Code file/ipl2017.ipynb

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   "source": [
    "#loading the required libraries\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
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   "source": [
    "#loading the ipl matches dataset\n",
    "ipl=pd.read_csv('../input/ipl-data-set/matches.csv')"
   ]
  },
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      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>1</td>\n",
       "      <td>IPL-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>2</td>\n",
       "      <td>IPL-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>3</td>\n",
       "      <td>IPL-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>4</td>\n",
       "      <td>IPL-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>5</td>\n",
       "      <td>IPL-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": [
       "   id    Season       city        date                        team1  \\\n",
       "0   1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1   2  IPL-2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2   3  IPL-2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3   4  IPL-2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4   5  IPL-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": 3,
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   ],
   "source": [
    "#having a glance at the first five records of the dataset\n",
    "ipl.head()"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(756, 18)"
      ]
     },
     "execution_count": 4,
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   ],
   "source": [
    "#Lookin at the number of rows and columns in the dataset\n",
    "ipl.shape"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "CH Gayle          21\n",
       "AB de Villiers    20\n",
       "RG Sharma         17\n",
       "MS Dhoni          17\n",
       "DA Warner         17\n",
       "                  ..\n",
       "PD Collingwood     1\n",
       "NV Ojha            1\n",
       "AC Voges           1\n",
       "J Theron           1\n",
       "S Hetmyer          1\n",
       "Name: player_of_match, Length: 226, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "#Getting the frequency of most man of the match awards\n",
    "ipl['player_of_match'].value_counts()"
   ]
  },
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     "data": {
      "text/plain": [
       "CH Gayle          21\n",
       "AB de Villiers    20\n",
       "RG Sharma         17\n",
       "MS Dhoni          17\n",
       "DA Warner         17\n",
       "YK Pathan         16\n",
       "SR Watson         15\n",
       "SK Raina          14\n",
       "G Gambhir         13\n",
       "MEK Hussey        12\n",
       "Name: player_of_match, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
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    }
   ],
   "source": [
    "#Getting the top 10 players with most man of the match awards\n",
    "ipl['player_of_match'].value_counts()[0:10]"
   ]
  },
  {
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   "execution_count": 7,
   "id": "ff8ee84e",
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     "data": {
      "text/plain": [
       "CH Gayle          21\n",
       "AB de Villiers    20\n",
       "RG Sharma         17\n",
       "MS Dhoni          17\n",
       "DA Warner         17\n",
       "Name: player_of_match, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    }
   ],
   "source": [
    "#Getting the top 5 players with most man of the match awards\n",
    "ipl['player_of_match'].value_counts()[0:5]"
   ]
  },
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#making a bar-plot for the top 5 players with most man of the match awards\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.bar(list(ipl['player_of_match'].value_counts()[0:5].keys()),list(ipl['player_of_match'].value_counts()[0:5]))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "68bcdc7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:48.208476Z",
     "iopub.status.busy": "2022-01-27T07:20:48.205638Z",
     "iopub.status.idle": "2022-01-27T07:20:48.211854Z",
     "shell.execute_reply": "2022-01-27T07:20:48.212508Z",
     "shell.execute_reply.started": "2022-01-27T07:10:47.870599Z"
    },
    "papermill": {
     "duration": 0.047427,
     "end_time": "2022-01-27T07:20:48.212689",
     "exception": false,
     "start_time": "2022-01-27T07:20:48.165262",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal       743\n",
       "tie            9\n",
       "no result      4\n",
       "Name: result, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Getting the frequency of result column\n",
    "ipl['result'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "323ad399",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:48.286522Z",
     "iopub.status.busy": "2022-01-27T07:20:48.285816Z",
     "iopub.status.idle": "2022-01-27T07:20:48.294597Z",
     "shell.execute_reply": "2022-01-27T07:20:48.295260Z",
     "shell.execute_reply.started": "2022-01-27T07:10:51.687908Z"
    },
    "papermill": {
     "duration": 0.047593,
     "end_time": "2022-01-27T07:20:48.295435",
     "exception": false,
     "start_time": "2022-01-27T07:20:48.247842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai Indians                 98\n",
       "Kolkata Knight Riders          92\n",
       "Chennai Super Kings            89\n",
       "Royal Challengers Bangalore    81\n",
       "Kings XI Punjab                81\n",
       "Delhi Daredevils               80\n",
       "Rajasthan Royals               80\n",
       "Sunrisers Hyderabad            46\n",
       "Deccan Chargers                43\n",
       "Pune Warriors                  20\n",
       "Gujarat Lions                  15\n",
       "Delhi Capitals                 10\n",
       "Kochi Tuskers Kerala            8\n",
       "Rising Pune Supergiants         7\n",
       "Rising Pune Supergiant          6\n",
       "Name: toss_winner, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out the number of toss wins w.r.t each team\n",
    "ipl['toss_winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed38181f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:48.369342Z",
     "iopub.status.busy": "2022-01-27T07:20:48.368696Z",
     "iopub.status.idle": "2022-01-27T07:20:48.376113Z",
     "shell.execute_reply": "2022-01-27T07:20:48.376684Z",
     "shell.execute_reply.started": "2022-01-27T07:10:59.318749Z"
    },
    "papermill": {
     "duration": 0.046397,
     "end_time": "2022-01-27T07:20:48.376875",
     "exception": false,
     "start_time": "2022-01-27T07:20:48.330478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Extracting the records where a team won batting first\n",
    "batting_first=ipl[ipl['win_by_runs']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ba4470a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:48.452717Z",
     "iopub.status.busy": "2022-01-27T07:20:48.452052Z",
     "iopub.status.idle": "2022-01-27T07:20:48.470320Z",
     "shell.execute_reply": "2022-01-27T07:20:48.470837Z",
     "shell.execute_reply.started": "2022-01-27T07:11:05.316845Z"
    },
    "papermill": {
     "duration": 0.056834,
     "end_time": "2022-01-27T07:20:48.471014",
     "exception": false,
     "start_time": "2022-01-27T07:20:48.414180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>1</td>\n",
       "      <td>IPL-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>4</th>\n",
       "      <td>5</td>\n",
       "      <td>IPL-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",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>11-04-2017</td>\n",
       "      <td>Delhi Daredevils</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>Delhi Daredevils</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>15-04-2017</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>15-04-2017</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>CJ Anderson</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>YC Barde</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id    Season       city        date                        team1  \\\n",
       "0    1  IPL-2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "4    5  IPL-2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "8    9  IPL-2017       Pune  11-04-2017             Delhi Daredevils   \n",
       "13  14  IPL-2017    Kolkata  15-04-2017        Kolkata Knight Riders   \n",
       "14  15  IPL-2017      Delhi  15-04-2017             Delhi Daredevils   \n",
       "\n",
       "                          team2                  toss_winner toss_decision  \\\n",
       "0   Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "4              Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "8        Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "13          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
       "14              Kings XI Punjab             Delhi Daredevils           bat   \n",
       "\n",
       "    result  dl_applied                       winner  win_by_runs  \\\n",
       "0   normal           0          Sunrisers Hyderabad           35   \n",
       "4   normal           0  Royal Challengers Bangalore           15   \n",
       "8   normal           0             Delhi Daredevils           97   \n",
       "13  normal           0        Kolkata Knight Riders           17   \n",
       "14  normal           0             Delhi Daredevils           51   \n",
       "\n",
       "    win_by_wickets player_of_match                                      venue  \\\n",
       "0                0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "4                0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "8                0       SV Samson    Maharashtra Cricket Association Stadium   \n",
       "13               0      RV Uthappa                               Eden Gardens   \n",
       "14               0     CJ Anderson                           Feroz Shah Kotla   \n",
       "\n",
       "        umpire1      umpire2 umpire3  \n",
       "0   AY Dandekar     NJ Llong     NaN  \n",
       "4           NaN          NaN     NaN  \n",
       "8   AY Dandekar       S Ravi     NaN  \n",
       "13  AY Dandekar     NJ Llong     NaN  \n",
       "14     YC Barde  Nitin Menon     NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the head\n",
    "batting_first.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b4b563e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:48.548216Z",
     "iopub.status.busy": "2022-01-27T07:20:48.547451Z",
     "iopub.status.idle": "2022-01-27T07:20:48.697635Z",
     "shell.execute_reply": "2022-01-27T07:20:48.698162Z",
     "shell.execute_reply.started": "2022-01-27T07:13:02.562368Z"
    },
    "papermill": {
     "duration": 0.190481,
     "end_time": "2022-01-27T07:20:48.698335",
     "exception": false,
     "start_time": "2022-01-27T07:20:48.507854",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a histogram \n",
    "plt.figure(figsize=(7,7))\n",
    "plt.hist(batting_first['win_by_runs'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1da634bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:48.775170Z",
     "iopub.status.busy": "2022-01-27T07:20:48.774425Z",
     "iopub.status.idle": "2022-01-27T07:20:48.781042Z",
     "shell.execute_reply": "2022-01-27T07:20:48.781582Z",
     "shell.execute_reply.started": "2022-01-27T07:11:24.448726Z"
    },
    "papermill": {
     "duration": 0.046813,
     "end_time": "2022-01-27T07:20:48.781748",
     "exception": false,
     "start_time": "2022-01-27T07:20:48.734935",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai Indians                 57\n",
       "Chennai Super Kings            52\n",
       "Kings XI Punjab                38\n",
       "Kolkata Knight Riders          36\n",
       "Royal Challengers Bangalore    35\n",
       "Sunrisers Hyderabad            30\n",
       "Rajasthan Royals               27\n",
       "Delhi Daredevils               25\n",
       "Deccan Chargers                18\n",
       "Pune Warriors                   6\n",
       "Rising Pune Supergiant          5\n",
       "Delhi Capitals                  3\n",
       "Kochi Tuskers Kerala            2\n",
       "Rising Pune Supergiants         2\n",
       "Gujarat Lions                   1\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out the number of wins w.r.t each team after batting first\n",
    "batting_first['winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "564fc728",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:48.861209Z",
     "iopub.status.busy": "2022-01-27T07:20:48.860546Z",
     "iopub.status.idle": "2022-01-27T07:20:49.024589Z",
     "shell.execute_reply": "2022-01-27T07:20:49.024005Z",
     "shell.execute_reply.started": "2022-01-27T07:12:16.403794Z"
    },
    "papermill": {
     "duration": 0.203577,
     "end_time": "2022-01-27T07:20:49.024728",
     "exception": false,
     "start_time": "2022-01-27T07:20:48.821151",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZEafXMXd8+8vK9mGSviTpLyPiLklf7/Ii/0nSqoYXxe+S9PI2rndKRFxo+whJ35C0/2QWvi2t257Yg6q+L+mN9fQJks4buWD0HpDt1fVVxoDt2+urmztsn2v79bavtr3O9ryG2z/A9jV1+rvr7exse3l9RXSL7WMaljHmnlLdK1hh+8I6hnPrk/TIl/TebvsGSW9uuM6Jtr9ST/+V7WvrK/gf2Z7dcF8X19teb/uDdfpOti+rr/ZX237rpB7l7jlE0uMR8eSTS0TcFBE/qWd3bvFYvdL2/9Q9rh/anlOnr7B9hu3r6rp9XZ1+ou3v2r68rst/GVme7a/ZHrK9xvbChukrbI/+w8tnqbwA/GUd6+8jYm2df4tX0SPbQl3nV9b1sNb2121vVy87om5fN9j+ju2d6/QN9X7cIOlvmjxufZLOl7QuIk5tscxW29lRddpKl72CkT31v6iv9FfV7etZba3BaWD7zyV9U9LREfGzOq1xz7LVdvBM2xe47PleXH+XBm3PqOtvdf2d/vvRy4yIX0v6hKSv1H+fjIiHJjDsKyXtVcexwfasenrQ9oqG+/C03+N62ci6bfn8I6mvruvb6rp/5gTGt/VExDb/T9IjKq9GLpS0g6RVkg6WdGm9/HRJJzfMv1rl1e6ApE2S9lOJ+UpJiyVZ5XsH/6vh+jdJ2lHla0zulrS7ypPDs+s8syTdqac+OflIq7HWnwdLeljlD6C3k3SNpNfW8d8tae86jgsa7seJkr5STz+nYVnvkvSvDWP9qaRn1DH9UtJMSW+R9M2Gcewy3ett1OPyQUlntris1WM1s97X/jrfW1X+HEKSVjQ8JkdJ+lHDY7he0i71sb5L0vPrZbvVnzPq9fdvuK3BJuP6lqSNKi+G3i5puzr9bEnHtVjnv5P0wrqMZZKOq+vpSkk71fk+qvKkJ0kbJH2kxeNyuqRfSTp/CtvZnnW+8xq2s+9JOqie3llS33RvHy3u/+P1/u/f5HE5eZzt4GRJ36inX6byPDAo6ZWSljXc1q5jLP8aSVeNmrbFum82XeWFxrUN63dWPT0oaUXDfXja7/Goddv0+UfleS0a1uFiNTz/ZfrXM3tQEXGzyoo5QWVvql0/j4hbImKzpDWSlkdZq7doy0M2l0TEYxHxgKQrVL5M15L+2fbNkn4kaQ9N7JDUdRFxT132qrq8l9QxravjOKfFdedK+qHtWySdIumlDZddFuUV/QMqT6Cz6/05vL6afF1EPDyBcWbQ7LHaR+XJZZntVSqHXuY2XOe79edKbbkul0fEwxHxO0m3SnpBnX583VO5UeXxHPPb+6Mc+jtM0nUqT3iL27wf66McqjtPJRavqsu6ut6P+Q1jksoeUitXSXqN7RePs8xm29n6iPh5nee8hvmvlvT5+qp914jY1Mb9mg6PqzyJnzTOfM22g9eqfAG2ImK1pJvr9PWSXmj7y7aPlPTrZjdoe66kOZJ2H9nbbcNn6/pd0MaYpea/x1sMQ62ff+6OiKvr6XNU7m86PROoaqmkz2nLXzapvDpqfCx2aDj9+4bTmxvOb9aW7+GN/oOyUHnV3C/plRFxoKT7R932eBqX/YQm9p7hl1X2pvaT9B61vk9PqLwCvkPSK1RC9Wk3vMmbxBqVV6+tNHusLGlNRBxY/+0XEUc0uc7ox/Zpt2V7T5XIHBYR+0u6TG2sy/ri5kyV9z3fUic/ub3VQ3jbN15l9E3U+7Gs4X7sGxGNT2CPjjGEKyV9WNIPRg5vNjGh7SwiPqOyV76jSjRfMtb802izpOMlzbP98THma7UdPE1EPCjpAJU9r/eq7CU380VJp6kc4TitzfGeUtfv4TWK0pbPTaO3t/HW21jPP822s3R6LVCLJS2MiFtGTd+g8uQs26+QtOckbvsYl09KPVflsMn1KoeJNkbE47YP0ZaveifrdkkDtl9Uz5/QYr5d9NQHQeaPd6O2d5f024g4R9JnVR+PRH4s6Rku344vSbK9/8h7Bi2sldTv+oko2zNtv3SM+cfybJUQPOzyft4bxpq5Hv8/uGHSgSqHC6WyvY3E9k0qhyJHzHP5+rDtVA5JXiXpfyUdZHvkfYmdxtkj2kJEXKTywuxy27u2ebW1KnsKA/X8k+9J2n5RDe8ZKtt51kApIn6r8t7z2223s1cy4mqVuMnlE5r71dOzVA7VXqSyR/603xPbb5D0PEn/IelTkt7syf9feRv01LbyljHma2as558/9VOfFPxble0snV75FJ8kKSLuUfk0z2gXSXqn7TWSrpV0xyRu/maVQ3uzJH0qIv7P9rmSvlcPsw2pxGVKIuJ39Un6Mtu/lfQTlTfkRztd0ndsP6jy5D5edPdTOcSwWeXQyN9NdaydFBFh+68lfcH2R1Xeq9mgsnewR4vr/MHlwwhfsr2Lyvb+BZW9sYku/ybbN6qsw7tVnsDGYkkfsf0NSY+pxO3Eetk3JV1i+yZJl2vLPaDrVd5Y30tle7o4IjbbPlHSeS6fBpTKk2Pb22lEfK2GdanLp8TGm/8x2+9TidqjdVwjPlyf8EYOe/+g3XFMh4j4VT0cd6Xt4Tav9m+Slti+VWWdr1F5r24PSf9eX0BI0scar2R7B5Vt7Lh6CP5R26eorNNDJzH8hZLOsv0plb22dozsDY31/LNW0vttL1Y5jP21SYyt6/iqIyCJusd1ckQcPc1DkVT2AiPiEdtW+d+z19XDlds8l/8xfGZ9Qfgilfdw9onyn7OmVY/g3BARnThaM+16ag8KwIS82/Z8lffIblT525xe8UxJV7j8PZslve+PIE67q+xlfW6ah9Ix7EEBAFLqtQ9JAAD+SBAoAEBKBAoAkBKBAgCkRKAAACn9P6is7cAQIed6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a bar-plot for top 3 teams with most wins after batting first\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.bar(list(batting_first['winner'].value_counts()[0:3].keys()),list(batting_first['winner'].value_counts()[0:3]),color=[\"Green\",\"Red\",\"Grey\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26bedbe1",
   "metadata": {
    "papermill": {
     "duration": 0.04005,
     "end_time": "2022-01-27T07:20:49.105378",
     "exception": false,
     "start_time": "2022-01-27T07:20:49.065328",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1dfef5cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:49.185446Z",
     "iopub.status.busy": "2022-01-27T07:20:49.184783Z",
     "iopub.status.idle": "2022-01-27T07:20:49.473361Z",
     "shell.execute_reply": "2022-01-27T07:20:49.473846Z",
     "shell.execute_reply.started": "2022-01-27T07:13:11.958008Z"
    },
    "papermill": {
     "duration": 0.330011,
     "end_time": "2022-01-27T07:20:49.474010",
     "exception": false,
     "start_time": "2022-01-27T07:20:49.143999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a pie chart\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.pie(list(batting_first['winner'].value_counts()),labels=list(batting_first['winner'].value_counts().keys()),autopct='%0.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f615748b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:49.558356Z",
     "iopub.status.busy": "2022-01-27T07:20:49.557607Z",
     "iopub.status.idle": "2022-01-27T07:20:49.560101Z",
     "shell.execute_reply": "2022-01-27T07:20:49.560553Z",
     "shell.execute_reply.started": "2022-01-27T07:13:28.608590Z"
    },
    "papermill": {
     "duration": 0.047669,
     "end_time": "2022-01-27T07:20:49.560725",
     "exception": false,
     "start_time": "2022-01-27T07:20:49.513056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#extracting those records where a team has won after batting second\n",
    "batting_second=ipl[ipl['win_by_wickets']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "53aacda2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:49.660122Z",
     "iopub.status.busy": "2022-01-27T07:20:49.659452Z",
     "iopub.status.idle": "2022-01-27T07:20:49.663734Z",
     "shell.execute_reply": "2022-01-27T07:20:49.663221Z",
     "shell.execute_reply.started": "2022-01-27T07:13:33.148254Z"
    },
    "papermill": {
     "duration": 0.062955,
     "end_time": "2022-01-27T07:20:49.663864",
     "exception": false,
     "start_time": "2022-01-27T07:20:49.600909",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>1</th>\n",
       "      <td>2</td>\n",
       "      <td>IPL-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>3</td>\n",
       "      <td>IPL-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>4</td>\n",
       "      <td>IPL-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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>09-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>A Deshmukh</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>IPL-2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>09-04-2017</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>N Rana</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id    Season       city        date                   team1  \\\n",
       "1   2  IPL-2017       Pune  06-04-2017          Mumbai Indians   \n",
       "2   3  IPL-2017     Rajkot  07-04-2017           Gujarat Lions   \n",
       "3   4  IPL-2017     Indore  08-04-2017  Rising Pune Supergiant   \n",
       "5   6  IPL-2017  Hyderabad  09-04-2017           Gujarat Lions   \n",
       "6   7  IPL-2017     Mumbai  09-04-2017   Kolkata Knight Riders   \n",
       "\n",
       "                    team2             toss_winner toss_decision  result  \\\n",
       "1  Rising Pune Supergiant  Rising Pune Supergiant         field  normal   \n",
       "2   Kolkata Knight Riders   Kolkata Knight Riders         field  normal   \n",
       "3         Kings XI Punjab         Kings XI Punjab         field  normal   \n",
       "5     Sunrisers Hyderabad     Sunrisers Hyderabad         field  normal   \n",
       "6          Mumbai Indians          Mumbai Indians         field  normal   \n",
       "\n",
       "   dl_applied                  winner  win_by_runs  win_by_wickets  \\\n",
       "1           0  Rising Pune Supergiant            0               7   \n",
       "2           0   Kolkata Knight Riders            0              10   \n",
       "3           0         Kings XI Punjab            0               6   \n",
       "5           0     Sunrisers Hyderabad            0               9   \n",
       "6           0          Mumbai Indians            0               4   \n",
       "\n",
       "  player_of_match                                      venue         umpire1  \\\n",
       "1       SPD Smith    Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
       "2         CA Lynn     Saurashtra Cricket Association Stadium     Nitin Menon   \n",
       "3      GJ Maxwell                     Holkar Cricket Stadium    AK Chaudhary   \n",
       "5     Rashid Khan  Rajiv Gandhi International Stadium, Uppal      A Deshmukh   \n",
       "6          N Rana                           Wankhede Stadium     Nitin Menon   \n",
       "\n",
       "         umpire2 umpire3  \n",
       "1         S Ravi     NaN  \n",
       "2      CK Nandan     NaN  \n",
       "3  C Shamshuddin     NaN  \n",
       "5       NJ Llong     NaN  \n",
       "6      CK Nandan     NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the head\n",
    "batting_second.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ba47c69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:49.752258Z",
     "iopub.status.busy": "2022-01-27T07:20:49.751581Z",
     "iopub.status.idle": "2022-01-27T07:20:49.982204Z",
     "shell.execute_reply": "2022-01-27T07:20:49.982684Z",
     "shell.execute_reply.started": "2022-01-27T07:13:38.774402Z"
    },
    "papermill": {
     "duration": 0.279391,
     "end_time": "2022-01-27T07:20:49.982845",
     "exception": false,
     "start_time": "2022-01-27T07:20:49.703454",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t7ve+zFO3LnkWAPh/3OoIgJEECoCRBAqAkQQKgJEECoCRBAqAkQQKgJEECoCRBAqAkQQKgJEECoCRdrwXH9/fkeMPXND+Z0/csaJJAH6wOIMCYCSBAmAkgQJgJIECYCSBAmAkgQJgJIECYCSBAmAkgQJgJIECYCSBAmAkgQJgpAN5s9gLuUGrm7MCHEzOoAAYSaAAGEmgABhJoAAYSaAAGEmgABhJoAAYSaAAGEmgABjpogJVVbdX1deq6omqOr6soQBgz4GqqkuS/IckP5HkhiTvraobljUYAOvtYs6g3prkie5+sru/m+R3k9y5nLEAWHfV3Xt7YdW7ktze3f96sf6+JP+ku3/2JfsdS3JssXp9kq/tfdwD4fVJ/nK/hzigvHd7573bO+/d3i3jvftH3b1xvidWfjfz7j6Z5OSqjzNFVZ3p7s39nuMg8t7tnfdu77x3e7fq9+5iLvE9neQNL1q/ZrENAC7axQTqj5NcV1XXVtWlSd6T5P7ljAXAutvzJb7ufqGqfjbJf0lySZKPdvdXljbZwbU2lzNXwHu3d967vfPe7d1K37s9f0kCAFbJnSQAGEmgABhJoJakqt5QVZ+vqker6itV9f79nukgqapLquqLVfWZ/Z7lIKmqy6vq3qr6alU9VlVv3++ZDoqq+oXF39VHqurjVfXq/Z5pqqr6aFWdq6pHXrTtyqp6sKoeXzxesezjCtTyvJDkl7r7hiRvS/Izbv10Qd6f5LH9HuIA+nCSz3b3m5O8Jd7DXamqq5P8fJLN7v7RbH/R6z37O9Vov5Pk9pdsO57kdHdfl+T0Yn2pBGpJuvuZ7v7CYvnb2f6H4ur9nepgqKprktyR5CP7PctBUlU/nOTHk9yTJN393e5+fl+HOlgOJXlNVR1K8tok/32f5xmru/8wyV+/ZPOdSU4tlk8luWvZxxWoFaiqI0luSvLQPo9yUPxmkl9O8nf7PMdBc22SrSS/vbg8+pGqumy/hzoIuvvpJL+e5BtJnknyP7r7c/s71YFzuLufWSw/m+Twsg8gUEtWVT+U5PeSfKC7v7Xf80xXVT+V5Fx3P7zfsxxAh5L8WJLf6u6bknwnK7jM8oNo8XnJndmO/I8kuayqfnp/pzq4evvnlZb+M0sCtURV9apsx+lj3f2p/Z7ngLg5yTur6my274h/S1X9x/0d6cB4KslT3f29M/V7sx0sdvaOJH/e3Vvd/TdJPpXkn+7zTAfNc1V1VZIsHs8t+wACtSRVVdn+LOCx7v6N/Z7noOjuX+3ua7r7SLY/pP797vZ/srvQ3c8m+Yuqun6x6dYkj+7jSAfJN5K8rapeu/i7e2t8weRC3Z/k6GL5aJL7ln0AgVqem5O8L9tnAF9a/PnJ/R6KH3g/l+RjVfWnSW5M8u/3d5yDYXHWeW+SLyT5crb/LXTLo5dRVR9P8kdJrq+qp6rq7iQnktxWVY9n+4z0xNKP61ZHAEzkDAqAkQQKgJEECoCRBAqAkQQKgJEECoCRBAqAkf4vpyav5t/CcFoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a histogram for frequency of wins w.r.t number of wickets\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.hist(batting_second['win_by_wickets'],bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "32ac6b0e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:50.071583Z",
     "iopub.status.busy": "2022-01-27T07:20:50.070867Z",
     "iopub.status.idle": "2022-01-27T07:20:50.073757Z",
     "shell.execute_reply": "2022-01-27T07:20:50.074376Z",
     "shell.execute_reply.started": "2022-01-27T07:13:44.269083Z"
    },
    "papermill": {
     "duration": 0.051087,
     "end_time": "2022-01-27T07:20:50.074548",
     "exception": false,
     "start_time": "2022-01-27T07:20:50.023461",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kolkata Knight Riders          56\n",
       "Mumbai Indians                 50\n",
       "Royal Challengers Bangalore    48\n",
       "Chennai Super Kings            48\n",
       "Rajasthan Royals               46\n",
       "Kings XI Punjab                42\n",
       "Delhi Daredevils               42\n",
       "Sunrisers Hyderabad            27\n",
       "Gujarat Lions                  12\n",
       "Deccan Chargers                11\n",
       "Pune Warriors                   6\n",
       "Delhi Capitals                  6\n",
       "Rising Pune Supergiant          5\n",
       "Kochi Tuskers Kerala            4\n",
       "Rising Pune Supergiants         3\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out the frequency of number of wins w.r.t each time after batting second\n",
    "batting_second['winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0927099e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:50.179969Z",
     "iopub.status.busy": "2022-01-27T07:20:50.177454Z",
     "iopub.status.idle": "2022-01-27T07:20:50.323897Z",
     "shell.execute_reply": "2022-01-27T07:20:50.323386Z",
     "shell.execute_reply.started": "2022-01-27T07:13:50.773019Z"
    },
    "papermill": {
     "duration": 0.207868,
     "end_time": "2022-01-27T07:20:50.324045",
     "exception": false,
     "start_time": "2022-01-27T07:20:50.116177",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a bar plot for top-3 teams with most wins after batting second\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.bar(list(batting_second['winner'].value_counts()[0:3].keys()),list(batting_second['winner'].value_counts()[0:3]),color=[\"blue\",\"green\",\"orange\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88dd6b66",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:50.411203Z",
     "iopub.status.busy": "2022-01-27T07:20:50.410499Z",
     "iopub.status.idle": "2022-01-27T07:20:50.682478Z",
     "shell.execute_reply": "2022-01-27T07:20:50.681774Z",
     "shell.execute_reply.started": "2022-01-27T07:13:54.493452Z"
    },
    "papermill": {
     "duration": 0.316831,
     "end_time": "2022-01-27T07:20:50.682622",
     "exception": false,
     "start_time": "2022-01-27T07:20:50.365791",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Making a pie chart for distribution of most wins after batting second\n",
    "plt.figure(figsize=(7,7))\n",
    "plt.pie(list(batting_second['winner'].value_counts()),labels=list(batting_second['winner'].value_counts().keys()),autopct='%0.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5fc6d9fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:50.780236Z",
     "iopub.status.busy": "2022-01-27T07:20:50.779391Z",
     "iopub.status.idle": "2022-01-27T07:20:50.783125Z",
     "shell.execute_reply": "2022-01-27T07:20:50.782616Z",
     "shell.execute_reply.started": "2022-01-27T07:17:43.295197Z"
    },
    "papermill": {
     "duration": 0.055078,
     "end_time": "2022-01-27T07:20:50.783272",
     "exception": false,
     "start_time": "2022-01-27T07:20:50.728194",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPL-2013    76\n",
       "IPL-2012    74\n",
       "IPL-2011    73\n",
       "IPL-2010    60\n",
       "IPL-2014    60\n",
       "IPL-2016    60\n",
       "IPL-2018    60\n",
       "IPL-2019    60\n",
       "IPL-2017    59\n",
       "IPL-2015    59\n",
       "IPL-2008    58\n",
       "IPL-2009    57\n",
       "Name: Season, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the number of matches played each season\n",
    "ipl['Season'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d2d995f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:50.874444Z",
     "iopub.status.busy": "2022-01-27T07:20:50.873742Z",
     "iopub.status.idle": "2022-01-27T07:20:50.880842Z",
     "shell.execute_reply": "2022-01-27T07:20:50.881488Z",
     "shell.execute_reply.started": "2022-01-27T07:14:20.741112Z"
    },
    "papermill": {
     "duration": 0.054465,
     "end_time": "2022-01-27T07:20:50.881665",
     "exception": false,
     "start_time": "2022-01-27T07:20:50.827200",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai            101\n",
       "Kolkata            77\n",
       "Delhi              74\n",
       "Bangalore          66\n",
       "Hyderabad          64\n",
       "Chennai            57\n",
       "Jaipur             47\n",
       "Chandigarh         46\n",
       "Pune               38\n",
       "Durban             15\n",
       "Bengaluru          14\n",
       "Visakhapatnam      13\n",
       "Centurion          12\n",
       "Ahmedabad          12\n",
       "Rajkot             10\n",
       "Mohali             10\n",
       "Indore              9\n",
       "Dharamsala          9\n",
       "Johannesburg        8\n",
       "Cuttack             7\n",
       "Ranchi              7\n",
       "Port Elizabeth      7\n",
       "Cape Town           7\n",
       "Abu Dhabi           7\n",
       "Sharjah             6\n",
       "Raipur              6\n",
       "Kochi               5\n",
       "Kanpur              4\n",
       "Nagpur              3\n",
       "Kimberley           3\n",
       "East London         3\n",
       "Bloemfontein        2\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at the number of matches played in each city\n",
    "ipl['city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "68a382fa",
   "metadata": {
    "execution": {
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    {
     "data": {
      "text/plain": [
       "393"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding out how many times a team has won the match after winning the toss\n",
    "import numpy as np\n",
    "np.sum(ipl['toss_winner']==ipl['winner'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e9607e83",
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    {
     "data": {
      "text/plain": [
       "0.5110062893081762"
      ]
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     "execution_count": 26,
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   ],
   "source": [
    "325/636"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6d8f8a05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-27T07:20:51.174648Z",
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   "outputs": [],
   "source": [
    "deliveries=pd.read_csv('../input/ipl-data-set/deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0a90bafc",
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   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
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       "      <td>TS Mills</td>\n",
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       "    <tr>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
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       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries.head()"
   ]
  },
  {
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       "       11143, 11144, 11145, 11146, 11147, 11148, 11149, 11150, 11151,\n",
       "       11152, 11153, 11309, 11310, 11311, 11312, 11313, 11314, 11315,\n",
       "       11316, 11317, 11318, 11319, 11320, 11321, 11322, 11323, 11324,\n",
       "       11325, 11326, 11327, 11328, 11329, 11330, 11331, 11332, 11333,\n",
       "       11334, 11335, 11336, 11337, 11338, 11339, 11340, 11341, 11342,\n",
       "       11343, 11344, 11345, 11346, 11347, 11412, 11413, 11414, 11415])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries['match_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2bb9fb1e",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "match_1=deliveries[deliveries['match_id']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1f9ab73b",
   "metadata": {
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    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
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       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_1.head()"
   ]
  },
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   "cell_type": "code",
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   "id": "ccf1282f",
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248, 21)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_1.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 33,
   "id": "919b96d5",
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   "outputs": [],
   "source": [
    "srh=match_1[match_1['inning']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d6ccb1a6",
   "metadata": {
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    },
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    57\n",
       "0    32\n",
       "4    17\n",
       "6     9\n",
       "2     9\n",
       "3     1\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2ff507b2",
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    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caught    3\n",
       "bowled    1\n",
       "Name: dismissal_kind, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh['dismissal_kind'].value_counts()"
   ]
  },
  {
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   "execution_count": 36,
   "id": "68214fd0",
   "metadata": {
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   "outputs": [],
   "source": [
    "rcb=match_1[match_1['inning']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "50c0fd96",
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "0    49\n",
       "1    44\n",
       "4    15\n",
       "6     8\n",
       "2     7\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcb['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "15f145dc",
   "metadata": {
    "execution": {
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    {
     "data": {
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       "caught     6\n",
       "bowled     2\n",
       "run out    2\n",
       "Name: dismissal_kind, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
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    }
   ],
   "source": [
    "rcb['dismissal_kind'].value_counts()"
   ]
  },
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