3  5.060606   \n",
              "3             0             0            6                    2  4.423077   \n",
              "4             4             0           23                    2  4.250000   \n",
              "\n",
              "       oppda  \n",
              "0  21.850000  \n",
              "1  17.695652  \n",
              "2  16.961538  \n",
              "3   9.446809  \n",
              "4  44.800000  "
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        "df['league'].value_counts()"
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          "data": {
            "text/plain": [
              "EPL           4560\n",
              "La_liga       4560\n",
              "Serie_A       4550\n",
              "Ligue_1       4358\n",
              "Bundesliga    3672\n",
              "RFPL          2880\n",
              "Name: league, dtype: int64"
            ]
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      "source": [
        "ligas = ['La_liga', 'EPL']\n",
        "df_ligas = df[df['league'].isin(ligas)]\n",
        "df_ligas.head()"
      ],
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              "     league  season                 date         team h_a result  pts  \\\n",
              "3672    EPL    2014  2014-08-16 15:00:00  Aston Villa   a      w    3   \n",
              "3673    EPL    2014  2014-08-23 12:45:00  Aston Villa   h      d    1   \n",
              "3674    EPL    2014  2014-08-31 13:30:00  Aston Villa   h      w    3   \n",
              "3675    EPL    2014  2014-09-13 17:30:00  Aston Villa   a      w    3   \n",
              "3676    EPL    2014  2014-09-20 15:00:00  Aston Villa   h      l    0   \n",
              "\n",
              "      goals_scored  goals_missed  deep_passes  deep_passes_allowed       ppda  \\\n",
              "3672             1             0            4                    3  14.043478   \n",
              "3673             0             0            4                    7  15.523810   \n",
              "3674             2             1            6                    7  28.153846   \n",
              "3675             1             0            1                    5  54.000000   \n",
              "3676             0             3            0                    7  44.250000   \n",
              "\n",
              "          oppda  \n",
              "3672   4.125000  \n",
              "3673   8.571429  \n",
              "3674  11.583333  \n",
              "3675   6.500000  \n",
              "3676   7.727273  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_agrupados = df_ligas.groupby(['season', 'team', 'league'])"
      ],
      "metadata": {
        "id": "KfrLZt6bsWOB"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_agrupados.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 424
        },
        "id": "tBHzM5VnyqTz",
        "outputId": "64a2a1a2-a4fe-45b8-d022-76fd61483e8a"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        league  season                 date         team h_a result  pts  \\\n",
              "3672       EPL    2014  2014-08-16 15:00:00  Aston Villa   a      w    3   \n",
              "3673       EPL    2014  2014-08-23 12:45:00  Aston Villa   h      d    1   \n",
              "3674       EPL    2014  2014-08-31 13:30:00  Aston Villa   h      w    3   \n",
              "3675       EPL    2014  2014-09-13 17:30:00  Aston Villa   a      w    3   \n",
              "3676       EPL    2014  2014-09-20 15:00:00  Aston Villa   h      l    0   \n",
              "...        ...     ...                  ...          ...  ..    ...  ...   \n",
              "12754  La_liga    2019  2019-08-17 19:00:00     Mallorca   h      w    3   \n",
              "12755  La_liga    2019  2019-08-25 16:00:00     Mallorca   h      l    0   \n",
              "12756  La_liga    2019  2019-09-01 16:00:00     Mallorca   a      l    0   \n",
              "12757  La_liga    2019  2019-09-13 19:00:00     Mallorca   h      d    1   \n",
              "12758  La_liga    2019  2019-09-22 10:00:00     Mallorca   a      l    0   \n",
              "\n",
              "       goals_scored  goals_missed  deep_passes  deep_passes_allowed  \\\n",
              "3672              1             0            4                    3   \n",
              "3673              0             0            4                    7   \n",
              "3674              2             1            6                    7   \n",
              "3675              1             0            1                    5   \n",
              "3676              0             3            0                    7   \n",
              "...             ...           ...          ...                  ...   \n",
              "12754             2             1            3                    6   \n",
              "12755             0             1            6                    7   \n",
              "12756             0             2            7                    5   \n",
              "12757             0             0            5                    3   \n",
              "12758             2             4            1                   13   \n",
              "\n",
              "            ppda      oppda  \n",
              "3672   14.043478   4.125000  \n",
              "3673   15.523810   8.571429  \n",
              "3674   28.153846  11.583333  \n",
              "3675   54.000000   6.500000  \n",
              "3676   44.250000   7.727273  \n",
              "...          ...        ...  \n",
              "12754  15.176471   6.954545  \n",
              "12755  16.714286   8.176471  \n",
              "12756  10.040000  13.421053  \n",
              "12757   9.421053   5.833333  \n",
              "12758   5.166667   6.850000  \n",
              "\n",
              "[1200 rows x 13 columns]"
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              "      <td>2019</td>\n",
              "      <td>2019-09-22 10:00:00</td>\n",
              "      <td>Mallorca</td>\n",
              "      <td>a</td>\n",
              "      <td>l</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>5.166667</td>\n",
              "      <td>6.850000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1200 rows × 13 columns</p>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def count_if_equal(series, value):\n",
        "    return (series == value).sum()"
      ],
      "metadata": {
        "id": "79SZKnl8yvjb"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict_valores = {\n",
        "    'points': df_agrupados['pts'].apply(np.sum),\n",
        "    'wins': df_agrupados['result'].apply(lambda x: count_if_equal(x, 'w')),\n",
        "    'draws':df_agrupados['result'].apply(lambda x: count_if_equal(x, 'd')),\n",
        "    'defeats': df_agrupados['result'].apply(lambda x: count_if_equal(x, 'l')),\n",
        "    'goals_scored': df_agrupados['goals_scored'].apply(np.sum),\n",
        "    'goals_missed': df_agrupados['goals_missed'].apply(np.sum),\n",
        "    'deep_passes_avg': df_agrupados['deep_passes'].apply(np.mean),\n",
        "    'deep_passes_allowed_avg': df_agrupados['deep_passes_allowed'].apply(np.mean),\n",
        "    'ppda_avg': df_agrupados['ppda'].apply(np.mean),\n",
        "    'oppda_avg': df_agrupados['oppda'].apply(np.mean)\n",
        "}\n",
        "df_final = pd.DataFrame.from_dict(dict_valores)\n",
        "\n",
        "df_final.sort_values(by=['season', 'points', 'goals_scored'], ascending=[True, False, False], inplace=True)\n",
        "\n",
        "df_final['position'] = df_final.groupby(['season', 'league'])['points'].rank(ascending=False, method='first')\n",
        "df_final['position'] = df_final['position'].astype(int)\n",
        "df_final.head(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Y-1wWd7md0Uz",
        "outputId": "8c6d3cad-8c85-46eb-ccfd-44aa7d26ac6f"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     points  wins  draws  defeats  \\\n",
              "season team                 league                                  \n",
              "2014   Barcelona            La_liga      94    30      4        4   \n",
              "       Real Madrid          La_liga      92    30      2        6   \n",
              "       Chelsea              EPL          87    26      9        3   \n",
              "       Manchester City      EPL          79    24      7        7   \n",
              "       Atletico Madrid      La_liga      78    23      9        6   \n",
              "       Valencia             La_liga      77    22     11        5   \n",
              "       Sevilla              La_liga      76    23      7        8   \n",
              "       Arsenal              EPL          75    22      9        7   \n",
              "       Manchester United    EPL          70    20     10        8   \n",
              "       Tottenham            EPL          64    19      7       12   \n",
              "       Liverpool            EPL          62    18      8       12   \n",
              "       Southampton          EPL          60    18      6       14   \n",
              "       Villarreal           La_liga      60    16     12       10   \n",
              "       Swansea              EPL          56    16      8       14   \n",
              "       Athletic Club        La_liga      55    15     10       13   \n",
              "       Stoke                EPL          54    15      9       14   \n",
              "       Celta Vigo           La_liga      51    13     12       13   \n",
              "       Malaga               La_liga      50    14      8       16   \n",
              "       Espanyol             La_liga      49    13     10       15   \n",
              "       Rayo Vallecano       La_liga      49    15      4       19   \n",
              "       Crystal Palace       EPL          48    13      9       16   \n",
              "       Everton              EPL          47    12     11       15   \n",
              "       West Ham             EPL          47    12     11       15   \n",
              "       Real Sociedad        La_liga      46    11     13       14   \n",
              "       West Bromwich Albion EPL          44    11     11       16   \n",
              "       Leicester            EPL          41    11      8       19   \n",
              "       Elche                La_liga      41    11      8       19   \n",
              "       Newcastle United     EPL          39    10      9       19   \n",
              "       Aston Villa          EPL          38    10      8       20   \n",
              "       Sunderland           EPL          38     7     17       14   \n",
              "\n",
              "                                     goals_scored  goals_missed  \\\n",
              "season team                 league                                \n",
              "2014   Barcelona            La_liga           110            21   \n",
              "       Real Madrid          La_liga           118            38   \n",
              "       Chelsea              EPL                73            32   \n",
              "       Manchester City      EPL                83            38   \n",
              "       Atletico Madrid      La_liga            67            29   \n",
              "       Valencia             La_liga            70            32   \n",
              "       Sevilla              La_liga            71            45   \n",
              "       Arsenal              EPL                71            36   \n",
              "       Manchester United    EPL                62            37   \n",
              "       Tottenham            EPL                58            53   \n",
              "       Liverpool            EPL                52            48   \n",
              "       Southampton          EPL                54            33   \n",
              "       Villarreal           La_liga            48            37   \n",
              "       Swansea              EPL                46            49   \n",
              "       Athletic Club        La_liga            42            41   \n",
              "       Stoke                EPL                48            45   \n",
              "       Celta Vigo           La_liga            47            44   \n",
              "       Malaga               La_liga            42            48   \n",
              "       Espanyol             La_liga            47            51   \n",
              "       Rayo Vallecano       La_liga            46            68   \n",
              "       Crystal Palace       EPL                47            51   \n",
              "       Everton              EPL                48            50   \n",
              "       West Ham             EPL                44            47   \n",
              "       Real Sociedad        La_liga            44            51   \n",
              "       West Bromwich Albion EPL                38            51   \n",
              "       Leicester            EPL                46            55   \n",
              "       Elche                La_liga            35            62   \n",
              "       Newcastle United     EPL                40            63   \n",
              "       Aston Villa          EPL                31            57   \n",
              "       Sunderland           EPL                31            53   \n",
              "\n",
              "                                     deep_passes_avg  deep_passes_allowed_avg  \\\n",
              "season team                 league                                              \n",
              "2014   Barcelona            La_liga        12.868421                 3.000000   \n",
              "       Real Madrid          La_liga         9.236842                 4.026316   \n",
              "       Chelsea              EPL            10.710526                 4.500000   \n",
              "       Manchester City      EPL            15.131579                 3.789474   \n",
              "       Atletico Madrid      La_liga         5.184211                 3.236842   \n",
              "       Valencia             La_liga         5.342105                 4.526316   \n",
              "       Sevilla              La_liga         8.026316                 4.421053   \n",
              "       Arsenal              EPL            10.473684                 4.500000   \n",
              "       Manchester United    EPL             7.026316                 5.105263   \n",
              "       Tottenham            EPL             5.526316                 6.105263   \n",
              "       Liverpool            EPL             8.052632                 5.289474   \n",
              "       Southampton          EPL             7.105263                 4.815789   \n",
              "       Villarreal           La_liga         6.368421                 4.500000   \n",
              "       Swansea              EPL             4.105263                 8.157895   \n",
              "       Athletic Club        La_liga         4.815789                 4.500000   \n",
              "       Stoke                EPL             5.131579                 6.289474   \n",
              "       Celta Vigo           La_liga         7.552632                 5.447368   \n",
              "       Malaga               La_liga         4.842105                 4.842105   \n",
              "       Espanyol             La_liga         4.552632                 5.394737   \n",
              "       Rayo Vallecano       La_liga         3.868421                 5.763158   \n",
              "       Crystal Palace       EPL             4.736842                 6.789474   \n",
              "       Everton              EPL             6.815789                 6.447368   \n",
              "       West Ham             EPL             5.710526                 8.315789   \n",
              "       Real Sociedad        La_liga         3.842105                 4.421053   \n",
              "       West Bromwich Albion EPL             4.236842                 7.447368   \n",
              "       Leicester            EPL             5.026316                 7.552632   \n",
              "       Elche                La_liga         2.815789                 5.684211   \n",
              "       Newcastle United     EPL             4.605263                 6.289474   \n",
              "       Aston Villa          EPL             4.842105                 6.236842   \n",
              "       Sunderland           EPL             4.026316                 8.236842   \n",
              "\n",
              "                                      ppda_avg  oppda_avg  position  \n",
              "season team                 league                                   \n",
              "2014   Barcelona            La_liga   5.683535  16.367593         1  \n",
              "       Real Madrid          La_liga  10.209085  12.929510         2  \n",
              "       Chelsea              EPL      10.942027  13.419520         1  \n",
              "       Manchester City      EPL       7.982490  15.076979         2  \n",
              "       Atletico Madrid      La_liga   8.982028   9.237091         3  \n",
              "       Valencia             La_liga   8.709827   7.870225         4  \n",
              "       Sevilla              La_liga   8.276148   9.477805         5  \n",
              "       Arsenal              EPL       8.660596  13.254169         3  \n",
              "       Manchester United    EPL       7.648715  15.521977         4  \n",
              "       Tottenham            EPL       8.000237  11.300694         5  \n",
              "       Liverpool            EPL       9.913851  12.121981         6  \n",
              "       Southampton          EPL       9.259540  14.879479         7  \n",
              "       Villarreal           La_liga  10.072085   8.679660         6  \n",
              "       Swansea              EPL      12.826741  11.455201         8  \n",
              "       Athletic Club        La_liga   7.462406   9.403965         7  \n",
              "       Stoke                EPL      10.405753  10.601440         9  \n",
              "       Celta Vigo           La_liga   6.056173  10.882769         8  \n",
              "       Malaga               La_liga   7.792069   7.019068         9  \n",
              "       Espanyol             La_liga   9.381754   7.158217        10  \n",
              "       Rayo Vallecano       La_liga   6.157978   9.735109        11  \n",
              "       Crystal Palace       EPL      11.577051   6.582148        10  \n",
              "       Everton              EPL      10.432774  11.682677        11  \n",
              "       West Ham             EPL      12.042643   8.433845        12  \n",
              "       Real Sociedad        La_liga   8.614235   8.829567        12  \n",
              "       West Bromwich Albion EPL      13.545803   9.869703        13  \n",
              "       Leicester            EPL      10.229308   8.019074        14  \n",
              "       Elche                La_liga   9.517572   8.623287        13  \n",
              "       Newcastle United     EPL      10.422385   8.840574        15  \n",
              "       Aston Villa          EPL      16.386892  11.065619        16  \n",
              "       Sunderland           EPL      11.421301   8.129492        17  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-90f631fd-6c77-4fea-911f-625363e76ae3\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>points</th>\n",
              "      <th>wins</th>\n",
              "      <th>draws</th>\n",
              "      <th>defeats</th>\n",
              "      <th>goals_scored</th>\n",
              "      <th>goals_missed</th>\n",
              "      <th>deep_passes_avg</th>\n",
              "      <th>deep_passes_allowed_avg</th>\n",
              "      <th>ppda_avg</th>\n",
              "      <th>oppda_avg</th>\n",
              "      <th>position</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <th>team</th>\n",
              "      <th>league</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"30\" valign=\"top\">2014</th>\n",
              "      <th>Barcelona</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>94</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>21</td>\n",
              "      <td>12.868421</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.683535</td>\n",
              "      <td>16.367593</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Real Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>92</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>118</td>\n",
              "      <td>38</td>\n",
              "      <td>9.236842</td>\n",
              "      <td>4.026316</td>\n",
              "      <td>10.209085</td>\n",
              "      <td>12.929510</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chelsea</th>\n",
              "      <th>EPL</th>\n",
              "      <td>87</td>\n",
              "      <td>26</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>73</td>\n",
              "      <td>32</td>\n",
              "      <td>10.710526</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>10.942027</td>\n",
              "      <td>13.419520</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manchester City</th>\n",
              "      <th>EPL</th>\n",
              "      <td>79</td>\n",
              "      <td>24</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>83</td>\n",
              "      <td>38</td>\n",
              "      <td>15.131579</td>\n",
              "      <td>3.789474</td>\n",
              "      <td>7.982490</td>\n",
              "      <td>15.076979</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atletico Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>78</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>67</td>\n",
              "      <td>29</td>\n",
              "      <td>5.184211</td>\n",
              "      <td>3.236842</td>\n",
              "      <td>8.982028</td>\n",
              "      <td>9.237091</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valencia</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>77</td>\n",
              "      <td>22</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>70</td>\n",
              "      <td>32</td>\n",
              "      <td>5.342105</td>\n",
              "      <td>4.526316</td>\n",
              "      <td>8.709827</td>\n",
              "      <td>7.870225</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sevilla</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>76</td>\n",
              "      <td>23</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>71</td>\n",
              "      <td>45</td>\n",
              "      <td>8.026316</td>\n",
              "      <td>4.421053</td>\n",
              "      <td>8.276148</td>\n",
              "      <td>9.477805</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arsenal</th>\n",
              "      <th>EPL</th>\n",
              "      <td>75</td>\n",
              "      <td>22</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>71</td>\n",
              "      <td>36</td>\n",
              "      <td>10.473684</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>8.660596</td>\n",
              "      <td>13.254169</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manchester United</th>\n",
              "      <th>EPL</th>\n",
              "      <td>70</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>62</td>\n",
              "      <td>37</td>\n",
              "      <td>7.026316</td>\n",
              "      <td>5.105263</td>\n",
              "      <td>7.648715</td>\n",
              "      <td>15.521977</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tottenham</th>\n",
              "      <th>EPL</th>\n",
              "      <td>64</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>58</td>\n",
              "      <td>53</td>\n",
              "      <td>5.526316</td>\n",
              "      <td>6.105263</td>\n",
              "      <td>8.000237</td>\n",
              "      <td>11.300694</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Liverpool</th>\n",
              "      <th>EPL</th>\n",
              "      <td>62</td>\n",
              "      <td>18</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>52</td>\n",
              "      <td>48</td>\n",
              "      <td>8.052632</td>\n",
              "      <td>5.289474</td>\n",
              "      <td>9.913851</td>\n",
              "      <td>12.121981</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Southampton</th>\n",
              "      <th>EPL</th>\n",
              "      <td>60</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>54</td>\n",
              "      <td>33</td>\n",
              "      <td>7.105263</td>\n",
              "      <td>4.815789</td>\n",
              "      <td>9.259540</td>\n",
              "      <td>14.879479</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Villarreal</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>60</td>\n",
              "      <td>16</td>\n",
              "      <td>12</td>\n",
              "      <td>10</td>\n",
              "      <td>48</td>\n",
              "      <td>37</td>\n",
              "      <td>6.368421</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>10.072085</td>\n",
              "      <td>8.679660</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Swansea</th>\n",
              "      <th>EPL</th>\n",
              "      <td>56</td>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>46</td>\n",
              "      <td>49</td>\n",
              "      <td>4.105263</td>\n",
              "      <td>8.157895</td>\n",
              "      <td>12.826741</td>\n",
              "      <td>11.455201</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Athletic Club</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>55</td>\n",
              "      <td>15</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>42</td>\n",
              "      <td>41</td>\n",
              "      <td>4.815789</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>7.462406</td>\n",
              "      <td>9.403965</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stoke</th>\n",
              "      <th>EPL</th>\n",
              "      <td>54</td>\n",
              "      <td>15</td>\n",
              "      <td>9</td>\n",
              "      <td>14</td>\n",
              "      <td>48</td>\n",
              "      <td>45</td>\n",
              "      <td>5.131579</td>\n",
              "      <td>6.289474</td>\n",
              "      <td>10.405753</td>\n",
              "      <td>10.601440</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Celta Vigo</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>51</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>47</td>\n",
              "      <td>44</td>\n",
              "      <td>7.552632</td>\n",
              "      <td>5.447368</td>\n",
              "      <td>6.056173</td>\n",
              "      <td>10.882769</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Malaga</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>50</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>16</td>\n",
              "      <td>42</td>\n",
              "      <td>48</td>\n",
              "      <td>4.842105</td>\n",
              "      <td>4.842105</td>\n",
              "      <td>7.792069</td>\n",
              "      <td>7.019068</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Espanyol</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>49</td>\n",
              "      <td>13</td>\n",
              "      <td>10</td>\n",
              "      <td>15</td>\n",
              "      <td>47</td>\n",
              "      <td>51</td>\n",
              "      <td>4.552632</td>\n",
              "      <td>5.394737</td>\n",
              "      <td>9.381754</td>\n",
              "      <td>7.158217</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rayo Vallecano</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>49</td>\n",
              "      <td>15</td>\n",
              "      <td>4</td>\n",
              "      <td>19</td>\n",
              "      <td>46</td>\n",
              "      <td>68</td>\n",
              "      <td>3.868421</td>\n",
              "      <td>5.763158</td>\n",
              "      <td>6.157978</td>\n",
              "      <td>9.735109</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Crystal Palace</th>\n",
              "      <th>EPL</th>\n",
              "      <td>48</td>\n",
              "      <td>13</td>\n",
              "      <td>9</td>\n",
              "      <td>16</td>\n",
              "      <td>47</td>\n",
              "      <td>51</td>\n",
              "      <td>4.736842</td>\n",
              "      <td>6.789474</td>\n",
              "      <td>11.577051</td>\n",
              "      <td>6.582148</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Everton</th>\n",
              "      <th>EPL</th>\n",
              "      <td>47</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>48</td>\n",
              "      <td>50</td>\n",
              "      <td>6.815789</td>\n",
              "      <td>6.447368</td>\n",
              "      <td>10.432774</td>\n",
              "      <td>11.682677</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Ham</th>\n",
              "      <th>EPL</th>\n",
              "      <td>47</td>\n",
              "      <td>12</td>\n",
              "      <td>11</td>\n",
              "      <td>15</td>\n",
              "      <td>44</td>\n",
              "      <td>47</td>\n",
              "      <td>5.710526</td>\n",
              "      <td>8.315789</td>\n",
              "      <td>12.042643</td>\n",
              "      <td>8.433845</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Real Sociedad</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>46</td>\n",
              "      <td>11</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>44</td>\n",
              "      <td>51</td>\n",
              "      <td>3.842105</td>\n",
              "      <td>4.421053</td>\n",
              "      <td>8.614235</td>\n",
              "      <td>8.829567</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>West Bromwich Albion</th>\n",
              "      <th>EPL</th>\n",
              "      <td>44</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>16</td>\n",
              "      <td>38</td>\n",
              "      <td>51</td>\n",
              "      <td>4.236842</td>\n",
              "      <td>7.447368</td>\n",
              "      <td>13.545803</td>\n",
              "      <td>9.869703</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Leicester</th>\n",
              "      <th>EPL</th>\n",
              "      <td>41</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>19</td>\n",
              "      <td>46</td>\n",
              "      <td>55</td>\n",
              "      <td>5.026316</td>\n",
              "      <td>7.552632</td>\n",
              "      <td>10.229308</td>\n",
              "      <td>8.019074</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Elche</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>41</td>\n",
              "      <td>11</td>\n",
              "      <td>8</td>\n",
              "      <td>19</td>\n",
              "      <td>35</td>\n",
              "      <td>62</td>\n",
              "      <td>2.815789</td>\n",
              "      <td>5.684211</td>\n",
              "      <td>9.517572</td>\n",
              "      <td>8.623287</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Newcastle United</th>\n",
              "      <th>EPL</th>\n",
              "      <td>39</td>\n",
              "      <td>10</td>\n",
              "      <td>9</td>\n",
              "      <td>19</td>\n",
              "      <td>40</td>\n",
              "      <td>63</td>\n",
              "      <td>4.605263</td>\n",
              "      <td>6.289474</td>\n",
              "      <td>10.422385</td>\n",
              "      <td>8.840574</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Aston Villa</th>\n",
              "      <th>EPL</th>\n",
              "      <td>38</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>20</td>\n",
              "      <td>31</td>\n",
              "      <td>57</td>\n",
              "      <td>4.842105</td>\n",
              "      <td>6.236842</td>\n",
              "      <td>16.386892</td>\n",
              "      <td>11.065619</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sunderland</th>\n",
              "      <th>EPL</th>\n",
              "      <td>38</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "      <td>14</td>\n",
              "      <td>31</td>\n",
              "      <td>53</td>\n",
              "      <td>4.026316</td>\n",
              "      <td>8.236842</td>\n",
              "      <td>11.421301</td>\n",
              "      <td>8.129492</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-42851e67-c66f-478f-a432-45fc73ccec70')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-42851e67-c66f-478f-a432-45fc73ccec70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Actividad 2"
      ],
      "metadata": {
        "id": "utwcxvkRh7uL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a considerar un momento de quiebre conocido de un equipo conocido como el Real Madrid. Este momento es la salida del Cristiano Ronaldo del equipo (2018)."
      ],
      "metadata": {
        "id": "GZklZ_-OqdUv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prime_rm = df_final.query('season >= 2016 and team == \"Real Madrid\"')\n",
        "prime_rm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "NhOByFP_qq0i",
        "outputId": "3ea67cfd-58e6-4cee-d902-fb63e4e1c235"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            points  wins  draws  defeats  goals_scored  \\\n",
              "season team        league                                                \n",
              "2016   Real Madrid La_liga      93    29      6        3           106   \n",
              "2017   Real Madrid La_liga      76    22     10        6            94   \n",
              "2018   Real Madrid La_liga      68    21      5       12            63   \n",
              "2019   Real Madrid La_liga      87    26      9        3            70   \n",
              "\n",
              "                            goals_missed  deep_passes_avg  \\\n",
              "season team        league                                   \n",
              "2016   Real Madrid La_liga            41         8.763158   \n",
              "2017   Real Madrid La_liga            44        11.921053   \n",
              "2018   Real Madrid La_liga            46         8.973684   \n",
              "2019   Real Madrid La_liga            25         8.789474   \n",
              "\n",
              "                            deep_passes_allowed_avg  ppda_avg  oppda_avg  \\\n",
              "season team        league                                                  \n",
              "2016   Real Madrid La_liga                 4.552632  9.742439  13.407533   \n",
              "2017   Real Madrid La_liga                 4.947368  9.303382  13.999744   \n",
              "2018   Real Madrid La_liga                 4.421053  8.896578  14.782501   \n",
              "2019   Real Madrid La_liga                 3.342105  9.262202  14.476897   \n",
              "\n",
              "                            position  \n",
              "season team        league             \n",
              "2016   Real Madrid La_liga         1  \n",
              "2017   Real Madrid La_liga         3  \n",
              "2018   Real Madrid La_liga         3  \n",
              "2019   Real Madrid La_liga         1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b5287b4-df4b-40a4-a444-7f0ea0ebd524\" 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 tbody tr th {\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>points</th>\n",
              "      <th>wins</th>\n",
              "      <th>draws</th>\n",
              "      <th>defeats</th>\n",
              "      <th>goals_scored</th>\n",
              "      <th>goals_missed</th>\n",
              "      <th>deep_passes_avg</th>\n",
              "      <th>deep_passes_allowed_avg</th>\n",
              "      <th>ppda_avg</th>\n",
              "      <th>oppda_avg</th>\n",
              "      <th>position</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <th>team</th>\n",
              "      <th>league</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <th>Real Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>93</td>\n",
              "      <td>29</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>106</td>\n",
              "      <td>41</td>\n",
              "      <td>8.763158</td>\n",
              "      <td>4.552632</td>\n",
              "      <td>9.742439</td>\n",
              "      <td>13.407533</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <th>Real Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>76</td>\n",
              "      <td>22</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>94</td>\n",
              "      <td>44</td>\n",
              "      <td>11.921053</td>\n",
              "      <td>4.947368</td>\n",
              "      <td>9.303382</td>\n",
              "      <td>13.999744</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <th>Real Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>68</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>12</td>\n",
              "      <td>63</td>\n",
              "      <td>46</td>\n",
              "      <td>8.973684</td>\n",
              "      <td>4.421053</td>\n",
              "      <td>8.896578</td>\n",
              "      <td>14.782501</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <th>Real Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>87</td>\n",
              "      <td>26</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>70</td>\n",
              "      <td>25</td>\n",
              "      <td>8.789474</td>\n",
              "      <td>3.342105</td>\n",
              "      <td>9.262202</td>\n",
              "      <td>14.476897</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b5287b4-df4b-40a4-a444-7f0ea0ebd524')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4b5287b4-df4b-40a4-a444-7f0ea0ebd524 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-4b5287b4-df4b-40a4-a444-7f0ea0ebd524');\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",
              "\n",
              "\n",
              "<div id=\"df-d4d62f79-0820-4290-b792-2c6b8144a1e8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d4d62f79-0820-4290-b792-2c6b8144a1e8')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d4d62f79-0820-4290-b792-2c6b8144a1e8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "quiebre_barca = df_final.query('season >= 2016 and team == \"Barcelona\"')\n",
        "quiebre_barca.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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      "execution_count": 157,
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        {
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          "data": {
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              "                          points  wins  draws  defeats  goals_scored  \\\n",
              "season team      league                                                \n",
              "2016   Barcelona La_liga      90    28      6        4           116   \n",
              "2017   Barcelona La_liga      93    28      9        1            99   \n",
              "2018   Barcelona La_liga      87    26      9        3            90   \n",
              "2019   Barcelona La_liga      82    25      7        6            86   \n",
              "\n",
              "                          goals_missed  deep_passes_avg  \\\n",
              "season team      league                                   \n",
              "2016   Barcelona La_liga            37        14.526316   \n",
              "2017   Barcelona La_liga            29        13.894737   \n",
              "2018   Barcelona La_liga            36        10.973684   \n",
              "2019   Barcelona La_liga            38        11.578947   \n",
              "\n",
              "                          deep_passes_allowed_avg  ppda_avg  oppda_avg  \\\n",
              "season team      league                                                  \n",
              "2016   Barcelona La_liga                 3.789474  6.702330  15.710111   \n",
              "2017   Barcelona La_liga                 4.947368  9.109883  13.939373   \n",
              "2018   Barcelona La_liga                 4.500000  9.015264  16.404885   \n",
              "2019   Barcelona La_liga                 3.842105  8.256988  17.961598   \n",
              "\n",
              "                          position  \n",
              "season team      league             \n",
              "2016   Barcelona La_liga         2  \n",
              "2017   Barcelona La_liga         1  \n",
              "2018   Barcelona La_liga         1  \n",
              "2019   Barcelona La_liga         2  "
            ],
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              "      <th>season</th>\n",
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              "      <th>2016</th>\n",
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              "      <th>2018</th>\n",
              "      <th>Barcelona</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>87</td>\n",
              "      <td>26</td>\n",
              "      <td>9</td>\n",
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              "      <td>1</td>\n",
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              "      <th>2019</th>\n",
              "      <th>Barcelona</th>\n",
              "      <th>La_liga</th>\n",
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              "      <td>7</td>\n",
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              "  @keyframes spin {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prime_rm['ppda_avg'].plot()\n",
        "quiebre_barca['ppda_avg'].plot()\n",
        "plt.legend()\n",
        "plt.xticks(rotation = 45)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "tCm1leVQrJOR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "outputId": "fdd644d9-bb7d-4e62-ee49-9d1111954a5a"
      },
      "execution_count": 180,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cambio_de_estilo(ppda_anterior, ppda_nuevo):\n",
        "    if (ppda_anterior - ppda_nuevo) > 0.4:\n",
        "        return 'Temporada a la baja'\n",
        "    elif (ppda_anterior - ppda_nuevo) < -0.3:\n",
        "        return 'Temporada de subida'\n",
        "    else:\n",
        "        return 'Temporada normal'"
      ],
      "metadata": {
        "id": "JX_-ElwX1TZv"
      },
      "execution_count": 181,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rm_temporadas = df_final.query('team == \"Real Madrid\"')\n",
        "barca_temporadas = df_final.query('team == \"Barcelona\"')"
      ],
      "metadata": {
        "id": "6L7YoIev3BjD"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ppda_rm = rm_temporadas['ppda_avg'].to_list()\n",
        "ppda_barca = barca_temporadas['ppda_avg'].to_list()"
      ],
      "metadata": {
        "id": "c6PLv8wV3TEe"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lista_estilo = []\n",
        "\n",
        "index = 0\n",
        "\n",
        "for season in ppda_rm:\n",
        "    if index > 1:\n",
        "        lista_estilo.append(cambio_de_estilo(ppda_rm[index - 1], season))\n",
        "    index += 1\n",
        "\n",
        "prime_rm['estilo'] = lista_estilo\n",
        "prime_rm.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "52ptX8u6250E",
        "outputId": "65eac4e7-eb91-4108-f65e-5075e632b3ae"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-182-e31c7062820c>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  prime_rm['estilo'] = lista_estilo\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            points  wins  draws  defeats  goals_scored  \\\n",
              "season team        league                                                \n",
              "2016   Real Madrid La_liga      93    29      6        3           106   \n",
              "2017   Real Madrid La_liga      76    22     10        6            94   \n",
              "2018   Real Madrid La_liga      68    21      5       12            63   \n",
              "2019   Real Madrid La_liga      87    26      9        3            70   \n",
              "\n",
              "                            goals_missed  deep_passes_avg  \\\n",
              "season team        league                                   \n",
              "2016   Real Madrid La_liga            41         8.763158   \n",
              "2017   Real Madrid La_liga            44        11.921053   \n",
              "2018   Real Madrid La_liga            46         8.973684   \n",
              "2019   Real Madrid La_liga            25         8.789474   \n",
              "\n",
              "                            deep_passes_allowed_avg  ppda_avg  oppda_avg  \\\n",
              "season team        league                                                  \n",
              "2016   Real Madrid La_liga                 4.552632  9.742439  13.407533   \n",
              "2017   Real Madrid La_liga                 4.947368  9.303382  13.999744   \n",
              "2018   Real Madrid La_liga                 4.421053  8.896578  14.782501   \n",
              "2019   Real Madrid La_liga                 3.342105  9.262202  14.476897   \n",
              "\n",
              "                            position               estilo  \n",
              "season team        league                                  \n",
              "2016   Real Madrid La_liga         1  Temporada de subida  \n",
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          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lista_estilo = []\n",
        "\n",
        "index = 0\n",
        "\n",
        "for season in ppda_barca:\n",
        "    if index > 1:\n",
        "        lista_estilo.append(cambio_de_estilo(ppda_barca[index - 1], season))\n",
        "    index += 1\n",
        "\n",
        "quiebre_barca['estilo'] = lista_estilo\n",
        "quiebre_barca.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "wPaqmbW73win",
        "outputId": "62126a12-cb72-42ec-95d6-41a7d818df09"
      },
      "execution_count": 183,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-183-9443efa545f1>:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  quiebre_barca['estilo'] = lista_estilo\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          points  wins  draws  defeats  goals_scored  \\\n",
              "season team      league                                                \n",
              "2016   Barcelona La_liga      90    28      6        4           116   \n",
              "2017   Barcelona La_liga      93    28      9        1            99   \n",
              "2018   Barcelona La_liga      87    26      9        3            90   \n",
              "2019   Barcelona La_liga      82    25      7        6            86   \n",
              "\n",
              "                          goals_missed  deep_passes_avg  \\\n",
              "season team      league                                   \n",
              "2016   Barcelona La_liga            37        14.526316   \n",
              "2017   Barcelona La_liga            29        13.894737   \n",
              "2018   Barcelona La_liga            36        10.973684   \n",
              "2019   Barcelona La_liga            38        11.578947   \n",
              "\n",
              "                          deep_passes_allowed_avg  ppda_avg  oppda_avg  \\\n",
              "season team      league                                                  \n",
              "2016   Barcelona La_liga                 3.789474  6.702330  15.710111   \n",
              "2017   Barcelona La_liga                 4.947368  9.109883  13.939373   \n",
              "2018   Barcelona La_liga                 4.500000  9.015264  16.404885   \n",
              "2019   Barcelona La_liga                 3.842105  8.256988  17.961598   \n",
              "\n",
              "                          position               estilo  \n",
              "season team      league                                  \n",
              "2016   Barcelona La_liga         2  Temporada de subida  \n",
              "2017   Barcelona La_liga         1  Temporada de subida  \n",
              "2018   Barcelona La_liga         1     Temporada normal  \n",
              "2019   Barcelona La_liga         2  Temporada a la baja  "
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              "      <td>2</td>\n",
              "      <td>Temporada a la baja</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e70112c2-9e32-4a47-9f99-4553befa1d84')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-e70112c2-9e32-4a47-9f99-4553befa1d84 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-e70112c2-9e32-4a47-9f99-4553befa1d84');\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",
              "\n",
              "\n",
              "<div id=\"df-f4b039b1-d98e-4ff8-8560-a22374ff6b82\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f4b039b1-d98e-4ff8-8560-a22374ff6b82')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f4b039b1-d98e-4ff8-8560-a22374ff6b82 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Actividad 3"
      ],
      "metadata": {
        "id": "-LFJxd1aEflD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_agrupados_a3 = df.groupby(['season', 'team', 'league'])"
      ],
      "metadata": {
        "id": "jc1HcOLOcZXJ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dict_valores_a3 = {\n",
        "    'points': df_agrupados_a3['pts'].apply(np.sum),\n",
        "    'wins': df_agrupados_a3['result'].apply(lambda x: count_if_equal(x, 'w')),\n",
        "    'draws':df_agrupados_a3['result'].apply(lambda x: count_if_equal(x, 'd')),\n",
        "    'defeats': df_agrupados_a3['result'].apply(lambda x: count_if_equal(x, 'l')),\n",
        "    'goals_scored': df_agrupados_a3['goals_scored'].apply(np.sum),\n",
        "    'goals_missed': df_agrupados_a3['goals_missed'].apply(np.sum),\n",
        "    'deep_passes_avg': df_agrupados_a3['deep_passes'].apply(np.mean),\n",
        "    'deep_passes_allowed_avg': df_agrupados_a3['deep_passes_allowed'].apply(np.mean),\n",
        "    'ppda_avg': df_agrupados_a3['ppda'].apply(np.mean),\n",
        "    'oppda_avg': df_agrupados_a3['oppda'].apply(np.mean),\n",
        "    'matches': df_agrupados_a3['pts'].count()\n",
        "}\n",
        "df_final_a3 = pd.DataFrame.from_dict(dict_valores_a3)\n",
        "\n",
        "df_final_a3.sort_values(by=['season', 'points', 'goals_scored'], ascending=[True, False, False], inplace=True)\n",
        "\n",
        "df_final_a3['position'] = df_final_a3.groupby(['season', 'league'])['points'].rank(ascending=False, method='first')\n",
        "df_final_a3['position'] = df_final_a3['position'].astype(int)\n",
        "df_final_a3.head(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "o40OPPHhcW15",
        "outputId": "a6261399-69de-4ae2-f526-10a66a7e1f6b"
      },
      "execution_count": 185,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        points  wins  draws  defeats  \\\n",
              "season team                 league                                     \n",
              "2014   Barcelona            La_liga         94    30      4        4   \n",
              "       Real Madrid          La_liga         92    30      2        6   \n",
              "       Chelsea              EPL             87    26      9        3   \n",
              "       Juventus             Serie_A         87    26      9        3   \n",
              "       Paris Saint Germain  Ligue_1         83    24     11        3   \n",
              "       Manchester City      EPL             79    24      7        7   \n",
              "       Bayern Munich        Bundesliga      79    25      4        5   \n",
              "       Atletico Madrid      La_liga         78    23      9        6   \n",
              "       Valencia             La_liga         77    22     11        5   \n",
              "       Sevilla              La_liga         76    23      7        8   \n",
              "       Lyon                 Ligue_1         75    22      9        7   \n",
              "       Arsenal              EPL             75    22      9        7   \n",
              "       Monaco               Ligue_1         71    20     11        7   \n",
              "       Manchester United    EPL             70    20     10        8   \n",
              "       Roma                 Serie_A         70    19     13        6   \n",
              "       Marseille            Ligue_1         69    21      6       11   \n",
              "       Wolfsburg            Bundesliga      69    20      9        5   \n",
              "       Lazio                Serie_A         69    21      6       11   \n",
              "       Saint-Etienne        Ligue_1         69    19     12        7   \n",
              "       Zenit St. Petersburg RFPL            67    20      7        3   \n",
              "       Borussia M.Gladbach  Bundesliga      66    19      9        6   \n",
              "       Fiorentina           Serie_A         64    18     10       10   \n",
              "       Tottenham            EPL             64    19      7       12   \n",
              "       Napoli               Serie_A         63    18      9       11   \n",
              "       Bordeaux             Ligue_1         63    17     12        9   \n",
              "       Liverpool            EPL             62    18      8       12   \n",
              "       Bayer Leverkusen     Bundesliga      61    17     10        7   \n",
              "       CSKA Moscow          RFPL            60    19      3        8   \n",
              "       Southampton          EPL             60    18      6       14   \n",
              "       FC Krasnodar         RFPL            60    17      9        4   \n",
              "\n",
              "                                        goals_scored  goals_missed  \\\n",
              "season team                 league                                   \n",
              "2014   Barcelona            La_liga              110            21   \n",
              "       Real Madrid          La_liga              118            38   \n",
              "       Chelsea              EPL                   73            32   \n",
              "       Juventus             Serie_A               72            24   \n",
              "       Paris Saint Germain  Ligue_1               83            36   \n",
              "       Manchester City      EPL                   83            38   \n",
              "       Bayern Munich        Bundesliga            80            18   \n",
              "       Atletico Madrid      La_liga               67            29   \n",
              "       Valencia             La_liga               70            32   \n",
              "       Sevilla              La_liga               71            45   \n",
              "       Lyon                 Ligue_1               72            33   \n",
              "       Arsenal              EPL                   71            36   \n",
              "       Monaco               Ligue_1               51            26   \n",
              "       Manchester United    EPL                   62            37   \n",
              "       Roma                 Serie_A               54            31   \n",
              "       Marseille            Ligue_1               76            42   \n",
              "       Wolfsburg            Bundesliga            72            38   \n",
              "       Lazio                Serie_A               71            38   \n",
              "       Saint-Etienne        Ligue_1               51            30   \n",
              "       Zenit St. Petersburg RFPL                  58            17   \n",
              "       Borussia M.Gladbach  Bundesliga            53            26   \n",
              "       Fiorentina           Serie_A               61            46   \n",
              "       Tottenham            EPL                   58            53   \n",
              "       Napoli               Serie_A               70            54   \n",
              "       Bordeaux             Ligue_1               47            44   \n",
              "       Liverpool            EPL                   52            48   \n",
              "       Bayer Leverkusen     Bundesliga            62            37   \n",
              "       CSKA Moscow          RFPL                  67            27   \n",
              "       Southampton          EPL                   54            33   \n",
              "       FC Krasnodar         RFPL                  52            27   \n",
              "\n",
              "                                        deep_passes_avg  \\\n",
              "season team                 league                        \n",
              "2014   Barcelona            La_liga           12.868421   \n",
              "       Real Madrid          La_liga            9.236842   \n",
              "       Chelsea              EPL               10.710526   \n",
              "       Juventus             Serie_A            8.789474   \n",
              "       Paris Saint Germain  Ligue_1            8.447368   \n",
              "       Manchester City      EPL               15.131579   \n",
              "       Bayern Munich        Bundesliga        11.411765   \n",
              "       Atletico Madrid      La_liga            5.184211   \n",
              "       Valencia             La_liga            5.342105   \n",
              "       Sevilla              La_liga            8.026316   \n",
              "       Lyon                 Ligue_1            5.315789   \n",
              "       Arsenal              EPL               10.473684   \n",
              "       Monaco               Ligue_1            6.000000   \n",
              "       Manchester United    EPL                7.026316   \n",
              "       Roma                 Serie_A            7.000000   \n",
              "       Marseille            Ligue_1            5.289474   \n",
              "       Wolfsburg            Bundesliga         5.058824   \n",
              "       Lazio                Serie_A            5.631579   \n",
              "       Saint-Etienne        Ligue_1            5.210526   \n",
              "       Zenit St. Petersburg RFPL               7.600000   \n",
              "       Borussia M.Gladbach  Bundesliga         6.500000   \n",
              "       Fiorentina           Serie_A            8.131579   \n",
              "       Tottenham            EPL                5.526316   \n",
              "       Napoli               Serie_A            7.394737   \n",
              "       Bordeaux             Ligue_1            5.368421   \n",
              "       Liverpool            EPL                8.052632   \n",
              "       Bayer Leverkusen     Bundesliga         7.235294   \n",
              "       CSKA Moscow          RFPL              10.800000   \n",
              "       Southampton          EPL                7.105263   \n",
              "       FC Krasnodar         RFPL               7.466667   \n",
              "\n",
              "                                        deep_passes_allowed_avg   ppda_avg  \\\n",
              "season team                 league                                           \n",
              "2014   Barcelona            La_liga                    3.000000   5.683535   \n",
              "       Real Madrid          La_liga                    4.026316  10.209085   \n",
              "       Chelsea              EPL                        4.500000  10.942027   \n",
              "       Juventus             Serie_A                    4.000000   8.715008   \n",
              "       Paris Saint Germain  Ligue_1                    3.052632   7.133794   \n",
              "       Manchester City      EPL                        3.789474   7.982490   \n",
              "       Bayern Munich        Bundesliga                 2.441176   5.761476   \n",
              "       Atletico Madrid      La_liga                    3.236842   8.982028   \n",
              "       Valencia             La_liga                    4.526316   8.709827   \n",
              "       Sevilla              La_liga                    4.421053   8.276148   \n",
              "       Lyon                 Ligue_1                    3.578947   8.994296   \n",
              "       Arsenal              EPL                        4.500000   8.660596   \n",
              "       Monaco               Ligue_1                    3.473684   8.338964   \n",
              "       Manchester United    EPL                        5.105263   7.648715   \n",
              "       Roma                 Serie_A                    4.605263   7.874105   \n",
              "       Marseille            Ligue_1                    3.315789   6.342344   \n",
              "       Wolfsburg            Bundesliga                 4.823529   9.136319   \n",
              "       Lazio                Serie_A                    4.552632   7.282635   \n",
              "       Saint-Etienne        Ligue_1                    3.263158   9.445227   \n",
              "       Zenit St. Petersburg RFPL                       2.900000   7.551196   \n",
              "       Borussia M.Gladbach  Bundesliga                 5.823529  12.751108   \n",
              "       Fiorentina           Serie_A                    4.473684   7.757888   \n",
              "       Tottenham            EPL                        6.105263   8.000237   \n",
              "       Napoli               Serie_A                    3.605263   8.836460   \n",
              "       Bordeaux             Ligue_1                    3.657895   9.811080   \n",
              "       Liverpool            EPL                        5.289474   9.913851   \n",
              "       Bayer Leverkusen     Bundesliga                 3.176471   5.693289   \n",
              "       CSKA Moscow          RFPL                       3.533333   8.137093   \n",
              "       Southampton          EPL                        4.815789   9.259540   \n",
              "       FC Krasnodar         RFPL                       4.366667   7.180436   \n",
              "\n",
              "                                        oppda_avg  matches  position  \n",
              "season team                 league                                    \n",
              "2014   Barcelona            La_liga     16.367593       38         1  \n",
              "       Real Madrid          La_liga     12.929510       38         2  \n",
              "       Chelsea              EPL         13.419520       38         1  \n",
              "       Juventus             Serie_A     13.017563       38         1  \n",
              "       Paris Saint Germain  Ligue_1     16.841499       38         1  \n",
              "       Manchester City      EPL         15.076979       38         2  \n",
              "       Bayern Munich        Bundesliga  25.022717       34         1  \n",
              "       Atletico Madrid      La_liga      9.237091       38         3  \n",
              "       Valencia             La_liga      7.870225       38         4  \n",
              "       Sevilla              La_liga      9.477805       38         5  \n",
              "       Lyon                 Ligue_1     11.970015       38         2  \n",
              "       Arsenal              EPL         13.254169       38         3  \n",
              "       Monaco               Ligue_1     10.394530       38         3  \n",
              "       Manchester United    EPL         15.521977       38         4  \n",
              "       Roma                 Serie_A     13.878387       38         2  \n",
              "       Marseille            Ligue_1      8.526785       38         4  \n",
              "       Wolfsburg            Bundesliga  10.289876       34         2  \n",
              "       Lazio                Serie_A     11.223855       38         3  \n",
              "       Saint-Etienne        Ligue_1     11.279169       38         5  \n",
              "       Zenit St. Petersburg RFPL        11.631755       30         1  \n",
              "       Borussia M.Gladbach  Bundesliga  12.783803       34         3  \n",
              "       Fiorentina           Serie_A     12.457943       38         4  \n",
              "       Tottenham            EPL         11.300694       38         5  \n",
              "       Napoli               Serie_A     13.312875       38         5  \n",
              "       Bordeaux             Ligue_1     11.579724       38         6  \n",
              "       Liverpool            EPL         12.121981       38         6  \n",
              "       Bayer Leverkusen     Bundesliga   7.347595       34         4  \n",
              "       CSKA Moscow          RFPL        15.635261       30         2  \n",
              "       Southampton          EPL         14.879479       38         7  \n",
              "       FC Krasnodar         RFPL         9.945586       30         3  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>points</th>\n",
              "      <th>wins</th>\n",
              "      <th>draws</th>\n",
              "      <th>defeats</th>\n",
              "      <th>goals_scored</th>\n",
              "      <th>goals_missed</th>\n",
              "      <th>deep_passes_avg</th>\n",
              "      <th>deep_passes_allowed_avg</th>\n",
              "      <th>ppda_avg</th>\n",
              "      <th>oppda_avg</th>\n",
              "      <th>matches</th>\n",
              "      <th>position</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>season</th>\n",
              "      <th>team</th>\n",
              "      <th>league</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"30\" valign=\"top\">2014</th>\n",
              "      <th>Barcelona</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>94</td>\n",
              "      <td>30</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>110</td>\n",
              "      <td>21</td>\n",
              "      <td>12.868421</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>5.683535</td>\n",
              "      <td>16.367593</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Real Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>92</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>118</td>\n",
              "      <td>38</td>\n",
              "      <td>9.236842</td>\n",
              "      <td>4.026316</td>\n",
              "      <td>10.209085</td>\n",
              "      <td>12.929510</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chelsea</th>\n",
              "      <th>EPL</th>\n",
              "      <td>87</td>\n",
              "      <td>26</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>73</td>\n",
              "      <td>32</td>\n",
              "      <td>10.710526</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>10.942027</td>\n",
              "      <td>13.419520</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Juventus</th>\n",
              "      <th>Serie_A</th>\n",
              "      <td>87</td>\n",
              "      <td>26</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>72</td>\n",
              "      <td>24</td>\n",
              "      <td>8.789474</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>8.715008</td>\n",
              "      <td>13.017563</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Paris Saint Germain</th>\n",
              "      <th>Ligue_1</th>\n",
              "      <td>83</td>\n",
              "      <td>24</td>\n",
              "      <td>11</td>\n",
              "      <td>3</td>\n",
              "      <td>83</td>\n",
              "      <td>36</td>\n",
              "      <td>8.447368</td>\n",
              "      <td>3.052632</td>\n",
              "      <td>7.133794</td>\n",
              "      <td>16.841499</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manchester City</th>\n",
              "      <th>EPL</th>\n",
              "      <td>79</td>\n",
              "      <td>24</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>83</td>\n",
              "      <td>38</td>\n",
              "      <td>15.131579</td>\n",
              "      <td>3.789474</td>\n",
              "      <td>7.982490</td>\n",
              "      <td>15.076979</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bayern Munich</th>\n",
              "      <th>Bundesliga</th>\n",
              "      <td>79</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>80</td>\n",
              "      <td>18</td>\n",
              "      <td>11.411765</td>\n",
              "      <td>2.441176</td>\n",
              "      <td>5.761476</td>\n",
              "      <td>25.022717</td>\n",
              "      <td>34</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Atletico Madrid</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>78</td>\n",
              "      <td>23</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>67</td>\n",
              "      <td>29</td>\n",
              "      <td>5.184211</td>\n",
              "      <td>3.236842</td>\n",
              "      <td>8.982028</td>\n",
              "      <td>9.237091</td>\n",
              "      <td>38</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Valencia</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>77</td>\n",
              "      <td>22</td>\n",
              "      <td>11</td>\n",
              "      <td>5</td>\n",
              "      <td>70</td>\n",
              "      <td>32</td>\n",
              "      <td>5.342105</td>\n",
              "      <td>4.526316</td>\n",
              "      <td>8.709827</td>\n",
              "      <td>7.870225</td>\n",
              "      <td>38</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sevilla</th>\n",
              "      <th>La_liga</th>\n",
              "      <td>76</td>\n",
              "      <td>23</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>71</td>\n",
              "      <td>45</td>\n",
              "      <td>8.026316</td>\n",
              "      <td>4.421053</td>\n",
              "      <td>8.276148</td>\n",
              "      <td>9.477805</td>\n",
              "      <td>38</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lyon</th>\n",
              "      <th>Ligue_1</th>\n",
              "      <td>75</td>\n",
              "      <td>22</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>72</td>\n",
              "      <td>33</td>\n",
              "      <td>5.315789</td>\n",
              "      <td>3.578947</td>\n",
              "      <td>8.994296</td>\n",
              "      <td>11.970015</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arsenal</th>\n",
              "      <th>EPL</th>\n",
              "      <td>75</td>\n",
              "      <td>22</td>\n",
              "      <td>9</td>\n",
              "      <td>7</td>\n",
              "      <td>71</td>\n",
              "      <td>36</td>\n",
              "      <td>10.473684</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>8.660596</td>\n",
              "      <td>13.254169</td>\n",
              "      <td>38</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monaco</th>\n",
              "      <th>Ligue_1</th>\n",
              "      <td>71</td>\n",
              "      <td>20</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>51</td>\n",
              "      <td>26</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>3.473684</td>\n",
              "      <td>8.338964</td>\n",
              "      <td>10.394530</td>\n",
              "      <td>38</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Manchester United</th>\n",
              "      <th>EPL</th>\n",
              "      <td>70</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>62</td>\n",
              "      <td>37</td>\n",
              "      <td>7.026316</td>\n",
              "      <td>5.105263</td>\n",
              "      <td>7.648715</td>\n",
              "      <td>15.521977</td>\n",
              "      <td>38</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Roma</th>\n",
              "      <th>Serie_A</th>\n",
              "      <td>70</td>\n",
              "      <td>19</td>\n",
              "      <td>13</td>\n",
              "      <td>6</td>\n",
              "      <td>54</td>\n",
              "      <td>31</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>4.605263</td>\n",
              "      <td>7.874105</td>\n",
              "      <td>13.878387</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Marseille</th>\n",
              "      <th>Ligue_1</th>\n",
              "      <td>69</td>\n",
              "      <td>21</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>76</td>\n",
              "      <td>42</td>\n",
              "      <td>5.289474</td>\n",
              "      <td>3.315789</td>\n",
              "      <td>6.342344</td>\n",
              "      <td>8.526785</td>\n",
              "      <td>38</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wolfsburg</th>\n",
              "      <th>Bundesliga</th>\n",
              "      <td>69</td>\n",
              "      <td>20</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>72</td>\n",
              "      <td>38</td>\n",
              "      <td>5.058824</td>\n",
              "      <td>4.823529</td>\n",
              "      <td>9.136319</td>\n",
              "      <td>10.289876</td>\n",
              "      <td>34</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lazio</th>\n",
              "      <th>Serie_A</th>\n",
              "      <td>69</td>\n",
              "      <td>21</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>71</td>\n",
              "      <td>38</td>\n",
              "      <td>5.631579</td>\n",
              "      <td>4.552632</td>\n",
              "      <td>7.282635</td>\n",
              "      <td>11.223855</td>\n",
              "      <td>38</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Saint-Etienne</th>\n",
              "      <th>Ligue_1</th>\n",
              "      <td>69</td>\n",
              "      <td>19</td>\n",
              "      <td>12</td>\n",
              "      <td>7</td>\n",
              "      <td>51</td>\n",
              "      <td>30</td>\n",
              "      <td>5.210526</td>\n",
              "      <td>3.263158</td>\n",
              "      <td>9.445227</td>\n",
              "      <td>11.279169</td>\n",
              "      <td>38</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zenit St. Petersburg</th>\n",
              "      <th>RFPL</th>\n",
              "      <td>67</td>\n",
              "      <td>20</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>58</td>\n",
              "      <td>17</td>\n",
              "      <td>7.600000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>7.551196</td>\n",
              "      <td>11.631755</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Borussia M.Gladbach</th>\n",
              "      <th>Bundesliga</th>\n",
              "      <td>66</td>\n",
              "      <td>19</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>53</td>\n",
              "      <td>26</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>5.823529</td>\n",
              "      <td>12.751108</td>\n",
              "      <td>12.783803</td>\n",
              "      <td>34</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fiorentina</th>\n",
              "      <th>Serie_A</th>\n",
              "      <td>64</td>\n",
              "      <td>18</td>\n",
              "      <td>10</td>\n",
              "      <td>10</td>\n",
              "      <td>61</td>\n",
              "      <td>46</td>\n",
              "      <td>8.131579</td>\n",
              "      <td>4.473684</td>\n",
              "      <td>7.757888</td>\n",
              "      <td>12.457943</td>\n",
              "      <td>38</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tottenham</th>\n",
              "      <th>EPL</th>\n",
              "      <td>64</td>\n",
              "      <td>19</td>\n",
              "      <td>7</td>\n",
              "      <td>12</td>\n",
              "      <td>58</td>\n",
              "      <td>53</td>\n",
              "      <td>5.526316</td>\n",
              "      <td>6.105263</td>\n",
              "      <td>8.000237</td>\n",
              "      <td>11.300694</td>\n",
              "      <td>38</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Napoli</th>\n",
              "      <th>Serie_A</th>\n",
              "      <td>63</td>\n",
              "      <td>18</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>70</td>\n",
              "      <td>54</td>\n",
              "      <td>7.394737</td>\n",
              "      <td>3.605263</td>\n",
              "      <td>8.836460</td>\n",
              "      <td>13.312875</td>\n",
              "      <td>38</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bordeaux</th>\n",
              "      <th>Ligue_1</th>\n",
              "      <td>63</td>\n",
              "      <td>17</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>47</td>\n",
              "      <td>44</td>\n",
              "      <td>5.368421</td>\n",
              "      <td>3.657895</td>\n",
              "      <td>9.811080</td>\n",
              "      <td>11.579724</td>\n",
              "      <td>38</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Liverpool</th>\n",
              "      <th>EPL</th>\n",
              "      <td>62</td>\n",
              "      <td>18</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>52</td>\n",
              "      <td>48</td>\n",
              "      <td>8.052632</td>\n",
              "      <td>5.289474</td>\n",
              "      <td>9.913851</td>\n",
              "      <td>12.121981</td>\n",
              "      <td>38</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bayer Leverkusen</th>\n",
              "      <th>Bundesliga</th>\n",
              "      <td>61</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>7</td>\n",
              "      <td>62</td>\n",
              "      <td>37</td>\n",
              "      <td>7.235294</td>\n",
              "      <td>3.176471</td>\n",
              "      <td>5.693289</td>\n",
              "      <td>7.347595</td>\n",
              "      <td>34</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CSKA Moscow</th>\n",
              "      <th>RFPL</th>\n",
              "      <td>60</td>\n",
              "      <td>19</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>67</td>\n",
              "      <td>27</td>\n",
              "      <td>10.800000</td>\n",
              "      <td>3.533333</td>\n",
              "      <td>8.137093</td>\n",
              "      <td>15.635261</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Southampton</th>\n",
              "      <th>EPL</th>\n",
              "      <td>60</td>\n",
              "      <td>18</td>\n",
              "      <td>6</td>\n",
              "      <td>14</td>\n",
              "      <td>54</td>\n",
              "      <td>33</td>\n",
              "      <td>7.105263</td>\n",
              "      <td>4.815789</td>\n",
              "      <td>9.259540</td>\n",
              "      <td>14.879479</td>\n",
              "      <td>38</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FC Krasnodar</th>\n",
              "      <th>RFPL</th>\n",
              "      <td>60</td>\n",
              "      <td>17</td>\n",
              "      <td>9</td>\n",
              "      <td>4</td>\n",
              "      <td>52</td>\n",
              "      <td>27</td>\n",
              "      <td>7.466667</td>\n",
              "      <td>4.366667</td>\n",
              "      <td>7.180436</td>\n",
              "      <td>9.945586</td>\n",
              "      <td>30</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-968f3698-f26f-4f7a-9a6b-a6adb36eadfe')\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-968f3698-f26f-4f7a-9a6b-a6adb36eadfe 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-968f3698-f26f-4f7a-9a6b-a6adb36eadfe');\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",
              "\n",
              "\n",
              "<div id=\"df-11918bd8-32e1-4dd5-b198-0f0dd9431049\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11918bd8-32e1-4dd5-b198-0f0dd9431049')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11918bd8-32e1-4dd5-b198-0f0dd9431049 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 185
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped = df_final_a3.groupby(level='league')[['goals_scored', 'matches']].sum()\n",
        "grouped['deep_passes_avg'] = df_final_a3.groupby(level='league')[['deep_passes_avg']].mean()\n",
        "grouped['ppda_avg'] = df_final_a3.groupby(level='league')[['ppda_avg']].mean()\n",
        "grouped"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "B0rmx7MBEhOW",
        "outputId": "a32cba68-48b6-46a1-bcba-12a0c8999875"
      },
      "execution_count": 186,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            goals_scored  matches  deep_passes_avg   ppda_avg\n",
              "league                                                       \n",
              "Bundesliga          5396     3672         5.968682  11.170879\n",
              "EPL                 6189     4560         6.567544  11.847171\n",
              "La_liga             6119     4560         5.631579   9.654516\n",
              "Ligue_1             5610     4358         4.741817  11.028938\n",
              "RFPL                3349     2880         5.992014  10.697584\n",
              "Serie_A             6299     4550         5.997967  11.054684"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5795c6e-cccf-4f1b-9ecd-02c1e01e3a42\" class=\"colab-df-container\">\n",
              "    <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>goals_scored</th>\n",
              "      <th>matches</th>\n",
              "      <th>deep_passes_avg</th>\n",
              "      <th>ppda_avg</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>league</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bundesliga</th>\n",
              "      <td>5396</td>\n",
              "      <td>3672</td>\n",
              "      <td>5.968682</td>\n",
              "      <td>11.170879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EPL</th>\n",
              "      <td>6189</td>\n",
              "      <td>4560</td>\n",
              "      <td>6.567544</td>\n",
              "      <td>11.847171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>La_liga</th>\n",
              "      <td>6119</td>\n",
              "      <td>4560</td>\n",
              "      <td>5.631579</td>\n",
              "      <td>9.654516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ligue_1</th>\n",
              "      <td>5610</td>\n",
              "      <td>4358</td>\n",
              "      <td>4.741817</td>\n",
              "      <td>11.028938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RFPL</th>\n",
              "      <td>3349</td>\n",
              "      <td>2880</td>\n",
              "      <td>5.992014</td>\n",
              "      <td>10.697584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Serie_A</th>\n",
              "      <td>6299</td>\n",
              "      <td>4550</td>\n",
              "      <td>5.997967</td>\n",
              "      <td>11.054684</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5795c6e-cccf-4f1b-9ecd-02c1e01e3a42')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f5795c6e-cccf-4f1b-9ecd-02c1e01e3a42 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-f5795c6e-cccf-4f1b-9ecd-02c1e01e3a42');\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",
              "\n",
              "\n",
              "<div id=\"df-4ff19220-d075-4a63-aa68-6daffc2d8817\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4ff19220-d075-4a63-aa68-6daffc2d8817')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4ff19220-d075-4a63-aa68-6daffc2d8817 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped['goals_per_match'] = grouped['goals_scored'] / grouped['matches']\n",
        "grouped"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "qeN9oo7Vei6_",
        "outputId": "dba92e1f-b5ac-47ec-e6bd-e1f3e45d4cbc"
      },
      "execution_count": 192,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            goals_scored  matches  deep_passes_avg   ppda_avg  goals_per_match\n",
              "league                                                                        \n",
              "Bundesliga          5396     3672         5.968682  11.170879         1.469499\n",
              "EPL                 6189     4560         6.567544  11.847171         1.357237\n",
              "La_liga             6119     4560         5.631579   9.654516         1.341886\n",
              "Ligue_1             5610     4358         4.741817  11.028938         1.287288\n",
              "RFPL                3349     2880         5.992014  10.697584         1.162847\n",
              "Serie_A             6299     4550         5.997967  11.054684         1.384396"
            ],
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              "      <th>goals_scored</th>\n",
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              "      <td>11.028938</td>\n",
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              "        document.querySelector('#df-7223b4d0-5c00-4c46-a1e8-fbf354f88c50 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7223b4d0-5c00-4c46-a1e8-fbf354f88c50');\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-3bbab7b7-7183-42e9-91d8-76a06bc6968a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 192
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped['deep_passes_avg'].plot()\n",
        "grouped['ppda_avg'].plot()\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "mOFcG8Gzc4mF",
        "outputId": "5abdf8fc-09c2-4c28-d219-49a37551352f"
      },
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grouped['goals_per_match'].plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "NQSerajOdofo",
        "outputId": "3695722a-b59b-4f6c-a07f-0dec4e40d271"
      },
      "execution_count": 189,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clasificar_equipo_liga(deep_passes_avg, ppda_avg, goals_per_match, position):\n",
        "    formula = 0.7 * deep_passes_avg + 0.9 * ppda_avg + 0.55 * goals_per_match + 0.15 * position\n",
        "    if formula > 16.5:\n",
        "        return 'EPL'\n",
        "    elif formula > 15.5:\n",
        "        return 'La_liga'\n",
        "    elif formula > 14.5:\n",
        "        return 'Serie_A'\n",
        "    elif formula > 13.8:\n",
        "        return 'Ligue_1'\n",
        "    elif formula > 13:\n",
        "        return 'Bundesliga'\n",
        "    else:\n",
        "        return 'RFPL'\n",
        "    return formula"
      ],
      "metadata": {
        "id": "pAFWQhm7fJP5"
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict = df_final_a3.apply(lambda row: clasificar_equipo_liga(row['deep_passes_avg'], row['ppda_avg'], row['goals_scored'] / row['matches'], row['position']), axis=1)\n",
        "correct_predictions = (predict == df_final_a3.index.get_level_values('league')).sum()\n",
        "total_predictions = len(df)\n",
        "accuracy = (correct_predictions / total_predictions) * 100\n",
        "print(\"--------- Accuracy ------------\")\n",
        "print(accuracy)\n",
        "print(\"--------- Predict -------------\")\n",
        "predict.head(15)\n",
        "correct_predictions = (predict == df_final_a3.index.get_level_values('league'))\n",
        "\n",
        "total_epl_predictions = (df_final_a3.index.get_level_values('league') == 'EPL').sum()\n",
        "accuracy_epl = (correct_predictions[df_final_a3.index.get_level_values('league') == 'EPL'].sum() / total_epl_predictions) * 100\n",
        "print(f\"Porcentaje de acierto para 'EPL' : {accuracy_epl:.2f}%\")\n",
        "\n",
        "total_epl_predictions = (df_final_a3.index.get_level_values('league') == 'La_liga').sum()\n",
        "accuracy_epl = (correct_predictions[df_final_a3.index.get_level_values('league') == 'La_liga'].sum() / total_epl_predictions) * 100\n",
        "print(f\"Porcentaje de acierto para 'La_liga' : {accuracy_epl:.2f}%\")\n",
        "\n",
        "total_epl_predictions = (df_final_a3.index.get_level_values('league') == 'Bundesliga').sum()\n",
        "accuracy_epl = (correct_predictions[df_final_a3.index.get_level_values('league') == 'Bundesliga'].sum() / total_epl_predictions) * 100\n",
        "print(f\"Porcentaje de acierto para 'Bundelisga': {accuracy_epl:.2f}%\")\n",
        "\n",
        "total_epl_predictions = (df_final_a3.index.get_level_values('league') == 'Serie_A').sum()\n",
        "accuracy_epl = (correct_predictions[df_final_a3.index.get_level_values('league') == 'Serie_A'].sum() / total_epl_predictions) * 100\n",
        "print(f\"Porcentaje de acierto para 'Serie_A': {accuracy_epl:.2f}%\")\n",
        "\n",
        "total_epl_predictions = (df_final_a3.index.get_level_values('league') == 'Ligue_1').sum()\n",
        "accuracy_epl = (correct_predictions[df_final_a3.index.get_level_values('league') == 'Ligue_1'].sum() / total_epl_predictions) * 100\n",
        "print(f\"Porcentaje de acierto para 'Ligue_a1': {accuracy_epl:.2f}%\")\n",
        "\n",
        "total_epl_predictions = (df_final_a3.index.get_level_values('league') == 'RFPL').sum()\n",
        "accuracy_epl = (correct_predictions[df_final_a3.index.get_level_values('league') == 'RFPL'].sum() / total_epl_predictions) * 100\n",
        "print(f\"Porcentaje de acierto para 'RFPL': {accuracy_epl:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "88dNOpbTmAod",
        "outputId": "770ec4ce-8fdd-4f3d-cdf4-6959e286ec00"
      },
      "execution_count": 193,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------- Accuracy ------------\n",
            "0.5899104963384866\n",
            "--------- Predict -------------\n",
            "Porcentaje de acierto para 'EPL' : 65.00%\n",
            "Porcentaje de acierto para 'La_liga' : 19.17%\n",
            "Porcentaje de acierto para 'Bundelisga': 8.33%\n",
            "Porcentaje de acierto para 'Serie_A': 14.17%\n",
            "Porcentaje de acierto para 'Ligue_a1': 11.67%\n",
            "Porcentaje de acierto para 'RFPL': 4.17%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict.head(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kW7yE9d_qex7",
        "outputId": "957ef67e-e4de-4fed-c412-1d7e1112a997"
      },
      "execution_count": 195,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season  team                  league    \n",
              "2014    Barcelona             La_liga          La_liga\n",
              "        Real Madrid           La_liga              EPL\n",
              "        Chelsea               EPL                  EPL\n",
              "        Juventus              Serie_A          Serie_A\n",
              "        Paris Saint Germain   Ligue_1       Bundesliga\n",
              "        Manchester City       EPL                  EPL\n",
              "        Bayern Munich         Bundesliga       Serie_A\n",
              "        Atletico Madrid       La_liga       Bundesliga\n",
              "        Valencia              La_liga       Bundesliga\n",
              "        Sevilla               La_liga          Serie_A\n",
              "        Lyon                  Ligue_1       Bundesliga\n",
              "        Arsenal               EPL                  EPL\n",
              "        Monaco                Ligue_1             RFPL\n",
              "        Manchester United     EPL           Bundesliga\n",
              "        Roma                  Serie_A       Bundesliga\n",
              "        Marseille             Ligue_1             RFPL\n",
              "        Wolfsburg             Bundesliga    Bundesliga\n",
              "        Lazio                 Serie_A             RFPL\n",
              "        Saint-Etienne         Ligue_1       Bundesliga\n",
              "        Zenit St. Petersburg  RFPL          Bundesliga\n",
              "        Borussia M.Gladbach   Bundesliga           EPL\n",
              "        Fiorentina            Serie_A          Ligue_1\n",
              "        Tottenham             EPL                 RFPL\n",
              "        Napoli                Serie_A          Serie_A\n",
              "        Bordeaux              Ligue_1          Ligue_1\n",
              "        Liverpool             EPL              La_liga\n",
              "        Bayer Leverkusen      Bundesliga          RFPL\n",
              "        CSKA Moscow           RFPL             La_liga\n",
              "        Southampton           EPL              Serie_A\n",
              "        FC Krasnodar          RFPL          Bundesliga\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    }
  ]
}