Data_Structure_Transfer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df =pd.read_csv('out.csv')\n",
    "df.head()\n",
    "Day = df['Day']\n",
    "Time = df['Time']\n",
    "Freq = df['Freq']\n",
    "PCT = df['PCT']\n",
    "\n",
    "for i in xrange(7):\n",
    "    for j in xrange(4)\n",
    "        # (i, j): [\n",
    "        for k in xrange(616):\n",
    "            if Day[k] == i && Time[k] == j\n",
    "                #( Freq[k], PCT[k]),\n",
    "        #\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df =pd.read_csv('out.csv')\n",
    "df.head()\n",
    "Day = df['Day']\n",
    "Time = df['Time']\n",
    "Freq = df['Freq']\n",
    "PCT = df['PCT']\n",
    "\n",
    "print Freq, PCT\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "( 0.410185185 , 5 ),\n",
      "( 0.373148148 , 6 ),\n",
      "( 0.44537037 , 7 ),\n",
      "( 0.393518519 , 9 ),\n",
      "( 0.37962963 , 10 ),\n",
      "( 0.667592593 , 13 ),\n",
      "( 0.781481481 , 14 ),\n",
      "( 0.338888889 , 17 ),\n",
      "( 0.657407407 , 18 ),\n",
      "( 0.675 , 19 ),\n",
      "( 0.408333333 , 20 ),\n",
      "( 0.000925926 , 22 ),\n",
      "( 0.625 , 23 ),\n",
      "( 0.621296296 , 24 ),\n",
      "( 0.457407407 , 25 ),\n",
      "( 0.326851852 , 26 ),\n",
      "( 0.500925926 , 28 ),\n",
      "( 0.437037037 , 30 ),\n",
      "( 0.662037037 , 32 ),\n",
      "( 0.375 , 33 ),\n",
      "( 0.501851852 , 34 )],\n",
      "( 5 , 2 ): [\n",
      "( 1.488888889 , 1 ),\n",
      "( 0.807407407 , 5 ),\n",
      "( 0.719444444 , 6 ),\n",
      "( 0.552777778 , 7 ),\n",
      "( 0.833333333 , 9 ),\n",
      "( 0.655555556 , 10 ),\n",
      "( 1.364814815 , 13 ),\n",
      "( 2.441666667 , 14 ),\n",
      "( 0.762037037 , 17 ),\n",
      "( 1.575 , 18 ),\n",
      "( 1.513888889 , 19 ),\n",
      "( 1.028703704 , 20 ),\n",
      "( 0.026851852 , 22 ),\n",
      "( 1.156481481 , 23 ),\n",
      "( 1.012037037 , 24 ),\n",
      "( 1.263888889 , 25 ),\n",
      "( 0.459259259 , 26 ),\n",
      "( 1.142592593 , 28 ),\n",
      "( 0.860185185 , 30 ),\n",
      "( 1.128703704 , 32 ),\n",
      "( 0.765740741 , 33 ),\n",
      "( 0.825925926 , 34 )],\n",
      "( 5 , 3 ): [\n",
      "( 0.837037037 , 1 ),\n",
      "( 0.651851852 , 5 ),\n",
      "( 0.813888889 , 6 ),\n",
      "( 0.753703704 , 7 ),\n",
      "( 0.976851852 , 9 ),\n",
      "( 0.598148148 , 10 ),\n",
      "( 1.052777778 , 13 ),\n",
      "( 1.952777778 , 14 ),\n",
      "( 0.517592593 , 17 ),\n",
      "( 1.573148148 , 18 ),\n",
      "( 1.112962963 , 19 ),\n",
      "( 0.530555556 , 20 ),\n",
      "( 0.02962963 , 22 ),\n",
      "( 1.381481481 , 23 ),\n",
      "( 0.92962963 , 24 ),\n",
      "( 1.281481481 , 25 ),\n",
      "( 0.515740741 , 26 ),\n",
      "( 1.094444444 , 28 ),\n",
      "( 0.703703704 , 30 ),\n",
      "( 1.435185185 , 32 ),\n",
      "( 0.734259259 , 33 ),\n",
      "( 0.888888889 , 34 )],\n",
      "( 6 , 0 ): [\n",
      "( 0.596296296 , 1 ),\n",
      "( 0.562037037 , 5 ),\n",
      "( 1.237962963 , 6 ),\n",
      "( 1.028703704 , 7 ),\n",
      "( 1.426851852 , 9 ),\n",
      "( 0.907407407 , 10 ),\n",
      "( 0.801851852 , 13 ),\n",
      "( 0.883333333 , 14 ),\n",
      "( 0.399074074 , 17 ),\n",
      "( 1.407407407 , 18 ),\n",
      "( 0.550925926 , 19 ),\n",
      "( 0.344444444 , 20 ),\n",
      "( 0.000925926 , 22 ),\n",
      "( 0.750925926 , 23 ),\n",
      "( 0.399074074 , 24 ),\n",
      "( 0.642592593 , 25 ),\n",
      "( 0.414814815 , 26 ),\n",
      "( 0.431481481 , 28 ),\n",
      "( 0.386111111 , 30 ),\n",
      "( 0.831481481 , 32 ),\n",
      "( 0.477777778 , 33 ),\n",
      "( 0.990740741 , 34 )],\n",
      "( 6 , 1 ): [\n",
      "( 0.387962963 , 1 ),\n",
      "( 0.287037037 , 5 ),\n",
      "( 0.271296296 , 6 ),\n",
      "( 0.278703704 , 7 ),\n",
      "( 0.336111111 , 9 ),\n",
      "( 0.310185185 , 10 ),\n",
      "( 0.510185185 , 13 ),\n",
      "( 0.719444444 , 14 ),\n",
      "( 0.306481481 , 17 ),\n",
      "( 0.57037037 , 18 ),\n",
      "( 0.509259259 , 19 ),\n",
      "( 0.389814815 , 20 ),\n",
      "( 0.0 , 22 ),\n",
      "( 0.541666667 , 23 ),\n",
      "( 0.380555556 , 24 ),\n",
      "( 0.408333333 , 25 ),\n",
      "( 0.276851852 , 26 ),\n",
      "( 0.361111111 , 28 ),\n",
      "( 0.293518519 , 30 ),\n",
      "( 0.613888889 , 32 ),\n",
      "( 0.303703704 , 33 ),\n",
      "( 0.432407407 , 34 )],\n",
      "( 6 , 2 ): [\n",
      "( 1.355555556 , 1 ),\n",
      "( 0.735185185 , 5 ),\n",
      "( 0.658333333 , 6 ),\n",
      "( 0.617592593 , 7 ),\n",
      "( 0.867592593 , 9 ),\n",
      "( 0.562037037 , 10 ),\n",
      "( 1.073148148 , 13 ),\n",
      "( 2.193518519 , 14 ),\n",
      "( 0.65 , 17 ),\n",
      "( 1.161111111 , 18 ),\n",
      "( 1.073148148 , 19 ),\n",
      "( 0.822222222 , 20 ),\n",
      "( 0.034259259 , 22 ),\n",
      "( 1.147222222 , 23 ),\n",
      "( 0.899074074 , 24 ),\n",
      "( 0.962037037 , 25 ),\n",
      "( 0.431481481 , 26 ),\n",
      "( 0.955555556 , 28 ),\n",
      "( 0.732407407 , 30 ),\n",
      "( 0.967592593 , 32 ),\n",
      "( 0.508333333 , 33 ),\n",
      "( 0.751851852 , 34 )],\n",
      "( 6 , 3 ): [\n",
      "( 0.781481481 , 1 ),\n",
      "( 0.465740741 , 5 ),\n",
      "( 0.60462963 , 6 ),\n",
      "( 0.65 , 7 ),\n",
      "( 0.84537037 , 9 ),\n",
      "( 0.480555556 , 10 ),\n",
      "( 0.946296296 , 13 ),\n",
      "( 1.608333333 , 14 ),\n",
      "( 0.467592593 , 17 ),\n",
      "( 0.919444444 , 18 ),\n",
      "( 0.652777778 , 19 ),\n",
      "( 0.536111111 , 20 ),\n",
      "( 0.010185185 , 22 ),\n",
      "( 1.164814815 , 23 ),\n",
      "( 0.690740741 , 24 ),\n",
      "( 0.937962963 , 25 ),\n",
      "( 0.451851852 , 26 ),\n",
      "( 0.949074074 , 28 ),\n",
      "( 0.562962963 , 30 ),\n",
      "( 1.062037037 , 32 ),\n",
      "( 0.541666667 , 33 ),\n",
      "( 0.902777778 , 34 )],\n",
      "}\n",
      "[[  7.40462963  12.36666666  24.15833333  16.24722222]\n",
      " [  7.59259259  12.42222222  25.91111111  18.93518519]\n",
      " [  8.2962963   14.05185185  27.79814815  21.08333333]\n",
      " [  9.34166667  13.94351852  28.90092593  22.55092592]\n",
      " [ 13.96759259  13.19351852  27.33703704  23.39351852]\n",
      " [ 17.46944445  10.61203704  22.38518519  20.36574074]\n",
      " [ 15.47222222   8.48888889  19.15925926  16.23240741]]\n",
      "[[ 26.30925926  17.9212963   18.78425926  17.53796296  22.77407407\n",
      "   17.09166667  28.99074074  44.32037037  14.26851852  32.69629629  25.925\n",
      "   16.83055556   0.76296296  29.15        19.87222222  27.50648148\n",
      "   12.47685185  22.91759259  17.24722222  26.82685185  17.28240741\n",
      "   21.58888889]]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy\n",
    "df =pd.read_csv('out.csv')\n",
    "df.head()\n",
    "Day = df['Day']\n",
    "Time = df['Time']\n",
    "Freq = df['Freq']\n",
    "PCT = df['PCT']\n",
    "\n",
    "\n",
    "Dict = {}\n",
    "\n",
    "crimes = numpy.zeros((7,4))\n",
    "num_pct = numpy.zeros((1,22))\n",
    "\n",
    "for i in xrange(7):\n",
    "    for j in xrange(4):\n",
    "        print '(',i,',', j,'): ['\n",
    "        num = 0;\n",
    "        for k in xrange(22):\n",
    "            num_pct[:,k] += Freq[i*4*22+j*22+k]\n",
    "            num += Freq[i*4*22+j*22+k]\n",
    "            if k != 21:\n",
    "                print '(',Freq[i*4*22+j*22+k], ',', PCT[i*4*22+j*22+k], '),' \n",
    "            if k == 21:\n",
    "                print '(',Freq[i*4*22+j*22+k], ',', PCT[i*4*22+j*22+k], ')],' \n",
    "        crimes[i,j] = num\n",
    "print '}'\n",
    "\n",
    "print crimes\n",
    "print num_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.]\n"
     ]
    }
   ],
   "source": [
    "num_pct = numpy.zeros((1,21))\n",
    "print num_pct[:,1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x113020b10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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R6+osnZaWhrNzKeirIiJFtgANgFst36sB+4DmQAgwyo7jxZGkp6fLrqO75O1N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\nEZE4oMAXERGJA/8Ds4Pb2asuW9UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113098e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = [1,2,3,4,5,6,7]\n",
    "\n",
    "\n",
    "plt.plot(x, crimes[:,0],'ko',label=\"Night\")\n",
    "plt.plot(x, crimes[:,0],'k-')\n",
    "plt.plot(x, crimes[:,1],'bo',label=\"Morning\")\n",
    "plt.plot(x, crimes[:,1],'b-')\n",
    "plt.plot(x, crimes[:,2],'go',label=\"Afternoon\")\n",
    "plt.plot(x, crimes[:,2],'g-')\n",
    "plt.plot(x, crimes[:,3],'ro',label=\"Evening\")\n",
    "plt.plot(x, crimes[:,3],'r-')\n",
    "plt.xlabel(\"Weekday\")\n",
    "plt.ylabel(\"Number of Crimes\")\n",
    "plt.title(\"The Number of Crimes under Different Day and Time\")\n",
    "plt.legend(bbox_to_anchor=(1.4, 1.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 26.30925926  17.9212963   18.78425926  17.53796296  22.77407407\n",
      "  17.09166667  28.99074074  44.32037037  14.26851852  32.69629629  25.925\n",
      "  16.83055556   0.76296296  29.15        19.87222222  27.50648148\n",
      "  12.47685185  22.91759259  17.24722222  26.82685185  17.28240741\n",
      "  21.58888889]\n",
      "(1, 22)\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'module' object has no attribute 'xticklabel'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-58-4ea1cbea8f0f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_pct\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m23\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum_pct\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxticklabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'G1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'G1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'G1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'G1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G2'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G3'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G5'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'G1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'G2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'xticklabel'"
     ]
    },
    {
     "data": {
      "image/png": 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AAAAGoAQCAAAAGIASCAAAAGAASiAAAACAASiBAAAAAAagBAIAAAAYgBIIAAAAYABKIAAA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\nMID/D5qNNB1X5IbdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117473950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#x = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]\n",
    "#print x\n",
    "#print np.shape(x)\n",
    "plt.figure(figsize=(20,7.5))\n",
    "print num_pct[0,:]\n",
    "print np.shape(num_pct)\n",
    "plt.bar(range(1,23),num_pct[0,:])\n",
    "plt.xticklabel(('G1', 'G2', 'G3', 'G4', 'G5','G1', 'G2', 'G3', 'G4', 'G5','G1', 'G2', 'G3', 'G4', 'G5','G1', 'G2', 'G3', 'G4', 'G5','G1', 'G2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x11b6d7510>,\n",
       " <matplotlib.text.Text at 0x11a21fa50>,\n",
       " <matplotlib.text.Text at 0x11be9b650>,\n",
       " <matplotlib.text.Text at 0x11be9bd90>,\n",
       " <matplotlib.text.Text at 0x11bea6510>,\n",
       " <matplotlib.text.Text at 0x11bea6c50>,\n",
       " <matplotlib.text.Text at 0x11beb03d0>,\n",
       " <matplotlib.text.Text at 0x11beb0b10>,\n",
       " <matplotlib.text.Text at 0x11beb9290>,\n",
       " <matplotlib.text.Text at 0x11beb99d0>,\n",
       " <matplotlib.text.Text at 0x11bec3150>,\n",
       " <matplotlib.text.Text at 0x11bec3890>,\n",
       " <matplotlib.text.Text at 0x11bec3fd0>,\n",
       " <matplotlib.text.Text at 0x11becf750>,\n",
       " <matplotlib.text.Text at 0x11a21f5d0>,\n",
       " <matplotlib.text.Text at 0x1198130d0>,\n",
       " <matplotlib.text.Text at 0x11b65fa50>,\n",
       " <matplotlib.text.Text at 0x11b6bc750>,\n",
       " <matplotlib.text.Text at 0x11bed82d0>,\n",
       " <matplotlib.text.Text at 0x11bed8a10>,\n",
       " <matplotlib.text.Text at 0x11bee4190>,\n",
       " <matplotlib.text.Text at 0x11bee48d0>]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k5d39Z1u2VZJ090eSfHuSDyd5+sKqAwAAAGCtjAmT7pjk/2x5fm22rJvU3Z9K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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a27f250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 22\n",
    "ind = np.arange(N)  # the x locations for the groups\n",
    "width = 0.7       # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,7.5))\n",
    "rects1 = ax.bar(ind, num_pct[0,:], width)#, yerr=men_std)\n",
    "ax.set_ylabel('Frequency(Cases per 6 hours)',fontsize=20)\n",
    "ax.set_xlabel('PCT',fontsize=20)\n",
    "ax.set_xticks(ind + width / 2)\n",
    "ax.set_xticklabels(('01', '05', '06', '07', '09', '10', '13', '14', '17', '18', '19', '20', '22', '23', '24', '25', '26', '28', '30', '32', '33', '34'),fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}