cricket _league.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/matche.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season               int64\n",
       "city                object\n",
       "date                object\n",
       "team1               object\n",
       "team2               object\n",
       "toss_winner         object\n",
       "toss_decision       object\n",
       "result              object\n",
       "dl_applied           int64\n",
       "winner              object\n",
       "win_by_runs          int64\n",
       "win_by_wickets       int64\n",
       "player_of_match     object\n",
       "venue               object\n",
       "umpire1             object\n",
       "umpire2             object\n",
       "umpire3            float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>636.000000</td>\n",
       "      <td>636.000000</td>\n",
       "      <td>636.000000</td>\n",
       "      <td>636.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2012.490566</td>\n",
       "      <td>0.025157</td>\n",
       "      <td>13.682390</td>\n",
       "      <td>3.372642</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.773026</td>\n",
       "      <td>0.156726</td>\n",
       "      <td>23.908877</td>\n",
       "      <td>3.420338</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2008.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            season  dl_applied  win_by_runs  win_by_wickets  umpire3\n",
       "count   636.000000  636.000000   636.000000      636.000000      0.0\n",
       "mean   2012.490566    0.025157    13.682390        3.372642      NaN\n",
       "std       2.773026    0.156726    23.908877        3.420338      NaN\n",
       "min    2008.000000    0.000000     0.000000        0.000000      NaN\n",
       "25%    2010.000000    0.000000     0.000000        0.000000      NaN\n",
       "50%    2012.000000    0.000000     0.000000        4.000000      NaN\n",
       "75%    2015.000000    0.000000    20.000000        7.000000      NaN\n",
       "max    2017.000000    1.000000   146.000000       10.000000      NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season       city        date                        team1  \\\n",
       "0    2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1    2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2    2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3    2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4    2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2  umpire3  \n",
       "0     AY Dandekar       NJ Llong      NaN  \n",
       "1  A Nand Kishore         S Ravi      NaN  \n",
       "2     Nitin Menon      CK Nandan      NaN  \n",
       "3    AK Chaudhary  C Shamshuddin      NaN  \n",
       "4             NaN            NaN      NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Counting the winnig rate of teams in particular seasons')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "srh = df[df[\"winner\"] == \"Royal Challengers Bangalore\"]\n",
    "winner_in_2017 = srh[srh[\"season\"] == 2017]\n",
    "winner_in_2009 = srh[srh[\"season\"] == 2009]\n",
    "xlabels = [\"2017 season\", \"2009 season\"]\n",
    "ylabels = [len(winner_in_2017), len(winner_in_2009)]\n",
    "plt.bar(xlabels, ylabels)\n",
    "plt.title(\"Counting the winnig rate of teams in particular seasons\")\n",
    "#shows SRH has scored more runs while batting first\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}