Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "776d77bd-b8f4-49b1-b227-0652d8fd4620",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bc702f1c-e7f2-4be8-9e15-f512d022d96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 0, 3]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mylist = [1,2,0,3]\n",
    "mylist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7842d9af-89b9-400a-86e8-6d33db5890fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 0, 3])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array(mylist)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65dec370-44bf-431d-b324-b66dccc74499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3e25938e-7663-491e-8c2d-e70a6cb53d59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d1edb42d-8bff-49e4-9b29-ecb357d1f9f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9ecfabc-d8c8-421d-87d5-3328674d5b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "08caa101-22be-4b22-8d58-3cfdebf8c324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7af0962-8cd0-4a02-948c-ced3d93c3f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 5, 9],\n",
       "       [9, 3, 2],\n",
       "       [2, 3, 6]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,10,(3,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adab258a-afd3-436a-b511-18c2a7b3846f",
   "metadata": {},
   "source": [
    "##oprations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "90f1ed5d-2da2-4e70-85f7-8410221eacce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47, 17, 41,  3, 82, 14,  5, 70, 37, 29])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e39c17e4-e876-4909-b7c6-9f1c000793e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([76, 28,  7,  3,  4, 68, 77, 49, 23, 50])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a =np.random.seed(12)\n",
    "b =np.random.randint(1,100,10)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2ec7b134-2e5a-40ba-9f43-0c790a6388ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "851e5a52-570c-4c1d-8e04-4047560a9fe5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20f90135-0221-4c20-b528-3928068c913c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "045408fe-360c-4e55-80ba-068690f9eb48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[76, 28,  7,  3,  4],\n",
       "       [68, 77, 49, 23, 50]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.reshape(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c55b8fc0-ac92-4dfc-a49b-d4e9625617aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat =np.arange(0,100).reshape(10,10)\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "694a1aa9-c195-4fef-9b61-5b086aa4da91",
   "metadata": {},
   "outputs": [],
   "source": [
    "row = 3\n",
    "col = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0a44e67e-1fd9-4430-8b53-0822d7d7bcda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[(row,col)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "63b95080-dee3-4ed3-9eea-a1f50e3e8401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f1ad8a17-d4b5-4d38-ab43-2178c00e8280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 18, 28, 38, 48, 58, 68, 78, 88, 98])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[:,col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe336d3f-f78a-4866-80fc-55fed7a155b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 31, 32, 33, 34, 35, 36, 37, 38, 39])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[row,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "720f05da-5ee0-4a05-aa83-c75dd23bbccb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [10, 11, 12],\n",
       "       [20, 21, 22]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[0:3,0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "113bb7b1-9f83-47c3-95e5-7e093c757e74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "         True]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat > 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c4a42078-b766-42a8-ba94-22e6fb7454ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat [mat>50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "55c8c8b8-0eb1-4cb4-8e83-17e4031c627e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[-1,-2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "065b5570-3d3e-47da-99ce-66d6f517d5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[-2,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a01e4741-af89-4e8b-9bd0-0de152bbda41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2d79ad7d-1f7c-4d00-bbac-8eb94037084e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "995c3d71-abac-4107-a1a9-92d97a6596f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat[2:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "14baf1d0-cc5c-4e53-bf8f-54b1ead10a19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47, 48, 49],\n",
       "       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59],\n",
       "       [60, 61, 62, 63, 64, 65, 66, 67, 68, 69],\n",
       "       [70, 71, 72, 73, 74, 75, 76, 77, 78, 79],\n",
       "       [80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
       "       [90, 91, 92, 93, 94, 95, 96, 97, 98, 99]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat1 = np.reshape(mat, (10, 10), order='f')\n",
    "mat1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e6b75604-5c12-4fe8-9439-043412ef4713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  4,  6,  8],\n",
       "       [10, 12, 14, 16, 18],\n",
       "       [20, 22, 24, 26, 28],\n",
       "       [30, 32, 34, 36, 38],\n",
       "       [40, 42, 44, 46, 48],\n",
       "       [50, 52, 54, 56, 58],\n",
       "       [60, 62, 64, 66, 68],\n",
       "       [70, 72, 74, 76, 78],\n",
       "       [80, 82, 84, 86, 88],\n",
       "       [90, 92, 94, 96, 98],\n",
       "       [ 1,  3,  5,  7,  9],\n",
       "       [11, 13, 15, 17, 19],\n",
       "       [21, 23, 25, 27, 29],\n",
       "       [31, 33, 35, 37, 39],\n",
       "       [41, 43, 45, 47, 49],\n",
       "       [51, 53, 55, 57, 59],\n",
       "       [61, 63, 65, 67, 69],\n",
       "       [71, 73, 75, 77, 79],\n",
       "       [81, 83, 85, 87, 89],\n",
       "       [91, 93, 95, 97, 99]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "mat2 =np.arange(0,100).reshape(10,10)\n",
    "\n",
    "matha = np.reshape(mat2, (20,5), order='F')\n",
    "matha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "61e2bba1-2820-4f6d-b56a-b4040f229bc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  2  4  6  8]\n",
      " [10 12 14 16 18]\n",
      " [20 22 24 26 28]\n",
      " [30 32 34 36 38]\n",
      " [40 42 44 46 48]\n",
      " [50 52 54 56 58]\n",
      " [60 62 64 66 68]\n",
      " [70 72 74 76 78]\n",
      " [80 82 84 86 88]\n",
      " [90 92 94 96 98]\n",
      " [ 1  3  5  7  9]\n",
      " [11 13 15 17 19]\n",
      " [21 23 25 27 29]\n",
      " [31 33 35 37 39]\n",
      " [41 43 45 47 49]\n",
      " [51 53 55 57 59]\n",
      " [61 63 65 67 69]\n",
      " [71 73 75 77 79]\n",
      " [81 83 85 87 89]\n",
      " [91 93 95 97 99]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "\n",
    "# Create a 10x10 matrix\n",
    "mat2 = np.arange(0, 100).reshape(10, 10)\n",
    "\n",
    "# Reshape into (20, 5) using column-major order ('F')\n",
    "matha = np.reshape(mat2, (20, 5), order='F')\n",
    "print(matha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7dd819c4-4d7d-4a01-8818-28126778bb2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]\n",
      " [15 16 17 18 19]\n",
      " [20 21 22 23 24]\n",
      " [25 26 27 28 29]\n",
      " [30 31 32 33 34]\n",
      " [35 36 37 38 39]\n",
      " [40 41 42 43 44]\n",
      " [45 46 47 48 49]\n",
      " [50 51 52 53 54]\n",
      " [55 56 57 58 59]\n",
      " [60 61 62 63 64]\n",
      " [65 66 67 68 69]\n",
      " [70 71 72 73 74]\n",
      " [75 76 77 78 79]\n",
      " [80 81 82 83 84]\n",
      " [85 86 87 88 89]\n",
      " [90 91 92 93 94]\n",
      " [95 96 97 98 99]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Create a 10x10 matrix\n",
    "mat2 = np.arange(0, 100).reshape(10, 10)\n",
    "\n",
    "# Reshape into (20, 5) using row-major order ('C')\n",
    "matha_c = np.reshape(mat2, (20, 5), order='C')\n",
    "print(matha_c)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0975da0f-0f06-44e7-980a-967148eb0f9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0a924517-6057-401c-8095-89fafa07b2b6",
   "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>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>981016</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981018</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>981020</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>1</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>481</td>\n",
       "      <td>499</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>981022</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>481</td>\n",
       "      <td>498</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>482</td>\n",
       "      <td>497</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>577 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0      335987  18-Apr-08             2                 1          1   \n",
       "1      335988  19-Apr-08             4                 3          1   \n",
       "2      335989  19-Apr-08             6                 5          1   \n",
       "3      335990  20-Apr-08             7                 2          1   \n",
       "4      335991  20-Apr-08             1                 8          1   \n",
       "..        ...        ...           ...               ...        ...   \n",
       "572    981016  22-May-16             6                 2          9   \n",
       "573    981018  24-May-16            13                 2          9   \n",
       "574    981020  25-May-16            11                 1          9   \n",
       "575    981022  27-May-16            13                11          9   \n",
       "576    981024  29-May-16             2                11          9   \n",
       "\n",
       "                                           Venue_Name  Toss_Winner_Id  \\\n",
       "0                               M Chinnaswamy Stadium               2   \n",
       "1          Punjab Cricket Association Stadium, Mohali               3   \n",
       "2                                    Feroz Shah Kotla               5   \n",
       "3                                    Wankhede Stadium               7   \n",
       "4                                        Eden Gardens               8   \n",
       "..                                                ...             ...   \n",
       "572  Shaheed Veer Narayan Singh International Stadium               2   \n",
       "573                             M Chinnaswamy Stadium               2   \n",
       "574                                  Feroz Shah Kotla               1   \n",
       "575                                  Feroz Shah Kotla              11   \n",
       "576                             M Chinnaswamy Stadium              11   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "0           field             0          1                  0     by runs   \n",
       "1             bat             0          1                  0     by runs   \n",
       "2             bat             0          1                  0  by wickets   \n",
       "3             bat             0          1                  0  by wickets   \n",
       "4             bat             0          1                  0  by wickets   \n",
       "..            ...           ...        ...                ...         ...   \n",
       "572         field             0          1                  0  by wickets   \n",
       "573         field             0          1                  0  by wickets   \n",
       "574         field             0          1                  0     by runs   \n",
       "575         field             0          1                  0  by wickets   \n",
       "576           bat             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "0     140.0              1.0                  2.0              470   \n",
       "1      33.0              3.0                 19.0              471   \n",
       "2       9.0              6.0                 90.0              472   \n",
       "3       5.0              2.0                 11.0              473   \n",
       "4       5.0              1.0                  4.0              474   \n",
       "..      ...              ...                  ...              ...   \n",
       "572     6.0              2.0                  8.0              511   \n",
       "573     4.0              2.0                110.0              495   \n",
       "574    22.0             11.0                163.0              481   \n",
       "575     4.0             11.0                187.0              481   \n",
       "576     8.0             11.0                385.0              482   \n",
       "\n",
       "     Second_Umpire_Id   City_Name Host_Country  \n",
       "0                 477   Bangalore        India  \n",
       "1                 487  Chandigarh        India  \n",
       "2                 512       Delhi        India  \n",
       "3                 476      Mumbai        India  \n",
       "4                 486     Kolkata        India  \n",
       "..                ...         ...          ...  \n",
       "572               497      Raipur        India  \n",
       "573               482   Bangalore        India  \n",
       "574               499       Delhi        India  \n",
       "575               498       Delhi        India  \n",
       "576               497   Bangalore        India  \n",
       "\n",
       "[577 rows x 19 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Replace 'your_file.csv' with your actual file name or path\n",
    "df = pd.read_csv(r'C:\\Users\\Rahul S\\OneDrive\\Desktop\\Datasets_for_naresh_it_projects\\Cricket_dataset/Match.csv')\n",
    "\n",
    "# Display the first few rows of the DataFrame\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6c69d0df-ca6a-4de8-b7b0-6ad5491f30be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 577 entries, 0 to 576\n",
      "Data columns (total 19 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Match_Id             577 non-null    int64  \n",
      " 1   Match_Date           577 non-null    object \n",
      " 2   Team_Name_Id         577 non-null    int64  \n",
      " 3   Opponent_Team_Id     577 non-null    int64  \n",
      " 4   Season_Id            577 non-null    int64  \n",
      " 5   Venue_Name           577 non-null    object \n",
      " 6   Toss_Winner_Id       577 non-null    int64  \n",
      " 7   Toss_Decision        577 non-null    object \n",
      " 8   IS_Superover         577 non-null    int64  \n",
      " 9   IS_Result            577 non-null    int64  \n",
      " 10  Is_DuckWorthLewis    577 non-null    int64  \n",
      " 11  Win_Type             577 non-null    object \n",
      " 12  Won_By               568 non-null    float64\n",
      " 13  Match_Winner_Id      574 non-null    float64\n",
      " 14  Man_Of_The_Match_Id  574 non-null    float64\n",
      " 15  First_Umpire_Id      577 non-null    int64  \n",
      " 16  Second_Umpire_Id     577 non-null    int64  \n",
      " 17  City_Name            577 non-null    object \n",
      " 18  Host_Country         577 non-null    object \n",
      "dtypes: float64(3), int64(10), object(6)\n",
      "memory usage: 85.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "daeabfe7-8714-4543-a40e-1918e14fbeee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2664b583-2134-44e3-acf6-bb24cf35cc7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2351582a-e7ca-4017-8015-03bf2f8a9d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[335987, '18-Apr-08', 2, ..., 477, 'Bangalore', 'India'],\n",
       "       [335988, '19-Apr-08', 4, ..., 487, 'Chandigarh', 'India'],\n",
       "       [335989, '19-Apr-08', 6, ..., 512, 'Delhi', 'India'],\n",
       "       ...,\n",
       "       [981020, '25-May-16', 11, ..., 499, 'Delhi', 'India'],\n",
       "       [981022, '27-May-16', 13, ..., 498, 'Delhi', 'India'],\n",
       "       [981024, '29-May-16', 2, ..., 497, 'Bangalore', 'India']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array = df.to_numpy()\n",
    "data_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f1211b67-f973-4eec-bf0c-45539e53882c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([335987, 335988, 335989, 335990, 335991, 335992, 335993, 335994,\n",
       "       335995, 335996, 335997, 335998, 335999, 336000, 336001, 336002,\n",
       "       336003, 336004, 336005, 336006, 336007, 336008, 336009, 336010,\n",
       "       336011, 336012, 336013, 336014, 336015, 336016, 336017, 336018,\n",
       "       336019, 336020, 336021, 336022, 336023, 336024, 336025, 336026,\n",
       "       336027, 336028, 336029, 336030, 336031, 336032, 336033, 336034,\n",
       "       336036, 336037, 336038, 336039, 336040, 336041, 336042, 336043,\n",
       "       336044, 336045, 392186, 392187, 392188, 392189, 392190, 392191,\n",
       "       392193, 392194, 392195, 392196, 392197, 392199, 392200, 392201,\n",
       "       392202, 392203, 392204, 392205, 392206, 392207, 392208, 392209,\n",
       "       392210, 392211, 392212, 392213, 392214, 392215, 392216, 392217,\n",
       "       392218, 392219, 392220, 392221, 392222, 392223, 392224, 392225,\n",
       "       392226, 392227, 392228, 392229, 392230, 392231, 392232, 392233,\n",
       "       392234, 392235, 392236, 392237, 392238, 392239, 392240, 392241,\n",
       "       392242, 392243, 392244, 419111, 419112, 419113, 419114, 419115,\n",
       "       419116, 419117, 419118, 419119, 419120, 419121, 419122, 419123,\n",
       "       419124, 419125, 419126, 419127, 419128, 419129, 419130, 419131,\n",
       "       419132, 419133, 419134, 419135, 419136, 419137, 419138, 419139,\n",
       "       419140, 419141, 419142, 419143, 419144, 419145, 419146, 419147,\n",
       "       419148, 419149, 419150, 419151, 419152, 419153, 419154, 419155,\n",
       "       419156, 419157, 419158, 419159, 419160, 419161, 419162, 419163,\n",
       "       419164, 419165, 419166, 419167, 419168, 419169, 419170, 501203,\n",
       "       501204, 501205, 501206, 501207, 501208, 501209, 501210, 501211,\n",
       "       501212, 501213, 501214, 501215, 501216, 501217, 501218, 501219,\n",
       "       501220, 501221, 501223, 501224, 501225, 501226, 501227, 501228,\n",
       "       501229, 501230, 501231, 501232, 501233, 501234, 501235, 501236,\n",
       "       501237, 501238, 501239, 501240, 501241, 501242, 501243, 501244,\n",
       "       501245, 501246, 501247, 501248, 501249, 501250, 501251, 501252,\n",
       "       501253, 501254, 501255, 501256, 501257, 501258, 501259, 501260,\n",
       "       501261, 501262, 501263, 501264, 501265, 501266, 501267, 501268,\n",
       "       501269, 501270, 501271, 501272, 501273, 501274, 501275, 501276,\n",
       "       548311, 548312, 548313, 548314, 548315, 548316, 548317, 548318,\n",
       "       548319, 548320, 548321, 548322, 548323, 548324, 548325, 548326,\n",
       "       548327, 548328, 548329, 548330, 548331, 548332, 548333, 548334,\n",
       "       548335, 548336, 548337, 548338, 548339, 548340, 548341, 548342,\n",
       "       548344, 548346, 548347, 548348, 548349, 548350, 548351, 548352,\n",
       "       548353, 548354, 548355, 548356, 548357, 548358, 548359, 548360,\n",
       "       548361, 548362, 548363, 548364, 548365, 548366, 548367, 548368,\n",
       "       548369, 548370, 548371, 548372, 548373, 548374, 548375, 548376,\n",
       "       548377, 548378, 548379, 548380, 548381, 548382, 548383, 548384,\n",
       "       548385, 548386, 598003, 598004, 598005, 598006, 598007, 598008,\n",
       "       598009, 598010, 598011, 598012, 598013, 598014, 598015, 598016,\n",
       "       598017, 598018, 598019, 598020, 598021, 598022, 598023, 598024,\n",
       "       598025, 598026, 598027, 598028, 598029, 598030, 598031, 598032,\n",
       "       598033, 598034, 598035, 598036, 598037, 598038, 598039, 598040,\n",
       "       598041, 598042, 598043, 598044, 598045, 598046, 598047, 598048,\n",
       "       598049, 598050, 598051, 598052, 598053, 598054, 598055, 598056,\n",
       "       598057, 598058, 598059, 598060, 598061, 598062, 598063, 598064,\n",
       "       598065, 598066, 598067, 598068, 598069, 598070, 598071, 598072,\n",
       "       598073, 598074, 598075, 598076, 598077, 598078, 729284, 729286,\n",
       "       729288, 729290, 729292, 729294, 729296, 729298, 729300, 729302,\n",
       "       729304, 729306, 729308, 729310, 729312, 729314, 729316, 729318,\n",
       "       729320, 729322, 733976, 733978, 733980, 733982, 733984, 733986,\n",
       "       733988, 733990, 733992, 733994, 733996, 733998, 734000, 734002,\n",
       "       734004, 734006, 734008, 734010, 734012, 734014, 734016, 734018,\n",
       "       734020, 734022, 734024, 734026, 734028, 734030, 734032, 734034,\n",
       "       734036, 734038, 734040, 734042, 734044, 734046, 734048, 734050,\n",
       "       734052, 734054, 829710, 829712, 829714, 829716, 829718, 829720,\n",
       "       829722, 829724, 829726, 829728, 829730, 829732, 829734, 829736,\n",
       "       829738, 829740, 829742, 829744, 829746, 829748, 829750, 829752,\n",
       "       829754, 829756, 829758, 829762, 829764, 829766, 829768, 829770,\n",
       "       829772, 829774, 829776, 829778, 829780, 829782, 829784, 829786,\n",
       "       829788, 829790, 829792, 829794, 829796, 829798, 829800, 829802,\n",
       "       829804, 829806, 829808, 829810, 829812, 829814, 829816, 829818,\n",
       "       829820, 829822, 829824, 829826, 829828, 980906, 980908, 980910,\n",
       "       980912, 980914, 980916, 980918, 980920, 980922, 980924, 980926,\n",
       "       980928, 980930, 980932, 980934, 980936, 980938, 980940, 980942,\n",
       "       980944, 980946, 980948, 980950, 980952, 980954, 980956, 980958,\n",
       "       980960, 980962, 980964, 980966, 980968, 980970, 980972, 980974,\n",
       "       980976, 980978, 980980, 980982, 980984, 980986, 980988, 980990,\n",
       "       980992, 980994, 980996, 980998, 981000, 981002, 981004, 981006,\n",
       "       981008, 981010, 981012, 981014, 981016, 981018, 981020, 981022,\n",
       "       981024], dtype=object)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_array[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fa5d8028-1c1c-4b2c-b3fc-f6ede3363bed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
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       "      <th>IS_Result</th>\n",
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       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>980996</td>\n",
       "      <td>15-May-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>510</td>\n",
       "      <td>481</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>980998</td>\n",
       "      <td>15-May-16</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>6</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>413.0</td>\n",
       "      <td>507</td>\n",
       "      <td>498</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>981000</td>\n",
       "      <td>16-May-16</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>505</td>\n",
       "      <td>511</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>981002</td>\n",
       "      <td>17-May-16</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>12</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>by runs</td>\n",
       "      <td>19.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>507</td>\n",
       "      <td>499</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>981004</td>\n",
       "      <td>18-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>4</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>by runs</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>510</td>\n",
       "      <td>481</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>981006</td>\n",
       "      <td>19-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Green Park</td>\n",
       "      <td>13</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>495</td>\n",
       "      <td>498</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>981008</td>\n",
       "      <td>20-May-16</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>6</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>981010</td>\n",
       "      <td>21-May-16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>482</td>\n",
       "      <td>507</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>981012</td>\n",
       "      <td>21-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>Green Park</td>\n",
       "      <td>13</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>495</td>\n",
       "      <td>498</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>981014</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>510</td>\n",
       "      <td>481</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>981016</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981018</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>981020</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>1</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>481</td>\n",
       "      <td>499</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>981022</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>481</td>\n",
       "      <td>498</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>482</td>\n",
       "      <td>497</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "562    980996  15-May-16             4                11          9   \n",
       "563    980998  15-May-16             7                 6          9   \n",
       "564    981000  16-May-16             1                 2          9   \n",
       "565    981002  17-May-16            12                 6          9   \n",
       "566    981004  18-May-16             2                 4          9   \n",
       "567    981006  19-May-16            13                 1          9   \n",
       "568    981008  20-May-16             6                11          9   \n",
       "569    981010  21-May-16            12                 4          9   \n",
       "570    981012  21-May-16            13                 7          9   \n",
       "571    981014  22-May-16             1                11          9   \n",
       "572    981016  22-May-16             6                 2          9   \n",
       "573    981018  24-May-16            13                 2          9   \n",
       "574    981020  25-May-16            11                 1          9   \n",
       "575    981022  27-May-16            13                11          9   \n",
       "576    981024  29-May-16             2                11          9   \n",
       "\n",
       "                                            Venue_Name  Toss_Winner_Id  \\\n",
       "562  Punjab Cricket Association IS Bindra Stadium, ...               4   \n",
       "563  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...               6   \n",
       "564                                       Eden Gardens               2   \n",
       "565  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...              12   \n",
       "566                              M Chinnaswamy Stadium               4   \n",
       "567                                         Green Park              13   \n",
       "568   Shaheed Veer Narayan Singh International Stadium               6   \n",
       "569  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...               4   \n",
       "570                                         Green Park              13   \n",
       "571                                       Eden Gardens              11   \n",
       "572   Shaheed Veer Narayan Singh International Stadium               2   \n",
       "573                              M Chinnaswamy Stadium               2   \n",
       "574                                   Feroz Shah Kotla               1   \n",
       "575                                   Feroz Shah Kotla              11   \n",
       "576                              M Chinnaswamy Stadium              11   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "562           bat             0          1                  0  by wickets   \n",
       "563         field             0          1                  0     by runs   \n",
       "564         field             0          1                  0  by wickets   \n",
       "565         field             0          1                  1     by runs   \n",
       "566         field             0          1                  1     by runs   \n",
       "567         field             0          1                  0  by wickets   \n",
       "568         field             0          1                  0  by wickets   \n",
       "569           bat             0          1                  0  by wickets   \n",
       "570         field             0          1                  0  by wickets   \n",
       "571         field             0          1                  0     by runs   \n",
       "572         field             0          1                  0  by wickets   \n",
       "573         field             0          1                  0  by wickets   \n",
       "574         field             0          1                  0     by runs   \n",
       "575         field             0          1                  0  by wickets   \n",
       "576           bat             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "562     7.0             11.0                427.0              510   \n",
       "563    80.0              7.0                413.0              507   \n",
       "564     9.0              2.0                  8.0              505   \n",
       "565    19.0             12.0                106.0              507   \n",
       "566    82.0              2.0                  8.0              510   \n",
       "567     6.0             13.0                147.0              495   \n",
       "568     6.0              6.0                339.0              511   \n",
       "569     4.0             12.0                 20.0              482   \n",
       "570     6.0             13.0                 21.0              495   \n",
       "571    22.0              1.0                 31.0              510   \n",
       "572     6.0              2.0                  8.0              511   \n",
       "573     4.0              2.0                110.0              495   \n",
       "574    22.0             11.0                163.0              481   \n",
       "575     4.0             11.0                187.0              481   \n",
       "576     8.0             11.0                385.0              482   \n",
       "\n",
       "     Second_Umpire_Id      City_Name Host_Country  \n",
       "562               481     Chandigarh        India  \n",
       "563               498  Visakhapatnam        India  \n",
       "564               511        Kolkata        India  \n",
       "565               499  Visakhapatnam        India  \n",
       "566               481      Bangalore        India  \n",
       "567               498         Kanpur        India  \n",
       "568               497         Raipur        India  \n",
       "569               507  Visakhapatnam        India  \n",
       "570               498         Kanpur        India  \n",
       "571               481        Kolkata        India  \n",
       "572               497         Raipur        India  \n",
       "573               482      Bangalore        India  \n",
       "574               499          Delhi        India  \n",
       "575               498          Delhi        India  \n",
       "576               497      Bangalore        India  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "964905e1-d3ba-4d56-9949-c1c4cd92284f",
   "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>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0    335987  18-Apr-08             2                 1          1   \n",
       "1    335988  19-Apr-08             4                 3          1   \n",
       "2    335989  19-Apr-08             6                 5          1   \n",
       "3    335990  20-Apr-08             7                 2          1   \n",
       "4    335991  20-Apr-08             1                 8          1   \n",
       "\n",
       "                                   Venue_Name  Toss_Winner_Id Toss_Decision  \\\n",
       "0                       M Chinnaswamy Stadium               2         field   \n",
       "1  Punjab Cricket Association Stadium, Mohali               3           bat   \n",
       "2                            Feroz Shah Kotla               5           bat   \n",
       "3                            Wankhede Stadium               7           bat   \n",
       "4                                Eden Gardens               8           bat   \n",
       "\n",
       "   IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  Won_By  \\\n",
       "0             0          1                  0     by runs   140.0   \n",
       "1             0          1                  0     by runs    33.0   \n",
       "2             0          1                  0  by wickets     9.0   \n",
       "3             0          1                  0  by wickets     5.0   \n",
       "4             0          1                  0  by wickets     5.0   \n",
       "\n",
       "   Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  Second_Umpire_Id  \\\n",
       "0              1.0                  2.0              470               477   \n",
       "1              3.0                 19.0              471               487   \n",
       "2              6.0                 90.0              472               512   \n",
       "3              2.0                 11.0              473               476   \n",
       "4              1.0                  4.0              474               486   \n",
       "\n",
       "    City_Name Host_Country  \n",
       "0   Bangalore        India  \n",
       "1  Chandigarh        India  \n",
       "2       Delhi        India  \n",
       "3      Mumbai        India  \n",
       "4     Kolkata        India  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "86e963bc-3926-4a21-8c68-c53dfdaa4c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Match_Id\n",
       "335987    1\n",
       "548354    1\n",
       "598062    1\n",
       "598063    1\n",
       "598064    1\n",
       "         ..\n",
       "501221    1\n",
       "501223    1\n",
       "501224    1\n",
       "501225    1\n",
       "981024    1\n",
       "Name: count, Length: 577, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Match_Id'].value_counts() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3d2a030d-59a3-4e68-a178-83cb89567321",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'Match_Id'}>,\n",
       "        <Axes: title={'center': 'Team_Name_Id'}>,\n",
       "        <Axes: title={'center': 'Opponent_Team_Id'}>,\n",
       "        <Axes: title={'center': 'Season_Id'}>],\n",
       "       [<Axes: title={'center': 'Toss_Winner_Id'}>,\n",
       "        <Axes: title={'center': 'IS_Superover'}>,\n",
       "        <Axes: title={'center': 'IS_Result'}>,\n",
       "        <Axes: title={'center': 'Is_DuckWorthLewis'}>],\n",
       "       [<Axes: title={'center': 'Won_By'}>,\n",
       "        <Axes: title={'center': 'Match_Winner_Id'}>,\n",
       "        <Axes: title={'center': 'Man_Of_The_Match_Id'}>,\n",
       "        <Axes: title={'center': 'First_Umpire_Id'}>],\n",
       "       [<Axes: title={'center': 'Second_Umpire_Id'}>, <Axes: >, <Axes: >,\n",
       "        <Axes: >]], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x3000 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(bins=50 ,figsize=(10,30))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "017597bd-3c50-4bcb-b6f1-d060a5639cf2",
   "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>Match_Id</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>568.000000</td>\n",
       "      <td>574.000000</td>\n",
       "      <td>574.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>577.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>591635.963605</td>\n",
       "      <td>5.102253</td>\n",
       "      <td>5.211438</td>\n",
       "      <td>5.029463</td>\n",
       "      <td>5.192374</td>\n",
       "      <td>0.010399</td>\n",
       "      <td>0.994801</td>\n",
       "      <td>0.025997</td>\n",
       "      <td>17.350352</td>\n",
       "      <td>4.991289</td>\n",
       "      <td>139.782230</td>\n",
       "      <td>484.098787</td>\n",
       "      <td>495.155979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>196366.642396</td>\n",
       "      <td>3.062703</td>\n",
       "      <td>3.024841</td>\n",
       "      <td>2.486247</td>\n",
       "      <td>3.024680</td>\n",
       "      <td>0.101530</td>\n",
       "      <td>0.071981</td>\n",
       "      <td>0.159263</td>\n",
       "      <td>21.908229</td>\n",
       "      <td>3.005218</td>\n",
       "      <td>117.304944</td>\n",
       "      <td>10.709012</td>\n",
       "      <td>12.353865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>335987.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>470.000000</td>\n",
       "      <td>471.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>419140.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>475.000000</td>\n",
       "      <td>488.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>548353.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>105.500000</td>\n",
       "      <td>482.000000</td>\n",
       "      <td>493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>734004.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>209.500000</td>\n",
       "      <td>493.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>981024.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>460.000000</td>\n",
       "      <td>511.000000</td>\n",
       "      <td>521.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Match_Id  Team_Name_Id  Opponent_Team_Id   Season_Id  \\\n",
       "count     577.000000    577.000000        577.000000  577.000000   \n",
       "mean   591635.963605      5.102253          5.211438    5.029463   \n",
       "std    196366.642396      3.062703          3.024841    2.486247   \n",
       "min    335987.000000      1.000000          1.000000    1.000000   \n",
       "25%    419140.000000      3.000000          3.000000    3.000000   \n",
       "50%    548353.000000      5.000000          5.000000    5.000000   \n",
       "75%    734004.000000      7.000000          7.000000    7.000000   \n",
       "max    981024.000000     13.000000         13.000000    9.000000   \n",
       "\n",
       "       Toss_Winner_Id  IS_Superover   IS_Result  Is_DuckWorthLewis  \\\n",
       "count      577.000000    577.000000  577.000000         577.000000   \n",
       "mean         5.192374      0.010399    0.994801           0.025997   \n",
       "std          3.024680      0.101530    0.071981           0.159263   \n",
       "min          1.000000      0.000000    0.000000           0.000000   \n",
       "25%          3.000000      0.000000    1.000000           0.000000   \n",
       "50%          5.000000      0.000000    1.000000           0.000000   \n",
       "75%          7.000000      0.000000    1.000000           0.000000   \n",
       "max         13.000000      1.000000    1.000000           1.000000   \n",
       "\n",
       "           Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "count  568.000000       574.000000           574.000000       577.000000   \n",
       "mean    17.350352         4.991289           139.782230       484.098787   \n",
       "std     21.908229         3.005218           117.304944        10.709012   \n",
       "min      1.000000         1.000000             1.000000       470.000000   \n",
       "25%      6.000000         3.000000            40.000000       475.000000   \n",
       "50%      8.000000         5.000000           105.500000       482.000000   \n",
       "75%     21.000000         7.000000           209.500000       493.000000   \n",
       "max    144.000000        13.000000           460.000000       511.000000   \n",
       "\n",
       "       Second_Umpire_Id  \n",
       "count        577.000000  \n",
       "mean         495.155979  \n",
       "std           12.353865  \n",
       "min          471.000000  \n",
       "25%          488.000000  \n",
       "50%          493.000000  \n",
       "75%          500.000000  \n",
       "max          521.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "42ca6b22-e2c1-4112-ab42-976ed9a03abf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(577, 19)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6370c23d-66b4-4aa5-93f8-9d64331cc3c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Match_Id', 'Match_Date', 'Team_Name_Id', 'Opponent_Team_Id',\n",
       "       'Season_Id', 'Venue_Name', 'Toss_Winner_Id', 'Toss_Decision',\n",
       "       'IS_Superover', 'IS_Result', 'Is_DuckWorthLewis', 'Win_Type', 'Won_By',\n",
       "       'Match_Winner_Id', 'Man_Of_The_Match_Id', 'First_Umpire_Id',\n",
       "       'Second_Umpire_Id', 'City_Name', 'Host_Country'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ba16827a-f259-4483-a0e3-3506f0251301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0    335987  18-Apr-08             2                 1          1   \n",
       "1    335988  19-Apr-08             4                 3          1   \n",
       "2    335989  19-Apr-08             6                 5          1   \n",
       "3    335990  20-Apr-08             7                 2          1   \n",
       "4    335991  20-Apr-08             1                 8          1   \n",
       "\n",
       "                                   Venue_Name  Toss_Winner_Id Toss_Decision  \\\n",
       "0                       M Chinnaswamy Stadium               2         field   \n",
       "1  Punjab Cricket Association Stadium, Mohali               3           bat   \n",
       "2                            Feroz Shah Kotla               5           bat   \n",
       "3                            Wankhede Stadium               7           bat   \n",
       "4                                Eden Gardens               8           bat   \n",
       "\n",
       "   IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  Won_By  \\\n",
       "0             0          1                  0     by runs   140.0   \n",
       "1             0          1                  0     by runs    33.0   \n",
       "2             0          1                  0  by wickets     9.0   \n",
       "3             0          1                  0  by wickets     5.0   \n",
       "4             0          1                  0  by wickets     5.0   \n",
       "\n",
       "   Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  Second_Umpire_Id  \\\n",
       "0              1.0                  2.0              470               477   \n",
       "1              3.0                 19.0              471               487   \n",
       "2              6.0                 90.0              472               512   \n",
       "3              2.0                 11.0              473               476   \n",
       "4              1.0                  4.0              474               486   \n",
       "\n",
       "    City_Name Host_Country  \n",
       "0   Bangalore        India  \n",
       "1  Chandigarh        India  \n",
       "2       Delhi        India  \n",
       "3      Mumbai        India  \n",
       "4     Kolkata        India  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "854ab13e-7a71-4277-9933-2e5c20e4f479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>981016</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981018</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>981020</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>1</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>481</td>\n",
       "      <td>499</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>981022</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>481</td>\n",
       "      <td>498</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>482</td>\n",
       "      <td>497</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>577 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0      335987  18-Apr-08             2                 1          1   \n",
       "1      335988  19-Apr-08             4                 3          1   \n",
       "2      335989  19-Apr-08             6                 5          1   \n",
       "3      335990  20-Apr-08             7                 2          1   \n",
       "4      335991  20-Apr-08             1                 8          1   \n",
       "..        ...        ...           ...               ...        ...   \n",
       "572    981016  22-May-16             6                 2          9   \n",
       "573    981018  24-May-16            13                 2          9   \n",
       "574    981020  25-May-16            11                 1          9   \n",
       "575    981022  27-May-16            13                11          9   \n",
       "576    981024  29-May-16             2                11          9   \n",
       "\n",
       "                                           Venue_Name  Toss_Winner_Id  \\\n",
       "0                               M Chinnaswamy Stadium               2   \n",
       "1          Punjab Cricket Association Stadium, Mohali               3   \n",
       "2                                    Feroz Shah Kotla               5   \n",
       "3                                    Wankhede Stadium               7   \n",
       "4                                        Eden Gardens               8   \n",
       "..                                                ...             ...   \n",
       "572  Shaheed Veer Narayan Singh International Stadium               2   \n",
       "573                             M Chinnaswamy Stadium               2   \n",
       "574                                  Feroz Shah Kotla               1   \n",
       "575                                  Feroz Shah Kotla              11   \n",
       "576                             M Chinnaswamy Stadium              11   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "0           field             0          1                  0     by runs   \n",
       "1             bat             0          1                  0     by runs   \n",
       "2             bat             0          1                  0  by wickets   \n",
       "3             bat             0          1                  0  by wickets   \n",
       "4             bat             0          1                  0  by wickets   \n",
       "..            ...           ...        ...                ...         ...   \n",
       "572         field             0          1                  0  by wickets   \n",
       "573         field             0          1                  0  by wickets   \n",
       "574         field             0          1                  0     by runs   \n",
       "575         field             0          1                  0  by wickets   \n",
       "576           bat             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "0     140.0              1.0                  2.0              470   \n",
       "1      33.0              3.0                 19.0              471   \n",
       "2       9.0              6.0                 90.0              472   \n",
       "3       5.0              2.0                 11.0              473   \n",
       "4       5.0              1.0                  4.0              474   \n",
       "..      ...              ...                  ...              ...   \n",
       "572     6.0              2.0                  8.0              511   \n",
       "573     4.0              2.0                110.0              495   \n",
       "574    22.0             11.0                163.0              481   \n",
       "575     4.0             11.0                187.0              481   \n",
       "576     8.0             11.0                385.0              482   \n",
       "\n",
       "     Second_Umpire_Id   City_Name Host_Country  \n",
       "0                 477   Bangalore        India  \n",
       "1                 487  Chandigarh        India  \n",
       "2                 512       Delhi        India  \n",
       "3                 476      Mumbai        India  \n",
       "4                 486     Kolkata        India  \n",
       "..                ...         ...          ...  \n",
       "572               497      Raipur        India  \n",
       "573               482   Bangalore        India  \n",
       "574               499       Delhi        India  \n",
       "575               498       Delhi        India  \n",
       "576               497   Bangalore        India  \n",
       "\n",
       "[577 rows x 19 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "556512a2-a153-4e8a-8fe3-54571bf8d716",
   "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>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>335992</td>\n",
       "      <td>21-Apr-08</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>472</td>\n",
       "      <td>513</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>335993</td>\n",
       "      <td>22-Apr-08</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>475</td>\n",
       "      <td>492</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>335994</td>\n",
       "      <td>23-Apr-08</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>7</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>476</td>\n",
       "      <td>512</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>335995</td>\n",
       "      <td>24-Apr-08</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>5</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>470</td>\n",
       "      <td>471</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>335996</td>\n",
       "      <td>25-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>7</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>472</td>\n",
       "      <td>492</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0    335987  18-Apr-08             2                 1          1   \n",
       "1    335988  19-Apr-08             4                 3          1   \n",
       "2    335989  19-Apr-08             6                 5          1   \n",
       "3    335990  20-Apr-08             7                 2          1   \n",
       "4    335991  20-Apr-08             1                 8          1   \n",
       "5    335992  21-Apr-08             5                 4          1   \n",
       "6    335993  22-Apr-08             8                 6          1   \n",
       "7    335994  23-Apr-08             3                 7          1   \n",
       "8    335995  24-Apr-08             8                 5          1   \n",
       "9    335996  25-Apr-08             4                 7          1   \n",
       "\n",
       "                                   Venue_Name  Toss_Winner_Id Toss_Decision  \\\n",
       "0                       M Chinnaswamy Stadium               2         field   \n",
       "1  Punjab Cricket Association Stadium, Mohali               3           bat   \n",
       "2                            Feroz Shah Kotla               5           bat   \n",
       "3                            Wankhede Stadium               7           bat   \n",
       "4                                Eden Gardens               8           bat   \n",
       "5                      Sawai Mansingh Stadium               4           bat   \n",
       "6   Rajiv Gandhi International Stadium, Uppal               8           bat   \n",
       "7             MA Chidambaram Stadium, Chepauk               7         field   \n",
       "8   Rajiv Gandhi International Stadium, Uppal               5         field   \n",
       "9  Punjab Cricket Association Stadium, Mohali               7         field   \n",
       "\n",
       "   IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  Won_By  \\\n",
       "0             0          1                  0     by runs   140.0   \n",
       "1             0          1                  0     by runs    33.0   \n",
       "2             0          1                  0  by wickets     9.0   \n",
       "3             0          1                  0  by wickets     5.0   \n",
       "4             0          1                  0  by wickets     5.0   \n",
       "5             0          1                  0  by wickets     6.0   \n",
       "6             0          1                  0  by wickets     9.0   \n",
       "7             0          1                  0     by runs     6.0   \n",
       "8             0          1                  0  by wickets     3.0   \n",
       "9             0          1                  0     by runs    66.0   \n",
       "\n",
       "   Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  Second_Umpire_Id  \\\n",
       "0              1.0                  2.0              470               477   \n",
       "1              3.0                 19.0              471               487   \n",
       "2              6.0                 90.0              472               512   \n",
       "3              2.0                 11.0              473               476   \n",
       "4              1.0                  4.0              474               486   \n",
       "5              5.0                 32.0              472               513   \n",
       "6              6.0                 41.0              475               492   \n",
       "7              3.0                 18.0              476               512   \n",
       "8              5.0                 31.0              470               471   \n",
       "9              4.0                 26.0              472               492   \n",
       "\n",
       "    City_Name Host_Country  \n",
       "0   Bangalore        India  \n",
       "1  Chandigarh        India  \n",
       "2       Delhi        India  \n",
       "3      Mumbai        India  \n",
       "4     Kolkata        India  \n",
       "5      Jaipur        India  \n",
       "6   Hyderabad        India  \n",
       "7     Chennai        India  \n",
       "8   Hyderabad        India  \n",
       "9  Chandigarh        India  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:10]"
   ]
  },
  {
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   "execution_count": 52,
   "id": "3dddc045-8914-41a1-96d4-7d9d2a9e3d45",
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   "outputs": [
    {
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       "      <th>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
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       "Empty DataFrame\n",
       "Columns: [Match_Id, Match_Date, Team_Name_Id, Opponent_Team_Id, Season_Id, Venue_Name, Toss_Winner_Id, Toss_Decision, IS_Superover, IS_Result, Is_DuckWorthLewis, Win_Type, Won_By, Match_Winner_Id, Man_Of_The_Match_Id, First_Umpire_Id, Second_Umpire_Id, City_Name, Host_Country]\n",
       "Index: []"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[12000:]"
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  {
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   "execution_count": 53,
   "id": "1516ecff-6486-4bea-8de8-604fd6efca5c",
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   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
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       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
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       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
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       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>482</td>\n",
       "      <td>497</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>981022</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>481</td>\n",
       "      <td>498</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>981020</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>1</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>481</td>\n",
       "      <td>499</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981018</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>981016</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>577 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "576    981024  29-May-16             2                11          9   \n",
       "575    981022  27-May-16            13                11          9   \n",
       "574    981020  25-May-16            11                 1          9   \n",
       "573    981018  24-May-16            13                 2          9   \n",
       "572    981016  22-May-16             6                 2          9   \n",
       "..        ...        ...           ...               ...        ...   \n",
       "4      335991  20-Apr-08             1                 8          1   \n",
       "3      335990  20-Apr-08             7                 2          1   \n",
       "2      335989  19-Apr-08             6                 5          1   \n",
       "1      335988  19-Apr-08             4                 3          1   \n",
       "0      335987  18-Apr-08             2                 1          1   \n",
       "\n",
       "                                           Venue_Name  Toss_Winner_Id  \\\n",
       "576                             M Chinnaswamy Stadium              11   \n",
       "575                                  Feroz Shah Kotla              11   \n",
       "574                                  Feroz Shah Kotla               1   \n",
       "573                             M Chinnaswamy Stadium               2   \n",
       "572  Shaheed Veer Narayan Singh International Stadium               2   \n",
       "..                                                ...             ...   \n",
       "4                                        Eden Gardens               8   \n",
       "3                                    Wankhede Stadium               7   \n",
       "2                                    Feroz Shah Kotla               5   \n",
       "1          Punjab Cricket Association Stadium, Mohali               3   \n",
       "0                               M Chinnaswamy Stadium               2   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "576           bat             0          1                  0     by runs   \n",
       "575         field             0          1                  0  by wickets   \n",
       "574         field             0          1                  0     by runs   \n",
       "573         field             0          1                  0  by wickets   \n",
       "572         field             0          1                  0  by wickets   \n",
       "..            ...           ...        ...                ...         ...   \n",
       "4             bat             0          1                  0  by wickets   \n",
       "3             bat             0          1                  0  by wickets   \n",
       "2             bat             0          1                  0  by wickets   \n",
       "1             bat             0          1                  0     by runs   \n",
       "0           field             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "576     8.0             11.0                385.0              482   \n",
       "575     4.0             11.0                187.0              481   \n",
       "574    22.0             11.0                163.0              481   \n",
       "573     4.0              2.0                110.0              495   \n",
       "572     6.0              2.0                  8.0              511   \n",
       "..      ...              ...                  ...              ...   \n",
       "4       5.0              1.0                  4.0              474   \n",
       "3       5.0              2.0                 11.0              473   \n",
       "2       9.0              6.0                 90.0              472   \n",
       "1      33.0              3.0                 19.0              471   \n",
       "0     140.0              1.0                  2.0              470   \n",
       "\n",
       "     Second_Umpire_Id   City_Name Host_Country  \n",
       "576               497   Bangalore        India  \n",
       "575               498       Delhi        India  \n",
       "574               499       Delhi        India  \n",
       "573               482   Bangalore        India  \n",
       "572               497      Raipur        India  \n",
       "..                ...         ...          ...  \n",
       "4                 486     Kolkata        India  \n",
       "3                 476      Mumbai        India  \n",
       "2                 512       Delhi        India  \n",
       "1                 487  Chandigarh        India  \n",
       "0                 477   Bangalore        India  \n",
       "\n",
       "[577 rows x 19 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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   ]
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  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3d8036cc-3e94-45a9-88cf-7f9bb34e5c73",
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   "outputs": [
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       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>336007</td>\n",
       "      <td>25-May-08</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>336027</td>\n",
       "      <td>17-May-08</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>6</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>by runs</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>479</td>\n",
       "      <td>477</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>392188</td>\n",
       "      <td>19-Apr-09</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Newlands</td>\n",
       "      <td>6</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>471</td>\n",
       "      <td>515</td>\n",
       "      <td>Cape Town</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>392210</td>\n",
       "      <td>2-May-09</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>St George's Park</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>483</td>\n",
       "      <td>480</td>\n",
       "      <td>Port Elizabeth</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>392230</td>\n",
       "      <td>14-May-09</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Kingsmead</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>478</td>\n",
       "      <td>476</td>\n",
       "      <td>Durban</td>\n",
       "      <td>South Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>419116</td>\n",
       "      <td>15-Mar-10</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>Sardar Patel Stadium, Motera</td>\n",
       "      <td>6</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>480</td>\n",
       "      <td>477</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>419136</td>\n",
       "      <td>29-Mar-10</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>6</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>485</td>\n",
       "      <td>490</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>419156</td>\n",
       "      <td>12-Apr-10</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Vidarbha Cricket Association Stadium, Jamtha</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>477</td>\n",
       "      <td>513</td>\n",
       "      <td>Nagpur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>501208</td>\n",
       "      <td>11-Apr-11</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>1</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>477</td>\n",
       "      <td>488</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>501229</td>\n",
       "      <td>24-Apr-11</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>8</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>37.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>482</td>\n",
       "      <td>517</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>501249</td>\n",
       "      <td>6-May-11</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>4</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>472</td>\n",
       "      <td>513</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>501269</td>\n",
       "      <td>21-May-11</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>Himachal Pradesh Cricket Association Stadium</td>\n",
       "      <td>4</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>82.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>470</td>\n",
       "      <td>492</td>\n",
       "      <td>Dharamsala</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>548323</td>\n",
       "      <td>12-Apr-12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>2</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>482</td>\n",
       "      <td>518</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>548344</td>\n",
       "      <td>25-Apr-12</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>472</td>\n",
       "      <td>497</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>548365</td>\n",
       "      <td>9-May-12</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>162.0</td>\n",
       "      <td>474</td>\n",
       "      <td>496</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>548385</td>\n",
       "      <td>25-May-12</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>6</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>86.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>185.0</td>\n",
       "      <td>478</td>\n",
       "      <td>490</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>598021</td>\n",
       "      <td>16-Apr-13</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>1</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>498</td>\n",
       "      <td>490</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>598041</td>\n",
       "      <td>29-Apr-13</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>5</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>351.0</td>\n",
       "      <td>481</td>\n",
       "      <td>503</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>598061</td>\n",
       "      <td>11-May-13</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>4</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>491</td>\n",
       "      <td>518</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>729288</td>\n",
       "      <td>18-Apr-14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>501</td>\n",
       "      <td>499</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>U.A.E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>733980</td>\n",
       "      <td>3-May-14</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>339.0</td>\n",
       "      <td>485</td>\n",
       "      <td>489</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>734020</td>\n",
       "      <td>18-May-14</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>500</td>\n",
       "      <td>498</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>829714</td>\n",
       "      <td>10-Apr-15</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>4</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>26.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>504</td>\n",
       "      <td>505</td>\n",
       "      <td>Pune</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>829754</td>\n",
       "      <td>24-Apr-15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>Sardar Patel Stadium, Motera</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>378.0</td>\n",
       "      <td>481</td>\n",
       "      <td>489</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>829796</td>\n",
       "      <td>9-May-15</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>980912</td>\n",
       "      <td>12-Apr-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>482</td>\n",
       "      <td>521</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>980952</td>\n",
       "      <td>28-Apr-16</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>507</td>\n",
       "      <td>518</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>980992</td>\n",
       "      <td>14-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>13</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>144.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>509</td>\n",
       "      <td>521</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0      335987  18-Apr-08             2                 1          1   \n",
       "20     336007  25-May-08             8                 2          1   \n",
       "40     336027  17-May-08             6                 4          1   \n",
       "60     392188  19-Apr-09             6                 4          2   \n",
       "80     392210   2-May-09             8                 5          2   \n",
       "100    392230  14-May-09             7                 5          2   \n",
       "120    419116  15-Mar-10             5                 6          3   \n",
       "140    419136  29-Mar-10             6                 1          3   \n",
       "160    419156  12-Apr-10             8                 2          3   \n",
       "180    501208  11-Apr-11             1                 8          4   \n",
       "200    501229  24-Apr-11             8                 7          4   \n",
       "220    501249   6-May-11             2                 4          4   \n",
       "240    501269  21-May-11             4                 8          4   \n",
       "260    548323  12-Apr-12             3                 2          5   \n",
       "280    548344  25-Apr-12             4                 7          5   \n",
       "300    548365   9-May-12             7                 2          5   \n",
       "320    548385  25-May-12             6                 3          5   \n",
       "340    598021  16-Apr-13             4                 1          6   \n",
       "360    598041  29-Apr-13             5                 2          6   \n",
       "380    598061  11-May-13             4                11          6   \n",
       "400    729288  18-Apr-14             3                 4          7   \n",
       "420    733980   3-May-14             6                 5          7   \n",
       "440    734020  18-May-14            11                 1          7   \n",
       "460    829714  10-Apr-15             4                 5          8   \n",
       "480    829754  24-Apr-15             5                 2          8   \n",
       "500    829796   9-May-15             1                 4          8   \n",
       "520    980912  12-Apr-16             2                11          9   \n",
       "540    980952  28-Apr-16             7                 1          9   \n",
       "560    980992  14-May-16             2                13          9   \n",
       "\n",
       "                                       Venue_Name  Toss_Winner_Id  \\\n",
       "0                           M Chinnaswamy Stadium               2   \n",
       "20      Rajiv Gandhi International Stadium, Uppal               8   \n",
       "40                               Feroz Shah Kotla               6   \n",
       "60                                       Newlands               6   \n",
       "80                               St George's Park               8   \n",
       "100                                     Kingsmead               5   \n",
       "120                  Sardar Patel Stadium, Motera               6   \n",
       "140                              Feroz Shah Kotla               6   \n",
       "160  Vidarbha Cricket Association Stadium, Jamtha               2   \n",
       "180                                  Eden Gardens               1   \n",
       "200     Rajiv Gandhi International Stadium, Uppal               8   \n",
       "220                         M Chinnaswamy Stadium               4   \n",
       "240  Himachal Pradesh Cricket Association Stadium               4   \n",
       "260               MA Chidambaram Stadium, Chepauk               2   \n",
       "280    Punjab Cricket Association Stadium, Mohali               4   \n",
       "300                              Wankhede Stadium               2   \n",
       "320               MA Chidambaram Stadium, Chepauk               6   \n",
       "340    Punjab Cricket Association Stadium, Mohali               1   \n",
       "360                        Sawai Mansingh Stadium               5   \n",
       "380    Punjab Cricket Association Stadium, Mohali               4   \n",
       "400                          Sheikh Zayed Stadium               3   \n",
       "420                              Feroz Shah Kotla               5   \n",
       "440     Rajiv Gandhi International Stadium, Uppal              11   \n",
       "460       Maharashtra Cricket Association Stadium               4   \n",
       "480                  Sardar Patel Stadium, Motera               2   \n",
       "500                                  Eden Gardens               4   \n",
       "520                         M Chinnaswamy Stadium              11   \n",
       "540                              Wankhede Stadium               7   \n",
       "560                         M Chinnaswamy Stadium              13   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "0           field             0          1                  0     by runs   \n",
       "20            bat             0          1                  0  by wickets   \n",
       "40            bat             0          1                  1     by runs   \n",
       "60          field             0          1                  1  by wickets   \n",
       "80            bat             0          1                  0  by wickets   \n",
       "100           bat             0          1                  0     by runs   \n",
       "120         field             0          1                  0  by wickets   \n",
       "140           bat             0          1                  0     by runs   \n",
       "160         field             0          1                  0     by runs   \n",
       "180           bat             0          1                  0     by runs   \n",
       "200         field             0          1                  0     by runs   \n",
       "220         field             0          1                  0     by runs   \n",
       "240         field             0          1                  0     by runs   \n",
       "260           bat             0          1                  0  by wickets   \n",
       "280           bat             0          1                  0  by wickets   \n",
       "300         field             0          1                  0  by wickets   \n",
       "320         field             0          1                  0     by runs   \n",
       "340         field             0          1                  0     by runs   \n",
       "360         field             0          1                  0  by wickets   \n",
       "380         field             0          1                  0     by runs   \n",
       "400           bat             0          1                  0  by wickets   \n",
       "420         field             0          1                  0  by wickets   \n",
       "440           bat             0          1                  0  by wickets   \n",
       "460         field             0          1                  0     by runs   \n",
       "480         field             0          1                  0  by wickets   \n",
       "500           bat             0          1                  0  by wickets   \n",
       "520         field             0          1                  0     by runs   \n",
       "540         field             0          1                  0  by wickets   \n",
       "560         field             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "0     140.0              1.0                  2.0              470   \n",
       "20      5.0              2.0                 81.0              470   \n",
       "40      6.0              4.0                 64.0              479   \n",
       "60     10.0              6.0                175.0              471   \n",
       "80      3.0              5.0                 31.0              483   \n",
       "100     2.0              5.0                 38.0              478   \n",
       "120     6.0              6.0                 41.0              480   \n",
       "140    40.0              6.0                187.0              485   \n",
       "160    13.0              8.0                256.0              477   \n",
       "180     9.0              1.0                  9.0              477   \n",
       "200    37.0              7.0                194.0              482   \n",
       "220    85.0              2.0                162.0              472   \n",
       "240    82.0              8.0                 42.0              470   \n",
       "260     5.0              3.0                303.0              482   \n",
       "280     4.0              7.0                208.0              472   \n",
       "300     9.0              2.0                162.0              474   \n",
       "320    86.0              3.0                185.0              478   \n",
       "340     4.0              4.0                120.0              498   \n",
       "360     4.0              5.0                351.0              481   \n",
       "380    30.0             11.0                 17.0              491   \n",
       "400     6.0              4.0                305.0              501   \n",
       "420     7.0              5.0                339.0              485   \n",
       "440     7.0              1.0                232.0              500   \n",
       "460    26.0              5.0                310.0              504   \n",
       "480     9.0              2.0                378.0              481   \n",
       "500     1.0              1.0                334.0              495   \n",
       "520    45.0              2.0                110.0              482   \n",
       "540     6.0              7.0                 57.0              507   \n",
       "560   144.0              2.0                110.0              509   \n",
       "\n",
       "     Second_Umpire_Id       City_Name  Host_Country  \n",
       "0                 477       Bangalore         India  \n",
       "20                477       Hyderabad         India  \n",
       "40                477           Delhi         India  \n",
       "60                515       Cape Town  South Africa  \n",
       "80                480  Port Elizabeth  South Africa  \n",
       "100               476          Durban  South Africa  \n",
       "120               477       Ahmedabad         India  \n",
       "140               490           Delhi         India  \n",
       "160               513          Nagpur         India  \n",
       "180               488         Kolkata         India  \n",
       "200               517       Hyderabad         India  \n",
       "220               513       Bangalore         India  \n",
       "240               492      Dharamsala         India  \n",
       "260               518         Chennai         India  \n",
       "280               497      Chandigarh         India  \n",
       "300               496          Mumbai         India  \n",
       "320               490         Chennai         India  \n",
       "340               490      Chandigarh         India  \n",
       "360               503          Jaipur         India  \n",
       "380               518      Chandigarh         India  \n",
       "400               499       Abu Dhabi         U.A.E  \n",
       "420               489           Delhi         India  \n",
       "440               498       Hyderabad         India  \n",
       "460               505            Pune         India  \n",
       "480               489       Ahmedabad         India  \n",
       "500               482         Kolkata         India  \n",
       "520               521       Bangalore         India  \n",
       "540               518          Mumbai         India  \n",
       "560               521       Bangalore         India  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[::20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7af9bb1f-b015-4f5b-91b1-9e09e6371827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      335987\n",
       "1      335988\n",
       "2      335989\n",
       "3      335990\n",
       "4      335991\n",
       "        ...  \n",
       "572    981016\n",
       "573    981018\n",
       "574    981020\n",
       "575    981022\n",
       "576    981024\n",
       "Name: Match_Id, Length: 577, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Match_Id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "04339ff7-4f59-490e-aea1-1ee02510f1f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Match_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>335987</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>335988</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>335989</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>335990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>335991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>22-May-16</td>\n",
       "      <td>981016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>24-May-16</td>\n",
       "      <td>981018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>25-May-16</td>\n",
       "      <td>981020</td>\n",
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       "      <th>575</th>\n",
       "      <td>27-May-16</td>\n",
       "      <td>981022</td>\n",
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       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>29-May-16</td>\n",
       "      <td>981024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>577 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Match_Date  Match_Id\n",
       "0    18-Apr-08    335987\n",
       "1    19-Apr-08    335988\n",
       "2    19-Apr-08    335989\n",
       "3    20-Apr-08    335990\n",
       "4    20-Apr-08    335991\n",
       "..         ...       ...\n",
       "572  22-May-16    981016\n",
       "573  24-May-16    981018\n",
       "574  25-May-16    981020\n",
       "575  27-May-16    981022\n",
       "576  29-May-16    981024\n",
       "\n",
       "[577 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df[['Match_Date','Match_Id']]"
   ]
  },
  {
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   "execution_count": 57,
   "id": "352e5716-7db5-4d6e-857c-0b4e5eccec55",
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       "      <th>Match_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>335987</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>335988</td>\n",
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       "      <th>2</th>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>335989</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>335990</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>335991</td>\n",
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      "text/plain": [
       "  Match_Date  Match_Id\n",
       "0  18-Apr-08    335987\n",
       "1  19-Apr-08    335988\n",
       "2  19-Apr-08    335989\n",
       "3  20-Apr-08    335990\n",
       "4  20-Apr-08    335991"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Match_Date','Match_Id']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3c0853c7-69d5-4778-b25d-7855d65f9293",
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   "outputs": [
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       "      <th>Match_Id</th>\n",
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       "  <tbody>\n",
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       "      <th>20</th>\n",
       "      <td>25-May-08</td>\n",
       "      <td>336007</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3-May-08</td>\n",
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       "      <th>22</th>\n",
       "      <td>4-May-08</td>\n",
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       "      <td>336013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8-May-08</td>\n",
       "      <td>336014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>8-May-08</td>\n",
       "      <td>336015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9-May-08</td>\n",
       "      <td>336016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>28-May-08</td>\n",
       "      <td>336017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10-May-08</td>\n",
       "      <td>336018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>11-May-08</td>\n",
       "      <td>336019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>11-May-08</td>\n",
       "      <td>336020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>12-May-08</td>\n",
       "      <td>336021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>13-May-08</td>\n",
       "      <td>336022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>14-May-08</td>\n",
       "      <td>336023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>28-May-08</td>\n",
       "      <td>336024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>15-May-08</td>\n",
       "      <td>336025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>16-May-08</td>\n",
       "      <td>336026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Match_Date  Match_Id\n",
       "20  25-May-08    336007\n",
       "21   3-May-08    336008\n",
       "22   4-May-08    336009\n",
       "23   4-May-08    336010\n",
       "24   5-May-08    336011\n",
       "25   6-May-08    336012\n",
       "26   7-May-08    336013\n",
       "27   8-May-08    336014\n",
       "28   8-May-08    336015\n",
       "29   9-May-08    336016\n",
       "30  28-May-08    336017\n",
       "31  10-May-08    336018\n",
       "32  11-May-08    336019\n",
       "33  11-May-08    336020\n",
       "34  12-May-08    336021\n",
       "35  13-May-08    336022\n",
       "36  14-May-08    336023\n",
       "37  28-May-08    336024\n",
       "38  15-May-08    336025\n",
       "39  16-May-08    336026"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[20:40][['Match_Date','Match_Id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "66384292-3929-45ef-a311-6654b657ed1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Match_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2-May-09</td>\n",
       "      <td>392210</td>\n",
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       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>2-May-09</td>\n",
       "      <td>392211</td>\n",
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       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>3-May-09</td>\n",
       "      <td>392212</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>3-May-09</td>\n",
       "      <td>392213</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>4-May-09</td>\n",
       "      <td>392214</td>\n",
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       "      <th>85</th>\n",
       "      <td>5-May-09</td>\n",
       "      <td>392215</td>\n",
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       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5-May-09</td>\n",
       "      <td>392216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>6-May-09</td>\n",
       "      <td>392217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>7-May-09</td>\n",
       "      <td>392218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>7-May-09</td>\n",
       "      <td>392219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>8-May-09</td>\n",
       "      <td>392220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>9-May-09</td>\n",
       "      <td>392221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>9-May-09</td>\n",
       "      <td>392222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>10-May-09</td>\n",
       "      <td>392223</td>\n",
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       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>10-May-09</td>\n",
       "      <td>392224</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>11-May-09</td>\n",
       "      <td>392225</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>12-May-09</td>\n",
       "      <td>392226</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>12-May-09</td>\n",
       "      <td>392227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>13-May-09</td>\n",
       "      <td>392228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>14-May-09</td>\n",
       "      <td>392229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>14-May-09</td>\n",
       "      <td>392230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>15-May-09</td>\n",
       "      <td>392231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>16-May-09</td>\n",
       "      <td>392232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>16-May-09</td>\n",
       "      <td>392233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>17-May-09</td>\n",
       "      <td>392234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>17-May-09</td>\n",
       "      <td>392235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>18-May-09</td>\n",
       "      <td>392236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>19-May-09</td>\n",
       "      <td>392237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>20-May-09</td>\n",
       "      <td>392238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>20-May-09</td>\n",
       "      <td>392239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>21-May-09</td>\n",
       "      <td>392240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>21-May-09</td>\n",
       "      <td>392241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>22-May-09</td>\n",
       "      <td>392242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>23-May-09</td>\n",
       "      <td>392243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>24-May-09</td>\n",
       "      <td>392244</td>\n",
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       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>12-Mar-10</td>\n",
       "      <td>419111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>13-Mar-10</td>\n",
       "      <td>419112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>13-Mar-10</td>\n",
       "      <td>419113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>14-Mar-10</td>\n",
       "      <td>419114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>14-Mar-10</td>\n",
       "      <td>419115</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    Match_Date  Match_Id\n",
       "80    2-May-09    392210\n",
       "81    2-May-09    392211\n",
       "82    3-May-09    392212\n",
       "83    3-May-09    392213\n",
       "84    4-May-09    392214\n",
       "85    5-May-09    392215\n",
       "86    5-May-09    392216\n",
       "87    6-May-09    392217\n",
       "88    7-May-09    392218\n",
       "89    7-May-09    392219\n",
       "90    8-May-09    392220\n",
       "91    9-May-09    392221\n",
       "92    9-May-09    392222\n",
       "93   10-May-09    392223\n",
       "94   10-May-09    392224\n",
       "95   11-May-09    392225\n",
       "96   12-May-09    392226\n",
       "97   12-May-09    392227\n",
       "98   13-May-09    392228\n",
       "99   14-May-09    392229\n",
       "100  14-May-09    392230\n",
       "101  15-May-09    392231\n",
       "102  16-May-09    392232\n",
       "103  16-May-09    392233\n",
       "104  17-May-09    392234\n",
       "105  17-May-09    392235\n",
       "106  18-May-09    392236\n",
       "107  19-May-09    392237\n",
       "108  20-May-09    392238\n",
       "109  20-May-09    392239\n",
       "110  21-May-09    392240\n",
       "111  21-May-09    392241\n",
       "112  22-May-09    392242\n",
       "113  23-May-09    392243\n",
       "114  24-May-09    392244\n",
       "115  12-Mar-10    419111\n",
       "116  13-Mar-10    419112\n",
       "117  13-Mar-10    419113\n",
       "118  14-Mar-10    419114\n",
       "119  14-Mar-10    419115"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Match_Date','Match_Id']][80:120]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3d4ba6e5-d1c4-4277-98e5-fb0db87b2903",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
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       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "      <td>47038180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "      <td>11087604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "      <td>3023901.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "      <td>1679950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "      <td>1679955.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0    335987  18-Apr-08             2                 1          1   \n",
       "1    335988  19-Apr-08             4                 3          1   \n",
       "2    335989  19-Apr-08             6                 5          1   \n",
       "3    335990  20-Apr-08             7                 2          1   \n",
       "4    335991  20-Apr-08             1                 8          1   \n",
       "\n",
       "                                   Venue_Name  Toss_Winner_Id Toss_Decision  \\\n",
       "0                       M Chinnaswamy Stadium               2         field   \n",
       "1  Punjab Cricket Association Stadium, Mohali               3           bat   \n",
       "2                            Feroz Shah Kotla               5           bat   \n",
       "3                            Wankhede Stadium               7           bat   \n",
       "4                                Eden Gardens               8           bat   \n",
       "\n",
       "   IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  Won_By  \\\n",
       "0             0          1                  0     by runs   140.0   \n",
       "1             0          1                  0     by runs    33.0   \n",
       "2             0          1                  0  by wickets     9.0   \n",
       "3             0          1                  0  by wickets     5.0   \n",
       "4             0          1                  0  by wickets     5.0   \n",
       "\n",
       "   Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  Second_Umpire_Id  \\\n",
       "0              1.0                  2.0              470               477   \n",
       "1              3.0                 19.0              471               487   \n",
       "2              6.0                 90.0              472               512   \n",
       "3              2.0                 11.0              473               476   \n",
       "4              1.0                  4.0              474               486   \n",
       "\n",
       "    City_Name Host_Country     winemp1  \n",
       "0   Bangalore        India  47038180.0  \n",
       "1  Chandigarh        India  11087604.0  \n",
       "2       Delhi        India   3023901.0  \n",
       "3      Mumbai        India   1679950.0  \n",
       "4     Kolkata        India   1679955.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['winemp1'] = df.Won_By * df.Match_Id\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "e5409c98-3678-44bb-a854-5f49e29eb2fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>981016</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981018</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>981020</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>1</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>481</td>\n",
       "      <td>499</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>981022</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>481</td>\n",
       "      <td>498</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>482</td>\n",
       "      <td>497</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>577 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0      335987  18-Apr-08             2                 1          1   \n",
       "1      335988  19-Apr-08             4                 3          1   \n",
       "2      335989  19-Apr-08             6                 5          1   \n",
       "3      335990  20-Apr-08             7                 2          1   \n",
       "4      335991  20-Apr-08             1                 8          1   \n",
       "..        ...        ...           ...               ...        ...   \n",
       "572    981016  22-May-16             6                 2          9   \n",
       "573    981018  24-May-16            13                 2          9   \n",
       "574    981020  25-May-16            11                 1          9   \n",
       "575    981022  27-May-16            13                11          9   \n",
       "576    981024  29-May-16             2                11          9   \n",
       "\n",
       "                                           Venue_Name  Toss_Winner_Id  \\\n",
       "0                               M Chinnaswamy Stadium               2   \n",
       "1          Punjab Cricket Association Stadium, Mohali               3   \n",
       "2                                    Feroz Shah Kotla               5   \n",
       "3                                    Wankhede Stadium               7   \n",
       "4                                        Eden Gardens               8   \n",
       "..                                                ...             ...   \n",
       "572  Shaheed Veer Narayan Singh International Stadium               2   \n",
       "573                             M Chinnaswamy Stadium               2   \n",
       "574                                  Feroz Shah Kotla               1   \n",
       "575                                  Feroz Shah Kotla              11   \n",
       "576                             M Chinnaswamy Stadium              11   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "0           field             0          1                  0     by runs   \n",
       "1             bat             0          1                  0     by runs   \n",
       "2             bat             0          1                  0  by wickets   \n",
       "3             bat             0          1                  0  by wickets   \n",
       "4             bat             0          1                  0  by wickets   \n",
       "..            ...           ...        ...                ...         ...   \n",
       "572         field             0          1                  0  by wickets   \n",
       "573         field             0          1                  0  by wickets   \n",
       "574         field             0          1                  0     by runs   \n",
       "575         field             0          1                  0  by wickets   \n",
       "576           bat             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "0     140.0              1.0                  2.0              470   \n",
       "1      33.0              3.0                 19.0              471   \n",
       "2       9.0              6.0                 90.0              472   \n",
       "3       5.0              2.0                 11.0              473   \n",
       "4       5.0              1.0                  4.0              474   \n",
       "..      ...              ...                  ...              ...   \n",
       "572     6.0              2.0                  8.0              511   \n",
       "573     4.0              2.0                110.0              495   \n",
       "574    22.0             11.0                163.0              481   \n",
       "575     4.0             11.0                187.0              481   \n",
       "576     8.0             11.0                385.0              482   \n",
       "\n",
       "     Second_Umpire_Id   City_Name Host_Country  \n",
       "0                 477   Bangalore        India  \n",
       "1                 487  Chandigarh        India  \n",
       "2                 512       Delhi        India  \n",
       "3                 476      Mumbai        India  \n",
       "4                 486     Kolkata        India  \n",
       "..                ...         ...          ...  \n",
       "572               497      Raipur        India  \n",
       "573               482   Bangalore        India  \n",
       "574               499       Delhi        India  \n",
       "575               498       Delhi        India  \n",
       "576               497   Bangalore        India  \n",
       "\n",
       "[577 rows x 19 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop('winemp1',axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "185e973f-cb99-41c5-9c52-49c7bf07d2d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "       ...  \n",
       "572     True\n",
       "573     True\n",
       "574     True\n",
       "575     True\n",
       "576     True\n",
       "Name: Won_By, Length: 577, dtype: bool"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.Won_By<30\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b36ae1fb-a4c0-4b56-a04c-70ffd0eaa402",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
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       "      <th>Won_By</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>829788</td>\n",
       "      <td>5-May-15</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>482</td>\n",
       "      <td>505</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>829792</td>\n",
       "      <td>7-May-15</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>Brabourne Stadium</td>\n",
       "      <td>5</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>494</td>\n",
       "      <td>499</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>829794</td>\n",
       "      <td>8-May-15</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>505</td>\n",
       "      <td>498</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>829796</td>\n",
       "      <td>9-May-15</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>829798</td>\n",
       "      <td>9-May-15</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>496</td>\n",
       "      <td>489</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>981016</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981018</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>495</td>\n",
       "      <td>482</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574</th>\n",
       "      <td>981020</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>1</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>481</td>\n",
       "      <td>499</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>981022</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>11</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>481</td>\n",
       "      <td>498</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>482</td>\n",
       "      <td>497</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "496    829788   5-May-15             7                 6          8   \n",
       "498    829792   7-May-15             5                11          8   \n",
       "499    829794   8-May-15             3                 7          8   \n",
       "500    829796   9-May-15             1                 4          8   \n",
       "501    829798   9-May-15             6                11          8   \n",
       "..        ...        ...           ...               ...        ...   \n",
       "572    981016  22-May-16             6                 2          9   \n",
       "573    981018  24-May-16            13                 2          9   \n",
       "574    981020  25-May-16            11                 1          9   \n",
       "575    981022  27-May-16            13                11          9   \n",
       "576    981024  29-May-16             2                11          9   \n",
       "\n",
       "                                           Venue_Name  Toss_Winner_Id  \\\n",
       "496                                  Wankhede Stadium               6   \n",
       "498                                 Brabourne Stadium               5   \n",
       "499                   MA Chidambaram Stadium, Chepauk               3   \n",
       "500                                      Eden Gardens               4   \n",
       "501  Shaheed Veer Narayan Singh International Stadium              11   \n",
       "..                                                ...             ...   \n",
       "572  Shaheed Veer Narayan Singh International Stadium               2   \n",
       "573                             M Chinnaswamy Stadium               2   \n",
       "574                                  Feroz Shah Kotla               1   \n",
       "575                                  Feroz Shah Kotla              11   \n",
       "576                             M Chinnaswamy Stadium              11   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "496           bat             0          1                  0  by wickets   \n",
       "498         field             0          1                  0     by runs   \n",
       "499           bat             0          1                  0  by wickets   \n",
       "500           bat             0          1                  0  by wickets   \n",
       "501           bat             0          1                  0     by runs   \n",
       "..            ...           ...        ...                ...         ...   \n",
       "572         field             0          1                  0  by wickets   \n",
       "573         field             0          1                  0  by wickets   \n",
       "574         field             0          1                  0     by runs   \n",
       "575         field             0          1                  0  by wickets   \n",
       "576           bat             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "496     5.0              7.0                 50.0              482   \n",
       "498     7.0             11.0                216.0              494   \n",
       "499     6.0              7.0                400.0              505   \n",
       "500     1.0              1.0                334.0              495   \n",
       "501     6.0             11.0                163.0              496   \n",
       "..      ...              ...                  ...              ...   \n",
       "572     6.0              2.0                  8.0              511   \n",
       "573     4.0              2.0                110.0              495   \n",
       "574    22.0             11.0                163.0              481   \n",
       "575     4.0             11.0                187.0              481   \n",
       "576     8.0             11.0                385.0              482   \n",
       "\n",
       "     Second_Umpire_Id  City_Name Host_Country  \n",
       "496               505     Mumbai        India  \n",
       "498               499     Mumbai        India  \n",
       "499               498    Chennai        India  \n",
       "500               482    Kolkata        India  \n",
       "501               489     Raipur        India  \n",
       "..                ...        ...          ...  \n",
       "572               497     Raipur        India  \n",
       "573               482  Bangalore        India  \n",
       "574               499      Delhi        India  \n",
       "575               498      Delhi        India  \n",
       "576               497  Bangalore        India  \n",
       "\n",
       "[70 rows x 19 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df1][400:500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "6a73a290-59c7-4949-b1f0-9e46aa1420b2",
   "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>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>335992</td>\n",
       "      <td>21-Apr-08</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>472</td>\n",
       "      <td>513</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>India</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>980990</td>\n",
       "      <td>13-May-16</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>482</td>\n",
       "      <td>498</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>980994</td>\n",
       "      <td>14-May-16</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>12</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>511</td>\n",
       "      <td>497</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>980996</td>\n",
       "      <td>15-May-16</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>510</td>\n",
       "      <td>481</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>981010</td>\n",
       "      <td>21-May-16</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>4</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>482</td>\n",
       "      <td>507</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>576</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>11</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>482</td>\n",
       "      <td>497</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>262 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "1      335988  19-Apr-08             4                 3          1   \n",
       "2      335989  19-Apr-08             6                 5          1   \n",
       "3      335990  20-Apr-08             7                 2          1   \n",
       "4      335991  20-Apr-08             1                 8          1   \n",
       "5      335992  21-Apr-08             5                 4          1   \n",
       "..        ...        ...           ...               ...        ...   \n",
       "559    980990  13-May-16             7                 4          9   \n",
       "561    980994  14-May-16             1                12          9   \n",
       "562    980996  15-May-16             4                11          9   \n",
       "569    981010  21-May-16            12                 4          9   \n",
       "576    981024  29-May-16             2                11          9   \n",
       "\n",
       "                                            Venue_Name  Toss_Winner_Id  \\\n",
       "1           Punjab Cricket Association Stadium, Mohali               3   \n",
       "2                                     Feroz Shah Kotla               5   \n",
       "3                                     Wankhede Stadium               7   \n",
       "4                                         Eden Gardens               8   \n",
       "5                               Sawai Mansingh Stadium               4   \n",
       "..                                                 ...             ...   \n",
       "559  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...               7   \n",
       "561                                       Eden Gardens              12   \n",
       "562  Punjab Cricket Association IS Bindra Stadium, ...               4   \n",
       "569  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...               4   \n",
       "576                              M Chinnaswamy Stadium              11   \n",
       "\n",
       "    Toss_Decision  IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  \\\n",
       "1             bat             0          1                  0     by runs   \n",
       "2             bat             0          1                  0  by wickets   \n",
       "3             bat             0          1                  0  by wickets   \n",
       "4             bat             0          1                  0  by wickets   \n",
       "5             bat             0          1                  0  by wickets   \n",
       "..            ...           ...        ...                ...         ...   \n",
       "559           bat             0          1                  0  by wickets   \n",
       "561           bat             0          1                  1  by wickets   \n",
       "562           bat             0          1                  0  by wickets   \n",
       "569           bat             0          1                  0  by wickets   \n",
       "576           bat             0          1                  0     by runs   \n",
       "\n",
       "     Won_By  Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  \\\n",
       "1      33.0              3.0                 19.0              471   \n",
       "2       9.0              6.0                 90.0              472   \n",
       "3       5.0              2.0                 11.0              473   \n",
       "4       5.0              1.0                  4.0              474   \n",
       "5       6.0              5.0                 32.0              472   \n",
       "..      ...              ...                  ...              ...   \n",
       "559     7.0              4.0                409.0              482   \n",
       "561     8.0              1.0                 31.0              511   \n",
       "562     7.0             11.0                427.0              510   \n",
       "569     4.0             12.0                 20.0              482   \n",
       "576     8.0             11.0                385.0              482   \n",
       "\n",
       "     Second_Umpire_Id      City_Name Host_Country  \n",
       "1                 487     Chandigarh        India  \n",
       "2                 512          Delhi        India  \n",
       "3                 476         Mumbai        India  \n",
       "4                 486        Kolkata        India  \n",
       "5                 513         Jaipur        India  \n",
       "..                ...            ...          ...  \n",
       "559               498  Visakhapatnam        India  \n",
       "561               497        Kolkata        India  \n",
       "562               481     Chandigarh        India  \n",
       "569               507  Visakhapatnam        India  \n",
       "576               497      Bangalore        India  \n",
       "\n",
       "[262 rows x 19 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.Toss_Decision == 'bat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "cb8d7ca5-07c7-42fc-b9f8-5fd59864defc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M Chinnaswamy Stadium',\n",
       "       'Punjab Cricket Association Stadium, Mohali', 'Feroz Shah Kotla',\n",
       "       'Wankhede Stadium', 'Eden Gardens', 'Sawai Mansingh Stadium',\n",
       "       'Rajiv Gandhi International Stadium, Uppal',\n",
       "       'MA Chidambaram Stadium, Chepauk', 'Dr DY Patil Sports Academy',\n",
       "       'Newlands', \"St George's Park\", 'Kingsmead', 'SuperSport Park',\n",
       "       'Buffalo Park', 'New Wanderers Stadium', 'De Beers Diamond Oval',\n",
       "       'OUTsurance Oval', 'Brabourne Stadium',\n",
       "       'Sardar Patel Stadium, Motera', 'Barabati Stadium',\n",
       "       'Vidarbha Cricket Association Stadium, Jamtha',\n",
       "       'Himachal Pradesh Cricket Association Stadium', 'Nehru Stadium',\n",
       "       'Holkar Cricket Stadium',\n",
       "       'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium',\n",
       "       'Subrata Roy Sahara Stadium',\n",
       "       'Shaheed Veer Narayan Singh International Stadium',\n",
       "       'JSCA International Stadium Complex', 'Sheikh Zayed Stadium',\n",
       "       'Sharjah Cricket Stadium', 'Dubai International Cricket Stadium',\n",
       "       'Maharashtra Cricket Association Stadium',\n",
       "       'Punjab Cricket Association IS Bindra Stadium, Mohali',\n",
       "       'Saurashtra Cricket Association Stadium', 'Green Park'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Venue_Name.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b7a90d3d-9dbe-48b6-ac43-7b49d9a9780e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "419e210b-fe23-4620-92bb-1e7e926b8c0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] =10,5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0184a161-e872-4b3e-9a18-809b7370d137",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "753bba87-4a0e-4ff4-b9d5-bf895e956cc8",
   "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>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name_Id</th>\n",
       "      <th>Opponent_Team_Id</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Toss_Winner_Id</th>\n",
       "      <th>Toss_Decision</th>\n",
       "      <th>IS_Superover</th>\n",
       "      <th>IS_Result</th>\n",
       "      <th>Is_DuckWorthLewis</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Man_Of_The_Match_Id</th>\n",
       "      <th>First_Umpire_Id</th>\n",
       "      <th>Second_Umpire_Id</th>\n",
       "      <th>City_Name</th>\n",
       "      <th>Host_Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>470</td>\n",
       "      <td>477</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>3</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>471</td>\n",
       "      <td>487</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>5</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>472</td>\n",
       "      <td>512</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>7</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>473</td>\n",
       "      <td>476</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>8</td>\n",
       "      <td>bat</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>474</td>\n",
       "      <td>486</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Match_Id Match_Date  Team_Name_Id  Opponent_Team_Id  Season_Id  \\\n",
       "0    335987  18-Apr-08             2                 1          1   \n",
       "1    335988  19-Apr-08             4                 3          1   \n",
       "2    335989  19-Apr-08             6                 5          1   \n",
       "3    335990  20-Apr-08             7                 2          1   \n",
       "4    335991  20-Apr-08             1                 8          1   \n",
       "\n",
       "                                   Venue_Name  Toss_Winner_Id Toss_Decision  \\\n",
       "0                       M Chinnaswamy Stadium               2         field   \n",
       "1  Punjab Cricket Association Stadium, Mohali               3           bat   \n",
       "2                            Feroz Shah Kotla               5           bat   \n",
       "3                            Wankhede Stadium               7           bat   \n",
       "4                                Eden Gardens               8           bat   \n",
       "\n",
       "   IS_Superover  IS_Result  Is_DuckWorthLewis    Win_Type  Won_By  \\\n",
       "0             0          1                  0     by runs   140.0   \n",
       "1             0          1                  0     by runs    33.0   \n",
       "2             0          1                  0  by wickets     9.0   \n",
       "3             0          1                  0  by wickets     5.0   \n",
       "4             0          1                  0  by wickets     5.0   \n",
       "\n",
       "   Match_Winner_Id  Man_Of_The_Match_Id  First_Umpire_Id  Second_Umpire_Id  \\\n",
       "0              1.0                  2.0              470               477   \n",
       "1              3.0                 19.0              471               487   \n",
       "2              6.0                 90.0              472               512   \n",
       "3              2.0                 11.0              473               476   \n",
       "4              1.0                  4.0              474               486   \n",
       "\n",
       "    City_Name Host_Country  \n",
       "0   Bangalore        India  \n",
       "1  Chandigarh        India  \n",
       "2       Delhi        India  \n",
       "3      Mumbai        India  \n",
       "4     Kolkata        India  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c74692a7-db9a-4398-a7e3-dbd70e1511a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.distplot(df[\"Match_Id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e72e29e5-47f1-4b46-a2e0-3236f464f912",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.distplot(df[\"Match_Id\"], bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "be6ccc07-9cec-463f-8e4f-346bb702ee8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.boxplot(data =df,x=\"Match_Id\",y=\"Toss_Decision\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "11380dfa-72d3-4693-bd33-c0c4f3763a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.lmplot(data =df, x=\"Match_Id\", y=\"Second_Umpire_Id\", fit_reg = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4d8b7deb-a215-4322-bc49-fedcb15d091b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.lmplot(data =df, x=\"Match_Id\", y=\"Second_Umpire_Id\", fit_reg = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "2731667a-88e0-45d3-af01-791e360e61d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 606.111x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.lmplot(data =df, x=\"Match_Id\", y=\"Second_Umpire_Id\", fit_reg = True, hue = 'Toss_Decision')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "afde8c49-0260-4209-b2fd-70ac1f82efed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1106.11x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vis1 = sns.lmplot(data =df, x=\"Match_Id\", y=\"Second_Umpire_Id\", fit_reg = True, hue = 'Toss_Decision', height =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b002bacc-d5c1-4a95-a7e4-633fa6ae93aa",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}