Self_Project_Cricket_Scorecard.ipynb

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      "name": "Copy of Self Project - Cricket Scorecard.ipynb",
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        "<a href=\"https://colab.research.google.com/github/GuptaRitik/T20_Cricket_Analysis/blob/main/Self_Project_Cricket_Scorecard.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "##<b> The International Cricket Council (ICC) is the world governing body of cricket. It was founded as the Imperial Cricket Conference in 1909 by representatives from Australia, England and South Africa. It was renamed as the International Cricket Conference in 1965, and took up its current name in 1989. It organises world championship events such as Cricket World Cup, Women's Cricket World Cup, ICC T20 World Cup, ICC Women's T20 World Cup, ICC Champions Trophy and Under-19 Cricket World Cup.</b>\n",
        "\n",
        "<br>\n",
        "\n",
        "## <b> You recently got a lucky break in the field of Data Science. You got a job of Data Scientist at ICC. You are part of the Analytics Team which is involved in pre-match analyses, live-match analyses and post-match analyses. </b>\n",
        "\n",
        "<br>\n",
        "\n",
        "## <b>This data consists of 1417 T20 matches. Each row corresponds to a single T20 match. As a data scientist, you have to first explore the data and check its sanity. </b>\n",
        "\n",
        "<br>\n",
        "\n",
        "## <u><b>Further you are required to do some analyses :</b></u>\n",
        "* ### Rename all the column names to their appropriate names, for example meta.created should be renamed as created_date\n",
        "* ### Find out the top three venues which hosted the greatest number of matches.\n",
        "* ### Find out the pair of cricket teams who played the most number of T20 matches against each other.\n",
        "* ### Print the top five teams by their win percentages. Win percentage is defined as the number of matches won divided by the number of matches played and then multiplied by 100.\n",
        "* ### Write a function to get the scorecard of each match. This function would take the innings value as argument and return two scorecard dataframes each for one team as shown below. So the first dataframe would contain the top 4 scorers of the team who batted first and the top 4 bowlers of the opponent team. And the second dataframe would contain the top 4 scorers of the team who batted second and the top 4 bowlers of the opponent team.\n",
        "\n",
        "\n"
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        "# <b><center> A sample scorecard </center></b>"
      ]
    },
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        "![T20 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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vq7iMfybnKNP"
      },
      "source": [
        "#Import package\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QdBbspBz3K0p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88baf774-3538-4e10-f687-e5f707dc26f0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUvwy0dY3Mcx"
      },
      "source": [
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/DS_Almabetter /Python/Week 3/Sample DataSet/International_T20_Data.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2DMjnnSMx2Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 681
        },
        "outputId": "aa015685-52d8-4872-da27-a2236092195f"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             innings  meta.data_version  \\\n",
              "0  [{'1st innings': {'team': 'Australia', 'delive...                0.9   \n",
              "1  [{'1st innings': {'team': 'Australia', 'delive...                0.9   \n",
              "2  [{'1st innings': {'team': 'Australia', 'delive...                0.9   \n",
              "3  [{'1st innings': {'team': 'Hong Kong', 'delive...                0.9   \n",
              "4  [{'1st innings': {'team': 'Zimbabwe', 'deliver...                0.9   \n",
              "\n",
              "  meta.created  meta.revision                    info.dates info.gender  \\\n",
              "0   2017-02-18              2  [datetime.date(2017, 2, 17)]        male   \n",
              "1   2017-02-19              2  [datetime.date(2017, 2, 19)]        male   \n",
              "2   2017-02-23              1  [datetime.date(2017, 2, 22)]        male   \n",
              "3   2016-09-12              1   [datetime.date(2016, 9, 5)]        male   \n",
              "4   2016-06-19              1  [datetime.date(2016, 6, 18)]        male   \n",
              "\n",
              "  info.match_type  info.outcome.by.wickets info.outcome.winner  info.overs  \\\n",
              "0             T20                      5.0           Sri Lanka          20   \n",
              "1             T20                      2.0           Sri Lanka          20   \n",
              "2             T20                      NaN           Australia          20   \n",
              "3             T20                      NaN           Hong Kong          20   \n",
              "4             T20                      NaN            Zimbabwe          20   \n",
              "\n",
              "   ... info.outcome.by.runs info.match_type_number info.neutral_venue  \\\n",
              "0  ...                  NaN                    NaN                NaN   \n",
              "1  ...                  NaN                    NaN                NaN   \n",
              "2  ...                 41.0                    NaN                NaN   \n",
              "3  ...                 40.0                    NaN                NaN   \n",
              "4  ...                  2.0                    NaN                NaN   \n",
              "\n",
              "  info.outcome.method info.outcome.result info.outcome.eliminator  \\\n",
              "0                 NaN                 NaN                     NaN   \n",
              "1                 NaN                 NaN                     NaN   \n",
              "2                 NaN                 NaN                     NaN   \n",
              "3                 NaN                 NaN                     NaN   \n",
              "4                 NaN                 NaN                     NaN   \n",
              "\n",
              "  info.supersubs.New Zealand  info.supersubs.South Africa  info.bowl_out  \\\n",
              "0                        NaN                          NaN            NaN   \n",
              "1                        NaN                          NaN            NaN   \n",
              "2                        NaN                          NaN            NaN   \n",
              "3                        NaN                          NaN            NaN   \n",
              "4                        NaN                          NaN            NaN   \n",
              "\n",
              "   info.outcome.bowl_out  \n",
              "0                    NaN  \n",
              "1                    NaN  \n",
              "2                    NaN  \n",
              "3                    NaN  \n",
              "4                    NaN  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "      <th></th>\n",
              "      <th>innings</th>\n",
              "      <th>meta.data_version</th>\n",
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              "      <th>info.outcome.result</th>\n",
              "      <th>info.outcome.eliminator</th>\n",
              "      <th>info.supersubs.New Zealand</th>\n",
              "      <th>info.supersubs.South Africa</th>\n",
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              "      <th>info.outcome.bowl_out</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[{'1st innings': {'team': 'Australia', 'delive...</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2017-02-18</td>\n",
              "      <td>2</td>\n",
              "      <td>[datetime.date(2017, 2, 17)]</td>\n",
              "      <td>male</td>\n",
              "      <td>T20</td>\n",
              "      <td>5.0</td>\n",
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              "      <td>20</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[{'1st innings': {'team': 'Australia', 'delive...</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2017-02-19</td>\n",
              "      <td>2</td>\n",
              "      <td>[datetime.date(2017, 2, 19)]</td>\n",
              "      <td>male</td>\n",
              "      <td>T20</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[{'1st innings': {'team': 'Australia', 'delive...</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2017-02-23</td>\n",
              "      <td>1</td>\n",
              "      <td>[datetime.date(2017, 2, 22)]</td>\n",
              "      <td>male</td>\n",
              "      <td>T20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Australia</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>41.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[{'1st innings': {'team': 'Hong Kong', 'delive...</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2016-09-12</td>\n",
              "      <td>1</td>\n",
              "      <td>[datetime.date(2016, 9, 5)]</td>\n",
              "      <td>male</td>\n",
              "      <td>T20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Hong Kong</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[{'1st innings': {'team': 'Zimbabwe', 'deliver...</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2016-06-19</td>\n",
              "      <td>1</td>\n",
              "      <td>[datetime.date(2016, 6, 18)]</td>\n",
              "      <td>male</td>\n",
              "      <td>T20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>20</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
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              "          const element = document.querySelector('#df-08146b30-c88f-4d44-b81d-114b66bb467a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "z4IjYbeIkSca"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# q1 solution"
      ],
      "metadata": {
        "id": "6hahJR8XkSay"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "column_list = list(df.columns)\n",
        "column_list\n",
        "\n",
        "column_dict = {\n",
        " 'meta.data_version':'data_version',\n",
        " 'meta.created':'created_date', \n",
        " 'meta.revision':'revision',\n",
        " 'info.dates': 'dates',\n",
        " 'info.gender':'gender',\n",
        " 'info.match_type':'match_type',\n",
        " 'info.outcome.by.wickets':'outcome.by.wickets',\n",
        " 'info.outcome.winner':'outcome.winner',\n",
        " 'info.overs':'overs',\n",
        " 'info.player_of_match':'player_of_match',\n",
        " 'info.teams':'teams',\n",
        " 'info.toss.decision':'toss.decision',\n",
        " 'info.toss.winner':'toss.winner',\n",
        " 'info.umpires':'umpires',\n",
        " 'info.venue':'venue',\n",
        " 'info.city':'city',\n",
        " 'info.outcome.by.runs':'outcome.by.runs',\n",
        " 'info.match_type_number':'match_type_number',\n",
        " 'info.neutral_venue':'neutral_venue',\n",
        " 'info.outcome.method':'outcome.method',\n",
        " 'info.outcome.result':'outcome.result',\n",
        " 'info.outcome.eliminator':'outcome.eliminator',\n",
        " 'info.supersubs.New Zealand':'supersubs.New Zealand',\n",
        " 'info.supersubs.South Africa':'supersubs.South Africa',\n",
        " 'info.bowl_out':'bowl_out',\n",
        " 'info.outcome.bowl_out':'outcome.bowl_out'}"
      ],
      "metadata": {
        "id": "GJsSaUZ1kSZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "XjJxC5FyI1QA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns = column_dict, inplace = True)\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fDVB3LGkkSXa",
        "outputId": "2db786bd-9682-471a-f625-bac81712b6b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['innings', 'data_version', 'created_date', 'revision', 'dates',\n",
              "       'gender', 'match_type', 'outcome.by.wickets', 'outcome.winner', 'overs',\n",
              "       'player_of_match', 'teams', 'toss.decision', 'toss.winner', 'umpires',\n",
              "       'venue', 'city', 'outcome.by.runs', 'match_type_number',\n",
              "       'neutral_venue', 'outcome.method', 'outcome.result',\n",
              "       'outcome.eliminator', 'supersubs.New Zealand', 'supersubs.South Africa',\n",
              "       'bowl_out', 'outcome.bowl_out'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "1P-qXmMgkSVV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# q2 solution\n",
        "\n",
        "df['venue'].value_counts().sort_values(ascending=False).nlargest(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XflZ6BuMkSTK",
        "outputId": "1903e5ad-e2f6-469d-9fd6-d1d3c49c9657"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dubai International Cricket Stadium    62\n",
              "Sheikh Zayed Stadium                   41\n",
              "Shere Bangla National Stadium          39\n",
              "Name: venue, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['teams']"
      ],
      "metadata": {
        "id": "uiHYbfkLIk2v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Q3 solution\n",
        "\n",
        "df['teams'] = df['teams'].apply(lambda x: eval(x))\n",
        "df['teams_new']= df['teams'].apply(lambda x: sorted(x))\n",
        "team_1= df['teams_new'].value_counts().sort_values(ascending=False).reset_index().loc[0,'index'][0]\n",
        "team_2= df['teams_new'].value_counts().sort_values(ascending=False).reset_index().loc[0,'index'][1]\n",
        "number = df['teams_new'].value_counts().sort_values(ascending=False).reset_index().loc[0,'teams_new']\n",
        "\n",
        "print(f'Highest number of matches played between {team_1} and {team_2} are {number}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PpROoa4QkSRl",
        "outputId": "45a5491a-446d-4ab5-c060-e989e91a78c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Highest number of matches played between Australia and England are 45\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "eA9A_5odoQlF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Ce--65R5oQiE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Q4 solution"
      ],
      "metadata": {
        "id": "tg1NGOakkSP7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unique_country_lst = list(df.explode('teams')['teams'].unique())\n",
        "winner_lst = list(df[~df['outcome.winner'].isna()]['outcome.winner'].values)\n",
        "played_matches_lst = list(df.explode('teams')['teams'].values)"
      ],
      "metadata": {
        "id": "UyXkprQNkSOU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "win_prcntge={'Country':[], 'Matches_played':[], 'Matches_won':[], 'Win_percentage':[]}\n",
        "\n",
        "for i in unique_country_lst:\n",
        "    won = winner_lst.count(i)\n",
        "    win_prcntge['Matches_won'].append(won)\n",
        "    played= played_matches_lst.count(i)\n",
        "    win_prcntge['Matches_played'].append(played)\n",
        "    win_pr= round((won/played)*100, 2)\n",
        "    win_prcntge['Country'].append(i)\n",
        "    win_prcntge['Win_percentage'].append(win_pr)"
      ],
      "metadata": {
        "id": "PFuXQPb2kSMh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percentage_df = pd.DataFrame(win_prcntge)\n",
        "percentage_df.sort_values('Matches_won', ascending=False).reset_index().iloc[:5,:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "hJ31GVXHkSLL",
        "outputId": "6e03f4a5-66c9-46e0-b9db-bf6cd5b6c350"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index       Country  Matches_played  Matches_won  Win_percentage\n",
              "0      0     Australia             214          132           61.68\n",
              "1      9       England             216          130           60.19\n",
              "2      5         India             204          121           59.31\n",
              "3     12      Pakistan             226          117           51.77\n",
              "4      8  South Africa             192          101           52.60"
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>61.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>England</td>\n",
              "      <td>216</td>\n",
              "      <td>130</td>\n",
              "      <td>60.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>India</td>\n",
              "      <td>204</td>\n",
              "      <td>121</td>\n",
              "      <td>59.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>226</td>\n",
              "      <td>117</td>\n",
              "      <td>51.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>192</td>\n",
              "      <td>101</td>\n",
              "      <td>52.60</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2c8c0ad-79cf-4e4b-a419-3ff082b0363d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [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",
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              "          document.querySelector('#df-b2c8c0ad-79cf-4e4b-a419-3ff082b0363d 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-b2c8c0ad-79cf-4e4b-a419-3ff082b0363d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "percentage_df.sort_values('Win_percentage',ascending=False).reset_index().iloc[:5,:]"
      ],
      "metadata": {
        "id": "cZm9IzPOkSJ6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "a9bb7187-578e-4788-c4c1-6d8b2647f6ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   index      Country  Matches_played  Matches_won  Win_percentage\n",
              "0     65      Belgium               3            3          100.00\n",
              "1     57        Spain               6            5           83.33\n",
              "2     34      Germany              17           13           76.47\n",
              "3     26      Namibia              34           25           73.53\n",
              "4     11  Afghanistan              75           51           68.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e746c049-af0f-48ee-91ac-89bfa422234d\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index</th>\n",
              "      <th>Country</th>\n",
              "      <th>Matches_played</th>\n",
              "      <th>Matches_won</th>\n",
              "      <th>Win_percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>65</td>\n",
              "      <td>Belgium</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>100.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>57</td>\n",
              "      <td>Spain</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>83.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34</td>\n",
              "      <td>Germany</td>\n",
              "      <td>17</td>\n",
              "      <td>13</td>\n",
              "      <td>76.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26</td>\n",
              "      <td>Namibia</td>\n",
              "      <td>34</td>\n",
              "      <td>25</td>\n",
              "      <td>73.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>75</td>\n",
              "      <td>51</td>\n",
              "      <td>68.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e746c049-af0f-48ee-91ac-89bfa422234d')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-e746c049-af0f-48ee-91ac-89bfa422234d button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e746c049-af0f-48ee-91ac-89bfa422234d');\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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              "          docLink.innerHTML = docLinkHtml;\n",
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              "  "
            ]
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          "metadata": {},
          "execution_count": 18
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nU33pHhqkSIi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Q5 solution"
      ],
      "metadata": {
        "id": "RiDX1Hr8kSHX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()"
      ],
      "metadata": {
        "id": "yn5wgAZHkSFf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1['innings'] = df['innings'].apply(lambda x: eval(x))"
      ],
      "metadata": {
        "id": "lX-f4Hv_kSDf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def first_inn_Scorecard(innings):\n",
        "\n",
        "    deliveries_list= innings[0]['1st innings']['deliveries']\n",
        "    keys = []\n",
        "    for i in deliveries_list:\n",
        "        keys += list(i.keys())\n",
        "\n",
        "\n",
        "    ball_number_zip = zip(keys, range(len(deliveries_list)))    \n",
        "\n",
        "\n",
        "    batsman=[]\n",
        "    runs=[]\n",
        "    bowlers=[]\n",
        "    wickets=[]\n",
        "\n",
        "    #For batsman\n",
        "    for ball, number in ball_number_zip:\n",
        "            batsman.append(deliveries_list[number][ball]['batsman'])      \n",
        "            runs.append(deliveries_list[number][ball]['runs']['batsman'])\n",
        "\n",
        "            #For bowlers\n",
        "            bowlers.append(deliveries_list[number][ball]['bowler'])\n",
        "            if 'wicket' in deliveries_list[number][ball]:\n",
        "                wickets.append(1)\n",
        "\n",
        "            else:\n",
        "                wickets.append(0)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    batsman_zip_lst = list(zip(batsman,runs))\n",
        "    bowler_zip_lst = list(zip(bowlers,wickets))\n",
        "\n",
        "\n",
        "    batsman_df = pd.DataFrame(pd.DataFrame(batsman_zip_lst, columns=['Batsman','Runs']).groupby('Batsman')['Runs'].sum().sort_values(ascending=False).reset_index()).loc[:3,:]\n",
        "    bowler_df = pd.DataFrame(pd.DataFrame(bowler_zip_lst, columns=['Bowler','Wickets']).groupby('Bowler')['Wickets'].sum().sort_values(ascending=False).reset_index()).loc[:3,:]\n",
        "    \n",
        "    inn_1_df = pd.concat([batsman_df,bowler_df], axis=1)\n",
        "    return inn_1_df"
      ],
      "metadata": {
        "id": "-7E97TDtMrcP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def second_inn_Scorecard(innings):\n",
        "\n",
        "    deliveries_list= innings[1]['2nd innings']['deliveries']\n",
        "    keys = []\n",
        "    for i in deliveries_list:\n",
        "        keys += list(i.keys())\n",
        "\n",
        "\n",
        "    ball_number_zip = zip(keys, range(len(deliveries_list)))    \n",
        "\n",
        "\n",
        "    batsman=[]\n",
        "    runs=[]\n",
        "    bowlers=[]\n",
        "    wickets=[]\n",
        "\n",
        "    #For batsman\n",
        "    for ball, number in ball_number_zip:\n",
        "            batsman.append(deliveries_list[number][ball]['batsman'])      \n",
        "            runs.append(deliveries_list[number][ball]['runs']['batsman'])\n",
        "\n",
        "            #For bowlers\n",
        "            bowlers.append(deliveries_list[number][ball]['bowler'])\n",
        "            if 'wicket' in deliveries_list[number][ball]:\n",
        "                wickets.append(1)\n",
        "\n",
        "            else:\n",
        "                wickets.append(0)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    batsman_zip_lst = list(zip(batsman,runs))\n",
        "    bowler_zip_lst = list(zip(bowlers,wickets))\n",
        "\n",
        "\n",
        "    batsman_df = pd.DataFrame(pd.DataFrame(batsman_zip_lst, columns=['Batsman','Runs']).groupby('Batsman')['Runs'].sum().sort_values(ascending=False).reset_index()).loc[:3,:]\n",
        "    bowler_df = pd.DataFrame(pd.DataFrame(bowler_zip_lst, columns=['Bowler','Wickets']).groupby('Bowler')['Wickets'].sum().sort_values(ascending=False).reset_index()).loc[:3,:]\n",
        "    \n",
        "    inn_2_df = pd.concat([batsman_df,bowler_df], axis=1)\n",
        "    return inn_2_df"
      ],
      "metadata": {
        "id": "t_scwLhtMrZ3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def Scorecard(innings):\n",
        "    a = first_inn_Scorecard(innings)\n",
        "    b = second_inn_Scorecard(innings)\n",
        "    display(a, b)\n"
      ],
      "metadata": {
        "id": "3HR8I-jIMrWz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Scorecard(df1.loc[896,'innings'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "GYnZQO-mMrTu",
        "outputId": "70c4eb08-13e0-4311-bfd3-9993ee21597f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        Batsman  Runs      Bowler  Wickets\n",
              "0   H Masakadza    44   DJG Sammy        5\n",
              "1  E Chigumbura    34     SJ Benn        4\n",
              "2       GA Lamb    11   R Rampaul        1\n",
              "3     AG Cremer     2  KA Pollard        0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f7e7099-391e-49aa-bf21-8aa0d35e8102\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Batsman</th>\n",
              "      <th>Runs</th>\n",
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              "      <th>Wickets</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>H Masakadza</td>\n",
              "      <td>44</td>\n",
              "      <td>DJG Sammy</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>E Chigumbura</td>\n",
              "      <td>34</td>\n",
              "      <td>SJ Benn</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GA Lamb</td>\n",
              "      <td>11</td>\n",
              "      <td>R Rampaul</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AG Cremer</td>\n",
              "      <td>2</td>\n",
              "      <td>KA Pollard</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f7e7099-391e-49aa-bf21-8aa0d35e8102')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "         Batsman  Runs     Bowler  Wickets\n",
              "0       D Ramdin    23  AG Cremer        3\n",
              "1  S Chanderpaul    20    GA Lamb        2\n",
              "2       DR Smith    12   P Utseya        1\n",
              "3      AB Barath     8   RW Price        1"
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              "      <th>Batsman</th>\n",
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              "      <th>0</th>\n",
              "      <td>D Ramdin</td>\n",
              "      <td>23</td>\n",
              "      <td>AG Cremer</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>S Chanderpaul</td>\n",
              "      <td>20</td>\n",
              "      <td>GA Lamb</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DR Smith</td>\n",
              "      <td>12</td>\n",
              "      <td>P Utseya</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AB Barath</td>\n",
              "      <td>8</td>\n",
              "      <td>RW Price</td>\n",
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    {
      "cell_type": "code",
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        ""
      ],
      "metadata": {
        "id": "LG2OOxnBMrRd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.loc[1,'innings'][0]"
      ],
      "metadata": {
        "id": "qkurugGV0H7B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "780df051-218a-4c95-865d-4535d7c5b0b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'1st innings': {'deliveries': [{0.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'AJ Finch',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {0.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'AJ Finch',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {0.3: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {0.4: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {0.5: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'extras': {'legbyes': 1},\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 1, 'total': 1}}},\n",
              "   {0.6: {'batsman': 'M Klinger',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'AJ Finch',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {1.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'extras': {'wides': 1},\n",
              "     'non_striker': 'AJ Finch',\n",
              "     'runs': {'batsman': 0, 'extras': 1, 'total': 1}}},\n",
              "   {1.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'AJ Finch',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {1.3: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {1.4: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {1.5: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {1.6: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {1.7: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {2.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'AJ Finch',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {2.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'AJ Finch',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {2.3: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {2.4: {'batsman': 'AJ Finch',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'kind': 'caught and bowled', 'player_out': 'AJ Finch'}}},\n",
              "   {2.5: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {2.6: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {3.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'extras': {'wides': 1},\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 0, 'extras': 1, 'total': 1}}},\n",
              "   {3.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {3.3: {'batsman': 'M Klinger',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {3.4: {'batsman': 'M Klinger',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {3.5: {'batsman': 'M Klinger',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {3.6: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {3.7: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {4.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {4.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {4.3: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {4.4: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {4.5: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {4.6: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {5.1: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'EMDY Munaweera',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {5.2: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'EMDY Munaweera',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {5.3: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'EMDY Munaweera',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 6, 'extras': 0, 'total': 6}}},\n",
              "   {5.4: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'EMDY Munaweera',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 6, 'extras': 0, 'total': 6}}},\n",
              "   {5.5: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'EMDY Munaweera',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {5.6: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'EMDY Munaweera',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 6, 'extras': 0, 'total': 6}}},\n",
              "   {6.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {6.2: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {6.3: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'BR Dunk',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {6.4: {'batsman': 'BR Dunk',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'kind': 'bowled', 'player_out': 'BR Dunk'}}},\n",
              "   {6.5: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {6.6: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {7.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {7.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {7.3: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {7.4: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {7.5: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {7.6: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {8.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {8.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {8.3: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {8.4: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {8.5: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {8.6: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {9.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {9.2: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {9.3: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {9.4: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {9.5: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {9.6: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {10.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 6, 'extras': 0, 'total': 6}}},\n",
              "   {10.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {10.3: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {10.4: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {10.5: {'batsman': 'M Klinger',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {10.6: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'DAS Gunaratne',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 6, 'extras': 0, 'total': 6}}},\n",
              "   {11.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {11.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {11.3: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {11.4: {'batsman': 'M Klinger',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {11.5: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {11.6: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {12.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0}}},\n",
              "   {12.2: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {12.3: {'batsman': 'M Klinger',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {12.4: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {12.5: {'batsman': 'M Klinger',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {12.6: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'M Klinger',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {13.1: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {13.2: {'batsman': 'M Klinger',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'fielders': ['N Dickwella'],\n",
              "      'kind': 'caught',\n",
              "      'player_out': 'M Klinger'}}},\n",
              "   {13.3: {'batsman': 'TM Head',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {13.4: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'TM Head',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {13.5: {'batsman': 'TM Head',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {13.6: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'extras': {'wides': 1},\n",
              "     'non_striker': 'TM Head',\n",
              "     'runs': {'batsman': 0, 'extras': 1, 'total': 1}}},\n",
              "   {13.7: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'TM Head',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {14.1: {'batsman': 'TM Head',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {14.2: {'batsman': 'TM Head',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'fielders': ['N Dickwella'],\n",
              "      'kind': 'caught',\n",
              "      'player_out': 'TM Head'}}},\n",
              "   {14.3: {'batsman': 'AJ Turner',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {14.4: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'AJ Turner',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {14.5: {'batsman': 'AJ Turner',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {14.6: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'AJ Turner',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {15.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'AJ Turner',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {15.2: {'batsman': 'AJ Turner',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {15.3: {'batsman': 'AJ Turner',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {15.4: {'batsman': 'AJ Turner',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {15.5: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'AJ Turner',\n",
              "     'runs': {'batsman': 3, 'extras': 0, 'total': 3}}},\n",
              "   {15.6: {'batsman': 'AJ Turner',\n",
              "     'bowler': 'S Prasanna',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'fielders': ['TAM Siriwardana'],\n",
              "      'kind': 'caught',\n",
              "      'player_out': 'AJ Turner'}}},\n",
              "   {16.1: {'batsman': 'JP Faulkner',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {16.2: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'JP Faulkner',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {16.3: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'JP Faulkner',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {16.4: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'JP Faulkner',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {16.5: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'JP Faulkner',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {16.6: {'batsman': 'JP Faulkner',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'kind': 'lbw', 'player_out': 'JP Faulkner'}}},\n",
              "   {17.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'TD Paine',\n",
              "     'runs': {'batsman': 6, 'extras': 0, 'total': 6}}},\n",
              "   {17.2: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'TD Paine',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {17.3: {'batsman': 'TD Paine',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 4, 'extras': 0, 'total': 4}}},\n",
              "   {17.4: {'batsman': 'TD Paine',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {17.5: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'TD Paine',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {17.6: {'batsman': 'TD Paine',\n",
              "     'bowler': 'JRMVB Sanjaya',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {18.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'TD Paine',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {18.2: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'TD Paine',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {18.3: {'batsman': 'TD Paine',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'kind': 'lbw', 'player_out': 'TD Paine'}}},\n",
              "   {18.4: {'batsman': 'PJ Cummins',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {18.5: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'PJ Cummins',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {18.6: {'batsman': 'PJ Cummins',\n",
              "     'bowler': 'SL Malinga',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {19.1: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'PJ Cummins',\n",
              "     'runs': {'batsman': 2, 'extras': 0, 'total': 2}}},\n",
              "   {19.2: {'batsman': 'MC Henriques',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'PJ Cummins',\n",
              "     'runs': {'batsman': 1, 'extras': 0, 'total': 1}}},\n",
              "   {19.3: {'batsman': 'PJ Cummins',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'kind': 'bowled', 'player_out': 'PJ Cummins'}}},\n",
              "   {19.4: {'batsman': 'AJ Tye',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'extras': {'legbyes': 4},\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 4, 'total': 4}}},\n",
              "   {19.5: {'batsman': 'AJ Tye',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'kind': 'lbw', 'player_out': 'AJ Tye'}}},\n",
              "   {19.6: {'batsman': 'JA Richardson',\n",
              "     'bowler': 'KMDN Kulasekara',\n",
              "     'non_striker': 'MC Henriques',\n",
              "     'runs': {'batsman': 0, 'extras': 0, 'total': 0},\n",
              "     'wicket': {'kind': 'bowled', 'player_out': 'JA Richardson'}}}],\n",
              "  'team': 'Australia'}}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = {'b': [1,2,3,4], 'j':[2,4,5,6]}\n",
        "\n",
        "b = {'b': [1,2,3,4], 'j':[2,4,5,6]}"
      ],
      "metadata": {
        "id": "vl1uOYnf2f33"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "aa = pd.DataFrame(a)\n",
        "bb = pd.DataFrame(b)"
      ],
      "metadata": {
        "id": "hSMLi6-JNUE8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "display(aa, bb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "MVgKLuU8NVHC",
        "outputId": "4364ecaf-5efe-4bd7-8f4a-af515360ef93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   b  j\n",
              "0  1  2\n",
              "1  2  4\n",
              "2  3  5\n",
              "3  4  6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a84c3fd0-d5d9-468f-9a5f-ba07b490b902\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "HUmuQ1r4NlhM"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}