CleanDataT20MaleCrickets.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CleanDataT20MaleCrickets.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeZMGJXDdi5Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fARBQ0pwlVE3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#counts runs and wickets in the last 5 overs\n",
        "def count_runs_wickets(data):\n",
        "  runs = data[\"runs\"]\n",
        "  wickets = data[\"wicket\"]\n",
        "  l = len(runs)\n",
        "  if l < 30:\n",
        "    l = l\n",
        "  else:\n",
        "    l = l + 1\n",
        "  last = 30\n",
        "  for i in range(0, l):\n",
        "    if i-30 < 0:\n",
        "      data.loc[i,\"last_runs5\"] = runs.iloc[i]\n",
        "      data.loc[i,\"last_wicket5\"] = wickets.iloc[i]\n",
        "    else:\n",
        "      sr = runs.iloc[i-30:i].sum()\n",
        "      sw = wickets.iloc[i-30:i].sum()\n",
        "      data.loc[i-1,\"last_runs5\"] = sr\n",
        "      data.loc[i-1,\"last_wicket5\"] = sw\n",
        "  return data\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FQT-6uXaxMz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "\n",
        "path = '/content/datasets'\n",
        "path1 = '/content/df/'\n",
        "files = []\n",
        "names = []\n",
        "# r=root, d=directories, f = files\n",
        "for r, d, f in os.walk(path):\n",
        "    for file in f:\n",
        "        if '.csv' in file:\n",
        "            names.append(file)\n",
        "            files.append(os.path.join(r, file))\n",
        "\n",
        "\n",
        "\n",
        "for i in range(0,len(files)):\n",
        "  with open(files[i],'r') as f:\n",
        "    with open(path1+names[i],'w') as f1:\n",
        "        f.readline() # skip header line\n",
        "        for line in f:\n",
        "            if line[0] != \"i\":\n",
        "              f1.write(line)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8k1QWaH0ydSu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b72a0096-a293-4216-a1df-8e889edffeea"
      },
      "source": [
        "df = pd.DataFrame(columns=['id','overs', 'teamBat', 'striker', 'non-striker', 'baller','wickets', 'teamBowl', 'venue', 'city','runs','cruns','run-rate','total'])\n",
        "for i in range(0,len(files)):\n",
        "  print(files[i], names[i])\n",
        "  df1 = pd.read_csv(files[i],nrows =11)\n",
        "  df2 = pd.read_csv(path1+names[i], error_bad_lines=False,header=None)\n",
        "  #df2 = df2.reset_index()\n",
        "  df2.rename(columns={0:'balls',1:'innings',2:'overs',3:'teamBat',4:'striker',5:'non-striker',6:'baller',7:'curr_runs',8:'extras',9:'wicket',10:'outname'},inplace=True)\n",
        "  df2 = df2[df2['innings']==1]\n",
        "  df2.drop(columns=['innings','balls','outname'],inplace=True)\n",
        "  df1 = df1.reset_index()\n",
        "  batting = df2.iloc[0]['teamBat']\n",
        "  if df1.loc[0]['1.3.0'] == batting:\n",
        "    df1.loc[0]['version'] = \"teamBat\"\n",
        "    df1.loc[1]['version'] = \"teamBowl\"\n",
        "  else:\n",
        "    df1.loc[1]['version'] = \"teamBat\"\n",
        "    df1.loc[0]['version'] = \"teamBowl\"\n",
        "\n",
        "  df1.set_index('version', inplace=True)\n",
        "  df1.drop(columns = ['index'],inplace=True)\n",
        "  df1.index.name=\"\" \n",
        "  df1 = df1.T\n",
        "  #df2[['teamBowl','venue','city','winner_runs']] = np.array(df1.iloc[0,:])\n",
        "  df2['teamBowl'] = str(df1['teamBowl']).split('    ')[1].split(\"\\n\")[0]\n",
        "  df2['venue'] = str(df1['venue']).split('    ')[1].split(\"\\n\")[0]\n",
        "  df2['city'] = str(df1['city']).split('    ')[1].split(\"\\n\")[0] \n",
        "  df2['runs'] = df2[['curr_runs','extras']].sum(axis=1)\n",
        "  df2['cruns'] = df2['runs'].cumsum(axis=0)\n",
        "  df2['run-rate'] = df2['cruns']/df2['overs']\n",
        "  df2['total'] = df2['curr_runs'].sum() + df2['extras'].sum()\n",
        "  df2.drop(columns = ['curr_runs','extras'],inplace=True)\n",
        "  #df2['wickets'].replace(np.nan, 0, inplace=True)\n",
        "  #df2['wickets'].replace(np.nan, 0, inplace=True)\n",
        "  df2.loc[~df2['wicket'].isnull(),'wicket'] = 1  # not nan\n",
        "  df2.loc[df2['wicket'].isnull(),'wicket'] = 0\n",
        "  df2['wickets'] = df2['wicket'].cumsum(axis=0)\n",
        "  df2['id'] = i\n",
        "  df2 = count_runs_wickets(df2)\n",
        "  df2.drop(columns = ['wicket'],inplace=True)\n",
        "  df = df.append(df2)\n",
        "\n",
        "\n",
        "df = df.reset_index()\n",
        "df.drop(columns=['index'], inplace=True)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
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          "name": "stdout"
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        {
          "output_type": "execute_result",
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>overs</th>\n",
              "      <th>teamBat</th>\n",
              "      <th>striker</th>\n",
              "      <th>non-striker</th>\n",
              "      <th>baller</th>\n",
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              "      <th>1</th>\n",
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              "      <td>BRM Taylor</td>\n",
              "      <td>KD Mills</td>\n",
              "      <td>0</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Eden Park</td>\n",
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              "      <td>Zimbabwe</td>\n",
              "      <td>H Masakadza</td>\n",
              "      <td>BRM Taylor</td>\n",
              "      <td>KD Mills</td>\n",
              "      <td>0</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Eden Park</td>\n",
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              "      <td>2</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>159</td>\n",
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              "      <th>3</th>\n",
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              "      <td>0.4</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>BRM Taylor</td>\n",
              "      <td>H Masakadza</td>\n",
              "      <td>KD Mills</td>\n",
              "      <td>0</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Eden Park</td>\n",
              "      <td>Auckland</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>159</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>BRM Taylor</td>\n",
              "      <td>H Masakadza</td>\n",
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              "      <td>Eden Park</td>\n",
              "      <td>Auckland</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>101592</th>\n",
              "      <td>838</td>\n",
              "      <td>19.2</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>E Chigumbura</td>\n",
              "      <td>R Mutumbami</td>\n",
              "      <td>Wahab Riaz</td>\n",
              "      <td>6</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Gaddafi Stadium</td>\n",
              "      <td>Lahore</td>\n",
              "      <td>0</td>\n",
              "      <td>168</td>\n",
              "      <td>8.750000</td>\n",
              "      <td>172</td>\n",
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              "    <tr>\n",
              "      <th>101593</th>\n",
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              "      <td>Wahab Riaz</td>\n",
              "      <td>6</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Gaddafi Stadium</td>\n",
              "      <td>Lahore</td>\n",
              "      <td>1</td>\n",
              "      <td>169</td>\n",
              "      <td>8.756477</td>\n",
              "      <td>172</td>\n",
              "      <td>51.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <th>101594</th>\n",
              "      <td>838</td>\n",
              "      <td>19.4</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>R Mutumbami</td>\n",
              "      <td>AG Cremer</td>\n",
              "      <td>Wahab Riaz</td>\n",
              "      <td>6</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Gaddafi Stadium</td>\n",
              "      <td>Lahore</td>\n",
              "      <td>1</td>\n",
              "      <td>170</td>\n",
              "      <td>8.762887</td>\n",
              "      <td>172</td>\n",
              "      <td>51.0</td>\n",
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              "    <tr>\n",
              "      <th>101595</th>\n",
              "      <td>838</td>\n",
              "      <td>19.5</td>\n",
              "      <td>Zimbabwe</td>\n",
              "      <td>AG Cremer</td>\n",
              "      <td>R Mutumbami</td>\n",
              "      <td>Wahab Riaz</td>\n",
              "      <td>6</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Gaddafi Stadium</td>\n",
              "      <td>Lahore</td>\n",
              "      <td>1</td>\n",
              "      <td>171</td>\n",
              "      <td>8.769231</td>\n",
              "      <td>172</td>\n",
              "      <td>51.0</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>101596</th>\n",
              "      <td>838</td>\n",
              "      <td>19.6</td>\n",
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              "      <td>R Mutumbami</td>\n",
              "      <td>AG Cremer</td>\n",
              "      <td>Wahab Riaz</td>\n",
              "      <td>6</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>Gaddafi Stadium</td>\n",
              "      <td>Lahore</td>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>8.775510</td>\n",
              "      <td>172</td>\n",
              "      <td>51.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>101597 rows × 16 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         id  overs   teamBat  ... total last_runs5 last_wicket5\n",
              "0         0    0.1  Zimbabwe  ...   159        1.0          0.0\n",
              "1         0    0.2  Zimbabwe  ...   159        0.0          0.0\n",
              "2         0    0.3  Zimbabwe  ...   159        1.0          0.0\n",
              "3         0    0.4  Zimbabwe  ...   159        0.0          0.0\n",
              "4         0    0.5  Zimbabwe  ...   159        1.0          0.0\n",
              "...     ...    ...       ...  ...   ...        ...          ...\n",
              "101592  838   19.2  Zimbabwe  ...   172       54.0          2.0\n",
              "101593  838   19.3  Zimbabwe  ...   172       51.0          2.0\n",
              "101594  838   19.4  Zimbabwe  ...   172       51.0          2.0\n",
              "101595  838   19.5  Zimbabwe  ...   172       51.0          2.0\n",
              "101596  838   19.6  Zimbabwe  ...   172       51.0          2.0\n",
              "\n",
              "[101597 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nf9QaMVmj1t0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df\n",
        "df.to_csv(\"updated.csv\",index=False)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}