Bowler_DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e46645bd-dcf2-41c7-89c2-59c78e726caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "fe2f13de-407a-4ac2-af21-7489f47fc2d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Span</th>\n",
       "      <th>Mat</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Mdns</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>BBI</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Econ</th>\n",
       "      <th>SR</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Zampa (AUS)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>553.2</td>\n",
       "      <td>11</td>\n",
       "      <td>2994</td>\n",
       "      <td>122</td>\n",
       "      <td>5/35</td>\n",
       "      <td>24.54</td>\n",
       "      <td>5.41</td>\n",
       "      <td>27.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Lamichhane (NEP)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>432.2</td>\n",
       "      <td>26</td>\n",
       "      <td>1916</td>\n",
       "      <td>104</td>\n",
       "      <td>6/11</td>\n",
       "      <td>18.42</td>\n",
       "      <td>4.43</td>\n",
       "      <td>24.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuldeep Yadav (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>540.2</td>\n",
       "      <td>19</td>\n",
       "      <td>2821</td>\n",
       "      <td>97</td>\n",
       "      <td>5/25</td>\n",
       "      <td>29.08</td>\n",
       "      <td>5.22</td>\n",
       "      <td>33.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>391.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1969</td>\n",
       "      <td>95</td>\n",
       "      <td>5/31</td>\n",
       "      <td>20.72</td>\n",
       "      <td>5.03</td>\n",
       "      <td>24.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>367.1</td>\n",
       "      <td>21</td>\n",
       "      <td>2024</td>\n",
       "      <td>93</td>\n",
       "      <td>5/18</td>\n",
       "      <td>21.76</td>\n",
       "      <td>5.51</td>\n",
       "      <td>23.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>KP Meiyappan (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>201.2</td>\n",
       "      <td>6</td>\n",
       "      <td>1114</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>30.10</td>\n",
       "      <td>5.53</td>\n",
       "      <td>32.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SR Taylor (USA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>247.5</td>\n",
       "      <td>13</td>\n",
       "      <td>1124</td>\n",
       "      <td>37</td>\n",
       "      <td>4/23</td>\n",
       "      <td>30.37</td>\n",
       "      <td>4.53</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>N Vanua (PNG)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>286.4</td>\n",
       "      <td>15</td>\n",
       "      <td>1528</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>41.29</td>\n",
       "      <td>5.33</td>\n",
       "      <td>46.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Basil Hameed (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>168.4</td>\n",
       "      <td>11</td>\n",
       "      <td>676</td>\n",
       "      <td>36</td>\n",
       "      <td>5/17</td>\n",
       "      <td>18.77</td>\n",
       "      <td>4.00</td>\n",
       "      <td>28.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>B Muzarabani (ZIM)</td>\n",
       "      <td>2020-2023</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>211.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1030</td>\n",
       "      <td>36</td>\n",
       "      <td>5/49</td>\n",
       "      <td>28.61</td>\n",
       "      <td>4.87</td>\n",
       "      <td>35.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player       Span  Mat  Inns  Overs  Mdns  Runs  Wkts   BBI  \\\n",
       "0          A Zampa (AUS)  2019-2023   61    61  553.2    11  2994   122  5/35   \n",
       "1     S Lamichhane (NEP)  2019-2023   48    47  432.2    26  1916   104  6/11   \n",
       "2    Kuldeep Yadav (IND)  2019-2023   65    64  540.2    19  2821    97  5/25   \n",
       "3       Bilal Khan (OMA)  2019-2023   45    45  391.0    29  1969    95  5/31   \n",
       "4   Mohammed Shami (IND)  2019-2023   46    46  367.1    21  2024    93  5/18   \n",
       "..                   ...        ...  ...   ...    ...   ...   ...   ...   ...   \n",
       "57    KP Meiyappan (UAE)  2019-2023   31    31  201.2     6  1114    37  4/37   \n",
       "58       SR Taylor (USA)  2019-2023   45    40  247.5    13  1124    37  4/23   \n",
       "59         N Vanua (PNG)  2019-2023   43    41  286.4    15  1528    37  4/37   \n",
       "60    Basil Hameed (UAE)  2019-2023   37    30  168.4    11   676    36  5/17   \n",
       "61    B Muzarabani (ZIM)  2020-2023   24    24  211.2    15  1030    36  5/49   \n",
       "\n",
       "      Ave  Econ    SR     4    5  \n",
       "0   24.54  5.41  27.2  11.0  1.0  \n",
       "1   18.42  4.43  24.9   7.0  3.0  \n",
       "2   29.08  5.22  33.4   5.0  1.0  \n",
       "3   20.72  5.03  24.6   4.0  4.0  \n",
       "4   21.76  5.51  23.6   4.0  4.0  \n",
       "..    ...   ...   ...   ...  ...  \n",
       "57  30.10  5.53  32.6   4.0  0.0  \n",
       "58  30.37  4.53  40.1   1.0  0.0  \n",
       "59  41.29  5.33  46.4   1.0  0.0  \n",
       "60  18.77  4.00  28.1   2.0  1.0  \n",
       "61  28.61  4.87  35.2   NaN  NaN  \n",
       "\n",
       "[62 rows x 14 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Bowlers.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d812ffb8-02a5-4e2c-a929-9b7cd3e14fb6",
   "metadata": {},
   "source": [
    "## DATA CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e7920a37-07bd-4208-ad56-5aeb9a6a8de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 14)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d79b83dc-0749-41ef-a365-095856f779c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player    0\n",
       "Span      0\n",
       "Mat       0\n",
       "Inns      0\n",
       "Overs     0\n",
       "Mdns      0\n",
       "Runs      0\n",
       "Wkts      0\n",
       "BBI       0\n",
       "Ave       0\n",
       "Econ      0\n",
       "SR        1\n",
       "4         2\n",
       "5         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "58d92398-0885-41c9-b43d-72ad3961effb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8fe01c-92e5-4cc4-b388-f16036ead085",
   "metadata": {},
   "source": [
    "## FILLING THE MISSING VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "57f0c3d5-38fe-45fb-abd4-8b8d94a3c16e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Span</th>\n",
       "      <th>Mat</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Mdns</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>BBI</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Econ</th>\n",
       "      <th>SR</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Zampa (AUS)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>553.2</td>\n",
       "      <td>11</td>\n",
       "      <td>2994</td>\n",
       "      <td>122</td>\n",
       "      <td>5/35</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Lamichhane (NEP)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>432.2</td>\n",
       "      <td>26</td>\n",
       "      <td>1916</td>\n",
       "      <td>104</td>\n",
       "      <td>6/11</td>\n",
       "      <td>18.42</td>\n",
       "      <td>4.43</td>\n",
       "      <td>24.9</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuldeep Yadav (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>540.2</td>\n",
       "      <td>19</td>\n",
       "      <td>2821</td>\n",
       "      <td>97</td>\n",
       "      <td>5/25</td>\n",
       "      <td>29.08</td>\n",
       "      <td>5.22</td>\n",
       "      <td>33.4</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>391.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1969</td>\n",
       "      <td>95</td>\n",
       "      <td>5/31</td>\n",
       "      <td>20.72</td>\n",
       "      <td>5.03</td>\n",
       "      <td>24.6</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>367.1</td>\n",
       "      <td>21</td>\n",
       "      <td>2024</td>\n",
       "      <td>93</td>\n",
       "      <td>5/18</td>\n",
       "      <td>21.76</td>\n",
       "      <td>5.51</td>\n",
       "      <td>23.6</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>KP Meiyappan (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>201.2</td>\n",
       "      <td>6</td>\n",
       "      <td>1114</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>30.10</td>\n",
       "      <td>5.53</td>\n",
       "      <td>32.6</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SR Taylor (USA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>247.5</td>\n",
       "      <td>13</td>\n",
       "      <td>1124</td>\n",
       "      <td>37</td>\n",
       "      <td>4/23</td>\n",
       "      <td>30.37</td>\n",
       "      <td>4.53</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>N Vanua (PNG)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>286.4</td>\n",
       "      <td>15</td>\n",
       "      <td>1528</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>41.29</td>\n",
       "      <td>5.33</td>\n",
       "      <td>46.4</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Basil Hameed (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>168.4</td>\n",
       "      <td>11</td>\n",
       "      <td>676</td>\n",
       "      <td>36</td>\n",
       "      <td>5/17</td>\n",
       "      <td>18.77</td>\n",
       "      <td>4.00</td>\n",
       "      <td>28.1</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>B Muzarabani (ZIM)</td>\n",
       "      <td>2020-2023</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>211.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1030</td>\n",
       "      <td>36</td>\n",
       "      <td>5/49</td>\n",
       "      <td>28.61</td>\n",
       "      <td>4.87</td>\n",
       "      <td>35.2</td>\n",
       "      <td>33.727869</td>\n",
       "      <td>33.727869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player       Span  Mat  Inns  Overs  Mdns  Runs  Wkts   BBI  \\\n",
       "0          A Zampa (AUS)  2019-2023   61    61  553.2    11  2994   122  5/35   \n",
       "1     S Lamichhane (NEP)  2019-2023   48    47  432.2    26  1916   104  6/11   \n",
       "2    Kuldeep Yadav (IND)  2019-2023   65    64  540.2    19  2821    97  5/25   \n",
       "3       Bilal Khan (OMA)  2019-2023   45    45  391.0    29  1969    95  5/31   \n",
       "4   Mohammed Shami (IND)  2019-2023   46    46  367.1    21  2024    93  5/18   \n",
       "..                   ...        ...  ...   ...    ...   ...   ...   ...   ...   \n",
       "57    KP Meiyappan (UAE)  2019-2023   31    31  201.2     6  1114    37  4/37   \n",
       "58       SR Taylor (USA)  2019-2023   45    40  247.5    13  1124    37  4/23   \n",
       "59         N Vanua (PNG)  2019-2023   43    41  286.4    15  1528    37  4/37   \n",
       "60    Basil Hameed (UAE)  2019-2023   37    30  168.4    11   676    36  5/17   \n",
       "61    B Muzarabani (ZIM)  2020-2023   24    24  211.2    15  1030    36  5/49   \n",
       "\n",
       "      Ave  Econ    SR          4          5  \n",
       "0   24.54  5.41  27.2  11.000000   1.000000  \n",
       "1   18.42  4.43  24.9   7.000000   3.000000  \n",
       "2   29.08  5.22  33.4   5.000000   1.000000  \n",
       "3   20.72  5.03  24.6   4.000000   4.000000  \n",
       "4   21.76  5.51  23.6   4.000000   4.000000  \n",
       "..    ...   ...   ...        ...        ...  \n",
       "57  30.10  5.53  32.6   4.000000   0.000000  \n",
       "58  30.37  4.53  40.1   1.000000   0.000000  \n",
       "59  41.29  5.33  46.4   1.000000   0.000000  \n",
       "60  18.77  4.00  28.1   2.000000   1.000000  \n",
       "61  28.61  4.87  35.2  33.727869  33.727869  \n",
       "\n",
       "[62 rows x 14 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.fillna(value=df['SR'].mean())\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2bb42871-ed83-4dec-b8fc-84cef09c16be",
   "metadata": {},
   "outputs": [
    {
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       "      <td>A Zampa (AUS)</td>\n",
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       "      <td>S Lamichhane (NEP)</td>\n",
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       "      <td>26</td>\n",
       "      <td>1916</td>\n",
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       "      <td>18.42</td>\n",
       "      <td>4.43</td>\n",
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       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuldeep Yadav (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>540.2</td>\n",
       "      <td>19</td>\n",
       "      <td>2821</td>\n",
       "      <td>97</td>\n",
       "      <td>5/25</td>\n",
       "      <td>29.08</td>\n",
       "      <td>5.22</td>\n",
       "      <td>33.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>391.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1969</td>\n",
       "      <td>95</td>\n",
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       "      <td>20.72</td>\n",
       "      <td>5.03</td>\n",
       "      <td>24.6</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>367.1</td>\n",
       "      <td>21</td>\n",
       "      <td>2024</td>\n",
       "      <td>93</td>\n",
       "      <td>5/18</td>\n",
       "      <td>21.76</td>\n",
       "      <td>5.51</td>\n",
       "      <td>23.6</td>\n",
       "      <td>4.0</td>\n",
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       "      <td>KP Meiyappan (UAE)</td>\n",
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       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>201.2</td>\n",
       "      <td>6</td>\n",
       "      <td>1114</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>30.10</td>\n",
       "      <td>5.53</td>\n",
       "      <td>32.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SR Taylor (USA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>247.5</td>\n",
       "      <td>13</td>\n",
       "      <td>1124</td>\n",
       "      <td>37</td>\n",
       "      <td>4/23</td>\n",
       "      <td>30.37</td>\n",
       "      <td>4.53</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>N Vanua (PNG)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>286.4</td>\n",
       "      <td>15</td>\n",
       "      <td>1528</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>41.29</td>\n",
       "      <td>5.33</td>\n",
       "      <td>46.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Basil Hameed (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>168.4</td>\n",
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       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>211.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1030</td>\n",
       "      <td>36</td>\n",
       "      <td>5/49</td>\n",
       "      <td>28.61</td>\n",
       "      <td>4.87</td>\n",
       "      <td>35.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player       Span  Mat  Inns  Overs  Mdns  Runs  Wkts   BBI  \\\n",
       "0          A Zampa (AUS)  2019-2023   61    61  553.2    11  2994   122  5/35   \n",
       "1     S Lamichhane (NEP)  2019-2023   48    47  432.2    26  1916   104  6/11   \n",
       "2    Kuldeep Yadav (IND)  2019-2023   65    64  540.2    19  2821    97  5/25   \n",
       "3       Bilal Khan (OMA)  2019-2023   45    45  391.0    29  1969    95  5/31   \n",
       "4   Mohammed Shami (IND)  2019-2023   46    46  367.1    21  2024    93  5/18   \n",
       "..                   ...        ...  ...   ...    ...   ...   ...   ...   ...   \n",
       "57    KP Meiyappan (UAE)  2019-2023   31    31  201.2     6  1114    37  4/37   \n",
       "58       SR Taylor (USA)  2019-2023   45    40  247.5    13  1124    37  4/23   \n",
       "59         N Vanua (PNG)  2019-2023   43    41  286.4    15  1528    37  4/37   \n",
       "60    Basil Hameed (UAE)  2019-2023   37    30  168.4    11   676    36  5/17   \n",
       "61    B Muzarabani (ZIM)  2020-2023   24    24  211.2    15  1030    36  5/49   \n",
       "\n",
       "      Ave  Econ    SR     4    5  \n",
       "0   24.54  5.41  27.2  11.0  1.0  \n",
       "1   18.42  4.43  24.9   7.0  3.0  \n",
       "2   29.08  5.22  33.4   5.0  1.0  \n",
       "3   20.72  5.03  24.6   4.0  4.0  \n",
       "4   21.76  5.51  23.6   4.0  4.0  \n",
       "..    ...   ...   ...   ...  ...  \n",
       "57  30.10  5.53  32.6   4.0  0.0  \n",
       "58  30.37  4.53  40.1   1.0  0.0  \n",
       "59  41.29  5.33  46.4   1.0  0.0  \n",
       "60  18.77  4.00  28.1   2.0  1.0  \n",
       "61  28.61  4.87  35.2   2.3  2.3  \n",
       "\n",
       "[62 rows x 14 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.fillna(value=df['4'].mean())\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "337cf570-f959-469d-9b5e-8b43e21f40e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>S Lamichhane (NEP)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuldeep Yadav (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>540.2</td>\n",
       "      <td>19</td>\n",
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       "      <td>5/25</td>\n",
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       "      <td>5.22</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>391.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>367.1</td>\n",
       "      <td>21</td>\n",
       "      <td>2024</td>\n",
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       "      <td>5/18</td>\n",
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       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SR Taylor (USA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>247.5</td>\n",
       "      <td>13</td>\n",
       "      <td>1124</td>\n",
       "      <td>37</td>\n",
       "      <td>4/23</td>\n",
       "      <td>30.37</td>\n",
       "      <td>4.53</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>N Vanua (PNG)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>286.4</td>\n",
       "      <td>15</td>\n",
       "      <td>1528</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>41.29</td>\n",
       "      <td>5.33</td>\n",
       "      <td>46.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Basil Hameed (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>168.4</td>\n",
       "      <td>11</td>\n",
       "      <td>676</td>\n",
       "      <td>36</td>\n",
       "      <td>5/17</td>\n",
       "      <td>18.77</td>\n",
       "      <td>4.00</td>\n",
       "      <td>28.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>B Muzarabani (ZIM)</td>\n",
       "      <td>2020-2023</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>211.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1030</td>\n",
       "      <td>36</td>\n",
       "      <td>5/49</td>\n",
       "      <td>28.61</td>\n",
       "      <td>4.87</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player       Span  Mat  Inns  Overs  Mdns  Runs  Wkts   BBI  \\\n",
       "0          A Zampa (AUS)  2019-2023   61    61  553.2    11  2994   122  5/35   \n",
       "1     S Lamichhane (NEP)  2019-2023   48    47  432.2    26  1916   104  6/11   \n",
       "2    Kuldeep Yadav (IND)  2019-2023   65    64  540.2    19  2821    97  5/25   \n",
       "3       Bilal Khan (OMA)  2019-2023   45    45  391.0    29  1969    95  5/31   \n",
       "4   Mohammed Shami (IND)  2019-2023   46    46  367.1    21  2024    93  5/18   \n",
       "..                   ...        ...  ...   ...    ...   ...   ...   ...   ...   \n",
       "57    KP Meiyappan (UAE)  2019-2023   31    31  201.2     6  1114    37  4/37   \n",
       "58       SR Taylor (USA)  2019-2023   45    40  247.5    13  1124    37  4/23   \n",
       "59         N Vanua (PNG)  2019-2023   43    41  286.4    15  1528    37  4/37   \n",
       "60    Basil Hameed (UAE)  2019-2023   37    30  168.4    11   676    36  5/17   \n",
       "61    B Muzarabani (ZIM)  2020-2023   24    24  211.2    15  1030    36  5/49   \n",
       "\n",
       "      Ave  Econ    SR     4    5  \n",
       "0   24.54  5.41  27.2  11.0  1.0  \n",
       "1   18.42  4.43  24.9   7.0  3.0  \n",
       "2   29.08  5.22  33.4   5.0  1.0  \n",
       "3   20.72  5.03  24.6   4.0  4.0  \n",
       "4   21.76  5.51  23.6   4.0  4.0  \n",
       "..    ...   ...   ...   ...  ...  \n",
       "57  30.10  5.53  32.6   4.0  0.0  \n",
       "58  30.37  4.53  40.1   1.0  0.0  \n",
       "59  41.29  5.33  46.4   1.0  0.0  \n",
       "60  18.77  4.00  28.1   2.0  1.0  \n",
       "61  28.61  4.87  35.2   0.9  0.9  \n",
       "\n",
       "[62 rows x 14 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df.fillna(value=df['5'].mean())\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05179bdc-da33-49b6-804a-0a52b92914a7",
   "metadata": {},
   "source": [
    "MAXIMUM AND MINIMUM VALUES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "615b523c-f2f9-4e28-bf3c-a9d780952394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player    Zahoor Khan (UAE)\n",
       "Span              2022-2023\n",
       "Mat                      69\n",
       "Inns                     66\n",
       "Overs                 553.2\n",
       "Mdns                     49\n",
       "Runs                   2994\n",
       "Wkts                    122\n",
       "BBI                    6/65\n",
       "Ave                   46.29\n",
       "Econ                   6.35\n",
       "SR                     49.8\n",
       "4                      11.0\n",
       "5                       4.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_max = df.max(axis=0)\n",
    "col_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4aa5f366-cb0a-4ea0-83f7-1bbdf37b6926",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Player    A Vala (PNG)\n",
       "Span         2019-2021\n",
       "Mat                 21\n",
       "Inns                21\n",
       "Overs            168.4\n",
       "Mdns                 2\n",
       "Runs               676\n",
       "Wkts                36\n",
       "BBI               3/17\n",
       "Ave              18.42\n",
       "Econ              3.96\n",
       "SR                23.6\n",
       "4                  0.0\n",
       "5                  0.0\n",
       "dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_min = df.min(axis=0)\n",
    "col_min"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93415e84-f802-4338-97f9-d1795cc6d43f",
   "metadata": {},
   "source": [
    "## Rename"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "78e566f8-e9c5-47c3-a198-5a3e19fea82f",
   "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>Players</th>\n",
       "      <th>Time span</th>\n",
       "      <th>Matches</th>\n",
       "      <th>Innings</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Maidens</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>BBI</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Econ</th>\n",
       "      <th>SR</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Zampa (AUS)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>553.2</td>\n",
       "      <td>11</td>\n",
       "      <td>2994</td>\n",
       "      <td>122</td>\n",
       "      <td>5/35</td>\n",
       "      <td>24.54</td>\n",
       "      <td>5.41</td>\n",
       "      <td>27.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Lamichhane (NEP)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>432.2</td>\n",
       "      <td>26</td>\n",
       "      <td>1916</td>\n",
       "      <td>104</td>\n",
       "      <td>6/11</td>\n",
       "      <td>18.42</td>\n",
       "      <td>4.43</td>\n",
       "      <td>24.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuldeep Yadav (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>540.2</td>\n",
       "      <td>19</td>\n",
       "      <td>2821</td>\n",
       "      <td>97</td>\n",
       "      <td>5/25</td>\n",
       "      <td>29.08</td>\n",
       "      <td>5.22</td>\n",
       "      <td>33.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>391.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1969</td>\n",
       "      <td>95</td>\n",
       "      <td>5/31</td>\n",
       "      <td>20.72</td>\n",
       "      <td>5.03</td>\n",
       "      <td>24.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>367.1</td>\n",
       "      <td>21</td>\n",
       "      <td>2024</td>\n",
       "      <td>93</td>\n",
       "      <td>5/18</td>\n",
       "      <td>21.76</td>\n",
       "      <td>5.51</td>\n",
       "      <td>23.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>KP Meiyappan (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>201.2</td>\n",
       "      <td>6</td>\n",
       "      <td>1114</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>30.10</td>\n",
       "      <td>5.53</td>\n",
       "      <td>32.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SR Taylor (USA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>247.5</td>\n",
       "      <td>13</td>\n",
       "      <td>1124</td>\n",
       "      <td>37</td>\n",
       "      <td>4/23</td>\n",
       "      <td>30.37</td>\n",
       "      <td>4.53</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>N Vanua (PNG)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>286.4</td>\n",
       "      <td>15</td>\n",
       "      <td>1528</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>41.29</td>\n",
       "      <td>5.33</td>\n",
       "      <td>46.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Basil Hameed (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>168.4</td>\n",
       "      <td>11</td>\n",
       "      <td>676</td>\n",
       "      <td>36</td>\n",
       "      <td>5/17</td>\n",
       "      <td>18.77</td>\n",
       "      <td>4.00</td>\n",
       "      <td>28.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>B Muzarabani (ZIM)</td>\n",
       "      <td>2020-2023</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>211.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1030</td>\n",
       "      <td>36</td>\n",
       "      <td>5/49</td>\n",
       "      <td>28.61</td>\n",
       "      <td>4.87</td>\n",
       "      <td>35.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Players  Time span  Matches  Innings  Overs  Maidens  Runs  \\\n",
       "0          A Zampa (AUS)  2019-2023       61       61  553.2       11  2994   \n",
       "1     S Lamichhane (NEP)  2019-2023       48       47  432.2       26  1916   \n",
       "2    Kuldeep Yadav (IND)  2019-2023       65       64  540.2       19  2821   \n",
       "3       Bilal Khan (OMA)  2019-2023       45       45  391.0       29  1969   \n",
       "4   Mohammed Shami (IND)  2019-2023       46       46  367.1       21  2024   \n",
       "..                   ...        ...      ...      ...    ...      ...   ...   \n",
       "57    KP Meiyappan (UAE)  2019-2023       31       31  201.2        6  1114   \n",
       "58       SR Taylor (USA)  2019-2023       45       40  247.5       13  1124   \n",
       "59         N Vanua (PNG)  2019-2023       43       41  286.4       15  1528   \n",
       "60    Basil Hameed (UAE)  2019-2023       37       30  168.4       11   676   \n",
       "61    B Muzarabani (ZIM)  2020-2023       24       24  211.2       15  1030   \n",
       "\n",
       "    Wkts   BBI    Ave  Econ    SR     4    5  \n",
       "0    122  5/35  24.54  5.41  27.2  11.0  1.0  \n",
       "1    104  6/11  18.42  4.43  24.9   7.0  3.0  \n",
       "2     97  5/25  29.08  5.22  33.4   5.0  1.0  \n",
       "3     95  5/31  20.72  5.03  24.6   4.0  4.0  \n",
       "4     93  5/18  21.76  5.51  23.6   4.0  4.0  \n",
       "..   ...   ...    ...   ...   ...   ...  ...  \n",
       "57    37  4/37  30.10  5.53  32.6   4.0  0.0  \n",
       "58    37  4/23  30.37  4.53  40.1   1.0  0.0  \n",
       "59    37  4/37  41.29  5.33  46.4   1.0  0.0  \n",
       "60    36  5/17  18.77  4.00  28.1   2.0  1.0  \n",
       "61    36  5/49  28.61  4.87  35.2   NaN  NaN  \n",
       "\n",
       "[62 rows x 14 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.rename(columns={'Player': 'Players', 'Span': 'Time span', 'Mat': 'Matches', 'Inns': 'Innings', 'Mdns': 'Maidens'})\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a8efd2-fa05-4196-af00-dcde01dac359",
   "metadata": {},
   "source": [
    "##  Players and their wickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "1ba3b368-39de-4a68-83e5-1930380405fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\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>Player</th>\n",
       "      <th>Wkts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Zampa (AUS)</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Lamichhane (NEP)</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuldeep Yadav (IND)</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>KP Meiyappan (UAE)</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SR Taylor (USA)</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>N Vanua (PNG)</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Basil Hameed (UAE)</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>B Muzarabani (ZIM)</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Player  Wkts\n",
       "0          A Zampa (AUS)   122\n",
       "1     S Lamichhane (NEP)   104\n",
       "2    Kuldeep Yadav (IND)    97\n",
       "3       Bilal Khan (OMA)    95\n",
       "4   Mohammed Shami (IND)    93\n",
       "..                   ...   ...\n",
       "57    KP Meiyappan (UAE)    37\n",
       "58       SR Taylor (USA)    37\n",
       "59         N Vanua (PNG)    37\n",
       "60    Basil Hameed (UAE)    36\n",
       "61    B Muzarabani (ZIM)    36\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.iloc[:, [0,7]]\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "3a738949-0cad-4b09-b38d-f773ff08b628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     62.000000\n",
       "mean      58.209677\n",
       "std       20.368842\n",
       "min       36.000000\n",
       "25%       42.000000\n",
       "50%       53.000000\n",
       "75%       70.750000\n",
       "max      122.000000\n",
       "Name: Wkts, dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Wkts'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f3351a-1019-4620-9cd1-fb682bacd34c",
   "metadata": {},
   "source": [
    "## OUTLIERS ESTIMATION FOR WICKETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "438868f8-cecc-44ba-8993-7c92353557d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "percentile25 = df['Wkts'].quantile(0.25)\n",
    "percentile75 = df['Wkts'].quantile(0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "690af575-a672-4eff-840b-271572fced44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "313386d3-201a-4abe-a692-8b875f7fcfd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.75"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentile75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "901d064c-9813-476a-bd61-8ed1c10935e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.75"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr = percentile75 - percentile25\n",
    "iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9f39a2fd-8ff1-463b-83a8-d9a7e44d26ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_limit = percentile75 + 1.5 * iqr\n",
    "lower_limit = percentile25 - 1.5 * iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "02435bc8-c28c-42b6-b41b-8b66d93f5b7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "113.875"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bcf0909a-c226-4c50-8fc3-8bf008001ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.125"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_limit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5b8abe-fdb9-49ab-a62a-ce5b6123b136",
   "metadata": {},
   "source": [
    "## DETECTING OUTLIERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b90b7230-d096-45ae-8bde-a10e8bdca788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player</th>\n",
       "      <th>Span</th>\n",
       "      <th>Mat</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Mdns</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>BBI</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Econ</th>\n",
       "      <th>SR</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Zampa (AUS)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>553.2</td>\n",
       "      <td>11</td>\n",
       "      <td>2994</td>\n",
       "      <td>122</td>\n",
       "      <td>5/35</td>\n",
       "      <td>24.54</td>\n",
       "      <td>5.41</td>\n",
       "      <td>27.2</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Player       Span  Mat  Inns  Overs  Mdns  Runs  Wkts   BBI    Ave  \\\n",
       "0  A Zampa (AUS)  2019-2023   61    61  553.2    11  2994   122  5/35  24.54   \n",
       "\n",
       "   Econ    SR     4    5  \n",
       "0  5.41  27.2  11.0  1.0  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Wkts'] > upper_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "33f6878c-c332-4e80-8f32-ed581540b232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Player</th>\n",
       "      <th>Span</th>\n",
       "      <th>Mat</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Mdns</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>BBI</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Econ</th>\n",
       "      <th>SR</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Player, Span, Mat, Inns, Overs, Mdns, Runs, Wkts, BBI, Ave, Econ, SR, 4, 5]\n",
       "Index: []"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Wkts'] < lower_limit]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e52114c-0331-4aee-bb07-25fc39cfdc3d",
   "metadata": {},
   "source": [
    "## TRIMMING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2ffae7bd-8af9-4a95-8928-a458c1190093",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Player</th>\n",
       "      <th>Span</th>\n",
       "      <th>Mat</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Mdns</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>BBI</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Econ</th>\n",
       "      <th>SR</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>S Lamichhane (NEP)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>48</td>\n",
       "      <td>47</td>\n",
       "      <td>432.2</td>\n",
       "      <td>26</td>\n",
       "      <td>1916</td>\n",
       "      <td>104</td>\n",
       "      <td>6/11</td>\n",
       "      <td>18.42</td>\n",
       "      <td>4.43</td>\n",
       "      <td>24.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kuldeep Yadav (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>65</td>\n",
       "      <td>64</td>\n",
       "      <td>540.2</td>\n",
       "      <td>19</td>\n",
       "      <td>2821</td>\n",
       "      <td>97</td>\n",
       "      <td>5/25</td>\n",
       "      <td>29.08</td>\n",
       "      <td>5.22</td>\n",
       "      <td>33.4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>391.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1969</td>\n",
       "      <td>95</td>\n",
       "      <td>5/31</td>\n",
       "      <td>20.72</td>\n",
       "      <td>5.03</td>\n",
       "      <td>24.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>367.1</td>\n",
       "      <td>21</td>\n",
       "      <td>2024</td>\n",
       "      <td>93</td>\n",
       "      <td>5/18</td>\n",
       "      <td>21.76</td>\n",
       "      <td>5.51</td>\n",
       "      <td>23.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shaheen Shah Afridi (PAK)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>47</td>\n",
       "      <td>46</td>\n",
       "      <td>397.2</td>\n",
       "      <td>21</td>\n",
       "      <td>2216</td>\n",
       "      <td>89</td>\n",
       "      <td>6/35</td>\n",
       "      <td>24.89</td>\n",
       "      <td>5.57</td>\n",
       "      <td>26.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>KP Meiyappan (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>201.2</td>\n",
       "      <td>6</td>\n",
       "      <td>1114</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>30.10</td>\n",
       "      <td>5.53</td>\n",
       "      <td>32.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>SR Taylor (USA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>40</td>\n",
       "      <td>247.5</td>\n",
       "      <td>13</td>\n",
       "      <td>1124</td>\n",
       "      <td>37</td>\n",
       "      <td>4/23</td>\n",
       "      <td>30.37</td>\n",
       "      <td>4.53</td>\n",
       "      <td>40.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>N Vanua (PNG)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>43</td>\n",
       "      <td>41</td>\n",
       "      <td>286.4</td>\n",
       "      <td>15</td>\n",
       "      <td>1528</td>\n",
       "      <td>37</td>\n",
       "      <td>4/37</td>\n",
       "      <td>41.29</td>\n",
       "      <td>5.33</td>\n",
       "      <td>46.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Basil Hameed (UAE)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>37</td>\n",
       "      <td>30</td>\n",
       "      <td>168.4</td>\n",
       "      <td>11</td>\n",
       "      <td>676</td>\n",
       "      <td>36</td>\n",
       "      <td>5/17</td>\n",
       "      <td>18.77</td>\n",
       "      <td>4.00</td>\n",
       "      <td>28.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>B Muzarabani (ZIM)</td>\n",
       "      <td>2020-2023</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>211.2</td>\n",
       "      <td>15</td>\n",
       "      <td>1030</td>\n",
       "      <td>36</td>\n",
       "      <td>5/49</td>\n",
       "      <td>28.61</td>\n",
       "      <td>4.87</td>\n",
       "      <td>35.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>61 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Player       Span  Mat  Inns  Overs  Mdns  Runs  Wkts  \\\n",
       "1          S Lamichhane (NEP)  2019-2023   48    47  432.2    26  1916   104   \n",
       "2         Kuldeep Yadav (IND)  2019-2023   65    64  540.2    19  2821    97   \n",
       "3            Bilal Khan (OMA)  2019-2023   45    45  391.0    29  1969    95   \n",
       "4        Mohammed Shami (IND)  2019-2023   46    46  367.1    21  2024    93   \n",
       "5   Shaheen Shah Afridi (PAK)  2019-2023   47    46  397.2    21  2216    89   \n",
       "..                        ...        ...  ...   ...    ...   ...   ...   ...   \n",
       "57         KP Meiyappan (UAE)  2019-2023   31    31  201.2     6  1114    37   \n",
       "58            SR Taylor (USA)  2019-2023   45    40  247.5    13  1124    37   \n",
       "59              N Vanua (PNG)  2019-2023   43    41  286.4    15  1528    37   \n",
       "60         Basil Hameed (UAE)  2019-2023   37    30  168.4    11   676    36   \n",
       "61         B Muzarabani (ZIM)  2020-2023   24    24  211.2    15  1030    36   \n",
       "\n",
       "     BBI    Ave  Econ    SR    4    5  \n",
       "1   6/11  18.42  4.43  24.9  7.0  3.0  \n",
       "2   5/25  29.08  5.22  33.4  5.0  1.0  \n",
       "3   5/31  20.72  5.03  24.6  4.0  4.0  \n",
       "4   5/18  21.76  5.51  23.6  4.0  4.0  \n",
       "5   6/35  24.89  5.57  26.7  4.0  3.0  \n",
       "..   ...    ...   ...   ...  ...  ...  \n",
       "57  4/37  30.10  5.53  32.6  4.0  0.0  \n",
       "58  4/23  30.37  4.53  40.1  1.0  0.0  \n",
       "59  4/37  41.29  5.33  46.4  1.0  0.0  \n",
       "60  5/17  18.77  4.00  28.1  2.0  1.0  \n",
       "61  5/49  28.61  4.87  35.2  NaN  NaN  \n",
       "\n",
       "[61 rows x 14 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df[df['Wkts'] < upper_limit]\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0206db74-ff8b-4629-a674-465e40e9dfef",
   "metadata": {},
   "source": [
    "## DATA VISUALISATION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "488b8591-e17c-45a8-80cb-a0c8b9246048",
   "metadata": {},
   "source": [
    "## Wickets taken by players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9ca878f0-ae5f-497d-97f6-c908a36a2a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "856aedef-c890-4e1c-8def-e978374600e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(x='Player', y='Wkts',data=df)\n",
    "plt.show\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "648a7aa0-dd77-4136-a265-0387445d7a14",
   "metadata": {},
   "source": [
    "## Players with 5 wickets hall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2a99d660-075b-42a5-9cc3-55cad93ba286",
   "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>Player</th>\n",
       "      <th>Span</th>\n",
       "      <th>Mat</th>\n",
       "      <th>Inns</th>\n",
       "      <th>Overs</th>\n",
       "      <th>Mdns</th>\n",
       "      <th>Runs</th>\n",
       "      <th>Wkts</th>\n",
       "      <th>BBI</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Econ</th>\n",
       "      <th>SR</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bilal Khan (OMA)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>391.0</td>\n",
       "      <td>29</td>\n",
       "      <td>1969</td>\n",
       "      <td>95</td>\n",
       "      <td>5/31</td>\n",
       "      <td>20.72</td>\n",
       "      <td>5.03</td>\n",
       "      <td>24.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammed Shami (IND)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>367.1</td>\n",
       "      <td>21</td>\n",
       "      <td>2024</td>\n",
       "      <td>93</td>\n",
       "      <td>5/18</td>\n",
       "      <td>21.76</td>\n",
       "      <td>5.51</td>\n",
       "      <td>23.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MA Starc (AUS)</td>\n",
       "      <td>2019-2023</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "      <td>393.1</td>\n",
       "      <td>15</td>\n",
       "      <td>2222</td>\n",
       "      <td>85</td>\n",
       "      <td>5/26</td>\n",
       "      <td>26.14</td>\n",
       "      <td>5.65</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Player       Span  Mat  Inns  Overs  Mdns  Runs  Wkts   BBI  \\\n",
       "3      Bilal Khan (OMA)  2019-2023   45    45  391.0    29  1969    95  5/31   \n",
       "4  Mohammed Shami (IND)  2019-2023   46    46  367.1    21  2024    93  5/18   \n",
       "7        MA Starc (AUS)  2019-2023   44    44  393.1    15  2222    85  5/26   \n",
       "\n",
       "     Ave  Econ    SR    4    5  \n",
       "3  20.72  5.03  24.6  4.0  4.0  \n",
       "4  21.76  5.51  23.6  4.0  4.0  \n",
       "7  26.14  5.65  27.7  3.0  4.0  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['5'] == df['5'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9eec7af-8867-4674-b4ce-dcbbb6dd5175",
   "metadata": {},
   "source": [
    "## Player-wise Bowling Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "3a04801b-3db9-430c-b4db-13549130ae18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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FCxeKiEhqaqp07tzZHn+Ruvp05cqVkpSUJElJSZKRkSHdu3eX66+/XgYOHCiqKt27d5dwOGzrv6+//lrGjx9fr/flKaecIl988YWrJ8KOHTvklFNOkaSkJFm6dKnN/wsXLpQnnnhCROrqnGg0Kscdd5zceeedsnDhQvnLX/5iz6VYc+fOlZtuukmSk5MlOztbNmzYIN98843rM5WVlTJ//nzJysoSVZX33ntPnnnmGTn00EPl+++/t/k0JydHEhMTJSsrS1asWCEiYt/bunWr+Hw+e96J1LVrROrqEXMsa2trJS0tzX5v/PjxcuONN8qbb75pv+csW1JTUyU1NVWysrKka9eu8re//c3mlXA4LHl5eSIitsdcr169ZP369ZKQkCCfffaZHHfccSIismXLFlmyZIkUFxdLq1at7P423w+FQrauue+++6R169YycuRIm7dHjBhh07xhwwYZNGiQZGZmysKFC2XFihUyd+5cWbJkiaxfv146duwo7dq1k/79+8vmzZulvLxc/v73v8vixYtFRGTjxo0yZMgQ+de//iW333673darr75aPvroI3n22Wdlx44dcsYZZ4hIXV0lIrbMvuGGG0REJBAIiIjIfffdJ+FwWNLT020b0/Rc2bFjh/j9fklJSZFQKCQDBw6UJUuWSHJystTW1tpyNBAISGpqqnTq1EnuuOMOOfXUU+22bty40ZY1+fn5snLlSrn99ttl5syZ8uCDD9rjpary4YcfSklJiUyZMsW2JU0+XLt2rYwZM0YikYhs3rxZLr/8chERufnmm+X444+XBx98ULKysiQ5OVlqamrq9RoybcRQKGTr5BdffFEOOeQQeeutt2z7fMKECSJSlzeDwaC8+eab0rZtW7u/0tPTZdGiRdKzZ0/byykSiYjf75eamhr55ptv7BMwoVDItg2Sk5Pl888/l0ceeUSmTp0qW7ZskS5dukhFRYU89dRTcuSRR0ogEJCDDz5YsrOz5eOPP7bn2pVXXmnLzZtuuklSU1PluOOOkxNOOEH++9//yqpVq+Tkk0+W/Px8SUhIkNWrV4uIyOjRo+3233333TJu3Dg56KCDpHnz5tKvXz8RqatnhwwZIvH8+c9/lhYtWsjSpUtFpK791759e9urMxAIyKZNm2TSpEmydu1aufXWW0Wk7lrDeS1h2hDBYFCCwaC0b99eHn30UXn88cclPz9fFi1aZNsXX331lWzdulUyMzPl7LPPlqKiIttGe/rpp2XNmjW2bBs8eLA8+OCDth2oqlJVVSWqKsXFxXL66afb885cB4wYMcLmxaSkJNmwYYOsWbNGPvjgA3uOiYi0a9dO/vznP0s4HJaamhrZsmWLbNmyRTIzM0VVpaamxtYLhYWFcvTRR4uIyKOPPioi4nrSYcSIETZPfvDBByIi8tRTTzV4zZqUlCQFBQVy3HHHSTQalUMPPVT8fr9MnTrV5oNf//rXMnv2bFm0aJF88sknMmbMGJtvTRtcpK78/+1vfysi4urhZPKziEi3bt3klFNOkYMPPlheeeUVGTFihN3H8XoQVVdXS58+faR3795y33332WsKv98vjz32mMyZM0dE6trbZttOPPFEmTdvnvTq1cu2mz/88EM5+OCDpbS0VEaOHGmvgT/44AObp9999117beVMa2JioixatMjVHjvqqKNk4MCB0rt3b3sNYbY1Go3K8uXLpaCgQAYPHiwiImlpabJ27VpbfyxcuFACgYC0bt1aROraJua6RqQuNnDmmWfWuw4aPny4zJ07V0Tq8tnWrVtl2rRp8vDDD0vnzp1l8+bNIiLy5Zdf2uMUiUQkOztbREQ+++wz+fLLL+WCCy6QLVu2SCAQkH79+tky0OfzyWWXXSZjxoyRN954Q6qqquTggw+WQYMGycCBA2X79u2yadMm6dixoyvvl5eXS1lZmTzwwAPyu9/9TkRETj31VPnwww8lHA7LokWLZOzYsbasu+OOOyQQCEhycrKNidx2220iIrJs2TJ7zWZ6zW/evFmeeuqpevnjq6++kuTkZDnhhBPsfm7Tpo2Ew2GJRqNSWlpq97HRvHlzGTdunBQUFMj27dttW61Zs2ZSVlYm999/vxQXF0vz5s3lq6++kvfff18ikYgkJSVJenq6K/5RWVkpW7ZscbW/REQ6d+4sW7dutdtx0UUXSWJiomRkZMjDDz8sTz/9tG0fmXw1YMAA8fl8MnToUDnllFMkGAxKJBKROXPmyKpVq2xdZcraHTt22N97//33Zfz48dK1a1d5+umnZeXKlTJ//nyJRqPy73//234uHA674icistN2vlNCQoLrWsdpzpw5MnPmTLn//vslJSVF+vbtKyJ1MSlTP77yyiuyfPlyWb58ucybN08CgYA9503+NNeZRlFRkc3vxoIFC+LGoE488UR54IEHXOfRbtnjkKLD6tWrVUR03rx5On78eL3mmmt263vmjo6q6qJFizQYDGrv3r1t9DX2rkpaWpr26tXLvm+i3S1atLB3jEKhkLZs2VIPP/xwTUtLU5G6Xg7du3dXv9+vCQkJ9rOnnnqqdu7c2d5pN7/j8/m0devWrjtYZt3m/7m5ubZHooi4vu9cT0FBgR544IH1eiT4fD794x//qPfcc4+90xb7/XA47Lobcu6552rr1q1dn4n9XjgcjpsWs0QikXq9kILBoLZv314TEhI0Ozs7bq+QpKQke0dgZ70jgsGg6w7jOeecoz6fz/bmcN5R8fv9euCBB9oeL+bOlPOOo8/n0759+2p5ebntWZmTk+Nax6uvvqo9evSIuw93tfj9fnvXNRqN2vTF3vmJ16PEmWaRul5FPp+v3jaaO6eRSMTuh50taWlpmpCQYO/mJSQk1NuvIrLTO9TmrlK8u73BYFALCgr0rrvusvvZvGd6hMXmVeffQ4cO1Y4dO+qZZ56pRxxxhN02Zx6bO3eu9u/fv95vZ2Vl2TuophdsbNqc+dfZK8Kka+TIkRoMBl3HJCEhQd98803t16+fa32BQECPOuoozczMbLCnSygU0mAwWO9upd/v1/T0dG3ZsmXc/di/f/9622h+w6TVrK9NmzYqUnd37fzzz9dIJKItWrSwv2N6zmVlZdnjYNaZkpKiJSUl2qxZM1faRESXLl2qEydOtPuutLS0Xl5NTEzUr7/+WktLS+Pmv8zMzAZ7tiQnJ+uqVatU5IfyKHY/FhcXa1FRke35k5mZqYcffrh+/PHHtve3c/8ceOCBrnO3vLxck5OTtaCgwPUbgUBAw+Gw3VbznvnbWZbvzmJ6M8VbnMc3Ly+v3l3ydu3a2V4VIuLqHRAIBDQpKUkTExNtrx1nb0TnMnbsWJtnYsv3UCikgUBA27Ztq+Fw2O43kz/M+e7sMZOTk6PRaDRu/iwqKtJZs2bpEUcc4eqt6vP5dNSoUXrttdfa/B/7XdMrVERsPWry5eWXX66zZs3S0tJSzczMtL1HTV4yva5j81G8fS1SvxyLRqN63HHHab9+/er1dklISLD7yllWm/PGuV8aKoed54Vzn7Rs2VKj0ai9291QvWzWN3bsWO3Ro4f26tXLvv7xxx/rli1bNDk5WadPn16vLp49e7b27NlTRep6nUQiEe3du7d26tTJte6kpCTt3r27q9efc3sOPvhguw3nnntug+dvr1697Pf69u3rOlamfv3Pf/5j82O8fBQOhzUQCOgXX3yh7du3r/dbJq8/9NBDDfbYMnnd9CxKSkpy5f/Y5bzzzqu3zeb/zl5S0Wg0bk8ds53Jycm2t4PZ723atNEOHTq4tsOs+5hjjtH169c3mC6Rup7A5mmQbt262X+dx9r0bFRV/eSTT+qtw7Rt+/bt63o9FAppKBTSsrIyuz6/36+nnnqqjho1ynVeOpfYdqFzuxrq9eYso8w5lZmZqdFo1PYac+5/U28nJiba88KUHYFAQI8++mgVqav/zf53fj8cDuvYsWNVpK6XnVm/z+fTK6+80rbFfD6f3fZXX33V7hNzvAoKCtTv99ueJn6/X6uqqlSkrk0RDAY1EAjY8tj8/m233aYzZsywZYgz/ZFIxLbfnPvEmUbz+wcffLBmZGS4yq1AIKChUEg7duxo87l5z/z/iSeesOsYNGiQ67dM23bIkCEqIvrMM8/o8OHD7WdMWky7JxwO69SpU207wdlTtFWrVjp69GhdsGCBXX9+fr5t95t9bz6blpamubm5+pe//EX79eunbdq0qfcERFpamm7fvt1VZmZkZOjUqVNd56Jzn++sp+Gll17aYL7t3r27zpo1y9alznMjGAxqVlaW7fXlfD8QCNjyMrb+MUtiYqKmpaU12DOpWbNmumPHDns+O9ffunVrPfLII/Xggw+2+TEcDrvqokAgoNnZ2Xr22WfH3f60tDRb7pnrmMLCQlcdKyL1egPHntOx++bggw/WrKysnfbmdn7X/G3KhunTp+vQoUNd+zoxMVEfffRR+5rpYR2v3D7ggAM0MzOzXvvy97//vQ4YMEBDoVC9cnrSpEmuvyORiJ511lm295czb8f+XklJyU7z2YMPPuhaRzAY1LS0NM3MzNTjjz9eRep64Zt61LkMHz7c9cSb2T7z/9ie6qaMDYVC+swzz8Q9dqZMKy8vt08mpqamateuXetdb5unKTIzMzUpKck+BRBvfUlJSfXKrNatW2tiYmK9dlW868WioiJ7/seea+b/zt939hIcPXq09uvXz1VPmTZFbJ3Trl27uE9VmXWaNpSz3ZaTk6N5eXm2R3G876WkpGheXp42a9ZMExMTXe0Mkbr6dcOGDXrYYYepSN21q8mjCQkJev3119snp/7+979r3759NS0tTVNTU9Xn8+l7772n69evV5/Pp88//7yKiD7++OM2NlRaWqqvvvqqrevnzp2roVCoXg/CTz/9VBMSEnTjxo2anp6uhx9+uOt9E8v67rvvVFV13bp1WlhYqMcff7zrc+Y8dP59ySWXaGpqqm7bts2+PnToUFvGOnsQqqq2aNHCPuW3u35SgPAvf/mL+v1+nT59uj7xxBPaqlUrra2t3eX3TIDw888/15ycHJ06dartCjpz5kyNRCI6YsQIfeaZZ/Sxxx6zhX7//v1V1f34TX5+vj755JP25IxGo3rJJZfoAw88YBsrw4YNs4WVSN2FoHn0xGTKrKwsveeee3Tw4MGuEyYjI0NFfmjotmzZssFHDc36nYG4Zs2aaZs2bWxDwBSINTU19U6ceA06Z+Hq8/lcv+33+3X8+PH272g0qk888YTr+3PnznVdyITDYR0xYoRrnY8++qhtNEYiEc3Pz7fdoMPhsB588MG2y3AwGNSvvvpK165d66rkc3JydMiQIbYhdPXVV+uwYcNcaTWFqXPbotGoLaRCoZB26NDBVliJiYn2sVBzYps0BYNBbd68ufp8Ph07dqx9ZNUcz7KyMpuWww8/3FbsJkgRuz7nsXFedJrHqpzLkUce6aosbrzxRu3Ro4frwqN///72b5NW5zqcF+4zZ860ha8pVEOhkJ566qmakZGxy8Cdea1Fixaux+mc+dgUnKYRbYKizuNuKlVTeZhjNWHCBPsbp556qj700EP2QsEEuEx39kAgoIcffrjdnrVr1+q4ceO0R48ersauSZdZt4ho586d7f8begQtEAi4us4fc8wx+sILL+iZZ56pPp9P09LSbF4yafb7/Tpq1CgNBoOakJDgaiSamwzOyskcm+TkZFshO/fl6aefXu9xzrvuust13vt8Pj399NPtNvl8Pr3uuutUROzFhN/vt5VWSkqKtm7d2lUGhMNhe+FkXjMXEu+8845taPbo0UPfeuuteo/biIgOGDBAfT6fFhcXa58+fVzvlZaW6gMPPGAfqzWNSJ/Pp4mJidq+fXsVkXpBGHPeX3/99dqpUydNTEzUvn376tKlS3XkyJHauXNnPeecc1xlXjQa1d/97nd2u0pKSrRLly6alJSkkUjEXnCbhobz/DCNFJMnY89js5jgntlf8cpp577s1KmT65Enn8+nI0aMcJVRU6dOdT1yaS6CTT4Oh8O6YsUKjUQirnWb88ycz4FAwPVIi7mwNJ81eXLChAnat29fDQaDmpGRYX/L7/drmzZtNCsry76WkpJif8OZb8zjWkVFRa5zrW3bturz+Wyj15mn4wVkzCN7InUXM+ZmlcmzpuwWERugN2VqvIC0OZ/M3ykpKa4L8B49emgkEnEde2d6EhMT9aKLLnLtfxN4MEtJSYkdnsF85/e//33cssT8Pz8/XxctWmTT0bFjR1fdZo6z84ZPenq6PvfcczZtw4cP1zlz5miLFi00Oztbhw8frgUFBa5GtFnfG2+8YfPLYYcdpgMHDrR54ZprrtFLLrmkXh40dYOpq1q0aOHahtiAZnFxsZaWlrpuqJgbE6a8M4+sm6VDhw5xg8ann366JiYm2m0x55XZptgAoTPPnX322a51OctNv9+vl1xyiev9mTNn1gtOmfrfrPeAAw7QDh062HLKGRTIyMjQgQMHavv27e2+uvrqq1XkhwulgoIC2yYyxzQtLU2vvfZau09btmxp3zPrcd4cNvt7wIABGgwGbYCkc+fO9nEkE4B1Lscee6xrP0yYMMGm3xkIMb/RokULTU5O1pSUFC0sLNRQKKQHHnigPUcKCgrqtQcmTpyo119/veu18vJy17kSDAY1Ozvb3sSORCK6YsUKm66MjAx97rnnXHls5syZ+sEHH9i6IhgM2vatSF2QzjxibAJXPp/PPu7pzDuJiYnarl07+/odd9yhImJvhi1dulT79Olj86NZV7t27bSkpMQeS5PvTDskOzvbvmfyR1lZmV5xxRX6+OOPuy7KO3bsWK9OPOCAA1xDypg6UKTu8caPPvpI//CHP7i+88Ybb9hAv89XN1yBSY9I3eNozvaSKVucZYJp0yQmJurFF1+sRUVF6vf7bXpNmpztMnN+pKWl2Rsdo0ePdl0DZGZm2naJCZiLiB599NH60Ucf6W9/+1sNBAKamZmpgwcP1vvuu8+1ba1atdKqqirt1auXradGjBjhap/Nnj3btS0FBQWamJioY8eO1eHDh+vatWv1oYcecpVBpr6/9tprXQHZU045xXVemvNh4MCBrhs4zjLE2RHhqKOOcqU/KSlJ/X6/RqNRTUtLs/Wks71nfmfYsGHarl07DYfDdp+Fw2H1+XzaqVMn2zbJyMjQAw44QAcOHOga1sfUleaa0TlshrPeNcfP3Mw3iwnOxb4WCoU0OTlZmzdvbtsGZnvNdW9CQoJecskltrw3+aW8vFwTEhLsTXlTX5v6JyEhQU855RTt2bOnvdYsKSlxBbuuuuoqzcrKsm2u2P2fnp6uH374oSvdCxcutGWJaTOZY2quOQoLCzUhIUF79OhhH2kWkXrDi3Tp0sX1d3p6uvr9fntOpaam2naH+Tc1NdXewPX7/XrNNdfY90xeN4Fesy+dnY3Mb5mbTSLu4HN+fr62atXK7ouhQ4e6Ap/muJlywOTThIQEm962bdtqhw4d4rZTzXli8lBCQoImJydr165dNRgM2mtn53kXDof1P//5j12f+a6p583NePN50+6PfRT5kEMOqXe9ao51MBjUoUOHujpxOW9Q9OzZUzt37uw6z5KTk/Wmm26yNxVM3iorK7P/z8rK0htuuMF2pjHXS6ruAGFSUpL+7ne/s+3GU0891R5vZyep4uJiPeSQQ2z5beIIV199tWZkZGhZWZmmpKTotGnT9LHHHtPHH39cV61apa+++qqK1NX/jz32mCYlJdkbfe3atdO//vWvKlI3XENJSYl93LuhAOGll16qY8eOVVXVhIQE7dixo+t9Z4CwqqpK+/btq927d3cN4eXcB+bRaVOvdOnSRe+77z5VVf3vf/+rkUhEP/roIxWpHyAcP368HVptd/2kAGGfPn30xBNP1IyMDNuL5LjjjtPly5fv9HuXX365du7cWbt27aoDBw7U6upqnTx5sq1QX3jhBdfn33rrLXvSqP6wU7OysuxYTtdcc409sKqqK1eutDv1sMMO09GjR9sC8Oijj7bj0TmDdKY3SGzh7hwjSeSHgI7zc86lbdu22rNnTy0uLtaUlBR98skndfLkya6T+fnnn3edrKWlpXHHZbvoootUpK7SdV4EmsJ2w4YNtkIy0WnnheX27dtd45oVFBTYCLMpJC644AJb+PXq1UtLSkoaHPcmHA7b4xI7Jozzbtixxx7rGnvR3NlISkqyn8vNzdX8/Hzbsy0ajWp5ebnW1NS4Gg3xemE6t+eTTz7R8847z3VRmp2dbRugw4cPt4XHjBkzbNBoZ3c6zdK8efN6d2AmTJjgurBZsGCBDhgwwDV2TWpqqubl5dm0mgZwvG1w5r9AIKD5+fnaunVrVVW95557dplGZz434xqYsTfMvnd+zow5EgwGbYPKOcZMSUmJPvfcc3Z7rrjiCvvdX/3qV/rmm2/av0tKStTv9+u9995rG77O3hHmTlhD+3rDhg1x7+o6Gygm35vtOOOMM1yfTUhIsA05n8+nzZo1c41dGI1G7d0g5zmUkJCgPXv2dKXNVILOvzMzM109fWIXMw5pbC/G2CDJ1VdfrT6fzzY+Tc9mcyEwcuRIV5mws8WZfxITE+0YeLHnqvn/pEmTXD1wzTH59NNPbcM1MzNT09LSXOO4ibh77sbeRc7IyLCvtW7d2t6wGDhwoB3HdVeLM3/4/X7bg8Lk39i8069fv7gNGLNPzD50jksTuyQmJmpJSYlu377dlc/MGGfmd8vLy103YS677DJ7sWDGKLr//vtdPQ/HjBmjbdu21eLiYk1KSrLj/DgDb2lpadqvXz+7j/Ly8vSggw7SX/3qV/raa6/FTfPkyZNdF6x+v9+WwaFQyJW3nXnB3OyZPn26du7c2XVjxPy/oR4It912mysvOxv6Pp/PrsM0Mp1B4di0/Pvf/3b91hlnnKGzZs2Km+aGzoPYm2oHH3xwvZ6qzh5S0WhU//znP9c7J2Pzcc+ePbVly5bq89X1/k9PT7fpND3nU1NTbRrbtm2rqmr/jkaj2qdPH83KytL58+drdXW1Zmdnxw2ULl26VCORiObk5Kiq6ujRo7VHjx72WKmqaxsWLFhQL0gfr93hbD+Yi35zEbSzY2ze27x5s2v/+v1+LSoqsttgLshMXjevO3/DLOaz5maRM11m//t8Pn3rrbdcwf7YHry/+c1v7HdM+X/SSSfp5s2b7XqcN1GdPSVNfWAChOY1My6e80I0LS1Nmzdvru3bt3f10nHejDjnnHNsb0Tn+kx978yjDfWUNOe7cz8484jf79cTTzzR7gtn4NXZWyleL0jz2876XaSunTxixAjXjfVAIKCdOnWyY4iL1I0ZbcbVKigoUFV19aCJRCKu45OTk6ORSMTV82f69OmuXvIZGRl6+eWX22Nn9nkwGLR1amJior15ZMqRpUuX6m9+85t6Nzn79+9vb8bGthvM9jrHJxs+fLgmJCTYsTSdx69Tp071xiM1F4Cmt4/zRsG1116rqu6xo/x+v9bW1tqb2meddZYdn9Q5pnO8/Ok8huZcCwaDunHjRltmm8+aJzbM36Z8iD2nY/NGaWmpLZN3Ng5mdna2Tps2rd5YZNFoVDdu3Ohq8/31r3919eYNBALapUuXevW0SYtpAzrfi30qwGxThw4dXNcRaWlpdhx6576KzfujR49WEal3PEV+uKmamppar91gjm9BQYGWlpbqpEmTtKSkRHfs2GEDgrHb5ff79auvvlJV1VatWmnXrl1t4DAUCtkyNPb4xqZr/PjxccdPNeexCfwOGTJEi4qKNCUlRefNm6cdO3Z01Qc+n0+PPPJIVVVXz07TxohGo7p582bXePvmfDRjwU2ePNmmN7YdeeaZZ2pWVlbc8YhNmzo23/znP/+xwX7Te9yZ10Tq2mddunSx9UdDPQNHjhzp+n6HDh00JSXFFbAz4w2a/5vXi4qKtGPHjjpw4EDbm8zn89n0jho1SkeNGlXvXDDpML2GzX4x5ZO5TnBe35kAoel8Ee98c54HzusWc+0X+3nnayY/Dh061OZb51OQPp9P58+fb7ffpG1nY0cmJSW5zmWRujrblDexi/PmUOxrCxYs0FatWtkxW83vmyfLTNkfDoc1NTVVS0tLbR5JTU21PZFNu/2UU07RioqKevvP9J40x7Fz586amZmpBxxwgO18YTpB5ebm2pvxpg5wji1qylsnU/7MnDlTW7Zsact70/tOpG5uCBGxY/vGCxDW1NRoUVGRPvnkk3ZcyGAwqJ9++qn9jMmHpow0QeQOHTq40mTSu2jRIvv3ggUL9KabbtJDDjlEVVWvvPJKPeqoo+xvxQYIzzvvPB04cKDuiR8dIFyxYoUGg0H9+uuvdfv27fqPf/xDe/ToodnZ2RoIBHTu3LkNfvfyyy/X9PR0LS0t1Q0bNqhqXQHVvXt3LS0t1fLyci0rK6tXUOXm5qrqDzu1Q4cOtkFtBoPu16+fqqo++eSTtqAZNGiQjh492l6YDxgwwN5NdRZGpnEVeyLHLs50OQtBc1LGnkDxlokTJ2paWpr9XP/+/W1gxXlC72o9zh4C5hGlnV0IxFvGjBlj70hPmDDBBgjz8vI0OTlZ27Zt67rYnzt3rlZVVdXrHu9Mb7t27ewAp879Evv5Zs2aaUZGhhYVFbkKS+f3zCMT5m5vixYtbCWcn5+voVDIFuJmSUlJsY++mMZqUVHRTrvwxx5b8/umMWYKa7/f7xp83TmwtXMxd4FFfrizGe+iIRKJ6KGHHlrvzpyzZ5tzMQ2e2CUajdq7B6mpqTsNNpngpbMgN/umuLhYH3nkkbhpveqqq2wj2uwfkboGh9kHzkfHV61apUcddZTrTr3pxm2+Z15v6NEzc/FlzivnRY1I3YVNp06ddprv41WSzjvK6enpNqhsKnoT/IoNGjq326T7rbfecuWB7OxsXbZsmasX7cSJE+3jBuYYm4agSN1dqauuusr1O5mZma5HjGMv2ETqAsPOXm6xi/MYO5eCggKdOXOmbSA4g3TO9ZkAoQlkmsBXYWGhq6ecs9xLSUlx9Rbq1KmT7T1jGvvmpoAzuOnsWWceqc3JydmtQf7NYranqKhol0GR2LLG+XkzOYHzPHrjjTfsYPomTT179nQFVsxNDbM+v9/v6r3h9/s1LS1NzzrrLB05cqSKiJ0gYdiwYXEHk49Go3raaadply5d7HFPTEx0Nfqdx+mYY46Jm1edS2xD3OfzuXo5ZmVl2Yt4czFeWFiojzzyiIrUb/iL/HCB7zxfkpOT1e/3q6q6bkTcd9999YbzcJ6b5v9m+IGcnBydPXu2a/1du3Z1HcOnn35aDz30UNf54+xRaLYzdttNDzCTJ5z1qLM3ulkOPPBAVVVXcMekKfaGYuxiAoS9e/dWVdVBgwbZc3x36vCkpCS9+OKLXa/l5ubqp59+asuFeEG72HXG3gQwx8dsSzgcdpU9sdsZe9Hm/Ixzexo693b1eiQS0a+++qrehWM4HLbH3PSmEKmru81TDs5guTPAb/afsyew83xwBhdj03b55ZfbtoS5iIzXnrjzzjt11apVrmB+7L5zDlHhTEdiYqKdeCgUCtmLqoaCzSJihwxw7p/YQPpBBx1U7yLe9Kwyf8e2fWLz4WWXXaarVq1y1eXO9/Py8uxFbEN51/QkMz3tzDAq5oa0ufBdunSpLly40PXdaDSq/fv3t70e7733Xls2ONP+9NNP2/+PGjVKu3btqsccc4z26dPHdV5mZ2fX63Fm6t8JEyZoWVmZq3dPvABhNBpVVXX1to8tK4LBoA1OnHLKKfax5tjJg5w9mOIF/swxSU5OtsfX2R4QqWtDmB7T5qkEU5aa42bSOG/ePP3nP/+pubm52qJFi3oBQlMeOstsE/Bzpi8235hriZ49e+rgwYN11apV9ga5cz2x+ce0H8zTHvHWbdYR+7qpB531nvm+c6IF04PO+USN+WyXLl108uTJruEj4tUVu1rMOncVIHSWN7G/1bx5cw2FQpqTk6Nt2rRpsEOD+f7555+vqmoDO85OIjtbfD6fPvLII7a3uXOfm3UnJye7Ap8m34vUXVufdtpp9vFds6xatUojkYg9NxuqD03PfNOLOd6+GjlypG3TmTyyO9uXlZWlF154oe1lbdoF5qaFiOgNN9xggz0N7Z/daXv6/X57I6l9+/YaCoV0+vTprqcOAoGAa+Kf3Tk25lwKBAK7DPQHg0Ht0KGDaygBn8/nelw8djn88MPrBdz79euno0ePrnfMAoGA3nzzzRqNRm1A3mx7IBDQF198Me4xCYVCOnfuXNsO69ChgxYWFtqe1iKiJ554ojZr1qxe4DG2HktISLA9H4PBoB500EFxh4Mx+8g5tNV///tfVVV96aWX7DkmIvr222/rhAkTtEWLFq5rpcmTJ2uHDh1sef/mm2/WBc1EbE/rxYsXq2r8AOFzzz2nmZmZWlVVpWvWrFGRus4zl1xyif2MKW9//etfaygU0ocfflhXrVqln3/+uStmZrbr2WeftX8vWLBAv/nmG41Go/rJJ59oixYt9JlnnmkwQPjrX/9ae/To0WBcLp4fPUnJnDlzZMeOHdKsWTNJTk6WESNGyNtvvy1bt26V4447zg5eHM8rr7wimzdvlieffNIOwChSN0Dms88+K++++6588sknMn36dFmwYIHcfffdIiL1piX3OQaBNf/3xxlMUh0Tk4jUDdZrBt8sKCiQYDAoycnJ8u6779oJBI4//ngRqRtYtV27dq4BUc1kHSI/TK5gBsUM/P9A0Wag/IYsXLhQvv/+ezsguHNbnBNhmMF8U1JSpKSkxA5y/cc//lEeeOABqa2ttfslJSXFNdCziMgdd9whF154oWs/mcGQMzIyRERkxYoVdgDrrKws+9lAICCBQEDy8/NlwIABkp6eLqFQSC6//HI588wzpbKy0u6D8vJyGTVqlIwbN05E6iY7WbBggWtdaWlpkpCQYH9XpG7iie+++05Wr14tVVVV4vP55OWXX7aD/KqqHbz9tddeE5G6wWTNNq5bt06qq6vlu+++c+3fLVu22N83g/evXr263ucMMzjzn/70J9fAuqpqJ1Qx+7lPnz7y9NNP288cddRRcsABB9TLe5FIxKbzlVdeEZEfJiVwOvzww+WTTz4RVbUDjtfW1romrHByDjztzJcVFRXym9/8RkTqJlSJncjBuZ3hcFjS0tKkc+fONg9v3LjRbvP48ePt4O5mEHiRumP9/fff27/z8vIkMTFRli9fbge0N9vs8/mkrKxMUlNTXZOQVFRU2L8jkYj9HednzGDmZlvMdvv9fvt544477pD//ve/oqoSjUYlOTnZDkhufmPJkiV235oB41VV8vPzxefzSWVlpfj9folEInb/bty4Ufx+vyQnJ8uxxx7rOr5//OMf7f937NghF110kRQVFdnXvvnmG+natas8//zz9rXFixfLjh07XOdnbW2tBINBycjIkA8++EAOO+ww+57f75fa2lqZMGGCnQBnzZo1IlI3qUVlZaVEIhF59tlnpU+fPrIzzvLFMAOam4Fru3btKoFAQDp37lwvjSJ1eeaMM86Q3//+9xKJRGTDhg0SCoWkpqZGWrduLX6/35b7W7ZsqTeRwrXXXisiIu+++66I1A0+bfa9yes5OTlSUlIiInWD14vUDbJvzj2/3y89e/Z05QHnfhcR1+/Glv3mGAaDQUlJSXFN5NK2bVu58sorbZkcCASksrLSdR717NlTHnzwQampqZGrrrrK/p6ZfCgcDtsJOfLy8mxZZ+oTs16RunPRpMfn88n27dtl4cKF0q5dOxFxT+BUUVEhkUhEtm7dKoFAQEKhkPh8Pns+m3WI1B2vDRs22L9NGTFq1Cg54ogj7OdjB1VWVVtWioj873//s5NBPPzwwxIOh8Xn80nXrl1FpK48zM3NlYSEBLtNpk5w5rfa2lr7vs/ns/nphBNOkEWLFkk8zsnAfD6f3daCggKbVpG6c7m6utr+Xnl5ufTq1ct+99tvv603sZgzDUYgEJCysjI7wP7WrVvtb3z88cc2/5kyyvyeczBs8//KykrZsWOHax+YwflFRP7xj3+IyA9l8RdffGEnbWrVqpUsW7ZMmjdvbj8/Y8YMSUlJsb+tqnZwcZN/1q9fb49VZWWl1NTUSDQatftOpO48iUQiEo1GJRwO27papC6P1NTU2G3YsWOHVFVV2fLGaeLEiSIict5559l9Zwb9N/vGtGHMfjbbarZLVW27oayszL7vrAMqKyuluLjYHofa2lrJyMiQcePGybJly+qVaeFwWNauXSvFxcVSUVFh940zbYmJiTJ37lzZsWOHTJs2TXr06CEiIkcffbStFxISEuSggw6yE/GY8iUUCtntMXksJydHSktLXWnJzs6Wqqoq+fDDD+vtO7N/zYQTw4YNs2lwvm/+/+CDD4pIXRlj8kDv3r3tZAwiYifkMmmrqqqSxYsX23PF7/fLV199VS8ttbW1rslk0tPT7eeTk5Nl2bJltiwWEenUqZMUFBTIkiVLxO/3S1pamqt8NXVBbJnrZCacUVVbbvp8PlvuONu+ZjB3k6YdO3bIBx98IF9++aVUV1fbSbV27NjhKgOck1ysWLFCtm7dKo899pgcdthh0qpVKwmFQhIIBKS6utrVzhD5YVKehx9+WD7++GM7WZJIXT0Uq6KiQj766CNZuXKliNRNZHLggQfafWnWZeqZr776SiorK+W7776T66+/3rUucz3lvJbp37+/iPxw3gQCATnmmGNc+cRMAhCNRuWzzz6zZcP3338vlZWVtvwz7ef//e9/UlNTIytWrJDBgwdLQkLCTq+bqqurbd5q1qyZLFu2zHWd8PLLL0teXp5Nd2pqqk1PcnKylJWVSYsWLUTkh3LATGY1ffp0+cMf/iAiYttsGRkZtrwdNGiQvPDCC/Xqk9i2scnHsZMItmrVyrZXq6qq7DE0+9NMSJCUlCSrVq2S77//Xt5++227flWtV1f4fD55/vnnpXv37tKmTRtp27atRCIRm/7a2lpJTEyUUaNG2f0waNCgehNCtGnTxtV+z8rKksMPP1xE6tpm11xzjWzfvt1OdmPOh4SEBNv+OeWUU0REZN68eTJ37lz561//KsFgUFJTU0VVpVevXjZ/mAkqnMehuLhYTjvtNKmqqpJgMCg5OTl2neb4JyQkSHl5uTz77LP19veLL74od911V73JiJYsWWLrwR07dtS7dhcRadGihQwdOlQGDhwo5eXl9trITFYVcEzgYtq/pm1lJsMRqTtPotGorTtatmwpbdu2lX79+skNN9wgM2fOtJOjiNTlHzOR2cyZM2XatGmudE2ePNlutykLRerqpg4dOtjP9evXT1q1aiXBYFBOOOEEW95/+OGHUl1dLX/605/sdptJm0y54Pv/SQEjkYhkZWVJ7969JSsry75vjpm53nfm+draWns++ByTiuXm5soHH3zgqrNV1dbz4XDY1lvm+x07dnRNSuYUW47X1NTIeeedJxUVFfZa0fne8OHDJRAI2Hal2W/mHDKTtPXp00dCoZCrPp07d66sW7fOlU9CoZCdkNKorKyUiooKqa2tlR07dshbb70lBQUF0rx5cwmFQtKmTRsRETniiCNk1apVkpWVZdukhimfTH459thj5dtvv5U777xT3njjDXtuJCYmuuIF5vWUlBR7TeosG1TV5lORuhjZt99+KwkJCfaa8t///rfce++99cqUG264QW677TYZN26clJWVuepdJzMxlZGVlSVHHHGEnHzyyVJRUWHjRfF8++239b6/Kz8qQLhjxw6ZN2+e/PGPf5Rly5bZZfny5dKsWTPZtm2ba6Ymp7///e/yr3/9S4qKiqS8vLze+9u2bRNVlaSkJPn3v/8tgwcPtgVFbHAl9gLUqV27dvUOginUTDBApK4g3rFjh0QiESkrK5P169eL3++3J2ZOTo6d0ctwBpBiBQIBCQaD8sUXX9jX7rnnHlvwmyCFczZbEXEFXZwBNBPIrKiokPXr19tGbE1NjRx77LGSnJxsC5NNmzbJkiVLXOk55phj5IADDrB/RyIR6d27t4j8EAxcuXJl3MBVbGXbqVMnqa6ulg0bNsgjjzxiZ2Y2lYDP57MngqrKxo0b7fbV1NSIz+cTv99vT1hzogYCAWnfvr1MnDhRwuGwzJs3zx67nJwcWbZsmYjUzVTapk0bG1QQqZu5Ki8vT4LBoKuyTUhIsIFFs66rr75azjjjDElKSrIXbKagNIVhbm6uq1Dw+Xz1LqTPPfdc134eOHCg5ObmugrUzMxMm5d9Pp+9YDfHNisryxZmCxcutAHlyspKOeaYYyQYDMpRRx0l7777rusCQqQuQGrEFiTOitrk07Zt20o8Pp9PSkpKJDExUXJzc+sVdieffLKISL0Zh535yVyIlpWV2QsSw7m+zZs32/SYfeLz+eS7776T2tpaSUpKcs2+5NyXpiBv1aqVVFdX29mvjGXLltljlJ2dLdu2bZPKykqbpysrK+Wmm26SHTt2SHJysq1ot23bJunp6ZKZmWln3DLrMGmoqampl7d8Pp+cffbZrrQuW7ZMjjnmGPvaLbfcIk888YQNIvl8PhtoNuWQz+ezacnOzpbvv/9eHnnkEbuOUCgkFRUV8tJLL9l9acoP54VNMBi0s66bgJKI2PNzy5YtUltbK1deeaV9L/D/s89u377dVmoFBQVSU1Nj12WYi5GEhAT55JNPZPz48VJUVCQjR4605VhmZqYkJSW5ZgJ0/v+DDz6wF1sPPfSQa90mD0ciEfsboVDIpt+57/1+v3z77beuC6SvvvpK+vXrZwPZJu84P2P2tzNPBv5/hnajqqpKevbsafONyUcDBgywn7nmmmvsRbuZSfj88893NR5GjRollZWV8vXXX8umTZskPT1d3n33XVvWOIN6TqbRdckll9jXzMyxIiJ33nmnfPXVV1JTU7PTOigajcrSpUvtfjCNroqKCtfsx1VVVVJQUOBqIHbu3Nn+Pzs7287kXFtbK1VVVRKNRm0gKhgMSps2bWT79u02P5pj7LzgNIHsePVMIBCQoqIiCQQC0qVLl7jbU11dbY+lCZ6abSopKbEzZoqIvP766zZ9hrNcMjOCm9lDk5OTJTMzU6qrq22jr7q6Wvr162e34brrrpMrrrhCRNx5qqKiwtWgvfrqqyUnJ0cyMjJk6tSprmP+7bff2jbFxRdf7Prepk2bpKamRkKhkCQmJkp+fr6rbdOnTx/XMdq2bZutO8x2d+3a1a7f5/NJ3759bXlmPpOUlCQ7duyQpKQk8fv9rnVWVFTIs88+K6pq2xhJSUly0EEH2TxhtiU9Pd0VyHPe5BWpyxemvWRu9Jlyrrq62s7WGA6H5b333pM1a9bYOsF54e/3+2XZsmWumyb5+fmSlZXluvlmVFZWyujRo2X79u1SXV0tLVu2FL/fb9sZZt+ZwNfvfvc7GzgzQZT09HTZsWOHJCQkSHZ2tqiqbQc5Z0Q1De1PP/1UevbsWS8tc+bMkYMOOkhimbaXuQgeMGCA9O3b15YJVVVV9oaNz+ez+zEUCklWVpb4/X67/SJ1ZY8JOIuInTF2yZIlNo/V1tbWm9kwISFBTjvtNBk+fLiIiPztb3+T7du3S0JCgtTW1orf75fCwkL56quvXOXWihUrpLKyUgKBgG2zmvczMjJkwoQJkpWVJbm5uSIirtmuRUSmTJkiInXnnamzzeySIuIKyDkDV7W1tTJ58mTZuHGjrF+/Xg444AA58cQT7We7dOlij8FXX31ly6OPPvpIPv30U8nJyZHf/OY30rFjR6murpaMjAxRVXvTQ6SuLDJlcElJiZSXl7tuSMab1V5EXDeL16xZI4mJia62s9/vtxf+Zh39+/eXt99+237G7K/t27dLUVGRqKoEAgE726rZL9u3b5fU1FQbtP7www/t+ZmdnS3r1q2zgUBVla+++srmLTOzr9/vl1AoJHfccYerHIq9kI5EIjJjxgzJzMyUnJwcSUpKki+//FJ27Njhqkd79uwpQ4cOtWWCad84L/jNPjKBQnPeTZo0SS644AIJBAJSUVEhJSUlUlFRYY9ls2bNZNCgQTavBAIBV7ll2rZmvzpvcInUBQpM4KaystJeW5prJlPOBoNB2bFjh/zvf/8TVZXVq1dLSkqKFBYW2utUc/1iglQrVqyQSy65RLZu3Spt27aVtLQ0+f777+26TLmTmZkp//rXv1w39kXqbmY588k333xjg4HmZkBlZaUkJSVJbW2tTJw4UQKBgGzfvt1eE5s2ZSAQkJNOOknS0tIkKSlJMjMzJRqNyrBhw+y+O/DAA+05ZY5Lq1atZOPGjfamSmZmpuumgTkGn332mSu4YM6ZwsJCWb58ueuaVaSuLhSpO4969OjhuhHr/Ex2drYkJyfbNp+I2KC4M28625LBYFC2bNniunHkbFOtW7fOFcB+9dVXJS8vz9bdmzZtsjMmn3DCCXaWX8O0Ucz6TRlcU1NjbxZ36tRJli1bJsnJyeLz+aRLly627gqHwxIMBmXGjBl2tnpTv5iyXVVlxYoVUlhYKCNGjJA2bdpIYmKiVFdX2yCzqto2iara/BONRl3Xr+bae82aNRIIBOwNeJPHzbFKS0uzsyKbbduyZYurfeQMcsW2lc2+bN68uaxYscK+Z8royspKmThxor0mMO0IEZEHH3zQlmHvv/++vfluApaXXHKJLF++XN59911XUHTYsGF2HVOmTLEz/Z555pmSnp4uCQkJ0qJFC4lEIlJdXW33c+/evaWsrEzKyspsffbGG2+ISF0bMTk5WdatWyciIqtWrZJLLrlEBg8eLOFw2B7/li1byrp16+rdYG7btq0NNhumo5Zpl/3vf/+Tp556Sh566CEbGysrK5MLL7xQvvvuO1tnm2vryZMn28B8QwKBgHTs2LHe6yeddJIsWrRIJk2a5Aqqx3rvvfdc1+67ZY/6G/6/BQsWaDgc1k8//VQPOeQQve+++3T58uX66aef2u7hJ510Ur3vffTRR5qenq7dunXTVq1a6cKFC+0ydOhQHTBggK5fv952Y8/Pz9cWLVq4upDee++9tntnOBy2jxibsVrMRCaqah936tatmw4YMMA+lpKbm6tPPfWUjho1ynZxTU9P19mzZ2u3bt1cj0mYR0qcXcHPOecc+//YLsPOrtJmMY9eO1876KCDtKioyD7CkJSU5Jq8wKzL+XibeSxD5Ieu/qFQyD5C4PP57Bh75junnnpqvYlOzDP4Zlws5+f79OmjzZs3d40rY8bhMuN7+Hw+vf322137JhgMaseOHe2YQ0lJSRqNRu1jb+Y3nI9PnHfeedquXTtNT0/XrKwsPeKII+oNZN+uXTvVutJMW7Zsqe3atXM9UuZ8hNPZtTocDrsGx05MTNShQ4fqWWedpUlJSbbbsfPxCZG6LvPO4xSvO/exxx7rGpfthBNOsI8KOtPt3Oemq7r53ezsbNd4FuFw2PUYn0hdV/hx48a5xudw5g/n/+M9LtjQ7FEidY9Pmn2SkpJix8By5vl4jzSZx2ycAxb7/X5duHChfczADEAtIvrII4/o6NGjtaCgwD7+UF5ebge9fvrpp+22xe57s1x44YUq8sOjG+acM38nJyfbcyDeIylmyczMrDfbXkZGhubm5tqZ4DIzM+3Mimb7QqGQ61HIMWPG6Nq1a13r9vv9+sorr9jvGKYLuTkWSUlJtmu7cxDjFi1aaIsWLeI+nuJcYh+VNuee+df5SGDsY/fOcTDNZDLmURaRuqEXzH5wlmt+v1/z8vLsAMJHHnmkpqamusZBSk1N1WAwaF8zedl5PEzandtvvp+bm2vHszT7K7bscA5c7szvoVBIhw4dWq8sdo4jZ85JZ3pi93EoFNK//e1v9R6xce7Tli1b6qJFi1yPQjzxxBOu9EyaNMn1fqdOnTQ7O9s1Q2haWppOmzbNPqpRVFRkHy12Po5s0uIcx8nn8+nAgQM1KSnJPo4o8sNkLm3atHGV6+ZxS/MoSLxyxCyxYyWa8WzMY96FhYV6xhln2HF2DjroIFtvOZfYx1OSk5O1W7du9R6ndO7nhmZddT4u8uyzz7rKpdhHylq2bKk33nijaxvnzZvn+swhhxzS4GyX5vecM5secMAB9cbY7dSpk32szWzT559/rnfeeaf6/X5t3ry5a5iTUChkh7xwzmJ5yy232HMlISFB27RpoyNHjnSV3WeeeaZrKIY2bdpobW2tK81z5851Pe7qfFzH+aiY7/+HlcjJyXGNTegc+sCkJyUlRe+77756Y7317t1bExIS7PEyY1Cb953/j/fosfld56RU8ZbMzExVVdeYcl27dtXx48fbWSidjxg7HyMz+1NE9NZbb7XbY8oyv99vZ+c0aXbmq4KCAm3RooVNo///J5cwj3yZ49q2bdt659Hs2bM1PT3d9YixacOaR+XM75pB5+PV6YWFhVpaWmrzqimDnbOxjhs3TpcsWeL6rpkF0lnPXXXVVfUeMf7Vr35l24I9e/a0Y2ia7/z2t791DSNwzjnn2DFaneWLs56ZM2eOqw0XOw6sKUvLysrsdiYlJdnXzbl+ww036EcffeTaL88++6zm5uaqz1c3WV8oFNJoNOqa6Vikbtw9sx3O1wcPHuyayCoajWrfvn0bfJw3OzvbVQYGg0H94IMPdM6cOfazHTt2dI1lGQ6H640XOnfuXFd7IRKJaJ8+fXTKlCn2NTMZXkpKimtMzpNOOsk+VmxeKykp0cLCQtv2MuWSGV8xtg1k8t7y5cvtdphz14xNnJmZqRkZGa48kpycrLNmzbLj2vn9fi0sLNRevXq5zpX+/ftrixYt6j0G6/v/4RomTpxoXzftdVOe3XDDDXrPPffYfd6+fXs76Y1I3XA6sUMEOMdnbWi4HbM4hzlxDhFUUFDgGprItHlMOz4QCGhWVpbm5OTYPFNYWGivDc0QPuZ87t69u93vzmsLkbry3jmrvFliJ2gy43+KiM6YMUOzsrJsPev3++tdYzj3o3PW3kgkYvOW8zFO58QZ5vum/WG+l5CQYK9JTZ4rLy/XtLQ01zWPsy02duxYTUtLqzdcSzQa1YkTJ9q6Inb7b775Zj3ssMO0devWrnTdcsstrs+Vl5e7vhtv6KTYMri0tFR79eqlF198sR1KwTk5m3n0OSMjw56v5l+zfrNPzbY6hxsw18dmMcMrOOvtUaNG6cCBA+sNW+Ecq9Ln8+kxxxxjH/H2+/1x80rsY/XOiZjMa1lZWa52V8eOHe3juKbciZ0VuXnz5q7zLRwO67hx41xDYIjUjUWdlJSk5557rh1uzPy2GYZJpK7Odk7M6MwPzvrItFXMUCklJSW6YMECfeKJJ2w+Kioqstf+InXX2a+99po+//zzesghh9iZwc2jwc70/uEPf9ApU6bopEmTXG3o0047TTMyMuzwSCbNxx9/vK5atUpnzJhhy7bHH39cc3Jy9K677lKRumEvVFXnz59v88Z9992nS5cute2RdevWqarqrFmztKCgwDVp7/nnn69jxozRcePG6dixY3X79u123y9evFiXL1/uWr744gv7XZG69pLz7wULFqiqam1trW7YsEErKytVVeM+Yrx161ZNSEjQl156afcDffojxyA84ogj9LDDDtOKigq96KKL9MADD9S0tDQ78LtI3VhNsZyTHcRbgsGgqtY1CJ0NYnPilpSUaF5ens1AnTp12mmA8IILLrCZMyEhwTX4ZceOHe0YDztLkzODm/87A27moi92LCXnOCHOxTQwg8GgK0DYvHlz16Df5jM7C3jEpi0tLc0OWhpbwMRbQqFQvUF7zfgisTMb7e7iLARSUlJcM0XHblO8iVDMZ01hWV5eXpdR/3+dJ510kusCpKyszBZ6sReczsBC8+bNtXv37nbfOAMjO1ucY6I50+hsxMX+XrzFTP7iHBcqGAy68ogJEMUGk03Fvat0Os+Z3R2zzaQ5tkJIT0+Puw4TILzzzjtd60hLS7MNVGeAMN5y4IEH2tnYKioq4o7L4dzn5vjG7pdgMKgTJ07UQYMGxR33x5RF8cbKFBEblI1XcTa0n+ItkUhEDzvsMDtzZHZ2ti2DTIPbzO7oPOZmjC8zgY5zXDvntu7usTQznZm/YwOEsdtRVFTkmsDBLObiy7newYMH1zuXd7X4/X6bnmHDhjU4Jmbr1q1tIyPeeG/FxcX1zsFdjc1qlngT4DgXZ97Iz8+P2/ByBsAOP/xwO3i4eS0zM9Mev9hZp80ybdo01ziusQHChvafuRhNTU3Vt956y677lFNOqRcgNHXJ2LFjXYGNPTlmDS05OTl61llnaZs2bfT44493lUeRSKTBMTCdv+0cZ1JEXI3X2GVnwSPnxAHRaNR1A8U5vqlZzA0PkR9m2Iy33obqAzMhgnNsSJ/Pp/n5+a6bid99952qqt500031Bp9v3ry5nXwi3mLSFAqF9JZbbqk3GYZzuf/++22daJZHHnnEFViOzUd7erxN/b1t27Z6+X13FnN+TpkyxRWECwQCcQPKsUs0GrUN4tjZPtPT0/XRRx+1gU1neyw2z95yyy22wZyWlqadOnWKWyaY9sZhhx3mqqPN+WNunOzOPjXvmTFURUTPPfdcFRHXTPfOJTc31wYvnfnu1FNPrTfeZOx+aqh+M8vIkSO1pqamXoAwXv6PvRA1+3hn33NOpBaJRFwzfscu5lg69+UFF1xgyxNzHhQUFNRrK82YMUODwaAecMAB9oLQeSxNWidOnLjL/Gra2klJSdq+ffsG0+v3+7Vdu3auG17OyYB++9vfaq9evXTChAmu/LIn54pIXTu2efPmOmXKlHrjImZmZromf4qd0CAhIUETEhL09NNPt/nMuW/M8TQBwnhL4P8n3frHP/5hXzMBwoqKChvANYGN8847z3XT35keUxfGK0/N2O/OtolzDLpmzZrVm+gqtu3bqlUrO3vpnpRtpkyOd1Pd7/frEUccoZMnT9aUlBSbPxISEuxNh+nTp7vONedxNjfwnIGwXV1fDBs2LO4MtvEWs49jt7ehm2rx8rHzutLsf2dQMnZx3niJXZcZU9Ncz8U7Ds2aNdNp06bpZZddtsfnQ0NLYWHhLss7ZxvA56sbQ94EjHb2vfPOO69eeeKcl6BLly5xx4cWEXujpX379vbGkAnsms9kZGTokCFDNCMjQ7t3717vZv/O2vmx57uz05QJgMZOpLg75VBmZmaD42M6/166dKkuWLDAdWPHOTO7uWGxJ3kzOztb//KXv7jyprmpIFLXiSq2neNcMjIybIekYDDoanO1atVKp0+frpMnT9bRo0fr6NGjXelLTU3V3NxcHT9+vC5cuFDbt2+vkUhEk5OTbdmyYMECnTlzpu3YYgKEJkZljkVeXp526dJFU1JS7PudO3fWs846yxX/ev/99zUhIUHnzp2r4XBYn3zyyV0eH0Pkh3FGzd8mQBgrXoBw/vz5dmK9PfGTZjH+pXFOKb03idQNvlxUVKSvvvrqXl33nnjvvfc0NzfXzsqzdu1azczMrDfg5U9x5plnaiQS2a11xqbnp5g5c6aeeuqpcd/r2bOnPvDAAz9qvbW1tXrQQQfp/Pnz9/i75q7wW2+9Ve91s9//+te/avv27bWmpsb1mWeffVbbt2+v1dXVOmjQID3nnHNc7y9fvlxzc3N1y5Ytqqp6yy232IZKQwWD01NPPaXBYNDOerQr7733niYmJuqUKVMaTGdDGtpG47bbbtOhQ4fuVjpUVYcNG6aHHnqonWFtd7333nvq8/n05Zdf3qPvxbO7+WLLli2ampqqjz/++C7z+9ixY/XKK6+0je1evXrp1KlTVbVum83/nSorK7W4uFhfeeWV3U770UcfrWlpaXHLonHjxuk111yz2+uKZfLhrrz33nt2RuSG8sWuXHjhhXraaaf9qO86j4Uz/8UrE03Pz48//lg3bNigmZmZrpnG4vkxZetP3feqqlOmTNGEhARbLhgN5R9jd/Pz7ua3H1NummNyySWXaGFh4S4/b2awW7hwYb33LrzwQp00aZJmZmbqXXfdZW8yOOv2bdu27bRObqgsNcdpZ+2FDRs2aDAY1FmzZu10G3Z1XIxdlaF7Yk/Pm7Fjx+q111670zr2p6Qxtg6JzTu7qr/3pH4/6qijNBKJ6OOPP+56/ae0hXa3zNuZhn4/3j7v2bOn9u7dWydOnLjTde4qfzdkV/tz+fLlmpOToxkZGTstB0866SQdOXKkqu6dtmbs9jz77LParl07LS4u1nvuucfVHopnd8+1WHvaPmkozabNVlhYuFv1dU1NjbZp08Y1aP2ufsOpofPVlOH/1979x0Rdx3Ecfx3KHdddRI0AlY1+cC0qwX44Cv9gZkuyUebo99YJ/igyk5GtwugHkK3QNlfDpbVTmxprxUau1cJmW/1FRdkWrYWerCVLJ8vJSkw+/eHuuzsOuB8gB97zsfEH3Be+n8/3+7n35/N98733N5pjNlwsa/bha9TRDJ9TJmKsBC6Ct2zZEvLzsc5lpPgWq+H9Gs96xZhzx8XhcFiJEWPGXjPEMwfHEksD8+/NN98c9lrgwQ3BSRNjjGlqajK5ublh7Yr12AePkWjXTdGuXUY6T5Ksmx46OjqMMSbiWjDaeSE4TgaLpl+lpaVm3bp1o7a7uLjYZGZmWuuQw4cPm5SUFPP9998bY+KfI8YSPJ8HxuCOHTui3s/wfo83HgTO06+//hrztVKwkWLfROdQAmutWERzDR5JvDmSuB9SglAOh0O7du0asy7i+Xb06FHt2rXLqiGQk5Oj999/P6Qe4nht3LhRFRUV8vv9MbdnPLKystTY2Bj28+PHj2vZsmV6+OGH4/q7NptN27ZtC6tXNhZjjPx+v5qampSdnW3VSgsIPu533323Vq9eHVYkfGBgQFu2bNGnn36qAwcOaM2aNSGvFxYW6o033rDqDT7++ONWbY5oHDlyRM8880xI/bSxBM6Vx+MJqT0xMDAgn88XUnNquNH6GJCammoV/x5Lf3+/2tra9NVXX6m3t1eLFi2Kqu0B//zzj2pra8cskh6tSONiaGhIf/31lxobG5WRkaF77rkn4nh/8803NTQ0pHnz5snlcqmrq0uLFi3Svn37dODAAevhQcF6e3tVV1cXUrB9LIODg7rxxhvV2toaFosGBwc1d+5c6+EC8QiMw+B6XCM5evSoWltb9cQTT4w6LiLZsGGD8vLywmrJRiP4XASPv5ycHK1atUoff/yx/H6/Ojo6tHr1ai1YsEBXX321/H6/WlparFpJo4k1tk7EsZfO1b55/fXXrbjQ398/5vgJiDbORTveYombLS0t6uzs1A8//KDHHntMb7/9tlUIfDQnT57U3r17lZKSYtUdDLZhwwa53W698847WrFihcrLy8O2cTqdMc/J0Z6nH3/8UQ888EBYgfOAaM9LQKQYGotY3zfNzc1yu92jzrHjbePwOSR47ESav6Od34eGhvTHH3/o2LFjysrKsh4QEDCetVC0MW8so+1/+DHv6+vTggUL9Oeff45YbyhYPOM7muNZWFio6upqvfDCCyPGwb///lvffPON9uzZo7Vr147Zv1gM78/AwIAaGxtVV1cnr9cbsh4KFut7bbho1yeR2lxYWKja2lpVVlaOGD+PHDmi7du367ffftPPP/+s6upqHT582HpwSzT7CDba+zUQw8c6ZqOJZc0+fI06muFzynjHSuABlykpKSF1zqSxz2Wk+Bar4f2KZ72yadMm/fTTT/r999/10Ucf6ezZs1q4cKGkyHNRrNcusVwrBc+/t99++6ixr7W1VZ2dnTp06JA++OADNTc3y+v1hrUr1mMfGCM9PT1Rr5uiXbsEzlNHR4fa29ut8bt582ZdccUV1nVWpLVgpHlhpDgZEM96cPj4CpxPl8uls2fPqq+vTy+++KJuvfVW3XTTTZLimyMiCZ7PA2MwJSUlqv2M1O/xxoPAeZoxY0ZM10rDjRT7JjqHElhrxSKaa/CxjCtHEndKcho6n3cQRnNXFy4M/f39xm63m6KiorBHicciLy/PpKenm+bm5qh/50Iea0uXLjWXXXaZsdvt5s4777QeSz8VBf6Dmpuba/3HMZLAuElPTzeZmZmmrq7OLF261MyZM8fU1dWF1KvA+bNz507j8XiMw+Ewc+bMMV6v1xw/fjzRzYrLdBg/NTU1ZtasWcbhcBiPx2MaGhoi/je0pqbGZGVlRR0b45nbI8XS8awXpsN5uZDEE4+noq6uLuN0Os2SJUvMiRMnEt2cEZWWlhqn02lqamoS3RRjzPR5r/X29pqSkhKTnp5uLr74YnPbbbeZr7/+OtHNmnZinRumsvvvv99cfvnlJi0tzVx33XVm69atiW6SMSbyMQ7E20ceeSTmuX2q+Pzzz831119vfYS9uLh4Qj9tNxFxMvgOwtHk5eVZdUKvueYac/Dgwbj3BwSzGTMBt9sAAAAAAAAAmJb4iDEAAAAAAACQxEgQAgAAAAAAAEmMBCEAAAAAAACQxEgQAgAAAAAAAEmMBCEAAMA0tGPHDmVkZCS6GQAAALgAkCAEAACYopYvXy6bzSabzSa73a78/Hw1NDTov//+S3TTAAAAcAGZmegGAAAAYHRlZWXy+Xw6ffq0PvvsM61Zs0apqamaNWtWopsmSTpz5oxSU1MT3QwAAACMA3cQAgAATGEOh0M5OTnKy8tTdXW17rjjDrW3t4dt19PTo3vvvVfZ2dlyu92aP3++Ojo6rNcbGhp0ww03hP3evHnzVF9fb33/3nvvqaCgQGlpabr22mvV0tJiveb3+2Wz2dTa2qrS0lKlpaVp9+7dE9xjAAAATDYShAAAANOI0+nU4OBg2M9PnTqlJUuWaP/+/erq6lJZWZnKy8vV29srSaqqqlJ3d7c6Ozut3+nq6tLBgwdVWVkpSdq9e7deeuklvfbaa+ru7tbGjRtVX1+vnTt3huzr+eef17p169Td3a3Fixefx94CAABgMvARYwAAgGnAGKP9+/friy++0Nq1a8NeLyoqUlFRkfV9Y2Oj2tra1N7erqeeekq5ublavHixfD6f5s+fL0ny+XwqLS3VVVddJUl6+eWXtXnzZi1btkySdOWVV+qXX37Ru+++K6/Xa/3tmpoaaxsAAABMf9xBCAAAMIXt27dPbrdbaWlpuuuuu/Tggw/qlVdeCdvu1KlTWr9+vQoKCpSRkSG3263u7m7rDkJJWrVqlfbu3at///1Xg4OD2rNnj6qqqiRJAwMD6unp0YoVK+R2u62vpqYm9fT0hOzrlltuOa99BgAAwOTiDkIAAIApbOHChdq6davsdrtmz56tmTNHXr6tX79eX375pTZt2qT8/Hw5nU5VVFSEfBy5vLxcDodDbW1tstvtOnPmjCoqKiSdSzBK0vbt21VcXBzyt2fMmBHyvcvlmsguAgAAIMFIEAIAAExhLpdL+fn5Ebf79ttvtXz5ct13332SziX8/H5/yDYzZ86U1+uVz+eT3W7XQw89JKfTKUnKzs7W7NmzdejQIT366KMT3g8AAABMXSQIAQAALgAej0effPKJysvLZbPZVF9fr6GhobDtVq5cqYKCAknnkorBXn31VT399NO65JJLVFZWptOnT+u7775Tf3+/amtrJ6UfAAAAmHwkCAEAAC4Ab731lqqqqlRSUqLMzEw999xzOnnyZNh2Ho9HJSUlOnHiRNhHiVeuXKmLLrpIzc3NevbZZ+VyuTR37lzV1NRMUi8AAACQCDZjjEl0IwAAADA5jDHyeDx68sknuSsQAAAAkriDEAAAIGkcO3ZMH374ofr6+lRZWZno5gAAAGCKIEEIAACQJLKyspSZmalt27bp0ksvTXRzAAAAMEWQIAQAAEgSVJYBAADASFIS3QAAAAAAAAAAiUOCEAAAAAAAAEhiJAgBAAAAAACAJEaCEAAAAAAAAEhiJAgBAAAAAACAJEaCEAAAAAAAAEhiJAgBAAAAAACAJEaCEAAAAAAAAEhiJAgBAAAAAACAJPY/PkV+k5SGD9IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(x='Player',y='Ave',data=df, edgecolor='none' )\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca48f5d0-d6db-4b4d-bce5-be41726b7207",
   "metadata": {},
   "source": [
    "## Maidens by a bowler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "dcf25b9e-d8f8-4129-be3d-940048585416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Player', ylabel='Mdns'>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "sns.barplot(x='Player',y='Mdns',data=df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08a5be10-916c-4085-8b7b-16f9afa61b2a",
   "metadata": {},
   "source": [
    "## SCATTER PLOT BETWEEN PLAYER AND MATCHES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f80bf278-0b82-4374-81c9-0581a10ec1f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Player'], df['Mat'])\n",
    "\n",
    "plt.xlabel('player')\n",
    "plt.ylabel('Mat')\n",
    "\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b7fa52-45e8-4c8d-acae-ec01a2268ddb",
   "metadata": {},
   "source": [
    "##  SCATTER PLOT BETWEEN PLAYER AND WICKETS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0a6690a8-5137-4ba9-9e62-29632589accc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Player'], df['Wkts'],s=10)\n",
    "\n",
    "plt.xlabel('player')\n",
    "plt.ylabel('Wkts')\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fa6cc6-a531-4c8c-83e3-17766c6cb268",
   "metadata": {},
   "source": [
    "## HISTOGRAM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d955c0f3-9e11-4732-9b66-d5747f54e6a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    " \n",
    "plt.hist(df['Wkts'], color ='Green')\n",
    " \n",
    "plt.title(\"Wickets\")\n",
    " \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f389889a-a81e-445a-94ed-9d240079f2cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Econ'], color = 'red')\n",
    " \n",
    "plt.title(\"Economy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e325e19d-8d9d-499e-a4e3-f0e9db21e457",
   "metadata": {},
   "source": [
    "## Pie chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37aa6802-1830-4c7d-9bfa-5e9b28a7af92",
   "metadata": {},
   "source": [
    "## Maiden overs bowled by first 5 bowlers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "86f6c584-785a-4ae2-9b90-c9a51263894c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Player = ['A Zampa', 'S Lamichhane ', 'Kuldeep Yadav ',\n",
    "        'Bilal Khan', 'Mohammed Shami']\n",
    " \n",
    "Mdns = [11,26,19,29,21]\n",
    " \n",
    "# Creating plot\n",
    "fig = plt.figure(figsize =(8, 7))\n",
    "plt.pie(Mdns, labels = Player)\n",
    " \n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7185c75-9c66-4f75-a27c-dc1e98a80720",
   "metadata": {},
   "source": [
    "## Wickets taken by first bowlers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b3335694-d156-4469-b28d-7b0fb7039242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ExyKiW2BpJbJUugpg31xgz2e1GwSQVcnVcwSwpdUYJCzaexErky5har8wPH57GJw1/LZIJFdc8orI0hhqgKQEYMeHQFmO6DTUQma6fYyVOX6iY9iUVi4avDIsGiM7txYdhYiug6WVyJKc2Qj8/gJQcE50Emph4xy/wp5C3iwkQo9QT7w9siOi/FxERyGiq7C0ElmCkixg/Yv/WxGAbEFfLMSlKs6zFEWtVGBirxD8844IuDjYiY5DRGBpJZI3oxE4OB/Y8i5QXSI6DZmJpFAirGoJJEkhOorN83HR4KW72uHeuDaioxDZPJZWIrnKTAbWzKjdJIBsitHBA2FFX4qOQVfpHuKBt0d2RLS/q+goRDaLpZVIbqrLakdWD3wDSAbRaUiAGrdQROT8W3QM+huVUoGp/ULx7B2R0KhVouMQ2Ryl6ABEdJWzm4G5PYH9X7Gw2jCdHUfz5MhglDBv+zkM+2wXDqcXio5DZHNYWonkoKoY+PX/gO/vBYozRKchwarsuGqAnJ3NLcN9X+/Fe+tSUVXDHy6JzIWllUi00xuAL3sCh78XnYRkokLFpZbkzmCU8M2Oc7j7s51IulggOg6RTWBpJRKlogBY9Tiw9AGgNFN0GpKRMgVLq6U4l1eO+7/ei3fWnOCoK1ELY2klEuHkOuDLHsDRH0UnIRkqBkurJTFKwIJd53HXHI66ErUkllYiczLUAOtfBpaPBcpzRachmSqUnEVHoCY4f6UcD8zbh883n4HRyIV5iEyNpZXIXIovAwnDgH1cf5Nu7orBSXQEaiKDUcLsjafx8IL9yC2pEh2HyKqwtBKZw5lNwLx+QMZ+0UnIAuSxtFq8PWn5uGvOTmw9yXdUiEyFpZWoJRkNwOZ3gB/uAyryRachC5GtY2m1BvnlOkxZdBDvrjmBGoNRdBwii8fSStRSSnOAxSOBnR8B4Pw2argsnYPoCGQikgR8u+s8xny1Bxfzy0XHIbJoLK1ELeH8ztrpABd2ik5CFuhSpUZ0BDKxo5eKMeyzXfjvES5vR9RULK1EpiRJwI4Pa0dYy3JEpyELJEGBS9UcabVGZdV6TF92GO+tS4WBqwsQNZpCkiT+yyEyhYoCYNVjwNlNopOQBZM0bggt/kp0DGph/SK88cXYOLg52YmOQmQxONJKZAoZB4Cv+7KwUrPpNe6iI5AZ7DxzBfd8uQunc0pFRyGyGCytRM215wtg4d1AyWXRScgK1Ni7iY5AZnIxvwKjv9yN9ceyRUe5qb1790KlUmHYsGG3PDYhIQEKheKGvyZPnmyGxGStOD2AqKkMNcCvTwNHl4tOQlakwK8v4i48JToGmZFCATwzKAL/HBIBhUIhOs41pk6dCq1WiwULFuDUqVMICAi44bGVlZUoLi6+5uNz587F+++/j61bt6J3794tGZesGEdaiZqiugz44X4WVjK5CpWr6AhkZpIEfLb5DB5bnISyar3oOPWUlZXhxx9/xJNPPolhw4YhISHhpsc7OjrCz8+v3q9Tp07hP//5D7788su6wpqfn4+xY8eidevWcHJyQkxMDJYtW1bvXAMGDMAzzzyDGTNmwMPDA76+vpg/fz7Ky8sxefJkuLi4IDw8HL///nvdc7Zt2waFQoG1a9ciNjYWDg4O6NmzJ44dO1Z3TEOuTfLE0krUWGW5QMLdwLmtopOQFSpTuoiOQIJsSs3BfV/tQXaxfLZ//emnn9CuXTtERUXh4YcfxnfffYfGvEF78eJF3H///Zg2bRqmTp1a9/Gqqip07doVa9euxbFjx/D4449jwoQJOHDgQL3nL1q0CN7e3jhw4ACeeeYZPPnkk7j//vvRu3dvHDp0CHfeeScmTJiAioqKes97/vnnMXv2bBw8eBA+Pj4YMWIEampqGnVtkh9ODyBqjCtnge/vBYouik5CVupA4KN44Mxg0TFIoAA3ByyachsifMX/ANOnTx888MAD+Mc//gG9Xg9/f3+sWLECAwYMuOVzKyoq0KdPH7i7u2Pjxo1Qq9U3PX748OFo164dPvroIwC1I60GgwE7d9aud20wGODm5oZ7770XixcvBgBkZ2fD398fe/fuRc+ePbFt2zYMHDgQy5cvx4MPPggAKCgoQJs2bZCQkIAHHnigQdcmeeJIK1FDXUoEvruThZVaVKGkFR2BBMssrsJ9X+/F/nNit34+deoUDhw4gLFjxwIA1Go1HnzwQSxYsKBBz3/00UdRVFSEFStWXFNYDQYD3nnnHcTExMDT0xNarRYbNmxAenp6veNiY2Pr/lulUsHLywsxMTF1H/P19QUA5Obm1nter1696v7b09MTUVFRSE1NbdS1SX5u/mMPEdU6tR5YORmoqbj1sUTNkG9wEh2BZKC4sgYTvjuATx7ojGGx/kIyLFiwAHq9vt6NV5IkQaPR4IsvvoCb241Xunj//ffx22+/Yffu3fD29r7m8Q8//BBz5szBp59+ipiYGDg7O2PGjBnQ6XT1jrOzq7+OrUKhqPexP29cMxqNDf68Gnptkh+OtBLdStIiYPk4FlYyizyDs+gIJBM6vRHPLDuE73adN/u19Xo9Fi9ejNmzZyM5Obnu15EjRxAQEHDTG5d+//13vPLKK1i4cCE6dep03WN2796NkSNH4uGHH0anTp0QFhaG06dPmyz/vn376v67sLAQp0+fRnR0tFmuTS2HI61EN7P1P8D2WaJTkA3JruEWrvQXowS8veYEsoor8fLd0WZbEmvNmjUoLCzEo48+es2I6pgxY7BgwQI88cQT1zzvzJkzGDduHKZOnYp+/fohO7v+GrT29vbw9PREREQEVq5ciT179sDDwwMff/wxcnJy0L59e5Pkf/vtt+Hl5QVfX1+88sor8Pb2xqhRowCgxa9NLYcjrUTXYzQA/53Owkpml1ntKDoCydD8necxfXkyagwNfxu8ORYsWIAhQ4ZcdwrAmDFjkJiYiKNHj17z2NKlS1FUVIR58+bB39//ml/33nsvAODVV19FXFwc4uPjMWDAAPj5+dWVSlOYNWsW/vGPf6Br167Izs7Gb7/9Bnt7e7Ncm1oOVw8g+jtdBbByCnD691sfS2RiQ5QLcLaCxZWub0h0K8wd3xX2ao45Xc+fqwcUFhbC3d1ddBwyMX7VE12tPB9YfA8LKwmTUaURHYFkbFNqLh5bnIiqGoPoKERmx9JK9KeSzNolrS4dFJ2EbJRkr0W1kS/LdHPbT+fh0UUHUaljcSXbwukBRABQlgcsvAvIPyM6CdkwvWsgwnPfFx2DLESPUE98N6k7nDW8p5psA3+kJ6osBJaMYmEl4WrsbrzuJdHf7T9fgEe+O4Cyar3oKERmwdJKtq26FPh+DJBzTHQSIlSxtFIjJV4sxMPf7kdJVY3oKEQtjqWVbJeuAvjhAeBykugkRACACpX4vebJ8iRnFGH8/P0oquCOTmTdWFrJNul1wI/jgfQ9opMQ1SlTsrRS06RcLsYjCw9yqgBZNZZWsj0GPbByMpC2RXQSonpKoRUdgSzYkYwiPJpwkMthkdViaSXbYjQCv0wDTq4RnYToGoUSSys1z/7zBXji+ySz7ZxFZE4srWQ7JAn4bTpwbKXoJETXlW9kaaXm23YqDzOWJ8No5IqWZF1YWsl2rH8JOLxEdAqiG8rTc/tWMo21KVl49VeuikLWhaWVbMPmt4H9X4lOQXRTOTUsrWQ6S/enY/Yfp0THIDIZllayfjtn1/4ikrlMHUsrmdbnW85i4e7zomMQmQRLK1m3fV/XjrISWYDLVQ6iI5AVenvNCaw5mik6BlGzsbSS9Tq5Flj/L9EpiBrsEksrtQBJAp776QgOpxeKjkLULCytZJ1yU4FV0wDw7lmyDJKdM8oNfEmmllGtN+KxxUm4VFghOgpRk/EVkqxPZSGwbCygKxWdhKjBDA7uoiOQlbtSVo2pixK5axZZLJZWsi5GA7BiMlDIGw/IstTYu4uOQDbgZHYpnll6CAau4UoWiKWVrMsfrwHntopOQdRo1XauoiOQjdh6Kg/vrDkhOgZRo7G0kvVIXgbs+1J0CqImqVSxtJL5JOy5gCV7L4iOQdQoLK1kHS4lAWtmiE5B1GRlShfREcjGvPnbCWw/nSc6BlGDsbSS5SvNBn4cD+irRCcharISaEVHIBtjMEp4eukhXMwvFx2FqEFYWsmy6auBHx8GSrNEJyFqliKWVhKgtEqPJ78/hKoag+goRLfE0kqWbc2zwKWDolMQNVuBwUl0BLJRJ7JK8Pqvx0THILolllayXPu+ApK/F52CyCTyDM6iI5AN+ynxEn46mCE6BtFNsbSSZTq3DfjjVdEpiEwmp4YjrSTWa78ew/HMYtExiG6IpZUsT8F5YMUkwMhdXch6ZOkcREcgG1etN+KpHw6hpKpGdBSi62JpJcui1wE/TajdqpXIilyqYmkl8S7mV+C5n45AkrhjFskPSytZlm3vAdkpolMQmVxGlUZ0BCIAwMYTOZi345zoGETXYGkly5G+H9j9megURCYnqR1RqleLjkFU58MNp3Aone9okbywtJJl0JUDv0wDJK4lSNbH6OAuOgJRPQajhOd+OoIKHe8dIPlgaSXLsOEVoPC86BRELaLG3k10BKJrnL9SjnfXpoqOQVSHpZXk78wmIGmh6BRELabajqWV5Gnp/nRsOZkjOgYRAJZWkruKAuC/T4tOQdSiKtWuoiMQ3dALK1NQUK4THYOIpZVkbu1zQGmW6BRELapMydJK8nWlrBovrToqOgYRSyvJWMpK4Pgq0SmIWlwptKIjEN3UhuM5+CmR27ySWCytJE8lWcC6maJTEJlFEZxFRyC6pbd/O4GMggrRMciGsbSSPP33ae56RTajwMjSSvJXVq3nblkkFEsryc/Bb4Gzm0SnIDKbK3qWVrIMBy4UYPlBThMgMVhaSV7y04A/XhedgsiscvSOoiMQNdh/1qUir7RadAyyQSytJB9GA7D6SaCmXHQSIrPK0rG0kuUoqdLj7TUnRMcgG8TSSvKxby6QsV90CiKzu1TlIDoCUaP8diQT207lio5BNoalleShLBfY9r7oFERCZFRpREcgarRXVx9Dpc4gOgbZEJZWkodNbwG6UtEpiMxOUjugqMZOdAyiRrtUWIlPNp0WHYNsCEsriZd5GEj+QXQKIiGMGjfREYia7Ltd53E8s1h0DLIRLK0k3u//AsB1/8g26e3dRUcgajK9UcLLq1JgNPI1nFoeSyuJlbISyNgnOgWRMNV2rqIjEDXLkUvF+JFbvJIZsLSSOLoKYOMbolMQCVWpZmklyzf7j9Moq9aLjkFWjqWVxNk9Byi5JDoFkVDlSpZWsnxXyqrx5dazomOQlWNpJTGKL9WWViIbV6rQio5AZBILdp1HRkGF6BhkxVhaSYyNrwP6StEpiIQrAksrWQed3oj3158UHYOsGEsrmd/FvcCxn0WnIJKFAqOz6AhEJrPmaBaSLhaIjkFWiqWVzMtoBNa/KDoFkWxcMTiJjkBkUm+vSYUkcQksMj2WVjKv5B+ArCOiUxDJRm4NSytZlyMZRfg1OVN0DLJCLK1kPtWlwOa3RacgkpUsnaPoCEQm9/76k6iqMYiOQVaGpZXMZ/sHQHmu6BREspKpcxAdgcjksoqr8P2+i6JjkJVhaSXzKMoA9n8tOgWR7GRUsrSSdfp6+zlU6jjaSqbD0krmsedzwKATnYJIViSVPfJ0dqJjELWIK2XVWLLvgugYZEVYWqnllV8BDi0WnYJIdowaN9ERiFrUvO3nUKHj9q5kGiyt1PL2zeVGAkTXode4i45A1KLyy3VYvJdzW8k0WFqpZVWVAAe+FZ2CSJZ0dhxpJev3zY5zKK/maCs1H0srtayD3wLVxaJTEMlSldpVdASiFldQrkPCnguiY5AVYGmlllNTBez7SnQKItkqV7qIjkBkFvN3nkMZR1upmVhaqeUcXsJ1WYluopSllWxEUUUNFu46LzoGWTiWVmoZBj2w+zPRKYhkrUjSio5AZDaL9l7gLlnULCyt1DJSVgDF6aJTEMlagdFZdAQis7lSpsPqw5dFxyALxtJKpidJwO5PRacgkr0rRifREYjM6ttd5yFJkugYZKFYWsn0Tq4B8k6KTkEke3k1LK1kW87mlmHbqTzRMchCsbSS6e38WHQCIouQVeMoOgKR2c3feU50BLJQLK1kWmlbgcxDolMQWYTMKgfREYjMbk9aPo5ncv1uajyWVjKtXRxlJWqoSyytZKO+3cnlr6jxWFrJdC4lAed3iE5BZBEkpRpZ1faiYxAJseZoJrKLq0THIAvD0kqms/9r0QmILIakcRcdgUiYGoOEhXs42kqNw9JKplFZCKT+V3QKIouh17iJjkAk1E8HM1Ct52YD1HAsrWQaR5YDer7VQ9RQOjuWVrJthRU12HA8R3QMsiAsrWQaSYtEJyCyKFVqV9ERiIT78SB3TqSGY2ml5ss4AOSlik5BZFHKVSytRHvS8pGeXyE6BlkIllZqPo6yEjVaqUIrOgKRcJIELOdoKzUQSys1T1UJcHyV6BREFqcELK1EALAy6RL0BqPoGGQBWFqpeVJWADV8a4eosQokllYiAMgtrcaWk7miY5AFYGmlZnmiYA/ejRuG4wEdRUchsihXDM6iIxDJxvKDGaIjkAVQiw5Alut88Xnszj4AAPhRA0TE9MUoaDH8XCI8y68ITkckb7k1jqIjEMnG9tN5yCquhL8b/13QjXGklZrst7Tf6v35TFk6Piw7gcF+rvhH3FBsDe8DvZI/FxFdTzZLK1Edg1HCqkOXRccgmWNppSaRJAlrz6297mN6ox5bCk9guiEDQyI7YHaX4UhrFWnmhETyllntIDoCkaz8diRTdASSOZZWapLEnERklt/6BSa/uhAJRUcxyrkK4zoNwE8d70SpA3cCIsqo0oiOQCQrJ7NLcTa3THQMkjGWVmqSv08NaIiUknN4p/wkBrVphRfj7sbe0NtgVPBLkGyPpFAis5qllejv1hzlaCvdGBsDNVqVvgobL25s+vMN1VhXeAyPIxtDo7vgi87DkOEVbMKERPImadwgSQrRMYhkZ83RLNERSMZYWqnRtl3ahrIa07yFk1WZh3nFKRjmCkzuPBj/jR6MSnsnk5ybSK4MGg/REYhk6WxuGVKzSkTHIJliaaVG25K+xeTnlCAhsfgMXqk6g4HBQXgjbhgOB3Yx+XWI5EBn7yo6ApFscYoA3QhLKzWKwWjA7su7W/Qa5foKrCpMwUR1PkZ07IlvO92NHLeAFr0mkTlVqXkzItGNcIoA3QhLKzVKcl4ySnTme+vmQnkm5pQcQ7yXBk92uRMbovpDp+INLGTZylUuoiMQydbF/AqkXCoWHYNkiCu/U6Nsv7RdyHUNkgG7ik5iFwC38Ajc7RSCUTnn0T7zuJA8RM1RpmBpJbqZNUczEdOG70hQfRxppUbZkbFDdAQU60qwrOgoHtSUYkxMXyyJGYpCZy/RsYgarBha0RGIZG1jao7oCCRDLK3UYJdKLyGtOE10jHpOl6Xjg7ITGOTnhn/G3YXt4X1gUKhExyK6qSLJWXQEIlk7l1eO9PwK0TFIZlhaqcFETQ1oCL1Rj02Fx/G0IQNDomLwcZdhONcqQnQsouu6YmBpJbqVLSc52kr1sbRSg+24JH5qQENcqS7AwqIUjHSuxvhOA7Ciwx0oc+ASQyQfuXquRUx0K1tP5YmOQDLDG7GoQSpqKpCYnSg6RqMdLTmHowA+aOOLwa59MarwCnqcPwgFJNHRyIZl1ziKjkAke/vO5aOqxgAHO075olocaaUG2Zu1FzqjTnSMJqsyVGNt4TE8hmzc1aEb5nYehsueQaJjkY3KrHYQHYFI9qr1RuxJuyI6BskISys1iKVMDWiIyxU5+Ko4BXe5KfBo5yH4LXoQt44ls7pUxdJK1BBbT3KKAP2F0wPoliRJws5LO0XHMDkJEg4Un8YBAO+FBCNeG4ZReZfROSNZdDSyYhIUuFRlLzoGkUXYeipXdASSEY600i2dyD+BvErr/mm3rKYcPxemYIK6APd07IUFsXchz9VPdCyyRhpXGCS+9BI1xKXCSpzJKRUdg2SCr5x0S9Y0NaAhzpdfxqelx3GHtyOe6hKPP6JuR42KI2NkGnqNu+gIRBZl+2nrHjShhmNppVuS8/qsLckgGbCzKBXP6S5gUHgU/tNlGE76txcdiyxcjb276AhEFmX/+QLREUgmOKeVbqpUV4oT+SdExxCuSFeMpboULHUA2sX2wyjJGcPSDsC9gi+m1DhVaq4ZTNQYiRcKIEkSFAqF6CgkGEda6aZSrqRA4pqm9ZwsvYhZZScwyN8Dz8bdhR1te3PrWGqwCpZWokYprKjB2dwy0TFIBjjSSjeVkpciOoJs1RhrsLHwODYCaNUuFsMdAjDq0kmE5qWJjkYyVqbQio5AZHEOXChAhK+L6BgkGEda6aaOXjkqOoJFyK3Kx3dFKbhHW4OHOw3ESm4dSzdQDH7jJWqsA5zXSuBIK90CR1ob70hJGo4A+KCNH4a49sOoglx0v5DIrWMJAFAoOYuOQGRxDrK0Elha6SYySjNQWF0oOobFqjRU4bfCFPymAFp36IaRdq0w8mIKAgrTRUcjgQoM3H2NqLEyi6twqbACbTz478eWcXoA3dDRPE4NMJXLFTmYW5yCoe4KTO08BGvaDUKVnaPoWCRAnoEjrURNcfACR1ttHUsr3VDKFU4NMDUJEvYXn8ZL1WcxKDQUb8UNw5HATqJjkRll1fCHFaKm4LxW4vQAuiHOZ21ZpTVlWFmYgpVqICymN0Yq3HDP+UPwLs0RHY1aUFa1g+gIRBbp0MUi0RFIMI600nXpDDqkFqSKjmEzzpVdwielx3GHjzOe7hKPTZH9UKO0Ex2LWkBGFUsrUVOk5ZWhqsYgOgYJxJFWuq6TBSdRY6wRHcPm6CU9thelYjsAj4h2GOYcjFGZ5xCVzV3JrIEEBUsrURPpjRJOZZeiU6C76CgkCEda6bp4E5Z4hbpifF94FPc5luGB2H74ISYexU4eomNRc2i0qDFyK0qipjqRVSI6AgnEkVa6Lm4qIC+ppReRCuBjfy8McOuJUcVF6H3uAFQS3yqzJAaNu+gIRBbteGax6AgkEEsrXRdvwpInnVGHPwqP4w/Ubh074n9bx4Zw61iLUGPnJjoCkUU7nsmRVlvG0krXKKgqwKWyS6Jj0C3kVuVjQVU+FmiBLq0HYZROifi0/XCuLhUdjW6giqWVqFlOZpXCaJSgVHKajS1iaaVrHLtyTHQEaqTDxWdxGMCsoADc4RKOUQW56MatY2WnQuUqOgKRRausMeDclTKEt3IRHYUE4I1YdI20Ir7VbKkq9ZX4b2EKpihycHeH7vi6093I8ggUHYv+p0zJb7REzcUpAraLpZWukVGaIToCmcClimx8WXIMQz1UeKzLHVjbbiCq1VxuSaQSaEVHILJ4J1habRanB9A10kvTRUcgEzJKRuwrOoV9AFzCwjBUG4pRuZcQe+mI6Gg2p1BiaSVqrlM5nLdvq1ha6RoZJRxptValNWVYUZiCFXZA25jeGKVww/BzSfAuyxUdzSYUGJ1FRyCyeBeulIuOQIJwegDVU2OoQXZFtugYZAZpZZcwu/Q47milxTNx8dgcwa1jW1qe3lF0BCKLd6mwEnqDUXQMEoAjrVRPRlkGjBJfDGyJXtJjW2EqtgHwjIzG3Y6BGJ2Zhsick6KjWZ2cGpZWoubSGyVcKqxEiDffubA1HGmlei6Vcn1WW1ZQXYTvi1IwxqkCD8bejmUd41Hs6C46ltXI1DmJjkBkFc7nc4qALeJIK9WTXsKbsKjWidILOAHgowBvDHTrhVFFheh9/gCUHIlvsktVGtERiKzCxSvlQJToFGRuLK1UD1cOoL/TGXXYUHgcGwD4RnfGPfb+GHkpFcFXzomOZnEyWFqJTOJCfoXoCCQASyvVw9JKN5NTeQXzK69gvgsQ1+bPrWP3wam6THQ02ZPsnVFZpRIdg8gqXOD0AJvE0kr1cLkraqhDxWdxCMB/glrjTpdwjMrPRreLSaJjyZZB4y46ApHV4LJXtomlleoYjAZklmeKjkEWplJfiV8LU/CrEgjscBtGqr0x8uJR+BXxpr6r1di7i45AZDX+XPZKreL95LaE/7epTmZ5JvRGvegYZMEyKrLxRckxxHuq8XiXO7Cu3QBuHfs/1XauoiMQWQ29UUJ2SZXoGGRmHGmlOpwaQKZilIzYW3QKe1G7dezd2lCMyklHx8spoqMJU6liaSUypdzSarTx4DJytoSllerwJixqCaU1ZfixMAU/2gPhMX0wSuGC4eeS4FWWJzqaWZUpWVqJTCmvtFp0BDIzTg+gOpfLLouOQFbubFkGPio9gSGtXDA9bii2RPSDXmkbPzuXQCs6ApFVYWm1Pbbx3YIapKCqQHQEshF6SY+thSewFYBnZAcMdwzEqMwziMg5JTpaiymUuOUkkSnlsrTaHJZWqlOiKxEdgWxQQXUhFlcXYrET0KFTf4w2aHBX2n64VhaLjmZSBUaWViJT4kir7WFppTqlulLREcjGHS85j+MAPmzdCoNc+2BUUQF6WsnWsXkG3jBCZEosrbaHpZXqcKSV5KLaUI3fC4/hdwB+0V0wwt4PozOOIzD/guhoTZZbw9JKZEp5ZSyttoallepwpJXkKLsyD/Mr8/CtqwJxgYMxSqfAnWf3wklnWTviZOm4Xi2RKeVxnVabw9JKdUqqOdJK8iVBQlLxGSQB+E9wIO50aYtRV7LRNd0yto69VMXSSmRKV8p0oiOQmbG0EgBAb9SjQl8hOgZRg1ToK7C6MAWrVUBQhx4YqfbCPRePwK9Ivsu2XWRpJTIpncGI0qoauDjYiY5CZsJ1WgkApwaQ5UqvyMLnJccQ72mHJ7rcifVRA6BTaUTHqkeyc0K5XiU6BpHVKa82iI5AZsSRVgLAm7DI8hklI3YXncRuAK7h4bjLOQSjc9LRQQZbxxo17gB/LiQyuXKdXnQEMiOWVgLAkVayLiW6Uvyoq906NiKmL0ZBi+HnEuFZfkVInhp7NyHXJbJ2FRxptSksrQSAN2GR9TpTlo4PAXzi54rb3bphVGkZ+qXtg9povhGaajtXs12LyJZwpNW2sLQSAKCkhqWVrJveqMeWwhPYAsArsgOGO7bB6Mtn0Db3dItfu1LF0krUEipYWm0KSysB4Egr2Zb86kIsqi7EImcgptMAjDLY466z++FS1TJbx5axtBK1CN6IZVtYWgkAb8Qi25VScg4pAD5oU7t17OjCK+hxIdGkW8eWwMVk5yKiv3Ck1bawtBIA3ohFdPXWsf7RXXCPvR9GZhxDYP7FZp+7CM7ND0hE1yjjSKtNYWklAECZrkx0BCLZyKrMw7zKPHzjqkDXwMEYVQ3cmbYXjrqmbcBRYGBpJWoJFdUcabUlLK0EoHaLTCKqT4KExOIzSATwn+AgxLu0xai8THTJONyo81wxOLVMQCIbV1nDkVZbwtJKAAClgpujEd1Mub4CqwpTsEoNhHTsiZEqT4y4kAzf4sxbPjenxtEMCYlsj0HigIstYVMhACytRI1xoTwTc0qOId5LU7t1bGT/m24dm83SStQi2FltC0daCQCgUnBfdKLGMkiGuq1j3cIjcLdTCEblnEf7zOP1jrtU5SAmIJGVMxrZWm0JSysBABQKhegIRBatWFeCZbqjWKYBIuu2jj0Ij/J8ZFTdeBSWiJqOndW2sLQSAI60EpnS6bJ0fADgYz83PKq6HY9e8YckcQoOkamF2vMHQlvC0koAONJK1BLa6LQYsjwD6X1zcSaLy14RmZpHa3fREciM+KM/AQCU/FIgMilvozM++NUFUkYm/PYtBu91JDI9JQdcbApfRgkAVw8gMiUnox0+2xAAnD4PALA7fwyBfqbbFpaIaimULK22hE2FALC0EpmKWlLiq12RUCen1vt4m9TVYgIRWTF+67It/N9NAHgjFpGpfHEoFo67j1zzcYfDm+Hrx39nRKbEkVbbwtJKAHgjFpEpfHQyDp5/JN7w8eDs7WZMQ2T9lCytNoWllQBwpJWouV5Lj0PQLwdueozz9uVw97IzUyIi68eRVtvC0koAONJK1BxP58YiZunBWx6nkCSEVh81QyIi26BSs8bYEv7fJgAcaSVqqvFF0bh90dEGb4LuvnEBnLRcIpvIFByc+W/JlrC0EgCWVqKmuKu8LUYlnAH0+gY/R1FdiTCHSy2Yish2aJw43caWsLQSAMBN4yY6ApFF6VXVBlMWZUGqrGr0c723zIfani+/RM3l4MzSakv4qkkAAA8HD9ERiCxG+xofPLu0HFJxSZOeryzIRphX055LRH9habUtLK0EAPBy8BIdgcgitNG74a0VKkg5ec06j9+eRVwYnaiZNJzTalP4kkkAAE8HT9ERiGTPw+iI2WvcIV1s/pxU9cUTCPIzmCAVke1y0HKk1ZawtBIATg8guhUHSY0vNgVCkZpmsnO2Ob7KZOcisjV2GhVUKtYYW8L/2wQAcFA7wEntJDoGkSypoMBXe9rBLumESc+rObINftzalahJODXA9rC0Uh1OESC6vs+OdIbzjuQWOXdQ1tYWOS+RteNNWLaHpZXqsLQSXWvWmTj4rLv1bldN5bzjJ27tStQELK22h6WV6rC0EtX30uUuCFt5oEWvoZAkhFUdadFrEFkjbixge1haqQ5vxiL6y7QrMejyfZJZruW2cQGcXDg/j6gxuHKA7WFppTocaSWqdX9JFAYnHAOMRrNcT6GrQlv7dLNci8haODjxBz1bw9JKdVhaiYAhFaF4YOF5oKbGrNf13DIfdhq+JBM1FEdabQ9fIakOpweQreteHYBpi/MgVVSY/dqqwlyEeRab/bpEloo3Ytkellaqw61cyZZF6L3wwrJqSIVFwjL47k6AUqkQdn0iS+Lq7SA6ApkZSyvV8XTk9ACyTX4GLf79swZSVo7QHOr0kwjyM++0BCJL5daKG+LYGpZWqsORVrJFbpIDPl3XCjgnjxuhAo6tFB2BSPbsNCo4u2lExyAzY2mlOj5OPtzKlWyKvaTCF1tCoDx2WnSUOg5Hd8Lfjy/NRDfj1spRdAQSgK+MVE+oW6joCERmoZCAuQc6QHPgmOgo1wi6vEV0BCJZc/PhAIstYmmlesLcwkRHIDKLOce6wHXLIdExrst55wp4ePPOaKIbcedIq01iaaV6wtxZWsn6vZsWB781B0XHuKnQisOiIxDJFm/Csk0srVQPpweQtZuZ1RmRPx0QHeOWXDd9B2dX7vhDdD0cabVNLK1UD6cHkDWbkt8Rty2xjBFMpa4aYXYXRccgkiWOtNomllaqJ9AlEGolR3fI+owujcRdi1IBg0F0lAbz2vQNt3Yl+ht7BxWcXO1FxyAB+GpI9aiVagS7BIuOQWRSAyqDMS7hIqTqatFRGkVZfAVhHkWiYxDJCkdZbRdLK12DN2ORNems88P/LSmEVFYuOkqTcGtXovo4n9V2sbTSNXgzFlmLML0HXllugJRfIDpKk6kzTiHITyc6BpFscKTVdrG00jV4MxZZg1YGLWatdoZ0OUt0lGZrfZRbuxL9iSOttoulla7BkVaydFrJHnM2+AJnLoiOYhKaY7vg78cpAkQAR1ptGUsrXSPULRQK8BskWSa1pMTc7eFQHTklOopJBV3aLDoCkXAKpQJerbWiY5AgLK10DUe1I/yd/UXHIGqSuUkxcNh7VHQMk3Pe9TM8vbkcHdk2T39n2GlUomOQICytdF2h7pwiQJbn4xNd4L4xSXSMFhNafkh0BCKhfMNcRUcggVha6brC3cJFRyBqlDcuxqHNrwdFx2hRLpsTuLUr2TS/UJZWW8bSStcV4xMjOgJRg03P6YQOy6y7sAL/29pVfV50DCJhfEPdREcggVha6bo6+3QWHYGoQSYUtUffxUcASRIdxSy8Ns2HvQPn9JHt0Tip4eHHlQNsGUsrXZevsy/8nP1ExyC6qeFl4bhn4WlArxcdxWyUJfkIc88XHYPI7FoFu0Ch4Mo2toyllW6Io60kZ32qAvHIokxIVVWio5hdq10JUKr4zZtsC6cGEEsr3VAnn06iIxBdVwddK8z4vhRSSYnoKEKoL51BsG+16BhEZuXLm7BsntlL64ABAzBjxoybHhMSEoJPP/3ULHnMadKkSRg1apToGA3WuVVn0RGIrhFkcMebKxSQ8q6IjiJUwJEVoiMQmZVfGEdabV2jSuv1StfKlSvh4OCA2bNnmzKXUJIkYciQIYiPj7/msblz58Ld3R2XLl0SkMy8ojyj4KByEB2DqI6X0Qkf/uoKKf2y6CjCaY7vQYA/pwiQbXBr5QgHZzvRMUiwZo20fvvttxg/fjy++uorPPfcc6bKJJxCocDChQuxf/9+zJs3r+7j58+fxwsvvIDPP/8cbdq0EZjQPOyUdujg3UF0DCIAgJPRDp9vbAPFqXOio8hG4MU/REcgMgtODSCgGaX1gw8+wDPPPIPly5dj8uTJAK4/EjtjxgwMGDDghufJzc3FiBEj4OjoiNDQUPzwww/XHFNUVISpU6fCx8cHrq6uGDRoEI4cOVLvmF9//RVxcXFwcHBAWFgY3nrrLeivuqNYoVDgq6++wl133QVHR0eEhYVh5cqVN8wVGBiIOXPmYObMmTh//jwkScKjjz6KO++8E+PGjcOjjz6K0NBQODo6IioqCnPmzKn3fIPBgGeffRbu7u7w8vLCCy+8AOlvS/KsX78effv2rTtm+PDhSEtLq3u8d+/eePHFF+s9Jy8vD3Z2dtixY8cNs5tSd7/uZrkO0c2ooMDc3VFQHzohOoqsOO9ZDS8fbjZA1s+PN2ERmlhaX3zxRbzzzjtYs2YNRo8e3awAkyZNQkZGBrZu3YqVK1di7ty5yM3NrXfM/fffj9zcXPz+++9ISkpCXFwcBg8ejIKCAgDAzp07MXHiRPzjH//AiRMnMG/ePCQkJODf//53vfO89tprGDNmDI4cOYLx48fjoYceQmpq6g2zPfLIIxg8eDCmTJmCL774AseOHcO8efNgNBrRpk0brFixAidOnMDrr7+Ol19+GT/99FPdc2fPno2EhAR899132LVrFwoKCvDLL7/UO395eTmeffZZJCYmYvPmzVAqlRg9ejSMRiMAYPz48Vi+fHm9svvjjz8iICAA/fr1a9pfeCN192VpJfG+ONwJTruSRceQpZAS6922luhPHGklAFBIfx/+u4lJkyZh2bJl0Ol02Lx5MwYNGnTN40VFRVi9enXdx2bMmIHk5GRs27YNQO2NWJ07d8ann36K06dPIyoqCgcOHED37rXl6OTJk4iOjsYnn3yCGTNmYNeuXRg2bBhyc3Oh0WjqzhseHo4XXngBjz/+OIYMGYLBgwfjpZdeqnv8+++/xwsvvIDMzMzaT1ShwBNPPIGvvvqq7piePXsiLi4Oc+fOveHnnJubiw4dOqCgoAA///zzDW+kevrpp5GdnV03ehsQEIB//vOfeP755wEAer0eoaGh6Nq1a72/n6tduXIFPj4+SElJQceOHZGXl4eAgABs2bKlrqT27t0bt99+O2bNmnXDzKZUbahG76W9oTPqzHI9or/74HQcQn4+IDqGbBnV9kgc9jnKim1nrVqyLWo7JR779HYoVVzwyNY1+isgNjYWISEheOONN1BWVtasi6empkKtVqNr1651H2vXrh3c3d3r/nzkyBGUlZXBy8sLWq227tf58+fr3ko/cuQI3n777XqPP/bYY8jKykJFRUXduXr16lXv+r169brpSCsAtGrVCtOmTUN0dHS9wvrll1+ia9eu8PHxgVarxTfffIP09HQAQHFxMbKystCjR4+649VqNbp161bv3GfOnMHYsWMRFhYGV1dXhISEAEDdeXx8fHDnnXfWTZk4f/489u7di/Hjx980sylpVBrE+sSa7XpEV3slowsL6y0o9TqEKTnPl6xXQKQ7CysBABo9Gap169ZYuXIlBg4ciKFDh+L333+Hi4sLAECpVF4zb7OmpqZZAcvKyuDv7183Unu1P8ttWVkZ3nrrLdx7773XHOPg0Py739VqNdTqv/6qli9fjpkzZ2L27Nno1asXXFxc8OGHH2L//v2NOu+IESMQHByM+fPnIyAgAEajER07doRO99eo5vjx4zF9+nR8/vnnWLp0KWJiYhATE9Psz6kxuvt1R2JOolmvSfRUXiw6/cCvu4bw3DQf9n3eg67KIDoKkckFd/QWHYFkokk/ugQHB2P79u3Izs7G0KFDUVpaCqB2ZDArK6vescnJyTc8T7t27aDX65GU9NecrFOnTqGoqKjuz3FxccjOzoZarUZ4eHi9X97e3nXHnDp16prHw8PDoVT+9Snu27ev3vX37duH6OjoRn/+u3fvRu/evfHUU0+hS5cuCA8Pr3cDlZubG/z9/euV2L9/nvn5+Th16hReffVVDB48GNHR0SgsLLzmWiNHjkRVVRXWr1+PpUuXmnWU9U+8GYvM7aHidhiQcBRo+Owlm6YsLUCYm22vW0vWKyTGS3QEkokmj7cHBgZi27ZtyM3NRXx8PEpKSjBo0CAkJiZi8eLFOHPmDN544w0cO3bshueIiorC0KFDMW3aNOzfvx9JSUmYOnUqHB0d644ZMmQIevXqhVGjRuGPP/7AhQsXsGfPHrzyyitITKwdhXn99dexePFivPXWWzh+/DhSU1OxfPlyvPrqq/Wut2LFCnz33Xc4ffo03njjDRw4cABPP/10oz/3iIgIJCYmYsOGDTh9+jRee+01HDx4sN4x//jHPzBr1iysXr0aJ0+exFNPPVWvjHt4eMDLywvffPMNzp49iy1btuDZZ5+95lrOzs4YNWoUXnvtNaSmpmLs2LGNzttcnXw6QaPS3PpAIhOIrwjDmIRzgJ5zNBuj1c7vuLUrWR0PPye4ejve+kCyCc2aJNKmTRts27YNV65cQXx8PHr16oXXXnsNL7zwArp3747S0lJMnDjxpudYuHAhAgIC0L9/f9x77714/PHH0apVq7rHFQoF1q1bh9tvvx2TJ09GZGQkHnroIVy8eBG+vr4AgPj4eKxZswZ//PEHunfvjp49e+KTTz5BcHBwvWu99dZbWL58OWJjY7F48WIsW7YM7du3b/TnPW3aNNx777148MEH0aNHD+Tn5+Opp56qd8xzzz2HCRMm4JFHHqmbQnD1SgtKpRLLly9HUlISOnbsiH/+85/48MMPr3u98ePH48iRI+jXrx+CgoIanbe57FX26Orb9dYHEjXTbdWtMXVRDqSr5qJTw6gzzyGEW7uSlQmJ4dQA+kujVg+wZAqFAr/88otFbaMqJz+e/BHv7n9XdAyyYlE13nh3iRFSTu6tD6br0kX3wC7fmw8UEFmSUc92QetID9ExSCZ4Ox41yKCgQVCAbz1Sy/A3uODdlXYsrM1kn7ofrbm1K1kJjZMa/m25qQD9haWVGsTHyYdLX1GLcDM64JO13pAuZIiOYhUCL2wQHYHIJAKjPbnUFdVjM18NkiRxakAzDQkaIjoCWRmNpMKXW0KgPH5GdBSr4bT3V27tSlaBqwbQ39lMaaXmGxw0WHQEsiIKCZi7rz3sD954hRFqmtDig7c+iEjGFAogqCNLK9XH0koNFugaiEiPSNExyEp8ntIFLtsOi45hlbRbFkPrxtFWslytQlzhqLUXHYNkhqWVGoVTBMgU3kuLQ6u1HA1sKUq9DmGKs6JjEDUZpwbQ9bC0UqMMChokOgJZuBcyOyP8pwOiY1g9z03zoXFUiY5B1CTBXJ+VroOllRolyjMKgS6BomOQhXosPwbdlhwSHcMmKMuKEOaaJzoGUaM5u2vgE+giOgbJEEsrNRpvyKKmGFMahTsTjgNGo+goNsNnxwJu7UoWJ5hTA+gGWFqp0VhaqbEGVYTgoYUXIOl0oqPYFHXWBYT6VomOQdQoUbf5iY5AMsXSSo3WyacTfBx9RMcgC9FF548nl+RDKi8XHcUm+R9aBm5mR5bC1ccR/uHcBYuuj6WVGk2hUPCGLGqQtnpPvLysBlJBoegoNsv+5EG09hedgqhh2vX0g0LBn7Lo+lhaqUk4RYBupZXRGf9Z5QgpM1t0FJsXlPa76AhEt6YAonpwagDdGEsrNUl3v+5w17iLjkEy5WLUYM46PyDtougoBMBx/xp4t+JmAyRvrSPc4ertKDoGyRhLKzWJWqnGiLYjRMcgGbKXVJi7PQyqlFOio9BVQgr3iY5AdFNRPTmPhW6OpZWa7P7I+0VHIJlRSMCXBztAsy9FdBT6G5ctS+Dibic6BtF1qTUqtI3jDb50cyyt1GShbqHo7tdddAySkU9OdIHbZm4eIEcKgx5hOC06BtF1te3iA3sHTmGhm2NppWbhaCv96a3zcQj470HRMegmPDbNh8aJW7uS/LTrxakBdGssrdQsQ4KGwNPBU3QMEuyf2Z0Q/SMLq9wpy4rR1iVXdAyielw8HdA60l10DLIALK3ULHYqO4xsO1J0DBLokcIO6LXkCCBJoqNQA3hvWwCVmutgknxEcW1WaiCWVmq2+yLvg4Jb7tike0ojMDzhFKDXi45CDaTOuYgQn0rRMYjqtOvFtVmpYVhaqdmCXIPQw7+H6BhkZv2qgjBh0SVIVdzb3tL4J3FrV5IH/3A3uPk4iY5BFoKllUyCN2TZlhhdK/xjSTGk0lLRUagJ7E8nog3veyEZaMe1WakRWFrJJAYGDYSXg5foGGQGwXp3vP4TYLySLzoKNUPg2bWiI5CN0zipEd6tlegYZEFYWskk7JR2GB0xWnQMamHeRmd8+KsLpIxM0VGomRwPrIMPt3YlgTre3pprs1KjsLSSyYyJGAOlgl9S1srJaIfP/ggATp8XHYVMJKRgr+gIZKNUaiViBrYRHYMsDBsGmUwblzboFdBLdAxqASoo8NWuSKgPp4qOQiak5dauJEhUD184u2lExyALw9JKJsUbsqzTl0md4Lj7iOgYZGIKowFtpZOiY5CtUQCd7wgSnYIsEEsrmVT/Nv3h58w196zJRyfj4PlHougY1ELcN30LB27tSmYUEuMNDz9n0THIArG0kkmplWpM6ThFdAwykdfS4xD0ywHRMagFKctLEKbNFh2DbEgXjrJSE7G0ksmNiRiDVo5cxsTS/V9uLGKWHhQdg8zAZ9t3UNnx2wG1PN9QVwREuIuOQRaKr1JkcvYqe0zuOFl0DGqGccXR6L/oKCBJoqOQGahy0xHqXS46BtkAjrJSc7C0Uou4L/I+eDt6i45BTXBXeVuMXngG0OtFRyEz8j/4AxTc2pVakJuPI8I6+4iOQRaMpZVahIPaAZM6TBIdgxqpV1UbTFmUBamySnQUMjO7s4fRxp8j69RyOg8JhELJn4yo6VhaqcU8EPUAPB08RcegBmpf44Nnl5ZDKi4RHYUEaXNmjegIZKUcXezQrpe/6Bhk4VhaqcU4qh0xsf1E0TGoAdro3fDWChWknDzRUUggx4Pr4ePL5a/I9Dr2bwO1Pb+2qHlYWqlFjW03Fu4ad9Ex6CY8jI6YvcYd0sVLoqOQDIRc2SM6AlkZtZ0SMQNai45BVoCllVqUk50TJrSfIDoG3YCDpMYXmwKhSE0THYVkQrv1B7h6cGtXMp2OA9rAUWsvOgZZAZZWanHj2o2Dq72r6Bj0NyooMHdvNOySToiOQjKiMBoQZuDXBJmGxkmNrkODRccgK8HSSi1Oa6/Fw9EPi45Bf/PZkc7Qbj8sOgbJkPvGb+HgrBYdg6xA16EhcHDmyD2ZBksrmcX49uPhYuciOgb9z6wzcfBZx92u6PqUlWUIc8oSHYMsnIunA2IHthEdg6wISyuZhau9K8ZGjxUdgwD863IXhK08IDoGyZzP1vlQc2tXaoYeI8O4PTCZFL+ayGwmtp8IZztn0TFs2rQrMYj7Pkl0DLIAqiuXEepTJjoGWSjvQC0ib/MVHYOsDEsrmY2bxg2PxTwmOobNur8kCoMTjgFGo+goZCF89y/h1q7UJL3HhEPBLx4yMZZWMquJ7ScixDVEdAybM6QiFA8sPA/U1IiOQhbEPu0oAv35Qw41TlB7TwS2426IZHosrWRWdio7vND9BdExbEq36gBMW5wHqaJCdBSyQG1O/ld0BLIgCgXQ695w0THISrG0ktn1a9MP/dv0Fx3DJkTUeOHFZdWQCotERyEL5XBoI1pxa1dqoMgefvBuoxUdg6wUSysJ8WL3F2Gv5A4pLcnPoMW/VzlAysoRHYUsXHDeLtERyAKo7JTocU+Y6BhkxVhaSYhA10BM7DBRdAyr5SY54NN1rYBzF0VHISug3bYMbp5cIJ5uLnZgG7h4OoiOQVaMpZWEeSzmMfg6cUkUU7OXVPhiSwiUx06LjkJWQmE0ILTmuOgYJGMOznboeleI6Bhk5VhaSRgnOyc82/VZ0TGsikIC5h7oAM2BY6KjkJVx3/gtHLm1K91At7tDoHHk1we1LJZWEurusLvR1ber6BhWY86xLnDdckh0DLJCyqpyhDldFh2DZMg7UIsYbtdKZsDSSsK9dNtLUCl4d3JzvXMuDn5rDoqOQVbMe8t8qO35bYP+olAAA8a3g1LJjQSo5fHVh4SL8ozCfZH3iY5h0WZmdUbUjwdExyArp8rPQqhXqegYJCMdB7SBb4ir6BhkI1haSRae6fIM3DXuomNYpMkFHXDbksOiY5CN8Nu3mFu7EgDA2V2DniO5xBWZD0sryYKbxg3PdHlGdAyLM7o0EncnnAQMBtFRyEbYnT/GrV0JAHD7g5Gwd+DNV2Q+LK0kG/dF3of2Xu1Fx7AYAyqDMS7hIqTqatFRyMa0SV0tOgIJFtrJG2FdfETHIBvD0kqyoVQo8e8+/+ZOWQ3QWeeH/1tSCKmsXHQUskEOhzfD1483T9oqewcVbn8oUnQMskEsrSQr4R7hmB43XXQMWQvTe+CV5QZI+QWio5ANC87eIToCCdLnvghoPbjzFZkfSyvJzsT2E9Hdr7voGLLUyqDFrNVaSJezREchG+e8nVu72qLAaA+07xsgOgbZKJZWkh2FQoF/9/k3tHZa0VFkRSvZY84GP+DMedFRiKCQJITpUkTHIDOyc1BhwMPtRMcgG8bSSrLkr/XHSz1eEh1DNtSSEl/uCIfqyEnRUYjquG/8Fo5a3j1uK3rfGw5XL0fRMciGsbSSbN3T9h7cEXyH6BiyMDcpBo57joqOQVSPoroSbR0uiY5BZtA6yh0d+lnGtIBt27ZBoVCgqKhIdJQWlZCQAHd3d+HnaKo333wTnTt3btRzWFpJ1l7v+Tq8Hb1FxxDq49Q4uG9MEh2D6Lq4tav1s3NQYdCEaChaaFeJSZMmQaFQ4Iknnrjmsf/7v/+DQqHApEmTWuTa1m779u0YNGgQPD094eTkhIiICDzyyCPQ6XSio2HmzJnYvHlzo57DVxqSNXcHd7zV+y3RMYR542Ic2qzm9qwkX8qCbIR5lYiOQS1o4Ph2cPVu2WkBgYGBWL58OSorK+s+VlVVhaVLlyIoKKhFr22tTpw4gaFDh6Jbt27YsWMHUlJS8Pnnn8Pe3h4GGWxIo9Vq4eXl1ajnsLSS7N3e5nbcF3mf6BhmNz2nEzosOyg6BtEt+e5ZBAW/m1il9v0CENHdt8WvExcXh8DAQKxataruY6tWrUJQUBC6dOlS79jq6mpMnz4drVq1goODA/r27YuDB699rUxKSkK3bt3g5OSE3r1749SpU3WPpaWlYeTIkfD19YVWq0X37t2xadOmes8PCQnBu+++i4kTJ0Kr1SI4OBj//e9/kZeXh5EjR0Kr1SI2NhaJiYl1z/nz7fY1a9YgKioKTk5OuO+++1BRUYFFixYhJCQEHh4emD59er3iWF1djZkzZ6J169ZwdnZGjx49sG3btnp5EhISEBQUBCcnJ4wePRr5+fk3/Tv9448/4Ofnhw8++AAdO3ZE27ZtMXToUMyfPx+OjvV/CNmwYQOio6Oh1WoxdOhQZGX9tULNwYMHcccdd8Db2xtubm7o378/Dh06VO/5CoUC8+bNw/Dhw+Hk5ITo6Gjs3bsXZ8+exYABA+Ds7IzevXsjLS2t7jmcHkBW6/luzyPIxXZ+2n64qD36Lj4CSJLoKES3ZHfxBIL8xI/ckGl5tdGi3wMRZrvelClTsHDhwro/f/fdd5g8efI1x73wwgv4+eefsWjRIhw6dAjh4eGIj49HQUH9tatfeeUVzJ49G4mJiVCr1ZgyZUrdY2VlZbj77ruxefNmHD58GEOHDsWIESOQnp5e7xyffPIJ+vTpg8OHD2PYsGGYMGECJk6ciIcffhiHDh1C27ZtMXHiREhXvVZXVFTgs88+w/Lly7F+/Xps27YNo0ePxrp167Bu3TosWbIE8+bNw8qVK+ue8/TTT2Pv3r1Yvnw5jh49ivvvvx9Dhw7FmTNnAAD79+/Ho48+iqeffhrJyckYOHAg3n333Zv+ffr5+SErKws7dtx8TeWKigp89NFHWLJkCXbs2IH09HTMnDmz7vHS0lI88sgj2LVrF/bt24eIiAjcfffdKC0trXeed955BxMnTkRycjLatWuHcePGYdq0aXjppZeQmJgISZLw9NNP3zTLrSgkid8VyTIk5yZj0vpJMEjW/c1xWFk4Jn17AVJllegoRA1W1Xkg9rjb3jsi1srOQYUHXuoOd1+nFr/WpEmTUFRUhPnz5yMwMLBuRLRdu3bIyMjA1KlT4e7ujoSEBJSXl8PDwwMJCQkYN24cAKCmpgYhISGYMWMGnn/+eWzbtg0DBw7Epk2bMHjwYADAunXrMGzYMFRWVsLB4fobI3Ts2BFPPPFEXbEKCQlBv379sGTJEgBAdnY2/P398dprr+Htt98GAOzbtw+9evVCVlYW/Pz8kJCQgMmTJ+Ps2bNo27YtAOCJJ57AkiVLkJOTA622dinHoUOHIiQkBF9//TXS09MRFhaG9PR0BAT8dbPbkCFDcNttt+G9997DuHHjUFxcjLVr19Y9/tBDD2H9+vU3vOHMYDBg6tSpSEhIgJ+fH3r27InBgwdj4sSJcHV1BYDr5p07dy7efvttZGdnX/e8RqMR7u7uWLp0KYYPHw6gdqT11VdfxTvvvFPv72XBggV1PywsX74ckydPrpsC8uabb2L16tVITk6+7nWuhyOtZDE6t+qMKR2n3PpAC9a7KhCTFmWysJLFcUjeCl8/fkuxFgPHtzNLYb2aj48Phg0bhoSEBCxcuBDDhg2Dt3f9G3HT0tJQU1ODPn361H3Mzs4Ot912G1JTU+sdGxsbW/ff/v7+AIDc3FwAtSOtM2fORHR0NNzd3aHVapGamnrNSOvV5/D1rZ0mERMTc83H/jwvADg5OdUVwD+PCQkJqSusf37sz+ekpKTAYDAgMjISWq227tf27dvr3k5PTU1Fjx496mXr1avXdf4W/6JSqbBw4UJcunQJH3zwAVq3bo333nsPHTp0qPf2/9/z+vv71/t8cnJy8NhjjyEiIgJubm5wdXVFWVlZk/6uqqqqUFLS9DnwXGCPLMqTnZ/Ersu7kFqQeuuDLUwHXSv884dSSM34B00kUkjWduQo+omOQc1krnms1zNlypS6kc4vv/yyWeeys/trx7Y/Vz4wGo0Aau9c37hxIz766COEh4fD0dER99133zV31V/vHDc7798f//OY633sz+eUlZVBpVIhKSkJKpWq3nFXF92mat26NSZMmIAJEybgnXfeQWRkJL7++mu89dZbN8x79ZvwjzzyCPLz8zFnzhwEBwdDo9GgV69eJvm7aiz+WEwWxU5ph48HfAx3jbvoKCYVZHDHmysUkHKviI5C1GROO36Euxe3drVk5p7H+ndDhw6FTqdDTU0N4uPjr3m8bdu2sLe3x+7du+s+VlNTg4MHD6J9+/YNvs7u3bsxadIkjB49GjExMfDz88OFCxdM8Sk0WpcuXWAwGJCbm4vw8PB6v/z8/AAA0dHR2L9/f73n7du3r9HX8vDwgL+/P8rLyxv8nN27d2P69Om4++670aFDB2g0Gly5IuZ7FUdayeK0cWmDD/t/iCc2PmEV81u9jE748FdXSOnnREchahaFJCG06ggOo+HlgeTDzkGFoY91hNpOdeuDW4hKpap7m//vo44A4OzsjCeffBLPP/88PD09ERQUhA8++AAVFRV49NFHG3ydiIgIrFq1CiNGjIBCocBrr73WrBHA5oiMjMT48eMxceJEzJ49G126dEFeXh42b96M2NhYDBs2DNOnT0efPn3w0UcfYeTIkdiwYQPWr19/0/POmzcPycnJGD16NNq2bYuqqiosXrwYx48fx+eff97gfBEREViyZAm6deuGkpISPP/889esPmAuHGkli9TTvyee6/ac6BjN5iCp8fnGNlCcYmEl6+C+cQGcXDgeYolEzGO9HldX17obha5n1qxZGDNmDCZMmIC4uDicPXsWGzZsgIeHR4Ov8fHHH8PDwwO9e/fGiBEjEB8fj7i4OFPEb5KFCxdi4sSJeO655xAVFYVRo0bh4MGDdWvU9uzZE/Pnz8ecOXPQqVMn/PHHH3j11Vdves7bbrsNZWVleOKJJ9ChQwf0798f+/btw+rVq9G/f/8GZ1uwYAEKCwsRFxeHCRMm1C03JgJXDyCL9uquV/Fr2q+iYzSJCgp8u6sDnHcmi45CZFK5Y17BsXzL2PKTarXvF4CB49uJjkF0UxxpJYv2eq/XEeMdc+sDZeiLw51YWMkqeW2ZDzsNv71YCtHzWIkaiq8qZNHsVfb4ZMAn8Hb0vvXBMvLB6Th4rU+89YFEFkhVmItQj2LRMagB7DTi57ESNRRLK1k8X2dffDLgE9gpLeOu5ZczuiDk5wOiYxC1KL89CVAqFaJj0E0olArc+WgHWcxjJWoIllayCp1bdcYrPV4RHeOWnsqLReelSaJjELU4dfpJBPrViI5BN9HvgQiExFrWu1Rk21hayWqMiRyDh6IeEh3jhh4qbocBCUcBQcuqEJlbm2M/i45AN9BpSCBiBrQRHYOoUVhayaq8eNuL6ObbTXSMa8RXhGFMwjlArxcdhchsNEd3wI9bu8pO2y4+6DMmXHQMokbjqwlZFbVSjdkDZsPf2V90lDq3VbfG1MW5kCoqREchMrugzK2iI9BVfENdMWRy+7otNYksCUsrWR1PB0/MGTgHjmoxO3ZcLarGG88vrYRUWCQ6CpEQ2h0/wcOLmw3IgauPI4Y9FQu1PVcKIMvE0kpWKdorGu/1fQ8qhbgXZ3+DC9792R5Sdq6wDERyEFqRLDqCzdM4qzHi6U5wdLEXHYWoyVhayWoNCR6CN3q9AQXM/zaYm+SAT9Z6QzqfbvZrE8mN2+aF3NpVIJVaibufiOXSVmTxWFrJqo2OGI3nuz9v1mtqJBW+3BwC5fEzZr0ukVwpdFVoa39RdAzbpAAGPdIOARHuopMQNRtLK1m9Ce0n4KlOT5nlWgoJmLuvPewPHjPL9Ygshdemb7i1qwA9R4Yhsruf6BhEJsFXELIJT3Z+EhPaT2jx63ye0gUu2w63+HWILI2y+ArCPIpEx7Ap7fsGoOvQENExiEyGpZVsxgvdX8C9Efe22PnfS4tDq7UHW+z8RJauFbd2NZvgjl7oPzZSdAwik2JpJZvyRq83cGfwnSY/7/OZnRH+0wGTn5fImtiln0IQt3ZtcUHtPTF0WkcoVfwWT9aFX9FkU5QKJWbdPgt9W/c12Tmn5ndE9+85JYCoIVqnrBQdwaoFdfDE3U/GQm3HtVjJ+rC0ks2xU9rhkwGfoKtv12af697SSMQnnAAMBhMkI7J+mpSd8Pfnt56WENTBE3c/EQuVHf9+yTrxK5tskoPaAV8M+gLtvdo3+RwDK0MwNuEiJJ3OhMmIrF9QxmbREaxOUAcvFlayevzqJpultddi3pB5aOvWttHP7aLzx1OL8yGVlbdAMiLr5rxrJTy9udmAqdQW1hgWVrJ6/Aonm+bu4I5v7vwGrbWtG/yctnpPvLysBlJBYQsmI7JuoeWcB24KwR1ZWMl28KucbF4rp1ZYGL8Qwa7Btz7W6Iz/rHKElJlthmRE1stl80I4u3K0tTmCY7xw1zQWVrId/EonAuCv9UfC0AREeUTd8BgXowZz1vkBadyOkqi5lLpqhKkviI5hsYJjvHDX4yysZFv41U70P96O3vhu6Hfo5NPpmsfsJRXmbg+DKuWUgGRE1slr0zewd+DSTI3FEVayVfyKJ7qKq70rvrnjG/T071nv418mdoRmX4qgVETWSVmSjzD3AtExLEpdYVXz2zfZHn7VE/2Nk50Tvhz8JQYHDQYAfHKiC9w2JQlORWSdWu1aCKWKW7s2RFhnHxZWsmkKSZIk0SGI5MhgNGDXz5+h1WvfiI5CZNXOj/sU5zPtRMeQtU6DA9FnTDgUShZ8sl38cY3oBlRKFfrf/094TZsmOgqRVQs48pPoCLKlUCpw+0OR6Ht/BAsr2TyWVqJbaPXPGfB9/TVAyX8uRC1Bc3wPAvxZyP5OrVHh7idiEDOgjegoRLLA78JEDeA5bhxaz/kUCo1GdBQiqxSUvlF0BFlxcrPHvc/FISTWW3QUItlgaSVqINc77kDQwu+gdHMTHYXI6jjt/gWePtxsAAA8A5xx34vd4BPkIjoKkaywtBI1glNcHEKW/gB1gL/oKERWJ7SUq3QERntgzPNd4eLpIDoKkeywtBI1kqZtW4SuXAmn224THYXIqrhsSrDprV2j+/hj+NOdYO9ou38HRDfD0krUBGpPTwQt/A6ekyeLjkJkNZR6HdqqzomOYX4KoMfIMAyaEA2lit+WiW6E67QSNVPJ778j85VXIVVUiI5CZPGMLp7Y0+c96KoMoqOYhUqtxOBHohHR3Vd0FCLZ4490RM3ketddCP1xOeyDg0VHIbJ4ytIChLnni45hFk6u9rhnRmcWVqIGYmklMgFNRARCVq6AdtAg0VGILF6rHQusfmvXNu088OCrtyEg3F10FCKLwdJKZCIqFxe0+fILeE9/hhsREDWDOvMcQnyrRcdoEQoFcNuIUNwzvTOcXO1FxyGyKJzTStQCynbswOXnX4CxuFh0FCKLpIvuiV2+E0THMCknN3vcOaUDWkd5iI5CZJFYWolaiC4jA5eemY7qkydFRyGySKfGzsXlLOv4FhUY7YEhkztwdJWoGfgeJlELsQ8MRMjyZXAdMUJ0FCKLFHjxD9ERmk2hVKDHPWEY8QynAxA1F0daicyg4PsfkPvBB5B0OtFRiCzKkfvnIT9PLzpGkzi72ePOqR0QEMHpAESmwJFWIjPwfHg8Qn9ZBYeYGNFRiCxKaMlB0RGaJKi9Z+3qACysRCbDkVYiM5L0euTPn4+8uV8BNTWi4xDJnlFtj8Rhn6Os2DJGW2unA4QiLj4YCoV1L9tFZG4caSUyI4VaDe8nn0Toip+gaddOdBwi2VPqdWirTBMdo0G0nhqMerYLug4NYWElagEcaSUSRKqpQd7cucif/y2gt4xRJCIRjFp37Ok3C7pKeW7tqlAAHfu3Qc9RYbB3UIuOQ2S1WFqJBKtMOYbMl/4F3VnLGE0iEiHrwXeRmiO/+aEe/s4YNKEd/MLcREchsnosrUQyYNTpkDdnDgoWJgBGo+g4RLJj8A/FzvbPw2iQx7cspVqBrvHB6HpXCFRqzrQjMgeWViIZqTh8GFn/egm6ixdFRyGSnQvjPsa5TI3oGPALc8XAh6PhGeAsOgqRTWFpJZIZY1UVcj/+GIVLvgf4z5Oojq5dd+zynwQI+mdhp1Gh56i2iOnfGgolb7QiMjeWViKZKj9wADnvvIPqM2dFRyGSjVNjv8TlLPNfNzjGC/3HRsHF08H8FyciACytRLImGQwoXL4cVz7/AoaiItFxiISr6DkC+xyGmu16ji526PtABCK7+5ntmkR0fSytRBbAUFyMvC++ROGyZVwei2ze0Qfm4Upuy/87iOrph773RcBBa9fi1yKiW2NpJbIg1WlpyJn1Psp37hQdhUiYkjumILGma4ud3zfUFb3HhCMg3L3FrkFEjcfSSmSByrZvR86s96E7f150FCKzk1RqJI6Yi9Ii026F7NbKEb1GtUXbuFYmPS8RmQZLK5GFkmpqUPDDD7gy9ysYS0pExyEyq/yRM3GkONQk53J0sUP3YaHo0C8AShXXXCWSK5ZWIgunLyxE3pw5KFqxEjDIc5tLIlMzat2w9/b3UV3R9K95tUaFzkMC0eWOIG6/SmQBWFqJrETVqdPI+c9/ULFvn+goRGaR9eDbSM3xavTzFEoFovv447bhoXB2E79ZARE1DEsrkZUp3boVV776GlVHj4qOQtSi9P4h2N3hBRj0Df82FtrJG71Gt4WHH3ezIrI0LK1EVqps927kfz0PFQcPio5C1GIujpuNtMxbL/jvF+aK3veGw58rAhBZLJZWIitXcegQrnz9Ncp3cJkssj66yG7Y1XryDbd29QlyQde7gtG2C1cEILJ0LK1ENqLy+HHkfz0PpZs2AfxnT1bk9LgvcSmz/sdaR7ojbmgwgto3fs4rEckTSyuRjak+cwZXvpmPknXruNoAWYWK2+7GPqdhgAIIjfVG3NBg+IW6iY5FRCbG0kpko3Tp6cifPx/Fq3+FVGPaRdqJzElhZ4fCl5YgamA4PP15gxWRtWJpJbJxNdnZyF/wHYpWrIBUVSU6DlGDqTw84DH2IXiMHQu1j4/oOETUwlhaiQhA7SYFxat/RdGKFdCdOyc6DtENaSLC4TFxItzuuQdKDddZJbIVLK1EdI2KpCQU/bQCJRs2cPSV5EGhgHPfvvB85BFo+/YRnYaIBGBpJaIbMpSUoPi331C0YiWqT54UHYdskH1wMNxGjYTbPffArnVr0XGISCCWViJqkMqUlNrR17VrYayoEB2HrJjS1RWud90Ft1Ej4dSli+g4RCQTLK1E1CjG8nIUr12LohUrUZWSIjoOWQu1Gto+feA2ehS0gwZBaW8vOhERyQxLKxE1WdXJk3Wjr4biYtFxyAJp2rWrfft/+HCovb1FxyEiGWNpJaJmk/R6VCQmoXTTJpRu2Qx9ZpboSCRjKh9vuA0bDrfRo+AQFSU6DhFZCJZWIjK5yuPHUbZ5M0o3bUb16dOi45AMqP39oe3XDy5DBsO5Tx8oVCrRkYjIwrC0ElGL0mVkoHTTZpRu3oTKQ4cBo1F0JDIHtRpOXbpA2/92ON9+OxwiI0UnIiILx9JKRGajLyhA2datKN20GeV79kCqrhYdiUxI7eMD5379oL39djj36Q2Vi4voSERkRVhaiUgIY0UFynbuQvmunahIOsRduCyRSgXH2Fhob+9XO5ravj0UCoXoVERkpVhaiUgW9IWFqDx0CBVJh1CZlITKEyeAmhrRsehv7AIC4NitK7S394e2bx+o3N1FRyIiG8HSSkSyZKyqQuXRo6hMSqotssnJMJaViY5lU1RubnCIiYFjbMz/fo+F2stLdCwislEsrURkESSjEdWnTtUW2EO1RVafkyM6ltVQaDRwiI6GQ2wMHGNi4RgbA/vgYNGxiIjqsLQSkcWqycpC1alTqD5zBrqzZ1F15gx0aed4g9etKJXQtA2Dw//KqWNsLDSRkVCo1aKTERHdEEsrEVkVyWCALj0d1WfPQpeWBt35C9BdvAjdxYswFBaKjmc2CgcH2LVpDfvAINgHBcKu7vdA2LduDQW3SSUiC8PSSkQ2w1BcXFdgdRcuoubyZegLC2DIL4ChoAD6wkJIlZWiYzaYyt0ddkFBsA8MhF1QYL2Cqm7lwzv5iciqsLQSEV3FWFEBfUEhDAX50BcUwFD334W1xbYg/38fK4CxqgowGCBJUu3vRiNgNNb+bjA0aCMFhZMTVC4uULm6QOniCpWLC5Suf/7uApWLa93vdce4ukDl5QWVVmuGvxEiInlgaSUiakF/Fti6YmswApIRkCQonZw4j5SIqIFYWomIiIhI9pSiAxARERER3QpLKxERERHJHksrEREREckeSysRERERyR5LKxERERHJHksrEREREckeSysRERERyR5LKxERERHJHksrEREREckeSysRERERyR5LK5ENuHDhAhQKBZKTkwEA27Ztg0KhQFFRUYPPMWDAAMyYMaNZOd5880107ty5WecgIiLbxNJKZOEmTZoEhUJR98vLywtDhw7F0aNH644JDAxEVlYWOnbs2GI5EhIS4O7uXu9jqampCAwMxP333w+dTtdi1yYiIuvH0kpkBYYOHYqsrCxkZWVh8+bNUKvVGD58eN3jKpUKfn5+UKvVZst08OBB9OvXD0OHDsWPP/4Ie3t7s12biIisD0srkRXQaDTw8/ODn58fOnfujH/961/IyMhAXl4egGunB/xdfn4+xo4di9atW8PJyQkxMTFYtmxZk/Ns2bIFgwYNwqOPPor58+dDqaz/UrNkyRKEhITAzc0NDz30EEpLS+seW79+Pfr27Qt3d3d4eXlh+PDhSEtLq3v8z89l1apVGDhwIJycnNCpUyfs3bu3yXmJiEj+WFqJrExZWRm+//57hIeHw8vLq0HPqaqqQteuXbF27VocO3YMjz/+OCZMmIADBw40+vq//PILhg0bhldffRXvv//+NY+npaVh9erVWLNmDdasWYPt27dj1qxZdY+Xl5fj2WefRWJiIjZv3gylUonRo0fDaDTWO88rr7yCmTNnIjk5GZGRkRg7diz0en2j8xIRkWUw33uFRNRi1qxZA61WC6C29Pn7+2PNmjXXjHDeSOvWrTFz5sy6Pz/zzDPYsGEDfvrpJ9x2220NzlFWVob7778fL7/8Ml588cXrHmM0GpGQkAAXFxcAwIQJE7B582b8+9//BgCMGTOm3vHfffcdfHx8cOLEiXpzcmfOnIlhw4YBAN566y106NABZ8+eRbt27Rqcl4iILAdHWomswMCBA5GcnIzk5GQcOHAA8fHxuOuuu3Dx4sUGPd9gMOCdd95BTEwMPD09odVqsWHDBqSnpzcqh6OjI+644w7Mnz8fqamp1z0mJCSkrrACgL+/P3Jzc+v+fObMGYwdOxZhYWFwdXVFSEgIAFyTJTY2tt45ANQ7DxERWReWViIr4OzsjPDwcISHh6N79+749ttvUV5ejvnz5zfo+R9++CHmzJmDF198EVu3bkVycjLi4+Mbfce/SqXC6tWrERcXh4EDB163uNrZ2dX7s0KhqPfW/4gRI1BQUID58+dj//792L9/PwBck+Xq8ygUCgC4ZgoBERFZD5ZWIiukUCigVCpRWVnZoON3796NkSNH4uGHH0anTp0QFhaG06dPN+naGo0Gq1atQvfu3TFw4ECcOHGiwc/Nz8/HqVOn8Oqrr2Lw4MGIjo5GYWFhk3IQEZF1YWklsgLV1dXIzs5GdnY2UlNT8cwzz6CsrAwjRoxo0PMjIiKwceNG7NmzB6mpqZg2bRpycnKanEej0eDnn39Gjx49MHDgQBw/frxBz/Pw8ICXlxe++eYbnD17Flu2bMGzzz7b5BxERGQ9WFqJrMD69evh7+8Pf39/9OjRAwcPHsSKFSswYMCABj3/1VdfRVxcHOLj4zFgwAD4+flh1KhRzcpkb2+PlStXonfv3hg4cCCOHTt2y+colUosX74cSUlJ6NixI/75z3/iww8/bFYOIiKyDgpJkiTRIYiIiIiIboYjrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQkeyytRERERCR7LK1EREREJHssrUREREQke/8Pz0B6u2NsN7YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Player = ['A Zampa', 'S Lamichhane ', 'Kuldeep Yadav ',\n",
    "        'Bilal Khan', 'Mohammed Shami']\n",
    " \n",
    "Wkts = [122,104,97,95,93]\n",
    " \n",
    "# Creating plot\n",
    "fig = plt.figure(figsize =(8, 7))\n",
    "plt.pie(Wkts, labels = Player)\n",
    " \n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c301b7d-0ea1-4838-87ec-c40f786b56b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}