FIFA_world_cup_analysis_project.ipynb

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      "name": "FIFA world cup analysis project.ipynb",
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      "cell_type": "markdown",
      "source": [
        "## Import the packages"
      ],
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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "import plotly as py\n",
        "import cufflinks as cf"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the dataset"
      ],
      "metadata": {
        "id": "3AKzX6ZmEMDu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "players = pd.read_csv(\"https://raw.githubusercontent.com/mohamedfawas/Analysis-on-FIFA-world-cup-data/main/WorldCupPlayers.csv\")\n",
        "matches = pd.read_csv(\"https://raw.githubusercontent.com/mohamedfawas/Analysis-on-FIFA-world-cup-data/main/WorldCupMatches.csv\")\n",
        "world_cup = pd.read_csv(\"https://raw.githubusercontent.com/mohamedfawas/Analysis-on-FIFA-world-cup-data/main/WorldCups.csv\")"
      ],
      "metadata": {
        "id": "2hx321WJDmPe"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Display first five rows of the data"
      ],
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        "id": "XEhym0QlEl1q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Player's data"
      ],
      "metadata": {
        "id": "lT38na35ErGF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "players.head()"
      ],
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              "   RoundID  MatchID Team Initials           Coach Name Line-up  Shirt Number  \\\n",
              "0      201     1096           FRA  CAUDRON Raoul (FRA)       S             0   \n",
              "1      201     1096           MEX     LUQUE Juan (MEX)       S             0   \n",
              "2      201     1096           FRA  CAUDRON Raoul (FRA)       S             0   \n",
              "3      201     1096           MEX     LUQUE Juan (MEX)       S             0   \n",
              "4      201     1096           FRA  CAUDRON Raoul (FRA)       S             0   \n",
              "\n",
              "        Player Name Position Event  \n",
              "0       Alex THEPOT       GK   NaN  \n",
              "1   Oscar BONFIGLIO       GK   NaN  \n",
              "2  Marcel LANGILLER      NaN  G40'  \n",
              "3      Juan CARRENO      NaN  G70'  \n",
              "4   Ernest LIBERATI      NaN   NaN  "
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        "players.tail()"
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              "       RoundID    MatchID Team Initials               Coach Name Line-up  \\\n",
              "37779   255959  300186501           ARG  SABELLA Alejandro (ARG)       N   \n",
              "37780   255959  300186501           GER       LOEW Joachim (GER)       N   \n",
              "37781   255959  300186501           ARG  SABELLA Alejandro (ARG)       N   \n",
              "37782   255959  300186501           GER       LOEW Joachim (GER)       N   \n",
              "37783   255959  300186501           ARG  SABELLA Alejandro (ARG)       N   \n",
              "\n",
              "       Shirt Number Player Name Position       Event  \n",
              "37779            19     ALVAREZ      NaN         NaN  \n",
              "37780             6     KHEDIRA      NaN         NaN  \n",
              "37781            20      AGUERO      NaN  IH46' Y65'  \n",
              "37782            21     MUSTAFI      NaN         NaN  \n",
              "37783            23     BASANTA      NaN         NaN  "
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      "source": [
        "#### Match data "
      ],
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        "id": "yLytRvl5Ewv8"
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        "matches.head()"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     Year              Datetime    Stage         Stadium         City  \\\n",
              "0  1930.0  13 Jul 1930 - 15:00   Group 1         Pocitos  Montevideo    \n",
              "1  1930.0  13 Jul 1930 - 15:00   Group 4  Parque Central  Montevideo    \n",
              "2  1930.0  14 Jul 1930 - 12:45   Group 2  Parque Central  Montevideo    \n",
              "3  1930.0  14 Jul 1930 - 14:50   Group 3         Pocitos  Montevideo    \n",
              "4  1930.0  15 Jul 1930 - 16:00   Group 1  Parque Central  Montevideo    \n",
              "\n",
              "  Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
              "0         France              4.0              1.0         Mexico   \n",
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              "2     Yugoslavia              2.0              1.0         Brazil   \n",
              "3        Romania              3.0              1.0           Peru   \n",
              "4      Argentina              1.0              0.0         France   \n",
              "\n",
              "  Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
              "0                     4444.0                   3.0                   0.0   \n",
              "1                    18346.0                   2.0                   0.0   \n",
              "2                    24059.0                   2.0                   0.0   \n",
              "3                     2549.0                   1.0                   0.0   \n",
              "4                    23409.0                   0.0                   0.0   \n",
              "\n",
              "                  Referee               Assistant 1  \\\n",
              "0  LOMBARDI Domingo (URU)     CRISTOPHE Henry (BEL)   \n",
              "1       MACIAS Jose (ARG)  MATEUCCI Francisco (URU)   \n",
              "2     TEJADA Anibal (URU)   VALLARINO Ricardo (URU)   \n",
              "3   WARNKEN Alberto (CHI)       LANGENUS Jean (BEL)   \n",
              "4     REGO Gilberto (BRA)      SAUCEDO Ulises (BOL)   \n",
              "\n",
              "                  Assistant 2  RoundID  MatchID Home Team Initials  \\\n",
              "0         REGO Gilberto (BRA)    201.0   1096.0                FRA   \n",
              "1       WARNKEN Alberto (CHI)    201.0   1090.0                USA   \n",
              "2         BALWAY Thomas (FRA)    201.0   1093.0                YUG   \n",
              "3    MATEUCCI Francisco (URU)    201.0   1098.0                ROU   \n",
              "4  RADULESCU Constantin (ROU)    201.0   1085.0                ARG   \n",
              "\n",
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              "0                MEX  \n",
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              "      Year Datetime Stage Stadium City Home Team Name  Home Team Goals  \\\n",
              "4567   NaN      NaN   NaN     NaN  NaN            NaN              NaN   \n",
              "4568   NaN      NaN   NaN     NaN  NaN            NaN              NaN   \n",
              "4569   NaN      NaN   NaN     NaN  NaN            NaN              NaN   \n",
              "4570   NaN      NaN   NaN     NaN  NaN            NaN              NaN   \n",
              "4571   NaN      NaN   NaN     NaN  NaN            NaN              NaN   \n",
              "\n",
              "      Away Team Goals Away Team Name Win conditions  Attendance  \\\n",
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              "4568              NaN            NaN            NaN         NaN   \n",
              "4569              NaN            NaN            NaN         NaN   \n",
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              "4571              NaN            NaN            NaN         NaN   \n",
              "\n",
              "      Half-time Home Goals  Half-time Away Goals Referee Assistant 1  \\\n",
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### World cup year by year data"
      ],
      "metadata": {
        "id": "Pj_La3FjE02Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "world_cup.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u-VPaF06E0XG",
        "outputId": "920c51b8-d0f2-4e19-cda3-a1dccc7993f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year      Country      Winner      Runners-Up    Third      Fourth  \\\n",
              "0  1930      Uruguay     Uruguay       Argentina      USA  Yugoslavia   \n",
              "1  1934        Italy       Italy  Czechoslovakia  Germany     Austria   \n",
              "2  1938       France       Italy         Hungary   Brazil      Sweden   \n",
              "3  1950       Brazil     Uruguay          Brazil   Sweden       Spain   \n",
              "4  1954  Switzerland  Germany FR         Hungary  Austria     Uruguay   \n",
              "\n",
              "   GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n",
              "0           70              13             18    590.549  \n",
              "1           70              16             17    363.000  \n",
              "2           84              15             18    375.700  \n",
              "3           88              13             22  1.045.246  \n",
              "4          140              16             26    768.607  "
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              "      <td>1938</td>\n",
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              "      <td>Sweden</td>\n",
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              "      <th>3</th>\n",
              "      <td>1950</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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      "cell_type": "code",
      "source": [
        "world_cup.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "x5l6yWZhFJGl",
        "outputId": "82dfc4b6-2e8a-40b8-8df9-6109cd0f75e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Year       Country   Winner   Runners-Up        Third          Fourth  \\\n",
              "15  1998        France   France       Brazil      Croatia     Netherlands   \n",
              "16  2002   Korea/Japan   Brazil      Germany       Turkey  Korea Republic   \n",
              "17  2006       Germany    Italy       France      Germany        Portugal   \n",
              "18  2010  South Africa    Spain  Netherlands      Germany         Uruguay   \n",
              "19  2014        Brazil  Germany    Argentina  Netherlands          Brazil   \n",
              "\n",
              "    GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n",
              "15          171              32             64  2.785.100  \n",
              "16          161              32             64  2.705.197  \n",
              "17          147              32             64  3.359.439  \n",
              "18          145              32             64  3.178.856  \n",
              "19          171              32             64  3.386.810  "
            ],
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning"
      ],
      "metadata": {
        "id": "dg86XN8OFQLL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Earlier we saw that at the end of the dataset of Worldcup matches there are a lot of empty rows. We need to remove those rows from our dataset. "
      ],
      "metadata": {
        "id": "2A9lwn1hFU1U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches.dropna(subset=['Year'], inplace=True)"
      ],
      "metadata": {
        "id": "Qir0BRaKFOMP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display the  data after removing empty rows\n",
        "matches.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "-eqQlCuiFkcD",
        "outputId": "146a2ae9-ae30-42b2-8026-2cdf5b00316e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Year              Datetime                     Stage  \\\n",
              "847  2014.0  05 Jul 2014 - 17:00             Quarter-finals   \n",
              "848  2014.0  08 Jul 2014 - 17:00                Semi-finals   \n",
              "849  2014.0  09 Jul 2014 - 17:00                Semi-finals   \n",
              "850  2014.0  12 Jul 2014 - 17:00   Play-off for third place   \n",
              "851  2014.0  13 Jul 2014 - 16:00                      Final   \n",
              "\n",
              "                 Stadium             City Home Team Name  Home Team Goals  \\\n",
              "847     Arena Fonte Nova        Salvador     Netherlands              0.0   \n",
              "848     Estadio Mineirao  Belo Horizonte          Brazil              1.0   \n",
              "849   Arena de Sao Paulo       Sao Paulo     Netherlands              0.0   \n",
              "850     Estadio Nacional        Brasilia          Brazil              0.0   \n",
              "851  Estadio do Maracana  Rio De Janeiro         Germany              1.0   \n",
              "\n",
              "     Away Team Goals Away Team Name                         Win conditions  \\\n",
              "847              0.0     Costa Rica  Netherlands win on penalties (4 - 3)    \n",
              "848              7.0        Germany                                          \n",
              "849              0.0      Argentina    Argentina win on penalties (2 - 4)    \n",
              "850              3.0    Netherlands                                          \n",
              "851              0.0      Argentina          Germany win after extra time    \n",
              "\n",
              "     Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
              "847     51179.0                   0.0                   0.0   \n",
              "848     58141.0                   0.0                   5.0   \n",
              "849     63267.0                   0.0                   0.0   \n",
              "850     68034.0                   0.0                   2.0   \n",
              "851     74738.0                   0.0                   0.0   \n",
              "\n",
              "                   Referee                  Assistant 1  \\\n",
              "847  Ravshan IRMATOV (UZB)  RASULOV Abduxamidullo (UZB)   \n",
              "848  RODRIGUEZ Marco (MEX)      TORRENTERA Marvin (MEX)   \n",
              "849     C�neyt �AKIR (TUR)         DURAN Bahattin (TUR)   \n",
              "850  HAIMOUDI Djamel (ALG)         ACHIK Redouane (MAR)   \n",
              "851   Nicola RIZZOLI (ITA)        Renato FAVERANI (ITA)   \n",
              "\n",
              "                  Assistant 2   RoundID      MatchID Home Team Initials  \\\n",
              "847  KOCHKAROV Bakhadyr (KGZ)  255953.0  300186488.0                NED   \n",
              "848     QUINTERO Marcos (MEX)  255955.0  300186474.0                BRA   \n",
              "849         ONGUN Tarik (TUR)  255955.0  300186490.0                NED   \n",
              "850   ETCHIALI Abdelhak (ALG)  255957.0  300186502.0                BRA   \n",
              "851      Andrea STEFANI (ITA)  255959.0  300186501.0                GER   \n",
              "\n",
              "    Away Team Initials  \n",
              "847                CRC  \n",
              "848                GER  \n",
              "849                ARG  \n",
              "850                NED  \n",
              "851                ARG  "
            ],
            "text/html": [
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              "<style scoped>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Datetime</th>\n",
              "      <th>Stage</th>\n",
              "      <th>Stadium</th>\n",
              "      <th>City</th>\n",
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              "      <th>Home Team Goals</th>\n",
              "      <th>Away Team Goals</th>\n",
              "      <th>Away Team Name</th>\n",
              "      <th>Win conditions</th>\n",
              "      <th>Attendance</th>\n",
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              "      <th>Half-time Away Goals</th>\n",
              "      <th>Referee</th>\n",
              "      <th>Assistant 1</th>\n",
              "      <th>Assistant 2</th>\n",
              "      <th>RoundID</th>\n",
              "      <th>MatchID</th>\n",
              "      <th>Home Team Initials</th>\n",
              "      <th>Away Team Initials</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>847</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>05 Jul 2014 - 17:00</td>\n",
              "      <td>Quarter-finals</td>\n",
              "      <td>Arena Fonte Nova</td>\n",
              "      <td>Salvador</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Costa Rica</td>\n",
              "      <td>Netherlands win on penalties (4 - 3)</td>\n",
              "      <td>51179.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Ravshan IRMATOV (UZB)</td>\n",
              "      <td>RASULOV Abduxamidullo (UZB)</td>\n",
              "      <td>KOCHKAROV Bakhadyr (KGZ)</td>\n",
              "      <td>255953.0</td>\n",
              "      <td>300186488.0</td>\n",
              "      <td>NED</td>\n",
              "      <td>CRC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>848</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>08 Jul 2014 - 17:00</td>\n",
              "      <td>Semi-finals</td>\n",
              "      <td>Estadio Mineirao</td>\n",
              "      <td>Belo Horizonte</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td></td>\n",
              "      <td>58141.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>RODRIGUEZ Marco (MEX)</td>\n",
              "      <td>TORRENTERA Marvin (MEX)</td>\n",
              "      <td>QUINTERO Marcos (MEX)</td>\n",
              "      <td>255955.0</td>\n",
              "      <td>300186474.0</td>\n",
              "      <td>BRA</td>\n",
              "      <td>GER</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>849</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>09 Jul 2014 - 17:00</td>\n",
              "      <td>Semi-finals</td>\n",
              "      <td>Arena de Sao Paulo</td>\n",
              "      <td>Sao Paulo</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Argentina win on penalties (2 - 4)</td>\n",
              "      <td>63267.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>C�neyt �AKIR (TUR)</td>\n",
              "      <td>DURAN Bahattin (TUR)</td>\n",
              "      <td>ONGUN Tarik (TUR)</td>\n",
              "      <td>255955.0</td>\n",
              "      <td>300186490.0</td>\n",
              "      <td>NED</td>\n",
              "      <td>ARG</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>12 Jul 2014 - 17:00</td>\n",
              "      <td>Play-off for third place</td>\n",
              "      <td>Estadio Nacional</td>\n",
              "      <td>Brasilia</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td></td>\n",
              "      <td>68034.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>HAIMOUDI Djamel (ALG)</td>\n",
              "      <td>ACHIK Redouane (MAR)</td>\n",
              "      <td>ETCHIALI Abdelhak (ALG)</td>\n",
              "      <td>255957.0</td>\n",
              "      <td>300186502.0</td>\n",
              "      <td>BRA</td>\n",
              "      <td>NED</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>851</th>\n",
              "      <td>2014.0</td>\n",
              "      <td>13 Jul 2014 - 16:00</td>\n",
              "      <td>Final</td>\n",
              "      <td>Estadio do Maracana</td>\n",
              "      <td>Rio De Janeiro</td>\n",
              "      <td>Germany</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>Germany win after extra time</td>\n",
              "      <td>74738.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Nicola RIZZOLI (ITA)</td>\n",
              "      <td>Renato FAVERANI (ITA)</td>\n",
              "      <td>Andrea STEFANI (ITA)</td>\n",
              "      <td>255959.0</td>\n",
              "      <td>300186501.0</td>\n",
              "      <td>GER</td>\n",
              "      <td>ARG</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the dataset we can see some countries established after 1930 are labeled with a tag rn\"> . We need to remove that tag from the names of the countries."
      ],
      "metadata": {
        "id": "gfm5BlLmQbGH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "names = matches[matches['Home Team Name'].str.contains('rn\">')]['Home Team Name'].value_counts()\n",
        "names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Izd_pAqDGHJo",
        "outputId": "14c45d33-b959-4b84-e2bd-5b0648fb08cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rn\">Republic of Ireland       5\n",
              "rn\">United Arab Emirates      1\n",
              "rn\">Trinidad and Tobago       1\n",
              "rn\">Serbia and Montenegro     1\n",
              "rn\">Bosnia and Herzegovina    1\n",
              "Name: Home Team Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a list of those countries which are labeled incorrectly \n",
        "wrong = list(names.index)\n",
        "wrong"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdfq9fGeQr45",
        "outputId": "a6ae9aaa-51b6-4c86-efed-41b729f89558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['rn\">Republic of Ireland',\n",
              " 'rn\">United Arab Emirates',\n",
              " 'rn\">Trinidad and Tobago',\n",
              " 'rn\">Serbia and Montenegro',\n",
              " 'rn\">Bosnia and Herzegovina']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Correct those incorrectly labeled country names\n",
        "correct = [name.split('>')[1] for name in wrong]\n",
        "correct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RTAlHXemQ9fS",
        "outputId": "7a78ff3b-e33c-4a4f-e654-09800f9d47cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Republic of Ireland',\n",
              " 'United Arab Emirates',\n",
              " 'Trinidad and Tobago',\n",
              " 'Serbia and Montenegro',\n",
              " 'Bosnia and Herzegovina']"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the stadium name data we can see that names of some stadiums are changed later , So we need to make both the old and stadium have name same. So, we will update the old stadium name with the present name. This will help us for an easier data analysis. "
      ],
      "metadata": {
        "id": "ogk1rio6RKLM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "old_name = ['Germany FR', 'Maracan� - Est�dio Jornalista M�rio Filho', 'Estadio do Maracana']\n",
        "new_name = ['Germany', 'Maracan Stadium', 'Maracan Stadium']"
      ],
      "metadata": {
        "id": "vPX1TQGBQx2B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wrong = wrong + old_name\n",
        "correct = correct + new_name"
      ],
      "metadata": {
        "id": "6QpJi_ZZRiQK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# display the names before and after data cleaning\n",
        "print(\"Before data cleaning\")\n",
        "print(wrong,\"\\n\")\n",
        "print(\"After data cleaning\")\n",
        "print(correct)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "31zG5wLIRxCZ",
        "outputId": "bf3eba08-da03-4b9e-df0a-af402bb9cad8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before data cleaning\n",
            "['rn\">Republic of Ireland', 'rn\">United Arab Emirates', 'rn\">Trinidad and Tobago', 'rn\">Serbia and Montenegro', 'rn\">Bosnia and Herzegovina', 'Germany FR', 'Maracan� - Est�dio Jornalista M�rio Filho', 'Estadio do Maracana'] \n",
            "\n",
            "After data cleaning\n",
            "['Republic of Ireland', 'United Arab Emirates', 'Trinidad and Tobago', 'Serbia and Montenegro', 'Bosnia and Herzegovina', 'Germany', 'Maracan Stadium', 'Maracan Stadium']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we need to make changes for the above mentioned labels in the whole dataset. This can be done with the help of the following code"
      ],
      "metadata": {
        "id": "tS58-qHxSSqR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for index, wr in enumerate(wrong):\n",
        "    world_cup = world_cup.replace(wrong[index], correct[index])\n",
        "    \n",
        "for index, wr in enumerate(wrong):\n",
        "    matches = matches.replace(wrong[index], correct[index])\n",
        "    \n",
        "for index, wr in enumerate(wrong):\n",
        "    players = players.replace(wrong[index], correct[index])"
      ],
      "metadata": {
        "id": "r0Q4DrcQSFpV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Analysis"
      ],
      "metadata": {
        "id": "vopM50PhSj4w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Which team had won more titles in FIFA international world cup?"
      ],
      "metadata": {
        "id": "lmlVDX4CSm7F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "winner = world_cup['Winner'].value_counts()\n",
        "winner"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "369-EcJiSebC",
        "outputId": "fabf5388-9968-4404-dc7d-b7ddf1172067"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Brazil       5\n",
              "Italy        4\n",
              "Germany      4\n",
              "Uruguay      2\n",
              "Argentina    2\n",
              "England      1\n",
              "France       1\n",
              "Spain        1\n",
              "Name: Winner, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above output we can see that Brazil had won more number of FIFA world cups, while Italy and Germany had won it 4 times."
      ],
      "metadata": {
        "id": "C1kiH23SSvpp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Which team had finished as runner up for more number of times?"
      ],
      "metadata": {
        "id": "iNqdKqZQTIqq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "runnerup = world_cup['Runners-Up'].value_counts()\n",
        "runnerup"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N55lfcLsSvB6",
        "outputId": "00e23ac0-dc64-4730-f701-ef5879d302c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Germany           4\n",
              "Argentina         3\n",
              "Netherlands       3\n",
              "Czechoslovakia    2\n",
              "Hungary           2\n",
              "Brazil            2\n",
              "Italy             2\n",
              "Sweden            1\n",
              "France            1\n",
              "Name: Runners-Up, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above data it is clear that Germany had lost more world cup finals than any other countries. Also, we can see that Argentina and Netherlands have lost the second most number of world cup finals."
      ],
      "metadata": {
        "id": "qOshOmAcTRKY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Which team had finished on the third spot more number of times?"
      ],
      "metadata": {
        "id": "Bk1r77D7TnN4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "third = world_cup['Third'].value_counts()\n",
        "third"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "onqEciXwTQL6",
        "outputId": "31fcc6fa-7fde-4d7b-e74b-2dc337db23f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Germany        4\n",
              "Brazil         2\n",
              "Sweden         2\n",
              "France         2\n",
              "Poland         2\n",
              "USA            1\n",
              "Austria        1\n",
              "Chile          1\n",
              "Portugal       1\n",
              "Italy          1\n",
              "Croatia        1\n",
              "Turkey         1\n",
              "Netherlands    1\n",
              "Name: Third, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the above data we can say that Germany had finished on third spot for more number times than any other countries in FIFA world cup."
      ],
      "metadata": {
        "id": "veZ1hYpBTuq_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Create a table that contains the data of countries which had reached top 3 positions at least one time."
      ],
      "metadata": {
        "id": "ecy2nGodT_k-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "teams = pd.concat([winner, runnerup, third], axis=1)\n",
        "teams.fillna(0, inplace=True)\n",
        "teams = teams.astype(int)\n",
        "teams"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "eWdeOPEaTtoi",
        "outputId": "bb6d42a6-1880-4698-9c38-2a959e359105"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Winner  Runners-Up  Third\n",
              "Brazil               5           2      2\n",
              "Italy                4           2      1\n",
              "Germany              4           4      4\n",
              "Uruguay              2           0      0\n",
              "Argentina            2           3      0\n",
              "England              1           0      0\n",
              "France               1           1      2\n",
              "Spain                1           0      0\n",
              "Netherlands          0           3      1\n",
              "Czechoslovakia       0           2      0\n",
              "Hungary              0           2      0\n",
              "Sweden               0           1      2\n",
              "Poland               0           0      2\n",
              "USA                  0           0      1\n",
              "Austria              0           0      1\n",
              "Chile                0           0      1\n",
              "Portugal             0           0      1\n",
              "Croatia              0           0      1\n",
              "Turkey               0           0      1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e18b54c3-5154-46a7-ba15-1635289193ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>0</td>\n",
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              "      <th>Sweden</th>\n",
              "      <td>0</td>\n",
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              "      <th>Poland</th>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
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              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <th>Turkey</th>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "                                                     [key], {});\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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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (10, 5))\n",
        "teams.plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
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        "id": "mRPllK0javat",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f980e6b96d0>"
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          },
          "metadata": {},
          "execution_count": 40
        },
        {
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              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Visualize the data in the above table using a bar plot"
      ],
      "metadata": {
        "id": "01-vROzQUfJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from plotly.offline import iplot\n",
        "py.offline.init_notebook_mode(connected=True)\n",
        "cf.go_offline()\n",
        "import plotly.io as pio \n",
        "pio.renderers.default = \"colab\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "vixbMvSMdBKq",
        "outputId": "e7b14590-614f-412d-fa4f-2bd2b361a3a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "teams.iplot(kind = 'bar', xTitle='Teams', yTitle='Count', title='FIFA World Cup Winning Count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "wFxs26dzdq0r",
        "outputId": "fe3ae486-0d91-41eb-8c89-db29219a178b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-2.8.3.min.js\"></script>                <div id=\"53ba718b-0ea7-40b6-8145-77a2dccb9f0e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### Number of goals scored by each country"
      ],
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      }
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              "     Year              Datetime    Stage         Stadium         City  \\\n",
              "0  1930.0  13 Jul 1930 - 15:00   Group 1         Pocitos  Montevideo    \n",
              "1  1930.0  13 Jul 1930 - 15:00   Group 4  Parque Central  Montevideo    \n",
              "\n",
              "  Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
              "0         France              4.0              1.0         Mexico   \n",
              "1            USA              3.0              0.0        Belgium   \n",
              "\n",
              "  Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
              "0                     4444.0                   3.0                   0.0   \n",
              "1                    18346.0                   2.0                   0.0   \n",
              "\n",
              "                  Referee               Assistant 1            Assistant 2  \\\n",
              "0  LOMBARDI Domingo (URU)     CRISTOPHE Henry (BEL)    REGO Gilberto (BRA)   \n",
              "1       MACIAS Jose (ARG)  MATEUCCI Francisco (URU)  WARNKEN Alberto (CHI)   \n",
              "\n",
              "   RoundID  MatchID Home Team Initials Away Team Initials  \n",
              "0    201.0   1096.0                FRA                MEX  \n",
              "1    201.0   1090.0                USA                BEL  "
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              "      <td>1930.0</td>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "home = matches[['Home Team Name', 'Home Team Goals']].dropna()\n",
        "away = matches[['Away Team Name', 'Away Team Goals']].dropna()"
      ],
      "metadata": {
        "id": "uqA_QdNhexLy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "home.columns = ['Countries', 'Goals']\n",
        "away.columns = home.columns"
      ],
      "metadata": {
        "id": "Y8Tsy314e2gx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "goals = home.append(away, ignore_index = True)"
      ],
      "metadata": {
        "id": "YhiTQFBwe4fy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "goals = goals.groupby('Countries').sum()\n",
        "goals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "MKrgUo1we54w",
        "outputId": "74dc9a0b-81c4-44d5-ca18-66a23d94f2ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Goals\n",
              "Countries                  \n",
              "Algeria                14.0\n",
              "Angola                  1.0\n",
              "Argentina             133.0\n",
              "Australia              11.0\n",
              "Austria                43.0\n",
              "...                     ...\n",
              "United Arab Emirates    2.0\n",
              "Uruguay                80.0\n",
              "Wales                   4.0\n",
              "Yugoslavia             60.0\n",
              "Zaire                   0.0\n",
              "\n",
              "[82 rows x 1 columns]"
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              "      <th>Goals</th>\n",
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              "      <th>Countries</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>14.0</td>\n",
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              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>133.0</td>\n",
              "    </tr>\n",
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              "      <th>Australia</th>\n",
              "      <td>11.0</td>\n",
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              "      <th>Austria</th>\n",
              "      <td>43.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>United Arab Emirates</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Uruguay</th>\n",
              "      <td>80.0</td>\n",
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              "      <td>4.0</td>\n",
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              "      <th>Yugoslavia</th>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zaire</th>\n",
              "      <td>0.0</td>\n",
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              "<p>82 rows × 1 columns</p>\n",
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              "            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",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "goals = goals.sort_values(by = 'Goals', ascending=False)\n",
        "goals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "_b-unir0e7M7",
        "outputId": "3ac0cee3-2316-4703-afcc-3a14e7acdcdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     Goals\n",
              "Countries                 \n",
              "Germany              235.0\n",
              "Brazil               225.0\n",
              "Argentina            133.0\n",
              "Italy                128.0\n",
              "France               108.0\n",
              "...                    ...\n",
              "Trinidad and Tobago    0.0\n",
              "Canada                 0.0\n",
              "China PR               0.0\n",
              "Dutch East Indies      0.0\n",
              "Zaire                  0.0\n",
              "\n",
              "[82 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Goals</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Countries</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>235.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Brazil</th>\n",
              "      <td>225.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>133.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Italy</th>\n",
              "      <td>128.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>108.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Trinidad and Tobago</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canada</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>China PR</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dutch East Indies</th>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>Zaire</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>82 rows × 1 columns</p>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5b6bb57c-63fa-45a4-bc85-4a3427661063 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-5b6bb57c-63fa-45a4-bc85-4a3427661063');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Top 20 countries sorted on the basis of goals scored"
      ],
      "metadata": {
        "id": "nN13qJkKfBTJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "goals[:20].iplot(kind='bar', xTitle = 'Country Names', yTitle = 'Goals', title = 'Countries Hits Number of Goals')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "sDKzQQ_Me-Xb",
        "outputId": "13012e77-113c-4d80-b468-0a14a432f5b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "### Attendance, Number of Teams, Goals, and Matches per Cup"
      ],
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    {
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      "source": [
        "In the attendance data we can see that the data have some dots in between the digits, we need to remove those dots for correct data analysis."
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        "id": "E1AOZvnDmns0"
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      "execution_count": null,
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              "    Year       Country   Winner   Runners-Up        Third          Fourth  \\\n",
              "15  1998        France   France       Brazil      Croatia     Netherlands   \n",
              "16  2002   Korea/Japan   Brazil      Germany       Turkey  Korea Republic   \n",
              "17  2006       Germany    Italy       France      Germany        Portugal   \n",
              "18  2010  South Africa    Spain  Netherlands      Germany         Uruguay   \n",
              "19  2014        Brazil  Germany    Argentina  Netherlands          Brazil   \n",
              "\n",
              "    GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n",
              "15          171              32             64  2.785.100  \n",
              "16          161              32             64  2.705.197  \n",
              "17          147              32             64  3.359.439  \n",
              "18          145              32             64  3.178.856  \n",
              "19          171              32             64  3.386.810  "
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      "cell_type": "code",
      "source": [
        "world_cup['Attendance'] = world_cup['Attendance'].str.replace(\".\", \"\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZEQDozrnFj_",
        "outputId": "8120b3db-d29a-4246-8664-eb46ea7ab985"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: FutureWarning:\n",
            "\n",
            "The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_cup.tail()"
      ],
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        "colab": {
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          "height": 206
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        "id": "cIwbDfV5nHSG",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Year       Country   Winner   Runners-Up        Third          Fourth  \\\n",
              "15  1998        France   France       Brazil      Croatia     Netherlands   \n",
              "16  2002   Korea/Japan   Brazil      Germany       Turkey  Korea Republic   \n",
              "17  2006       Germany    Italy       France      Germany        Portugal   \n",
              "18  2010  South Africa    Spain  Netherlands      Germany         Uruguay   \n",
              "19  2014        Brazil  Germany    Argentina  Netherlands          Brazil   \n",
              "\n",
              "    GoalsScored  QualifiedTeams  MatchesPlayed Attendance  \n",
              "15          171              32             64    2785100  \n",
              "16          161              32             64    2705197  \n",
              "17          147              32             64    3359439  \n",
              "18          145              32             64    3178856  \n",
              "19          171              32             64    3386810  "
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "world_cup['Year'].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciGcAjINpCpe",
        "outputId": "036c623e-d619-4fc3-a8a2-a019f53a7dd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1930"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "world_cup['Year'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c_VFVflhprYm",
        "outputId": "a988eb98-b78c-474b-aef5-039f6caf7414"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2014"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Visualize the attendance data "
      ],
      "metadata": {
        "id": "2nJCiH0jnVPD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = (10,5))\n",
        "sns.despine(right = True)\n",
        "g = sns.barplot(x = world_cup['Year'], y = world_cup['Attendance'])\n",
        "# g.set_xticklabels(g.get_xticklabels(), rotation = 80)\n",
        "g.set_xlim(world_cup['Year'].min(),world_cup['Year'].max())\n",
        "g.set_ylim()\n",
        "g.set_title('Attendance Per Year')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "W56v3FwZn37O",
        "outputId": "380cbb04-b238-48fb-885f-e18c73b79120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Attendance Per Year')"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Number of Qualified Teams Per Year"
      ],
      "metadata": {
        "id": "ArV2qy7rqcyd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = (10,5))\n",
        "sns.despine(right = True)\n",
        "g = sns.barplot(x = 'Year', y = 'QualifiedTeams', data = world_cup)\n",
        "g.set_xticklabels(g.get_xticklabels(), rotation = 80)\n",
        "g.set_title('Qualified Teams Per Year')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "Noa3DB3vqgCE",
        "outputId": "a647db1b-9a77-418c-af08-afea8df5712d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Qualified Teams Per Year')"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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WJEnS4Om38Hqmfn80IrYAVgHWaqdJkiRJg6nfsxpPi4jVgH+mTAexIvDB1lolSZI0gPo9q/E/649XAc9vrzmSJEmDq69DjRExIyJOj4jv1N83r/NwSZIkqU/9jvE6A/gu8Lz6+53A8W00SJIkaVD1W3itmZlfp14gOzPnAvNaa5UkSdIAWmjhFRFDY8Ceiog1KJcJIiK2BR5ruW2SJEkDZbTB9T8HXg68h3I24wsi4hpgOrBfy22TJEkaKKMVXgGQmddFxA7Ai+uyOzLzmYX+pSRJkv7KaIXX9Ij4+xGW7xoRZOa/tdEoSZKkQTRa4TWFMllqdNAWSZKkgTZa4TUrMz/SSUskSZIG3GjTSdjTJUmS1JDRerx2jYjVF3RjZv6x4fZIkiQNrH6mk0hKz9cGwCP151WB3wMbt9o6SZKkAbLQQ42ZuXFmPh/4HrBXZq6ZmWsAewKXddFASZKkQdHvJYO2zcxLhn7JzO8A27XTJEmSpME02qHGIQ9ExD8BZ9ffDwYeaKdJkiRJg6nfHq+DKJcJuhC4oP58UFuNkiRJGkR99XjVsxePi4gVMvOpltskSZI0kPrq8YqI7SLiNuD2+vuWEXFKqy2TJEkaMP0eavw08HrgDwCZeRPwmrYaJUmSNIj6LbzIzHuHLZrXcFskSZIGWr9nNd4bEdsBGRHTgOOohx0lSZLUn357vN4JvAtYF7gf2Kr+LkmSpD71e1bjw5S5uyadB0/9WCvrXfvof2plvePZeV/arbV173f4pc9Z9n/Pen1ree845LvPWXbi19vJO/GA52YB7P7NfVvJ+87e54+4fI8L2/lfuGSfkf8X3nDBqa3kfftNR4+4fM/zzmkl7+L9JuWuU9ICLLTwioh/yMxPRsRnKNds/CuZeWxrLZMkSRowo/V43Va/X9t2QyRJkgbdaIXXgcDFwKqZ+R8dtEeSJGlgjTa4/n9ExPOAIyJitYhYvfdrYX8YEV+MiIci4paeZSdGxP0RcWP92qOJByFJkjQRjNbj9XngCuD5wHVA9NyWdfmCnAF8FvjysOWfzsxPLVozJUmSJr6F9nhl5v/JzM2AL2bm8zNz456vhRVdZOYPgT822VhJkqSJbKGFV0SsXH/8wPDDjKMdalyIYyLi5noocrXFXIckSdKEM9qhxq8Ae1IOMyaLdqhxJKcCH61/+1HgZOCIke4YEUcBRwFssMEGixgzcf3qc3u3st5N3/XNVtYrSZL6t9DCKzP3rN83biIsM2cP/RwRX6CcMbmg+54GnAYwc+bM58whJkmSNNGMNoHqyxd2e2ZevyhhEbFOZs6qv+4D3LKw+0uSJA2S0Q41nryQ2xLYeUE3RsRXgR2BNSPiPuBDwI4RsVX923uAdyxKYyVJkiay0Q417rS4K87Mg0ZYfPrirk+SJGmi6+si2QARsQWwObDs0LLMHD5HlyRJkhagr8IrIj5EOWy4OXAJsDtwNc+dHFWSJEkLMNolg4bsB+wCPJiZhwNbAqu01ipJkqQB1G/h9efMnA/MrZOqPgSs316zJEmSBk+/Y7yujYhVgS9QJlN9EvhJa62SJEkaQH0VXpn5d/XHz0fEpcDKmXlze82SJEkaPP0Orn/NSMvqhbAlSZLUh34PNZ7Q8/OywDaUQ44LnEBVkiRJf63fQ4179f4eEesD/95KiyRJkgZUv2c1DncfsFmTDZEkSRp0/Y7x+gzl+opQirWtgUW6QLYkSdJk1+8Yr18BU+rPfwC+mpnXtNOkhZtz6tmtrHf60W9tZb3j2ZVfeENr697xb7/d2rolSZqoFlp4RcQ04H8DhwL31MUzgM8A10TEVpl5Y6stlCRJGhCj9XidDCwPbJiZTwDUmes/FRGnArsBG7fbREmSpMEwWuG1B7BJZg6N7yIzH4+Io4GHKRfLliRJUh9GO6txfm/RNSQz5wFzMvOn7TRLkiRp8IxWeN0WEYcOXxgRbwVub6dJkiRJg2m0Q43vAi6IiCMoM9UDzASWA/Zps2GSJEmDZqGFV2beD7wiInYGXlIXX5KZV7TeMkmSpAHT7yWDvg98v+W2SJIkDbTFvWSQJEmSFpGFlyRJUkcsvCRJkjpi4SVJktQRCy9JkqSOWHhJkiR1xMJLkiSpIxZekiRJHbHwkiRJ6oiFlyRJUkcsvCRJkjrSWuEVEV+MiIci4paeZatHxOUR8ev6fbW28iVJksabNnu8zgB2G7bsfcAVmbkJcEX9XZIkaVJorfDKzB8Cfxy2eG/gzPrzmcAb28qXJEkab7oe4zUjM2fVnx8EZnScL0mSNGbGbHB9ZiaQC7o9Io6KiGsj4to5c+Z02DJJkqR2dF14zY6IdQDq94cWdMfMPC0zZ2bmzOnTp3fWQEmSpLZ0XXhdBBxWfz4M+GbH+ZIkSWOmzekkvgr8BHhxRNwXEUcCJwGvi4hfA6+tv0uSJE0KU9tacWYetICbdmkrU5IkaTxz5npJkqSOWHhJkiR1xMJLkiSpIxZekiRJHbHwkiRJ6oiFlyRJUkcsvCRJkjpi4SVJktQRCy9JkqSOWHhJkiR1xMJLkiSpIxZekiRJHbHwkiRJ6oiFlyRJUkcsvCRJkjpi4SVJktQRCy9JkqSOWHhJkiR1xMJLkiSpIxZekiRJHbHwkiRJ6oiFlyRJUkcsvCRJkjpi4SVJktQRCy9JkqSOWHhJkiR1xMJLkiSpIxZekiRJHbHwkiRJ6oiFlyRJUkcsvCRJkjpi4SVJktSRqWMRGhH3AE8A84C5mTlzLNohSZLUpTEpvKqdMvPhMcyXJEnqlIcaJUmSOjJWhVcCl0XEdRFx1Bi1QZIkqVNjdajxVZl5f0SsBVweEb/KzB/23qEWZEcBbLDBBmPRRkmSpEaNSY9XZt5fvz8EXAhsM8J9TsvMmZk5c/r06V03UZIkqXGdF14RsUJErDT0M7ArcEvX7ZAkSeraWBxqnAFcGBFD+V/JzEvHoB2SJEmd6rzwysy7gS27zpUkSRprTichSZLUEQsvSZKkjlh4SZIkdcTCS5IkqSMWXpIkSR2x8JIkSeqIhZckSVJHLLwkSZI6YuElSZLUEQsvSZKkjlh4SZIkdcTCS5IkqSMWXpIkSR2x8JIkSeqIhZckSVJHLLwkSZI6YuElSZLUEQsvSZKkjlh4SZIkdcTCS5IkqSMWXpIkSR2x8JIkSeqIhZckSVJHLLwkSZI6YuElSZLUEQsvSZKkjlh4SZIkdcTCS5IkqSMWXpIkSR2x8JIkSeqIhZckSVJHxqTwiojdIuKOiPhNRLxvLNogSZLUtc4Lr4iYAnwO2B3YHDgoIjbvuh2SJEldG4ser22A32Tm3Zn5F+BrwN5j0A5JkqROjUXhtS5wb8/v99VlkiRJAy0ys9vAiP2A3TLz7fX3Q4BXZOYxw+53FHBU/fXFwB2LEbcm8PASNNe8yZM3yI/NPPPMG7u8QX5s5i3Yw5m520g3TF2y9iyW+4H1e35fry77K5l5GnDakgRFxLWZOXNJ1mHe5Mgb5MdmnnnmjV3eID828xbPWBxq/AWwSURsHBFLA28GLhqDdkiSJHWq8x6vzJwbEccA3wWmAF/MzFu7bockSVLXxuJQI5l5CXBJB1FLdKjSvEmVN8iPzTzzzBu7vEF+bOYths4H10uSJE1WXjJIkiSpIxZekiRJHRnowisiYqzbMAiix1i3pQ2D+rjGituL+uW2MvEzB/W1g7/aPhutlRzj1ZCeF2apzJzbUWbkAL6A9bkMynP5TEeZA/lcTgaTZXvpMnNQ/x8GdVupxc80YFpmPtVmVs3bGvgN8FRmzo+INYDVgPsy879ayJtKmch0O2AVyhVvfp2Z9zSdVfNWzszHhy1bLzPva2T9g/a/FRHLAusAr6JMV/E74M7MfM4krQ3lrZKZjw1btmFm/q6lvOnALsBtmXlzRMwAVgR+3/SOJCJWBGYCrwHmAXcCN1GutTm/yayat2ZmPjxs2Ysz8442dl4R8QLgAMpz+c2I2AxYGrijjZ3HCPmt7pAjYurQh4COdv5uL83mLQM8D1i+iyl3IuINwNVD+7OIeFm96c6mH5/bSrOiXOnln4HfA7cDx2bmn3racneTjzEi7gG2ysxHI2IPYC/KLAm/B05q4b3o48DuwGXAM8DKwNPAtzPzB01m1bxrgNnAo8BvKdvmPwAHNlFLDFThFRFrAicBWwHfpmzoKwJPAudl5nUN520IXEzZ0J8EfgU8CLwd2Ckz5zWctz5wBmVjmEeZkmN7yie4X2bmfzSYtQ5wav31e8AalDeBpYBvZWajk95GxMbAjZTn8EngVuAe4Ghgs6Z7ESNiI+CrwA+BtSiv4UuBlYBfA+9vYeexPOUi8Stm5sVNrnsBeW8H/t/QG05E7ETZWd2cmU82nOX20uD2UnsQPgy8lnK5tNnA/8rMp+qn/20z8+oG8zYDvpGZW0TECsBhlA+vy/Ls42tkf+a20vi28mLgK5RtZUPK+89jmfmB+lpempmvbjBvE+CczNwmIjao2adTCpS3Ao9k5gkN5m0OfL1umzOAZYDVKYX7O4HPZ+Z/Npi3JuV9/XrgO8DWwKbA3sAPKM/twUuSMWhjvPYE1qjT+58CfJ5SqDwCfL5W5k1anVJ9306ZfX9Zyoa3GfCtiPhEw3mvBh7KzH2BHwN/S9lxfR3YISL+vsGs1wHzMvONwFnAycCJwKXAsRFxRINZULrJr6nr/xClp3IvynN8a0Sc3XDetsBvM/MfgZ8B+wGfBU6gdGV/qMmwusP4IvBB4E0RcU5ErFtvWzoiDmg4bzPguMx8OCJWjoj/DziG8sHktIhYuck83F4a3V6A1wMzMnNT4P2UffV7620v7/m5KZvx7PVwdwb2AT5NeXwrUbbbpritNOtllJ7CRzLzRuATwJYRsRfwIsp7VJNWAH4bEdMo28avMvNLmXkl8DlKkdmkGZSeNDJzdmb+PjNvrMXWuyhXv2lM/aC6G7Ac8OrM/AhwJnAL8L8oReYSGZMJVFu0PKXHicycXZf9DrguIv4E7EFDE7fW7ukbIuJA4JPAHzPzI/UN7mHgI5SNsknrUDdAyvUub8vMc2t7NgC2bDBrCvAYQM+h1CeB8yNiLUrPzRebCKrP5Z0RcTDwv4Gd63P5BOU4/lH1U1aTpgOz6s+bArdk5s9qe35G+ZTTpJ2BzMyd6yfi4yk74uMpPbSHUAropryYMgZjKHsX4CDKtvlByvZ5fIN5U4HHwe2lIS8BfgmQmbdGxL8Cn42InSm9Qw81nPc48GSUy7j9hdJL8guAiLieso02xX1Ls1YC7qrrn5KZ99bt5e8pz+svmgqqz+eNEfED4OeUTocZEfG3wJWUguXepvKqqykfVm+ldHDcRnl+Z1MK6EaHEUXEUvUQ6ruA90bEkZTX7PrM/HU9zLpEBq3H6xxgvYj4aUScGBEHR8RrI2JLYAdKN28jMjPrRv5rStfuRhHxJsob3BWZ+StKV2WTLgO2iYg5lE8BUyJix4hYm3JY4M4Gs84C/hIRd0bElyPiPRGxX0TsSPnnurmpoJ7n8hHgWPjvMQtv49mdxm8W8OeL60LKp8L5lE/Ez0TEG+tOeCfKYYkmbUHtUcgyIPQjlG31LcALeXZH3ZQHgD/XxzOb8kY6p47zeIByGL5JZwBPd7i9xAjby2G0t72cD2w1bHvZu8Xt5c/U4qo+1ruBfweOpDzmK5sKquv/HvBTyj7rA8D+EXFsRGwF7EgtAhvyZcq+5dcRcVZEvDci9m9xW1mqZ1tZKiLeSbv7lgt4dltZBpjb8r7lfODMiFg+M+fV1/PHwDeAQymHVRvR83x+vq77Ksr+ZH/K67oMZaxZYzLzmcx8N2Xbn0PZl74ZOJvyv/i+hvPm1+9/Ar5AKWxfCXy/3mWJD7kP1BivIRGxK6U7fi1gVeAVlDEFZzQ9tqUnczNgX8oGcWD9lNrGoM0VKF3Lv6ccjtiGcqz7JuCDTQz8G5a3OaWo2wTYCFiP8snxkjYGidbM9YG3UAanHpyZv2rjuaxZG1I+7b+Ocqh6e8qO7JM5bDDuEuYcRxmLd0pm/qUu25oyYPMVlHEfX2soK+oO8g2UcULLUXZQX6PsPI6mvH5nNpE3LHsLyplHG1MOczyPbraXAynbzFta3l42poyx3AfSBmoAAAxjSURBVBl4I+WxtrG9BKXn5A/1zXRaZj4TEYdQDnu8qr65NirKQOztKP8P0ynbzsnAZZn5dMNZm1G2/Y2BzSm9+J+k3W1lBmVbORQ4JDNvb3nf8jBlW9mPMlTkPBreVmrWVCApRcIzWc9sjIi9KT01jfZCRcQymfl0/d+bCjxai9tWRcQqlPf0ednQGYZ9Zu4C/Cgz5zSxvQxU4VWLkqUy84koA5kBVsnMpnsThvKWA6YMFXNRjnnvnZnntZS3AuU1e7Jn2VbAnCYLrrrTX7ut520BeTMy88Fhy1+VDQ4gHpb3nMcXEWtk5h+azutZ/9I9RddSWU7D3pFSDO2QmT9qIXM65dPa9pRD0U9QCqHrs6FBxXW7fzNlAOzT9fldPls6rb3mHVjzhp7P5SiHA36aDZ8VV/MOAs7tLT4iYjXg8Wz4JJq67jWANTPzjp5la2XmQxHxSuAXTb1+PXlrZebtPcuWr5/6GxXlcOY0Sq/9spQ30TsW/ldLlDet5q1H6UlcljIW+Kct5S1DKUbWrN9nU94nHlvoHy5e1hRKb9N7Kb1Nd1J6oG6nFMuN9ubVvP0owySWrjn3U3rxvt90Xs1cGXhypP/riFgpM5+YaHkDU3jVTzLvoQx++z6lW/lASm/GVZl5Q4t5P6Bs+HtTDg/8NDNvajHvyvrzgZSN/keZ2dihgCinkR+XmUfWN9GXUw7j/A74XguPbXje31B6L+6gnN7e5GGO4XlLUXoM30bZeVyZmY0d6qh5Uyjd478c+meOiFUpb3R3RsTzgNlNvYHXvM0pYwDn9Syf2uSbdc96XwH8hHLo5pTenrShArOrvDYMyzs1M89oOW8jSs/PDjXzZOBgyv/61Zl5eQt5n6AcUvwF8CnKSUIP1LzLGsyaThma8WbKvnlV4FuUcUEXDv/w1ULempQeyt9QetYaHSs3LO8Jyhmb36IMc7konx173FTeKyknkb2OUuRtQtnXvJYy5cI7miyGRsh7UU/eSi3kvZBycsmtlJ7mOfXrt5R5wy7KzMbG6I2Q9xCl1/J3lBMjGskbpDFe+1DeyKYBN1DOyJlC+RR8SkRs2mLedcDHKZ+qtqecQfniFvOurXnPUM6g+ULDj28mMNTV/3pKkReUAb+nRvODUYfnHU957V4B/N+W83ateXMp20obedsAX649XCtExNuAzwBHRcTBmflAw70m2wBn10NUK0bEERHxFeDkKIOMm/ZiyllbbwcOiIgrauZqTRddI+TtX/OOrL02bejN26/n8a3eUt6elJ60GZQxV++n9GTMAz4aEbu1kPdET94Hat5c4CMN5x0IvCgzt6QUd1dRDke/Gvh4/dDQpOF5V1AOab4e+ETLeQfXvLUohxtPaiFvY+DWzHyo7keuyszPZeY+lKEFH20578rM/GyWM1TbyHsD5aS4uZQzb3em9D4fXbOaPtw4PG/opKR3NJk3SIXXljw7mH01yimuH8rMwyiD0vdvMW91ysR4H8zMQ2teo9MDLCDvxJr3XZp9fNtRzzqivInfkJnvzswjKDuSRk/fXUje2zrMOzYzD6dMz9F03kspxTKUf+q9Kae23wwcFhFvbTFvd+B/Us7mva6lvFdS/t9+SZlX53xKb827IuJF8N+Hd9vIO7rmvQY4usO8HYC/aylvPconeihnMv8kMz+Z5bT2CymPtUld5q1LHVyeZZLp3wI/zsy3AH+iPL9NGinvmsw8qMO8H7eYdzOwXER8LCJeGhEz6tfqlDG5jfYgjkHeo5Qe31Mohc/nKT2I11Ce66ZPYOskb5Cmk3iaZ99szqeeXlttREPTSEySvKeBN0fEDpQ3nd4icgPK5LRNGvS8P1FO64YyweF3MvMc+O/J+jZvOe+SzDy7xbylqdtmHcR7SkT8nHJY/EcRsX9m/tC8vv0UODgizqNMAB21B30W5bD/NxvM6jrvJ8DbIuIwSpG3C2XMIZSeoabHdA50XmbeEhHvpszR9y/AU5RemQ0pg+1Pmsh5lDMzh8YBPkb5wPwrgChnhN8yEfMGaYzXWpQZZZ+ugzcTmF8Pt1wFvLN34Kh5o+YtR6nwX0Kp8u/LzIyIK4Gjm8yaJHlfppwl9lvKoZWzKeNM3k+ZXf4rEzUvImYC19Xnbxp1u6y3bQH8rskBsIOeV9f7Gsp4nR9RpljYCFibMoD62Gz+7OXO8qJM7LkH5QPCxZQxsX+u+7GjM/O2prImSd5ydf3LUQ4FbgDc3/TY2DHMG/Ekjygn0M3NeoLNRMobmMILICJWzGHTRUQZlL5dZl5o3iJlPefsjSjXV9s+M7/bZNZkyKvrfz3lMOe2lEGb0ymHcs4a6R99IuWN9Hy2aRLkLZs9UypExOsoUztcku2cINFZXvSc3duzLChnGjY6zcIg59UP4DtRhpm8ilLoXU8ZLnFlNn+iwljk7UyZpuk1wHzK5KnfAy7O5qfI6CxvIAqvenz51cCbKIPb/0iZ1+pSyqf7pq+ZOLB5PVn71qw/9GRdlM1fv3Cg83py//ti6lHOpFwlW5z3pqu8KAPaX005+WPo+byZ8nx+O5u/uPKg5/Vun9tRLnf2c8q1Ztu4GHBneT1Z+1E+DDxMmY7gYspz2fQ8YYOetxPl7NcPULbJTSgnJM2kzPP4scx8fEDybqp5f1Pz7gP+ZaLmDcoYr4MpO8UPUGYBP4ZyKG5PymM8N6LRSfIGOW+QH1vneXVn/Cpg34jYnjL49Abgsoj4fjY811XXeZRpPxb0fE4Bvt7w6zfoeSNtn8sAh0fEjMz82gTOGylrLuWyL9Po5n99kPKGDoFfVtf7IOVQMRFxKvBPlAmamzLWebOpY+Qmet6gFF6vpPRW3AXcFRHbUZ6wu4F/j4hZ2ezg10HOG+THNhZ5w3fG76Ycwtm3fm/7jbvtvNGezwc7fv0GPe+BCZw36P/rXefdDLwsInbJzCuG3fYXyjxiTTKvIYNSeN0AbBoRK1Em5NuFcsz5rohYlnKmjnnjL2sy5I23N+6m8wb99TNvYmYNdF798PTdut6PR7l0zyzKBLhPUU4cOtu88ZlHZk74L8oMvedTxidcT5mHael629XAuuaNv6xJkncC5YLYK1Embrwa2KnedjmwxwTPG/TXz7wJmDVJ8jamnD35WsqZy7tSzlr+W2DVJrPMazav0YaPhy/K9b+Wooy/WBs41LzxnzWoeYO+8x/018+8iZ81iHm1MDgf+CHPTvC5fM/th5s3fvMm/KHGiHg+5VIvt1Nml57dc9tfKJOhmTfOsiZDHkCWs2D2rRkzKNcZi4hYGzgtG56Pqcu8QX/9zHPfMl7zKFdOeCYzX1MzjqOM39yPcqmbw4EvmTc+8yZ84QX8HXAo5dpiq0TEY5RJI6+izMWxDHCaeeMua+DzJsHOf6BfP/Pct4zjvM0pUzgQZVLf/0M5meZdlPm1bm4wy7yG8wah8FoX+DDl2kobA8+nXKtuE8oFS483b1xmTYa8Qd/5D/rrZ97EzJoMeTcCS0fE1KxzD0bESZQpD95EGetp3njNa/K45Vh8UTbyzYApI9z2Y8pM5OaNs6xJkvdVyiemoOyIX0u5vt8nKNeLPHqC5w3662feBMyaDHl1vcvx7CToQ9+3pVzHdxvzxm/eQMxcvyAR8WbKzO6Nzi49GfMG+bG1lRcRG1MO792Zw64uEBE/Bk7IzGsmat4obZnwr5957lsmaN7qwKOZOd+88Zk30IWXNF4N+s5fkjQyCy9JkqSOLDXWDZAkSZosLLwkSZI6YuElaaBEcXVE7N6zbP+IuHQs2yVJ4BgvSQMoIrYAvkGZUHYq5QLGu2W5ePiirmtqZs5tuImSJikLL0kDKSI+CTwFrFC/bwhsAUwDTszMb0bERsBZ9T4Ax2TmjyNiR+CjlGtebpqZL+q29ZIGlYWXpIEUEStQLhT+F+Bi4NbMPDsiVgV+TukNS2B+Zv5XRGwCfDUzZ9bC69vAFpn527F5BJIG0SBcMkiSniMzn4qIc4EngQOAvSLivfXmZYENgAeAz0bEVsA8oLdn6+cWXZKaZuElaZDNr18B7JuZd/TeGBEnArOBLSknG/VOMPtUR22UNIl4VqOkyeC7wLsjIgAiYuu6fBVgVr0cyCHAlDFqn6RJwsJL0mTwUcqg+psj4tb6O8ApwGERcROwKfZySWqZg+slSZI6Yo+XJElSRyy8JEmSOmLhJUmS1BELL0mSpI5YeEmSJHXEwkuSJKkjFl6SJEkdsfCSJEnqyP8P9MdAhy3PfUgAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Number of goals Goals Scored by Teams Per Year"
      ],
      "metadata": {
        "id": "4JMSTdaGqmKb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = (10,5))\n",
        "sns.despine(right = True)\n",
        "g = sns.barplot(x = 'Year', y = 'GoalsScored', data = world_cup)\n",
        "g.set_xticklabels(g.get_xticklabels(), rotation = 80)\n",
        "g.set_title('Goals Scored by Teams Per Year')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "Wlqo3ij-qlJd",
        "outputId": "74095293-adf4-4abe-eb6e-f892bde85748"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Goals Scored by Teams Per Year')"
            ]
          },
          "metadata": {},
          "execution_count": 93
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Number of matches played in each year"
      ],
      "metadata": {
        "id": "F_u-oeXWqr_r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = (10,5))\n",
        "sns.despine(right = True)\n",
        "g = sns.barplot(x = 'Year', y = 'MatchesPlayed', data = world_cup)\n",
        "g.set_xticklabels(g.get_xticklabels(), rotation = 80)\n",
        "g.set_title('Number of matches played in each year')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "xxkDg7COqrgb",
        "outputId": "afe344a2-b2e3-4b8d-da8f-ab0f979c0532"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Number of matches played in each year')"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Goals scored by each team in each world cup"
      ],
      "metadata": {
        "id": "7-uAZ0jN4wQ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "05wkYvqz4tDZ",
        "outputId": "eb7c74ad-3eb6-4759-a1df-b4b20d4e7ac6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Year              Datetime    Stage         Stadium         City  \\\n",
              "0  1930.0  13 Jul 1930 - 15:00   Group 1         Pocitos  Montevideo    \n",
              "1  1930.0  13 Jul 1930 - 15:00   Group 4  Parque Central  Montevideo    \n",
              "\n",
              "  Home Team Name  Home Team Goals  Away Team Goals Away Team Name  \\\n",
              "0         France              4.0              1.0         Mexico   \n",
              "1            USA              3.0              0.0        Belgium   \n",
              "\n",
              "  Win conditions  Attendance  Half-time Home Goals  Half-time Away Goals  \\\n",
              "0                     4444.0                   3.0                   0.0   \n",
              "1                    18346.0                   2.0                   0.0   \n",
              "\n",
              "                  Referee               Assistant 1            Assistant 2  \\\n",
              "0  LOMBARDI Domingo (URU)     CRISTOPHE Henry (BEL)    REGO Gilberto (BRA)   \n",
              "1       MACIAS Jose (ARG)  MATEUCCI Francisco (URU)  WARNKEN Alberto (CHI)   \n",
              "\n",
              "   RoundID  MatchID Home Team Initials Away Team Initials  \n",
              "0    201.0   1096.0                FRA                MEX  \n",
              "1    201.0   1090.0                USA                BEL  "
            ],
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              "      <td>1930.0</td>\n",
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              "      <td>Pocitos</td>\n",
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              "      <td>4.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>18346.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>MATEUCCI Francisco (URU)</td>\n",
              "      <td>WARNKEN Alberto (CHI)</td>\n",
              "      <td>201.0</td>\n",
              "      <td>1090.0</td>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "home = matches.groupby(['Year', 'Home Team Name'])['Home Team Goals'].sum()\n",
        "home"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_9LX2Cgl41B4",
        "outputId": "2a78991c-6ff5-4c0e-8a56-fcb31bd35df1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year    Home Team Name\n",
              "1930.0  Argentina         16.0\n",
              "        Brazil             4.0\n",
              "        Chile              4.0\n",
              "        France             4.0\n",
              "        Paraguay           1.0\n",
              "                          ... \n",
              "2014.0  Russia             1.0\n",
              "        Spain              1.0\n",
              "        Switzerland        4.0\n",
              "        USA                2.0\n",
              "        Uruguay            3.0\n",
              "Name: Home Team Goals, Length: 366, dtype: float64"
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          "metadata": {},
          "execution_count": 108
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      "source": [
        "away = matches.groupby(['Year', 'Away Team Name'])['Away Team Goals'].sum()\n",
        "away"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "kXnAo4mz44Vo",
        "outputId": "80de37c4-4a5f-4a42-c3f9-096d59491186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year    Away Team Name\n",
              "1930.0  Argentina         2.0\n",
              "        Belgium           0.0\n",
              "        Bolivia           0.0\n",
              "        Brazil            1.0\n",
              "        Chile             1.0\n",
              "                         ... \n",
              "2014.0  Russia            1.0\n",
              "        Spain             3.0\n",
              "        Switzerland       3.0\n",
              "        USA               4.0\n",
              "        Uruguay           1.0\n",
              "Name: Away Team Goals, Length: 411, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 109
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      "cell_type": "code",
      "source": [
        "goals = pd.concat([home, away], axis=1)\n",
        "goals.fillna(0, inplace=True)\n",
        "goals['Goals'] = goals['Home Team Goals'] + goals['Away Team Goals']\n",
        "goals = goals.drop(labels = ['Home Team Goals', 'Away Team Goals'], axis = 1)\n",
        "goals"
      ],
      "metadata": {
        "colab": {
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        "outputId": "2ff2d011-56da-44f2-bd98-e56a58dcc65c"
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      "execution_count": null,
      "outputs": [
        {
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              "                    Goals\n",
              "Year                     \n",
              "1930.0 Argentina     18.0\n",
              "       Belgium        0.0\n",
              "       Bolivia        0.0\n",
              "       Brazil         5.0\n",
              "       Chile          5.0\n",
              "...                   ...\n",
              "2014.0 Russia         2.0\n",
              "       Spain          4.0\n",
              "       Switzerland    7.0\n",
              "       USA            6.0\n",
              "       Uruguay        4.0\n",
              "\n",
              "[427 rows x 1 columns]"
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              "      <th rowspan=\"5\" valign=\"top\">1930.0</th>\n",
              "      <th>Argentina</th>\n",
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              "      <th rowspan=\"5\" valign=\"top\">2014.0</th>\n",
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        "goals = goals.reset_index()"
      ],
      "metadata": {
        "id": "Y2i1jIFT47x4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "goals.columns = ['Year', 'Country', 'Goals']\n",
        "goals = goals.sort_values(by = ['Year', 'Goals'], ascending = [True, False])\n",
        "goals"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "DRmr8g214_c4",
        "outputId": "d24c6e04-6a0f-440b-b184-424f1a7360cf"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "       Year     Country  Goals\n",
              "0    1930.0   Argentina   18.0\n",
              "11   1930.0     Uruguay   15.0\n",
              "10   1930.0         USA    7.0\n",
              "12   1930.0  Yugoslavia    7.0\n",
              "3    1930.0      Brazil    5.0\n",
              "..      ...         ...    ...\n",
              "416  2014.0       Japan    2.0\n",
              "422  2014.0      Russia    2.0\n",
              "401  2014.0    Cameroon    1.0\n",
              "413  2014.0    Honduras    1.0\n",
              "414  2014.0     IR Iran    1.0\n",
              "\n",
              "[427 rows x 3 columns]"
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              "    <tr>\n",
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "top5 = goals.groupby('Year').head()\n",
        "top5.head(10)"
      ],
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          "height": 363
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        "id": "54mkdwU15Bjo",
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      "execution_count": null,
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Year         Country  Goals\n",
              "0   1930.0       Argentina   18.0\n",
              "11  1930.0         Uruguay   15.0\n",
              "10  1930.0             USA    7.0\n",
              "12  1930.0      Yugoslavia    7.0\n",
              "3   1930.0          Brazil    5.0\n",
              "22  1934.0           Italy   12.0\n",
              "20  1934.0         Germany   11.0\n",
              "17  1934.0  Czechoslovakia    9.0\n",
              "14  1934.0         Austria    7.0\n",
              "21  1934.0         Hungary    5.0"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Country</th>\n",
              "      <th>Goals</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1930.0</td>\n",
              "      <td>Argentina</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1930.0</td>\n",
              "      <td>Uruguay</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1930.0</td>\n",
              "      <td>USA</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1930.0</td>\n",
              "      <td>Yugoslavia</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1930.0</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1934.0</td>\n",
              "      <td>Italy</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1934.0</td>\n",
              "      <td>Germany</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1934.0</td>\n",
              "      <td>Czechoslovakia</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1934.0</td>\n",
              "      <td>Austria</td>\n",
              "      <td>7.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1934.0</td>\n",
              "      <td>Hungary</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go"
      ],
      "metadata": {
        "id": "Fnxkm4095H-H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x, y = goals['Year'].values, goals['Goals'].values"
      ],
      "metadata": {
        "id": "WzPXiVeL5JWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = []\n",
        "for team in top5['Country'].drop_duplicates().values:\n",
        "    year = top5[top5['Country'] == team]['Year']\n",
        "    goal = top5[top5['Country'] == team]['Goals']\n",
        "    \n",
        "    data.append(go.Bar(x = year, y = goal, name = team))\n",
        "layout = go.Layout(barmode = 'stack', title = 'Top 5 Teams with most Goals in each world cup', showlegend = False)\n",
        "\n",
        "fig = go.Figure(data = data, layout = layout)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "XG7_CzzB5LcA",
        "outputId": "8a1e68aa-71c8-4aad-cf3b-78eded85c266"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Matches With highest Number Of Attendance"
      ],
      "metadata": {
        "id": "Yt4wKzDL5WmL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches['Datetime'] = pd.to_datetime(matches['Datetime'])"
      ],
      "metadata": {
        "id": "RGAkBl9i5Mxw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "matches['Datetime'] = matches['Datetime'].apply(lambda x: x.strftime('%d %b, %y'))"
      ],
      "metadata": {
        "id": "k7OxCa6G5cCQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top10 = matches.sort_values(by = 'Attendance', ascending = False)[:10]\n",
        "top10['vs'] = top10['Home Team Name'] + \" vs \" + top10['Away Team Name']\n",
        "\n",
        "plt.figure(figsize = (12,10))\n",
        "\n",
        "ax = sns.barplot(y = top10['vs'], x = top10['Attendance'])\n",
        "sns.despine(right = True)\n",
        "\n",
        "plt.ylabel('Match Teams')\n",
        "plt.xlabel('Attendence')\n",
        "plt.title('Matches with the highest number of Attendence')\n",
        "\n",
        "for i, s in enumerate(\"Stadium: \" + top10['Stadium'] +\", Date: \" + top10['Datetime']):\n",
        "    ax.text(2000, i, s, fontsize = 12, color = 'white')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "iZMSNC5Z5dan",
        "outputId": "9e4dff20-f44d-4f88-f288-4b8a00eb6115"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Stadium with Highest Average Attendance"
      ],
      "metadata": {
        "id": "Vx8rqX576Wfd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matches['Year'] = matches['Year'].astype(int)\n",
        "\n",
        "std = matches.groupby(['Stadium', 'City'])['Attendance'].mean().reset_index().sort_values(by = 'Attendance', ascending =False)\n",
        "\n",
        "top10 = std[:10]\n",
        "\n",
        "plt.figure(figsize = (12,9))\n",
        "ax = sns.barplot(y = top10['Stadium'], x = top10['Attendance'])\n",
        "sns.despine(right = True)\n",
        "\n",
        "plt.ylabel('Stadium Names')\n",
        "plt.xlabel('Attendance')\n",
        "plt.title('Stadium with the heighest number of attendance')\n",
        "for i, s in enumerate(\"City: \" + top10['City']):\n",
        "        ax.text(2000, i, s, fontsize = 12, color = 'b')\n",
        "        \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "id": "xq1qtQeC6LRo",
        "outputId": "de1fadc7-3a9c-4975-9678-d9e209291ea5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 1 Axes>"
            ],
            "image/png": 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\n"
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            "needs_background": "light"
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      ]
    }
  ]
}