Exploring-geo.ipynb

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   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    }
   ],
   "source": [
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot\n",
    "from plotly.offline import iplot as iplot_offline\n",
    "from plotly.plotly import iplot as iplot\n",
    "from plotly.graph_objs import Scatter\n",
    "init_notebook_mode() # run at the start of every ipython notebook to use plotly.offline\n",
    "                     # this injects the plotly.js source files into the notebook\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as plt\n",
    "plt.style.use('ggplot')\n",
    "import pandas as pd\n",
    "import cufflinks as cf\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "update_string = ' (updated January 30, 2018)'\n",
    "# markets_csv = '/Users/coreypetty/PycharmProjects/TBP_metrics/raw_data/us-markets_2016-06-25.csv'\n",
    "world_csv = '/Users/coreypetty/Dropbox/Projects/PycharmProjects/TBP_metrics/raw_data/2018-01-28_export_11344.csv'\n",
    "states_csv = '/Users/coreypetty/Dropbox/Projects/PycharmProjects/TBP_metrics/raw_data/2018-01-26_export_11467.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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          "California",
          "Colorado",
          "Florida",
          "Georgia",
          "Illinois",
          "Massachusetts",
          "New Jersey",
          "New York",
          "North Carolina",
          "Pennsylvania",
          "Texas",
          "Virginia",
          "Washington",
          "other"
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        "title": "US States Download Breakdown",
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       "<div id=\"04d79391-5cf3-40ee-bdfe-e24879118b27\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"04d79391-5cf3-40ee-bdfe-e24879118b27\", [{\"type\": \"pie\", \"values\": [4152, 399, 964, 412, 656, 447, 555, 1551, 452, 379, 1828, 380, 726, 4652], \"labels\": [\"California\", \"Colorado\", \"Florida\", \"Georgia\", \"Illinois\", \"Massachusetts\", \"New Jersey\", \"New York\", \"North Carolina\", \"Pennsylvania\", \"Texas\", \"Virginia\", \"Washington\", \"other\"], \"marker\": {\"colors\": [\"rgba(226, 74, 51, 1.0)\", \"rgba(62, 111, 176, 1.0)\", \"rgba(132, 118, 202, 1.0)\", \"rgba(101, 101, 101, 1.0)\", \"rgba(251, 193, 94, 1.0)\", \"rgba(142, 186, 66, 1.0)\", \"rgba(255, 181, 184, 1.0)\", \"rgba(255, 181, 184, 0.89999999999999991)\", \"rgba(226, 74, 51, 0.89999999999999991)\", \"rgba(62, 111, 176, 0.89999999999999991)\", \"rgba(132, 118, 202, 0.89999999999999991)\", \"rgba(101, 101, 101, 0.89999999999999991)\", \"rgba(251, 193, 94, 0.89999999999999991)\", \"rgba(142, 186, 66, 0.89999999999999991)\"]}, \"name\": \"\"}], {\"title\": \"US States Download Breakdown\", \"paper_bgcolor\": \"#FFFFFF\", \"legend\": {\"font\": {\"color\": \"#666666\"}, \"bgcolor\": \"#FFFFFF\"}, \"titlefont\": {\"color\": \"#151516\"}, \"plot_bgcolor\": \"#E5E5E5\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
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   ],
   "source": [
    "states = pd.DataFrame.from_csv(states_csv)\n",
    "states['perc'] = states.requests / states.requests.sum() * 100\n",
    "states.reset_index(inplace=True)\n",
    "states['grouping'] = np.where(states.perc <= 2, 'other', states.region)\n",
    "\n",
    "states.groupby('grouping').sum().reset_index().iplot(kind='pie', labels='grouping', values='requests', \n",
    "                                                      filename='TBP_plots/US-states-pie',\n",
    "                                                      title='US States Download Breakdown'\n",
    "                                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'iplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-0f7437221fbd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'grouping'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperc\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'other'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m world.groupby('grouping').sum().reset_index().iplot(kind='pie', labels='grouping', values='total_downloads', \n\u001b[0m\u001b[1;32m      7\u001b[0m                                                       \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'TBP_plots/world-pie'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m                                                       \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Country Download Breakdown'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3081\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3083\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'iplot'"
     ]
    }
   ],
   "source": [
    "world = pd.DataFrame.from_csv(world_csv)\n",
    "world['perc'] = world.requests / world.requests.sum() * 100\n",
    "world.reset_index(inplace=True)\n",
    "world['grouping'] = np.where(world.perc <= 2, 'other', world.region)\n",
    "\n",
    "world.groupby('grouping').sum().reset_index().iplot(kind='pie', labels='grouping', values='total_downloads', \n",
    "                                                      filename='TBP_plots/world-pie',\n",
    "                                                      title='Country Download Breakdown'\n",
    "                                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'total_downloads'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-dbe9eb23b622>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mworld\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworld_csv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mworld\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'perc'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_downloads\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtotal_downloads\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'grouping'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperc\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'other'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworld\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcountry_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   3079\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3080\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3081\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3083\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'total_downloads'"
     ]
    }
   ],
   "source": [
    "world = pd.DataFrame.from_csv(world_csv)\n",
    "world['perc'] = world.total_downloads / world.total_downloads.sum() * 100\n",
    "world.reset_index(inplace=True)\n",
    "world['grouping'] = np.where(world.perc <= 2, 'other', world.country_name)\n",
    "\n",
    "world.groupby('grouping').sum().reset_index().iplot(kind='pie', labels='grouping', values='total_downloads', \n",
    "                                                      filename='TBP_plots/world-pie',\n",
    "                                                      title='Country Download Breakdown'\n",
    "                                                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scripts.geo_dicts import states as states_dict\n",
    "from scripts.geo_dicts import countries as countries_dict\n",
    "states_dict = dict((k,v) for v, k in states_dict.items())\n",
    "countries_dict = dict((k,v) for v, k in countries_dict.items())\n",
    "def get_abbrev(state_text):\n",
    "    state = state_text.split(',')[0]\n",
    "    if state in states_dict.keys():\n",
    "        return states_dict[state]\n",
    "def get_state_name(state_text):\n",
    "    return state_text.split(',')[0]\n",
    "def get_abbrev_country(name):\n",
    "    if name in countries_dict.keys():\n",
    "        return countries_dict[name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pycountry\n",
    "\n",
    "countries = {}\n",
    "for country in pycountry.countries:\n",
    "    countries[country.name] = country.alpha_3\n",
    "countries['United States'] = 'None'\n",
    "codes = [countries.get(country, 'None') for country in world.region]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world[world['region'] == \"United States\"].index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>requests</th>\n",
       "      <th>perc</th>\n",
       "      <th>grouping</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anonymous Proxy</td>\n",
       "      <td>12</td>\n",
       "      <td>0.039556</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>83</td>\n",
       "      <td>0.273593</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albania</td>\n",
       "      <td>6</td>\n",
       "      <td>0.019778</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>3</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>132</td>\n",
       "      <td>0.435112</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Austria</td>\n",
       "      <td>129</td>\n",
       "      <td>0.425223</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australia</td>\n",
       "      <td>1493</td>\n",
       "      <td>4.921383</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Aland Islands</td>\n",
       "      <td>5</td>\n",
       "      <td>0.016482</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Barbados</td>\n",
       "      <td>5</td>\n",
       "      <td>0.016482</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>17</td>\n",
       "      <td>0.056037</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>207</td>\n",
       "      <td>0.682335</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>55</td>\n",
       "      <td>0.181297</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>5</td>\n",
       "      <td>0.016482</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Benin</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Bermuda</td>\n",
       "      <td>31</td>\n",
       "      <td>0.102185</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Bolivia</td>\n",
       "      <td>11</td>\n",
       "      <td>0.036259</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>191</td>\n",
       "      <td>0.629594</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Bahamas</td>\n",
       "      <td>9</td>\n",
       "      <td>0.029667</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Botswana</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Belarus</td>\n",
       "      <td>7</td>\n",
       "      <td>0.023074</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Canada</td>\n",
       "      <td>1660</td>\n",
       "      <td>5.471866</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>196</td>\n",
       "      <td>0.646076</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Chile</td>\n",
       "      <td>26</td>\n",
       "      <td>0.085704</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>China</td>\n",
       "      <td>93</td>\n",
       "      <td>0.306556</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>34</td>\n",
       "      <td>0.112074</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>104</td>\n",
       "      <td>0.342816</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Cuba</td>\n",
       "      <td>6</td>\n",
       "      <td>0.019778</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Philippines</td>\n",
       "      <td>89</td>\n",
       "      <td>0.293371</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>9</td>\n",
       "      <td>0.029667</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Poland</td>\n",
       "      <td>124</td>\n",
       "      <td>0.408742</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>7</td>\n",
       "      <td>0.023074</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>56</td>\n",
       "      <td>0.184593</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Paraguay</td>\n",
       "      <td>3</td>\n",
       "      <td>0.009889</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Qatar</td>\n",
       "      <td>17</td>\n",
       "      <td>0.056037</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Romania</td>\n",
       "      <td>59</td>\n",
       "      <td>0.194482</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Serbia</td>\n",
       "      <td>15</td>\n",
       "      <td>0.049445</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Russian Federation</td>\n",
       "      <td>95</td>\n",
       "      <td>0.313149</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>40</td>\n",
       "      <td>0.131852</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>314</td>\n",
       "      <td>1.035040</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>130</td>\n",
       "      <td>0.428520</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>Slovenia</td>\n",
       "      <td>63</td>\n",
       "      <td>0.207667</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>Slovakia</td>\n",
       "      <td>34</td>\n",
       "      <td>0.112074</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>Swaziland</td>\n",
       "      <td>4</td>\n",
       "      <td>0.013185</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Togo</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>Thailand</td>\n",
       "      <td>181</td>\n",
       "      <td>0.596631</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Tunisia</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Turkey</td>\n",
       "      <td>95</td>\n",
       "      <td>0.313149</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Taiwan</td>\n",
       "      <td>66</td>\n",
       "      <td>0.217556</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Ukraine</td>\n",
       "      <td>48</td>\n",
       "      <td>0.158223</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Uganda</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>United States</td>\n",
       "      <td>0</td>\n",
       "      <td>58.489633</td>\n",
       "      <td>United States</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>10</td>\n",
       "      <td>0.032963</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>2</td>\n",
       "      <td>0.006593</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>7</td>\n",
       "      <td>0.023074</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Vietnam</td>\n",
       "      <td>82</td>\n",
       "      <td>0.270297</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>231</td>\n",
       "      <td>0.761446</td>\n",
       "      <td>other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     region  requests       perc       grouping\n",
       "0           Anonymous Proxy        12   0.039556          other\n",
       "1      United Arab Emirates        83   0.273593          other\n",
       "2               Afghanistan         1   0.003296          other\n",
       "3       Antigua and Barbuda         1   0.003296          other\n",
       "4                   Albania         6   0.019778          other\n",
       "5                   Armenia         3   0.009889          other\n",
       "6                 Argentina       132   0.435112          other\n",
       "7                   Austria       129   0.425223          other\n",
       "8                 Australia      1493   4.921383      Australia\n",
       "9             Aland Islands         5   0.016482          other\n",
       "10   Bosnia and Herzegovina         1   0.003296          other\n",
       "11                 Barbados         5   0.016482          other\n",
       "12               Bangladesh        17   0.056037          other\n",
       "13                  Belgium       207   0.682335          other\n",
       "14                 Bulgaria        55   0.181297          other\n",
       "15                  Bahrain         5   0.016482          other\n",
       "16                    Benin         2   0.006593          other\n",
       "17                  Bermuda        31   0.102185          other\n",
       "18                  Bolivia        11   0.036259          other\n",
       "19                   Brazil       191   0.629594          other\n",
       "20                  Bahamas         9   0.029667          other\n",
       "21                 Botswana         1   0.003296          other\n",
       "22                  Belarus         7   0.023074          other\n",
       "23                   Canada      1660   5.471866         Canada\n",
       "24              Switzerland       196   0.646076          other\n",
       "25                    Chile        26   0.085704          other\n",
       "26                    China        93   0.306556          other\n",
       "27                 Colombia        34   0.112074          other\n",
       "28               Costa Rica       104   0.342816          other\n",
       "29                     Cuba         6   0.019778          other\n",
       "..                      ...       ...        ...            ...\n",
       "97              Philippines        89   0.293371          other\n",
       "98                 Pakistan         9   0.029667          other\n",
       "99                   Poland       124   0.408742          other\n",
       "100             Puerto Rico         7   0.023074          other\n",
       "101                Portugal        56   0.184593          other\n",
       "102                Paraguay         3   0.009889          other\n",
       "103                   Qatar        17   0.056037          other\n",
       "104                 Romania        59   0.194482          other\n",
       "105                  Serbia        15   0.049445          other\n",
       "106      Russian Federation        95   0.313149          other\n",
       "107                  Rwanda         2   0.006593          other\n",
       "108            Saudi Arabia        40   0.131852          other\n",
       "109                  Sweden       314   1.035040          other\n",
       "110               Singapore       130   0.428520          other\n",
       "111                Slovenia        63   0.207667          other\n",
       "112                Slovakia        34   0.112074          other\n",
       "113               Swaziland         4   0.013185          other\n",
       "114                    Togo         2   0.006593          other\n",
       "115                Thailand       181   0.596631          other\n",
       "116                 Tunisia         1   0.003296          other\n",
       "117                  Turkey        95   0.313149          other\n",
       "118                  Taiwan        66   0.217556          other\n",
       "119                 Ukraine        48   0.158223          other\n",
       "120                  Uganda         1   0.003296          other\n",
       "121           United States         0  58.489633  United States\n",
       "122                 Uruguay        10   0.032963          other\n",
       "123              Uzbekistan         2   0.006593          other\n",
       "124               Venezuela         7   0.023074          other\n",
       "125                 Vietnam        82   0.270297          other\n",
       "126            South Africa       231   0.761446          other\n",
       "\n",
       "[127 rows x 4 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world.set_value(col='requests', index=world[world['region'] == \"United States\"].index[0], value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~CoreyPetty/132.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scl = [[0.0, '#f1f1f1'],[0.2, '#efe7de'],[0.4, '#ecceaf'],\\\n",
    "            [0.6, '#ecb983'],[0.8, '#eca559'],[1.0, '#ef9534']]\n",
    "\n",
    "us_data = go.Choropleth(\n",
    "#     colorscale = scl,\n",
    "    name = 'US States',\n",
    "    autocolorscale = True,\n",
    "    locations = states.region.apply(get_abbrev),\n",
    "    z = states.requests,\n",
    "    locationmode = 'USA-states',\n",
    "    text = states.region.apply(get_state_name),\n",
    "    marker = dict(\n",
    "        line = dict (\n",
    "            width = 0.5\n",
    "        ) ),\n",
    "    colorbar = dict(\n",
    "        title = \"USA\")\n",
    ")\n",
    "world_data = go.Choropleth(\n",
    "    colorscale = scl,\n",
    "    name = 'World',\n",
    "    autocolorscale = True,\n",
    "    locations = codes,\n",
    "    z = world.requests.astype('float'),\n",
    "    text = world.region,\n",
    "    locationmode = 'countries',\n",
    "    marker = dict(\n",
    "        line = dict (\n",
    "            width = 1\n",
    "        ) ),\n",
    "    colorbar = dict(\n",
    "        title = \"World\",\n",
    "        x = 0.9,\n",
    "    )\n",
    ")\n",
    "data = [us_data, world_data]\n",
    "layout = dict(\n",
    "    title = 'Downloads by Location' + update_string,\n",
    "    geo = dict(\n",
    "        scope='world',\n",
    "        projection=dict( type='orthographic', ),\n",
    "        showlakes = True,\n",
    "        showocean = True,\n",
    "        lakecolor = 'rgba(0, 204, 255, 1)',\n",
    "        oceancolor = 'rgba(0, 204, 255, 0.5)',\n",
    "        bgcolor = 'rgba(250,250,250,0)',\n",
    "    ),\n",
    "    \n",
    "    paper_bgcolor='rgba(250,250,250,0)',\n",
    ")\n",
    "fig=dict(data=data, layout=layout)\n",
    "iplot(fig, filename='TBP_plots/geo_choropleth')"
   ]
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