Fiddling With IPL Matches data.ipynb

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       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as mlt\n",
    "import seaborn as sns\n",
    "mlt.style.use('fivethirtyeight')\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data = pd.read_csv(\"matches.csv\")\n",
    "ball_by_ball_data = pd.read_csv(\"deliveries.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                team1  \\\n",
       "0   1    2017  Hyderabad  2017-04-05  Sunrisers Hyderabad   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "\n",
       "   result  dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "0  normal           0  Sunrisers Hyderabad           35               0   \n",
       "\n",
       "  player_of_match                                      venue      umpire1  \\\n",
       "0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal  AY Dandekar   \n",
       "\n",
       "    umpire2  umpire3  \n",
       "0  NJ Llong      NaN  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         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",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ball_by_ball_data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "Team_Names = {'Sunrisers Hyderabad':\"SRH\", 'Rising Pune Supergiant':\"RPS\",\n",
    "       'Kolkata Knight Riders':\"KKR\", 'Kings XI Punjab':\"KXIP\",\n",
    "       'Royal Challengers Bangalore':\"RCB\", 'Mumbai Indians':\"MI\",\n",
    "       'Delhi Daredevils':\"DD\", 'Gujarat Lions':\"GL\", 'Chennai Super Kings':\"CSK\",\n",
    "       'Rajasthan Royals':\"RR\", 'Deccan Chargers':\"DC\", 'Pune Warriors':\"PWI\",\n",
    "       'Kochi Tuskers Kerala':\"KTK\", 'Rising Pune Supergiants':\"RPS\"}\n",
    "raw_data = raw_data.replace(Team_Names.keys() , Team_Names.values())\n",
    "ball_by_ball_data = ball_by_ball_data.replace(Team_Names.keys() , Team_Names.values())\n",
    "raw_data = raw_data.drop([\"umpire3\"], axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>city</th>\n",
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       "      <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",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
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       "      <td>0</td>\n",
       "      <td>SRH</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>RPS</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date team1 team2 toss_winner toss_decision  \\\n",
       "0   1    2017  Hyderabad  2017-04-05   SRH   RCB         RCB         field   \n",
       "1   2    2017       Pune  2017-04-06    MI   RPS         RPS         field   \n",
       "\n",
       "   result  dl_applied winner  win_by_runs  win_by_wickets player_of_match  \\\n",
       "0  normal           0    SRH           35               0    Yuvraj Singh   \n",
       "1  normal           0    RPS            0               7       SPD Smith   \n",
       "\n",
       "                                       venue         umpire1   umpire2  \n",
       "0  Rajiv Gandhi International Stadium, Uppal     AY Dandekar  NJ Llong  \n",
       "1    Maharashtra Cricket Association Stadium  A Nand Kishore    S Ravi  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
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       "    <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>SRH</td>\n",
       "      <td>RCB</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",
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       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
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       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>3</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <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>SRH</td>\n",
       "      <td>RCB</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",
       "      <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>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning batting_team bowling_team  over  ball    batsman  \\\n",
       "0         1       1          SRH          RCB     1     1  DA Warner   \n",
       "1         1       1          SRH          RCB     1     2  DA Warner   \n",
       "2         1       1          SRH          RCB     1     3  DA Warner   \n",
       "3         1       1          SRH          RCB     1     4  DA Warner   \n",
       "4         1       1          SRH          RCB     1     5  DA Warner   \n",
       "\n",
       "  non_striker    bowler  is_super_over  ...  bye_runs  legbye_runs  \\\n",
       "0    S Dhawan  TS Mills              0  ...         0            0   \n",
       "1    S Dhawan  TS Mills              0  ...         0            0   \n",
       "2    S Dhawan  TS Mills              0  ...         0            0   \n",
       "3    S Dhawan  TS Mills              0  ...         0            0   \n",
       "4    S Dhawan  TS Mills              0  ...         0            0   \n",
       "\n",
       "   noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \\\n",
       "0            0             0             0           0           0   \n",
       "1            0             0             0           0           0   \n",
       "2            0             0             4           0           4   \n",
       "3            0             0             0           0           0   \n",
       "4            0             0             0           2           2   \n",
       "\n",
       "   player_dismissed dismissal_kind fielder  \n",
       "0               NaN            NaN     NaN  \n",
       "1               NaN            NaN     NaN  \n",
       "2               NaN            NaN     NaN  \n",
       "3               NaN            NaN     NaN  \n",
       "4               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ball_by_ball_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping non-required columns from ball_by_ball dataframe\n",
    "\n",
    "ball_by_ball_data_refined = ball_by_ball_data.drop(['is_super_over', 'wide_runs',\n",
    "       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs',\n",
    "       'batsman_runs', 'extra_runs','player_dismissed',\n",
    "       'dismissal_kind', 'fielder'], axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We want only first innings data\n",
    "#Including only those entries to the dataset which correspond to the first innings of a match\n",
    "first_innings_ball_by_ball_data_refined = ball_by_ball_data_refined[ball_by_ball_data_refined['inning'] == 1]\n",
    "first_innings_ball_by_ball_data_refined.tail()\n",
    "\n",
    "\n",
    "#Grouping the dataframe in terms of separate matches.\n",
    "#Each group is a dataframe consisting of data of first innings of one particular match\n",
    "sep_matches = first_innings_ball_by_ball_data_refined.groupby([\"match_id\"])\n",
    "\n",
    "\n",
    "#Iterating over each group,i.e., effectively iterating over each match.\n",
    "#Grouping each match in terms of overs and balls to find out those matches where 20 overs have not been bowled.\n",
    "#If (20,6) is not present in the groups obtained, it implies theinnings was over before the 6th ball of the 20th over.\n",
    "#In that case, we are discarding those matches and only including matches where entire 20 overs have been bowled.\n",
    "completed_first_innings_ball_by_ball_data_refined = pd.DataFrame()\n",
    "for group in sep_matches.groups:\n",
    "    a = sep_matches.get_group(group)\n",
    "    if (20,6) in a.groupby(['over','ball']).groups.keys():\n",
    "        completed_first_innings_ball_by_ball_data_refined = pd.concat([completed_first_innings_ball_by_ball_data_refined,a], axis = 0)\n",
    "    \n",
    "    \n",
    "    \n",
    "# To check if completed_first_innings_ball_by_ball_data_refined contains data of only\n",
    "# those matches which have had complete 20 overs in the first innings\n",
    "# For completed matches, the lengths of dataframes of each group of matches should be greater than equal to 120\n",
    "sep_overs_and_balls = completed_first_innings_ball_by_ball_data_refined.groupby(['match_id'])\n",
    "for group in sep_overs_and_balls.groups:\n",
    "    a = sep_overs_and_balls.get_group(group)\n",
    "    if len(a)<120:\n",
    "        print(len(a) + \"Error\")\n",
    "        \n",
    "        \n",
    "#Calculating the total runs scored in the first innings of each match\n",
    "#Iterating through each group (each match), and summing the 'total_runs' column \n",
    "#'total_runs' represents runs scored in each ball. Adding the runs scored in each ball to calculate total score.\n",
    "total = []\n",
    "\n",
    "for group in sep_matches.groups:\n",
    "    a = sep_matches.get_group(group)\n",
    "    total.append(a['total_runs'].sum())\n",
    "    \n",
    "#Creating a new extended dataframe to account for the new columns.\n",
    "extended_raw_data = copy.deepcopy(raw_data)\n",
    "extended_raw_data['First_Innings_Total'] = total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>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>First_Innings_Total</th>\n",
       "      <th>Second_Innings_Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>SRH</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>207</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>RPS</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>184</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>KKR</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>183</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>KXIP</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>163</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>DD</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>RCB</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>157</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date team1 team2 toss_winner toss_decision  \\\n",
       "0   1    2017  Hyderabad  2017-04-05   SRH   RCB         RCB         field   \n",
       "1   2    2017       Pune  2017-04-06    MI   RPS         RPS         field   \n",
       "2   3    2017     Rajkot  2017-04-07    GL   KKR         KKR         field   \n",
       "3   4    2017     Indore  2017-04-08   RPS  KXIP        KXIP         field   \n",
       "4   5    2017  Bangalore  2017-04-08   RCB    DD         RCB           bat   \n",
       "\n",
       "   result  dl_applied winner  win_by_runs  win_by_wickets player_of_match  \\\n",
       "0  normal           0    SRH           35               0    Yuvraj Singh   \n",
       "1  normal           0    RPS            0               7       SPD Smith   \n",
       "2  normal           0    KKR            0              10         CA Lynn   \n",
       "3  normal           0   KXIP            0               6      GJ Maxwell   \n",
       "4  normal           0    RCB           15               0       KM Jadhav   \n",
       "\n",
       "                                       venue         umpire1        umpire2  \\\n",
       "0  Rajiv Gandhi International Stadium, Uppal     AY Dandekar       NJ Llong   \n",
       "1    Maharashtra Cricket Association Stadium  A Nand Kishore         S Ravi   \n",
       "2     Saurashtra Cricket Association Stadium     Nitin Menon      CK Nandan   \n",
       "3                     Holkar Cricket Stadium    AK Chaudhary  C Shamshuddin   \n",
       "4                      M Chinnaswamy Stadium             NaN            NaN   \n",
       "\n",
       "   First_Innings_Total  Second_Innings_Total  \n",
       "0                  207                   172  \n",
       "1                  184                   187  \n",
       "2                  183                   184  \n",
       "3                  163                   164  \n",
       "4                  157                   142  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We want only second innings data\n",
    "#Including only those entries to the dataset which correspond to the second innings of a match\n",
    "second_innings_ball_by_ball_data_refined = ball_by_ball_data_refined[ball_by_ball_data_refined['inning'] == 2]\n",
    "second_innings_ball_by_ball_data_refined.head()\n",
    "\n",
    "\n",
    "#Grouping the dataframe in terms of separate matches.\n",
    "#Each group is a dataframe consisting of data of second innings of one particular match\n",
    "sep_matches_2nd = second_innings_ball_by_ball_data_refined.groupby([\"match_id\"])\n",
    "\n",
    "\n",
    "#Calculating the total runs scored in the first innings of each match\n",
    "#Iterating through each group (each match), and summing the 'total_runs' column \n",
    "#'total_runs' represents runs scored in each ball. Adding the runs scored in each ball to calculate total score.\n",
    "total_2nd = []\n",
    "for group in sep_matches_2nd.groups:\n",
    "    a = sep_matches_2nd.get_group(group)\n",
    "    total_2nd.append(a['total_runs'].sum())\n",
    "    \n",
    "len(total_2nd)#Length of total_2nd is 634 whereas total_1st is 636 because there were 2 matches where 2nd innings wasn't played.\n",
    "\n",
    "#Removing the matches which ended as no result from the extended_raw_data dataframe\n",
    "extended_raw_data = extended_raw_data[extended_raw_data['result'] != 'no result']\n",
    "\n",
    "\n",
    "#3 matches were no result, out of which 2nd innings didn't start on 2 occassions and it started once but didn't go upto 5 overs.\n",
    "#Upon searching through the data, I found the anomalous match was match id 569.\n",
    "#Here we omit the 568th index from the total_2nd list as it corresponds to the 569th match.\n",
    "total_2nd_new = total_2nd[:568] + total_2nd[569:]\n",
    "\n",
    "\n",
    "extended_raw_data['Second_Innings_Total'] = total_2nd_new\n",
    "\n",
    "extended_raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\py3-TF2.0\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning:\n",
      "\n",
      "Boolean Series key will be reindexed to match DataFrame index.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 609 entries, 1 to 636\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   season                609 non-null    int64 \n",
      " 1   city                  609 non-null    object\n",
      " 2   date                  609 non-null    object\n",
      " 3   team1                 609 non-null    object\n",
      " 4   team2                 609 non-null    object\n",
      " 5   toss_winner           609 non-null    object\n",
      " 6   toss_decision         609 non-null    object\n",
      " 7   result                609 non-null    object\n",
      " 8   winner                609 non-null    object\n",
      " 9   win_by_runs           609 non-null    int64 \n",
      " 10  win_by_wickets        609 non-null    int64 \n",
      " 11  player_of_match       609 non-null    object\n",
      " 12  venue                 609 non-null    object\n",
      " 13  umpire1               609 non-null    object\n",
      " 14  umpire2               609 non-null    object\n",
      " 15  First_Innings_Total   609 non-null    int64 \n",
      " 16  Second_Innings_Total  609 non-null    int64 \n",
      "dtypes: int64(5), object(12)\n",
      "memory usage: 85.6+ KB\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
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       "      <th>toss_decision</th>\n",
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       "      <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>First_Innings_Total</th>\n",
       "      <th>Second_Innings_Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</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>207</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>RPS</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>184</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</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>183</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KXIP</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>163</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</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>135</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    season       city        date team1 team2 toss_winner toss_decision  \\\n",
       "id                                                                        \n",
       "1     2017  Hyderabad  2017-04-05   SRH   RCB         RCB         field   \n",
       "2     2017       Pune  2017-04-06    MI   RPS         RPS         field   \n",
       "3     2017     Rajkot  2017-04-07    GL   KKR         KKR         field   \n",
       "4     2017     Indore  2017-04-08   RPS  KXIP        KXIP         field   \n",
       "6     2017  Hyderabad  2017-04-09    GL   SRH         SRH         field   \n",
       "\n",
       "    result winner  win_by_runs  win_by_wickets player_of_match  \\\n",
       "id                                                               \n",
       "1   normal    SRH           35               0    Yuvraj Singh   \n",
       "2   normal    RPS            0               7       SPD Smith   \n",
       "3   normal    KKR            0              10         CA Lynn   \n",
       "4   normal   KXIP            0               6      GJ Maxwell   \n",
       "6   normal    SRH            0               9     Rashid Khan   \n",
       "\n",
       "                                        venue         umpire1        umpire2  \\\n",
       "id                                                                             \n",
       "1   Rajiv Gandhi International Stadium, Uppal     AY Dandekar       NJ Llong   \n",
       "2     Maharashtra Cricket Association Stadium  A Nand Kishore         S Ravi   \n",
       "3      Saurashtra Cricket Association Stadium     Nitin Menon      CK Nandan   \n",
       "4                      Holkar Cricket Stadium    AK Chaudhary  C Shamshuddin   \n",
       "6   Rajiv Gandhi International Stadium, Uppal      A Deshmukh       NJ Llong   \n",
       "\n",
       "    First_Innings_Total  Second_Innings_Total  \n",
       "id                                             \n",
       "1                   207                   172  \n",
       "2                   184                   187  \n",
       "3                   183                   184  \n",
       "4                   163                   164  \n",
       "6                   135                   140  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a new dataframe consisting of data of only those matches which weren't affected by rain or any other issue.\n",
    "#Basically including only complete 20 over matches.\n",
    "only_full_matches_data = extended_raw_data[raw_data[\"dl_applied\"] == 0]\n",
    "only_full_matches_data = only_full_matches_data.drop(['dl_applied'], axis = 1)\n",
    "#Removing entries which have missing data\n",
    "only_full_matches_data = only_full_matches_data.dropna(axis=0)\n",
    "#Setting the id column as index\n",
    "only_full_matches_data.set_index(\"id\",inplace=True)\n",
    "only_full_matches_data.head(3)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CSK     5\n",
       "RCB     4\n",
       "MI      3\n",
       "RR      2\n",
       "KKR     2\n",
       "DC      1\n",
       "SRH     1\n",
       "DD      1\n",
       "KXIP    1\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Sorting the only_full_matches_data by the win_by_runs column \n",
    "test = only_full_matches_data.sort_values(by=\"win_by_runs\", ascending = False)\n",
    "#Creating a new dataframe consisting of matches where a team won by 80 runs or more  \n",
    "#Its purpose is to see which team has won most one sided matches while batting first\n",
    "test_2 = test[test[\"win_by_runs\"]>80]\n",
    "test_2\n",
    "test_2[\"winner\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Biggest wins in terms of runs has been registered by Mumbai Indians - 146 runs.\n",
    "Chennai Super Kings have 5 out of the top 20 biggest wins by runs, most for any team.\n",
    "Royal Challengers Bangalore have 4.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Grouping the only_full_matches_data dataframe by team1 and team2\n",
    "#Basically creating groups containing all the matches between any two particular teams.\n",
    "gb = only_full_matches_data.groupby(['team1','team2'])\n",
    "\n",
    "\n",
    "head_to_head = [[]]\n",
    "# Creates a list of all teams which have played head to head in IPL\n",
    "for key in gb.groups.keys():\n",
    "    key = list(key)\n",
    "    head_to_head.append(key)\n",
    "\n",
    "#Create a copy of the head_to_head list.\n",
    "head_to_heads = copy.deepcopy(head_to_head)\n",
    "\n",
    "#print(head_to_heads)\n",
    "#Removes duplicates from the list\n",
    "#For example, ['CSK',\"MI\"] and [\"MI\",\"CSK\"] are two different values in the list\n",
    "for i in range(1,len(head_to_heads)):\n",
    "    team1 = head_to_heads[i][0]\n",
    "    team2 = head_to_heads[i][1]\n",
    "    for j in range(1,i):\n",
    "        if head_to_heads[j][0] == team2 and head_to_heads[j][1] == team1:   #Checks every inner list unpto the ith list to see if there is any list containing the elements of ith list but in reverse order\n",
    "            head_to_heads[i][0] = 'x'   #If a reverse duplicate is found, one element of the duplicate list is set to 'x' to flag it as duplicate.\n",
    "            break\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checks if any inner list is flagged. If yes, then it removes the inner list.\n",
    "i = 1\n",
    "while i < len(head_to_heads):\n",
    "    if head_to_heads[i][0] == 'x':\n",
    "        head_to_heads.pop(i)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates a dictionary of dataframes where team names are the keys and the corresponding head to head matches data are the values \n",
    "\n",
    "frames = {}\n",
    "\n",
    "for i in range(1,len(head_to_heads)):\n",
    "    a = gb.get_group((head_to_heads[i][0],head_to_heads[i][1]))\n",
    "    if [head_to_heads[i][1],head_to_heads[i][0]] in head_to_head:\n",
    "        b = gb.get_group((head_to_heads[i][1],head_to_heads[i][0]))\n",
    "        frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = pd.concat([a,b],axis = 0)\n",
    "        frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]].drop([\"team1\",\"team2\",\"umpire1\",\"umpire2\",\"result\"], axis = 1)\n",
    "    else:\n",
    "        frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = a\n",
    "        frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]].drop([\"team1\",\"team2\",\"umpire1\",\"umpire2\",\"result\"], axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "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>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</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>First_Innings_Total</th>\n",
       "      <th>Second_Innings_Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2008</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2008-04-23</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>208</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2008</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2008-05-14</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>ST Jayasuriya</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>156</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>2010</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010-03-25</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>SR Tendulkar</td>\n",
       "      <td>Brabourne Stadium</td>\n",
       "      <td>180</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>2010</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2010-04-06</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>165</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2010</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2010-04-25</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>Dr DY Patil Sports Academy</td>\n",
       "      <td>168</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>2012</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012-04-04</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>RE Levi</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>112</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>2012</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2012-05-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>DR Smith</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>173</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>2012</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2012-05-23</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>187</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>2013</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013-05-21</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>192</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>2015</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2015-05-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>158</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2009</td>\n",
       "      <td>Cape Town</td>\n",
       "      <td>2009-04-18</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>MI</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>SR Tendulkar</td>\n",
       "      <td>Newlands</td>\n",
       "      <td>165</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>2009</td>\n",
       "      <td>Port Elizabeth</td>\n",
       "      <td>2009-05-16</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>St George's Park</td>\n",
       "      <td>147</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2011</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2011-04-22</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>MI</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>164</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>2013</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2013-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>MI</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>148</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>2013</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2013-05-05</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>MI</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>MG Johnson</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>139</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>2013</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2013-05-26</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>MI</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>148</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>2014</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2014-05-10</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>DR Smith</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>157</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>2014</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2014-05-28</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>Brabourne Stadium</td>\n",
       "      <td>173</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>2015</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2015-04-17</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>183</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>2015</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2015-05-19</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>MI</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>187</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>2015</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2015-05-24</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>MI</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>202</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season            city        date toss_winner toss_decision winner  \\\n",
       "id                                                                         \n",
       "67     2008         Chennai  2008-04-23          MI         field    CSK   \n",
       "96     2008          Mumbai  2008-05-14          MI         field     MI   \n",
       "194    2010          Mumbai  2010-03-25          MI         field     MI   \n",
       "211    2010         Chennai  2010-04-06         CSK           bat    CSK   \n",
       "234    2010          Mumbai  2010-04-25         CSK           bat    CSK   \n",
       "308    2012         Chennai  2012-04-04          MI         field     MI   \n",
       "355    2012          Mumbai  2012-05-06          MI         field     MI   \n",
       "379    2012       Bangalore  2012-05-23          MI         field    CSK   \n",
       "454    2013           Delhi  2013-05-21         CSK           bat    CSK   \n",
       "559    2015         Chennai  2015-05-08         CSK           bat     MI   \n",
       "118    2009       Cape Town  2009-04-18         CSK         field     MI   \n",
       "162    2009  Port Elizabeth  2009-05-16          MI           bat    CSK   \n",
       "257    2011          Mumbai  2011-04-22         CSK         field     MI   \n",
       "386    2013         Chennai  2013-04-06          MI           bat     MI   \n",
       "430    2013          Mumbai  2013-05-05          MI           bat     MI   \n",
       "457    2013         Kolkata  2013-05-26          MI           bat     MI   \n",
       "490    2014          Mumbai  2014-05-10         CSK         field    CSK   \n",
       "515    2014          Mumbai  2014-05-28         CSK         field    CSK   \n",
       "530    2015          Mumbai  2015-04-17          MI           bat    CSK   \n",
       "573    2015          Mumbai  2015-05-19          MI           bat     MI   \n",
       "576    2015         Kolkata  2015-05-24         CSK         field     MI   \n",
       "\n",
       "     win_by_runs  win_by_wickets  player_of_match  \\\n",
       "id                                                  \n",
       "67             6               0        ML Hayden   \n",
       "96             0               9    ST Jayasuriya   \n",
       "194            0               5     SR Tendulkar   \n",
       "211           24               0         SK Raina   \n",
       "234           22               0         SK Raina   \n",
       "308            0               8          RE Levi   \n",
       "355            0               2         DR Smith   \n",
       "379           38               0         MS Dhoni   \n",
       "454           48               0       MEK Hussey   \n",
       "559            0               6        HH Pandya   \n",
       "118           19               0     SR Tendulkar   \n",
       "162            0               7        ML Hayden   \n",
       "257            8               0  Harbhajan Singh   \n",
       "386            9               0       KA Pollard   \n",
       "430           60               0       MG Johnson   \n",
       "457           23               0       KA Pollard   \n",
       "490            0               4         DR Smith   \n",
       "515            0               7         SK Raina   \n",
       "530            0               6          A Nehra   \n",
       "573           25               0       KA Pollard   \n",
       "576           41               0        RG Sharma   \n",
       "\n",
       "                               venue  First_Innings_Total  \\\n",
       "id                                                          \n",
       "67   MA Chidambaram Stadium, Chepauk                  208   \n",
       "96                  Wankhede Stadium                  156   \n",
       "194                Brabourne Stadium                  180   \n",
       "211  MA Chidambaram Stadium, Chepauk                  165   \n",
       "234       Dr DY Patil Sports Academy                  168   \n",
       "308  MA Chidambaram Stadium, Chepauk                  112   \n",
       "355                 Wankhede Stadium                  173   \n",
       "379            M Chinnaswamy Stadium                  187   \n",
       "454                 Feroz Shah Kotla                  192   \n",
       "559  MA Chidambaram Stadium, Chepauk                  158   \n",
       "118                         Newlands                  165   \n",
       "162                 St George's Park                  147   \n",
       "257                 Wankhede Stadium                  164   \n",
       "386  MA Chidambaram Stadium, Chepauk                  148   \n",
       "430                 Wankhede Stadium                  139   \n",
       "457                     Eden Gardens                  148   \n",
       "490                 Wankhede Stadium                  157   \n",
       "515                Brabourne Stadium                  173   \n",
       "530                 Wankhede Stadium                  183   \n",
       "573                 Wankhede Stadium                  187   \n",
       "576                     Eden Gardens                  202   \n",
       "\n",
       "     Second_Innings_Total  \n",
       "id                         \n",
       "67                    202  \n",
       "96                    158  \n",
       "194                   181  \n",
       "211                   141  \n",
       "234                   146  \n",
       "308                   115  \n",
       "355                   174  \n",
       "379                   149  \n",
       "454                   144  \n",
       "559                   159  \n",
       "118                   146  \n",
       "162                   151  \n",
       "257                   156  \n",
       "386                   139  \n",
       "430                    79  \n",
       "457                   125  \n",
       "490                   160  \n",
       "515                   176  \n",
       "530                   189  \n",
       "573                   162  \n",
       "576                   161  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames[\"CSKvMI\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mumbai Indians vs Chennai Super Kings\n",
    "Matches - 21\n",
    "MI - 12\n",
    "CSK - 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal    626\n",
       "tie         7\n",
       "Name: result, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extended_raw_data[\"result\"].value_counts()\n",
    "only_full_matches_data[\"result\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185ae2759c8>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extracting the Man of The Match data from the extended_raw_data dataframe\n",
    "most_MoMs = extended_raw_data[\"player_of_match\"].value_counts()\n",
    "most_MoMs = most_MoMs[:20]\n",
    "most_MoMs\n",
    "\n",
    "most_MoMs.plot.bar(title = \"Most Man Of The Match Awards\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185ae86e808>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extracting the venues data\n",
    "venues = extended_raw_data[\"venue\"].value_counts()\n",
    "venues = venues[:20]\n",
    "venues.plot.barh(figsize=(6,8), fontsize=10, title = \"Venues Which Have Hosted Most IPL Matches\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 89 entries, HDPK Dharmasena to SD Fry\n",
      "Data columns (total 1 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   no_of_matches  89 non-null     int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 1.4+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x185aefcdd88>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "umpires = pd.DataFrame()\n",
    "umpires2 = pd.DataFrame()\n",
    "umpires[\"no_of_matches\"] = extended_raw_data[\"umpire1\"].value_counts()\n",
    "umpires2[\"no_of_matches\"] = extended_raw_data[\"umpire2\"].value_counts()\n",
    "umpires = pd.concat([umpires,umpires2], axis = 0)\n",
    "umpires.sort_values(by=\"no_of_matches\", ascending = False)\n",
    "top_umpires = umpires[:20]\n",
    "\n",
    "top_umpires.plot.barh(figsize = (10,7), title = \"Top 20 Umpires of IPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sep_overs = first_innings_ball_by_ball_data_refined.groupby([\"over\"])\n",
    "over_by_over_runs_1st_innings = pd.DataFrame()\n",
    "over_by_over_runs_1st_innings[['Id','Season','Winner','Won_by___wickets',\n",
    "                  'Won_by___runs','Venue',\n",
    "                  'First_Innings_Total','Second_Innings_Total']] = extended_raw_data[['id','season','winner','win_by_wickets',\n",
    "                                                                            'win_by_runs','venue',\n",
    "                                                                           'First_Innings_Total','Second_Innings_Total']]\n",
    "\n",
    "#Initialising the columns Batting Team and Bowling team with dashes\n",
    "#Initialising each column for PCT runs and Runs for every over with -1\n",
    "#Not initialising with 0 because there may be maiden overs where runs scored were 0, so we have to keep track of them as well.\n",
    "over_by_over_runs_1st_innings[\"Batting_Team\"] = \"---\"\n",
    "over_by_over_runs_1st_innings[\"Bowling_Team\"] = \"---\"\n",
    "for i in range(1,21):\n",
    "    x = \"PCT_Runs_in_over\" + str(i)\n",
    "    over_by_over_runs_1st_innings[x] = -1\n",
    "for i in range(1,21):\n",
    "    x = \"Runs_in_over\" + str(i)\n",
    "    over_by_over_runs_1st_innings[x] = -1\n",
    "                \n",
    "                \n",
    "over_by_over_runs_1st_innings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "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>Venue</th>\n",
       "      <th>Batting_Team</th>\n",
       "      <th>Bowling_Team</th>\n",
       "      <th>Runs_in_over1</th>\n",
       "      <th>Runs_in_over2</th>\n",
       "      <th>Runs_in_over3</th>\n",
       "      <th>Runs_in_over4</th>\n",
       "      <th>Runs_in_over5</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_Runs_in_over16</th>\n",
       "      <th>PCT_Runs_in_over17</th>\n",
       "      <th>PCT_Runs_in_over18</th>\n",
       "      <th>PCT_Runs_in_over19</th>\n",
       "      <th>PCT_Runs_in_over20</th>\n",
       "      <th>First_Innings_Total</th>\n",
       "      <th>Second_Innings_Total</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Won_by___wickets</th>\n",
       "      <th>Won_by___runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>---</td>\n",
       "      <td>---</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>207</td>\n",
       "      <td>172</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>---</td>\n",
       "      <td>---</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>184</td>\n",
       "      <td>187</td>\n",
       "      <td>RPS</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>---</td>\n",
       "      <td>---</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>183</td>\n",
       "      <td>184</td>\n",
       "      <td>KKR</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Season                                      Venue Batting_Team  \\\n",
       "0   1    2017  Rajiv Gandhi International Stadium, Uppal          ---   \n",
       "1   2    2017    Maharashtra Cricket Association Stadium          ---   \n",
       "2   3    2017     Saurashtra Cricket Association Stadium          ---   \n",
       "\n",
       "  Bowling_Team  Runs_in_over1  Runs_in_over2  Runs_in_over3  Runs_in_over4  \\\n",
       "0          ---             -1             -1             -1             -1   \n",
       "1          ---             -1             -1             -1             -1   \n",
       "2          ---             -1             -1             -1             -1   \n",
       "\n",
       "   Runs_in_over5  ...  PCT_Runs_in_over16  PCT_Runs_in_over17  \\\n",
       "0             -1  ...                  -1                  -1   \n",
       "1             -1  ...                  -1                  -1   \n",
       "2             -1  ...                  -1                  -1   \n",
       "\n",
       "   PCT_Runs_in_over18  PCT_Runs_in_over19  PCT_Runs_in_over20  \\\n",
       "0                  -1                  -1                  -1   \n",
       "1                  -1                  -1                  -1   \n",
       "2                  -1                  -1                  -1   \n",
       "\n",
       "   First_Innings_Total  Second_Innings_Total  Winner  Won_by___wickets  \\\n",
       "0                  207                   172     SRH                 0   \n",
       "1                  184                   187     RPS                 7   \n",
       "2                  183                   184     KKR                10   \n",
       "\n",
       "   Won_by___runs  \n",
       "0             35  \n",
       "1              0  \n",
       "2              0  \n",
       "\n",
       "[3 rows x 50 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rearranging the columns\n",
    "over_by_over_runs_1st_innings = over_by_over_runs_1st_innings[['Id', 'Season', 'Venue', 'Batting_Team',\n",
    "       'Bowling_Team','Runs_in_over1','Runs_in_over2','Runs_in_over3','Runs_in_over4',\n",
    "       'Runs_in_over5','Runs_in_over6','Runs_in_over7','Runs_in_over8','Runs_in_over9',\n",
    "       'Runs_in_over10','Runs_in_over11','Runs_in_over12','Runs_in_over13','Runs_in_over14',\n",
    "       'Runs_in_over15','Runs_in_over16','Runs_in_over17','Runs_in_over18','Runs_in_over19',\n",
    "       'Runs_in_over20', 'PCT_Runs_in_over1', 'PCT_Runs_in_over2',\n",
    "       'PCT_Runs_in_over3', 'PCT_Runs_in_over4', 'PCT_Runs_in_over5',\n",
    "       'PCT_Runs_in_over6', 'PCT_Runs_in_over7', 'PCT_Runs_in_over8',\n",
    "       'PCT_Runs_in_over9', 'PCT_Runs_in_over10', 'PCT_Runs_in_over11',\n",
    "       'PCT_Runs_in_over12', 'PCT_Runs_in_over13', 'PCT_Runs_in_over14',\n",
    "       'PCT_Runs_in_over15', 'PCT_Runs_in_over16', 'PCT_Runs_in_over17',\n",
    "       'PCT_Runs_in_over18', 'PCT_Runs_in_over19', 'PCT_Runs_in_over20',\n",
    "       'First_Innings_Total', 'Second_Innings_Total','Winner', 'Won_by___wickets', 'Won_by___runs']]\n",
    "\n",
    "over_by_over_runs_1st_innings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating each match into groups. Excluding match nos. 301,546 and 571 as they were no result matches.\n",
    "#Extracting he First Innings total from the extended_raw_data for each match and storing it in total_runs\n",
    "#Similarly extracting Batting team and Bowling team\n",
    "#Grouping each atch into groups of overs, calculating total runs in an over, cummulative runs upto that over\n",
    "#Entering the calculated values of run rate and total runs score for every over in the corresponding columns\n",
    "\n",
    "for group in sep_matches.groups:\n",
    "    #print(group)\n",
    "    if(group == 301 or group == 546 or group == 571):\n",
    "        continue\n",
    "    else:\n",
    "        a = sep_matches.get_group(group)\n",
    "        match_id = a['match_id'].unique()\n",
    "        idx = match_id - 1\n",
    "        total_runs = extended_raw_data.loc[idx][\"First_Innings_Total\"]\n",
    "        #print(total_runs)\n",
    "        Batting_Team = a[\"batting_team\"].unique()\n",
    "        Bowling_Team = a[\"bowling_team\"].unique()\n",
    "        over_by_over_runs_1st_innings.loc[idx,\"Batting_Team\"] = Batting_Team\n",
    "        over_by_over_runs_1st_innings.loc[idx,\"Bowling_Team\"] = Bowling_Team\n",
    "        PCT_runs_in_over = [0]*20\n",
    "        Runs_in_over = [0]*20\n",
    "        cummulative_runs = 0\n",
    "        sep_overs = a.groupby(['over'])\n",
    "        for i in range (1,21):\n",
    "            \n",
    "            if i in sep_overs.groups.keys():\n",
    "    \n",
    "                b = sep_overs.get_group(i)\n",
    "                runs_in_over = b['total_runs'].sum()\n",
    "                cummulative_runs += runs_in_over\n",
    "                PCT_runs_in_over[i-1] = round((cummulative_runs/i), 2)\n",
    "                Runs_in_over[i-1] = runs_in_over \n",
    "                #print(PCT_runs_in_over[i-1])\n",
    "                #break\n",
    "                y = \"PCT_Runs_in_over\" + str(i)\n",
    "                z = \"Runs_in_over\" + str(i)\n",
    "                over_by_over_runs_1st_innings.loc[idx,y] = PCT_runs_in_over[i-1]\n",
    "                over_by_over_runs_1st_innings.loc[idx,z] = Runs_in_over[i-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "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>Venue</th>\n",
       "      <th>Batting_Team</th>\n",
       "      <th>Bowling_Team</th>\n",
       "      <th>Runs_in_over1</th>\n",
       "      <th>Runs_in_over2</th>\n",
       "      <th>Runs_in_over3</th>\n",
       "      <th>Runs_in_over4</th>\n",
       "      <th>Runs_in_over5</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_Runs_in_over16</th>\n",
       "      <th>PCT_Runs_in_over17</th>\n",
       "      <th>PCT_Runs_in_over18</th>\n",
       "      <th>PCT_Runs_in_over19</th>\n",
       "      <th>PCT_Runs_in_over20</th>\n",
       "      <th>First_Innings_Total</th>\n",
       "      <th>Second_Innings_Total</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Won_by___wickets</th>\n",
       "      <th>Won_by___runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>9.69</td>\n",
       "      <td>9.53</td>\n",
       "      <td>9.94</td>\n",
       "      <td>10.05</td>\n",
       "      <td>10.35</td>\n",
       "      <td>207</td>\n",
       "      <td>172</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>7.94</td>\n",
       "      <td>8.06</td>\n",
       "      <td>8.06</td>\n",
       "      <td>8.11</td>\n",
       "      <td>9.20</td>\n",
       "      <td>184</td>\n",
       "      <td>187</td>\n",
       "      <td>RPS</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>8.12</td>\n",
       "      <td>8.29</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.89</td>\n",
       "      <td>9.15</td>\n",
       "      <td>183</td>\n",
       "      <td>184</td>\n",
       "      <td>KKR</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Season                                      Venue Batting_Team  \\\n",
       "0   1    2017  Rajiv Gandhi International Stadium, Uppal          SRH   \n",
       "1   2    2017    Maharashtra Cricket Association Stadium           MI   \n",
       "2   3    2017     Saurashtra Cricket Association Stadium           GL   \n",
       "\n",
       "  Bowling_Team  Runs_in_over1  Runs_in_over2  Runs_in_over3  Runs_in_over4  \\\n",
       "0          RCB              7             16              6              4   \n",
       "1          RPS             11              8              9             13   \n",
       "2          KKR              8              8              6              7   \n",
       "\n",
       "   Runs_in_over5  ...  PCT_Runs_in_over16  PCT_Runs_in_over17  \\\n",
       "0              9  ...                9.69                9.53   \n",
       "1              7  ...                7.94                8.06   \n",
       "2             11  ...                8.12                8.29   \n",
       "\n",
       "   PCT_Runs_in_over18  PCT_Runs_in_over19  PCT_Runs_in_over20  \\\n",
       "0                9.94               10.05               10.35   \n",
       "1                8.06                8.11                9.20   \n",
       "2                8.50                8.89                9.15   \n",
       "\n",
       "   First_Innings_Total  Second_Innings_Total  Winner  Won_by___wickets  \\\n",
       "0                  207                   172     SRH                 0   \n",
       "1                  184                   187     RPS                 7   \n",
       "2                  183                   184     KKR                10   \n",
       "\n",
       "   Won_by___runs  \n",
       "0             35  \n",
       "1              0  \n",
       "2              0  \n",
       "\n",
       "[3 rows x 50 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_by_over_runs_1st_innings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Using similar logic as above, creating an over_by_over_runs_2nd_innings dataframe.\n",
    "\n",
    "#sep_overs = first_innings_ball_by_ball_data_refined.groupby([\"over\"])\n",
    "over_by_over_runs_2nd_innings = pd.DataFrame()\n",
    "over_by_over_runs_2nd_innings[['Id','Season','Winner','Won_by___wickets',\n",
    "                  'Won_by___runs','Venue',\n",
    "                  'First_Innings_Total','Second_Innings_Total']] = extended_raw_data[['id','season','winner','win_by_wickets',\n",
    "                                                                            'win_by_runs','venue',\n",
    "                                                                           'First_Innings_Total','Second_Innings_Total']]\n",
    "\n",
    "\n",
    "over_by_over_runs_2nd_innings[\"Batting_Team\"] = \"---\"\n",
    "over_by_over_runs_2nd_innings[\"Bowling_Team\"] = \"---\"\n",
    "for i in range(1,21):\n",
    "    x = \"PCT_Runs_in_over\" + str(i)\n",
    "    over_by_over_runs_2nd_innings[x] = -1\n",
    "for i in range(1,21):\n",
    "    x = \"Runs_in_over\" + str(i)\n",
    "    over_by_over_runs_2nd_innings[x] = -1\n",
    "                \n",
    "        \n",
    "over_by_over_runs_2nd_innings = over_by_over_runs_2nd_innings[['Id', 'Season', 'Venue', 'Batting_Team',\n",
    "       'Bowling_Team', 'Runs_in_over1','Runs_in_over2','Runs_in_over3','Runs_in_over4',\n",
    "       'Runs_in_over5','Runs_in_over6','Runs_in_over7','Runs_in_over8','Runs_in_over9',\n",
    "       'Runs_in_over10','Runs_in_over11','Runs_in_over12','Runs_in_over13','Runs_in_over14',\n",
    "       'Runs_in_over15','Runs_in_over16','Runs_in_over17','Runs_in_over18','Runs_in_over19',\n",
    "       'Runs_in_over20','PCT_Runs_in_over1', 'PCT_Runs_in_over2',\n",
    "       'PCT_Runs_in_over3', 'PCT_Runs_in_over4', 'PCT_Runs_in_over5',\n",
    "       'PCT_Runs_in_over6', 'PCT_Runs_in_over7', 'PCT_Runs_in_over8',\n",
    "       'PCT_Runs_in_over9', 'PCT_Runs_in_over10', 'PCT_Runs_in_over11',\n",
    "       'PCT_Runs_in_over12', 'PCT_Runs_in_over13', 'PCT_Runs_in_over14',\n",
    "       'PCT_Runs_in_over15', 'PCT_Runs_in_over16', 'PCT_Runs_in_over17',\n",
    "       'PCT_Runs_in_over18', 'PCT_Runs_in_over19', 'PCT_Runs_in_over20',\n",
    "       'First_Innings_Total', 'Second_Innings_Total','Winner', 'Won_by___wickets', 'Won_by___runs']]\n",
    "\n",
    "\n",
    "\n",
    "for group in sep_matches_2nd.groups:\n",
    "    #print(group)\n",
    "    if(group == 571):\n",
    "        continue\n",
    "    else:\n",
    "        a = sep_matches_2nd.get_group(group)\n",
    "        match_id = a['match_id'].unique()\n",
    "        idx = match_id - 1\n",
    "        total_runs = extended_raw_data.loc[idx][\"First_Innings_Total\"]\n",
    "        #print(total_runs)\n",
    "        Batting_Team = a[\"batting_team\"].unique()\n",
    "        Bowling_Team = a[\"bowling_team\"].unique()\n",
    "        over_by_over_runs_2nd_innings.loc[idx,\"Batting_Team\"] = Batting_Team\n",
    "        over_by_over_runs_2nd_innings.loc[idx,\"Bowling_Team\"] = Bowling_Team\n",
    "        PCT_runs_in_over = [0]*20\n",
    "        Runs_in_over = [0]*20\n",
    "        cummulative_runs = 0\n",
    "        sep_overs = a.groupby(['over'])\n",
    "        for i in range (1,21):\n",
    "            \n",
    "            if i in sep_overs.groups.keys():\n",
    "    \n",
    "                b = sep_overs.get_group(i)\n",
    "                runs_in_over = b['total_runs'].sum()\n",
    "                cummulative_runs += runs_in_over\n",
    "                PCT_runs_in_over[i-1] = round((cummulative_runs/i), 2)\n",
    "                Runs_in_over[i-1] = runs_in_over                 \n",
    "                y = \"PCT_Runs_in_over\" + str(i)\n",
    "                z = \"Runs_in_over\" + str(i)\n",
    "                over_by_over_runs_2nd_innings.loc[idx,y] = PCT_runs_in_over[i-1]\n",
    "                over_by_over_runs_2nd_innings.loc[idx,z] = Runs_in_over[i-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "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>Venue</th>\n",
       "      <th>Batting_Team</th>\n",
       "      <th>Bowling_Team</th>\n",
       "      <th>Runs_in_over1</th>\n",
       "      <th>Runs_in_over2</th>\n",
       "      <th>Runs_in_over3</th>\n",
       "      <th>Runs_in_over4</th>\n",
       "      <th>Runs_in_over5</th>\n",
       "      <th>...</th>\n",
       "      <th>PCT_Runs_in_over16</th>\n",
       "      <th>PCT_Runs_in_over17</th>\n",
       "      <th>PCT_Runs_in_over18</th>\n",
       "      <th>PCT_Runs_in_over19</th>\n",
       "      <th>PCT_Runs_in_over20</th>\n",
       "      <th>First_Innings_Total</th>\n",
       "      <th>Second_Innings_Total</th>\n",
       "      <th>Winner</th>\n",
       "      <th>Won_by___wickets</th>\n",
       "      <th>Won_by___runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>9.44</td>\n",
       "      <td>9.06</td>\n",
       "      <td>8.67</td>\n",
       "      <td>8.68</td>\n",
       "      <td>8.60</td>\n",
       "      <td>207</td>\n",
       "      <td>172</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>RPS</td>\n",
       "      <td>MI</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>9.25</td>\n",
       "      <td>9.29</td>\n",
       "      <td>9.17</td>\n",
       "      <td>9.05</td>\n",
       "      <td>9.35</td>\n",
       "      <td>184</td>\n",
       "      <td>187</td>\n",
       "      <td>RPS</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>KKR</td>\n",
       "      <td>GL</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>183</td>\n",
       "      <td>184</td>\n",
       "      <td>KKR</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Season                                      Venue Batting_Team  \\\n",
       "0   1    2017  Rajiv Gandhi International Stadium, Uppal          RCB   \n",
       "1   2    2017    Maharashtra Cricket Association Stadium          RPS   \n",
       "2   3    2017     Saurashtra Cricket Association Stadium          KKR   \n",
       "\n",
       "  Bowling_Team  Runs_in_over1  Runs_in_over2  Runs_in_over3  Runs_in_over4  \\\n",
       "0          SRH             11              1             16             15   \n",
       "1           MI              8             15             12              4   \n",
       "2           GL              7             13             17              9   \n",
       "\n",
       "   Runs_in_over5  ...  PCT_Runs_in_over16  PCT_Runs_in_over17  \\\n",
       "0              5  ...                9.44                9.06   \n",
       "1              4  ...                9.25                9.29   \n",
       "2              7  ...               -1.00               -1.00   \n",
       "\n",
       "   PCT_Runs_in_over18  PCT_Runs_in_over19  PCT_Runs_in_over20  \\\n",
       "0                8.67                8.68                8.60   \n",
       "1                9.17                9.05                9.35   \n",
       "2               -1.00               -1.00               -1.00   \n",
       "\n",
       "   First_Innings_Total  Second_Innings_Total  Winner  Won_by___wickets  \\\n",
       "0                  207                   172     SRH                 0   \n",
       "1                  184                   187     RPS                 7   \n",
       "2                  183                   184     KKR                10   \n",
       "\n",
       "   Won_by___runs  \n",
       "0             35  \n",
       "1              0  \n",
       "2              0  \n",
       "\n",
       "[3 rows x 50 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_by_over_runs_2nd_innings.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
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       "    <tr>\n",
       "      <th>Over</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over1</th>\n",
       "      <td>5.31</td>\n",
       "      <td>5.46</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.33</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5.01</td>\n",
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       "      <td>5.52</td>\n",
       "      <td>6.1</td>\n",
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       "    <tr>\n",
       "      <th>PCT_Runs_in_over2</th>\n",
       "      <td>6.67</td>\n",
       "      <td>6.26</td>\n",
       "      <td>7.57</td>\n",
       "      <td>6.27</td>\n",
       "      <td>5.51</td>\n",
       "      <td>5.78</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.67</td>\n",
       "      <td>6.02</td>\n",
       "      <td>6.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over3</th>\n",
       "      <td>6.71</td>\n",
       "      <td>6.63</td>\n",
       "      <td>7.76</td>\n",
       "      <td>6.68</td>\n",
       "      <td>6.19</td>\n",
       "      <td>6.34</td>\n",
       "      <td>6.51</td>\n",
       "      <td>7.46</td>\n",
       "      <td>6.19</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over4</th>\n",
       "      <td>6.92</td>\n",
       "      <td>6.85</td>\n",
       "      <td>7.71</td>\n",
       "      <td>6.66</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.78</td>\n",
       "      <td>7.61</td>\n",
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       "    <tr>\n",
       "      <th>PCT_Runs_in_over5</th>\n",
       "      <td>7.25</td>\n",
       "      <td>7.09</td>\n",
       "      <td>7.82</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.86</td>\n",
       "      <td>6.68</td>\n",
       "      <td>7.17</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.14</td>\n",
       "      <td>7.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   2008  2009  2010  2011  2012  2013  2014  2015  2016  2017\n",
       "Over                                                                         \n",
       "PCT_Runs_in_over1  5.31  5.46  6.53  6.33  4.99  5.01  6.53  5.96  5.52   6.1\n",
       "PCT_Runs_in_over2  6.67  6.26  7.57  6.27  5.51  5.78  6.15  6.67  6.02  6.71\n",
       "PCT_Runs_in_over3  6.71  6.63  7.76  6.68  6.19  6.34  6.51  7.46  6.19  7.46\n",
       "PCT_Runs_in_over4  6.92  6.85  7.71  6.66  6.63  6.33  6.88  7.73  6.78  7.61\n",
       "PCT_Runs_in_over5  7.25  7.09  7.82  6.81  6.86  6.68  7.17  7.78  7.14  7.69"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def Over_by_Over_PCT_Runs_Breakdown(over_by_over_runs):\n",
    "    over_by_over_PCT_Runs_breakdown = pd.DataFrame(columns = [\"Over\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\"], index = range(0,20))\n",
    " \n",
    "\n",
    "    for i in range(1,21):\n",
    "        x = \"PCT_Runs_in_over\" + str(i)\n",
    "        j = i-1\n",
    "        over_by_over_PCT_Runs_breakdown[\"Over\"][j] = x \n",
    "\n",
    "    years = over_by_over_runs.groupby(['Season'])\n",
    "\n",
    "    for group in years.groups:\n",
    "        c = years.get_group(group)\n",
    "        for r in range(25,45):\n",
    "            total_pct = 0\n",
    "            total_matches = 0\n",
    "            for j in range(len(c)):\n",
    "                if c.iloc[j,r] != -1:\n",
    "                    total_pct = total_pct + c.iloc[j,r]\n",
    "                    total_matches = total_matches + 1\n",
    "            avg = round(total_pct/total_matches, 2)\n",
    "            k = r - 25\n",
    "            over_by_over_PCT_Runs_breakdown.loc[k,str(group)] = avg\n",
    "    \n",
    "    \n",
    "    over_by_over_PCT_Runs_breakdown = over_by_over_PCT_Runs_breakdown.set_index(['Over'])\n",
    "\n",
    "    return over_by_over_PCT_Runs_breakdown\n",
    "\n",
    "\n",
    "over_by_over_PCT_Runs_breakdown_1st_inngs = Over_by_Over_PCT_Runs_Breakdown(over_by_over_runs_1st_innings)\n",
    "over_by_over_PCT_Runs_breakdown_2nd_inngs = Over_by_Over_PCT_Runs_Breakdown(over_by_over_runs_2nd_innings)\n",
    "\n",
    "over_by_over_PCT_Runs_breakdown_1st_inngs.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>2008</th>\n",
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       "      <th>2011</th>\n",
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       "      <th>Over</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over1</th>\n",
       "      <td>5.31</td>\n",
       "      <td>5.46</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.33</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5.01</td>\n",
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       "      <td>5.96</td>\n",
       "      <td>5.52</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over2</th>\n",
       "      <td>6.67</td>\n",
       "      <td>6.26</td>\n",
       "      <td>7.57</td>\n",
       "      <td>6.27</td>\n",
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       "      <td>6.02</td>\n",
       "      <td>6.71</td>\n",
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       "    <tr>\n",
       "      <th>PCT_Runs_in_over3</th>\n",
       "      <td>6.71</td>\n",
       "      <td>6.63</td>\n",
       "      <td>7.76</td>\n",
       "      <td>6.68</td>\n",
       "      <td>6.19</td>\n",
       "      <td>6.34</td>\n",
       "      <td>6.51</td>\n",
       "      <td>7.46</td>\n",
       "      <td>6.19</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over4</th>\n",
       "      <td>6.92</td>\n",
       "      <td>6.85</td>\n",
       "      <td>7.71</td>\n",
       "      <td>6.66</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.33</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.78</td>\n",
       "      <td>7.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over5</th>\n",
       "      <td>7.25</td>\n",
       "      <td>7.09</td>\n",
       "      <td>7.82</td>\n",
       "      <td>6.81</td>\n",
       "      <td>6.86</td>\n",
       "      <td>6.68</td>\n",
       "      <td>7.17</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.14</td>\n",
       "      <td>7.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over6</th>\n",
       "      <td>7.48</td>\n",
       "      <td>7.34</td>\n",
       "      <td>7.83</td>\n",
       "      <td>7</td>\n",
       "      <td>7.12</td>\n",
       "      <td>6.84</td>\n",
       "      <td>7.38</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.31</td>\n",
       "      <td>7.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over7</th>\n",
       "      <td>7.43</td>\n",
       "      <td>7.13</td>\n",
       "      <td>7.73</td>\n",
       "      <td>6.83</td>\n",
       "      <td>6.93</td>\n",
       "      <td>6.83</td>\n",
       "      <td>7.24</td>\n",
       "      <td>7.58</td>\n",
       "      <td>7.21</td>\n",
       "      <td>7.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over8</th>\n",
       "      <td>7.35</td>\n",
       "      <td>7.04</td>\n",
       "      <td>7.58</td>\n",
       "      <td>6.92</td>\n",
       "      <td>6.99</td>\n",
       "      <td>6.82</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over9</th>\n",
       "      <td>7.3</td>\n",
       "      <td>6.97</td>\n",
       "      <td>7.54</td>\n",
       "      <td>6.94</td>\n",
       "      <td>6.98</td>\n",
       "      <td>6.78</td>\n",
       "      <td>7.23</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.27</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over10</th>\n",
       "      <td>7.31</td>\n",
       "      <td>6.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>6.97</td>\n",
       "      <td>7</td>\n",
       "      <td>6.79</td>\n",
       "      <td>7.24</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.33</td>\n",
       "      <td>7.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over11</th>\n",
       "      <td>7.4</td>\n",
       "      <td>6.82</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.07</td>\n",
       "      <td>7.08</td>\n",
       "      <td>6.88</td>\n",
       "      <td>7.32</td>\n",
       "      <td>7.52</td>\n",
       "      <td>7.29</td>\n",
       "      <td>7.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over12</th>\n",
       "      <td>7.42</td>\n",
       "      <td>6.86</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.11</td>\n",
       "      <td>7.19</td>\n",
       "      <td>6.89</td>\n",
       "      <td>7.39</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.33</td>\n",
       "      <td>7.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over13</th>\n",
       "      <td>7.36</td>\n",
       "      <td>6.85</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.12</td>\n",
       "      <td>7.2</td>\n",
       "      <td>6.96</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.52</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over14</th>\n",
       "      <td>7.42</td>\n",
       "      <td>6.9</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.2</td>\n",
       "      <td>7.27</td>\n",
       "      <td>6.98</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.62</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over15</th>\n",
       "      <td>7.53</td>\n",
       "      <td>7</td>\n",
       "      <td>7.62</td>\n",
       "      <td>7.31</td>\n",
       "      <td>7.36</td>\n",
       "      <td>7.07</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.58</td>\n",
       "      <td>7.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over16</th>\n",
       "      <td>7.6</td>\n",
       "      <td>7.09</td>\n",
       "      <td>7.67</td>\n",
       "      <td>7.41</td>\n",
       "      <td>7.43</td>\n",
       "      <td>7.16</td>\n",
       "      <td>7.8</td>\n",
       "      <td>7.83</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over17</th>\n",
       "      <td>7.79</td>\n",
       "      <td>7.21</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.32</td>\n",
       "      <td>7.86</td>\n",
       "      <td>8</td>\n",
       "      <td>7.72</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over18</th>\n",
       "      <td>7.96</td>\n",
       "      <td>7.35</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.59</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.95</td>\n",
       "      <td>8.13</td>\n",
       "      <td>7.86</td>\n",
       "      <td>8.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over19</th>\n",
       "      <td>8.16</td>\n",
       "      <td>7.38</td>\n",
       "      <td>8.05</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.81</td>\n",
       "      <td>7.64</td>\n",
       "      <td>8.09</td>\n",
       "      <td>8.3</td>\n",
       "      <td>8.06</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCT_Runs_in_over20</th>\n",
       "      <td>8.41</td>\n",
       "      <td>7.52</td>\n",
       "      <td>8.31</td>\n",
       "      <td>7.92</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.85</td>\n",
       "      <td>8.29</td>\n",
       "      <td>8.47</td>\n",
       "      <td>8.2</td>\n",
       "      <td>8.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2008  2009  2010  2011  2012  2013  2014  2015  2016  2017\n",
       "Over                                                                          \n",
       "PCT_Runs_in_over1   5.31  5.46  6.53  6.33  4.99  5.01  6.53  5.96  5.52   6.1\n",
       "PCT_Runs_in_over2   6.67  6.26  7.57  6.27  5.51  5.78  6.15  6.67  6.02  6.71\n",
       "PCT_Runs_in_over3   6.71  6.63  7.76  6.68  6.19  6.34  6.51  7.46  6.19  7.46\n",
       "PCT_Runs_in_over4   6.92  6.85  7.71  6.66  6.63  6.33  6.88  7.73  6.78  7.61\n",
       "PCT_Runs_in_over5   7.25  7.09  7.82  6.81  6.86  6.68  7.17  7.78  7.14  7.69\n",
       "PCT_Runs_in_over6   7.48  7.34  7.83     7  7.12  6.84  7.38   7.8  7.31  7.88\n",
       "PCT_Runs_in_over7   7.43  7.13  7.73  6.83  6.93  6.83  7.24  7.58  7.21  7.75\n",
       "PCT_Runs_in_over8   7.35  7.04  7.58  6.92  6.99  6.82  7.27  7.54   7.2  7.62\n",
       "PCT_Runs_in_over9    7.3  6.97  7.54  6.94  6.98  6.78  7.23   7.6  7.27  7.65\n",
       "PCT_Runs_in_over10  7.31  6.89  7.55  6.97     7  6.79  7.24  7.66  7.33   7.6\n",
       "PCT_Runs_in_over11   7.4  6.82   7.5  7.07  7.08  6.88  7.32  7.52  7.29  7.61\n",
       "PCT_Runs_in_over12  7.42  6.86   7.5  7.11  7.19  6.89  7.39  7.49  7.33  7.65\n",
       "PCT_Runs_in_over13  7.36  6.85  7.54  7.12   7.2  6.96  7.47  7.52   7.4  7.72\n",
       "PCT_Runs_in_over14  7.42   6.9   7.6   7.2  7.27  6.98  7.51  7.62  7.43  7.82\n",
       "PCT_Runs_in_over15  7.53     7  7.62  7.31  7.36  7.07  7.64  7.71  7.58  7.86\n",
       "PCT_Runs_in_over16   7.6  7.09  7.67  7.41  7.43  7.16   7.8  7.83   7.6  7.89\n",
       "PCT_Runs_in_over17  7.79  7.21  7.79  7.49  7.54  7.32  7.86     8  7.72     8\n",
       "PCT_Runs_in_over18  7.96  7.35  7.89  7.59  7.66  7.47  7.95  8.13  7.86  8.08\n",
       "PCT_Runs_in_over19  8.16  7.38  8.05  7.76  7.81  7.64  8.09   8.3  8.06   8.3\n",
       "PCT_Runs_in_over20  8.41  7.52  8.31  7.92  7.99  7.85  8.29  8.47   8.2  8.51"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_by_over_PCT_Runs_breakdown_1st_inngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Over_by_Over_Runs_breakdown(over_by_over_runs):\n",
    "    over_by_over_Runs_breakdown = pd.DataFrame(columns = [\"Over\",\"2008\",\"2009\",\"2010\",\"2011\",\"2012\",\"2013\",\"2014\",\"2015\",\"2016\",\"2017\"], index = range(0,20))\n",
    " \n",
    "\n",
    "    for i in range(1,21):\n",
    "        x = \"Runs_in_over\" + str(i)\n",
    "        j = i-1\n",
    "        over_by_over_Runs_breakdown[\"Over\"][j] = x \n",
    "\n",
    "    years = over_by_over_runs.groupby(['Season'])\n",
    "\n",
    "    for group in years.groups:\n",
    "        c = years.get_group(group)\n",
    "        for r in range(5,25):\n",
    "            total_runs = 0\n",
    "            total_matches = 0\n",
    "            for j in range(len(c)):\n",
    "                if c.iloc[j,r] != -1:\n",
    "                    total_runs = total_runs + c.iloc[j,r]\n",
    "                    total_matches = total_matches + 1\n",
    "            avg = round(total_runs/total_matches, 2)\n",
    "            k = r - 5\n",
    "            over_by_over_Runs_breakdown.loc[k,str(group)] = avg\n",
    "    \n",
    "    over_by_over_Runs_breakdown = over_by_over_Runs_breakdown.set_index(['Over'])\n",
    "    \n",
    "    return over_by_over_Runs_breakdown\n",
    "\n",
    "\n",
    "over_by_over_Runs_breakdown_1st_inngs = Over_by_Over_Runs_breakdown(over_by_over_runs_1st_innings)\n",
    "over_by_over_Runs_breakdown_2nd_inngs = Over_by_Over_Runs_breakdown(over_by_over_runs_2nd_innings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "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>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Over</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Runs_in_over1</th>\n",
       "      <td>5.31</td>\n",
       "      <td>5.46</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.33</td>\n",
       "      <td>4.99</td>\n",
       "      <td>5.01</td>\n",
       "      <td>6.53</td>\n",
       "      <td>5.96</td>\n",
       "      <td>5.52</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over2</th>\n",
       "      <td>8.03</td>\n",
       "      <td>7.07</td>\n",
       "      <td>8.6</td>\n",
       "      <td>6.21</td>\n",
       "      <td>6.03</td>\n",
       "      <td>6.54</td>\n",
       "      <td>5.77</td>\n",
       "      <td>7.37</td>\n",
       "      <td>6.52</td>\n",
       "      <td>7.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over3</th>\n",
       "      <td>6.78</td>\n",
       "      <td>7.37</td>\n",
       "      <td>8.13</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.57</td>\n",
       "      <td>7.46</td>\n",
       "      <td>7.22</td>\n",
       "      <td>9.05</td>\n",
       "      <td>6.55</td>\n",
       "      <td>8.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over4</th>\n",
       "      <td>7.55</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.58</td>\n",
       "      <td>6.58</td>\n",
       "      <td>7.93</td>\n",
       "      <td>6.32</td>\n",
       "      <td>8</td>\n",
       "      <td>8.53</td>\n",
       "      <td>8.55</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over5</th>\n",
       "      <td>8.57</td>\n",
       "      <td>8.09</td>\n",
       "      <td>8.27</td>\n",
       "      <td>7.4</td>\n",
       "      <td>7.78</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.32</td>\n",
       "      <td>8</td>\n",
       "      <td>8.58</td>\n",
       "      <td>8.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over6</th>\n",
       "      <td>8.64</td>\n",
       "      <td>8.56</td>\n",
       "      <td>7.88</td>\n",
       "      <td>7.96</td>\n",
       "      <td>8.43</td>\n",
       "      <td>7.64</td>\n",
       "      <td>8.47</td>\n",
       "      <td>7.89</td>\n",
       "      <td>8.15</td>\n",
       "      <td>8.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over7</th>\n",
       "      <td>7.1</td>\n",
       "      <td>5.88</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.77</td>\n",
       "      <td>6.76</td>\n",
       "      <td>6.38</td>\n",
       "      <td>6.23</td>\n",
       "      <td>6.63</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over8</th>\n",
       "      <td>6.79</td>\n",
       "      <td>6.44</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.39</td>\n",
       "      <td>6.76</td>\n",
       "      <td>7.47</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.1</td>\n",
       "      <td>6.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over9</th>\n",
       "      <td>6.95</td>\n",
       "      <td>6.42</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.07</td>\n",
       "      <td>6.97</td>\n",
       "      <td>7.15</td>\n",
       "      <td>6.88</td>\n",
       "      <td>8.11</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over10</th>\n",
       "      <td>7.38</td>\n",
       "      <td>6.18</td>\n",
       "      <td>7.58</td>\n",
       "      <td>7.28</td>\n",
       "      <td>7.11</td>\n",
       "      <td>6.93</td>\n",
       "      <td>7.4</td>\n",
       "      <td>8.21</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over11</th>\n",
       "      <td>8.34</td>\n",
       "      <td>6.12</td>\n",
       "      <td>7.05</td>\n",
       "      <td>8.03</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.76</td>\n",
       "      <td>8.05</td>\n",
       "      <td>7.27</td>\n",
       "      <td>6.97</td>\n",
       "      <td>7.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over12</th>\n",
       "      <td>8.19</td>\n",
       "      <td>7.28</td>\n",
       "      <td>7.5</td>\n",
       "      <td>7.62</td>\n",
       "      <td>8.42</td>\n",
       "      <td>6.97</td>\n",
       "      <td>8.22</td>\n",
       "      <td>8.07</td>\n",
       "      <td>7.72</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over13</th>\n",
       "      <td>6.65</td>\n",
       "      <td>7.05</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.21</td>\n",
       "      <td>7.45</td>\n",
       "      <td>7.76</td>\n",
       "      <td>8.42</td>\n",
       "      <td>7.85</td>\n",
       "      <td>8.17</td>\n",
       "      <td>8.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over14</th>\n",
       "      <td>8.3</td>\n",
       "      <td>7.52</td>\n",
       "      <td>8.32</td>\n",
       "      <td>8.15</td>\n",
       "      <td>8.12</td>\n",
       "      <td>7.35</td>\n",
       "      <td>8</td>\n",
       "      <td>8.98</td>\n",
       "      <td>7.93</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over15</th>\n",
       "      <td>8.98</td>\n",
       "      <td>8.41</td>\n",
       "      <td>7.98</td>\n",
       "      <td>8.89</td>\n",
       "      <td>8.63</td>\n",
       "      <td>8.29</td>\n",
       "      <td>9.55</td>\n",
       "      <td>9.02</td>\n",
       "      <td>9.67</td>\n",
       "      <td>8.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over16</th>\n",
       "      <td>8.72</td>\n",
       "      <td>8.46</td>\n",
       "      <td>8.4</td>\n",
       "      <td>8.86</td>\n",
       "      <td>8.44</td>\n",
       "      <td>8.48</td>\n",
       "      <td>9.32</td>\n",
       "      <td>9.52</td>\n",
       "      <td>9.54</td>\n",
       "      <td>8.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over17</th>\n",
       "      <td>9.85</td>\n",
       "      <td>9.18</td>\n",
       "      <td>9.68</td>\n",
       "      <td>8.3</td>\n",
       "      <td>9.4</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.86</td>\n",
       "      <td>10.83</td>\n",
       "      <td>9.63</td>\n",
       "      <td>8.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over18</th>\n",
       "      <td>10.3</td>\n",
       "      <td>9.71</td>\n",
       "      <td>9.6</td>\n",
       "      <td>9.35</td>\n",
       "      <td>9.6</td>\n",
       "      <td>10.03</td>\n",
       "      <td>9.81</td>\n",
       "      <td>10.19</td>\n",
       "      <td>10.2</td>\n",
       "      <td>9.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over19</th>\n",
       "      <td>11.4</td>\n",
       "      <td>8.87</td>\n",
       "      <td>10.98</td>\n",
       "      <td>9.9</td>\n",
       "      <td>10.49</td>\n",
       "      <td>10.56</td>\n",
       "      <td>10.5</td>\n",
       "      <td>11.37</td>\n",
       "      <td>10.32</td>\n",
       "      <td>11.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Runs_in_over20</th>\n",
       "      <td>11.41</td>\n",
       "      <td>10.25</td>\n",
       "      <td>12</td>\n",
       "      <td>10.2</td>\n",
       "      <td>10.34</td>\n",
       "      <td>11.61</td>\n",
       "      <td>11.46</td>\n",
       "      <td>10.53</td>\n",
       "      <td>10.72</td>\n",
       "      <td>11.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 2008   2009   2010  2011   2012   2013   2014   2015   2016  \\\n",
       "Over                                                                           \n",
       "Runs_in_over1    5.31   5.46   6.53  6.33   4.99   5.01   6.53   5.96   5.52   \n",
       "Runs_in_over2    8.03   7.07    8.6  6.21   6.03   6.54   5.77   7.37   6.52   \n",
       "Runs_in_over3    6.78   7.37   8.13  7.51   7.57   7.46   7.22   9.05   6.55   \n",
       "Runs_in_over4    7.55   7.49   7.58  6.58   7.93   6.32      8   8.53   8.55   \n",
       "Runs_in_over5    8.57   8.09   8.27   7.4   7.78   8.05   8.32      8   8.58   \n",
       "Runs_in_over6    8.64   8.56   7.88  7.96   8.43   7.64   8.47   7.89   8.15   \n",
       "Runs_in_over7     7.1   5.88    7.1  5.83   5.77   6.76   6.38   6.23   6.63   \n",
       "Runs_in_over8    6.79   6.44    6.5  7.54   7.39   6.76   7.47    7.3    7.1   \n",
       "Runs_in_over9    6.95   6.42    7.3  7.07   6.97   7.15   6.88   8.11   7.82   \n",
       "Runs_in_over10   7.38   6.18   7.58  7.28   7.11   6.93    7.4   8.21   7.87   \n",
       "Runs_in_over11   8.34   6.12   7.05  8.03   7.93   7.76   8.05   7.27   6.97   \n",
       "Runs_in_over12   8.19   7.28    7.5  7.62   8.42   6.97   8.22   8.07   7.72   \n",
       "Runs_in_over13   6.65   7.05   7.98  7.21   7.45   7.76   8.42   7.85   8.17   \n",
       "Runs_in_over14    8.3   7.52   8.32  8.15   8.12   7.35      8   8.98   7.93   \n",
       "Runs_in_over15   8.98   8.41   7.98  8.89   8.63   8.29   9.55   9.02   9.67   \n",
       "Runs_in_over16   8.72   8.46    8.4  8.86   8.44   8.48   9.32   9.52   9.54   \n",
       "Runs_in_over17   9.85   9.18   9.68   8.3    9.4   9.96   8.86  10.83   9.63   \n",
       "Runs_in_over18   10.3   9.71    9.6  9.35    9.6  10.03   9.81  10.19   10.2   \n",
       "Runs_in_over19   11.4   8.87  10.98   9.9  10.49  10.56   10.5  11.37  10.32   \n",
       "Runs_in_over20  11.41  10.25     12  10.2  10.34  11.61  11.46  10.53  10.72   \n",
       "\n",
       "                 2017  \n",
       "Over                   \n",
       "Runs_in_over1     6.1  \n",
       "Runs_in_over2    7.32  \n",
       "Runs_in_over3    8.97  \n",
       "Runs_in_over4    8.05  \n",
       "Runs_in_over5    8.02  \n",
       "Runs_in_over6    8.83  \n",
       "Runs_in_over7    6.98  \n",
       "Runs_in_over8    6.64  \n",
       "Runs_in_over9     7.9  \n",
       "Runs_in_over10    7.2  \n",
       "Runs_in_over11   7.73  \n",
       "Runs_in_over12    8.1  \n",
       "Runs_in_over13   8.46  \n",
       "Runs_in_over14   9.15  \n",
       "Runs_in_over15   8.37  \n",
       "Runs_in_over16   8.36  \n",
       "Runs_in_over17   8.93  \n",
       "Runs_in_over18   9.33  \n",
       "Runs_in_over19  11.02  \n",
       "Runs_in_over20  11.59  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_by_over_Runs_breakdown_1st_inngs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = []\n",
    "for i in range(2008,2018):\n",
    "    x.append(over_by_over_Runs_breakdown_1st_inngs[str(i)])\n",
    "mlt.figure(figsize=(15,10))\n",
    "\n",
    "#mlt.plot(x[0], label = \"2008\", color='orange', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[1], label = \"2009\", color='red', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[2], label = \"2010\", color='blue', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[3], label = \"2011\", color='green', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[4], label = \"2012\", color='black', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[5], label = \"2013\", color='cyan', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[6], label = \"2014\", color='pink', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[7], label = \"2015\", color='yellow', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "mlt.plot(x[8], label = \"2016\", color='magenta', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "mlt.plot(x[9], label = \"2017 Runs\", color='orange', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "\n",
    "\n",
    "y = []\n",
    "for i in range(2008,2018):\n",
    "    y.append(over_by_over_PCT_Runs_breakdown_1st_inngs[str(i)])\n",
    "#mlt.figure(figsize=(15,10))\n",
    "x = x[:10]\n",
    "#mlt.plot(y[0], label = \"2008\", color='orange', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[1], label = \"2009\", color='red', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[2], label = \"2010\", color='blue', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[3], label = \"2011\", color='green', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[4], label = \"2012\", color='black', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[5], label = \"2013\", color='cyan', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[6], label = \"2014\", color='pink', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[7], label = \"2015\", color='yellow', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[8], label = \"2016\", color='magenta', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[9], label = \"2017 PCT Runs\", color='brown', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "\n",
    "\n",
    "#Overs = [_ for _ in range(1,21)]\n",
    "a = [_ for _ in range(1,21)]\n",
    "mlt.xticks(a , a)\n",
    "mlt.xlabel(\"Overs\")\n",
    "mlt.ylabel(\"Runs\")\n",
    "mlt.title(\"Avg Runs Scored in each Over\\n2016 V 2017\")\n",
    "mlt.legend()\n",
    "mlt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = []\n",
    "for i in range(2008,2018):\n",
    "    x.append(over_by_over_Runs_breakdown_1st_inngs[str(i)])\n",
    "mlt.figure(figsize=(15,10))\n",
    "\n",
    "#mlt.plot(x[0], label = \"2008\", color='orange', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[1], label = \"2009\", color='red', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[2], label = \"2010\", color='blue', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[3], label = \"2011\", color='green', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[4], label = \"2012\", color='black', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[5], label = \"2013\", color='cyan', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[6], label = \"2014\", color='pink', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(x[7], label = \"2015\", color='yellow', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "lt.plot(x[8], label = \"2016\", color='magenta', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "mlt.plot(x[9], label = \"2017 Runs\", color='orange', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "\n",
    "\n",
    "y = []\n",
    "for i in range(2008,2018):\n",
    "    y.append(over_by_over_PCT_Runs_breakdown_1st_inngs[str(i)])\n",
    "#mlt.figure(figsize=(15,10))\n",
    "x = x[:10]\n",
    "#mlt.plot(y[0], label = \"2008\", color='orange', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[1], label = \"2009\", color='red', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[2], label = \"2010\", color='blue', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[3], label = \"2011\", color='green', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[4], label = \"2012\", color='black', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[5], label = \"2013\", color='cyan', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[6], label = \"2014\", color='pink', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[7], label = \"2015\", color='yellow', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[8], label = \"2016\", color='magenta', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "#mlt.plot(y[9], label = \"2017 PCT Runs\", color='brown', marker='o', linestyle='dashed', linewidth=2, markersize=4)\n",
    "\n",
    "\n",
    "#Overs = [_ for _ in range(1,21)]\n",
    "a = [_ for _ in range(1,21)]\n",
    "mlt.xticks(a , a)\n",
    "mlt.xlabel(\"Overs\")\n",
    "mlt.ylabel(\"Runs\")\n",
    "mlt.title(\"Avg Runs Scored in each Over\\n2016 V 2017\")\n",
    "mlt.legend()\n",
    "mlt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(mlt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('Toss Decisions in %\\n',((raw_data['toss_decision']).value_counts())/636*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlt.subplots(figsize=(12,6))\n",
    "sns.countplot(x='season',hue='toss_decision',data=raw_data, palette='Set1_r')\n",
    "mlt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlt.subplots(figsize=(13,6))\n",
    "ax=raw_data['toss_winner'].value_counts().plot.bar(width=0.8,color=sns.color_palette('RdYlGn',5))\n",
    "for p in ax.patches:\n",
    "    ax.annotate(format(p.get_height()), (p.get_x()+0.25, p.get_height()+2))\n",
    "mlt.show()"
   ]
  }
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