Enhanced_License_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AmLEGyDZgjdZ6PTAT6KyUOgY8ZnxuE8UiAaK1Dtdad7d1HOJfqampbNq0ieeff14SIEII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58gJUV2k/KhMWHCBFavXs3Tp0/zPS4lJYXr168XqBFRp0wdBnkMYuDfA7VS3i/fUQxqFkSFEtKq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x108a50f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "#adding graphics\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline\n",
    "from pylab import rcParams\n",
    "from collections import defaultdict\n",
    "from collections import OrderedDict as od\n",
    "rcParams['figure.figsize'] = 18, 10\n",
    " \n",
    "#from IPython.html import widgets\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    " \n",
    "#import gmaps\n",
    " \n",
    "###################################\n",
    " \n",
    "all_lic = pd.read_table('ready_data.csv', sep=',')\n",
    "tsxdata = pd.read_csv(\"tsx.csv\")\n",
    "#this will get the tsx data\n",
    "def getgain(year):\n",
    "    row=tsxdata[tsxdata['year']==year]\n",
    "    if len(row.index) >0 :\n",
    "        return row.iloc[0]['gain']\n",
    "    else :\n",
    "        return np.nan\n",
    " \n",
    "#fix the dates\n",
    "def addyear(x):\n",
    "    if x is nan :\n",
    "        return nan\n",
    "    ystr = x[6]+x[7]\n",
    "    if (ystr>'18') :\n",
    "        ystr = '19'+ystr\n",
    "    else :\n",
    "        ystr = '20'+ystr\n",
    "    return ystr\n",
    " \n",
    "def getarea(po) :\n",
    "    area = po[:3]\n",
    "    return area\n",
    "# get rid of nulls in postal codes\n",
    "all_lic= all_lic[pd.notnull(all_lic['Licence Address Line 3'])]\n",
    "all_lic['year_issued']=all_lic[\"Issued\"].map(addyear)\n",
    "all_lic['year_cancel']=all_lic[\"Cancel Date\"].map(addyear)\n",
    " \n",
    " \n",
    "all_lic['area']=all_lic['postal'].map(getarea)\n",
    " \n",
    "cat = all_lic['Category']\n",
    "d = defaultdict(int)\n",
    "for c in cat :  \n",
    "    d[c]+=1\n",
    "df = DataFrame(d.items(),columns=['category','count'])\n",
    "indf = df.set_index(['category'])\n",
    "indf=indf.sort_index()\n",
    "#####################################\n",
    "def limit_year(dataset,yearfield,start=None,end=None,year=None) :\n",
    "    target = dataset\n",
    "    if (start is not None):\n",
    "        target= target [target[yearfield]>= start]\n",
    "    if (end is not None) :\n",
    "        target = target[target[yearfield]<= end]\n",
    "    if (year is not None) :\n",
    "        target = target[target[yearfield]== year]\n",
    "    return target\n",
    " \n",
    "# here we build the weights of each area\n",
    "def getweighted(dataset) :\n",
    "    target = dataset\n",
    "    areadict = defaultdict(int)\n",
    "    for c in dataset['area'] :  \n",
    "        areadict[c]+=1\n",
    "    f = lambda x : areadict[x]\n",
    "    target['w'] = target['area'].map(f)\n",
    "    return target\n",
    " \n",
    "# function gets total of a category through years in a single dataset\n",
    "def get_totals_by(dataset,cat) :\n",
    "    d = defaultdict(int)\n",
    "    catseries=dataset[cat]\n",
    "    for c in catseries :  \n",
    "        if c is not nan :\n",
    "            d[c]+=1\n",
    "    df = DataFrame(d.items(),columns=[cat,'count'])\n",
    "    indf = df.set_index([cat])\n",
    "    indf= indf.sort_index()\n",
    "    return indf\n",
    " \n",
    "#this function will take totals and make sure that if there is any missing years in the series\n",
    "# the years will have 'nan' it and be inserted in the 'target ' data frame\n",
    "def spread_totals_by_years(totals,starty,endy):\n",
    "    myindex = range (starty,endy+1)\n",
    "    strindex = map(str, myindex)\n",
    "    mydf = pd.DataFrame(index=strindex)\n",
    "    targetpdf = pd.concat([mydf,totals],axis=1)\n",
    "    return targetpdf\n",
    " \n",
    " \n",
    "def limit_by_area (dataset,area) :\n",
    "    target = dataset[dataset['area']==area]\n",
    "    return target\n",
    " \n",
    "def get_category(cat):\n",
    "    cat_target= all_lic [all_lic['Category']==cat]\n",
    "    return cat_target\n",
    "################################################\n",
    "year_rest = limit_year(get_category(cat='EATING ESTABLISHMENT'),year='2012',yearfield='year_issued')\n",
    " \n",
    " \n",
    "year_w = getweighted(year_rest)\n",
    " \n",
    "#ignore errors\n",
    "year_w=year_w[year_w['lat']!=0]\n",
    " \n",
    "year_w_heatdata= year_w[['lat','lng','w']]\n",
    "heatmapdata= year_w_heatdata.to_records(index=False)\n",
    " \n",
    "# Time to put an interface on this\n",
    "def yearly_graph(category,starty,endy,postal,postal2,plot_i,plot_c,plot_t,plot_tsx,printme,saveme):  \n",
    "    # first get the Year Issued  range\n",
    "    if len(postal)!=3 :\n",
    "        print \" Postal Code \", postal, \" is invalid, Enter only first 3 letters of the postal code \"\n",
    "        return\n",
    "    if postal2!=\"NONE\" and len (postal2) != 3:\n",
    "        print \" Second Postal Code \", postal2, \" is invalid, Enter only first 3 letters of the postal code \"\n",
    "        return\n",
    "    postal=postal.upper()\n",
    "    if postal==\"ALL\" :\n",
    "        issued_dataset = limit_year(get_category(cat=category),start=str(starty),end=str(endy),yearfield=\"year_issued\")\n",
    "        cancel_dataset = limit_year(get_category(cat=category),start=str(starty),end=str(endy),yearfield=\"year_cancel\")\n",
    "    else :\n",
    "        issued_dataset = limit_by_area(limit_year(get_category(cat=category),yearfield=\"year_issued\",start=str(starty),end=str(endy)),postal)\n",
    "        cancel_dataset = limit_by_area(limit_year(get_category(cat=category),yearfield=\"year_cancel\",start=str(starty),end=str(endy)),postal)\n",
    "    #\n",
    "    if postal2!=\"NONE\" :\n",
    "        postal2=postal2.upper()\n",
    "        issued2_dataset = limit_by_area(limit_year(get_category(cat=category),yearfield=\"year_issued\",start=str(starty),end=str(endy)),postal2)\n",
    "        cancel2_dataset = limit_by_area(limit_year(get_category(cat=category),yearfield=\"year_cancel\",start=str(starty),end=str(endy)),postal2)\n",
    " \n",
    "    \n",
    "    # set column names\n",
    "    icol1 = postal + \" Issued\"\n",
    "    ccol1 = postal + \" Canceled\"\n",
    "    tcol1 = postal + \" Total\"\n",
    "    icol2 = postal2 + \" Issued\"\n",
    "    ccol2 = postal2 + \" Canceled\"\n",
    "    tcol2 = postal2 + \" Total\"\n",
    "    \n",
    "    # Build Serieses of Data to be ready for display\n",
    "    finalissued = get_totals_by(issued_dataset,'year_issued')\n",
    "    finalissued.rename(columns={'count':icol1}, inplace=True)\n",
    "    finalissued = spread_totals_by_years(finalissued,starty,endy)\n",
    "    finalcanceled= get_totals_by(cancel_dataset,'year_cancel')\n",
    "    finalcanceled.rename(columns={'count':ccol1}, inplace=True)\n",
    "    finalcanceled = spread_totals_by_years(finalcanceled,starty,endy)\n",
    "    finaltotal=pd.DataFrame(index=finalissued.index)\n",
    "    finaltotal[tcol1]= finalissued[icol1]-finalcanceled[ccol1]\n",
    "   \n",
    "    if postal2!=\"NONE\" :\n",
    "        finalissued2 = get_totals_by(issued2_dataset,'year_issued')\n",
    "        finalissued2.rename(columns={'count':icol2}, inplace=True)\n",
    "        finalissued2 = spread_totals_by_years(finalissued2,starty,endy)\n",
    " \n",
    "        finalcanceled2= get_totals_by(cancel2_dataset,'year_cancel')\n",
    "        finalcanceled2.rename(columns={'count':ccol2}, inplace=True)\n",
    "        finalcanceled2 = spread_totals_by_years(finalcanceled2,starty,endy)\n",
    " \n",
    "        if len (finalissued2.index)!=0 :\n",
    "            finaltotal2=pd.DataFrame(index=finalissued2.index)\n",
    "            finaltotal2[tcol2]= finalissued2[icol2]-finalcanceled2[ccol2]\n",
    "    \n",
    "    final = pd.DataFrame(index=finaltotal.index)\n",
    "    if plot_i :\n",
    "        final = pd.concat ([final,finalissued],axis=1)\n",
    "        if postal2!=\"NONE\" :\n",
    "            final = pd.concat ([final,finalissued2],axis=1)\n",
    "    if plot_c :\n",
    "        final = pd.concat ([final,finalcanceled],axis=1)\n",
    "        if postal2!=\"NONE\" :\n",
    "            final = pd.concat ([final,finalcanceled2],axis=1)\n",
    "    if plot_t :\n",
    "        final = pd.concat ([final,finaltotal],axis=1)\n",
    "        if (postal2!=\"NONE\") and (len (finalissued2.index)!=0 ) :\n",
    "            final = pd.concat ([final,finaltotal2],axis=1)\n",
    "   \n",
    "    if plot_tsx :\n",
    "        tsxyears = limit_year(tsxdata,start=starty,end=endy,yearfield='year')\n",
    "        tsxyears [['year']] = tsxyears[['year']].astype(str)\n",
    "        tsxyears=tsxyears.set_index('year')\n",
    "        final =pd.concat([final,tsxyears],axis=1)\n",
    " \n",
    "    if len(final.index)==0 :\n",
    "        print \"There is no data in the current selection\"\n",
    "        plt.clf()\n",
    "        return\n",
    "    else :\n",
    "        mycolors = ['green','red','blue']\n",
    "        fig=plt.figure()\n",
    "        linesplt=fig.add_subplot(2,1,1)\n",
    "#        somevar, axarr = plt.subplots(2, sharex=True)\n",
    "#        plt.subplots_adjust(hspace = .2 )\n",
    "#        linesplt=axarr[0]\n",
    "        linesplt.grid('on', which='major', axis='x' )\n",
    "        linesplt.grid('on', which='major', axis='y' )\n",
    "#        lplt =final.plot(ax=linesplt,color=mycolors)\n",
    "        justlicenses = final\n",
    "        if plot_tsx :\n",
    "             justlicenses = final.drop(['gain', 'first','last'], axis=1) # drop this data\n",
    "        lplt =justlicenses.plot(ax=linesplt,x=final.index)\n",
    "        lplt.xaxis.grid(True, which='major', linestyle='-', linewidth=0.25)\n",
    "        lplt.yaxis.grid(True, which='major', linestyle='-', linewidth=0.25)\n",
    "        lplt.set_xlabel(\"Year\")\n",
    "        lplt.set_ylabel(\"Number of Licenses\")\n",
    "        lplt.set_xticks(range(0,endy-starty+1))\n",
    "        lplt.set_xticklabels(final.index,rotation=90)\n",
    "        if plot_tsx :\n",
    "            ax2 = linesplt.twinx() # Create another axes that shares the same x-axis as ax.\n",
    "            ax2.set_ylabel(\"TSX Index\")\n",
    "            final['gain'].plot(ax=ax2,color='black',x=final.index)\n",
    "            \n",
    "#        barsplt=axarr[1]\n",
    "        barsplt=fig.add_subplot(2,1,2,sharex=lplt)\n",
    "#        barsplt.xaxis.tick_top()\n",
    "#        bplt=final.plot(ax=barsplt, kind='bar',color=mycolors)\n",
    "        bplt=justlicenses.plot(ax=barsplt, kind='bar')\n",
    "        bplt.xaxis.grid(True, which='major', linestyle='-', linewidth=0.25)\n",
    "        bplt.yaxis.grid(True, which='major', linestyle='-', linewidth=0.25)\n",
    "        bplt.set_xlabel(\"Year\")\n",
    "        bplt.set_ylabel(\"Number of Licenses\")\n",
    "        bplt.set_xticks(range(0,endy-starty+1))\n",
    "        bplt.set_xticklabels(final.index,rotation=90)\n",
    "        title = category+\" for postal code : \"+ postal\n",
    "        if postal2!=\"NONE\" :\n",
    "            title=title+ \" compared to postal code \" + postal2\n",
    "        plt.suptitle(title )\n",
    "#        plt.show()  \n",
    "        weighted_issued = getweighted(issued_dataset)\n",
    "        year_w_heatdata= weighted_issued[['lat','lng','w']]\n",
    "        heatmapdata= year_w_heatdata.to_records(index=False)\n",
    "#        m = gmaps.heatmap(heatmapdata, max_intensity=10, point_radius=4)\n",
    "#        gmaps.display(m)\n",
    "    if (printme) :\n",
    "        print tcol1,final[tcol1].sum()\n",
    "        if postal2!=\"NONE\" :\n",
    "            print tcol2,final[tcol2].sum()\n",
    "        print \" final data\"\n",
    "        print final\n",
    "        print issued_dataset\n",
    "        print cancel_dataset\n",
    "    if (saveme) :\n",
    "        fig.savefig(category+\"_\"+postal+\"_\"+str(starty)+\"_\"+str(endy)+\".png\")\n",
    "#    return fig\n",
    "       \n",
    "catlist_w = widgets.Select()\n",
    "catlist_w.options = list( indf.index)\n",
    "catlist_w.label =\" Category \"\n",
    "yearrange= range(1980,2016)\n",
    "stryearrange =[str(i) for i in yearrange]\n",
    " \n",
    "startyear_w = widgets.IntSlider(value=1980,min=1919,max=2015,step=1,description='Start YEAR !')\n",
    "endyear_w = widgets.IntSlider(value=2015,min=1919,max=2015,step=1,description='End YEAR !')\n",
    " \n",
    "postalfield = widgets.Text()\n",
    "postalfield.description = \"Postal Code\"\n",
    "postalfield.value =\"ALL\"\n",
    "postalfield2 = widgets.Text()\n",
    "postalfield2.description = \"Second Postal (Optaional)\"\n",
    "postalfield2.value =\"NONE\"\n",
    " \n",
    " \n",
    "plot_i_w = widgets.Checkbox(\n",
    "    description='Plot Issued',\n",
    "    value=True\n",
    ")\n",
    " \n",
    "plot_c_w = widgets.Checkbox(\n",
    "    description='Plot Canceled',\n",
    "    value=True\n",
    ")\n",
    " \n",
    "plot_t_w = widgets.Checkbox(\n",
    "    description='Plot Total',\n",
    "    value=True\n",
    ")\n",
    " \n",
    "plot_tsx_w = widgets.Checkbox(\n",
    "    description='Plot TSX Returns',\n",
    "    value=True\n",
    ")\n",
    " \n",
    "print_data_w = widgets.Checkbox(\n",
    "    description='Print Data',\n",
    "    value=False\n",
    ")\n",
    " \n",
    "save_image_w = widgets.ToggleButton(\n",
    "    description='Save as png',\n",
    "    value=False\n",
    ")\n",
    "start_range = widgets.IntSlider(min=1980,max=2015,step=1,description='Start YEAR !')\n",
    "wid=widgets.interact( yearly_graph,\n",
    "                 category    = catlist_w,\n",
    "                 starty  = startyear_w,\n",
    "                 endy = endyear_w,\n",
    "                 postal = postalfield,\n",
    "                 postal2 = postalfield2,\n",
    "                 plot_i = plot_i_w,\n",
    "                 plot_c = plot_c_w,\n",
    "                 plot_t = plot_t_w,\n",
    "                 plot_tsx = plot_tsx_w,\n",
    "                 printme = print_data_w,\n",
    "                 saveme = save_image_w\n",
    "             )"
   ]
  },
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   "source": []
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