Practice-1 (Numpy,Pandas,Matplotlib).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c45cd487",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "791319a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Num=numerical  py = python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e485e16",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d473a2d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr=np.array([1,2,3,4,5])  #to create array use np.array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9875e44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33a97273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  6, 78, 77],\n",
       "       [44, 52, 66, 54,  3,  2, 76, 34],\n",
       "       [79, 68, 49,  4, 45,  5,  7, 77]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_list=np.array([[1,2,3,4,5,6,78,77],[44,52,66,54,3,2,76,34],[79,68,49,4,45,5,7,77]])\n",
    "list_of_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "469d40c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_list[2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "af13a92e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_list[2][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3da53049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 66, 49])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_list[:,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b426c182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[66],\n",
       "       [49]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_list[1:3,2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b6b5885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  2,   4,   6,   8,  10,  12, 156, 154],\n",
       "       [ 88, 104, 132, 108,   6,   4, 152,  68],\n",
       "       [158, 136,  98,   8,  90,  10,  14, 154]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_of_list+list_of_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ca02d783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.71828183e+00, 7.38905610e+00, 2.00855369e+01, 5.45981500e+01,\n",
       "        1.48413159e+02, 4.03428793e+02, 7.49841700e+33, 2.75851345e+33],\n",
       "       [1.28516001e+19, 3.83100800e+22, 4.60718663e+28, 2.83075330e+23,\n",
       "        2.00855369e+01, 7.38905610e+00, 1.01480039e+33, 5.83461743e+14],\n",
       "       [2.03828107e+34, 3.40427605e+29, 1.90734657e+21, 5.45981500e+01,\n",
       "        3.49342711e+19, 1.48413159e+02, 1.09663316e+03, 2.75851345e+33]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9cc0c9a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.41421356, 1.73205081, 2.        , 2.23606798,\n",
       "        2.44948974, 8.83176087, 8.77496439],\n",
       "       [6.63324958, 7.21110255, 8.1240384 , 7.34846923, 1.73205081,\n",
       "        1.41421356, 8.71779789, 5.83095189],\n",
       "       [8.88819442, 8.24621125, 7.        , 2.        , 6.70820393,\n",
       "        2.23606798, 2.64575131, 8.77496439]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "595a1600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 0.69314718, 1.09861229, 1.38629436, 1.60943791,\n",
       "        1.79175947, 4.35670883, 4.34380542],\n",
       "       [3.78418963, 3.95124372, 4.18965474, 3.98898405, 1.09861229,\n",
       "        0.69314718, 4.33073334, 3.52636052],\n",
       "       [4.36944785, 4.21950771, 3.8918203 , 1.38629436, 3.80666249,\n",
       "        1.60943791, 1.94591015, 4.34380542]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e855b2ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aedb5ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eaf8d486",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.82271776718522"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dff59172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   1,    4,    9,   16,   25,   36, 6084, 5929],\n",
       "       [1936, 2704, 4356, 2916,    9,    4, 5776, 1156],\n",
       "       [6241, 4624, 2401,   16, 2025,   25,   49, 5929]], dtype=int32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.square(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cac2401a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "950.0399305555555"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e7e47cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35.041666666666664"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f859494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(list_of_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7d38a65",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "452747c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3790876f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5e99875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 10, 15, 20, 25])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5,30,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9a6d7636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "535d5f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10,int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "23479c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(10,int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "244a10d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "39a8dd8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,5)    #linspace=linearlyspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4be5ebca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  2.22222222,  4.44444444,  6.66666667,  8.88888889,\n",
       "       11.11111111, 13.33333333, 15.55555556, 17.77777778, 20.        ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ced68d74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   2.04081633,   4.08163265,   6.12244898,\n",
       "         8.16326531,  10.20408163,  12.24489796,  14.28571429,\n",
       "        16.32653061,  18.36734694,  20.40816327,  22.44897959,\n",
       "        24.48979592,  26.53061224,  28.57142857,  30.6122449 ,\n",
       "        32.65306122,  34.69387755,  36.73469388,  38.7755102 ,\n",
       "        40.81632653,  42.85714286,  44.89795918,  46.93877551,\n",
       "        48.97959184,  51.02040816,  53.06122449,  55.10204082,\n",
       "        57.14285714,  59.18367347,  61.2244898 ,  63.26530612,\n",
       "        65.30612245,  67.34693878,  69.3877551 ,  71.42857143,\n",
       "        73.46938776,  75.51020408,  77.55102041,  79.59183673,\n",
       "        81.63265306,  83.67346939,  85.71428571,  87.75510204,\n",
       "        89.79591837,  91.83673469,  93.87755102,  95.91836735,\n",
       "        97.95918367, 100.        ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4ae1c441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(10,dtype=int)   #identity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a1f80e43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.13944545, 0.65436632, 0.98841255, 0.53394256, 0.90617741],\n",
       "       [0.86086515, 0.29957723, 0.37775117, 0.36283089, 0.38384897],\n",
       "       [0.01816724, 0.43685038, 0.54678801, 0.11344572, 0.17891221]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.random.rand(3,5)   #random numbers #uniform distribution ranging(0 to 3)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "48a792b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.13944545, 0.86086515, 0.01816724],\n",
       "       [0.65436632, 0.29957723, 0.43685038],\n",
       "       [0.98841255, 0.37775117, 0.54678801],\n",
       "       [0.53394256, 0.36283089, 0.11344572],\n",
       "       [0.90617741, 0.38384897, 0.17891221]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d6363370",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.28910199, -1.77574745,  0.73980495,  0.51585005, -0.07715117],\n",
       "       [-0.62570745,  0.42365924, -0.72088364,  1.11586928,  2.31952864]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(2,5) #standard normal distribution(-3 to 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "67da9bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "639"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,1000)   #random integer umber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6ea32ef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([61, 62, 83, 71, 59, 46, 73,  6, 58, 25])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100,10,dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ee9a9628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "835c3644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1=np.arange(30)\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "afd3e89c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.reshape(5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c4eb26ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47, 33, 96, 22, 42, 86, 44, 53, 78, 48, 68,  4, 43, 76,  5, 52, 29,\n",
       "       26, 75, 32])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2=np.random.randint(0,100,20)\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b795e82d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "65ca3ee8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "eb77dd24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "374df67b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ede7b1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.eye(5)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "73218c71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3=np.arange(2,28)\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f13b9608",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18,  3, 25,  2, 16])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr4=np.random.randint(2,28,5)\n",
    "arr4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2dfe0a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "aba50111",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "54da484f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "73d1c6db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "25b92590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5=np.arange(30)\n",
    "arr5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "3fbc34fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5.reshape(5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e61e0d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr5[2]=55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c691230d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([55,  3,  4])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8d731aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1, 33, 33, 33,  5],\n",
       "       [ 6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17],\n",
       "       [18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5[2:5]=33\n",
    "arr5.reshape(5,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "959fdf4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['chapathi', 'dosa', 'idli', 'vada'], dtype='<U8')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food=np.array(['idli','vada','dosa','chapathi'])\n",
    "np.unique(food)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f00f7fc6",
   "metadata": {},
   "source": [
    "## Pandas ( Loan )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6c57963f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "50c86979",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/Premalatha-success/Yhills_July12_Analytics/main/loan_prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "31e75c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2aa38c31",
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "290d3f63",
   "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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "609  LP002978  Female      No          0  Graduate            No   \n",
       "610  LP002979    Male     Yes         3+  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "612  LP002984    Male     Yes          2  Graduate            No   \n",
       "613  LP002990  Female      No          0  Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "8c87d14d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>LP001570</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4167</td>\n",
       "      <td>1447.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>LP001098</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3500</td>\n",
       "      <td>1667.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>LP001585</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>51763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>LP002087</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID  Gender Married Dependents Education Self_Employed  \\\n",
       "163  LP001570    Male     Yes          2  Graduate            No   \n",
       "33   LP001098    Male     Yes          0  Graduate            No   \n",
       "611  LP002983    Male     Yes          1  Graduate            No   \n",
       "171  LP001585     NaN     Yes         3+  Graduate            No   \n",
       "329  LP002087  Female      No          0  Graduate            No   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "163             4167             1447.0       158.0             360.0   \n",
       "33              3500             1667.0       114.0             360.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "171            51763                0.0       700.0             300.0   \n",
       "329             2500                0.0        67.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "163             1.0         Rural           Y  \n",
       "33              1.0     Semiurban           Y  \n",
       "611             1.0         Urban           Y  \n",
       "171             1.0         Urban           Y  \n",
       "329             1.0         Urban           Y  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.sample(n=5,replace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4615327a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               object\n",
       "Gender                object\n",
       "Married               object\n",
       "Dependents            object\n",
       "Education             object\n",
       "Self_Employed         object\n",
       "ApplicantIncome        int64\n",
       "CoapplicantIncome    float64\n",
       "LoanAmount           float64\n",
       "Loan_Amount_Term     float64\n",
       "Credit_History       float64\n",
       "Property_Area         object\n",
       "Loan_Status           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "5e033585",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5 entries, 163 to 329\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            5 non-null      object \n",
      " 1   Gender             4 non-null      object \n",
      " 2   Married            5 non-null      object \n",
      " 3   Dependents         5 non-null      object \n",
      " 4   Education          5 non-null      object \n",
      " 5   Self_Employed      5 non-null      object \n",
      " 6   ApplicantIncome    5 non-null      int64  \n",
      " 7   CoapplicantIncome  5 non-null      float64\n",
      " 8   LoanAmount         5 non-null      float64\n",
      " 9   Loan_Amount_Term   5 non-null      float64\n",
      " 10  Credit_History     5 non-null      float64\n",
      " 11  Property_Area      5 non-null      object \n",
      " 12  Loan_Status        5 non-null      object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 560.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c622b15e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               1\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "03cf801e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14000.400000</td>\n",
       "      <td>670.800000</td>\n",
       "      <td>258.400000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21215.402666</td>\n",
       "      <td>818.601674</td>\n",
       "      <td>256.225096</td>\n",
       "      <td>26.832816</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3500.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4167.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>158.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8072.000000</td>\n",
       "      <td>1447.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>51763.000000</td>\n",
       "      <td>1667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count         5.000000           5.000000    5.000000          5.000000   \n",
       "mean      14000.400000         670.800000  258.400000        348.000000   \n",
       "std       21215.402666         818.601674  256.225096         26.832816   \n",
       "min        2500.000000           0.000000   67.000000        300.000000   \n",
       "25%        3500.000000           0.000000  114.000000        360.000000   \n",
       "50%        4167.000000         240.000000  158.000000        360.000000   \n",
       "75%        8072.000000        1447.000000  253.000000        360.000000   \n",
       "max       51763.000000        1667.000000  700.000000        360.000000   \n",
       "\n",
       "       Credit_History  \n",
       "count             5.0  \n",
       "mean              1.0  \n",
       "std               0.0  \n",
       "min               1.0  \n",
       "25%               1.0  \n",
       "50%               1.0  \n",
       "75%               1.0  \n",
       "max               1.0  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fd8c1d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>5</td>\n",
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       "      <td>5.000000</td>\n",
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       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>Y</td>\n",
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       "      <td>670.800000</td>\n",
       "      <td>258.400000</td>\n",
       "      <td>348.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>25%</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8072.000000</td>\n",
       "      <td>1447.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51763.000000</td>\n",
       "      <td>1667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>360.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Loan_ID Gender Married Dependents Education Self_Employed  \\\n",
       "count          5      4       5          5         5             5   \n",
       "unique         5      2       2          4         1             1   \n",
       "top     LP001570   Male     Yes          0  Graduate            No   \n",
       "freq           1      3       4          2         5             5   \n",
       "mean         NaN    NaN     NaN        NaN       NaN           NaN   \n",
       "std          NaN    NaN     NaN        NaN       NaN           NaN   \n",
       "min          NaN    NaN     NaN        NaN       NaN           NaN   \n",
       "25%          NaN    NaN     NaN        NaN       NaN           NaN   \n",
       "50%          NaN    NaN     NaN        NaN       NaN           NaN   \n",
       "75%          NaN    NaN     NaN        NaN       NaN           NaN   \n",
       "max          NaN    NaN     NaN        NaN       NaN           NaN   \n",
       "\n",
       "        ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count          5.000000           5.000000    5.000000          5.000000   \n",
       "unique              NaN                NaN         NaN               NaN   \n",
       "top                 NaN                NaN         NaN               NaN   \n",
       "freq                NaN                NaN         NaN               NaN   \n",
       "mean       14000.400000         670.800000  258.400000        348.000000   \n",
       "std        21215.402666         818.601674  256.225096         26.832816   \n",
       "min         2500.000000           0.000000   67.000000        300.000000   \n",
       "25%         3500.000000           0.000000  114.000000        360.000000   \n",
       "50%         4167.000000         240.000000  158.000000        360.000000   \n",
       "75%         8072.000000        1447.000000  253.000000        360.000000   \n",
       "max        51763.000000        1667.000000  700.000000        360.000000   \n",
       "\n",
       "        Credit_History Property_Area Loan_Status  \n",
       "count              5.0             5           5  \n",
       "unique             NaN             3           1  \n",
       "top                NaN         Urban           Y  \n",
       "freq               NaN             3           5  \n",
       "mean               1.0           NaN         NaN  \n",
       "std                0.0           NaN         NaN  \n",
       "min                1.0           NaN         NaN  \n",
       "25%                1.0           NaN         NaN  \n",
       "50%                1.0           NaN         NaN  \n",
       "75%                1.0           NaN         NaN  \n",
       "max                1.0           NaN         NaN  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a83a05aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "idli        1\n",
       "vada        2\n",
       "dosa        3\n",
       "chapathi    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "food=pd.Series([1,2,3,4],index=['idli','vada','dosa','chapathi'])\n",
    "food"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "f7220555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.224902</td>\n",
       "      <td>0.769802</td>\n",
       "      <td>0.025563</td>\n",
       "      <td>0.069332</td>\n",
       "      <td>0.612553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.048296</td>\n",
       "      <td>0.360444</td>\n",
       "      <td>0.747001</td>\n",
       "      <td>0.130672</td>\n",
       "      <td>0.212818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.470180</td>\n",
       "      <td>0.799292</td>\n",
       "      <td>0.906312</td>\n",
       "      <td>0.586179</td>\n",
       "      <td>0.250539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.696423</td>\n",
       "      <td>0.817854</td>\n",
       "      <td>0.459694</td>\n",
       "      <td>0.590388</td>\n",
       "      <td>0.526453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.626487</td>\n",
       "      <td>0.536166</td>\n",
       "      <td>0.911662</td>\n",
       "      <td>0.624513</td>\n",
       "      <td>0.435490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.072602</td>\n",
       "      <td>0.608860</td>\n",
       "      <td>0.461528</td>\n",
       "      <td>0.928496</td>\n",
       "      <td>0.937905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3         4         5\n",
       "A  0.224902  0.769802  0.025563  0.069332  0.612553\n",
       "B  0.048296  0.360444  0.747001  0.130672  0.212818\n",
       "C  0.470180  0.799292  0.906312  0.586179  0.250539\n",
       "D  0.696423  0.817854  0.459694  0.590388  0.526453\n",
       "E  0.626487  0.536166  0.911662  0.624513  0.435490\n",
       "F  0.072602  0.608860  0.461528  0.928496  0.937905"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.DataFrame(np.random.rand(6,5),index='A B C D E F'.split(),columns='1 2 3 4 5'.split())\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f78a6ded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0.069332\n",
       "B    0.130672\n",
       "C    0.586179\n",
       "D    0.590388\n",
       "E    0.624513\n",
       "F    0.928496\n",
       "Name: 4, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a['4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "c6fb9102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.224902</td>\n",
       "      <td>0.769802</td>\n",
       "      <td>0.025563</td>\n",
       "      <td>0.069332</td>\n",
       "      <td>0.612553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.048296</td>\n",
       "      <td>0.360444</td>\n",
       "      <td>0.747001</td>\n",
       "      <td>0.130672</td>\n",
       "      <td>0.212818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.470180</td>\n",
       "      <td>0.799292</td>\n",
       "      <td>0.906312</td>\n",
       "      <td>0.586179</td>\n",
       "      <td>0.250539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.696423</td>\n",
       "      <td>0.817854</td>\n",
       "      <td>0.459694</td>\n",
       "      <td>0.590388</td>\n",
       "      <td>0.526453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.626487</td>\n",
       "      <td>0.536166</td>\n",
       "      <td>0.911662</td>\n",
       "      <td>0.624513</td>\n",
       "      <td>0.435490</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3         4         5\n",
       "A  0.224902  0.769802  0.025563  0.069332  0.612553\n",
       "B  0.048296  0.360444  0.747001  0.130672  0.212818\n",
       "C  0.470180  0.799292  0.906312  0.586179  0.250539\n",
       "D  0.696423  0.817854  0.459694  0.590388  0.526453\n",
       "E  0.626487  0.536166  0.911662  0.624513  0.435490"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.drop('F',axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "35799068",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>0.224902</td>\n",
       "      <td>0.769802</td>\n",
       "      <td>0.025563</td>\n",
       "      <td>0.069332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.048296</td>\n",
       "      <td>0.360444</td>\n",
       "      <td>0.747001</td>\n",
       "      <td>0.130672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.470180</td>\n",
       "      <td>0.799292</td>\n",
       "      <td>0.906312</td>\n",
       "      <td>0.586179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.696423</td>\n",
       "      <td>0.817854</td>\n",
       "      <td>0.459694</td>\n",
       "      <td>0.590388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.626487</td>\n",
       "      <td>0.536166</td>\n",
       "      <td>0.911662</td>\n",
       "      <td>0.624513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.072602</td>\n",
       "      <td>0.608860</td>\n",
       "      <td>0.461528</td>\n",
       "      <td>0.928496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1         2         3         4\n",
       "A  0.224902  0.769802  0.025563  0.069332\n",
       "B  0.048296  0.360444  0.747001  0.130672\n",
       "C  0.470180  0.799292  0.906312  0.586179\n",
       "D  0.696423  0.817854  0.459694  0.590388\n",
       "E  0.626487  0.536166  0.911662  0.624513\n",
       "F  0.072602  0.608860  0.461528  0.928496"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.drop('5',axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ac56a2f",
   "metadata": {},
   "source": [
    "## Matplotlib  (visualization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5a2b9c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f1ba370e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xpoints=np.array([1,2])\n",
    "ypoints=np.array([5,10])\n",
    "plt.plot(xpoints,ypoints)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "97787545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c94eec9f40>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ypoints=np.array([3,8,1,12,7,9])\n",
    "plt.plot(ypoints,marker='*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8f26a5cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c94f198820>]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ypoints=np.array([5,3,6,7.8,9])\n",
    "plt.plot(ypoints,'*'':y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7249570c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c94ef4d550>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ypoints=np.array([5,10])\n",
    "plt.plot(xpoints,ypoints,marker='*',ms=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "10a2d525",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1c9543b7be0>]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ypoints=np.array([5,10])\n",
    "plt.plot(xpoints,ypoints,marker='*',ms=14,mfc='y',mec='g',linewidth=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d20df0c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.array(['hockey','Football','Fencing','chess'])\n",
    "y=np.array([22,45,67,87])\n",
    "plt.title(\"Sports-2022\")\n",
    "plt.xlabel(\"sport\",loc=\"left\")\n",
    "plt.ylabel(\"scores\",loc=\"bottom\")\n",
    "plt.plot(x,y,marker='*',markersize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "809148d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c95857dca0>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.array([2,3,5,4,6,8,9])\n",
    "y=np.array([4,2,3,4,6,7,5])\n",
    "plt.scatter(x,y,color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "8332e96f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1c958a77d90>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.array([2,3,5,4,6,8,9])\n",
    "y=np.array([4,2,3,4,6,7,5])\n",
    "colors=([\"red\",\"yellow\",\"pink\",\"blue\",\"green\",\"black\",\"orange\"])\n",
    "size=([50])\n",
    "plt.scatter(x,y,c=colors,alpha=0.5,s=size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "6da002a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.array([\"a\",\"b\",\"c\",\"d\",\"e\"])\n",
    "y=np.array([4,2,3,4,6])\n",
    "plt.bar(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "9f358b92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.array([\"a\",\"b\",\"c\",\"d\",\"e\"])\n",
    "y=np.array([4,2,3,4,6])\n",
    "plt.barh(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "9af08474",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1c95a2c7310>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95a2c7820>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95a2c7d00>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95a2d2220>],\n",
       " [Text(0.8757023583064643, 0.6656916550900253, ''),\n",
       "  Text(-0.8757023894697387, 0.6656916140954386, ''),\n",
       "  Text(-0.059552831359321656, -1.0983867535058351, ''),\n",
       "  Text(1.0424184681733868, -0.3512317428864455, '')])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([12,22,18,6])\n",
    "plt.pie(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "38345c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1c95b4c43d0>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b4c48b0>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b4c4d90>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b4d22b0>],\n",
       " [Text(-0.665691655090025, 0.8757023583064644, 'rice'),\n",
       "  Text(-0.6656916140954386, -0.8757023894697387, 'idli'),\n",
       "  Text(1.0983867535058351, -0.059552831359321726, 'dosa'),\n",
       "  Text(0.35123174288644554, 1.0424184681733868, 'puri')])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([12,22,18,6])\n",
    "mylabels=[\"rice\",\"idli\",\"dosa\",\"puri\"]\n",
    "plt.pie(y,labels=mylabels,startangle=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "c29ca9f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1c95b64f820>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b64fd00>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b65e220>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b65e700>],\n",
       " [Text(-0.665691655090025, 0.8757023583064644, 'rice'),\n",
       "  Text(-1.0287961308747688, -1.3533582382714142, 'idli'),\n",
       "  Text(1.2980934359614416, -0.0703806188791984, 'dosa'),\n",
       "  Text(0.5108825351075571, 1.5162450446158353, 'puri')])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([12,22,18,6])\n",
    "mylabels=[\"rice\",\"idli\",\"dosa\",\"puri\"]\n",
    "myexplode=[0,0.6,0.2,0.5]\n",
    "plt.pie(y,labels=mylabels,startangle=90,explode=myexplode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "c0c2aeb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x1c95b6f4640>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b6f4ac0>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b6f4fa0>,\n",
       "  <matplotlib.patches.Wedge at 0x1c95b7024c0>],\n",
       " [Text(0.8757023583064643, 0.6656916550900253, 'rice'),\n",
       "  Text(-0.8757023894697387, 0.6656916140954386, 'idli'),\n",
       "  Text(-0.059552831359321656, -1.0983867535058351, 'dosa'),\n",
       "  Text(1.0424184681733868, -0.3512317428864455, 'puri')])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([12,22,18,6])\n",
    "mylabels=[\"rice\",\"idli\",\"dosa\",\"puri\"]\n",
    "mycolors=([\"red\",\"yellow\",\"pink\",\"blue\"])\n",
    "plt.pie(y,labels=mylabels,colors=mycolors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "126b7634",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1c958a387c0>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y=np.array([12,22,18,6])\n",
    "mylabels=[\"rice\",\"idli\",\"dosa\",\"puri\"]\n",
    "mycolors=([\"orange\",\"m\",\"hotpink\",\"blue\"])\n",
    "plt.pie(y,colors=mycolors)\n",
    "plt.legend(mylabels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a89f30ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f5a462d",
   "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",
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