simgeekiz/librarydataexample1.ipynb

## librarydataexample1.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:ce3b79e0e65bf93559fee523303acf8497e47f3fa53af35cdd6e2a4a9e79d299"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import DataFrame, read_csv\n",
      "import pandas as pd\n",
      "import os\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# databaseimizde 3 farkl\u0131 tablo var ilk olarak bunlar\u0131 dataframe olarak y\u00fckleyelim.\n",
      "data1=pd.read_csv('dosya/hakandatabase/library_count_types.csv')\n",
      "data2=pd.read_csv('dosya/hakandatabase/library_stats.csv')\n",
      "data3=pd.read_csv('dosya/hakandatabase/library_types.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#ve biz bunlar\u0131 birle\u015ftirip bir tane tablo yapal\u0131m\n",
      "newdf= pd.merge(data2,data3)\n",
      "# newdf2=pd.merge(newdf,hakan1, left_on='count_type_id' ,right_on='library_type_id')\n",
      "newdf2= pd.merge(data1,newdf)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Kullan\u0131lmayan kolonlar\u0131 drop edelim.\n",
      "newdf2 = newdf2.drop(['library_type_id', 'count_type_id'], axis=1)\n",
      "#ve bu yapt\u0131\u011f\u0131m\u0131z iki ayr\u0131 merge i\u015flemini tek ad\u0131mda da yapabilirdik.\n",
      "#newdff= pd.merge(pd.merge(data1,data2),data3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#y\u0131llara g\u00f6re ulusal k\u00fct\u00fcphanelerde olan kitap say\u0131lar\u0131\n",
      "numberofbook_national =newdf2[newdf2['library_type_name'] == 'National Library']\n",
      "numberofbook_national =numberofbook_national[numberofbook_national['count_type_name'] =='Number of Books']\n",
      "numberofbook_national = numberofbook_national.set_index('year')[['count']]\n",
      "numberofbook_national.columns=['National']\n",
      "numberofbook_national\n",
      "\n",
      "'''n_book_national2 = numberofbook_national.set_index('year')\n",
      "n_book_national2 = n_book_national2[['count']]\n",
      "n_book_national2.columns=['National']\n",
      "n_book_national2'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "\"n_book_national2 = numberofbook_national.set_index('year')\\nn_book_national2 = n_book_national2[['count']]\\nn_book_national2.columns=['National']\\nn_book_national2\""
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numberofbook_national.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f057b5f5550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEPCAYAAACOU4kjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XmcFNW5//HPIIgQkGHAHwIKg8YluCEYhVyEQa+Ixqui\nIqBBEJfoqCGJGhUSAbfoDYqIAaLIqiyKXtRIEAKM4gJEBUSJsoMMuLGjEJap3x/PaaYYBmaheqqr\n+vt+vfo11aera85Daz9T5zl1CkREREREREREREREREREREREREREREQCNwL4BlhUzGt3AwVAlq/t\nAWAp8AXQ3tfewh1jKTDI114VmOja5wCNfa91B5a4xw2+9ibAXPeeCUCVMsYkIiIhOx84mwOTy/HA\nVGAlhcmlKbAA+7LPBpYBGe61ecC5bnsK0MFt5wJD3HZnLFngjrkcyHSP5UAt99rLwLVueyhwWzlj\nExGREGVzYHJ5BTiT/ZPLA8B9vn2mAi2B+sC/fe1dgGG+fc5z25WB79x2VyxxJAxz78tw+1Ry7S3d\nMUREJIVUKnmXA1wBrAU+LdLewLUnrAUaFtOe79pxP79y23uALUCdQxwrC9iMDccVPZaIiKSIymXc\nvzrQG7jI15ZxkH2D5lXQ7xERkcNU1uRyIjZMttA9Pw74GBvaysdqMfheW+vajyumHfdaI2Cd60st\nYINrz/G953hgJrARq8FUws5ejnP7HqBBgwbeunXryhieiEjaWw78tCJ+UTbFzxaD4gv6R2IzupZT\neFYzF0tAGRxY0E/UVrqwf0F/BZZIavu2wQr6nd32MA5e0PfirG/fvmF3IaniHF+cY/M8xRd1BDRK\nVFLNZTzwAXAyVhu5segXuG97MfbFvxj4B5Y4Eq/nAsOx6cPLKCzCv4DVWJYCvwXud+0bgYeBf2Ez\nzfpjtRawSQO/d++p7Y6RdlatWhV2F5IqzvHFOTZQfGJKGhbrWsLrJxR5/ph7FPUxcEYx7f+hcFpx\nUSPdo6iVFM4wExGRFFSe2WKSAnr06BF2F5IqzvHFOTZQfGIqaqZXGNzwoYiIlFZGRgYEkBvS7swl\nKyuLjIwMPSrgkZWVVfIHchB5eXnBfegpJs6xgeITU9apyJG3adMmdEZTMdxfQCKShuL8f3+xw2IZ\nGRlKLhVE/9Yi0aNhMRERSVlKLpKS4jyuHefYQPGJUXKJqUsvvZSxY8cm/fdUqlSJFStWJP33iEjy\nTZkS3LFUc0kR2dnZ7Nixg5UrV1K9enUAhg8fzksvvcSsWbMO+d5+/fqxfPnyCkkmRVWqVIlly5Zx\nwglFr6dN3X9rETnQ4sXQpg1s2KCaS+wUFBQwaNCgkncUEQnQrl3QrRs8+mhwx1RySREZGRncc889\nDBgwgC1bthzweq9evWjUqBG1atXinHPO4b333gNg6tSp/PnPf2bixInUrFmTs88+G4CcnBxeeMGW\nXfM8j0ceeYTs7Gzq1atH9+7d2bp1K2DrJFWqVIkxY8bQuHFjjjnmGB57rHAFn3nz5tGqVStq165N\ngwYNuOuuu9i9e3ey/zliPa4d59hA8UXRww/DscfCrbcGd0wllxRyzjnnkJOTw4ABAw547dxzz2Xh\nwoVs2rSJ6667jk6dOrFr1y46dOhA79696dKlC9u2bWP+/PkA+y5kBBg5ciSjR48mLy+PFStWsH37\ndu688879jv/++++zZMkSZsyYwUMPPcSXX34JQOXKlRk0aBAbNmzgww8/ZMaMGQwZMgQRiYc5c+C5\n52D4cNClaaVz0OWkD73cdDCPssrOzvZmzJjhffbZZ16tWrW87777znv++ee9nJycYvevXbu29+mn\nn3qeZ0uA/+pXv9rv9ZycHO+FF17wPM/zLrjgAm/o0KH7Xvvyyy+9KlWqeHv37vVWrlzpZWRkePn5\n+fteP/fcc70JEyYU+3sHDhzodezYcd/zjIwMb/ny5cXuW9K/tYiEa/t2z/vpTz3vlVcK26igJffT\nTlDppbxOO+00LrvsMh5//PH9rnAfMGAATZs2JTMzk9q1a7Nlyxa+//77Uh1z/fr1NG7ceN/zRo0a\nsWfPHr755pt9bccee+y+7erVq/PDDz8AsGTJEi677DLq169PrVq16NOnDxs2bCh/gCKSMv7wB2jZ\nEq65JvhjK7mkoP79+/P888+Tn2832Xzvvff4y1/+wiuvvMLmzZvZtGkTtWrV2jcTq6RlVho0aLDf\nPSjWrFlD5cqVqVevXol9uf3222natCnLli1jy5YtPProoxQUFJQ/uFKK47h2QpxjA8UXFW+/DW++\nCYMHJ+f4Si4p6MQTT6Rz5877Zo5t27aNypUrU7duXXbt2sVDDz20ryAPdtaxatWqg0777dq1KwMH\nDmTVqlVs3759X42mUqWSP/7t27dTs2ZNqlevzhdffMHQoUNLfI+IpLaNG+Gmm2DkSMjMLHn/8lBy\nSVEPPvggP/74IxkZGVx88cV06NCBk08+mezsbKpVq0ajRo327dupUycA6tSpwznnnHPAsXr27Em3\nbt1o06YNJ5xwAtWrV2ew78+VQ535DBgwgHHjxnH00Udz66230qVLl/32T9bilDk5OUk5biqIc2yg\n+KIgNxeuvhouvDB5vyPOcwO84v6S14V9FUf/1iKpZ/x4eOgh+OQTqFbtwNe1cKXEWlzGtYsT59hA\n8aWy/Hzo1QvGji0+sQRJyUVEJA14HvTsCXfeCcWMngdOw2KSNPq3Fkkdf/0rjBkD778PlQ9xm8ig\nhsWUXCRp9G8tkhq+/BJat7bEcvLJh95XNReJtSiPa5ckzrGB4ks1e/bADTdAv34lJ5YgKbmIiMTY\nY49BrVpw++0V+3vTblgsKyuLTZs2hdCd9FO7dm02btwYdjdE0tZHH8Gll8L8+dCwYeneE9Sw2CHK\nOvGkLzsRSQc7dtg9WgYNKn1iCZKGxSIqauO+ZRXn+OIcGyi+VPHAA3DWWdC1azi/P+3OXERE4m7G\nDJg0CT79NLw+lDSuNgL4JfAtcIZrexi4HFvzfwPQA/gKyAb+DXzh9vsQyHXbLYBRwFHAFKCXa68K\njAGau2N1Bla717oDfdz2I24/gCbABCAL+BjoBhR3a8Riay4iInG2eTOceSY8/zxcfHHZ319R17mc\nD2zHvtgTyaUmsM1t3wWcBdyMJZc3ffv5zQPudD+nAM8AU7Hkc7r72RnoCHTBEse/sKQElkSaA1uA\nl4FJ7udQYCEwrJjfqeQiImnnhhugRg0o7w1jK+o6l9lA0alV23zbNYCS7lhVH0tI89zzMcCVbvty\nYLTbfhVIrNF5MTAN2Owe04FLsIDbYckF997EsdJKVMZ9yyvO8cU5NlB8YXr1Vbtt8V/+EnZPyl9z\neRQbjvoRaOlrbwLMx84w/gi8BzQE1vr2yXdtuJ9fue097n11gAZF3rPW7ZuFJZvE3ar8xxIRSVvr\n18Mdd8DkyfCTn4Tdm/LPFusDNMLqKANd2zrgeOBs4PfAOOyMJSga4/KJwz0lDiXO8cU5NlB8YfA8\nuPlmuOUWu21xKjjc2WLjsBoKwC73APgEWA6chJ1dHOd7z3EUnpXkY0lqnetLLaywnw/k+N5zPDAT\n2AhkYkmxwB0r/2Cd69GjB9nZ2QBkZmbSrFmzff9hJE5t9VzP9VzPo/78nnvyWLoUJk8u+/vz8vIY\nNWoUwL7vy4qSDSzyPT/Jt30XMNZt1wWOcNsnYAkkcQPNucB5WM1kCtDBtediRXmwQv4Et50FrHDv\nr+3bBivkd3bbw4DbDtJvL85mzZoVdheSKs7xxTk2z1N8FW3ZMs+rW9fzPv88mOMR0ChRSWcu44G2\nWOL4CugLXAqcAuzFzk4SK9a0AR7CpgUXAL/G6iNgSWQUUA1LLlNd+wtYclqKnbF0ce0bsSnP/3LP\n+/uOdR+WhB7BzpBeKHW0IiIxsnevzQ7r0weaNg27N/tLu7XFRETi4vHHYdo0+Oc/oVJA663ofi4l\nU3IRkdhasADat7fFKRs1Cu64up9LmksU5OIqzvHFOTZQfBVh505blHLAgGATS5CUXEREIuZPf7Ib\nf3XrFnZPDk7DYiIiEfLOO7bS8aefQt26wR9fw2IiImlm61bo0cMWpUxGYgmSkktEpcK4bzLFOb44\nxwaKL5l+9zu46CL45S9D60Kp6X4uIiIR8PrrkJdns8SiQDUXEZEU9+23dlfJV16B1q2T+7t0nUvJ\nlFxEJPI8Dzp2hFNPtYsmk00F/TSnce3oinNsoPiCNno0rFwJ/ftX6K89bKq5iIikqFWr4N57YeZM\nqFo17N6UjYbFRERS0M6d0K4dXHWVJZiKoppLyZRcRCSSPA+uv95+jhsHGRX4Ta2aS5rTuHZ0xTk2\nUHxB6N/f6iwjR1ZsYgmSai4iIilk3Dgr4s+ZA0cdFXZvyi+iObFUNCwmIpHywQdw5ZVWwD/99HD6\noGExEZEYWbkSrr4axowJL7EEScklojSuHV1xjg0UX3ls2QKXXWa3K+7QIfDDh0LJRUQkRHv2wLXX\nwgUXwJ13ht2b4KjmIiISEs+DO+6wiyXfeAMqp8AUq6BqLikQiohIeho8GGbPhvffT43EEiQNi0WU\nxrWjK86xgeIrrbfesoUo//53OProQA6ZUmKWK0VEUt+nn8KNN9pQWOPGYfcmOVRzERGpQF9/DS1b\nwhNPQOfOYffmQLrORUQkYnbsgCuugJtuSs3EEiQll4jSuHZ0xTk2UHwHU1AA3bvDSSfBH/8YbJ9S\nkWouIiIV4MEHIT8fZsyI7mKUZRHnEFVzEZGUMGYM9OsHc+fCMceE3ZtD0/1cSqbkIiKhmz3b1gzL\ny4OmTcPuTckqqqA/AvgGWORrexhYCCwAZgDH+157AFgKfAG097W3cMdYCgzytVcFJrr2OYB/Ul53\nYIl73OBrbwLMde+ZAFQpIYZY0rh2dMU5NlB8fsuWQadO8NJL0UgsQSopuYwEii6j9r/AWUAzYDLQ\n17U3BTq7nx2AIRRmv6HATcBJ7pE45k3ABtc2EHjCtWcBDwLnukdfoJZ77QngSfeeTe4YIiIpZdMm\nW4yyXz+46KKwe1PxSnPqkw28CZxRzGsPYF/697vtAgoTxFSgH7AamAn8zLV3AXKA29w+fbEzkcrA\neuAYoCvQBrjdvWcYkIed5XwL1HO/q6X7HcWtI6phMREJxe7dcMklcMYZMHBg2L0pm7DXFnsU6Abs\nwM4sABpgQ1sJa4GGwG63nZDv2nE/v3Lbe4AtQB13LP97EsfKAjZjiaXosUREQud5trpxtWowYEDY\nvQlPeZNLH/e4H3gauDGwHh1cmU9DevToQXZ2NgCZmZk0a9aMnJwcoHDcNKrPn3766VjFk07x+cfs\nU6E/ii/Y+AYOhH/+M4/Bg+GII8Lvb2niGTVqFMC+78uKks3+BX2/RsBnbvt+90iYCpwHHAv829fe\nFavBJPZp6bYrA9+57S7YUFjC37B6TobbJ1ErauWOURwvzmbNmhV2F5IqzvHFOTbPS+/4Xn/d8xo2\n9Lw1ayquP0GjHH/IF6c8NZeTsJlaAHdhw2LdsEL+OPe8IfBP4Keuo3OB3wDzgLeAZ7CkkOuOezuW\nUK50P7OAj4Dmro8fu+3NwMvAq1j9ZRg2a82fiBLcv5OISPItWADt29tqxz//edi9Kb+KqrmMB9oC\ndbHaSF/gUuAUYC+wnMKi+2Lsi38xVj/JpTAD5gKjgGrAFArPNl4AxmLJagOWWAA2YlOe/+We98cS\nC8B92BTkR4BP3DFEREKzbh1cfjkMGRLtxBIkXUQZUXl5efvGT+MozvHFOTZIv/h++AHatoWrroLe\nvcPrV1C0KrKISMgKCuCGG+D00+GBB8LuTWrRmYuISDndfz988AFMnw5Vq4bdm2CEfZ2LiEhaGzkS\nJk2COXPik1iCpGGxiPLPtY+jOMcX59ggPeLLy7Ozlrfegrp1w+5RalJyEREpg7Vr7S6S48fDKaeE\n3ZvUpZqLiEgpbdwIrVrBvffCzTeH3Zvk0GwxEZEK9NVXcOGFcOWV8U0sQVJyiah0GNeOqzjHBvGM\nb948aNkSrrsOOnTIC7s7kaDkIiJyCBMn2n1Zhg614bCMOBcTAhTnfybVXESk3DwP+veHUaPgjTfg\nzDPD7lHF0HUuIiJJsmMH9OgBa9bA3LlQr17YPYoeDYtFVBzHtf3iHF+cY4Pox7d+va0VVqUKzJp1\nYGKJenwVRclFRMSZPx/OOw+uuALGjoWjjgq7R9GlmouICPDaa/DrX8OwYXD11WH3JjyquYiIBMDz\n4PHH7V4sU6dCixZh9ygeNCwWUXEf941zfHGODaIV386dtmT+a69Z4b40iSVK8YVJyUVE0tK339oV\n9//5D7zzDjRoEHaP4kU1FxFJO4sW2W2Jb7gB+vaFSvozex/VXEREyuHvf4eePWHQIOjaNezexJfy\ndUTFfdw3zvHFOTZI3fg8D5580maEvflm+RNLqsaXanTmIiKxt2sX5ObCRx/Bhx9Co0Zh9yj+VHMR\nkVj7/nu7bqV2bXjxRahRI+wepTbdz0VEpASLF9sV961a2XRjJZaKo+QSUXEf941zfHGODVInvrff\nhpwcePBBu0gyqBlhqRJfqlPNRURixfPg2WfhscfsbKV167B7lJ5UcxGR2Ni9G3r1gnfftRlhTZqE\n3aPo0XUuIiI+mzZBp05QtSp88AEcfXTYPUpvqrlEVNzHfeMcX5xjg3DiW7rU7nF/5pl218hkJpa4\nf35BKSm5jAC+ARb52v4C/BtYCLwG1HLt2cAOYL57DPG9p4U7xlJgkK+9KjDRtc8BGvte6w4scY8b\nfO1NgLnuPROAKiXEICIxNnOm1VXuuQeeegqOOCLsHgmUPK52PrAdGAOc4douAmYABcDjru1+LLm8\n6dvPbx5wp/s5BXgGmArkAqe7n52BjkAXIAv4F5aUAD4GmgNbgJeBSe7nUCzJDSvmd6rmIhJjBQUw\neLAV7idMgHbtwu5RPFTUdS6zgU1F2qZjiQXsDOK4Eo5RH6iJJRawRHWl274cGO22XwUudNsXA9OA\nze4xHbgEC7gdllxw700cS0TSxPr1cOmlMH681VeUWFLP4dZcemJnIglNsCGxPCAxAbAhsNa3T75r\nS7z2ldveg52Z1AEaFHnPWrdvFpZsEsnNf6y0Evdx3zjHF+fYIPnxvf46nH22XRw5ezaceGJSf90B\n4v75BeVwZov1AXYB49zzdcDx2JlOc2AycNph9W5/ZR7j6tGjB9nZ2QBkZmbSrFkzcnJygML/QKL6\nfMGCBSnVH8Wn58l+vmMHTJ6cw/Tp8Mc/5nH66VClSur0L6rP8/LyGDVqFMC+78sglGZcLZsDayk9\ngFuwYaydB3nfLOBuYD0wE/iZa+8KtAFux+ou/bBifmW37zFY3SUHuM2952/uGC8D3wL1sLOXVkBf\noEMxv181F5GY+OgjuP56mxE2eLCmGSdTmGuLdQDuBa5g/8RSF0jM0zgBOAlYgSWMrcB5WIe7Aa+7\n/d7AZoUBXINNFACrt7QHMoHa2CSCt7Gzl1lAJ7dfd+wMSURiaO9e+POfrb7y8MMwerQSS1SUlFzG\nAx8Ap2C1kZ7AYKAGVmT3Tzlui83cmg+8Avwaq4+AzQYbjk0fXoadsQC8gNVYlgK/xWadAWwEHsZm\njM0D+vuOdR/we/ee2u4YaSdxWhtXcY4vzrFBcPGtXg0XXADTpsHHH8O11wZy2MMW988vKCXVXIq7\nnc6Ig+z7qnsU52OKn6L8H+Bg/8mMdI+iVmJnQSISU+PH2zIu99wDd9+ta1eiSGuLiUjK2LIF7rjD\naizjxkHz5mH3KP3ofi4iEiuzZ8NZZ1lN5ZNPlFiiTsklouI+7hvn+OIcG5Q9vt27oU8fq6k8+ywM\nGQLVqyenb0GI++cXFK2KLCKhWbrUphgfcwwsWAD16oXdIwmKai4iUuE8D4YPh969oV8/yM2FjDh/\nG0WI7uciIpH0/fdwyy2wciW88w40bRp2jyQZVHOJqLiP+8Y5vjjHBoeOb9o0aNYMfvpTmDs3mokl\n7p9fUHTmIiJJt3MnPPAATJoEY8bYxZESb3Ee5VTNRSQFLFpkRftTT4VhwyArK+weyaHoOhcRSWkF\nBTBokJ2l3H03TJyoxJJOlFwiKu7jvnGOL86xgcW3fj1ccondIXLOHOjePT6zweL++QVFyUVEAvXe\ne3Yzr1/8IpybeUlqiMnfEsVSzUWkAnzzjSWR2bPh3Xdh61Z48UVo1Srsnkl5BFVzUXIRkVLzPLs+\nxZ9MvvsOWreG88+3R4sWcOSRYfdUyksF/TQX93HfOMcXpdgKCuCzz2y9r65d4fjjLZFMmWJDX5Mm\nwYYN8Oab8Ic/2NnKBx/khdzr5IrS5xcmXeciIvvs3m0rEr/7rp2ZvP++zfA6/3xo397uBnniifEp\nzkvyxPk/EQ2LiZTgxx9tNldiiGvePDjhBGjTpnCYq379sHspFUk1l5IpuYgUsWmTzeZK1Ew+/dTu\noZJIJP/1X1C7dti9lDCp5pLm4j7uG+f4KjK2bdvs4sU77oAzz4RGjezCxho14LHHrBj/wQfwxBNw\n2WXBJJY4f3YQ//iCopqLSAwtXmxF+HHjrMjerp1dyHj22VClSti9k3SgYTGRmNi9G15/Hf76V/ji\nC1vW/tZb4bjjwu6ZRInu5yIiAKxbB88/D889Z0vZ5+ZCx4661kTCpZpLRMV93DfO8QURm+dBXp7d\nd/700+0q+alT7eZbnTuHm1ji/NlB/OMLis5cRCJk61YYO9bqKZ5nhfrhw+Hoo8Pumcj+VHMRiYDE\nVfITJsCFF1pSadtWFzNK8FRzEYm5Xbtg8mQr0C9dasX5RYugYcOweyZSMtVcIiru475xjq+k2PLz\noW9fyM62s5U774TVq6Ffv2gkljh/dhD/+IKi5CKSAjwPZs6Ea66BM86wxSCnT7eifadOujZFoqek\ncbURwC+Bb4EzXNtfgMuAXcBy4EZgi3vtAaAnsBf4DTDNtbcARgFHAVOAXq69KjAGaA5sADoDq91r\n3YE+bvsRtx9AE2ACkAV8DHQDdhfTd9VcJOVt2QJjxtgZyhFH2DTibt2gZs2weybpqqKWfxkJdCjS\nNg04DTgLWIIlFICmWHJo6t4zxNfBocBNwEnukTjmTVhSOQkYCDzh2rOAB4Fz3aMvUMu99gTwpHvP\nJncMkUhZtAhuu82Gvt57D4YNs7bcXCUWiYeSksts7AvcbzpQ4LbnAonrf68AxmNnEauAZcB5QH2g\nJjDP7TcGuNJtXw6MdtuvAhe67YuxJLbZPaYDl2DJqh0wye032nestBL3cd84xvfDD3aWcsYZeXTo\nAA0awOef29pfcZr5FcfPzi/u8QXlcGeL9cQSCkADYI7vtbVAQyzZrPW157t23M+v3PYebHitjjuW\n/z2JY2VhySaR3PzHEkk5ngdz58KIEfDKK3Zf+Wuugd69VUeReDuc5NIHq7uMC6gvJSlzAaVHjx5k\nZ2cDkJmZSbNmzcjJyQEK//qI6vNEW6r0R/Ht//y11/KYNg3eeSeHvXshJyeP556DTp1ygJzQ+5fM\n5zk5ii9Kz/Py8hg1ahTAvu/LIJTmRDwbeJPCgj5AD+AWbBhrp2u73/183P2citVKVgOzgJ+59q5A\nG+B2t08/7IynMrAeOAboAuQAt7n3/A2YCbyMTS6oh529tHK/o2hdCFTQlwq2eze89RaMHGk33rrq\nKrjxRrtHSlyGvCT+wryfSwfgXqzGstPX/gaWFI7EZnSdhNVZvga2YvWXDGx21+u+93R329cAM9z2\nNKA9kAnUBi4C3sbOXmYBndx+3YHJ5Ygh8hJ/ecRVlOL7/HO45x5bffjJJ+HKK+Grr+CFF+x+80UT\nS5RiKw/FJ1DysNh4oC1QF6uN9MVmhx2JFdkBPgRygcXYmcVirH6SS+FQVi42FbkaNhV5qmt/ARgL\nLMVmjXVx7RuBh4F/uef9sVoLwH3YVORHgE/cMUQq1JYtthTLiBGwdq3dK2X2bDj55LB7JpIa4nyy\nrmExCVRBgV3UOHIkvPkm/Pd/Q8+e0L49VNZCShITQQ2LKbmIlGD1ahg92pJKzZpw001w/fVQt27Y\nPRMJXpg1F0kBcR/3DTu+nTtt2Kt9e2je3O6XMmkSLFwIvXodXmIJO7ZkU3wCWhVZZB/Pg08+sTrK\nhAnQooUNe73xBhx1VNi9E4kWDYtJ2vvuO3jpJUsq27dDjx5WoG/cOOyeiVQ81VxKpuQiJRo71oa5\nfvlLO0tp2xYqabBY0phqLmku7uO+yY5v71647z67R8rs2ZZk2rWrmMSizy7a4h5fUFRzkbSzdavN\n9tq2zdb90qwvkeBpWEzSyooVcPnltiTL4MFw5JFh90gktWhYTKSM3nnHViW+7Ta7f4oSi0jyKLlE\nVNzHfYOO7/nn4dpr4cUX7Z70YS4kqc8u2uIeX1BUc5FY27MH7r4b3n5ba3+JVCTVXCS2Nm2Czp3t\nLGXiRMjMDLtHIqlPNReRQ1iyBFq2hNNOs3usKLGIVCwll4iK+7jv4cQ3fTqcfz7cey8MHJh6Kxbr\ns4u2uMcXlBT7306k/DwPnn0WHnvM7lffpk3YPRJJX6q5SCzs2gV33QUffGALTTZpEnaPRKIpqJqL\nzlwk8r7/Hq65Bo4+2pJLzZph90hEVHOJqLiP+5Y2vs8+g3PPhVat4P/+LxqJRZ9dtMU9vqDozEUi\n6+9/t5WMn3oKfvWrsHsjIn6quUjkeB4MGABPPw2vvmpTjkUkGKq5SFrauRN+/WtYtAjmzIHjjw+7\nRyJSHNVcIiru477Fxff113DBBbBjhy3lEtXEko6fXZzEPb6gKLlIJMyfD+edBx062FIuP/lJ2D0S\nkUNRzUVS3quv2jL5Q4falGMRSR7VXCT2PA8efhiGD7dVjZs3D7tHIlJaGhaLqLiP+06dmkeXLvCP\nf8C8efFKLHH/7BSfgJKLpKD8fOjVC6pWhVmz4Nhjw+6RiJSVai6SMtauheeeg7/9zW7wde+94d4x\nUiQdVdT9XEYA3wCLfG2dgM+BvYB/sCIb2AHMd48hvtdauGMsBQb52qsCE137HKCx77XuwBL3uMHX\n3gSY695oWLhRAAAM8ElEQVQzAahSQgySwgoKYNo06NgRzjwTNm6EmTPhD39QYhGJspKSy0igQ5G2\nRUBH4N1i9l8GnO0eub72ocBNwEnukTjmTcAG1zYQeMK1ZwEPAue6R1+glnvtCeBJ955N7hhpJ+rj\nvhs2wJNPwimnWCK55BJYs8aWzD/ttOjHdyhxjg0Un5iSksts7Avc7wvsbKK06gM1gXnu+RjgSrd9\nOTDabb8KXOi2LwamAZvdYzpwCXaq1g6Y5PYb7TuWpDjPg7lzoXt3OPFEWLgQxoyxa1huvRVq1Ai7\nhyISlKAL+k2wIbE8oLVrawis9e2T79oSr33ltvcAW4A6QIMi71nr9s3Ckk1BMcdKKzk5OWF3odR+\n+MGmE7doAdddB6efDsuWWWJp1ar44a8oxVdWcY4NFJ+YIK9zWQccj53pNAcmA6cFeHxV5yPm3/+2\nCx9feglat7Y7RLZvD5U0R1Ek9oJMLrvcA+ATYDlWF8kHjvPtdxyFZyX5QCMsMVXG6iobXHuO7z3H\nAzOBjUAmdsZV4I6Vf7AO9ejRg+zsbAAyMzNp1qzZvr86EuOmUX3+9NNPp2Q8v/hFDpMnw2OP5bFm\nDeTm5jB/PqxYYa9XqhTt+IJ47h+zT4X+KL70ji8vL49Ro0YB7Pu+rCjZ7D9bLGEWNgssoS5whNs+\nAUsgme75XOA8rGYyhcKCfi5W7Afogs3+Ahv+WuHeX9u3DfAy0NltDwNuO0i/vTibNWtW2F3Yz5o1\nnvfHP3pe/fqe17at502c6Hn/+U/5j5dq8QUpzrF5nuKLOgIaJSppsud4oC2WOL7BZm1tBAa7ti1Y\njeUS4GqgP7AbO6t4EHjLHacFMAqohiWX37j2qsBYbHbZBizBrHKv3Qj0dtuPUFj4b4IloSzsDOlX\n7ncW5f6dJFkKCmD6dBv6evddu2HXbbdB06Zh90xEyiuo61zifCWBkkuSbNgAI0fCsGF2W+HcXOja\nVbO9ROKgoi6ilBTlH/etCJ5nN+dKTCNetAhefBE++QRuuSX4xFLR8VWkOMcGik+MVkWWQyoosCnD\nzzwDW7fasNdTT0GdOmH3TERSmYbF5KC++AJuvtkSTN++cNFFmkYsEncaFpOk2bULHnkEzj/fainv\nvQcXX6zEIiKlp6+LiErWuO/cuXYl/Zw5Vk+5445wkkqcx7XjHBsoPjGquQgA27fDn/4EEybAwIHQ\nubNWJRaR8ovz14dqLqX09ttWqG/TRsV6kXQXVM1FZy5pbMMG+N3vYPZsu2bl4ovD7pGIxIVqLhF1\nOOO+ngfjx9t9U+rUsWtWUi2xxHlcO86xgeITozOXNLNmDdx+u/18/XU477yweyQicaSaS5ooKIAh\nQ6B/f/jtb+3+9EceGXavRCTVqOYipbZ4sV0MecQRVl859dSweyQicaeaS0SVZtx31y546CFo2xa6\ndYN33olOYonzuHacYwPFJ0ZnLjE1Z46drZxwgt2j/rjjSn6PiEhQVHOJme3boU8fePllGDQIOnXS\nxZAiUnpaW0wOMHUqnH66rV782Wdw7bVKLCISDiWXiPKP+37/vd0FMjcXnn/ebuQV9avs4zyuHefY\nQPGJUXKJMM+Dl16ys5V69exiyIsuCrtXIiKquUTW6tV2MWR+PgwfDj//edg9EpE4UM0lTW3dCgMG\n2LL4rVvDRx8psYhI6lFyiYhly6BXL8jOhnnz4Kmn8ujdG6pUCbtnyRHnce04xwaKT4ySSwrzPJg+\nHf7nf6BVK6heHRYutGnGjRqF3TsRkYNTzSUF/fADjB0LgwfbXSB79YLrr4dq1cLumYjEndYWi6HV\nq+Gvf4URI6yeMngwtGuna1VEJHo0LBYyz4N334Wrr4bmzWHPHqupTJ4MF1xw8MQS93HfOMcX59hA\n8YnRmUtIdu60G3Y98wz8+CP85jcwejTUqBF2z0REDl+cB1xSsuaybh0MHQrPPWdnKr16Qfv2VlsR\nEQmbrnOJmLlz4brr7Gr6TZtsKOwf/4AOHZRYRCR+SvpaGwF8AyzytXUCPgf2As2L7P8AsBT4Amjv\na2/hjrEUGORrrwpMdO1zgMa+17oDS9zjBl97E2Cue88EIGWv9Ni1C8aNs1sJd+0K55wDK1bAs8/C\nKacc3rHjPu4b5/jiHBsoPjElJZeRQIcibYuAjsC7RdqbAp3dzw7AEApPrYYCNwEnuUfimDcBG1zb\nQOAJ154FPAic6x59gVrutSeAJ917NrljpJRvv4WHH7YLHocPh969YelS+P3vITMzmN+xYMGCYA6U\nouIcX5xjA8UnpqTkMhv7Avf7AjubKOoKYDywG1gFLAPOA+oDNYF5br8xwJVu+3JgtNt+FbjQbV8M\nTAM2u8d04BIsWbUDJrn9RvuOFboFC+DGG+2sZM0aePttmDkTrrjCbjEcpM2bNwd7wBQT5/jiHBso\nPjFBzhZrgA1tJawFGmLJZq2vPd+1435+5bb3AFuAOu5Y/vckjpWFJZuCYo4VmilT4PHHYeVKW/Z+\n6VKoWzfsXomIhCdKU5FTb+qX8/XXcOed0LFjxa31tWrVqor5RSGJc3xxjg0Un5ReNvsX9BNmsX9B\n/373SJiKDYsdC/zb194Vq8Ek9mnptisD37ntLsAw33v+htVzMtw+ieG8Vu4YxVmGJSQ99NBDDz1K\n/1hGBcnm4Mmlhe95U2ABcCQ2o2s5hQX9uViiyQCmUFjQz6Uw0XTBZn+BDX+tADKB2r5tgJexRAOW\ngG4rV1QiIhKa8cA6YBdWG+mJFdC/AnYAXwP/8O3fG8t6X2BF+YTEVORlwDO+9qpYskhMRc72vXaj\na1+KTUtO8E9FnkgKT0UWEREREZE0dDw2FPc58BnwG9eehU1VXoJNX/ZfSVLWizrDFFR81YC3sDrX\nZ8Cfk93xUgjys0t4g+KHa8MQZHxHAs8BX2Kf4VXJ7HgpBRnfjdjnthAb9aiTzI6XUlnjy3L7bwMG\nFzlWHL5bDhZfKn63BOJYoJnbroH9z/cz4H+BP7j2+4DH3XaiBlQFG25bRmENaB52cSbsXwMKU1Dx\nVQPaun2qYBe7hh1fELH5r8m6CngJ+DSZnS6DIP/b7A885Dt2Knz5BhXfkdhF01luvyewC6TDVtb4\nqgP/BfyaA5NLHL5bDhZfKn63JMVk4L+xv4zqubZj3XOwv5zu8+2fmJlWn/1nrxWdmZYqyhtfUU+T\neqsYHE5sNbCLe39G6py5FFWe+M5z22uw/4lTWXnjq4QlmkZYshkK3FwB/S2rkuJL6MH+X75x+W5J\n6MGBydPvkN8tUV0yMRs4Gyvs18PWP8P9TPxjHexCzKLtKXEhZhHZlD8+v0zgf4AZyepoOWRTvtga\nuO2HgQHAj8nuaDllU/7PLjEs8QjwMTbZ5f8lt7tllk354jsOu/i5Fzakko/9gTAi6T0um2xKji/B\nK/K8IfH4bkkoGp9fid8tUUwuNbClYnphY4J+iXnaUXY48flfq4zN9huELceTCg4ntgzs1P4E4HVS\n83YRh/vfZmXsS/h9bOz+QyyRporD/W/zaGy26FlYAlqEneWkCn23lE6pvluillyqYP84Y7FTO7CM\ne6zbrg9867bzsUJWwnHYXxX5btvfnp+k/pbV4cbnjyNRFPZP/Q5TEJ9dS+AcYCU2NHYyMDOpvS69\nID67DdgZ2WuufRIHrjweliDi+xn22a107a8Av0hel8ukLPEdTFy+W0pSqu+WKCWXDOAFYDE21pfw\nBoXXwXSn8B/uDWzMM3FR50lYse1rYCuFF3V2870nTEHFBzascjTwu+R2udSCim0YNszQBGiNzXK5\nIMl9L42g4vOAN7HFWcEWcv08mR0vpaDiWwGcCiRW3rvIHTNsZY3P/z6/9cTju8X/vqJS7bslEK2x\nMdsFwHz36IDNPPknxU+HLOtFnWEKKr7E2PbnvuP0TH73DynIzy4hm9SZLRZkfI2Ad7CputPZ/y/h\nsAQZ3w0UTkV+HVuBI2zliW8Vdqa5Dbuo/FTXHpfvllUcGF8qfreIiIiIiIiIiIiIiIiIiIiIiIiI\niIiIiIiIiEhERWkFDZH96D9ekWD0xxYETHgUuynTvdjSJwuBfr7X/w/4CFsh+BZf+3ZsscoFFH8L\nBRERSSONsWXyofC+JdcCf/O1vQmc754nlj2phi0XknheAFyT7M6KJFvlsDsgEhOrsbWYmmErzc4H\nfo7d4ne+2+cnwE+xFZ17AVe69uMpXNxxL7Z6rYiICGBnKoOACcAl2PDWrcXsl4MlmKPc81lAG7dd\n9D4bIiKS5qpg97lI3DP+ImAOdsYCdruAY4DLseXOwVab3YGSi8SMhsVEgrMbu3nZJuzeLNOxG2R9\n6F7fBvwKu6f8bdj9Nb70vQ7Rv9uhiIgErBJWXzkx7I6IhE1TkUWC0RRYit18aXnIfRERERERERER\nERERERERERERERERERERiaL/D3eoDhgggxN2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f057b8be9d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#y\u0131llara g\u00f6re halka a\u00e7\u0131k k\u00fct\u00fcphanelerde olan kitap say\u0131lar\u0131\n",
      "numberofbook_public = newdf2[newdf2['library_type_name'] == 'Public Library']\n",
      "numberofbook_public = numberofbook_public[numberofbook_public['count_type_name'] =='Number of Books']\n",
      "numberofbook_public = numberofbook_public.set_index('year')[['count']]\n",
      "numberofbook_public.columns=[\"Public\"]\n",
      "numberofbook_public"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Public</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td> 12398913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td> 12433310</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td> 12684084</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 12984801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> 12948460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 12958376</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 13198814</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 13662483</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 14093896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 14528550</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 15621478</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> 15785280</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "        Public\n",
        "year          \n",
        "2001  12398913\n",
        "2002  12433310\n",
        "2003  12684084\n",
        "2004  12984801\n",
        "2005  12948460\n",
        "2006  12958376\n",
        "2007  13198814\n",
        "2008  13662483\n",
        "2009  14093896\n",
        "2010  14528550\n",
        "2011  15621478\n",
        "2012  15785280\n",
        "\n",
        "[12 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numberofbook_public.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f057b511110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEVCAYAAAAGrllxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f05985e0bd0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#y\u0131llara g\u00f6re \u00fcniversite k\u00fct\u00fcphanelerinde olan kitap say\u0131lar\u0131\n",
      "numberofbook_university = newdf2[newdf2['library_type_name'] == 'University Library']\n",
      "numberofbook_university = numberofbook_university[numberofbook_university['count_type_name'] =='Number of Books']\n",
      "numberofbook_university = numberofbook_university.set_index('year')[['count']]\n",
      "numberofbook_university.columns=[\"University\"]\n",
      "numberofbook_university"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>University</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td>        0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td>  6017934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td>  6449641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td>  7087667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td>  8073808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td>  8043531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td>  8565905</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td>  9313197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td>  9929087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 10837368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 11697709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> 12584612</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "      University\n",
        "year            \n",
        "2001           0\n",
        "2002     6017934\n",
        "2003     6449641\n",
        "2004     7087667\n",
        "2005     8073808\n",
        "2006     8043531\n",
        "2007     8565905\n",
        "2008     9313197\n",
        "2009     9929087\n",
        "2010    10837368\n",
        "2011    11697709\n",
        "2012    12584612\n",
        "\n",
        "[12 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numberofbook_university.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f057b343150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEVCAYAAAABwEUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt8VOW1//FPCOAFDgZQUQKYClTAKtEiIKCkFTQoqCBH\nBATjDevtp7anIh4FvKH2WGvVU7RajYIggrXcgoDiREQQqQRBJBIgFINCUTiAggKZ3x9rEoYYyCTZ\nM3vmme/79ZqXs2d2ZtZi4sqetZ/9PCAiIiIiIiIiIiIiIiIiIiIiIiIiInHoJWALsDKCfZ8Elodu\nhcD2KMYlIiIeOg84i8iKfbjbgBe9D0dERKIlg0OLfWtgDrAMeB84rZKf+RC4IOqRiYiIZzI4tNi/\nC7QJ3e8S2g53CrAZSIl6ZCIiDqvr43s3BM4FpoY9Vr/CPleFng/GKigREam9DA4e2TfCjtqP5BOg\nazQDEhFJBnUi2CfSUTTnAPuBARG+905gAzAwtJ0CnBn2fDugMbAkwtcTEZFaiGQUTSqwAJgFXHGY\nfSZjR/I/ApuAa7Ej/TlAAfAZcF/Y/mOAcbWIW0REqimDIxf7O4FbgJc5fLEXERGfRNLGqUo6cBkw\nPrStk6kiInHGi2L/FHAPVuRT0DBJEZG448XQy18Cr4fuHw/0AfYBM8J3at68eXDz5qoG34iISAXr\nOHg9Uo15cWR/KvCz0G0acDMVCj3A5s2bCQaDzt7GjBnjewzKT7kpP/du2EwDtRbJkf1koCd21L4J\nGyVTL/Tc814E4YLi4mK/Q4gql/NzOTdQfmIiKfaDq/F619Y0EBERiR4v2jgC5OTk+B1CVLmcn8u5\ngfITE8uRM8FQ/0lERCKUkpICHtRq34/smzRpQkpKim5xeGvSpEn55xQIBPz7JYkyl3MD5SfGz1kv\nAdi+fTs64o9PoSMKEXGA722clJQUFfs4pc9GxH/OtHFERCT6VOwlIi73RV3ODZSfGBX7KLn55pt5\n+OGHY/qer732GhdddFFM31NEEoN69odRp04dioqKOPXUU8sfGzt2LOvWrWPChAk+Rha5ynKojnj9\nbESSiXr2PvB7dErYXBnV+hkRERX7aggvnIFAgBYtWvDkk0/SrFkzmjdvTm5ubvnzOTk53H///QC0\nb9+e2bNnlz+3f/9+TjjhBAoKCgBYsmQJ3bp1o3HjxmRmZpKfn1++b1ZWFvfddx/du3enQYMGrF+/\nntzcXFq3bk2jRo049dRTmTRpEgC5ubmcd955AJx//vkAdOzYkUaNGvHGG29wxhlnMGvWrPLX3rdv\nH8cffzwrVqyoMneX+6Iu5wbKT4zv4+wT2ZYtW9i5cyebN29m3rx5DBw4kP79+3PccceVX5gEMGTI\nECZPnswll1wCwNy5cznxxBPJzMykpKSEvn37MnHiRLKzs3nnnXe44oorKCwspGnTpgBMnDiROXPm\ncNppp7Fr1y7uuOMOli1bRtu2bdmyZQvffPPNT2J7//33qVOnDp9++ml5G2fjxo1MnDiRvn37ApCX\nl0d6ejodO3aMxT+XiPgo7o/sU1K8uUVDvXr1GD16NKmpqfTp04eGDRtSWFhY/nzZN4HBgwczY8YM\n9u7dC8CkSZMYPNjml5s4cSIXX3wx2dnZAPTq1YtOnTqVfxNISUkhJyeH9u3bU6dOHerWrUudOnVY\nuXIle/bsoVmzZnTo0CGieIcOHcrs2bPZvXs3ABMmTGDYsGER/WxWVlZE+yUil3MD5Scm7ot9MOjN\nrbpSU1PZt2/fIY/t27ePevXqlW83bdqUOnUO/hMee+yx5YU0XJs2bWjfvj0zZszg+++/Z+bMmQwZ\nMgSwo+2pU6fSuHHj8tuiRYv4+uuvy3++ZcuW5fcbNGjAlClTeO6552jevDl9+/Y95A/MkTRv3pzu\n3bszbdo0duzYwdtvv83QoUMj+wcRkYQW98XeL61atWLDhg2HPLZhwwYyMjJq9HqDBw9m8uTJTJ8+\nnQ4dOpS3Vlq1asWwYcPYvn17+W3Xrl3cfffd5T9b8cTwhRdeyLx58/j6669p164dN954Y8RxXHPN\nNUycOJGpU6fSrVs3Tj755Ih+zuW+qMu5gfITo2J/GIMGDeLhhx+mpKSE0tJS3nnnHWbNmsXAgQMj\n+vmKo2Cuuuoq5s6dy3PPPXfI0fTVV1/NzJkzmTdvHgcOHGDv3r0EAgFKSkoqfa2tW7cyffp0vvvu\nO+rVq0eDBg1ITU2tNIZmzZqxbt26Qx7r378/n3zyCU8//TTDhw+PKBcRSXwq9ocxevRounXrRo8e\nPWjSpAn33HMPkyZNOqQ/fqShmOEnaAFOOukkunXrxuLFixk0aFD54y1atGD69OmMGzeOE088kVat\nWvHHP/7xkAIf/jqlpaX86U9/Ij09naZNm7Jw4ULGjx9f6XuOHTuWa665hsaNGzNt2jQAjj76aAYM\nGEBxcTEDBgyI+N/D5b6oy7mB8hOji6qS0EMPPcTatWt59dVXj7ifPhsR/wSDMH8+XHSRLqqSGvj2\n22956aWXGDFiRLV+zuW+qMu5gfJLNMEgvPsunHce3HGHd6+rYp9EXnjhBVq1akWfPn3o0aOH3+GI\nSAX5+ZCVBTffbLdVq7x7bbVx5LD02YjExgcfwJgxsHEjjB4NQ4ZA3dAlr17NjaMraEVEfLJ4sRX5\ntWvh/vth2DAIu5THU2rjSERc64uGczk3UH7x6OOP4eKL4aqrYOBAKCyE666LXqGHyIr9S8AWYOVh\nnh8KrAA+BRYBZ3oTmoiIWz75BPr1gwED7L9ffAEjRkD9+tF/70j6QOcBu4FXgTMqef5cYDXwf0A2\nMBboWsl+lfbsmzRpwvbt2yMMV2KpcePGfPvtt36HIZLwVqyAsWNh6VK45x648UY4+ujIftarnn2k\nL5ABzKTyYh+uMfYNoEUlz1Va7EVEXLVqFTzwgJ2AHTkSbroJjjmmeq8Rr4uXXA/kefyaCSER+4bV\n4XJ+LucGys8Pn39u/fhevaBLFygqgjvvrH6h95KXo3F+BVwHdD/cDjk5OeUTiaWlpZGZmVl+qXPZ\nB5ao22ULkcRLPMpP29qO/Xbz5lk8+CDMmhXgyiuhqCiLhg2r93qBQKB8IaSaTrxYGa/aOGcCf8d6\n9kWH2UdtHBFxUlERPPQQ5OXZEfztt0OjRt68djy1cVphhf5qDl/oRUScs2EDXH89dO0KrVtb0f/v\n//au0HspkmI/GfgQOA3YhLVqbgrdAEZjJ2bHA8uBpd6HGf/Kvoa5yuX8XM4NlF80bNxoQyY7dYL0\ndLsoavRoOO64mIcSsUh69oOreP6G0E1ExGlffgnjxsGUKfCb39g4+dBS0XHP97lxRETi3caN8Nhj\nVuRvuAF+/3s44YTYvHc89exFRJy0fr0V97PPhsaNbVqDP/whdoXeSyr2HlFfNHG5nBsov5r44gvI\nyYHOnaF5c9seNy4xi3wZzXopIhLy+efwyCMwd64NnywqgrQ0v6Pyhnr2IpL0Vq6Ehx+G996Du+6C\nW2+Nn+GT6tmLiNRSQQFccQX07m3DKNevh1Gj4qfQe0nF3iPqiyYul3MD5VeZZcvgsstsTvkePWDd\nOhth07Ch9/HFCxV7EUkaS5bAJZfA5ZfbJGXr1lnbpkEDvyOLPvXsRcR5H3wADz5oQydHjYJrr4Wj\njvI7qshoDVoRkSMIBiE/34p8cTHcey8MHx6bVaHikdo4HlFfNHG5nBskX37BIMyfD+efbytCDR9u\nR/Q33JC8hR50ZC8ijggG4e237Uh+xw647z4YNAjqqsoB6tmLSIILBmHWLCvye/bA/ffDwIGQmup3\nZN5Qz15EklowCNOn2xqvpaU2xXD//lBHzelK6Z/FI8nWF3WJy7mBe/kFgzadQefOMHYsXHFFgOXL\n7eIoFfrD0z+NiCSMhQuhZ0+44w67COqTT+yiKBX5qqlnLyJxb9kyO+FaWAhjxsDVVyfPiVfNjSMi\nzlu1CgYMsKkNLr3Uin1OTvIUei+p2HvEtb5oRS7n53JukJj5FRXB0KFwwQXQvbut8XrLLZWPk0/E\n/PygYi8icWPTJrsQqmtXaNfOiv7vfgfHHut3ZIlPPXsR8d2WLbYS1MSJMGKEnXxt0sTvqOKDevYi\nkvC+/dYmJuvQAVJS4LPP4NFHVeijQcXeI673DV3Oz+XcID7z27XLrnj9+c/hm29sEZGnnoKTTqr+\na8VjfvEokmL/ErAFWHmEfZ4G1gIrgLM8iEtEHLRnDzzxBLRpY4t4L1kCf/0rtGzpd2Tui6QPdB6w\nG3gVOKOS5y8Gbgv9twvwZ6BrJfupZy+SpH78EV580Rbz7tLFjup/8Qu/o0oMsZwbZyGQcYTnLwVe\nCd3/CEgDmmHfBkQkie3fbyddH3jARtdMn25rvUrsedGzTwc2hW1/CbTw4HUTiut9Q5fzczk38Ce/\n0lJ44w07en/5ZXj1VZgzJzqF3vXPzyteXYdW8StGpf2anJwcMjIyAEhLSyMzM5OsrCzg4AeWqNsF\nBQVxFY/y07Yf2z17ZjF7Ntx5Z4DUVHjmmSx694b8/ACBgP/xJcJ2IBAgNzcXoLxeeiHSPlAGMJPK\ne/bPAQHg9dD2GqAnP23jqGcv4qh162DyZJg0yeaRf+ghm+IgJZZX8jgqnsbZzwCGh+53BXagfr2I\n877+Gp5+2q527dbNLoz629/g00/h8stV6ONNJMV+MvAhcBrWm78OuCl0A8gD1gNFwPPALd6HGf/K\nvoa5yuX8XM4NvM1v50545RW46CJo3x7++U87+VpSAs88A+eeG/si7/rn55VIevaDI9jnttoGIiLx\nae9eO7k6aRLMmwe/+pUt3v3WW5qzJpFobhwR+YkDByAQsAL/1luQmQlDhthqUI0b+x1dcvGqZ69i\nLyKALfe3bJkV+ClT4OSTbZrhQYMgPd3v6JJXPJ2gFdzvG7qcn8u5QdX5FRbaWq6nnWZH740awYIF\n1o//7W/jv9C7/vl5Reu9iCShkhI7ep80ye5fdRW89ppd9KRRNG5SG0ckSWzfDm++aQW+oAD697cj\n+awsGxsv8Uk9exGp0t69MHOmHbW/9x707m0F/uKL4eij/Y5OIqGefZxxvW/ocn4u5rZpE9x7L7Rq\nBY89FuDyy+Ff/4Jp02wBb5cKvYufXzSoZy/iiGAQPvjArmp99124+mpYuBC++spaNZLc1MYRSXB7\n9lgf/pln7P7tt8Pw4TaqRhKfevYiSe5f/4K//MXmo+nSxYp8795QR81Zp6hnH2dc7xu6nF8i5RYM\n2pWtAwbAWWfBDz/A4sUwa5bNV1NZoU+k/GrC9fy8op69SAL4/ntb8emZZ2wqg9tuswVBGjb0OzJJ\nFGrjiMSxDRusVfPyy9C9u7VqLrhAFz4lE7VxRBwVDNpomssvh3POse2lS2391l69VOilZlTsPeJ6\n39Dl/OIlt927Yfx4W7f1jjugTx/YuBGeeAJOPbXmrxsv+UWL6/l5RT17EZ+tWwf/+7+2KMj558Oz\nz9q4eB3Bi5fUsxfxQTAI8+fbCdclS+C66+Dmm8HD9aXFEV717HVkLxJD27bZbJPPPgv169sJ1ylT\ntOKTRJ969h5xvW/ocn7RzC0YhM8+g8cegx49oHVryM+H55+3mSdvuCH6hd7lzw7cz88rOrIX8dgP\nP1hBnznTLnYqLYV+/eD++6FnT7cmIZPEoZ69iAe2bIG8PCvu774Lp58Offva7Re/0MlWqTnNjSPi\no2AQPv304NH7mjU2L02/fjZk8oQT/I5QXKGLquKM631Dl/OLNLc9e+zo/ZZb4JRTbH6abdvgkUdg\n61aYOtVmm4y3Qu/yZwfu5+eVSHr22cBTQCrwIvB4heePByYCJ4Ve7wkg17sQRfyzeTPMnm1H74EA\nZGZaa2buXGjXTu0ZSRxV/aqmAoVAL6AE+BgYDHwets9Y4ChgFFb4C4FmwP4Kr6U2jsS90lJYvvxg\ne2b9eptNsl8/yM6GJk38jlCSTazG2XcGioDi0PbrwGUcWuy/As4M3W8EfMNPC71IRHbutHbI/v1Q\nty7Uq3fofyt77EjPHW7/8CPy776zk6qzZtmtUSM7en/iCZt8rF49//49RLxSVbFPBzaFbX8JdKmw\nzwvAAmAz8B/AlZ5Fl0ACgQBZDq/9Fu389u+Hl16CMWPg3HOt771/P+zbd+h/K3usJs/VqXOw8O/f\nH6B79yz69oXf/x7ato1amr7Q76ZA1cU+kr7LvUABkAW0BuYDHYFdFXfMyckhI3Q9eFpaGpmZmeUf\nUtlJlkTdLigoiKt4Eim/+fNhxIgAxx0Hs2Zl8ctfRjefYBAWLAhw4AB065bFhx9C/fr2fNu23r+f\ntrVdne1AIEBubi5Aeb30QlV9oK5YTz47tD0KKOXQk7R5wCPAotD2u8BIYFmF11LPXg6xerUdSX/x\nBfzP/8Bll+mEp0hFsRp6uQxoC2QA9YFBwIwK+6zBTuCCnZg9DVhf28DEXf/+N9x6q11N2quXTSdw\n+eUq9CLRVFWx3w/cBswFVgNTsJOzN4VuAOOATsAK4B3gbuDbaAQbz8q+hrnKi/x++MFOenboAKmp\ndiHSXXfZhGB+0meX2FzPzyuRjLOfE7qFez7s/jagn2cRiXOCQXjzTRg50qYRWLjQxqiLSOxougSJ\nqo8/ht/+1oZUPvmkrZ8qIpHTdAkS1zZtgmHD7KRrTg588okKvYifVOw94nrfMNL8du+G0aNtWoFT\nToHCQrj+euvRxyt9donN9fy8omIvnjhwwC6KOu00m2Jg+XJ4+GH4j//wOzIRAfXsxQMLFsDvfgcN\nGlhfvnNnvyMScYfWoBXfffGFXRS1ciU8/jgMHKix8iLxSm0cj7jeNwzP79tv4c47oVs3myhs9Wr4\nz/9M3EKfTJ+di1zPzysq9hKxH3+Ep56yMfI//mhF/u67taaqSCJQz16qFAzCjBnWsmnTxuaxOf10\nv6MSSQ7q2UvUff89/OMf8Nxz1rp55hlbyENEEo/aOB5xpW9YWmqja669FtLT4dVX4eab4c9/Djhb\n6F357A5H+QnoyF5CVq+GCRPgtdds6b3hw2HcODj5ZHte/z+JJDb17JPY1q0webIV+a++gqFDbYqD\nM87wOzIRKeNVz17FPsns2WMnWydMgA8+gEsvtQL/61/H95QGIslKE6HFmXjuG5aWQn4+3HCD9eH/\n9jcYNAi+/NJ68r17V13o4zm/2nI5N1B+YtSzd1hhoR3BT5xoc9QMH25Xu6an+x2ZiMSa2jiO2bYN\nXn/djtg3bYIhQ6xN07Fj4l7hKpLM1LOXcnv3wqxZVuDffx8uucQKfK9eUFff3UQSmnr2cSbWfcNg\n0E6wjhhhbZnx42HAADuaf+01yM72ttC73Bd1OTdQfmJ03JdAvv8eFi+2i54mT4ZjjrE+fEEBtGzp\nd3QiEs/Uxolju3fDhx/aSJr8fCvqZ54JPXvaLJNnnaU+vIjr1LN30M6dsGiRXa2anw+rVllBz8qy\nAn/uubZAiIgkD/Xs40xN+oY7dsDMmfBf/wXnnAPNm8Mf/mDtmUcfhX//GxYuhIcespOtfhZ6l/ui\nLucGyk9MJD37bOApIBV4EXi8kn2ygD8B9YBtoW2p4JtvrHjn59vRe1ERdOliR+5ly/kddZTfUYqI\ni6r6apAKFAK9gBLgY2Aw8HnYPmnAIuAi4EvgeKzgV5R0bZytW20oZFnPvbjYVnfq2dNunTpB/fp+\nRyki8SxW89l3BoqA4tD268BlHFrshwBvYoUeKi/0SeHrrw8W9kAANm+2Zft69oQXX4Szz9a4dxHx\nR1U9+3RgU9j2l6HHwrUFmgDvAcuAYZ5FlyCefhpatgzQoQNMmmSrOU2YYG2b2bNt6b7OnRO70Lvc\nF3U5N1B+YqoqP5H0XeoBZwMXAMcCi4ElwNqKO+bk5JCRkQFAWloamZmZZGVlAQc/sETb7t49izFj\nYPjwAi69FC644ODzCxf6H59X2wUFBXEVj7a17ep2IBAgNzcXoLxeeqGqPlBXYCx2khZgFFDKoSdp\nRwLHhPYDO4n7NjCtwms52bMPBOzIfelSvyMRERfFaujlMqxNkwHUBwYBMyrsMx3ogZ3MPRboAqyu\nbWCJYvZsuPhiv6MQETmyqor9fuA2YC5WwKdgJ2dvCt0A1mBH8p8CHwEvkETFPi/Pin3Z1zBXuZyf\ny7mB8hMTySnDOaFbuOcrbD8RuiWV4mKbUrhTJxtiKSISrzRdQi385S/w0Ufwyit+RyIirtJ0CXFg\n9mybO15EJN6p2NfQnj029cGFF9q2631Dl/NzOTdQfmJU7GsoELAZKdPS/I5ERKRq6tnX0G232YIh\nI0f6HYmIuEw9ex8Fg+rXi0hiUbGvgTVr4MABOP30g4+53jd0OT+XcwPlJ0bFvgbKLqTSkoAikijU\ns6+BX/8a7roL+vXzOxIRcZ3WoPXJzp2Qnm5z12s9WBGJNp2g9cn8+bYgScVC73rf0OX8XM4NlJ8Y\nFftqKuvXi4gkErVxqqG01Fo4CxfaalQiItGmNo4PCgqgUSMVehFJPCr21ZCXd/gLqVzvG7qcn8u5\ngfITo2JfDVqVSkQSlXr2Edq2DVq3hq1b4aij/I5GRJKFevYx9vbbdjGVCr2IJCIV+whVNeTS9b6h\ny/m5nBsoPzEq9hHYvx/mzlW/XkQSl3r2EVi0CG691YZeiojEknr2MaS560Uk0anYRyCSKRJc7xu6\nnJ/LuYHyExNJsc8G1gBrgSMtwncOsB8Y4EFccaOkBDZtgi5d/I5ERKTmquoDpQKFQC+gBPgYGAx8\nXsl+84HvgZeBNyt5rYTs2b/wArz3Hkya5HckIpKMYtWz7wwUAcXAPuB14LJK9rsdmAb8u7YBxRvN\ncikiLqiq2KcDm8K2vww9VnGfy4Dxoe3EO3w/jB9+gAULIDu76n1d7xu6nJ/LuYHyE1O3iucjKdxP\nAfeE9k3hCF83cnJyyMjIACAtLY3MzEyysrKAgx9YPG3/85/QoUMWxx9f9f4FoXGZ8RS/l9uu56dt\nbcfLdiAQIDc3F6C8Xnqhqj5QV2AsdpIWYBRQCjwets/6sNc5Huvb3wjMqPBaCdezv+suaNoU7rvP\n70hEJFl51bOv6sh+GdAWyAA2A4OwE7ThTg27/zIwk58W+oSUlweTJ/sdhYhI7VXVs98P3AbMBVYD\nU7CRODeFbs4qKoJdu+CssyLbv+xrmKtczs/l3ED5ianqyB5gTugW7vnD7Htt7cKJH3l50KcPpMRy\nQgkRkSjR3DiHkZ0NN94IV1zhdyQiksy86tmr2Ffiu+/gpJPs6tlGjfyORkSSmSZCi6IFC+Ccc6pX\n6F3vG7qcn8u5gfITo2JfCc1yKSKuURungmAQTjnFFitp397vaEQk2amNEyWrVkHdutCund+RiIh4\nR8W+grKJz6o75NL1vqHL+bmcGyg/MSr2FahfLyIuUs8+zPbt0KoVbN0KxxzjdzQiIurZR8W8eXD+\n+Sr0IuIeFfsweXk1b+G43jd0OT+XcwPlJ0bFPqS0FObM0apUIuIm9exDli6Fa6+Fzz7zOxIRkYPU\ns/eY1poVEZep2IfUdsil631Dl/NzOTdQfmJU7IEtW2yxku7d/Y5ERCQ61LMHcnPtyH7qVL8jERE5\nlHr2HlK/XkRcl/TFft8+mD/fliCsDdf7hi7n53JuoPzEJH2x//BDaN3aVqYSEXFV0vfsR46Eo46C\nBx/0OxIRkZ9Sz94jmuVSRJJBUhf7jRtthstOnWr/Wq73DV3Oz+XcQPmJibTYZwNrgLXAyEqeHwqs\nAD4FFgFnehJdlOXlQXY2pKb6HYmISHRF0gdKBQqBXkAJ8DEwGPg8bJ9zgdXA/2F/GMYCXSu8Ttz1\n7Pv1g6FD4aqr/I5ERKRysezZdwaKgGJgH/A6cFmFfRZjhR7gI6BFbQOLtj17ID8fLrzQ70hERKIv\nkmKfDmwK2/4y9NjhXA/k1SaoWMjPh44doUkTb17P9b6hy/m5nBsoPzF1I9inOr2XXwHXAZXOMpOT\nk0NGRgYAaWlpZGZmkpWVBRz8wGK1/fzzAdq1A/Dm9QoKCmIaf6y3Xc9P29qOl+1AIEBubi5Aeb30\nQiR9oK5YDz47tD0KKAUer7DfmcDfQ/sVVfI6cdOzDwahTRt46y04MyFOJYtIsoplz34Z0BbIAOoD\ng4AZFfZphRX6q6m80MeVL76AH3+EM87wOxIRkdiIpNjvB24D5mIjbqZgI3FuCt0ARgONgfHAcmCp\n55F6aPZsm/gsxcPrh8u+hrnK5fxczg2Un5hIevYAc0K3cM+H3b8hdEsIeXlw++1+RyEiEjtJNzfO\nrl3QvDl89RU0bOh3NCIiR6a5cWronXfg3HNV6EUkuSRdsS/r13vN9b6hy/m5nBsoPzFJVeyDQevX\na5ZLEUk2SdWzX74crrwS1q71NQwRkYipZ18DOqoXkWSVVMU+Wv16cL9v6HJ+LucGyk9M0hT7bdtg\n1Sro2dPvSEREYi9pevaTJsGUKTB9um8hiIhUm3r21aS1ZkUkmSVFsT9wAObOhT59ovcervcNXc7P\n5dxA+YlJimL/0Uc2RULLln5HIiLij6To2d93nx3dP/qoL28vIlJj6tlXQzSHXIqIJALni31JCWzc\naJOfRZPrfUOX83M5N1B+Ypwv9nPmwIUXQt1IZ+4XEXGQ8z37AQOgf38YNizmby0iUmte9eydLvY/\n/ggnnmgTn51wQkzfWkTEEzpBG4GFC6Fdu9gUetf7hi7n53JuoPzEOF3s8/I0CkdEBBxv47RrBxMn\nQqdOMX1bERHPqI1ThXXrYMcOOPtsvyMREfFfJMU+G1gDrAVGHmafp0PPrwDO8ia02snLs7lw6sTo\nz5nrfUOX83M5N1B+YqoqhanAs1jB7wAMBtpX2OdioA3QFhgBjPc4xhqJ9apUBQUFsXszH7icn8u5\ngfITU1Wx7wwUAcXAPuB14LIK+1wKvBK6/xGQBjTzLsTq++47+OAD6N07du+5Y8eO2L2ZD1zOz+Xc\nQPmJqarYpwObwra/DD1W1T4tah9azb33np2UPe44P6MQEYkfVRX7SIfPVDxT7N+SVPgz5LK4uDi2\nbxhjLuf035XVAAAFMklEQVTncm6g/MRUNZynKzAW69kDjAJKgcfD9nkOCGAtHrCTuT2BLRVeqwho\nXfNQRUSS0jrsvGhU1Q29UQZQHyig8hO0eaH7XYEl0Q5KRES81wcoxI7MR4Ueuyl0K/Ns6PkVgEa2\ni4iIiIgkkpbAe8BnwCrg/4UebwLMB74A5mFDMcuMwi6+WgNcGPb4L4GVoef+HNWoI+dVfscAs4HP\nQ68TD4sjevnZlZmBfYbxwMv86gN/xb7dfg4MiGbgEfIyv2uxz20FMAdoGs3AI1Td/JqE9t8FPFPh\ntVyoLYfLL2a15SQgM3S/IfY/Q3vgD8DdocdHAo+F7nfAev71sHMARRw8QbwUG9MP1v8vOyHsJ6/y\nOwY7YU3ouffxPz8vcgsfyTUAeA34NJpBV4OXv5sPAA+GvXY8FEOv8qsPfIMVE7CBF2OiG3pEqpvf\nsUB3rLVcsdi7UFsOl59vteUfQC/syKHsoqqTQttgRxbh0y28jZ3QPRn7y1TmKmyET7ypaX4VPQVc\nH6UYa6o2uTUEFmK/rPFyZF9RTfLrErr/L+x/qnhW0/zqYIW/FVb8xwM3xCDe6qoqvzI5HFoMXakt\nZXL46R+zcEesLV7NHJOBzYnzERZs2bDLLRwMvjl2wVWZsgu0Kj5ewk8v3PJbBjXPL1wa0A94N1qB\n1kAGNcuteej+Q8ATwPfRDrSGMqj5Z1f2Nfph4J/AG8CJ0Q232jKoWX4tsGHUd2AtgBLsD/ZLUY+4\nejKoOr8yFa/vSceN2lLmSNcvVVlbvCj2DYE3sV+aXRWeC+LzBVYeqE1+4c/VBSZjfcNiD+Orjdrk\nloJ9FT0VmE5sp8uOVG1/N+tiRXER1vtdjP1hixe1/d1shE1i2BH7g7CSgyPu4oFqS2Qiqi21Lfb1\nsGAnYF9FwP4inRS6fzKwNXS/BDsxUaYF9le3hEOnV2gReiwe1Da/8DzKTvI9Ha1gq8mLz64r0AnY\ngLVyfg4siGrUkfPis/sG+8by99Dj04ifocVe5Nce++w2hB6fCnSLXsjVUp38DseV2lKViGpLbYp9\nCvA3YDXWKyozA7gmdP8aDiYyA+uZ1Qd+hs2SuRT4GtiJ9RBTgGFhP+Mnr/IDawM0Au6KbsgR8yq3\n57CvxT8DemCjCH4d5dgj4VV+QWAm8KvQfhdgIyj85lV+64F2wPGh/XqHXtNv1c0v/OfCfYUbtSX8\n5yqKSW3pgfX8CoDloVs2dmb/HSof/nUvdkJoDXBR2ONlw6OKiJ8jX6/yK+uNfhb2OtdFP/wj8vKz\nK5NB/IzG8TK/VkA+NjRxPj5P8hfiZX7DOTj0cjrQOMqxR6Im+RVj38R2YRMztgs97kptKean+cVj\nbRERERERERERERERERERERERERERERERERHHeDVxoEjM6ZdXXPUANsFUmUewRSJ+j00VsAIYG/b8\nW8AybAbIG8Me341NflZA5VNWi4iIj07BpiWGg/O2Xwk8H/bYTOC80HbZNAHHYJfXl22XAgOjHaxI\ntNX1OwCRKNmIzSWSic0kuBw4B1uSb3lonwZAG2zGzjuAy0OPt+TgZGEHsNkJRUQkTl2JzfH9OtAH\na8eMqGS/LKzgHx3afg84P3S/4jzjIiISZ+ph83yXrbnaG1iCHdGDTc98AnApNr0s2GyCe1CxF8eo\njSMu24ctprIdm5t+PrZgx+LQ87uAq7E1WX+DzS9eGPY8JP5qSCIizquD9edb+x2IiN809FJc1QFY\niy0Gsc7nWERERERERERERERERERERERERERERCRx/H9uOLAEEgxfOAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f057b4ff110>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total =pd.concat([numberofbook_national,numberofbook_public,numberofbook_university],axis=1)\n",
      "total\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>National</th>\n",
        "      <th>Public</th>\n",
        "      <th>University</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2001</th>\n",
        "      <td> 1100975</td>\n",
        "      <td> 12398913</td>\n",
        "      <td>        0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2002</th>\n",
        "      <td> 1117190</td>\n",
        "      <td> 12433310</td>\n",
        "      <td>  6017934</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2003</th>\n",
        "      <td> 1141281</td>\n",
        "      <td> 12684084</td>\n",
        "      <td>  6449641</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 1156715</td>\n",
        "      <td> 12984801</td>\n",
        "      <td>  7087667</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td> 1187765</td>\n",
        "      <td> 12948460</td>\n",
        "      <td>  8073808</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td> 1202087</td>\n",
        "      <td> 12958376</td>\n",
        "      <td>  8043531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 1217692</td>\n",
        "      <td> 13198814</td>\n",
        "      <td>  8565905</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 1226676</td>\n",
        "      <td> 13662483</td>\n",
        "      <td>  9313197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 1253232</td>\n",
        "      <td> 14093896</td>\n",
        "      <td>  9929087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 1284639</td>\n",
        "      <td> 14528550</td>\n",
        "      <td> 10837368</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 1328895</td>\n",
        "      <td> 15621478</td>\n",
        "      <td> 11697709</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> 1383181</td>\n",
        "      <td> 15785280</td>\n",
        "      <td> 12584612</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "      National    Public  University\n",
        "year                                \n",
        "2001   1100975  12398913           0\n",
        "2002   1117190  12433310     6017934\n",
        "2003   1141281  12684084     6449641\n",
        "2004   1156715  12984801     7087667\n",
        "2005   1187765  12948460     8073808\n",
        "2006   1202087  12958376     8043531\n",
        "2007   1217692  13198814     8565905\n",
        "2008   1226676  13662483     9313197\n",
        "2009   1253232  14093896     9929087\n",
        "2010   1284639  14528550    10837368\n",
        "2011   1328895  15621478    11697709\n",
        "2012   1383181  15785280    12584612\n",
        "\n",
        "[12 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total.plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f057b275390>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEVCAYAAAABwEUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f057b286e50>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f057b1e3310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAW8AAAEjCAYAAAD9poADAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKeOz2gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f057b4f2410>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total.plot(colormap='jet')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f057b015d50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEVCAYAAAABwEUhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEIIIYQQQgghhBBCCCGE8B7/Hyfjv18MkrELAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f057b02fb90>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total.to_csv('dosya/hakandatabase//total.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}