Untitled3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6flEYfwXCOWe",
        "outputId": "fec22d24-6402-497b-9321-294bb3931fde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount ('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as ols\n",
        "import seaborn as sns\n",
        "path = \"/content/drive/MyDrive/messidata.csv\"\n",
        "df = pd.read_csv(path,encoding='latin1')\n",
        "\n",
        "df.head\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wScKEbPbCRWL",
        "outputId": "5d0c32c2-04a3-441a-aa04-276661241afe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of     Season            Competition     Matchday      Date Venue  \\\n",
              "0    04/05                 LaLiga           34  05-01/05     H   \n",
              "1    05/06  UEFA Champions League  Group Stage  11-02/05     H   \n",
              "2    05/06                 LaLiga           13  11/27/05     H   \n",
              "3    05/06                 LaLiga           19   1/15/06     H   \n",
              "4    05/06                 LaLiga           20   1/22/06     H   \n",
              "..     ...                    ...          ...       ...   ...   \n",
              "692  22/23                Ligue 1            9   10/1/22     H   \n",
              "693  22/23  UEFA Champions League  Group Stage   10/5/22     A   \n",
              "694  22/23                Ligue 1           12  10/21/22     A   \n",
              "695  22/23  UEFA Champions League  Group Stage  10/25/22     H   \n",
              "696  22/23  UEFA Champions League  Group Stage  10/25/22     H   \n",
              "\n",
              "                    Club              Opponent Result Playing_Position Minute  \\\n",
              "0           FC Barcelona     Albacete Balompie   2:00               CF   90+1   \n",
              "1           FC Barcelona  Panathinaikos Athens   5:00               RW     34   \n",
              "2           FC Barcelona      Racing Santander   4:01               RW     51   \n",
              "3           FC Barcelona       Athletic Bilbao   2:01               RW     50   \n",
              "4           FC Barcelona      Deportivo Alaves   2:00               CF     82   \n",
              "..                   ...                   ...    ...              ...    ...   \n",
              "692  Paris Saint-Germain              OGC Nice    2:1               AM    28'   \n",
              "693  Paris Saint-Germain            SL Benfica    1:1               AM    22'   \n",
              "694  Paris Saint-Germain            AC Ajaccio    0:3               CF    78'   \n",
              "695  Paris Saint-Germain         Maccabi Haifa    7:2               SS    19'   \n",
              "696  Paris Saint-Germain         Maccabi Haifa    7:2               SS    44'   \n",
              "\n",
              "    At_score              Type        Goal_assist  \n",
              "0       2:00  Left-footed shot  Ronaldinho Gaacho  \n",
              "1       3:00  Left-footed shot                NaN  \n",
              "2       2:00  Left-footed shot        Samuel Etoo  \n",
              "3       2:01  Left-footed shot    Mark van Bommel  \n",
              "4       2:00  Left-footed shot  Ronaldinho Gaacho  \n",
              "..       ...               ...                ...  \n",
              "692      1:0  Direct free kick                NaN  \n",
              "693      0:1  Left-footed shot             Neymar  \n",
              "694      0:2  Left-footed shot      Kylian Mbappé  \n",
              "695      1:0  Left-footed shot      Kylian Mbappé  \n",
              "696      4:1  Left-footed shot      Kylian Mbappé  \n",
              "\n",
              "[697 rows x 13 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.isnull().sum())\n",
        "sns.heatmap(df.isnull(),cbar=False, cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "M804H12OEJ4Y",
        "outputId": "e2b15667-a1e6-4188-a281-ed4e262d5665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Season                0\n",
            "Competition           0\n",
            "Matchday              0\n",
            "Date                  0\n",
            "Venue                 0\n",
            "Club                  0\n",
            "Opponent              0\n",
            "Result                0\n",
            "Playing_Position      0\n",
            "Minute                0\n",
            "At_score              0\n",
            "Type                  1\n",
            "Goal_assist         213\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d6ec2d4f0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.fillna('place kick',inplace=True)\n",
        "print(df.isnull().sum())\n",
        "sns.heatmap(df.isnull(),cbar=False, cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "xdbXDj7VET_w",
        "outputId": "5d4fb4b4-e44f-48b6-e46f-8f1e5124f5ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Season              0\n",
            "Competition         0\n",
            "Matchday            0\n",
            "Date                0\n",
            "Venue               0\n",
            "Club                0\n",
            "Opponent            0\n",
            "Result              0\n",
            "Playing_Position    0\n",
            "Minute              0\n",
            "At_score            0\n",
            "Type                0\n",
            "Goal_assist         0\n",
            "dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2d6c0ee220>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.Date[0:5])\n",
        "df.Date = pd.to_datetime(df.Date)\n",
        "df.Date[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPpbHKnrEYAG",
        "outputId": "f20d96d0-93bf-421e-b9c7-b42688c4cb10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    05-01/05\n",
            "1    11-02/05\n",
            "2    11/27/05\n",
            "3     1/15/06\n",
            "4     1/22/06\n",
            "Name: Date, dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0   2005-05-01\n",
              "1   2005-11-02\n",
              "2   2005-11-27\n",
              "3   2006-01-15\n",
              "4   2006-01-22\n",
              "Name: Date, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.insert(12, \"goals\", 1, True)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zOZRkHx3EbUQ",
        "outputId": "99d8d0b1-8d7d-4682-a4fb-3a84e049b08c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Season            Competition     Matchday       Date Venue          Club  \\\n",
              "0  04/05                 LaLiga           34 2005-05-01     H  FC Barcelona   \n",
              "1  05/06  UEFA Champions League  Group Stage 2005-11-02     H  FC Barcelona   \n",
              "2  05/06                 LaLiga           13 2005-11-27     H  FC Barcelona   \n",
              "3  05/06                 LaLiga           19 2006-01-15     H  FC Barcelona   \n",
              "4  05/06                 LaLiga           20 2006-01-22     H  FC Barcelona   \n",
              "\n",
              "               Opponent Result Playing_Position Minute At_score  \\\n",
              "0     Albacete Balompie   2:00               CF   90+1     2:00   \n",
              "1  Panathinaikos Athens   5:00               RW     34     3:00   \n",
              "2      Racing Santander   4:01               RW     51     2:00   \n",
              "3       Athletic Bilbao   2:01               RW     50     2:01   \n",
              "4      Deportivo Alaves   2:00               CF     82     2:00   \n",
              "\n",
              "               Type  goals        Goal_assist  \n",
              "0  Left-footed shot      1  Ronaldinho Gaacho  \n",
              "1  Left-footed shot      1         place kick  \n",
              "2  Left-footed shot      1        Samuel Etoo  \n",
              "3  Left-footed shot      1    Mark van Bommel  \n",
              "4  Left-footed shot      1  Ronaldinho Gaacho  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-08e39482-29fc-4b7b-9e1b-ad7e7647a93b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .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>Season</th>\n",
              "      <th>Competition</th>\n",
              "      <th>Matchday</th>\n",
              "      <th>Date</th>\n",
              "      <th>Venue</th>\n",
              "      <th>Club</th>\n",
              "      <th>Opponent</th>\n",
              "      <th>Result</th>\n",
              "      <th>Playing_Position</th>\n",
              "      <th>Minute</th>\n",
              "      <th>At_score</th>\n",
              "      <th>Type</th>\n",
              "      <th>goals</th>\n",
              "      <th>Goal_assist</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>04/05</td>\n",
              "      <td>LaLiga</td>\n",
              "      <td>34</td>\n",
              "      <td>2005-05-01</td>\n",
              "      <td>H</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Albacete Balompie</td>\n",
              "      <td>2:00</td>\n",
              "      <td>CF</td>\n",
              "      <td>90+1</td>\n",
              "      <td>2:00</td>\n",
              "      <td>Left-footed shot</td>\n",
              "      <td>1</td>\n",
              "      <td>Ronaldinho Gaacho</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>05/06</td>\n",
              "      <td>UEFA Champions League</td>\n",
              "      <td>Group Stage</td>\n",
              "      <td>2005-11-02</td>\n",
              "      <td>H</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Panathinaikos Athens</td>\n",
              "      <td>5:00</td>\n",
              "      <td>RW</td>\n",
              "      <td>34</td>\n",
              "      <td>3:00</td>\n",
              "      <td>Left-footed shot</td>\n",
              "      <td>1</td>\n",
              "      <td>place kick</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>05/06</td>\n",
              "      <td>LaLiga</td>\n",
              "      <td>13</td>\n",
              "      <td>2005-11-27</td>\n",
              "      <td>H</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Racing Santander</td>\n",
              "      <td>4:01</td>\n",
              "      <td>RW</td>\n",
              "      <td>51</td>\n",
              "      <td>2:00</td>\n",
              "      <td>Left-footed shot</td>\n",
              "      <td>1</td>\n",
              "      <td>Samuel Etoo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>05/06</td>\n",
              "      <td>LaLiga</td>\n",
              "      <td>19</td>\n",
              "      <td>2006-01-15</td>\n",
              "      <td>H</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Athletic Bilbao</td>\n",
              "      <td>2:01</td>\n",
              "      <td>RW</td>\n",
              "      <td>50</td>\n",
              "      <td>2:01</td>\n",
              "      <td>Left-footed shot</td>\n",
              "      <td>1</td>\n",
              "      <td>Mark van Bommel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>05/06</td>\n",
              "      <td>LaLiga</td>\n",
              "      <td>20</td>\n",
              "      <td>2006-01-22</td>\n",
              "      <td>H</td>\n",
              "      <td>FC Barcelona</td>\n",
              "      <td>Deportivo Alaves</td>\n",
              "      <td>2:00</td>\n",
              "      <td>CF</td>\n",
              "      <td>82</td>\n",
              "      <td>2:00</td>\n",
              "      <td>Left-footed shot</td>\n",
              "      <td>1</td>\n",
              "      <td>Ronaldinho Gaacho</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "goal_seasons= pd.DataFrame(df.groupby(df['Season'].tolist()).size(),columns=['goals'])\n",
        "goal_seasons[0:5]"
      ],
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        "colab": {
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       goals\n",
              "04/05      1\n",
              "05/06      8\n",
              "06/07     17\n",
              "07/08     16\n",
              "08/09     38"
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              "      <td>16</td>\n",
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              "      <th>08/09</th>\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "goal_competition= pd.DataFrame(df.groupby(df.Competition.tolist()).size(),columns=['goals'])\n",
        "goal_competition"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "XoXnDf5NEizq",
        "outputId": "4ba0d99c-3916-402a-95ad-e1556bf84aca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        goals\n",
              "Champions League            1\n",
              "Copa del Rey               56\n",
              "FIFA Club World Cup         5\n",
              "LaLiga                    474\n",
              "Ligue 1                    14\n",
              "Supercopa                  14\n",
              "TrophÃ©e des Champions      1\n",
              "Trophée des Champions       1\n",
              "UEFA Champions League     128\n",
              "UEFA Super Cup              3"
            ],
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              "      <td>56</td>\n",
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              "      <td>5</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "np.random.seed(42)\n",
        "\n",
        "# Generate graph using Figure Constructor\n",
        "layout = go.Layout(\n",
        "    title=\"Scoring over the seasons\",\n",
        "    xaxis_title=\"Seasons\",\n",
        "    yaxis_title=\"Goals\"\n",
        ")\n",
        "\n",
        "fig = go.Figure(\n",
        "    data=go.Scatter(x= goal_seasons.index, y=goal_seasons.goals),\n",
        "    layout=layout\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
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          "height": 542
        },
        "id": "AOeVkBYKEnQM",
        "outputId": "a67c1fc9-48d2-457f-d5b3-451276dd9a33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "layout = go.Layout(\n",
        "    title=\"Scoring over the competitions\",\n",
        "    xaxis_title=\"competitions\",\n",
        "    yaxis_title=\"Goals\"\n",
        ")\n",
        "\n",
        "fig = go.Figure(\n",
        "    data=go.Bar(x= goal_competition.index,y= goal_competition.goals),\n",
        "    layout=layout\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "VnDs3hslEuBk",
        "outputId": "c6aeee2a-395f-4e8f-ba1f-4f3fb391861d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "select_indices = list(np.where(df[\"Competition\"] == 'LaLiga')[0])\n",
        "LaLiga = df.iloc[select_indices]"
      ],
      "metadata": {
        "id": "pnR2vYCrE0BF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LaLiga_seasons= pd.DataFrame(LaLiga.groupby(LaLiga['Season'].tolist()).size(),columns=['goals'])\n",
        "layout1 = go.Layout(\n",
        "    title=\"Scoring over LaLiga seasons\",\n",
        "    xaxis_title=\"LaLiga seasons\",\n",
        "    yaxis_title=\"Goals\"\n",
        ")\n",
        "\n",
        "fig1 = go.Figure(\n",
        "    data=go.Bar(x= sorted(LaLiga_seasons.index),y= LaLiga_seasons.goals),\n",
        "    layout=layout1\n",
        ")\n",
        "fig1.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "2nI_Y0VZE26q",
        "outputId": "a169c664-6597-4770-a1dc-9a6c1e8d0a14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "source": [
        "select_indices = list(np.where(df[\"Competition\"] == 'UEFA Champions League')[0])\n",
        "Champions_League = df.iloc[select_indices]\n",
        "Champions_League = pd.DataFrame(Champions_League.groupby(Champions_League['Season'].tolist()).size(),columns=['goals'])\n",
        "layout2 = go.Layout(\n",
        "    title=\"Scoring over the UEFA Champions League\",\n",
        "    xaxis_title=\"Champions League Seasons\",\n",
        "    yaxis_title=\"Goals\"\n",
        ")\n",
        "\n",
        "fig2 = go.Figure(\n",
        "    data=go.Bar(x= Champions_League.index,y= Champions_League.goals),\n",
        "    layout=layout2\n",
        ")\n",
        "fig2.show()"
      ],
      "metadata": {
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          "height": 542
        },
        "id": "x4WCbsDcE9wD",
        "outputId": "15cc6f0a-dc7b-42f0-db20-6c67553ae45d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Competition\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yb9sRmhMGCHg",
        "outputId": "27fc1ac0-dea1-4226-8a94-30196034ef38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['LaLiga', 'UEFA Champions League', 'Copa del Rey', 'Supercopa',\n",
              "       'FIFA Club World Cup', 'UEFA Super Cup', 'Ligue 1',\n",
              "       'TrophÃ©e des Champions', 'Trophée des Champions',\n",
              "       'Champions League'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "Copa = list(np.where(df[\"Competition\"] == 'Copa del Rey')[0])\n",
        "Supercopa = list(np.where(df[\"Competition\"] == 'Supercopa')[0])\n",
        "FIFA  = list(np.where(df[\"Competition\"] == 'FIFA Club World Cup')[0])\n",
        "UEFA  = list(np.where(df[\"Competition\"] == 'UEFA Super Cup')[0])\n",
        "Ligue  = list(np.where(df[\"Competition\"] == 'Ligue 1')[0])\n",
        "\n",
        "Champions = df.iloc[Copa + Supercopa + FIFA + UEFA + Ligue]\n",
        "\n",
        "data = px.bar( x= Champions.Season, y= Champions.goals, color= Champions.Competition ,barmode='group')\n",
        "data.update_layout(    \n",
        "    title=\"Scoring on competitions for each season\",\n",
        "    xaxis_title=\"competitions\",\n",
        "    yaxis_title=\"Goals\")\n",
        "data.update_xaxes(showgrid=True)\n",
        "\n",
        "data.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "OHwAPaR1GH6-",
        "outputId": "5461d9a6-4591-4662-cd70-23d6c57dd755"
      },
      "execution_count": null,
      "outputs": [
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "goal_match= pd.DataFrame(df.groupby(df.Date.tolist()).size(),columns=['goals'])\n",
        "goal_match = goal_match.rename_axis('Date').reset_index()\n",
        "goal_match = goal_match.merge(df, how='inner', on='Date')\n",
        "import plotly.express as px\n",
        "\n",
        "fig = px.scatter(x= sorted(goal_match.Minute), y= goal_match.goals_x, color= sorted(goal_match.Season))\n",
        "fig.update_layout(    \n",
        "    title=\"Minutes scored goals per season\",\n",
        "    xaxis_title=\"Minute\",\n",
        "    yaxis_title=\"Goals\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "gO4NQKqDGQi4",
        "outputId": "ef407ccf-ecf5-4390-81d2-5e941cf283ce"
      },
      "execution_count": null,
      "outputs": [
        {
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    },
    {
      "cell_type": "code",
      "source": [
        "df['Playing_Position'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a5xfPoTuGpbP",
        "outputId": "bb8ffa35-5016-4a40-a70f-f0d1a7b03296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['CF', 'RW', 'LW', 'SS', 'CF ', 'AM', 'RW ', 'AM ', 'SS '],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Playing_Position= df.Playing_Position.str.replace('CF ','CF')\n",
        "df.Playing_Position= df.Playing_Position.str.replace('RW ','RW')\n",
        "df.Playing_Position= df.Playing_Position.str.replace('AM ','AM')\n",
        "df.Playing_Position= df.Playing_Position.str.replace('SS ','SS')\n",
        "df['Playing_Position'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p9F88MlmGsB7",
        "outputId": "e3f7f94a-6373-43fb-f7bc-b183718179da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['CF', 'RW', 'LW', 'SS', 'AM'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pos = pd.DataFrame(df.groupby(df['Playing_Position'].tolist()).size(),columns=['goals'])\n",
        "pos = pos.rename_axis('Playing_Position').reset_index()\n",
        "pos = pos.merge(df, how='inner', on='Playing_Position')\n",
        "pos = pos[['Playing_Position','goals_x','Season']]\n",
        "pos"
      ],
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "aqChrS6sGvMd",
        "outputId": "b01a1b5c-d409-485d-c6a8-41ce9aafbabf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Playing_Position  goals_x Season\n",
              "0                 AM       15  14/15\n",
              "1                 AM       15  15/16\n",
              "2                 AM       15  15/16\n",
              "3                 AM       15  16/17\n",
              "4                 AM       15  16/17\n",
              "..               ...      ...    ...\n",
              "692               SS       53  20/21\n",
              "693               SS       53  20/21\n",
              "694               SS       53  22/23\n",
              "695               SS       53  22/23\n",
              "696               SS       53  22/23\n",
              "\n",
              "[697 rows x 3 columns]"
            ],
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              "  <thead>\n",
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              "      <th>Playing_Position</th>\n",
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              "      <td>14/15</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AM</td>\n",
              "      <td>15</td>\n",
              "      <td>15/16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AM</td>\n",
              "      <td>15</td>\n",
              "      <td>15/16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AM</td>\n",
              "      <td>15</td>\n",
              "      <td>16/17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AM</td>\n",
              "      <td>15</td>\n",
              "      <td>16/17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>692</th>\n",
              "      <td>SS</td>\n",
              "      <td>53</td>\n",
              "      <td>20/21</td>\n",
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              "    <tr>\n",
              "      <th>693</th>\n",
              "      <td>SS</td>\n",
              "      <td>53</td>\n",
              "      <td>20/21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>694</th>\n",
              "      <td>SS</td>\n",
              "      <td>53</td>\n",
              "      <td>22/23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>SS</td>\n",
              "      <td>53</td>\n",
              "      <td>22/23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>696</th>\n",
              "      <td>SS</td>\n",
              "      <td>53</td>\n",
              "      <td>22/23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>697 rows × 3 columns</p>\n",
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            ]
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.pie(values= pos.goals_x, names= pos.Playing_Position,\n",
        "             title=\"Most of Messi's position scored goals\",\n",
        "             hover_data=[pos.Season])\n",
        "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
        "fig.show()"
      ],
      "metadata": {
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          "height": 542
        },
        "id": "2B_mrk8XGzAZ",
        "outputId": "0a15e83e-f5cb-4588-c133-71895b4156d9"
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