numpy and pandas codes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]] (2, 3)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a1=np.array([[1,2,3],[4,5,6]])\n",
    "print(a1,a1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.  ,  2.75,  4.5 ,  6.25,  8.  ,  9.75, 11.5 , 13.25, 15.  ,\n",
       "       16.75, 18.5 , 20.25, 22.  , 23.75, 25.5 , 27.25, 29.  , 30.75,\n",
       "       32.5 , 34.25, 36.  , 37.75, 39.5 , 41.25, 43.  , 44.75, 46.5 ,\n",
       "       48.25, 50.  ])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1,50,29)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,4),dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1],\n",
       "       [1, 1]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,2),dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.06414279, 0.62999527, 0.87084008],\n",
       "       [0.54101503, 0.15775871, 0.09682105],\n",
       "       [0.13436507, 0.31254744, 0.04932712]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0],\n",
       "       [0, 2, 0, 0],\n",
       "       [0, 0, 3, 0],\n",
       "       [0, 0, 0, 4]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.28137181,  0.22045423,  1.00634757,  0.49934395],\n",
       "       [ 0.82084935, -0.76600068, -0.66794118, -0.76340023],\n",
       "       [-1.00834407,  0.5463997 , -0.61773512,  0.08693321]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[29, 18],\n",
       "       [43, 89],\n",
       "       [89, 63]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100,(3,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[54 90 58 38]\n",
      " [15  7 67 60]\n",
      " [72  2 38 99]]\n"
     ]
    }
   ],
   "source": [
    "a2=np.random.randint(1,100,(3,4))\n",
    "print(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7, 67],\n",
       "       [ 2, 38]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2[1:3,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([90, 67, 99])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(a2, axis=1) # axis=1 rowwise and axis=0 columnwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38,  7,  2])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(a2,axis=1) # axis=1 rowwise and axis=0 columnwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[52 56 29 18  7 28 38  3 45 22] 1\n",
      "[52 56 29 18  7 28 38  3 45 22] 7\n"
     ]
    }
   ],
   "source": [
    "a3=np.random.randint(1,57,10)\n",
    "print(a3,np.argmax(a3))\n",
    "print(a3,np.argmin(a3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4],\n",
       "       [5, 5, 5]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,1,1],[2,2,2]])+np.array([[3,3,3]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4],\n",
       "       [5, 5, 5]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(np.array([[1,1,1],[2,2,2]]),np.array([[3,3,3]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2, -2, -2],\n",
       "       [-1, -1, -1]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,1,1],[2,2,2]])-np.array([[3,3,3]]) #np.subtract(a1,a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3, 3],\n",
       "       [6, 6, 6]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,1,1],[2,2,2]])*np.array([[3,3,3]])# np.multiply(a1,a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.33333333, 0.33333333, 0.33333333],\n",
       "       [0.66666667, 0.66666667, 0.66666667]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,1,1],[2,2,2]])/np.array([[3,3,3]])#np.divide(a1,a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[179  65  33 283 326 350]\n",
      " [469 308  57 488 484 171]\n",
      " [306 186  61 327 461 261]\n",
      " [513 123 227 490 520 410]\n",
      " [147 165 580 139  76 470]]\n"
     ]
    }
   ],
   "source": [
    "a5=np.random.randint(1,600,(5,6))\n",
    "print(a5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[179,  33, 326],\n",
       "       [306,  61, 461],\n",
       "       [147, 580,  76]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a5[0:5:2,0:6:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10],\n",
       "       [10]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(np.array([[1,2,3],[1,2,3]]),np.array([[1],[3],[1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([60.  , 37.25, 52.75])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a2,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 566.        , 1628.66666667,  146.88888889,  636.22222222])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(a2,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.86796226, 26.51768278, 36.40999176])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a2,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[54 90 58 38]\n",
      " [15  7 67 60]\n",
      " [72  2 38 99]] (3, 4)\n"
     ]
    }
   ],
   "source": [
    "print(a2,a2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54, 90, 58],\n",
       "       [38, 15,  7],\n",
       "       [67, 60, 72],\n",
       "       [ 2, 38, 99]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(a2,(4,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[54, 90, 58, 38, 15],\n",
       "       [ 7, 67, 60, 72,  2],\n",
       "       [38, 99, 54, 90, 58],\n",
       "       [38, 15,  7, 67, 60],\n",
       "       [72,  2, 38, 99, 54]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.resize(a2,(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Series\n",
    "import pandas as pd\n",
    "l=[1,2,3]\n",
    "arr=np.array([10,20,30])\n",
    "d={'a':1,'b':\"hello\",'c':3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>r</th>\n",
       "      <th>s</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.710793</td>\n",
       "      <td>0.126856</td>\n",
       "      <td>0.817080</td>\n",
       "      <td>0.482452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.008872</td>\n",
       "      <td>0.280510</td>\n",
       "      <td>0.757130</td>\n",
       "      <td>0.266038</td>\n",
       "      <td>0.903187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.056305</td>\n",
       "      <td>0.551470</td>\n",
       "      <td>0.056024</td>\n",
       "      <td>0.340156</td>\n",
       "      <td>0.626754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.890947</td>\n",
       "      <td>0.874463</td>\n",
       "      <td>0.788550</td>\n",
       "      <td>0.550429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.941108</td>\n",
       "      <td>0.190264</td>\n",
       "      <td>0.593913</td>\n",
       "      <td>0.549445</td>\n",
       "      <td>0.234686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          p         q         r         s         t\n",
       "a  0.428336  0.710793  0.126856  0.817080  0.482452\n",
       "b  0.008872  0.280510  0.757130  0.266038  0.903187\n",
       "c  0.056305  0.551470  0.056024  0.340156  0.626754\n",
       "d  0.016861  0.890947  0.874463  0.788550  0.550429\n",
       "e  0.941108  0.190264  0.593913  0.549445  0.234686"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.DataFrame(np.random.random((5,5)),index=['a','b','c','d','e'],\n",
    "               columns=['p','q','r','s','t'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>roll_no</th>\n",
       "      <th>sub</th>\n",
       "      <th>section</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>x</td>\n",
       "      <td>1</td>\n",
       "      <td>INT246</td>\n",
       "      <td>KM025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>y</td>\n",
       "      <td>2</td>\n",
       "      <td>INT246</td>\n",
       "      <td>KM025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>z</td>\n",
       "      <td>3</td>\n",
       "      <td>INT246</td>\n",
       "      <td>KM025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Name  roll_no     sub section\n",
       "0    x        1  INT246   KM025\n",
       "1    y        2  INT246   KM025\n",
       "2    z        3  INT246   KM025"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Name':['x','y','z'],\n",
    "                'roll_no':[1,2,3],\n",
    "                'sub':['INT246']*3,\n",
    "                'section':['KM025']*3})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p</th>\n",
       "      <th>q</th>\n",
       "      <th>r</th>\n",
       "      <th>s</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.710793</td>\n",
       "      <td>0.126856</td>\n",
       "      <td>0.817080</td>\n",
       "      <td>0.482452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.008872</td>\n",
       "      <td>0.280510</td>\n",
       "      <td>0.757130</td>\n",
       "      <td>0.266038</td>\n",
       "      <td>0.903187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.056305</td>\n",
       "      <td>0.551470</td>\n",
       "      <td>0.056024</td>\n",
       "      <td>0.340156</td>\n",
       "      <td>0.626754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.890947</td>\n",
       "      <td>0.874463</td>\n",
       "      <td>0.788550</td>\n",
       "      <td>0.550429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.941108</td>\n",
       "      <td>0.190264</td>\n",
       "      <td>0.593913</td>\n",
       "      <td>0.549445</td>\n",
       "      <td>0.234686</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          p         q         r         s         t\n",
       "a  0.428336  0.710793  0.126856  0.817080  0.482452\n",
       "b  0.008872  0.280510  0.757130  0.266038  0.903187\n",
       "c  0.056305  0.551470  0.056024  0.340156  0.626754\n",
       "d  0.016861  0.890947  0.874463  0.788550  0.550429\n",
       "e  0.941108  0.190264  0.593913  0.549445  0.234686"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['p'] # Accessing a single column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <tbody>\n",
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       "      <th>a</th>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.817080</td>\n",
       "      <td>0.126856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.008872</td>\n",
       "      <td>0.266038</td>\n",
       "      <td>0.757130</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.056305</td>\n",
       "      <td>0.340156</td>\n",
       "      <td>0.056024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.788550</td>\n",
       "      <td>0.874463</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
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      "text/plain": [
       "          p         s         r\n",
       "a  0.428336  0.817080  0.126856\n",
       "b  0.008872  0.266038  0.757130\n",
       "c  0.056305  0.340156  0.056024\n",
       "d  0.016861  0.788550  0.874463\n",
       "e  0.941108  0.549445  0.593913"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[['p','s','r']] # Accessing multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.710793</td>\n",
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     "metadata": {},
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   "source": [
    "df1.loc[['a']]"
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  {
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   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "      <th>a</th>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.710793</td>\n",
       "      <td>0.126856</td>\n",
       "      <td>0.817080</td>\n",
       "      <td>0.482452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.056305</td>\n",
       "      <td>0.551470</td>\n",
       "      <td>0.056024</td>\n",
       "      <td>0.340156</td>\n",
       "      <td>0.626754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.890947</td>\n",
       "      <td>0.874463</td>\n",
       "      <td>0.788550</td>\n",
       "      <td>0.550429</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "          p         q         r         s         t\n",
       "a  0.428336  0.710793  0.126856  0.817080  0.482452\n",
       "c  0.056305  0.551470  0.056024  0.340156  0.626754\n",
       "d  0.016861  0.890947  0.874463  0.788550  0.550429"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[['a','c','d']] # Accessing Multiple rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>a</th>\n",
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       "          p         q\n",
       "a  0.428336  0.710793\n",
       "c  0.056305  0.551470"
      ]
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     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.loc[['a','c'],['p','q']] # Accessing rows and colums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>roll_no</th>\n",
       "      <th>sub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>INT246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>INT246</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>INT246</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   roll_no     sub\n",
       "0        1  INT246\n",
       "1        2  INT246\n",
       "2        3  INT246"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:3,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.290296</td>\n",
       "      <td>0.524797</td>\n",
       "      <td>0.481677</td>\n",
       "      <td>0.552254</td>\n",
       "      <td>0.559502</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.403525</td>\n",
       "      <td>0.291959</td>\n",
       "      <td>0.370753</td>\n",
       "      <td>0.251436</td>\n",
       "      <td>0.241954</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.008872</td>\n",
       "      <td>0.190264</td>\n",
       "      <td>0.056024</td>\n",
       "      <td>0.266038</td>\n",
       "      <td>0.234686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.016861</td>\n",
       "      <td>0.280510</td>\n",
       "      <td>0.126856</td>\n",
       "      <td>0.340156</td>\n",
       "      <td>0.482452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.056305</td>\n",
       "      <td>0.551470</td>\n",
       "      <td>0.593913</td>\n",
       "      <td>0.549445</td>\n",
       "      <td>0.550429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.428336</td>\n",
       "      <td>0.710793</td>\n",
       "      <td>0.757130</td>\n",
       "      <td>0.788550</td>\n",
       "      <td>0.626754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.941108</td>\n",
       "      <td>0.890947</td>\n",
       "      <td>0.874463</td>\n",
       "      <td>0.817080</td>\n",
       "      <td>0.903187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              p         q         r         s         t\n",
       "count  5.000000  5.000000  5.000000  5.000000  5.000000\n",
       "mean   0.290296  0.524797  0.481677  0.552254  0.559502\n",
       "std    0.403525  0.291959  0.370753  0.251436  0.241954\n",
       "min    0.008872  0.190264  0.056024  0.266038  0.234686\n",
       "25%    0.016861  0.280510  0.126856  0.340156  0.482452\n",
       "50%    0.056305  0.551470  0.593913  0.549445  0.550429\n",
       "75%    0.428336  0.710793  0.757130  0.788550  0.626754\n",
       "max    0.941108  0.890947  0.874463  0.817080  0.903187"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>a</th>\n",
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       "      <td>0.817080</td>\n",
       "      <td>0.482452</td>\n",
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       "      <th>b</th>\n",
       "      <td>0.008872</td>\n",
       "      <td>0.280510</td>\n",
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       "      <td>0.266038</td>\n",
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       "      <th>c</th>\n",
       "      <td>0.056305</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.941108</td>\n",
       "      <td>0.190264</td>\n",
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      ],
      "text/plain": [
       "          p         q         r         s         t\n",
       "a  0.428336  0.710793  0.126856  0.817080  0.482452\n",
       "b  0.008872  0.280510  0.757130  0.266038  0.903187\n",
       "c  0.056305  0.551470  0.056024  0.340156  0.626754\n",
       "d  0.016861  0.890947  0.874463  0.788550  0.550429\n",
       "e  0.941108  0.190264  0.593913  0.549445  0.234686"
      ]
     },
     "execution_count": 86,
     "metadata": {},
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   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "          p         q         r        s         t\n",
       "a       NaN  0.710793       NaN  0.81708       NaN\n",
       "b       NaN       NaN  0.757130      NaN  0.903187\n",
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       "d       NaN  0.890947  0.874463  0.78855       NaN\n",
       "e  0.941108       NaN       NaN      NaN       NaN"
      ]
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     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1>0.6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     sepallength  sepalwidth  petallength  petalwidth          target\n",
      "0            5.1         3.5          1.4         0.2     Iris-setosa\n",
      "1            4.9         3.0          1.4         0.2     Iris-setosa\n",
      "2            4.7         3.2          1.3         0.2     Iris-setosa\n",
      "3            4.6         3.1          1.5         0.2     Iris-setosa\n",
      "4            5.0         3.6          1.4         0.2     Iris-setosa\n",
      "..           ...         ...          ...         ...             ...\n",
      "145          6.7         3.0          5.2         2.3  Iris-virginica\n",
      "146          6.3         2.5          5.0         1.9  Iris-virginica\n",
      "147          6.5         3.0          5.2         2.0  Iris-virginica\n",
      "148          6.2         3.4          5.4         2.3  Iris-virginica\n",
      "149          5.9         3.0          5.1         1.8  Iris-virginica\n",
      "\n",
      "[150 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv(\"C:\\\\Users\\\\hp\\\\Downloads\\\\iris (2).csv\",header=None,\n",
    "              names=['sepallength','sepalwidth','petallength','petalwidth','target'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
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      ],
      "text/plain": [
       "     0    1    2    3            4\n",
       "0  5.1  3.5  1.4  0.2  Iris-setosa\n",
       "1  4.9  3.0  1.4  0.2  Iris-setosa\n",
       "2  4.7  3.2  1.3  0.2  Iris-setosa\n",
       "3  4.6  3.1  1.5  0.2  Iris-setosa\n",
       "4  5.0  3.6  1.4  0.2  Iris-setosa"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    150.000000\n",
       "mean       5.843333\n",
       "std        0.828066\n",
       "min        4.300000\n",
       "25%        5.100000\n",
       "50%        5.800000\n",
       "75%        6.400000\n",
       "max        7.900000\n",
       "Name: sepallength, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sepallength'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.550429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.941108</td>\n",
       "      <td>0.190264</td>\n",
       "      <td>0.593913</td>\n",
       "      <td>0.549445</td>\n",
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      ],
      "text/plain": [
       "          p         q         r         s         t\n",
       "a  0.428336  0.710793  0.126856  0.817080  0.482452\n",
       "b  0.008872  0.280510  0.757130  0.266038  0.903187\n",
       "c  0.056305  0.551470  0.056024  0.340156  0.626754\n",
       "d  0.016861  0.890947  0.874463  0.788550  0.550429\n",
       "e  0.941108  0.190264  0.593913  0.549445  0.234686"
      ]
     },
     "execution_count": 96,
     "metadata": {},
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    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['f']=df1['p']+df1['r']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
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       "      <td>0.190264</td>\n",
       "      <td>0.593913</td>\n",
       "      <td>0.549445</td>\n",
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       "      <td>1.535021</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "          p         q         r         s         t         f\n",
       "a  0.428336  0.710793  0.126856  0.817080  0.482452  0.555192\n",
       "b  0.008872  0.280510  0.757130  0.266038  0.903187  0.766002\n",
       "c  0.056305  0.551470  0.056024  0.340156  0.626754  0.112329\n",
       "d  0.016861  0.890947  0.874463  0.788550  0.550429  0.891325\n",
       "e  0.941108  0.190264  0.593913  0.549445  0.234686  1.535021"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop('f',axis=1, inplace=True)  #AXis=1 for column inplace=True changes permanent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>0.941108</td>\n",
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      ],
      "text/plain": [
       "          p         q         r         s         t\n",
       "a  0.428336  0.710793  0.126856  0.817080  0.482452\n",
       "b  0.008872  0.280510  0.757130  0.266038  0.903187\n",
       "c  0.056305  0.551470  0.056024  0.340156  0.626754\n",
       "d  0.016861  0.890947  0.874463  0.788550  0.550429\n",
       "e  0.941108  0.190264  0.593913  0.549445  0.234686"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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