3. Most Runs  Average Strikerate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>batsman</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>out</th>\n",
       "      <th>numberofballs</th>\n",
       "      <th>average</th>\n",
       "      <th>strikerate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>5426</td>\n",
       "      <td>152</td>\n",
       "      <td>4111</td>\n",
       "      <td>35.697368</td>\n",
       "      <td>131.987351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>5386</td>\n",
       "      <td>160</td>\n",
       "      <td>3916</td>\n",
       "      <td>33.662500</td>\n",
       "      <td>137.538304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>4902</td>\n",
       "      <td>161</td>\n",
       "      <td>3742</td>\n",
       "      <td>30.447205</td>\n",
       "      <td>130.999466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>4717</td>\n",
       "      <td>114</td>\n",
       "      <td>3292</td>\n",
       "      <td>41.377193</td>\n",
       "      <td>143.286756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>4601</td>\n",
       "      <td>137</td>\n",
       "      <td>3665</td>\n",
       "      <td>33.583942</td>\n",
       "      <td>125.538881</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>ND Doshi</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>J Denly</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>S Ladda</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>V Pratap Singh</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>S Kaushik</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  total_runs  out  numberofballs    average  strikerate\n",
       "0           V Kohli        5426  152           4111  35.697368  131.987351\n",
       "1          SK Raina        5386  160           3916  33.662500  137.538304\n",
       "2         RG Sharma        4902  161           3742  30.447205  130.999466\n",
       "3         DA Warner        4717  114           3292  41.377193  143.286756\n",
       "4          S Dhawan        4601  137           3665  33.583942  125.538881\n",
       "..              ...         ...  ...            ...        ...         ...\n",
       "511        ND Doshi           0    1             13   0.000000    0.000000\n",
       "512         J Denly           0    1              1   0.000000    0.000000\n",
       "513         S Ladda           0    2              9   0.000000    0.000000\n",
       "514  V Pratap Singh           0    1              1   0.000000    0.000000\n",
       "515       S Kaushik           0    1              1   0.000000    0.000000\n",
       "\n",
       "[516 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StrikeRare=pd.read_csv(\"C:\\\\Users\\\\Ashhh Choudhary\\\\Downloads\\\\archive (1)\\\\most_runs_average_strikerate.csv\")\n",
    "StrikeRare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\",None)\n",
    "pd.set_option(\"display.max_columns\",None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashhh Choudhary\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='average', ylabel='Density'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(StrikeRare[\"average\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashhh Choudhary\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='total_runs', ylabel='Density'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(StrikeRare[\"total_runs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>batsman</th>\n",
       "      <th>strikerate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>131.987351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>137.538304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>130.999466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>143.286756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>125.538881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>ND Doshi</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>J Denly</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>S Ladda</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>V Pratap Singh</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>S Kaushik</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>516 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  strikerate\n",
       "0           V Kohli  131.987351\n",
       "1          SK Raina  137.538304\n",
       "2         RG Sharma  130.999466\n",
       "3         DA Warner  143.286756\n",
       "4          S Dhawan  125.538881\n",
       "..              ...         ...\n",
       "511        ND Doshi    0.000000\n",
       "512         J Denly    0.000000\n",
       "513         S Ladda    0.000000\n",
       "514  V Pratap Singh    0.000000\n",
       "515       S Kaushik    0.000000\n",
       "\n",
       "[516 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_strikeratplayer=StrikeRare.drop(columns=[\"total_runs\",\"out\",\"numberofballs\",\"average\"])\n",
    "top_strikeratplayer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Top ten most strike rate Player\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 21.,  24.,  56.,  84., 159., 127.,  35.,   4.,   4.,   2.]),\n",
       " array([  0.,  25.,  50.,  75., 100., 125., 150., 175., 200., 225., 250.]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADLZUtFXVd1bVh6vqd6vq4ar6i1X18qq6r6q+tHi8eLcmCwAAsG6WvdL2C0k+3t1/LslfSPJwkmNJ7u/ua5Lcv3gNAADADuw42qrqzyT5gSTvT5Lu/kZ3/88kNya5Y7HbHUnevtwUAQAA1tcyV9q+O8nZJP+6qj5fVb9cVS9Ncll3P5Eki8dLd2GeAAAAa2mZaDuQ5PVJfqm7X5fkf+c53ApZVUer6kRVnTh79uwS0wAAANi/lom200lOd/enFq8/nM2Ie7KqLk+SxeOZ7d7c3bd196HuPrSxsbHENAAAAPavHUdbd/9+kseq6tWLoTcneSjJPUmOLMaOJPnYUjMEAABYYweWfP97k3yoql6U5MtJ/lY2Q/Duqnp3kkeTvGPJYwAAAKytpaKtu7+Q5NA2m968zOcCAACwadnf0wYAAMAKiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAZb6pdrA8+fg8fuXfkxTh2/YeXHAADguXGlDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGBLR1tVXVRVn6+qf794/fKquq+qvrR4vHj5aQIAAKyn3bjS9mNJHt7y+liS+7v7miT3L14DAACwA0tFW1VdmeSGJL+8ZfjGJHcsnt+R5O3LHAMAAGCdLXul7Z8n+Ykk39oydll3P5Eki8dLlzwGAADA2tpxtFXVjyQ5092f3eH7j1bViao6cfbs2Z1OAwAAYF9b5krbG5O8rapOJbkryQ9W1a8kebKqLk+SxeOZ7d7c3bd196HuPrSxsbHENAAAAPavHUdbd9/S3Vd298Ekh5P8x+5+Z5J7khxZ7HYkyceWniUAAMCaWsXvaTue5C1V9aUkb1m8BgAAYAcO7MaHdPdvJvnNxfP/nuTNu/G5AAAA624VV9oAAADYJaINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADDYgb2eAADATh08du/Kj3Hq+A0rPwbAM3GlDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAY7MBeT4D97+Cxe1d+jFPHb1j5MQAAYC+40gYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYLAd/3LtqroqyQeTfFeSbyW5rbt/oapenuTfJjmY5FSSH+3u/7H8VJ9/fik0AACw15a50vZ0kn/U3X8+yRuS3FxV1yY5luT+7r4myf2L1wAAAOzAjqOtu5/o7s8tnv9hkoeTXJHkxiR3LHa7I8nbl5wjAADA2tqVn2mrqoNJXpfkU0ku6+4nks2wS3LpbhwDAABgHS0dbVX1siS/muTHu/sPnsP7jlbViao6cfbs2WWnAQAAsC8tFW1V9R3ZDLYPdfdHFsNPVtXli+2XJzmz3Xu7+7buPtTdhzY2NpaZBgAAwL6142irqkry/iQPd/fPb9l0T5Iji+dHknxs59MDAABYbzv+yv8kb0zyriRfrKovLMZ+KsnxJHdX1buTPJrkHUvNEAAAYI3tONq6+78kqfNsfvNOPxcAAIA/tivfHgkAAMBqiDYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMdmCvJwAAwN46eOzelR/j1PEbVn6MvbbqP8d1+DNke660AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYL49EgAAGMO3cP7/XGkDAAAYTLQBAAAMtrJoq6q3VtUjVXWyqo6t6jgAAAD72UqiraouSvIvk/xwkmuT3FRV167iWAAAAPvZqq60XZfkZHd/ubu/keSuJDeu6FgAAAD71qqi7Yokj215fXoxBgAAwHNQ3b37H1r1jiTXd/ffWbx+V5Lruvu9W/Y5muTo4uWrkzzyLD76kiRf2+Xpwk5Yi0xiPTKJ9cgU1iKTPJv1+Ge7e2O7Dav6PW2nk1y15fWVSR7fukN335bktufyoVV1orsPLT89WI61yCTWI5NYj0xhLTLJsutxVbdHfibJNVV1dVW9KMnhJPes6FgAAAD71kqutHX301X1niS/nuSiJLd394OrOBYAAMB+tqrbI9Pdv5bk13b5Y5/T7ZSwQtYik1iPTGI9MoW1yCRLrceVfBEJAAAAu2NVP9MGAADALnhBRFtVvbWqHqmqk1V1bK/nw/qpqlNV9cWq+kJVnViMvbyq7quqLy0eL97rebI/VdXtVXWmqh7YMnbe9VdVtyzOl49U1fV7M2v2o/OsxZ+pqq8uzo9fqKq/tmWbtchKVNVVVfWfqurhqnqwqn5sMe7cyPPuGdbjrp0fx98eWVUXJfm9JG/J5q8S+EySm7r7oT2dGGulqk4lOdTdX9sy9rNJvt7dxxf/mXBxd//kXs2R/auqfiDJHyX5YHe/djG27fqrqmuT3JnkuiSvSPIbSV7V3d/co+mzj5xnLf5Mkj/q7p87Z19rkZWpqsuTXN7dn6uqP53ks0nenuRvxrmR59kzrMcfzS6dH18IV9quS3Kyu7/c3d9IcleSG/d4TpBsrsM7Fs/vyOZfTth13f3JJF8/Z/h86+/GJHd191Pd/ZUkJ7N5HoWlnWctno+1yMp09xPd/bnF8z9M8nCSK+LcyB54hvV4Ps95Pb4Qou2KJI9teX06z/yHAKvQST5RVZ+tqqOLscu6+4lk8y9rkkv3bHaso/OtP+dM9sJ7qup3FrdPfvt2NGuR50VVHUzyuiSfinMje+yc9Zjs0vnxhRBttc3Y7Hs62Y/e2N2vT/LDSW5e3CIEEzln8nz7pSTfk+T7kjyR5J8uxq1FVq6qXpbkV5P8eHf/wTPtus2Y9ciu2mY97tr58YUQbaeTXLXl9ZVJHt+jubCmuvvxxeOZJB/N5iXsJxf3MH/7XuYzezdD1tD51p9zJs+r7n6yu7/Z3d9K8q/yx7f4WIusVFV9Rzb/gfyh7v7IYti5kT2x3XrczfPjCyHaPpPkmqq6uqpelORwknv2eE6skap66eKHSlNVL03yQ0keyOY6PLLY7UiSj+3NDFlT51t/9yQ5XFUvrqqrk1yT5NN7MD/WxLf/gbzw17N5fkysRVaoqirJ+5M83N0/v2WTcyPPu/Otx908Px7Y3Snvvu5+uqrek+TXk1yU5PbufnCPp8V6uSzJRzf/PuZAkn/T3R+vqs8kubuq3p3k0STv2MM5so9V1Z1J3pTkkqo6neR9SY5nm/XX3Q9W1d1JHkrydJKbfTsau+U8a/FNVfV92by151SSv5tYi6zcG5O8K8kXq+oLi7GfinMje+N86/Gm3To/jv/KfwAAgHX2Qrg9EgAAYG2JNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABvt/5Hs2sXPJ3mwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.hist(StrikeRare[\"strikerate\"],bins=10,rwidth=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>batsman</th>\n",
       "      <th>strikerate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>B Stanlake</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>KMDN Kulasekara</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>Umar Gul</td>\n",
       "      <td>205.263158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>S Sharma</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>RS Sodhi</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>VRV Singh</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>AD Russell</td>\n",
       "      <td>188.164894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>LJ Wright</td>\n",
       "      <td>176.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>Shahid Afridi</td>\n",
       "      <td>176.086957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>I Malhotra</td>\n",
       "      <td>175.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             batsman  strikerate\n",
       "443       B Stanlake  250.000000\n",
       "449  KMDN Kulasekara  250.000000\n",
       "301         Umar Gul  205.263158\n",
       "441         S Sharma  200.000000\n",
       "461         RS Sodhi  200.000000\n",
       "462        VRV Singh  200.000000\n",
       "49        AD Russell  188.164894\n",
       "225        LJ Wright  176.666667\n",
       "250    Shahid Afridi  176.086957\n",
       "433       I Malhotra  175.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tenlargeststrk=top_strikeratplayer.nlargest(10,[\"strikerate\"])\n",
    "tenlargeststrk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "toptenBatsman=list(tenlargeststrk[\"batsman\"])\n",
    "toptenstrk=list(tenlargeststrk[\"strikerate\"])\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.title(\"Ten Most strike Rate Player\",fontsize=25,color=\"r\")\n",
    "plt.bar(toptenBatsman,toptenstrk,width=.3,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashhh Choudhary\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='out', ylabel='Density'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.distplot(StrikeRare[\"out\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# # Top ten player with maximum Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([413.,  40.,  19.,  16.,   7.,   7.,   3.,   4.,   4.,   3.]),\n",
       " array([1.000e+00, 4.120e+02, 8.230e+02, 1.234e+03, 1.645e+03, 2.056e+03,\n",
       "        2.467e+03, 2.878e+03, 3.289e+03, 3.700e+03, 4.111e+03]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAAI/CAYAAADkwzGCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAeDUlEQVR4nO3dX4yl9X3f8c83QIgVuwoWAyIs6VJrUxUiBacr5Aopcu0oEDsK9gXRWorLhSusCleOmiqC3MS5QKJVnKSVaks4trLNP7pSYhnZzh9CYkWRIuPFwTYLpt4GatYgdpM0in1DBP72Yh7kEZndnd2Z2fnuzOsljc5zfud5zvnN/PSA3pxzHqq7AwAAwEzftdMTAAAA4PREGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADHbpTk8gSa688srev3//Tk8DAABgRzz22GN/090r6z02Itr279+fo0eP7vQ0AAAAdkRV/d/TPebjkQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAa7dKcnMNn+ez6z7a/x7P3v3PbXAAAALl7eaQMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABttwtFXVJVX1V1X16eX+G6vq4ar62nJ7xZp9762q41X1dFXduh0TBwAA2AvO5Z22DyZ5as39e5I80t0Hkjyy3E9V3ZDkUJIbk9yW5CNVdcnWTBcAAGBv2VC0VdW+JO9M8utrhm9PcnjZPpzkXWvGH+zul7r7mSTHk9y8JbMFAADYYzb6TtuvJfn5JN9eM3Z1d7+QJMvtVcv4tUmeW7PfiWUMAACAc3TWaKuqn0xysrsf2+Bz1jpjvc7z3lVVR6vq6KlTpzb41AAAAHvLRt5puyXJT1XVs0keTPK2qvqtJC9W1TVJstyeXPY/keS6NcfvS/L8a5+0ux/o7oPdfXBlZWUTvwIAAMDuddZo6+57u3tfd+/P6gVG/rS7fybJQ0nuXHa7M8mnlu2Hkhyqqsur6vokB5I8uuUzBwAA2AMu3cSx9yc5UlXvS/L1JHckSXcfq6ojSZ5M8nKSu7v7lU3PFAAAYA86p2jr7s8l+dyy/bdJ3n6a/e5Lct8m5wYAALDnncv/pw0AAIALTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgZ422qvqeqnq0qr5UVceq6peW8Q9V1Teq6vHl5x1rjrm3qo5X1dNVdet2/gIAAAC72aUb2OelJG/r7m9V1WVJ/qKq/mB57Fe7+5fX7lxVNyQ5lOTGJN+f5E+q6ge7+5WtnDgAAMBecNZ32nrVt5a7ly0/fYZDbk/yYHe/1N3PJDme5OZNzxQAAGAP2tB32qrqkqp6PMnJJA939+eXhz5QVV+uqk9U1RXL2LVJnltz+IllDAAAgHO0oWjr7le6+6Yk+5LcXFU/lOSjSd6U5KYkLyT58LJ7rfcUrx2oqruq6mhVHT116tR5TB0AAGD3O6erR3b33yf5XJLbuvvFJea+neRj+c5HIE8kuW7NYfuSPL/Ocz3Q3Qe7++DKysr5zB0AAGDX28jVI1eq6vuW7dcl+bEkX62qa9bs9u4kTyzbDyU5VFWXV9X1SQ4keXRLZw0AALBHbOTqkdckOVxVl2Q18o5096er6jer6qasfvTx2STvT5LuPlZVR5I8meTlJHe7ciQAAMD5OWu0dfeXk7x5nfH3nuGY+5Lct7mpAQAAcE7faQMAAODCEm0AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADDYWaOtqr6nqh6tqi9V1bGq+qVl/I1V9XBVfW25vWLNMfdW1fGqerqqbt3OXwAAAGA328g7bS8leVt3/3CSm5LcVlVvSXJPkke6+0CSR5b7qaobkhxKcmOS25J8pKou2Ya5AwAA7HpnjbZe9a3l7mXLTye5PcnhZfxwknct27cnebC7X+ruZ5IcT3LzVk4aAABgr9jQd9qq6pKqejzJySQPd/fnk1zd3S8kyXJ71bL7tUmeW3P4iWUMAACAc7ShaOvuV7r7piT7ktxcVT90ht1rvaf4JztV3VVVR6vq6KlTpzY0WQAAgL3mnK4e2d1/n+RzWf2u2otVdU2SLLcnl91OJLluzWH7kjy/znM90N0Hu/vgysrKuc8cAABgD9jI1SNXqur7lu3XJfmxJF9N8lCSO5fd7kzyqWX7oSSHquryqro+yYEkj27xvAEAAPaESzewzzVJDi9XgPyuJEe6+9NV9ZdJjlTV+5J8PckdSdLdx6rqSJInk7yc5O7ufmV7pg8AALC7nTXauvvLSd68zvjfJnn7aY65L8l9m54dAADAHndO32kDAADgwhJtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgsLNGW1VdV1V/VlVPVdWxqvrgMv6hqvpGVT2+/LxjzTH3VtXxqnq6qm7dzl8AAABgN7t0A/u8nOTnuvuLVfWGJI9V1cPLY7/a3b+8duequiHJoSQ3Jvn+JH9SVT/Y3a9s5cQBAAD2grO+09bdL3T3F5ftbyZ5Ksm1Zzjk9iQPdvdL3f1MkuNJbt6KyQIAAOw15/Sdtqran+TNST6/DH2gqr5cVZ+oqiuWsWuTPLfmsBM5c+QBAABwGhuOtqp6fZLfS/Kz3f0PST6a5E1JbkryQpIPv7rrOof3Os93V1Udraqjp06dOtd5AwAA7Akbiraquiyrwfbb3f37SdLdL3b3K9397SQfy3c+AnkiyXVrDt+X5PnXPmd3P9DdB7v74MrKymZ+BwAAgF1rI1ePrCQfT/JUd//KmvFr1uz27iRPLNsPJTlUVZdX1fVJDiR5dOumDAAAsHds5OqRtyR5b5KvVNXjy9gvJHlPVd2U1Y8+Ppvk/UnS3ceq6kiSJ7N65cm7XTkSAADg/Jw12rr7L7L+99Q+e4Zj7kty3ybmBQAAQM7x6pEAAABcWKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGO2u0VdV1VfVnVfVUVR2rqg8u42+sqoer6mvL7RVrjrm3qo5X1dNVdet2/gIAAAC72UbeaXs5yc91979K8pYkd1fVDUnuSfJIdx9I8shyP8tjh5LcmOS2JB+pqku2Y/IAAAC73Vmjrbtf6O4vLtvfTPJUkmuT3J7k8LLb4STvWrZvT/Jgd7/U3c8kOZ7k5i2eNwAAwJ5wTt9pq6r9Sd6c5PNJru7uF5LVsEty1bLbtUmeW3PYiWUMAACAc7ThaKuq1yf5vSQ/293/cKZd1xnrdZ7vrqo6WlVHT506tdFpAAAA7Ckbiraquiyrwfbb3f37y/CLVXXN8vg1SU4u4yeSXLfm8H1Jnn/tc3b3A919sLsPrqysnO/8AQAAdrWNXD2yknw8yVPd/StrHnooyZ3L9p1JPrVm/FBVXV5V1yc5kOTRrZsyAADA3nHpBva5Jcl7k3ylqh5fxn4hyf1JjlTV+5J8PckdSdLdx6rqSJIns3rlybu7+5WtnjgAAMBecNZo6+6/yPrfU0uSt5/mmPuS3LeJeQEAAJBzvHokAAAAF5ZoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwc4abVX1iao6WVVPrBn7UFV9o6oeX37eseaxe6vqeFU9XVW3btfEAQAA9oKNvNP2G0luW2f8V7v7puXns0lSVTckOZTkxuWYj1TVJVs1WQAAgL3mrNHW3X+e5O82+Hy3J3mwu1/q7meSHE9y8ybmBwAAsKdt5jttH6iqLy8fn7xiGbs2yXNr9jmxjAEAAHAezjfaPprkTUluSvJCkg8v47XOvr3eE1TVXVV1tKqOnjp16jynAQAAsLudV7R194vd/Up3fzvJx/Kdj0CeSHLdml33JXn+NM/xQHcf7O6DKysr5zMNAACAXe+8oq2qrllz991JXr2y5ENJDlXV5VV1fZIDSR7d3BQBAAD2rkvPtkNV/W6Stya5sqpOJPnFJG+tqpuy+tHHZ5O8P0m6+1hVHUnyZJKXk9zd3a9sy8wBAAD2gLNGW3e/Z53hj59h//uS3LeZSQEAALBqM1ePBAAAYJuJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMNhZo62qPlFVJ6vqiTVjb6yqh6vqa8vtFWseu7eqjlfV01V163ZNHAAAYC/YyDttv5HktteM3ZPkke4+kOSR5X6q6oYkh5LcuBzzkaq6ZMtmCwAAsMecNdq6+8+T/N1rhm9PcnjZPpzkXWvGH+zul7r7mSTHk9y8NVMFAADYe873O21Xd/cLSbLcXrWMX5vkuTX7nVjGAAAAOA9bfSGSWmes192x6q6qOlpVR0+dOrXF0wAAANgdzjfaXqyqa5JkuT25jJ9Ict2a/fYleX69J+juB7r7YHcfXFlZOc9pAAAA7G7nG20PJblz2b4zyafWjB+qqsur6vokB5I8urkpAgAA7F2Xnm2HqvrdJG9NcmVVnUjyi0nuT3Kkqt6X5OtJ7kiS7j5WVUeSPJnk5SR3d/cr2zR3AACAXe+s0dbd7znNQ28/zf73JblvM5MCAABg1VZfiAQAAIAtJNoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGCwSzdzcFU9m+SbSV5J8nJ3H6yqNyb5X0n2J3k2yU939//b3DQBAAD2pq14p+3fdvdN3X1wuX9Pkke6+0CSR5b7AAAAnIft+Hjk7UkOL9uHk7xrG14DAABgT9hstHWSP66qx6rqrmXs6u5+IUmW26s2+RoAAAB71qa+05bklu5+vqquSvJwVX11owcukXdXkvzAD/zAJqcBAACwO23qnbbufn65PZnkk0luTvJiVV2TJMvtydMc+0B3H+zugysrK5uZBgAAwK513tFWVd9bVW94dTvJjyd5IslDSe5cdrszyac2O0kAAIC9ajMfj7w6ySer6tXn+Z3u/sOq+kKSI1X1viRfT3LH5qcJAACwN513tHX3Xyf54XXG/zbJ2zczKQAAAFZtxyX/AQAA2CKiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwc77f64NG7X/ns9s+2s8e/87t/01AABgJ3inDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABhNtAAAAg4k2AACAwUQbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACDiTYAAIDBRBsAAMBgog0AAGAw0QYAADCYaAMAABhMtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYLBLd3oCwMbsv+cz2/4az97/zm1/DQAAzo132gAAAAYTbQAAAIOJNgAAgMF8pw3YU7b7u4G+FwgAbDXvtAEAAAwm2gAAAAYTbQAAAIOJNgAAgMFEGwAAwGCiDQAAYDDRBgAAMJhoAwAAGEy0AQAADCbaAAAABrt0pycAwMVn/z2f2dbnf/b+d27r80+w3X/DZG/8HQH2Au+0AQAADCbaAAAABhNtAAAAg/lOGwDAHuc7llvD933ZLtsWbVV1W5L/luSSJL/e3fdv12sBAHuT2IDdR/z+U9vy8ciquiTJ/0jyE0luSPKeqrphO14LAABgN9uu77TdnOR4d/91d/9jkgeT3L5NrwUAALBrbVe0XZvkuTX3TyxjAAAAnIPq7q1/0qo7ktza3f9+uf/eJDd3939cs89dSe5a7v7LJE9v4KmvTPI3Wzxdzo+1mMV6zGEtZrEec1iLWazHHNZilp1cj3/e3SvrPbBdFyI5keS6Nff3JXl+7Q7d/UCSB87lSavqaHcf3Pz02CxrMYv1mMNazGI95rAWs1iPOazFLFPXY7s+HvmFJAeq6vqq+u4kh5I8tE2vBQAAsGttyztt3f1yVX0gyR9l9ZL/n+juY9vxWgAAALvZtv1/2rr7s0k+u8VPe04fp2RbWYtZrMcc1mIW6zGHtZjFesxhLWYZuR7bciESAAAAtsZ2facNAACALXBRRFtV3VZVT1fV8aq6Z6fnsxdU1bNV9ZWqeryqji5jb6yqh6vqa8vtFWv2v3dZn6er6tadm/nuUFWfqKqTVfXEmrFz/vtX1b9e1vF4Vf33qqoL/bvsBqdZjw9V1TeWc+TxqnrHmsesxzapquuq6s+q6qmqOlZVH1zGnR8X2BnWwrmxA6rqe6rq0ar60rIev7SMOzcusDOshXNjB1XVJVX1V1X16eX+xXVudPfon6xeyOT/JPkXSb47yZeS3LDT89rtP0meTXLla8b+a5J7lu17kvyXZfuGZV0uT3L9sl6X7PTvcDH/JPnRJD+S5InN/P2TPJrk3ySpJH+Q5Cd2+ne7GH9Osx4fSvKf19nXemzvWlyT5EeW7Tck+d/L39z5MWctnBs7sx6V5PXL9mVJPp/kLc6NUWvh3NjZdflPSX4nyaeX+xfVuXExvNN2c5Lj3f3X3f2PSR5McvsOz2mvuj3J4WX7cJJ3rRl/sLtf6u5nkhzP6rpxnrr7z5P83WuGz+nvX1XXJPln3f2XvfpPmv+55hjOwWnW43Ssxzbq7he6+4vL9jeTPJXk2jg/LrgzrMXpWItt1Ku+tdy9bPnpODcuuDOsxelYi21WVfuSvDPJr68ZvqjOjYsh2q5N8tya+ydy5n8psDU6yR9X1WNVddcydnV3v5Cs/ss6yVXLuDW6MM7173/tsv3acbbOB6rqy7X68clXP1ZhPS6Qqtqf5M1Z/a/Yzo8d9Jq1SJwbO2L5+NfjSU4mebi7nRs75DRrkTg3dsqvJfn5JN9eM3ZRnRsXQ7St91lRl7zcfrd0948k+Ykkd1fVj55hX2u0s07397cu2+ujSd6U5KYkLyT58DJuPS6Aqnp9kt9L8rPd/Q9n2nWdMeuxhdZZC+fGDunuV7r7piT7svrOwA+dYXfrsY1OsxbOjR1QVT+Z5GR3P7bRQ9YZ2/H1uBii7USS69bc35fk+R2ay57R3c8vtyeTfDKrH3d8cXlrOMvtyWV3a3RhnOvf/8Sy/dpxtkB3v7j8S/nbST6W73wk2Hpss6q6LKuR8Nvd/fvLsPNjB6y3Fs6Nndfdf5/kc0lui3NjR61dC+fGjrklyU9V1bNZ/ZrV26rqt3KRnRsXQ7R9IcmBqrq+qr47yaEkD+3wnHa1qvreqnrDq9tJfjzJE1n9u9+57HZnkk8t2w8lOVRVl1fV9UkOZPWLmmytc/r7L2/1f7Oq3rJc3ejfrTmGTXr1H/SLd2f1HEmsx7Za/nYfT/JUd//KmoecHxfY6dbCubEzqmqlqr5v2X5dkh9L8tU4Ny64062Fc2NndPe93b2vu/dntSP+tLt/JhfZuXHphXqh89XdL1fVB5L8UVavJPmJ7j62w9Pa7a5O8snlKqaXJvmd7v7DqvpCkiNV9b4kX09yR5J097GqOpLkySQvJ7m7u1/ZmanvDlX1u0nemuTKqjqR5BeT3J9z//v/hyS/keR1Wb3K0R9cwF9j1zjNery1qm7K6kcjnk3y/sR6XAC3JHlvkq8s3xdJkl+I82MnnG4t3uPc2BHXJDlcVZdk9T/KH+nuT1fVX8a5caGdbi1+07kxykX1741avfgJAAAAE10MH48EAADYs0QbAADAYKINAABgMNEGAAAwmGgDAAAYTLQBAAAMJtoAAAAGE20AAACD/X/BMyV5wfK/SQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.hist(StrikeRare[\"numberofballs\"],bins=10,rwidth=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>batsman</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>out</th>\n",
       "      <th>numberofballs</th>\n",
       "      <th>average</th>\n",
       "      <th>strikerate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>5426</td>\n",
       "      <td>152</td>\n",
       "      <td>4111</td>\n",
       "      <td>35.697368</td>\n",
       "      <td>131.987351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>5386</td>\n",
       "      <td>160</td>\n",
       "      <td>3916</td>\n",
       "      <td>33.662500</td>\n",
       "      <td>137.538304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>4902</td>\n",
       "      <td>161</td>\n",
       "      <td>3742</td>\n",
       "      <td>30.447205</td>\n",
       "      <td>130.999466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>4717</td>\n",
       "      <td>114</td>\n",
       "      <td>3292</td>\n",
       "      <td>41.377193</td>\n",
       "      <td>143.286756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>4601</td>\n",
       "      <td>137</td>\n",
       "      <td>3665</td>\n",
       "      <td>33.583942</td>\n",
       "      <td>125.538881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>4525</td>\n",
       "      <td>110</td>\n",
       "      <td>2972</td>\n",
       "      <td>41.136364</td>\n",
       "      <td>152.254374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>4450</td>\n",
       "      <td>118</td>\n",
       "      <td>3206</td>\n",
       "      <td>37.711864</td>\n",
       "      <td>138.802246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>4420</td>\n",
       "      <td>156</td>\n",
       "      <td>3381</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>130.730553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>4414</td>\n",
       "      <td>104</td>\n",
       "      <td>2902</td>\n",
       "      <td>42.442308</td>\n",
       "      <td>152.101999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>4219</td>\n",
       "      <td>134</td>\n",
       "      <td>3400</td>\n",
       "      <td>31.485075</td>\n",
       "      <td>124.088235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          batsman  total_runs  out  numberofballs    average  strikerate\n",
       "0         V Kohli        5426  152           4111  35.697368  131.987351\n",
       "1        SK Raina        5386  160           3916  33.662500  137.538304\n",
       "2       RG Sharma        4902  161           3742  30.447205  130.999466\n",
       "3       DA Warner        4717  114           3292  41.377193  143.286756\n",
       "4        S Dhawan        4601  137           3665  33.583942  125.538881\n",
       "5        CH Gayle        4525  110           2972  41.136364  152.254374\n",
       "6        MS Dhoni        4450  118           3206  37.711864  138.802246\n",
       "7      RV Uthappa        4420  156           3381  28.333333  130.730553\n",
       "8  AB de Villiers        4414  104           2902  42.442308  152.101999\n",
       "9       G Gambhir        4219  134           3400  31.485075  124.088235"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_ten_most_rum=StrikeRare.nlargest(10,[\"total_runs\"])\n",
    "top_ten_most_rum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2cce0519f70>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "sns.displot(StrikeRare[\"total_runs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,10))\n",
    "plt.title(\"TOP Ten Player With Maximum Run\",fontsize=25,color=\"r\")\n",
    "plt.bar(top_ten_most_rum[\"batsman\"],top_ten_most_rum[\"total_runs\"],width=.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top ten player play maximum number of balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([413.,  40.,  19.,  16.,   7.,   7.,   3.,   4.,   4.,   3.]),\n",
       " array([1.000e+00, 4.120e+02, 8.230e+02, 1.234e+03, 1.645e+03, 2.056e+03,\n",
       "        2.467e+03, 2.878e+03, 3.289e+03, 3.700e+03, 4.111e+03]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.hist(StrikeRare[\"numberofballs\"],bins=10,rwidth=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>batsman</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>out</th>\n",
       "      <th>numberofballs</th>\n",
       "      <th>average</th>\n",
       "      <th>strikerate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>5426</td>\n",
       "      <td>152</td>\n",
       "      <td>4111</td>\n",
       "      <td>35.697368</td>\n",
       "      <td>131.987351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>5386</td>\n",
       "      <td>160</td>\n",
       "      <td>3916</td>\n",
       "      <td>33.662500</td>\n",
       "      <td>137.538304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>4902</td>\n",
       "      <td>161</td>\n",
       "      <td>3742</td>\n",
       "      <td>30.447205</td>\n",
       "      <td>130.999466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>4601</td>\n",
       "      <td>137</td>\n",
       "      <td>3665</td>\n",
       "      <td>33.583942</td>\n",
       "      <td>125.538881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>4219</td>\n",
       "      <td>134</td>\n",
       "      <td>3400</td>\n",
       "      <td>31.485075</td>\n",
       "      <td>124.088235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>4420</td>\n",
       "      <td>156</td>\n",
       "      <td>3381</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>130.730553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>4717</td>\n",
       "      <td>114</td>\n",
       "      <td>3292</td>\n",
       "      <td>41.377193</td>\n",
       "      <td>143.286756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>4450</td>\n",
       "      <td>118</td>\n",
       "      <td>3206</td>\n",
       "      <td>37.711864</td>\n",
       "      <td>138.802246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>3834</td>\n",
       "      <td>117</td>\n",
       "      <td>3133</td>\n",
       "      <td>32.769231</td>\n",
       "      <td>122.374721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>4525</td>\n",
       "      <td>110</td>\n",
       "      <td>2972</td>\n",
       "      <td>41.136364</td>\n",
       "      <td>152.254374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       batsman  total_runs  out  numberofballs    average  strikerate\n",
       "0      V Kohli        5426  152           4111  35.697368  131.987351\n",
       "1     SK Raina        5386  160           3916  33.662500  137.538304\n",
       "2    RG Sharma        4902  161           3742  30.447205  130.999466\n",
       "4     S Dhawan        4601  137           3665  33.583942  125.538881\n",
       "9    G Gambhir        4219  134           3400  31.485075  124.088235\n",
       "7   RV Uthappa        4420  156           3381  28.333333  130.730553\n",
       "3    DA Warner        4717  114           3292  41.377193  143.286756\n",
       "6     MS Dhoni        4450  118           3206  37.711864  138.802246\n",
       "10   AM Rahane        3834  117           3133  32.769231  122.374721\n",
       "5     CH Gayle        4525  110           2972  41.136364  152.254374"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_ten_player_play_most_balls=StrikeRare.nlargest(10,[\"numberofballs\"])\n",
    "top_ten_player_play_most_balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ashhh Choudhary\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2551: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='numberofballs', ylabel='Density'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.distplot(StrikeRare[\"numberofballs\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7l4UWg1tkOLxleVWHZ8uf29fvgD+h6+GCDOcoJVuWdW9w2Hj/wbnPDh3DPHjucze2sE38zNhBx8J5XqcuMf6/jff3SdVS58NN2a6q1+3xtxkCyjeNj1d7yYMN2RJm5q/vcI7kcoEnqfq5JI/JsMPnp7IlwJ6wm7ayfH5m+LC5YwyHIN56ZyzMDjZ7COXsuYWz673Ue7++l0xYSvfX031mtuxYuGnmdy4EzCHMwd7lTdnSuvbMVD1iyTGrvjnJazP8sH41yf/d0Qu3TYaeExf+vJ6QquX/BAznkOwJFs4Ru8cSz/9CkqWudTbrtRlaUu6YofOO/bJ8Bx1/m+H6VPsk+cux5Wa+qpuMh99Nya6q123X/ekkv5zhGl6/k+6l/pwv9qVs6Qxp6/Udrk25fMdFw/Xk/nR89Efpfne635jhENUkeXGq7jx32l1l6DBloWOVH1tirGfupKXZ0X5iZnjTzPAXZ4bnvfe3zHDtvvVTtW+Wv07n12aG1/MwbNijCXOwNxk6XTg+yccz/Ll8far+LFXfecM4VbdK1QkZLup6jww/qj+b7o/sgiVerd/P8Ods3yT/mKpfSdWWzlCGdTo2Vadny6GmU7fQPf4Pp+r/pmrooGC4LMRvJvnzrOYSFMPheKeNj75vvF+6g46hdfbk8dHDk7wzVfe7IdQNlwb4jlT9SoZW06V3GOyedk29bq/uv0j3r6X799YwzdXZ0tL6J6l68A1/tquOTPLWDBeBn99bZNX+GXrTvFmGnmB/e+bZX83w/h+Y5FVjMNydLFw642dS9YtZuAB81WGpemmGXkG/utTEu72qO6TqD5KcMJa8N1su1ZAM51l+Yhx+WaqOnpn2vhl2+q13L6CHZjgn7rdSdc8bbRNV35Wh1+Nk2N7es86vDXssYQ72NsOf8ftkuMbRvkmemuSiVH0tVZ/P0JPZaRnOf7kyySPS/ar5M9tNDOd6/GCSD2Xode15Sa5K1edT9cUM6/S2JE/IlkPRpu7l2RJMfy/Jl1P1uQxB45QMoeRFS0y72IuzpYVm5Q46ul+QoZOQ6zL0SvevSb6aqs9k6Mn0Ixneg++cme9U7Lp63TWenuHP8yEZejb8aqq+lOH8pe/PcK7fZ5aY9o+z5XpyP5HZazwOPSoel6G15XuS/O6OWfxt9kcZAs1+Ga5BePX4/feJDN8TJybZvMuWbi22vjTIFzJ8dy+0Lp6f5MdutCNhGH5yhu3ybkk2peorqfpKhvM9vyNDoF1v35ph59sHknw9VZ9N1TUZvrsflKHH2BPnnL8HLEGYg71R9+Z0H5vh3J+XJrk4w4/oARk6MHhbhh/6u6b7H5ecz+6k+9IMrQhPyHAtoyszBLtvynB42+sz9NZ33121iOtq6NDmhzL8Sf6vJN/I0OPcuRm6xf/RrPZQpe5LMnRlnqy2g47uP87wh+/5ST6coTv7Wye5OsN1tZ6ToYOFV65qGXYXu7ped7bu9ye5d5IzMoS2m2QIZ2ck+d50/+3c6aoenuSXxke/mHnXk+u+MFsuVXByqr5/XZd9ewytkvdP8icZvh+uzfBevy7JfdP96mWm3t0ctOh2swwdFb09Qxg/Jt1XbDVV95sztBq/JcMOr30zbAN/k+Re6T5rq2m2z6cyfH6en6GlcOHan9dmCNZ/meTIdP/9Or8u7NFqRx3xAcBEVN0hyScz/Jl7aLrfsYuXaM+gXgHYwbTMAfALGQLHJdkRHXTsvdQrADuUMAewN6s6JlsOhfuTHdZBx95GvQKwEzjMEmBvVHVZkv2z5cLAH0zyPeM5Y2wr9QrATiTMAeyNqha+/P8nQw+NJ4/XK2N7qFcAdqLdPszd7na3640bN+7qxQAAANgl3v/+93+muzcsLt/dLuK5lY0bN2bTpk27ejEAAAB2iar673nlOkABAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmaN9dvQBTtfHkt+zQ+V/27Ifv0PkDAADTpmUOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJWnWYq6p9quqDVfXm8fFtq+qdVfXR8f42M+M+o6ouqaqLq+qhM+VHV9X543MvqKpa39UBAADYO6ylZe5pST4y8/jkJGd19+FJzhofp6qOSHJckrslOTbJC6tqn3GaFyU5Kcnh4+3Y7Vp6AACAvdSqwlxVHZrk4UleOlP8yCSnj8OnJ3nUTPmru/ua7r40ySVJ7l1VByc5sLvP6e5O8vKZaQAAAFiD1bbM/WmS30hy/UzZQd19ZZKM97cfyw9J8smZ8S4fyw4ZhxeXb6WqTqqqTVW1afPmzatcRAAAgL3HimGuqh6R5Krufv8q5znvPLhepnzrwu5Tu/uY7j5mw4YNq3xZAACAvce+qxjnfkl+tKoeluSmSQ6sqr9L8umqOri7rxwPobxqHP/yJIfNTH9okivG8kPnlAMAALBGK7bMdfczuvvQ7t6YoWOTf+run0xyZpITxtFOSPLGcfjMJMdV1f5VdecMHZ2cOx6K+eWqus/Yi+UTZqYBAABgDVbTMreUZyc5o6qemOQTSR6bJN19YVWdkeSiJNcmeXJ3XzdO86QkpyU5IMnbxhsAAABrtKYw191nJzl7HP5skocsMd4pSU6ZU74pyZFrXUgAAABubC3XmQMAAGA3IcwBAABMkDAHAAAwQcIcAADABAlzAAAAE7Q9lyaA7bbx5Lfs0Plf9uyH79D5AwDArqJlDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggvbd1QsAbJ+NJ79lh7/GZc9++A5/DQAA1kbLHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQS4aDhAXXwcApkfLHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAErRjmquqmVXVuVX2oqi6sqt8dy59VVZ+qqvPG28NmpnlGVV1SVRdX1UNnyo+uqvPH515QVbVjVgsAAGDPtu8qxrkmyYO7++qq2i/Jv1bV28bnnt/dz50duaqOSHJckrsluWOSd1XVt3X3dUlelOSkJO9N8tYkxyZ5WwAAAFiTFVvmenD1+HC/8dbLTPLIJK/u7mu6+9IklyS5d1UdnOTA7j6nuzvJy5M8aruWHgAAYC+1qnPmqmqfqjovyVVJ3tnd7xufekpVfbiqXlZVtxnLDknyyZnJLx/LDhmHF5fPe72TqmpTVW3avHnz6tcGAABgL7GqMNfd13X3UUkOzdDKdmSGQybvkuSoJFcmed44+rzz4HqZ8nmvd2p3H9Pdx2zYsGE1iwgAALBXWVNvlt39hSRnJzm2uz89hrzrk7wkyb3H0S5PctjMZIcmuWIsP3ROOQAAAGu0mt4sN1TVrcfhA5L8QJL/HM+BW/DoJBeMw2cmOa6q9q+qOyc5PMm53X1lki9X1X3GXiyfkOSN67cqAAAAe4/V9GZ5cJLTq2qfDOHvjO5+c1X9bVUdleFQycuS/HySdPeFVXVGkouSXJvkyWNPlknypCSnJTkgQy+WerIEAADYBiuGue7+cJJ7zin/qWWmOSXJKXPKNyU5co3LCAAAwCJrOmcOAACA3YMwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAEreai4QCwKhtPfssOf43Lnv3wHf4aADAFWuYAAAAmSMscAOxmdnQLp9ZNgD2DljkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZo3129AAAAO8LGk9+yQ+d/2bMfvkPnD7ASLXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEzQvrt6AQAA2D1tPPktO/w1Lnv2w3f4a8CeSsscAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBLhoOAAA7kIuvs6NomQMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJigFcNcVd20qs6tqg9V1YVV9btj+W2r6p1V9dHx/jYz0zyjqi6pqour6qEz5UdX1fnjcy+oqtoxqwUAALBnW03L3DVJHtzd90hyVJJjq+o+SU5OclZ3H57krPFxquqIJMcluVuSY5O8sKr2Gef1oiQnJTl8vB27fqsCAACw91gxzPXg6vHhfuOtkzwyyelj+elJHjUOPzLJq7v7mu6+NMklSe5dVQcnObC7z+nuTvLymWkAAABYg1WdM1dV+1TVeUmuSvLO7n5fkoO6+8okGe9vP45+SJJPzkx++Vh2yDi8uHze651UVZuqatPmzZvXsDoAAAB7h1WFue6+rruPSnJohla2I5cZfd55cL1M+bzXO7W7j+nuYzZs2LCaRQQAANirrKk3y+7+QpKzM5zr9unx0MmM91eNo12e5LCZyQ5NcsVYfuiccgAAANZoNb1ZbqiqW4/DByT5gST/meTMJCeMo52Q5I3j8JlJjquq/avqzhk6Ojl3PBTzy1V1n7EXyyfMTAMAAMAa7LuKcQ5OcvrYI+VNkpzR3W+uqnOSnFFVT0zyiSSPTZLuvrCqzkhyUZJrkzy5u68b5/WkJKclOSDJ28YbAADAsjae/JYd/hqXPfvhO/w11tOKYa67P5zknnPKP5vkIUtMc0qSU+aUb0qy3Pl2AAAArMKazpkDAABg9yDMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEzQimGuqg6rqndX1Ueq6sKqetpY/qyq+lRVnTfeHjYzzTOq6pKquriqHjpTfnRVnT8+94Kqqh2zWgAAAHu2fVcxzrVJfrW7P1BVt0zy/qp65/jc87v7ubMjV9URSY5Lcrckd0zyrqr6tu6+LsmLkpyU5L1J3prk2CRvW59VAQAA2Hus2DLX3Vd29wfG4S8n+UiSQ5aZ5JFJXt3d13T3pUkuSXLvqjo4yYHdfU53d5KXJ3nU9q4AAADA3mhN58xV1cYk90zyvrHoKVX14ap6WVXdZiw7JMknZya7fCw7ZBxeXD7vdU6qqk1VtWnz5s1rWUQAAIC9wqrDXFXdIsnrkjy9u7+U4ZDJuyQ5KsmVSZ63MOqcyXuZ8q0Lu0/t7mO6+5gNGzasdhEBAAD2GqsKc1W1X4Yg94ru/ock6e5Pd/d13X19kpckufc4+uVJDpuZ/NAkV4zlh84pBwAAYI1W05tlJfnrJB/p7j+ZKT94ZrRHJ7lgHD4zyXFVtX9V3TnJ4UnO7e4rk3y5qu4zzvMJSd64TusBAACwV1lNb5b3S/JTSc6vqvPGst9McnxVHZXhUMnLkvx8knT3hVV1RpKLMvSE+eSxJ8skeVKS05IckKEXSz1ZAgAAbIMVw1x3/2vmn+/21mWmOSXJKXPKNyU5ci0LCAAAwNbW1JslAAAAuwdhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCCVgxzVXVYVb27qj5SVRdW1dPG8ttW1Tur6qPj/W1mpnlGVV1SVRdX1UNnyo+uqvPH515QVbVjVgsAAGDPtpqWuWuT/Gp3f2eS+yR5clUdkeTkJGd19+FJzhofZ3zuuCR3S3JskhdW1T7jvF6U5KQkh4+3Y9dxXQAAAPYaK4a57r6yuz8wDn85yUeSHJLkkUlOH0c7PcmjxuFHJnl1d1/T3ZcmuSTJvavq4CQHdvc53d1JXj4zDQAAAGuwpnPmqmpjknsmeV+Sg7r7ymQIfEluP452SJJPzkx2+Vh2yDi8uHze65xUVZuqatPmzZvXsogAAAB7hVWHuaq6RZLXJXl6d39puVHnlPUy5VsXdp/a3cd09zEbNmxY7SICAADsNVYV5qpqvwxB7hXd/Q9j8afHQycz3l81ll+e5LCZyQ9NcsVYfuiccgAAANZoNb1ZVpK/TvKR7v6TmafOTHLCOHxCkjfOlB9XVftX1Z0zdHRy7ngo5per6j7jPJ8wMw0AAABrsO8qxrlfkp9Kcn5VnTeW/WaSZyc5o6qemOQTSR6bJN19YVWdkeSiDD1hPrm7rxune1KS05IckORt4w0AAIA1WjHMdfe/Zv75bknykCWmOSXJKXPKNyU5ci0LCAAAwNbW1JslAAAAuwdhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCCVgxzVfWyqrqqqi6YKXtWVX2qqs4bbw+bee4ZVXVJVV1cVQ+dKT+6qs4fn3tBVdX6rw4AAMDeYTUtc6clOXZO+fO7+6jx9tYkqaojkhyX5G7jNC+sqn3G8V+U5KQkh4+3efMEAABgFVYMc939niSfW+X8Hpnk1d19TXdfmuSSJPeuqoOTHNjd53R3J3l5kkdt4zIDAADs9bbnnLmnVNWHx8MwbzOWHZLkkzPjXD6WHTIOLy6fq6pOqqpNVbVp8+bN27GIAAAAe6ZtDXMvSnKXJEcluTLJ88byeefB9TLlc3X3qd19THcfs2HDhm1cRAAAgD3XNoW57v50d1/X3dcneUmSe49PXZ7ksJlRD01yxVh+6JxyAAAAtsE2hbnxHLgFj06y0NPlmUmOq6r9q+rOGTo6Obe7r0zy5aq6z9iL5ROSvHE7lhsAAGCvtu9KI1TVq5I8KMntquryJL+T5EFVdVSGQyUvS/LzSdLdF1bVGUkuSnJtkid393XjrJ6UoWfMA5K8bbwBAACwDVYMc919/Jziv15m/FOSnDKnfFOSI9e0dAAAAMy1Pb1ZAgAAsIsIcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATtGKYq6qXVdVVVXXBTNltq+qdVfXR8f42M889o6ouqaqLq+qhM+VHV9X543MvqKpa/9UBAADYO6ymZe60JMcuKjs5yVndfXiSs8bHqaojkhyX5G7jNC+sqn3GaV6U5KQkh4+3xfMEAABglVYMc939niSfW1T8yCSnj8OnJ3nUTPmru/ua7r40ySVJ7l1VByc5sLvP6e5O8vKZaQAAAFijbT1n7qDuvjJJxvvbj+WHJPnkzHiXj2WHjMOLy+eqqpOqalNVbdq8efM2LiIAAMCea707QJl3HlwvUz5Xd5/a3cd09zEbNmxYt4UDAADYU2xrmPv0eOhkxvurxvLLkxw2M96hSa4Yyw+dUw4AAMA22NYwd2aSE8bhE5K8cab8uKrav6runKGjk3PHQzG/XFX3GXuxfMLMNAAAAKzRviuNUFWvSvKgJLerqsuT/E6SZyc5o6qemOQTSR6bJN19YVWdkeSiJNcmeXJ3XzfO6kkZesY8IMnbxhsAAADbYMUw193HL/HUQ5YY/5Qkp8wp35TkyDUtHQAAAHOtdwcoAAAA7ATCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAECXMAAAATJMwBAABMkDAHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEHCHAAAwAQJcwAAABMkzAEAAEyQMAcAADBBwhwAAMAEbVeYq6rLqur8qjqvqjaNZbetqndW1UfH+9vMjP+Mqrqkqi6uqodu78IDAADsrdajZe77u/uo7j5mfHxykrO6+/AkZ42PU1VHJDkuyd2SHJvkhVW1zzq8PgAAwF5nRxxm+cgkp4/Dpyd51Ez5q7v7mu6+NMklSe69A14fAABgj7e9Ya6TvKOq3l9VJ41lB3X3lUky3t9+LD8kySdnpr18LNtKVZ1UVZuqatPmzZu3cxEBAAD2PPtu5/T36+4rqur2Sd5ZVf+5zLg1p6znjdjdpyY5NUmOOeaYueMAAADszbarZa67rxjvr0ry+gyHTX66qg5OkvH+qnH0y5McNjP5oUmu2J7XBwAA2Fttc5irqptX1S0XhpP8UJILkpyZ5IRxtBOSvHEcPjPJcVW1f1XdOcnhSc7d1tcHAADYm23PYZYHJXl9VS3M55Xd/Y9V9R9JzqiqJyb5RJLHJkl3X1hVZyS5KMm1SZ7c3ddt19IDAADspbY5zHX3x5PcY075Z5M8ZIlpTklyyra+JgAAAIMdcWkCAAAAdjBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCChDkAAIAJEuYAAAAmSJgDAACYIGEOAABggoQ5AACACRLmAAAAJkiYAwAAmCBhDgAAYIKEOQAAgAkS5gAAACZImAMAAJggYQ4AAGCCdnqYq6pjq+riqrqkqk7e2a8PAACwJ9ipYa6q9knyl0l+OMkRSY6vqiN25jIAAADsCXZ2y9y9k1zS3R/v7v9N8uokj9zJywAAADB51d0778WqHpPk2O7+2fHxTyX5nu5+yqLxTkpy0vjw25NcvNMWcse5XZLP7OqF2AOox/WhHrefOlwf6nF9qMf1oR7Xh3pcH+px++1JdXin7t6wuHDfnbwQNadsqzTZ3acmOXXHL87OU1WbuvuYXb0cU6ce14d63H7qcH2ox/WhHteHelwf6nF9qMfttzfU4c4+zPLyJIfNPD40yRU7eRkAAAAmb2eHuf9IcnhV3bmqvinJcUnO3MnLAAAAMHk79TDL7r62qp6S5O1J9knysu6+cGcuwy60Rx02ugupx/WhHrefOlwf6nF9qMf1oR7Xh3pcH+px++3xdbhTO0ABAABgfez0i4YDAACw/YQ5AACACRLmVqGqzq6qhy4qe3pVvXDOuFfPDD+sqj5aVd+yxHw3VtUFSzx32nhdvlTVS6vqiO1bi52vqp5ZVRdW1Yer6ryq+p6x/OyqOmYc3jjW0eL63VhVXxunu6iqXl5V+63wej9aVSfvuDXasarqunF9L6iqN1XVrWeeO7yq3lxVH6uq91fVu6vq++bM42ZV9YqqOn+cz79W1S2W29b2VEttf4vGOa2qLq2qD1XVf43b2SEzz1+9eJq9RVUdVFWvrKqPj9vcOVX16CXGXdX2uY3LccP3xaLyZ1XVry0xzb+vx2tvj3mf56o6sapetWi821XV5qraf1H5jdZ79jNcVUdV1cNmnluyLvYUM/V54fh5/ZWqusmicd5YVecsMX1V1Weq6jbj44Orqqvq/jPjbK6qb96xa7L7GNf/b2ce7zvWwZvHxweNn+sPjb/Db11iPku+N+M2/xfrtLy/UFVPWI95bedyPHqsu++YKds4lv3+TNntquob89Z/rJfNY739Z1X98iped+534Z6iqu5QVa8ef0cuqqq3VtW3zfv/ssL3/0+Ov/sL2+NLa+b/1BqXaRJ1Lsytzqsy9Lw567ixfK6qekiSP89wkfRPbM+Ld/fPdvdF2zOPna2q7pvkEUnu1d3fleQHknxy0TiHZugM51e7++1zZvOx7j4qyd0zXMbix5d7ze4+s7ufvQ6Lv6t8rbuP6u4jk3wuyZOTpKpumuQtSU7t7rt099FJfinJt86Zx9OSfLq77z7O54lJvrG9C1ZVO/ualNtlNdvfjF/v7nsk+fYkH0zy7hp6291rVVUleUOS93T3t47b3HEZPoeLx13L9rlTdPf3Li6rqn128mLM+zz/Q5IfrKqbzYz3mCRndvc1a5j3UUkettJIe5iF+rxbkh/MsP6/s/Dk+GftXkluXVV3XjxxDx0EvC/Jfcei783wef/ecfpvT/KZ7v7sSgsyBsPt+v+0m3ynfiXJkVV1wPj4B5N8aub530vyzu6+R3cfkWSpnaXLvjfrpbtf3N0vX+/5boPjk/xrtv5f+PEMvzsLHptkuU7+XjP+x7lfkmdW1WHLjLtHG39zXp/k7PF35Igkv5nkoDXO59gkv5zkh8ft8V5J/n2t85kaYW51/j7JIxb2nFbVxiR3zPBh3kpVPSDJS5I8vLs/Npb9yriH9oKqevrM6PtU1UvGPQjvmPlSnZ3fJPYMLHJwhh/Ga5Kkuz/T3bPXFLxDknck+a3uXvbyFN19XZJzkxySJFX1I1X1vqr6YFW9q6oOGstv2ANYQ4vLC6rq32toWVho5bxFVZ1VVR+oofXqkeu94uvknIzrm+TxSc6ZrafuvqC7T5sz3cGZ+THu7otn/iTO3daq6ueq6j/GPVivW/ijOdbhn1TVu5P80fj4RTW0uny8qh5YVS+rqo9U1Q3LMo6zaXyd313HOlmLlba/rfTg+Un+J8kPL5RX1Slj3bx3Zltbahs8v4YWmKqqz9a4F7mq/raqfmDcw/gv4/b3gapa+CP5oPFz/vc17KV9xfjjtqs8OMn/dveLFwq6+7+7+8/njLvs9llV9x4/hx8c7799LD+xqt5QQ6vVpVX1lPF78oNjXd925jV+cpz2gqq690z5EWO9fbyqnrpQWGOL6liv766qVyY5fx3qZVudk+SQ7v5Skvck+ZGZ55bdMbhYDTsafi/J42rYq/+48aml6uINNbSWXlhVJ82UX11Vzxu3w7OqasNYfnZV/eni+l7qfdwVuvuqJCclecrM5+THkrwpyauz9Z/sBf+WMbyN93+SG4e7f1/qN2L87H6khiNyPpDkAePjed+pd6mqfxzr/V9qbMFZ/J26nnWyHd6W5OHj8PG58bZ4cIbrAydJuvvDK81siffmjmN9fLSqnrMwblUdX1uOIvmjmfKrl/je3eUt0FV1iwzh64nZejv7WpKP1Jb/a49LcsZK8xx3IFySob5TVb9dw2/yBVV16qLfgsdW1bk1HEnygHH8Nf+uVNXRVfXP4zb69qo6eNtrZV18f5JvLPrNOa+7/2WN83lmkl/r7k+N87iuu1/W3Rcn8+t2/Lx+YGEGNRxp8v7FM66qH6rhCJUPVNVrx21h99Ddbqu4Zdjz/Mhx+OQkf7zEeN/IsBf2u2bKjs7wR+LmSW6RYU/NPZNsTHJtkqPG8c5I8pPj8GlJHjMOn53kmF1dB2usr1skOS/JfyV5YZIHzjx39lhHv7jM9BuTXDAO3zTJuxfqNMltsqUn1p9N8rxx+MQkfzFTf6/NsMPiiCSXjOX7JjlwHL5dhi/Q2tX1NS7P1eP9PuOyHzs+/pMkT1vlPI5KclWGP49/kOTwmfpcalv75pnp/yDJL83U4ZuT7DPz+NVJKskjk3wpQ6vpTZK8f2bet51Zj7NnPwu7w/a3aLwbPmczZX+a5P+Mw53kR8bh52TY+bDcNvjiDH+MjsxwXc2XjOUfHZfpZkluOpYdnmTTOPygJF/M0PJ1k/H9u/8u3BafmuT5qxx32e0zyYFJ9h2HfyDJ68bhE8fP3y2TbBjX/xfG556f5Onj8Nkz9fh92fK98KwMe1z3z/BZ/myS/RZ9lh6UofXhzrugDpf6PD82yevH4TsmuWLhM7Zo+rMz872fG38nnpjxu24VdbHweTwgyQUZP+/jtv34cfi3s+W7c6n6nvs+7uz6XFT2+SQHjcPvSvKAJN+W5MNLzONBSf5pHP6XDJ/Jhc/gS5L8TJb4jRjr//ok95l5P5b6Tj0rW757v2fmNU/LzHfqrr4luTrJd2XYYX3TDN+ZD0ry5vH5hyb5Qobf32cmueNa35txW/14kluNr/HfSQ4bt/1PZPjs75vkn5I8ambbnPe9+6wMf9R3ZZ39ZJK/Hof/PcPRHwvbwwVJfjTJczN8l5+VRZ/VmfncUJ7kW8a6X/htuO3MeH87UxdnZ8tvzcOSvGscXtPvSpL9xmXfMI73uAyXCtuV9brkb85Yt18b62jh9j/ztoUM/y1vtczrLFW3786Wz/L/y5b/QWcnOSbDd8F7ktx8LP8/SX57V9bZ7E3L3OrNHmq53J7Ub2T4kDxxpuz+GX68v9LdV2c41OYB43OXdvd54/D7M2y0kzeu59EZ9tBtTvKaqjpxZpR3JfmpuvHhRovdparOy/DH5BO9Za/goUneXlXnJ/n1JHdbYvo3dPf1PRyiutDEXkn+X1V9eFyGQ7L7NL8fMLO+t03yznkjVdXrx71K/7D4uXFb+tYkfzzO4z+q6jvHp5fa1o4c9+qdn6GVZbY+X9tDy+iCN/XwTXZ+hsM5z+/u6zPsoFiY34+Pe7k+OM5rp5/vuYrtbzmze0H/N8Ofr+TGdbbUNvgvGf4Af1+SFyW5ew3n4H1uXKb9krxknO61uXHdnNvdl4/1eV52o++CqvrLcS/5f6xi3MXb562SvLaGcx6enxtvX+/u7i939+YMfzreNJafnxuv/6uSpLvfk+TA2nL+w1u6+5ru/kyGnRjzPsvndvelq1rR9bXU5/nNSe5fVQdmOHT87xd9xhb0KssWLFUXT62qDyV5b4Y/0YeP5dcnec04/HcZfqcWzKvv5d7HXWWhleGgJHdN8q/d/V9Jrq2qI+eMf26Se1bVzTOE3auTfLyq7pqxZS7L/0b8d3e/d2Z+W32njnvrvzdDXZ2X5K8ytriMFn+n7lLj7+rGDK1yb1303Nsz/J68JMl3JPlgjS24qzD7PXpWd3+xu7+e5KIkd0ry3RkOqdvc3dcmeUWG781k6e/d3cHxGXZqZrw/ftHz/5jhUNPjs+XztZTHVdWFGcLun431kyTfX8ORH+dnOEpi9rO28L06Wy9r/V359gw7HN85bqO/lTmH0O9mPtbDobxH9XBo6otXmqCq7l7D0Qsfqy1HMCxVty9N8tM1HI7/uCSvXDS7+2So138b6+yEDNvxbkGYW703JHlIVd0ryQHd/YElxrs+ww/0d1fVb45lyx0uNXuexHXZyRdy35F6aN4+u7t/J8lTMhwGs+A5Gc5feG0tfe7Awjlzd01yn6r60bH8zzPs0bp7kp/PsLdvntm6XXgPHp9hT+DR47w/vcz0O9vXxmW6U5JvynjOXIagdK+Fkbr70Rn26t02c3T31d39D939ixn+pC2cW7PUtnZakqeM9fm7uXF9fGXR7Bfmcf2i+V2fZN8azlX5tSQP6eFctbdkF9XvCtvfcu6Z5CPj8DfG8JrcuM6W2gbfk2FHzQMy7NHbnOGcqIVDRX45wzZ3jwx7+2bPzdudvgsWb3NPTvKQDJ+dlcZdvH3+fobQdmSGwwtnt4fF29Ds9jW7/otDzMLj1dTZ4m14Z5n7ee7ur2X4w/foLL9j8LMZWoAX3DbJZ5Z5va3qoqoelKEV7b49nBf6wSz9eewlhhceL/c+7nRV9a0Z1vOqDH++bpPk0qq6LMMf1q0Otezur2ZoafuZDIdKJkPIfViS2ye5OMv/Riz1fZhs2f5ukuQLs388u/s7Z8bbVdvjcs7M0Jq01bbY3Z/r7ld2909lONpgxY6NFr03yfx6Wu5/0VLfu7tUDZ3jPDjJS8ft7NczBLIb1qW7/zdD0PrVJK9bYZav6eG8rgckeV4NHYDcNMPRJI8Zf19ekvnfmbP1stbflUpy4cz2effu/qHV1MEOdGGGHbDrMZ97Jcm4s/moDIcSH7BC3b4uw+kVj0jy/t763NnKcP7oQp0d0d1PzG5CmFulcQ/e2UlelhXObxh/MB6R5PFV9cQMf/AeVUNPgzfP8CO+1uOAJ6Wqvr2qDp8pOirD4RWzfjnDoXp/veiY8Bvp7iszHNr6jLHoVtlyXtgJa1y0WyW5qru/UVXfn91oz8qC7v5ihkMOfq2GHjxfmeR+M2E2GQ6r2EpV3a+29Nb2TRn2JC2u98VumeTK8bUev52Lf2CGPytfHPeW//AK4+8Qq9z+Fk9TNZxrdHCGP9vLmbsNdvcnMxyOcXh3fzzDebW/li2f91sluXLcS/pTGQ7B2x39U5KbVtWTZsqWakVfafucrasTt3F5HpckNfQ8+MXxMzIJcz7PyfAb8isZWnzeu8SkZ2c4V3Dhu/GEDIcCJcmXM3xuV3KrJJ/v7q/WcN7WfWaeu0mGHQ1J8hO58Tng8+p7Pd7HdTG2Dr04ww6VztAKcmx3b+zujRn+FC533tzTMxxylvH+aUneO85ru34jejgv8tKqeuy4rFVV91jLPHaBlyX5ve6+0XmlVfXg2nIO9S2T3CXDoZFLmvPeLOV9SR5YQ4+P+2R4D/95O9ZhZ3hMkpd3953Gbe2wJJfmxq3aSfK8DIfqr9iZTpJ09zkZDvl7WraEi8+MrbyPWXLCLdb6u3Jxkg01dBSWqtqvqnZ1S/s/Jdm/qn5uoaCqvruqHrjG+fxhkufW0MHegoW+KJas27FV9O0Zjqj5mznzfW+G37m7jst2s6r6tjUu2w4jzK3NqzLs+Xj1SiN29+eSHJuh+fqwDK0f52b4Antpd39wxy3mbuEWSU6voXvZD2cIFc+aHWH8oj8hw5/n52w1hxt7Q5Kb1XDC77MytOj9S5bfUz3PK5IcU1WbMgSX/1zj9DvFuH18KMlx4578RyT5hRo6Nzgnw3b1B3MmvUuSfx4PIfhgkk1Zee/g/82wXb4z21kf3f2h8XUvzPAH4d+2Z37bYcXtb8Yfj4eh/VeGQ3++f9y7upxnZelt8H3jvJIhxB2SLX+UX5jkhKp6b4Zze3bHvfQLn81HZfizdWlVnZvk9AznCSwed6Xt8zlJ/rCq/i3bHl4/X8PlBl6cGx/CPgmzn+ex6B0Zzhl6zTJ/eE/NENo+NG6ft8jQepIMoe6IunEHKPP8Y4YWug9naFmbDY5fSXK3Gk70f3CGTlUWzKvv9Xgft8cB4/pemOHwx3ck+d0aOiT7lsys23hY7ZdqzuVIMnwnfWu2hLkPZDjEbOFyFuvxG/H4JE8c37cLM5xjvNsaD8P7szlPHZ1k07j9nJPhv8u8Q63nvjcrvOaVGXbQvjvDZ+MD3f3G7VmPneD4DD0uznpdhp0hN+juC7v79DXO+4+S/HSG1rOXZDjU/A0ZWkNXsqbflfH37TEZOjb7UIbDL7fqAXhnGr8HH52ht9+PjdvSszKcU7yW+bw1yQuSvG38/f/3DHX69u7+Qpav21dkOArhHXPmuznDTqxXjZ+H92Y49Hi3UEv/jgAAe6Kqurq7t+qNrarOztCxwKadv1QAu0YNPaXeqrv/765elrXaLY5DBgAA2Nmq6vUZjmx68K5elm2hZQ4AAGCCnDMHAAAwQcIcAADABAlzAAAAEyTMAQAATJAwBwAAMEH/P2uZqfA34TG6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"TOP Ten Player Play Maximum Balls\",fontsize=25,color=\"r\")\n",
    "plt.bar(top_ten_player_play_most_balls[\"batsman\"],top_ten_player_play_most_balls[\"numberofballs\"],width=.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top ten player with maximum average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([133., 149., 107.,  66.,  22.,   2.,   2.,   0.,   0.,   1.]),\n",
       " array([ 0. ,  8.8, 17.6, 26.4, 35.2, 44. , 52.8, 61.6, 70.4, 79.2, 88. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.hist(StrikeRare[\"average\"],bins=10,rwidth=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>batsman</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>out</th>\n",
       "      <th>numberofballs</th>\n",
       "      <th>average</th>\n",
       "      <th>strikerate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>106.024096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>J Bairstow</td>\n",
       "      <td>459</td>\n",
       "      <td>8</td>\n",
       "      <td>284</td>\n",
       "      <td>57.375000</td>\n",
       "      <td>161.619718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>MN van Wyk</td>\n",
       "      <td>167</td>\n",
       "      <td>3</td>\n",
       "      <td>132</td>\n",
       "      <td>55.666667</td>\n",
       "      <td>126.515152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>PD Collingwood</td>\n",
       "      <td>203</td>\n",
       "      <td>4</td>\n",
       "      <td>156</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>130.128205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>HM Amla</td>\n",
       "      <td>577</td>\n",
       "      <td>13</td>\n",
       "      <td>407</td>\n",
       "      <td>44.384615</td>\n",
       "      <td>141.769042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>4414</td>\n",
       "      <td>104</td>\n",
       "      <td>2902</td>\n",
       "      <td>42.442308</td>\n",
       "      <td>152.101999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>2031</td>\n",
       "      <td>49</td>\n",
       "      <td>1633</td>\n",
       "      <td>41.448980</td>\n",
       "      <td>124.372321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DA Warner</td>\n",
       "      <td>4717</td>\n",
       "      <td>114</td>\n",
       "      <td>3292</td>\n",
       "      <td>41.377193</td>\n",
       "      <td>143.286756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>4525</td>\n",
       "      <td>110</td>\n",
       "      <td>2972</td>\n",
       "      <td>41.136364</td>\n",
       "      <td>152.254374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>1107</td>\n",
       "      <td>27</td>\n",
       "      <td>802</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>138.029925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  total_runs  out  numberofballs    average  strikerate\n",
       "243   Iqbal Abdulla          88    1             83  88.000000  106.024096\n",
       "109      J Bairstow         459    8            284  57.375000  161.619718\n",
       "180      MN van Wyk         167    3            132  55.666667  126.515152\n",
       "159  PD Collingwood         203    4            156  50.750000  130.128205\n",
       "97          HM Amla         577   13            407  44.384615  141.769042\n",
       "8    AB de Villiers        4414  104           2902  42.442308  152.101999\n",
       "30        JP Duminy        2031   49           1633  41.448980  124.372321\n",
       "3         DA Warner        4717  114           3292  41.377193  143.286756\n",
       "5          CH Gayle        4525  110           2972  41.136364  152.254374\n",
       "61        ML Hayden        1107   27            802  41.000000  138.029925"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "top_Ten_most_average_player=StrikeRare.nlargest(10,[\"average\"])\n",
    "top_Ten_most_average_player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.title(\"Top ten player with maximum average\",fontsize=25,color=\"r\")\n",
    "plt.bar(top_Ten_most_average_player[\"batsman\"],top_Ten_most_average_player[\"average\"],width=.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}