NoBalls_bowled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>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",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 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",
       "\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",
       "\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",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Ipl Data/deliveries.csv\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\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",
       "      <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>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <th>182</th>\n",
       "      <td>1</td>\n",
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       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>TM Head</td>\n",
       "      <td>MC Henriques</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>JJ Bumrah</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1545</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>SP Narine</td>\n",
       "      <td>MJ McClenaghan</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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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       "    <tr>\n",
       "      <th>190972</th>\n",
       "      <td>1216537</td>\n",
       "      <td>1</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Kartik Tyagi</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192737</th>\n",
       "      <td>1237178</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>S Nadeem</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192824</th>\n",
       "      <td>1237178</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>Washington Sundar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192950</th>\n",
       "      <td>1237180</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>S Nadeem</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193011</th>\n",
       "      <td>1237180</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>S Hetmyer</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>S Nadeem</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>764 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning                 batting_team  \\\n",
       "9              1       1          Sunrisers Hyderabad   \n",
       "182            1       2  Royal Challengers Bangalore   \n",
       "1440           7       1        Kolkata Knight Riders   \n",
       "1443           7       1        Kolkata Knight Riders   \n",
       "1545           7       1        Kolkata Knight Riders   \n",
       "...          ...     ...                          ...   \n",
       "190972   1216537       1              Kings XI Punjab   \n",
       "192737   1237178       1  Royal Challengers Bangalore   \n",
       "192824   1237178       2          Sunrisers Hyderabad   \n",
       "192950   1237180       1               Delhi Capitals   \n",
       "193011   1237180       1               Delhi Capitals   \n",
       "\n",
       "                       bowling_team  over  ball         batsman  \\\n",
       "9       Royal Challengers Bangalore     2     3       DA Warner   \n",
       "182             Sunrisers Hyderabad    10     1       KM Jadhav   \n",
       "1440                 Mumbai Indians     3     3         CA Lynn   \n",
       "1443                 Mumbai Indians     3     6         CA Lynn   \n",
       "1545                 Mumbai Indians    20     2       MK Pandey   \n",
       "...                             ...   ...   ...             ...   \n",
       "190972             Rajasthan Royals    19     2      GJ Maxwell   \n",
       "192737          Sunrisers Hyderabad    11     5  AB de Villiers   \n",
       "192824  Royal Challengers Bangalore     5     2       DA Warner   \n",
       "192950          Sunrisers Hyderabad     6     2        S Dhawan   \n",
       "193011          Sunrisers Hyderabad    15     6       S Hetmyer   \n",
       "\n",
       "           non_striker             bowler  is_super_over  ...  bye_runs  \\\n",
       "9             S Dhawan        A Choudhary              0  ...         0   \n",
       "182            TM Head       MC Henriques              0  ...         0   \n",
       "1440         G Gambhir          JJ Bumrah              0  ...         0   \n",
       "1443         G Gambhir          JJ Bumrah              0  ...         0   \n",
       "1545         SP Narine     MJ McClenaghan              0  ...         0   \n",
       "...                ...                ...            ...  ...       ...   \n",
       "190972        CH Gayle       Kartik Tyagi              0  ...         0   \n",
       "192737  AB de Villiers           S Nadeem              0  ...         0   \n",
       "192824       MK Pandey  Washington Sundar              0  ...         0   \n",
       "192950      MP Stoinis           S Nadeem              0  ...         0   \n",
       "193011        S Dhawan           S Nadeem              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "9                 0            1             0             0           1   \n",
       "182               0            1             0             1           1   \n",
       "1440              0            1             0             1           1   \n",
       "1443              0            1             0             1           1   \n",
       "1545              0            1             0             4           1   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "190972            0            5             0             0           5   \n",
       "192737            0            2             0             0           2   \n",
       "192824            0            1             0             0           1   \n",
       "192950            0            5             0             0           5   \n",
       "193011            0            3             0             0           3   \n",
       "\n",
       "        total_runs  player_dismissed dismissal_kind fielder  \n",
       "9                1               NaN            NaN     NaN  \n",
       "182              2               NaN            NaN     NaN  \n",
       "1440             2               NaN            NaN     NaN  \n",
       "1443             2               NaN            NaN     NaN  \n",
       "1545             5               NaN            NaN     NaN  \n",
       "...            ...               ...            ...     ...  \n",
       "190972           5               NaN            NaN     NaN  \n",
       "192737           2               NaN            NaN     NaN  \n",
       "192824           1               NaN            NaN     NaN  \n",
       "192950           5               NaN            NaN     NaN  \n",
       "193011           3               NaN            NaN     NaN  \n",
       "\n",
       "[764 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"noball_runs\"]!=0]     # all the no-ball deliveries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9               A Choudhary\n",
       "182            MC Henriques\n",
       "1440              JJ Bumrah\n",
       "1443              JJ Bumrah\n",
       "1545         MJ McClenaghan\n",
       "                ...        \n",
       "190972         Kartik Tyagi\n",
       "192737             S Nadeem\n",
       "192824    Washington Sundar\n",
       "192950             S Nadeem\n",
       "193011             S Nadeem\n",
       "Name: bowler, Length: 764, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowlers=df[df[\"noball_runs\"]!=0].bowler   #list of all bowlers who have bowled a no-ball\n",
    "bowlers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Counting the total number of no-balls bowled by the bowlers**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "A Ashish Reddy     2\n",
       "A Choudhary        1\n",
       "A Kumble           5\n",
       "A Mishra          21\n",
       "A Mithun           9\n",
       "                  ..\n",
       "Y Nagar            4\n",
       "YA Abdulla         1\n",
       "YS Chahal          8\n",
       "Yuvraj Singh       1\n",
       "Z Khan             2\n",
       "Length: 204, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'Name' : bowlers}) \n",
    "\n",
    "# counting the duplicates \n",
    "bowler_names = df2.pivot_table(index = ['Name'], aggfunc ='size') \n",
    "\n",
    "# displaying the duplicate Series \n",
    "bowler_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Result: the bowler who has bowled the max number of no-balls**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "JJ Bumrah      23\n",
       "S Sreesanth    23\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bowler_names[bowler_names==max(bowler_names)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name\n",
       "JJ Bumrah      23\n",
       "S Sreesanth    23\n",
       "A Mishra       21\n",
       "I Sharma       21\n",
       "UT Yadav       19\n",
       "SL Malinga     18\n",
       "AB Dinda       14\n",
       "SR Watson      13\n",
       "B Lee          13\n",
       "JA Morkel      13\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 10 bowlers who have bowled Most number of no-balls\n",
    "\n",
    "top_10=bowler_names.sort_values(ascending=False)[:10]\n",
    "top_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Top 10 bowlers to have bowled Most no. of No-Balls'),\n",
       " Text(0.5, 0, 'Number of no-balls')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1=sns.barplot(x=top_10,y=top_10.index)\n",
    "plot1.set(title=\"Top 10 bowlers to have bowled Most no. of No-Balls\",xlabel=\"Number of no-balls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#all more than 1 runs scored on no-balls\n",
    "runs=df[(df[\"noball_runs\"]!=0) & (df[\"noball_runs\"]!=1)].noball_runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Calculating the frequency of extra-runs scored on no-balls**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "runs_scored\n",
       "2    23\n",
       "3     6\n",
       "5    14\n",
       "7     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=pd.DataFrame({\"runs_scored\" : runs})\n",
    "\n",
    "no_ball_runs = df3.pivot_table(index = [\"runs_scored\"], aggfunc = 'size')\n",
    "no_ball_runs\n",
    "#NO-sixes were scored on a No-Ball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Runs scored on No-Balls'), Text(0, 0.5, 'frequency')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot2=sns.barplot(x=[\"Singles\",\"Doubles\",\"Fours\",\"Sixes\"],y=no_ball_runs)\n",
    "plot2.set(title=\"Runs scored on No-Balls\",ylabel=\"frequency\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot2.figure.savefig(\"ss7.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot1.figure.savefig(\"ss8.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30510     5\n",
       "42590     5\n",
       "47878     5\n",
       "54477     5\n",
       "134929    5\n",
       "142424    5\n",
       "179230    5\n",
       "179584    7\n",
       "179907    7\n",
       "179908    7\n",
       "182284    5\n",
       "183483    7\n",
       "184304    5\n",
       "185608    5\n",
       "186784    5\n",
       "186787    5\n",
       "190972    5\n",
       "192950    5\n",
       "Name: noball_runs, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of Boundaries in No Balls\n",
    "boundaries = df[df[\"noball_runs\"]>3].noball_runs\n",
    "boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total Number of Boundaries in No Balls\n",
    "boundaries_on_no_balls=len(boundaries)\n",
    "boundaries_on_no_balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "764"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total number of No Balls\n",
    "noballs = df[df[\"noball_runs\"]!=0].noball_runs\n",
    "total_no_balls=len(noballs)\n",
    "total_no_balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.356020942408377"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percentage of Boundaries in No Balls\n",
    "percentage_of_boundaries = (boundaries_on_no_balls/total_no_balls)*100\n",
    "percentage_of_boundaries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}