29-June-19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# String join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "o m a r\n",
      "o m a r\n",
      "o m a r\n"
     ]
    }
   ],
   "source": [
    "s=input()\n",
    "print(s)\n",
    "''.join(s)\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "o m a r\n",
      "omar\n"
     ]
    }
   ],
   "source": [
    "t=int(input())\n",
    "l=input().split()\n",
    "j=''.join(l)\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "o m a r\n",
      "omar\n"
     ]
    }
   ],
   "source": [
    "t=int(input())\n",
    "l=list(input().split())\n",
    "j=''.join(l)\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# print odd  numbers in the range of 10 in reverse order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "7\n",
      "5\n",
      "3\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "def revereodd(n):\n",
    "    for i in reversed(range(0,n)):\n",
    "        if i%2!=0:\n",
    "            print(i)\n",
    "    return \n",
    "n=int(input())\n",
    "revereodd(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "9\n",
      "7\n",
      "5\n",
      "3\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "def revereodd(n):\n",
    "    for i in range(n,0,-1):\n",
    "        if i%2!=0:\n",
    "            print(i)\n",
    "    return \n",
    "n=int(input())\n",
    "revereodd(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': '4',\n",
       " '1': '5',\n",
       " '2': '6',\n",
       " '3': '7',\n",
       " '4': '8',\n",
       " '5': '9',\n",
       " '6': '0',\n",
       " '7': '1',\n",
       " '8': '2',\n",
       " '9': '3'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def constructkeyfile(keyfile):\n",
    "    d={}\n",
    "    with open(keyfile,'r') as f:\n",
    "        for i in f:\n",
    "            i=i.split()\n",
    "            d[i[0]]=i[1]\n",
    "    return d\n",
    "keyfile=\"data files/key.txt\"\n",
    "constructkeyfile(keyfile)      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function to encryption based on a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encrypt(filename,keyfile):\n",
    "    key=constructkeyfile(keyfile)\n",
    "    with open(filename,'r') as f:\n",
    "        filedata=f.read().split('\\n')\n",
    "    with open('data files\\encryptmarks.txt','w') as f:\n",
    "        for mark in filedata:\n",
    "            line=' '\n",
    "            for n in mark:\n",
    "                line+=key[n]\n",
    "            f.write(line+'\\n')\n",
    "    return\n",
    "filename=\"data files/marks.txt\"\n",
    "encrypt(filename,keyfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "' '",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-75-067f24376e2b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mencryptfile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'data files\\encryptmarks.txt'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mdecrypt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mencryptfile\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkeyfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-75-067f24376e2b>\u001b[0m in \u001b[0;36mdecrypt\u001b[1;34m(encryptfile, keyfile)\u001b[0m\n\u001b[0;32m     12\u001b[0m             \u001b[0mline\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m''\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m                 \u001b[0mline\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[0mnewkey\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m             \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: ' '"
     ]
    }
   ],
   "source": [
    "def decrypt(encryptfile,keyfile):\n",
    "    key=constructkeyfile(keyfile)\n",
    "    newkey={}\n",
    "    for key,value in key.items():\n",
    "        newkey[value]=key\n",
    "        \n",
    "    with open(encryptfile,'r') as f:\n",
    "        encrytpdata=f.read().split('\\n')\n",
    "    with open('data files/decyptmarks.txt','w') as f:\n",
    "      \n",
    "        for i in encrytpdata:\n",
    "            line=''\n",
    "            for n in i:\n",
    "                line+=newkey[n]\n",
    "            f.write(line+'\\n')\n",
    "    return\n",
    "encryptfile='data files\\encryptmarks.txt'\n",
    "decrypt(encryptfile,keyfile)        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comprehensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': '4',\n",
       " '1': '5',\n",
       " '2': '6',\n",
       " '3': '7',\n",
       " '4': '8',\n",
       " '5': '9',\n",
       " '6': '0',\n",
       " '7': '1',\n",
       " '8': '2',\n",
       " '9': '3'}"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyfile='data files\\key.txt'\n",
    "key=constructkeyfile(keyfile)\n",
    "key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('0', '4'), ('2', '6'), ('4', '8'), ('6', '0'), ('8', '2')}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "even={i for i in key.items()  if int(i[0])%2==0}\n",
    "even"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0', '1', '2', '3', '4', '5', '6', '7', '8', '9'}"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x={item for item in key}\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Libraries"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "# Scify\n",
    "https://www.scipy.org/\n",
    "#ForNumpy= N-Dimensional data\n",
    "Scipy-Sicentific computation\n",
    "matplotlib\n",
    "Sympy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NUMPY Library(np)\n",
    "### >. Processing N-dimensional arrays\n",
    "### >.https://github.com/Akash-Sinha/PythonDataScienceHandbook/blob/master/notebooks/02.02-The-Basics-Of-NumPy-Arrays.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "li=[1,2,3]\n",
    "a=np.array(li)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', 'z'], dtype='<U11')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "li=[1,2,3,'z']\n",
    "a=np.array(li)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=np.arange(0,10) #arange means array range\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([47, 47, 16, 45, 40,  8, 46, 40, 22,  3])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rn=np.random.randint(0,50,10) #range is 0 -50 and size is 10\n",
    "rn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 4],\n",
       "       [4, 3, 3],\n",
       "       [2, 3, 0]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3=np.random.randint(0,5,size=(3,3)) #generate 3x3 array with range of 0-5\n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 6, 4],\n",
       "       [4, 3, 3],\n",
       "       [2, 3, 0]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[0][1]=6#assign new value in a index\n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 0],\n",
       "        [4, 2],\n",
       "        [1, 3]],\n",
       "\n",
       "       [[4, 3],\n",
       "        [3, 4],\n",
       "        [3, 2]],\n",
       "\n",
       "       [[2, 2],\n",
       "        [1, 1],\n",
       "        [4, 4]]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3=np.random.randint(0,5,size=(3,3,2))#return 3x3 array in 2 \n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[2][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 2],\n",
       "       [1, 1],\n",
       "       [4, 4]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[0][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [4, 2],\n",
       "       [1, 3]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3, 2)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 0],\n",
       "        [4, 2],\n",
       "        [1, 3]],\n",
       "\n",
       "       [[4, 3],\n",
       "        [3, 4],\n",
       "        [3, 2]],\n",
       "\n",
       "       [[2, 2],\n",
       "        [1, 1],\n",
       "        [4, 4]]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3[1:4:1]\n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 12, 78])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=[1,2,3,4,5,6,7,8,9,12,78]\n",
    "s=np.array(x)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7, 8, 9, 12, 78]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[78, 12, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0:9:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1],\n",
       "        [5, 3],\n",
       "        [2, 4]],\n",
       "\n",
       "       [[5, 4],\n",
       "        [4, 5],\n",
       "        [4, 3]],\n",
       "\n",
       "       [[3, 3],\n",
       "        [2, 2],\n",
       "        [5, 5]]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=m3+1 #Sclar addition 1 add to all elements in array\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mat M:\n",
      " [[1. 1. 1.]\n",
      " [1. 1. 1.]\n",
      " [1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "m=np.ones((3,3))\n",
    "print(\"mat M:\\n\",m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Use Cases\n",
    "## For Data analysis-Data cleaning\n",
    "## Data Transformation\n",
    "## Data Analysis\n",
    "Notations\n",
    "-Series\n",
    "-Data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s1    21\n",
       "s2    18\n",
       "s3    23\n",
       "s4    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd #1 dimensional dataa\n",
    "int1={'s1':21,'s2':18,'s3':23,'s4':12}\n",
    "int1=pd.Series(int1)\n",
    "int1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s1    13\n",
       "s2     8\n",
       "s3    13\n",
       "s4    22\n",
       "dtype: int64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd #1 dimensional dataa\n",
    "int2={'s1':13,'s2':8,'s3':13,'s4':22}\n",
    "int2=pd.Series(int2)\n",
    "int2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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>int1</th>\n",
       "      <th>int2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s1</th>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    int1  int2\n",
       "s1    21    13\n",
       "s2    18     8\n",
       "s3    23    13\n",
       "s4    12    22"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final={'int1':int1,'int2':int2}\n",
    "final=pd.DataFrame(final)\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['int1', 'int2'], dtype='object')"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.columns#list of columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21, 13],\n",
       "       [18,  8],\n",
       "       [23, 13],\n",
       "       [12, 22]], dtype=int64)"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.values# list of rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([21, 13], dtype=int64)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23\n",
      "13\n"
     ]
    }
   ],
   "source": [
    "print(final.values[2,0])\n",
    "print(final.values[2][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "internal1- 21 internal2- 13\n",
      "internal1- 18 internal2- 8\n",
      "internal1- 23 internal2- 13\n",
      "internal1- 12 internal2- 22\n"
     ]
    }
   ],
   "source": [
    "for row in final.values:\n",
    "    print('internal1-',row[0],'internal2-',row[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>int1</th>\n",
       "      <th>int2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s1</th>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    int1  int2\n",
       "s1    21    13\n",
       "s2    18     8\n",
       "s3    23    13\n",
       "s4    12    22\n",
       "3     15    18"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.loc[3]=[15,18]\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>int1</th>\n",
       "      <th>int2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s1</th>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    int1  int2\n",
       "s1    21    13\n",
       "s2    18     8\n",
       "s3    12    25\n",
       "s4    12    22"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=final.drop(3)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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>int1</th>\n",
       "      <th>int2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>s1</th>\n",
       "      <td>21</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>12</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    int1  int2\n",
       "s1    21    13\n",
       "s2    18     8\n",
       "s3    12    25\n",
       "s4    12    22\n",
       "3     15    18"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.values[2]=[12,25]\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>GEOID</th>\n",
       "      <th>State</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>04000US01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>37150</td>\n",
       "      <td>37952</td>\n",
       "      <td>42212</td>\n",
       "      <td>44476</td>\n",
       "      <td>39980</td>\n",
       "      <td>40933</td>\n",
       "      <td>42590</td>\n",
       "      <td>43464</td>\n",
       "      <td>41381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04000US02</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>55891</td>\n",
       "      <td>56418</td>\n",
       "      <td>62993</td>\n",
       "      <td>63989</td>\n",
       "      <td>61604</td>\n",
       "      <td>57848</td>\n",
       "      <td>57431</td>\n",
       "      <td>63648</td>\n",
       "      <td>61137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>04000US04</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>45245</td>\n",
       "      <td>46657</td>\n",
       "      <td>47215</td>\n",
       "      <td>46914</td>\n",
       "      <td>45739</td>\n",
       "      <td>46896</td>\n",
       "      <td>48621</td>\n",
       "      <td>47044</td>\n",
       "      <td>50602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04000US05</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>36658</td>\n",
       "      <td>37057</td>\n",
       "      <td>40795</td>\n",
       "      <td>39586</td>\n",
       "      <td>36538</td>\n",
       "      <td>38587</td>\n",
       "      <td>41302</td>\n",
       "      <td>39018</td>\n",
       "      <td>39919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04000US06</td>\n",
       "      <td>California</td>\n",
       "      <td>51755</td>\n",
       "      <td>55319</td>\n",
       "      <td>55734</td>\n",
       "      <td>57014</td>\n",
       "      <td>56134</td>\n",
       "      <td>54283</td>\n",
       "      <td>53367</td>\n",
       "      <td>57020</td>\n",
       "      <td>57528</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GEOID       State   2005   2006   2007   2008   2009   2010   2011  \\\n",
       "0  04000US01     Alabama  37150  37952  42212  44476  39980  40933  42590   \n",
       "1  04000US02      Alaska  55891  56418  62993  63989  61604  57848  57431   \n",
       "2  04000US04     Arizona  45245  46657  47215  46914  45739  46896  48621   \n",
       "3  04000US05    Arkansas  36658  37057  40795  39586  36538  38587  41302   \n",
       "4  04000US06  California  51755  55319  55734  57014  56134  54283  53367   \n",
       "\n",
       "    2012   2013  \n",
       "0  43464  41381  \n",
       "1  63648  61137  \n",
       "2  47044  50602  \n",
       "3  39018  39919  \n",
       "4  57020  57528  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading csv file data\n",
    "import pandas as pd\n",
    "filepath='data files/income.csv'\n",
    "incomedf=pd.read_csv(filepath)\n",
    "incomedf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['04000US01', 'Alabama', 37150, 37952, 42212, 44476, 39980, 40933,\n",
       "        42590, 43464, 41381],\n",
       "       ['04000US02', 'Alaska', 55891, 56418, 62993, 63989, 61604, 57848,\n",
       "        57431, 63648, 61137],\n",
       "       ['04000US04', 'Arizona', 45245, 46657, 47215, 46914, 45739, 46896,\n",
       "        48621, 47044, 50602],\n",
       "       ['04000US05', 'Arkansas', 36658, 37057, 40795, 39586, 36538,\n",
       "        38587, 41302, 39018, 39919],\n",
       "       ['04000US06', 'California', 51755, 55319, 55734, 57014, 56134,\n",
       "        54283, 53367, 57020, 57528]], dtype=object)"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Extract income of all states in  2013\n",
    "#california:57528\n",
    "incomedf.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alabama : 41381\n",
      "Alaska : 61137\n",
      "Arizona : 50602\n",
      "Arkansas : 39919\n",
      "California : 57528\n"
     ]
    }
   ],
   "source": [
    "for row in incomedf.values:\n",
    "    print(row[1],':',row[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47214.77777777778"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average income of Arizona in 2013\n",
    "s=0\n",
    "for i in range(2,11):\n",
    "    s+=incomedf.values[2][i]\n",
    "s/(len(incomedf.values[2][2:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50038.8"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average income of all states in 2012\n",
    "s=0\n",
    "for row in incomedf.values:\n",
    "    s+=row[-2]\n",
    "s/len(incomedf.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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