TaxiServiceProject.ipynb

{
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  "name": "",
  "signature": "sha256:0f4be014339124c5e94daa42e562f75ba8e30c6ac716f0cd3a9f4d1172bff719"
 },
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import matplotlib\n",
      "from matplotlib import animation, colors\n",
      "import numpy as np\n",
      "import pylab as plt\n",
      "from time import time, sleep\n",
      "\n",
      "def distributionHistogram(data, numberOfBin, center=True):\n",
      "    if type(data) != np.ndarray:\n",
      "        data = np.array(data)\n",
      "    y, x = np.histogram(data, numberOfBin, density=True)\n",
      "    N = len(data)\n",
      "    if center:\n",
      "        x = (x[:-1] + x[1:])/2\n",
      "    return (x, y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plotCorrelationMatix(data, normalize=True, column_list=column_list, saveName=-1, **keyargs):\n",
      "    fig = plt.figure(figsize=(700/80,700/80))\n",
      "    ax = fig.add_subplot(1, 1, 1)\n",
      "    ax.xaxis.set_ticks_position('top')\n",
      "    ##ax.yaxis.grid(linestyle = '-', color = 'gray')\n",
      "    ##ax.set_title('asdasdasdasdasdasd')\n",
      "    \n",
      "    aa=ax.imshow(data, cmap=plt.cm.hot) ##, cmap=plt.cm.hot {hot, autumn, Blues, spring, summer, winter, gray, cool}\n",
      "                                         ##, interpolation='nearest' {'none', 'nearest', 'bilinear', 'bicubic'}\n",
      "    ax.set_yticks(np.arange(len(column_list)))\n",
      "    ax.set_xticks(np.arange(len(column_list)))\n",
      "    ax.set_yticklabels(column_list, **keyargs)\n",
      "    ax.set_xticklabels(column_list, rotation='vertical', **keyargs)\n",
      "    \n",
      "    if normalize == True:\n",
      "        norm = colors.Normalize(vmin=-1.0, vmax=1.0)\n",
      "        aa.set_norm(norm)\n",
      "    \n",
      "    plt.colorbar(aa)\n",
      "    plt.show()\n",
      "    \n",
      "    if saveName != -1:\n",
      "        fig.savefig(saveName, bbox_inches='tight', pad_inches=0.1, dpi=150) # , dpi=80, transparent=True\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 351
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "New"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%time\n",
      "folder = r'D:\\Document\\Ebook\\University\\University\\MSc\\data\\Taxi - Isfahan\\\\'\n",
      "filename = 'taxiPaymentEntity-31662_en.xls'\n",
      "# filename = 'taxiPaymentEntity-58228_en.xls'\n",
      "excel = pd.read_excel(folder + filename, header=4, skiprows=[48902], encoding='utf-8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Wall time: 13 s\n"
       ]
      }
     ],
     "prompt_number": 415
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print excel['Corporation'] # same as bellow\n",
      "# print excel.get(excel.columns[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1          \u062a\u0639\u0627\u0648\u0646\u064a \u0647\u0645\u0627\u064a \u0633\u0639\u0627\u062f\u062a \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "2          \u062a\u0639\u0627\u0648\u0646\u064a \u0647\u0645\u0627\u064a \u0633\u0639\u0627\u062f\u062a \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "3     \u0645\u0633\u0627\u0641\u0631\u0628\u0631\u064a \u0631\u0647\u067e\u0648\u064a\u0627\u0646 \u0633\u0628\u0632 \u0627\u0633\u067e\u0627\u062f\u0627\u0646\u0627\n",
        "4          \u062a\u0639\u0627\u0648\u0646\u064a \u0647\u0645\u0627\u064a \u0633\u0639\u0627\u062f\u062a \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "5                      \u0633\u064a\u0631 \u0627\u0633\u067e\u0627\u062f\u0627\u0646\u0627\n",
        "6              \u0641\u0627\u0646\u0648\u0633 \u0622\u0628\u064a \u0632\u0627\u064a\u0646\u062f\u0647 \u0631\u0648\u062f\n",
        "7                 \u0627\u062a\u062d\u0627\u062f \u0643\u0648\u062b\u0631 \u0633\u067e\u0627\u0647\u0627\u0646\n",
        "8                 \u0627\u062a\u062d\u0627\u062f \u0643\u0648\u062b\u0631 \u0633\u067e\u0627\u0647\u0627\u0646\n",
        "9          \u062a\u0639\u0627\u0648\u0646\u064a \u0647\u0645\u0627\u064a \u0633\u0639\u0627\u062f\u062a \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "10             \u0634\u0631\u0643\u062a \u0627\u0645\u064a\u0646 \u0633\u064a\u0631 \u0627\u0635\u0641\u0647\u0627\u0646\n",
        "11         \u062a\u0639\u0627\u0648\u0646\u064a \u0647\u0645\u0627\u064a \u0633\u0639\u0627\u062f\u062a \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "12                \u0627\u062a\u062d\u0627\u062f \u0643\u0648\u062b\u0631 \u0633\u067e\u0627\u0647\u0627\u0646\n",
        "13                \u0627\u062a\u062d\u0627\u062f \u0643\u0648\u062b\u0631 \u0633\u067e\u0627\u0647\u0627\u0646\n",
        "14              \u064a\u0627\u0631\u0627\u0646 \u067e\u0627\u0631\u0633 \u0627\u0631\u062f\u0633\u062a\u0627\u0646\u064a\n",
        "15              \u064a\u0627\u0631\u0627\u0646 \u067e\u0627\u0631\u0633 \u0627\u0631\u062f\u0633\u062a\u0627\u0646\u064a\n",
        "...\n",
        "59578    \u0633\u0628\u0632 \u0627\u064a\u062b\u0627\u0631  \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "59579        \u0628\u0647\u0631\u0647 \u0648\u0631\u0633\u067e\u0627\u0647\u0627\u0646\n",
        "59580        \u0628\u0647\u0631\u0647 \u0648\u0631\u0633\u067e\u0627\u0647\u0627\u0646\n",
        "59581    \u0622\u0648\u0627\u064a \u0645\u0647\u062a\u0627\u0628 \u0633\u067e\u0627\u0647\u0627\u0646\n",
        "59582    \u0622\u0648\u0627\u064a \u0645\u0647\u062a\u0627\u0628 \u0633\u067e\u0627\u0647\u0627\u0646\n",
        "59583      \u062c\u0645\u0627\u0644 \u0632\u0627\u064a\u0646\u062f\u0647 \u0631\u0648\u062f\n",
        "59584      \u062c\u0645\u0627\u0644 \u0632\u0627\u064a\u0646\u062f\u0647 \u0631\u0648\u062f\n",
        "59585      \u062c\u0645\u0627\u0644 \u0632\u0627\u064a\u0646\u062f\u0647 \u0631\u0648\u062f\n",
        "59586      \u062c\u0645\u0627\u0644 \u0632\u0627\u064a\u0646\u062f\u0647 \u0631\u0648\u062f\n",
        "59587     \u0646\u064a\u0643 \u0627\u0646\u062f\u064a\u0634 \u0627\u0635\u0641\u0647\u0627\u0646\n",
        "59588       \u0628\u0639\u062b\u062a \u0633\u067e\u0627\u0647\u0627\u0646\u0634\u0647\u0631\n",
        "59589       \u0628\u0639\u062b\u062a \u0633\u067e\u0627\u0647\u0627\u0646\u0634\u0647\u0631\n",
        "59590     \u0633\u0628\u0632 \u0627\u064a\u062b\u0627\u0631 \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "59591      \u067e\u0627\u0631\u0633\u064a\u0627\u0646 \u0645\u0647\u0631 \u0641\u062f\u0643\n",
        "\u0645\u062c\u0645\u0648\u0639                  NaN\n",
        "Name: Corporation, Length: 59591, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 404
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print excel.ix[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Taxi                                                                   63470\n",
        "Corporation                                         \u062a\u0639\u0627\u0648\u0646\u064a \u0647\u0645\u0627\u064a \u0633\u0639\u0627\u062f\u062a \u0635\u0641\u0627\u0647\u0627\u0646\n",
        "Customer                                                                 NaN\n",
        "taxiPayment_dgsDatabaseId                                            1098973\n",
        "taxiPayment_dgsSendPacketId                                         17337559\n",
        "Discount                                                                   0\n",
        "Distance                                                               18961\n",
        "Drain                                                           \u0645\u0631\u0643\u0632\u064a-online\n",
        "Successful Payment                                                         +\n",
        "Line                                                                     ---\n",
        "taxiPayment_missionId                                                    NaN\n",
        "Transaction Date                                                  \u06f1\u06f3\u06f9\u06f3/\u06f0\u06f3/\u06f2\u06f5\n",
        "Transaction Time                                                    \u06f0\u06f0:\u06f0\u06f2:\u06f4\u06f5\n",
        "taxiPayment_occuranceTimeLong                                            NaN\n",
        "Calculating Version of Cost                                             6005\n",
        "Card Reader                                                              NaN\n",
        "Real Transaction Date                                             \u06f1\u06f3\u06f9\u06f3/\u06f0\u06f3/\u06f2\u06f5\n",
        "Real Transaction Time                                               \u06f0\u06f0:\u06f0\u06f2:\u06f4\u06f5\n",
        "Receipt Date                                                      \u06f1\u06f3\u06f9\u06f3/\u06f0\u06f3/\u06f2\u06f4\n",
        "Receipt Time                                                        \u06f2\u06f3:\u06f5\u06f9:\u06f2\u06f5\n",
        "taxiPayment_receiveDateLong                                              NaN\n",
        "Stopping Time Length                                                     NaN\n",
        "Taxi Type                                                     \u06af\u0631\u062f\u0634\u064a \u2013 \u062f\u0631\u0628\u0633\u062a\u064a\n",
        "Terminal                                                               31293\n",
        "Tracking Code                                                            NaN\n",
        "taxiPayment_tripEndAddressPointID                                        NaN\n",
        "End Latitude                                                        32710133\n",
        "End Longitude                                                       51615933\n",
        "End Date                                                          \u06f1\u06f3\u06f9\u06f3/\u06f0\u06f3/\u06f2\u06f5\n",
        "End Time                                                            \u06f0\u06f0:\u06f0\u06f2:\u06f4\u06f5\n",
        "taxiPayment_tripStartAddressPointID                                      NaN\n",
        "Start Latitude                                                      32600594\n",
        "Start Longitude                                                     51667337\n",
        "Start Date                                                        \u06f1\u06f3\u06f9\u06f3/\u06f0\u06f3/\u06f2\u06f4\n",
        "Start Time                                                          \u06f2\u06f3:\u06f3\u06f9:\u06f4\u06f4\n",
        "Transaction Serial                     3129363470519280381140001402774365000\n",
        "Cost                                                                  114000\n",
        "Name: 2, dtype: object\n"
       ]
      }
     ],
     "prompt_number": 405
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot(dataFrame, figsize=(15,15), kind=None, basex=1, basey=1, rotation=90, legend=False, **kwargs):\n",
      "    # fff.plot(fontfamily='Arial')\n",
      "    # plt.rcParams['text.unicode'] = True\n",
      "    plt.rc('font', family='Arial') # 'sans-serif'\n",
      "    import matplotlib.font_manager as font_manager\n",
      "    prop = font_manager.FontProperties(fname=u'C:\\\\Windows\\\\Fonts\\\\tahoma.ttf')\n",
      "    plt.rcParams['font.family'] = prop.get_name()\n",
      "    # plt.rcParams['text.usetex']=True\n",
      "    # plt.rcParams['text.latex.unicode']=True\n",
      "    # plt.rc('font', encoding='utf-8')\n",
      "    # matplotlib.rc('font', family='Arial')\n",
      "    # matplotlib.rc('font', **{'sans-serif' : 'Arial', 'family' : 'sans-serif'})\n",
      "\n",
      "    # plt.xlabel('hi',rotation=90)\n",
      "    # plt.xticks(rotation=90)\n",
      "\n",
      "    fig, ax = plt.subplots(figsize=figsize)\n",
      "    if kind==None:\n",
      "        dataFrame.plot(ax=ax, **kwargs)\n",
      "    else:\n",
      "        dataFrame.plot(ax=ax, kind='bar', **kwargs)\n",
      "\n",
      "    if basex!=1:\n",
      "        ax.set_xscale('log', basex=basex) # basex=4\n",
      "    if basey!=1:\n",
      "        ax.set_yscale('log', basey=basey) # basey=4\n",
      "#     ax.set_xticklabels(ax.xaxis.get_ticklabels(), rotation=rotation)\n",
      "#     ax.xaxis.get_majorticklabels()\n",
      "#     ax.xaxis.get_minorticklabels()\n",
      "#     ax.xaxis.get_ticklabels()\n",
      "#     ax.set_xticklabels(ax.get_xticklabels() ,rotation='vertical')\n",
      "    fig.autofmt_xdate(rotation=rotation)\n",
      "#     ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
      "    if legend:\n",
      "        ax.legend(loc='best')\n",
      "    # fig.savefig('results-tilted.png', bbox_inches='tight')\n",
      "    return ax\n",
      "\n",
      "    # plt.xticks(rotation=90)\n",
      "    # fff.plot()\n",
      "    \n",
      "def plotGroupBy(group, func, field):\n",
      "    ff = excel.groupby(group).func()[field]\n",
      "    print ff.size\n",
      "    fff = ff.copy().sort(inplace=False, ascending=False)\n",
      "\n",
      "    print ff.sum()\n",
      "    plot(fff)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 406
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ff = excel.groupby('Corporation').count()['Distance']\n",
      "print ff.size\n",
      "fff = ff.copy().sort(inplace=False, ascending=False)\n",
      "# fff = fff[:-1]\n",
      "fff.set_axis(0, np.arange(1, fff.size+1))\n",
      "\n",
      "print fff.describe()\n",
      "fff = fff/float(ff.sum())\n",
      "print 'sum: {:.2f}'.format(ff.sum())\n",
      "print 'avg: {:.2f}'.format(ff.mean())\n",
      "\n",
      "ax = plot(fff, kind='bar', figsize=(20,20))\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "ax = plot(fff, basex=1, basey=10, figsize=(10,10), linewidth=2)\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "\n",
      "from scipy.optimize import curve_fit\n",
      "def fit(x, a, m):\n",
      "    return a*x**(-m)\n",
      "def fit2(x, a, m):\n",
      "    return m*x+a\n",
      "\n",
      "xdata = np.arange(1, fff.size+1)\n",
      "ydata = np.array(fff.tolist())\n",
      "ydata2 = np.log10(np.array(fff.tolist()))\n",
      "# print ydata\n",
      "# print ydata2\n",
      "\n",
      "# params, paramsCov = curve_fit(fit, 10**xdata, ydata) # , maxfev = 1000\n",
      "params, paramsCov = curve_fit(fit2, xdata, ydata2) # , maxfev = 1000\n",
      "print params\n",
      "# ax.plot(xdata, fit(10**xdata, params[0], params[1]))\n",
      "ax.plot(xdata, 10**fit2(xdata, params[0], params[1]), 'r--', linewidth=3)\n",
      "# ax.set_yscale('linear')\n",
      "ax.legend(('Number of Journeys', 'Power-law Fitted: ${} x{}$'.format(round(params[0], 2), round(params[1], 2))), loc='best')\n",
      "# plt.figure()\n",
      "# plt.plot(xdata, ydata2)\n",
      "# plt.plot(xdata, fit2(xdata, params[0], params[1]))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "129\n",
        "count     129.000000\n",
        "mean      379.046512\n",
        "std       812.729505\n",
        "min         1.000000\n",
        "25%        12.000000\n",
        "50%        50.000000\n",
        "75%       250.000000\n",
        "max      4478.000000\n",
        "dtype: float64\n",
        "sum: 48897.00\n",
        "avg: 379.05\n",
        "[-1.35539786 -0.02446991]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 416,
       "text": [
        "<matplotlib.legend.Legend at 0x4a3ff3c8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YEx8wXa58vs87kr6pTOokbV3VSdra1kna2tZJ2trWSdq6qpO0ta2T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       "text": [
        "<matplotlib.figure.Figure at 0x22f08978>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9ip89ip9nKFHzpoULoV492LrVqY+VKJHZQffii9ofVEREJFCpRs1btm41a60l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\nJEmSJEmSJEmSJEmSJEmSJEmSis//B5NZQeIaHFE4AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4a090470>"
       ]
      }
     ],
     "prompt_number": 416
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ff = excel.groupby('Corporation').sum()['Distance']\n",
      "print ff.size\n",
      "fff = ff.copy().sort(inplace=False, ascending=False)\n",
      "fff = fff[:-1]\n",
      "fff.set_axis(0, np.arange(1, fff.size+1))\n",
      "\n",
      "print fff.describe()\n",
      "fff = fff/float(ff.sum())\n",
      "print 'sum: {:.2f}'.format(ff.sum())\n",
      "print 'avg: {:.2f}'.format(ff.mean())\n",
      "\n",
      "ax = plot(fff, kind='bar', figsize=(20,20))\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "ax = plot(fff, basex=1, basey=10, figsize=(10,10), linewidth=2)\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "\n",
      "from scipy.optimize import curve_fit\n",
      "def fit(x, a, m):\n",
      "    return a*x**(-m)\n",
      "def fit2(x, a, m):\n",
      "    return m*x+a\n",
      "\n",
      "xdata = np.arange(1, fff.size+1)\n",
      "ydata = np.array(fff.tolist())\n",
      "ydata2 = np.log10(np.array(fff.tolist()))\n",
      "# print ydata\n",
      "# print ydata2\n",
      "\n",
      "# params, paramsCov = curve_fit(fit, 10**xdata, ydata) # , maxfev = 1000\n",
      "params, paramsCov = curve_fit(fit2, xdata, ydata2) # , maxfev = 1000\n",
      "print params\n",
      "# ax.plot(xdata, fit(10**xdata, params[0], params[1]))\n",
      "ax.plot(xdata, 10**fit2(xdata, params[0], params[1]), 'r--', linewidth=3)\n",
      "# ax.set_yscale('linear')\n",
      "ax.legend(('Sum of Distances', 'Power-law Fitted: ${} x{}$'.format(round(params[0], 2), round(params[1], 2))), loc='best')\n",
      "# plt.figure()\n",
      "# plt.plot(xdata, ydata2)\n",
      "# plt.plot(xdata, fit2(xdata, params[0], params[1]))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "129\n",
        "count         128.000000\n",
        "mean      2125758.445312\n",
        "std       4161701.175432\n",
        "min            35.000000\n",
        "25%        129069.500000\n",
        "50%        421137.500000\n",
        "75%       1414390.000000\n",
        "max      21046830.000000\n",
        "dtype: float64\n",
        "sum: 272097081.00\n",
        "avg: 2109279.70\n",
        "[-1.29798317 -0.02374662]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 417,
       "text": [
        "<matplotlib.legend.Legend at 0x4a0e2ac8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Zn52rv1Hua2fmPTPzipn55rma0/TumfktM/N3r/+bp2+6NAAAAACf7NMecf43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       "text": [
        "<matplotlib.figure.Figure at 0x44325048>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Mc40TERHxIxr6FJccOQLXXQfHj8O0aXB36VXw1lvw+edmi4NLxcVBnjw531AR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\nSZLUev4/8bzACwKfo3oAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x44325208>"
       ]
      }
     ],
     "prompt_number": 417
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ff = excel.groupby('Corporation').sum()['Cost']\n",
      "print ff.size\n",
      "fff = ff.copy().sort(inplace=False, ascending=False)\n",
      "fff.set_axis(0, np.arange(1, fff.size+1))\n",
      "\n",
      "print fff.describe()\n",
      "fff = fff/float(ff.sum())\n",
      "print 'sum: {:.2f}'.format(ff.sum())\n",
      "print 'avg: {:.2f}'.format(ff.mean())\n",
      "\n",
      "ax = plot(fff, kind='bar', figsize=(20,20))\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "ax = plot(fff, basex=1, basey=10, figsize=(10,10), linewidth=2)\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "\n",
      "from scipy.optimize import curve_fit\n",
      "def fit(x, a, m):\n",
      "    return a*x**(-m)\n",
      "def fit2(x, a, m):\n",
      "    return m*x+a\n",
      "\n",
      "xdata = np.arange(1, fff.size+1)\n",
      "ydata = np.array(fff.tolist())\n",
      "ydata2 = np.log10(np.array(fff.tolist()))\n",
      "# print ydata\n",
      "# print ydata2\n",
      "\n",
      "# params, paramsCov = curve_fit(fit, 10**xdata, ydata) # , maxfev = 1000\n",
      "params, paramsCov = curve_fit(fit2, xdata, ydata2) # , maxfev = 1000\n",
      "print params\n",
      "# ax.plot(xdata, fit(10**xdata, params[0], params[1]))\n",
      "ax.plot(xdata, 10**fit2(xdata, params[0], params[1]), 'r--', linewidth=3)\n",
      "# ax.set_yscale('linear')\n",
      "ax.legend(('Sum of Costs', 'Power-law Fitted: ${} x{}$'.format(round(params[0], 2), round(params[1], 2))), loc='best')\n",
      "# plt.figure()\n",
      "# plt.plot(xdata, ydata2)\n",
      "# plt.plot(xdata, fit2(xdata, params[0], params[1]))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "129\n",
        "count    1.290000e+02\n",
        "mean     1.060176e+07\n",
        "std      2.034097e+07\n",
        "min      1.000000e+03\n",
        "25%      8.550000e+05\n",
        "50%      2.522920e+06\n",
        "75%      7.347020e+06\n",
        "max      1.235629e+08\n",
        "dtype: float64\n",
        "sum: 1367627180.00\n",
        "avg: 10601761.09\n",
        "[-1.342372   -0.02160081]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 418,
       "text": [
        "<matplotlib.legend.Legend at 0x4a1754a8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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StrZ1kra2dZK2tnWStq7qJG1t6yRtbeskbW3rJG1t6yRt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       "text": [
        "<matplotlib.figure.Figure at 0x49f10d30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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w+DC8+aatDr3uOvjxx2xteyDQPA3fKXbuKH7uKH7uKH45I9zfDZC85bzz7Ei2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\nKY4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x49f10b38>"
       ]
      }
     ],
     "prompt_number": 418
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ff = excel.groupby('Corporation').mean()['Distance']\n",
      "print ff.size\n",
      "fff = ff.copy().sort(inplace=False, ascending=False)\n",
      "fff.set_axis(0, np.arange(1, fff.size+1))\n",
      "fff = fff[:-1]\n",
      "\n",
      "print fff.describe()\n",
      "fff = fff/float(ff.sum())\n",
      "print 'sum: {:.2f}'.format(ff.sum())\n",
      "print 'avg: {:.2f}'.format(ff.mean())\n",
      "\n",
      "ax = plot(fff, kind='bar', figsize=(20,20))\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "ax = plot(fff, basex=1, basey=10, figsize=(10,10), linewidth=2)\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "\n",
      "from scipy.optimize import curve_fit\n",
      "def fit(x, a, m):\n",
      "    return a*x**(-m)\n",
      "def fit2(x, a, m):\n",
      "    return m*x+a\n",
      "\n",
      "xdata = np.arange(1, fff.size+1)\n",
      "ydata = np.array(fff.tolist())\n",
      "ydata2 = np.log10(np.array(fff.tolist()))\n",
      "# print ydata\n",
      "# print ydata2\n",
      "\n",
      "# params, paramsCov = curve_fit(fit, 10**xdata, ydata) #\n",
      "params, paramsCov = curve_fit(fit2, xdata, ydata2) # , maxfev = 1000\n",
      "print params\n",
      "# ax.plot(xdata, fit(10**xdata, params[0], params[1]))\n",
      "ax.plot(xdata, 10**fit2(xdata, params[0], params[1]), 'r--', linewidth=3)\n",
      "# ax.set_yscale('linear')\n",
      "ax.legend(('Mean of Distances', 'Power-law Fitted: ${} x{}$'.format(round(params[0], 2), round(params[1], 2))), loc='best')\n",
      "# plt.figure()\n",
      "# plt.plot(xdata, ydata2)\n",
      "# plt.plot(xdata, fit2(xdata, params[0], params[1]))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "129\n",
        "count       128.000000\n",
        "mean      11304.007898\n",
        "std       23508.087542\n",
        "min           7.000000\n",
        "25%        4513.345555\n",
        "50%        6867.458333\n",
        "75%       12124.188048\n",
        "max      260932.000000\n",
        "dtype: float64\n",
        "sum: 1446913.01\n",
        "avg: 11216.38\n",
        "[-1.69352655 -0.00955722]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 419,
       "text": [
        "<matplotlib.legend.Legend at 0x4b385748>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tk7SlrZO0xZnP71x55qYvlwAAAADYwZ1L7lwCAAAAWHLnEgAAAADb3fTl0mP9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\nD0ryfUlenuS5Sd7i6mJXhcbI2d8YOfsbI2d/Y+Tsb4yc/Y3pljNljJz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       "text": [
        "<matplotlib.figure.Figure at 0x4a5fb9b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kiuihz8ps2mR62TZsgORkeP99MyQqIiIiUtdUo+aDw4fhd7+DTz+FuDhYuhQG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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4ad48f28>"
       ]
      }
     ],
     "prompt_number": 419
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ff = excel.groupby('Corporation').mean()['Cost']\n",
      "print ff.size\n",
      "fff = ff.copy().sort(inplace=False, ascending=False)\n",
      "fff.set_axis(0, np.arange(1, fff.size+1))\n",
      "\n",
      "print fff.describe()\n",
      "fff = fff/float(ff.sum())\n",
      "print 'sum: {:.2f}'.format(ff.sum())\n",
      "print 'avg: {:.2f}'.format(ff.mean())\n",
      "\n",
      "ax = plot(fff, kind='bar', figsize=(20,20))\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "ax = plot(fff, basex=1, basey=10, figsize=(10,10), linewidth=2)\n",
      "ax.set_xlabel('Rank', fontsize=18)\n",
      "ax.set_ylabel('PDF', fontsize=18)\n",
      "\n",
      "from scipy.optimize import curve_fit\n",
      "def fit(x, a, m):\n",
      "    return a*x**(-m)\n",
      "def fit2(x, a, m):\n",
      "    return m*x+a\n",
      "\n",
      "xdata = np.arange(1, fff.size+1)\n",
      "ydata = np.array(fff.tolist())\n",
      "ydata2 = np.log10(np.array(fff.tolist()))\n",
      "# print ydata\n",
      "# print ydata2\n",
      "\n",
      "# params, paramsCov = curve_fit(fit, 10**xdata, ydata) # , maxfev = 1000\n",
      "params, paramsCov = curve_fit(fit2, xdata, ydata2) # , maxfev = 1000\n",
      "print params\n",
      "# ax.plot(xdata, fit(10**xdata, params[0], params[1]))\n",
      "ax.plot(xdata, 10**fit2(xdata, params[0], params[1]), 'r--', linewidth=3)\n",
      "# ax.set_yscale('linear')\n",
      "ax.legend(('Mean of Costs', 'Power-law Fitted: ${} x{}$'.format(round(params[0], 2), round(params[1], 2))), loc='best')\n",
      "# plt.figure()\n",
      "# plt.plot(xdata, ydata2)\n",
      "# plt.plot(xdata, fit2(xdata, params[0], params[1]))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "129\n",
        "count        129.000000\n",
        "mean       74471.868673\n",
        "std       138088.405483\n",
        "min          500.000000\n",
        "25%        22068.761905\n",
        "50%        42226.666667\n",
        "75%        83815.789474\n",
        "max      1464000.000000\n",
        "dtype: float64\n",
        "sum: 9606871.06\n",
        "avg: 74471.87\n",
        "[-1.6414037  -0.01069193]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 420,
       "text": [
        "<matplotlib.legend.Legend at 0x4bfe3320>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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25ognmE42by10raOc3YzRLadS1245lbp2y6nU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       "text": [
        "<matplotlib.figure.Figure at 0x44539a90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qxcyZY5bp6NJF20mJiIhI9SivRk2JWjEHD5qFbyMi4NAhiIryQstEREREitFk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\nkiRJkiRJkiSpef4PxFRiUCxcW6QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4a7a3860>"
       ]
      }
     ],
     "prompt_number": 420
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plotDistributionOfField(columnName, NumberOfRemoveFromEnd=0, bins=50, figsize=(12,12), basex=1, basey=1, **kwargs):\n",
      "    ff = excel[columnName].copy()\n",
      "    fff = np.asanyarray(ff)\n",
      "    fff.sort()\n",
      "    if NumberOfRemoveFromEnd != 0:\n",
      "        fff = fff[:-NumberOfRemoveFromEnd]\n",
      "#     print fff\n",
      "    # print max(fff)\n",
      "    # a=plt.hist(fff, 150);\n",
      "\n",
      "    a=np.histogram(fff, bins, density=True);\n",
      "    print a[0], a[1]\n",
      "    plt.figure(figsize=figsize)\n",
      "    plt.plot(a[1][:-1], a[0], **kwargs)\n",
      "#     print plt.xlim()\n",
      "    if basex!=1:\n",
      "        plt.xscale('log', basex=basex) # basex=4\n",
      "    if basey!=1:\n",
      "        plt.yscale('log', basey=basey) # basey=4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 421
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotDistributionOfField('Cost', NumberOfRemoveFromEnd=100, bins=50, basex=10, basey=10, linewidth=2)\n",
      "\n",
      "# ff = excel['Cost'].copy()\n",
      "# fff = np.asanyarray(ff)\n",
      "# fff.sort()\n",
      "# fff = fff[:-1]\n",
      "# print fff\n",
      "# # print max(fff)\n",
      "# # a=plt.hist(fff, 150);\n",
      "\n",
      "# a=np.histogram(fff, 50, density=True);\n",
      "# print a[0], a[1]\n",
      "# plt.figure(figsize=(12,12))\n",
      "# plt.plot(a[1][:-1], a[0])\n",
      "# print plt.xlim()\n",
      "# plt.yscale('log')\n",
      "# plt.xscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  5.48652530e-05   1.77281570e-05   3.02813768e-06   3.61504653e-06\n",
        "   2.24429264e-06   2.31448772e-06   1.93621424e-06   1.71003010e-06\n",
        "   1.34735553e-06   1.02757795e-06   8.16992715e-07   6.04457618e-07\n",
        "   5.12814043e-07   3.64624434e-07   3.76323614e-07   3.97772110e-07\n",
        "   3.10028262e-07   2.80780313e-07   2.53482227e-07   2.35933457e-07\n",
        "   1.48189609e-07   1.38440293e-07   8.77438477e-08   8.77438477e-08\n",
        "   5.45961719e-08   7.21449414e-08   6.43454883e-08   5.26463086e-08\n",
        "   5.65460352e-08   4.09471289e-08   3.70474024e-08   4.09471289e-08\n",
        "   2.14484961e-08   4.09471289e-08   2.92479492e-08   3.11978125e-08\n",
        "   1.75487695e-08   2.92479492e-08   9.74931641e-09   2.14484961e-08\n",
        "   1.36490430e-08   1.55989063e-08   1.36490430e-08   7.79945313e-09\n",
        "   5.84958985e-09   1.16991797e-08   3.89972656e-09   1.36490430e-08\n",
        "   5.84958985e-09   3.89972656e-09] [  5.00000000e+02   1.10100000e+04   2.15200000e+04   3.20300000e+04\n",
        "   4.25400000e+04   5.30500000e+04   6.35600000e+04   7.40700000e+04\n",
        "   8.45800000e+04   9.50900000e+04   1.05600000e+05   1.16110000e+05\n",
        "   1.26620000e+05   1.37130000e+05   1.47640000e+05   1.58150000e+05\n",
        "   1.68660000e+05   1.79170000e+05   1.89680000e+05   2.00190000e+05\n",
        "   2.10700000e+05   2.21210000e+05   2.31720000e+05   2.42230000e+05\n",
        "   2.52740000e+05   2.63250000e+05   2.73760000e+05   2.84270000e+05\n",
        "   2.94780000e+05   3.05290000e+05   3.15800000e+05   3.26310000e+05\n",
        "   3.36820000e+05   3.47330000e+05   3.57840000e+05   3.68350000e+05\n",
        "   3.78860000e+05   3.89370000e+05   3.99880000e+05   4.10390000e+05\n",
        "   4.20900000e+05   4.31410000e+05   4.41920000e+05   4.52430000e+05\n",
        "   4.62940000e+05   4.73450000e+05   4.83960000e+05   4.94470000e+05\n",
        "   5.04980000e+05   5.15490000e+05   5.26000000e+05]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6zOPGxa5MiscxDEmStI3HHoMzz4T586GsDM45B6ZNg+7dY1cm5Z5jGJIkaRtj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\nAAAAAAAAAAAAAAAAAHr5H2CJafK/VojqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4343ba58>"
       ]
      }
     ],
     "prompt_number": 422
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotDistributionOfField('Distance', NumberOfRemoveFromEnd=100, bins=50, basex=10, basey=10, linewidth=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  1.99477223e-04   1.38991653e-04   7.48789910e-05   5.34612752e-05\n",
        "   4.16604586e-05   2.85186401e-05   2.16092875e-05   1.13155016e-05\n",
        "   7.36912465e-06   5.70883660e-06   4.77652100e-06   4.92977836e-06\n",
        "   3.35889044e-06   3.37166188e-06   3.12900440e-06   2.49043207e-06\n",
        "   2.46488918e-06   1.89017409e-06   1.95403132e-06   1.76245962e-06\n",
        "   1.37931622e-06   9.32315597e-07   7.66286792e-07   7.79058238e-07\n",
        "   6.25800880e-07   6.38572326e-07   5.36400754e-07   3.19286163e-07\n",
        "   3.83143396e-07   4.72543522e-07   3.83143396e-07   3.19286163e-07\n",
        "   3.19286163e-07   2.68200377e-07   2.42657484e-07   1.66028805e-07\n",
        "   2.29886037e-07   1.78800251e-07   1.53257358e-07   1.27714465e-07\n",
        "   1.02171572e-07   7.66286792e-08   8.94001257e-08   7.66286792e-08\n",
        "   1.02171572e-07   1.02171572e-07   1.53257358e-07   3.83143396e-08\n",
        "   6.38572326e-08   6.38572326e-08] [     0.    1604.6   3209.2   4813.8   6418.4   8023.    9627.6  11232.2\n",
        "  12836.8  14441.4  16046.   17650.6  19255.2  20859.8  22464.4  24069.\n",
        "  25673.6  27278.2  28882.8  30487.4  32092.   33696.6  35301.2  36905.8\n",
        "  38510.4  40115.   41719.6  43324.2  44928.8  46533.4  48138.   49742.6\n",
        "  51347.2  52951.8  54556.4  56161.   57765.6  59370.2  60974.8  62579.4\n",
        "  64184.   65788.6  67393.2  68997.8  70602.4  72207.   73811.6  75416.2\n",
        "  77020.8  78625.4  80230. ]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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HPPII7LRT7KoklQp3w5Ak5bW+feHFF8MezLNnwz77wBNPxK5KkjauecTvrkhd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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x43dcd0b8>"
       ]
      }
     ],
     "prompt_number": 423
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plotDistributionOfField('Distance', NumberOfRemoveFromEnd=100, bins=50, basex=10, basey=10, linewidth=2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}