{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of the clusters\n",
    "\n",
    "So the time is here to do some analysis.   \n",
    "For fun of it let see what is going on in Pittsburg, PA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "#general\n",
    "import numpy as np\n",
    "import scipy\n",
    "from matplotlib import pyplot as plt\n",
    "%pylab inline\n",
    "import pandas as pd\n",
    "import MySQLdb\n",
    "import os\n",
    "import sys\n",
    "sys.setrecursionlimit(3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user = 'root'\n",
    "passwd = 'Moj_0q5_0e48e4'\n",
    "dbname = 'IP_db'\n",
    "host = 'localhost'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data loaded\n"
     ]
    }
   ],
   "source": [
    "con=MySQLdb.connect(user=user, passwd=passwd, db=dbname, host=host)\n",
    "df = pd.read_sql(\"SELECT * FROM business WHERE state ='PA' \", con)\n",
    "print \"data loaded\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>X</th>\n",
       "      <th>categories</th>\n",
       "      <th>city</th>\n",
       "      <th>full_address</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>name</th>\n",
       "      <th>open</th>\n",
       "      <th>review_count</th>\n",
       "      <th>stars</th>\n",
       "      <th>state</th>\n",
       "      <th>type</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>cluster</th>\n",
       "      <th>E_div</th>\n",
       "      <th>EI</th>\n",
       "      <th>LQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>139465</td>\n",
       "      <td>139466</td>\n",
       "      <td>2</td>\n",
       "      <td>Nightlife</td>\n",
       "      <td>Dravosburg</td>\n",
       "      <td>Dravosburg, PA 15034</td>\n",
       "      <td>40.350519</td>\n",
       "      <td>-79.88693</td>\n",
       "      <td>Clancy's Pub</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>business</td>\n",
       "      <td>15034</td>\n",
       "      <td>0</td>\n",
       "      <td>0.047206</td>\n",
       "      <td>3006</td>\n",
       "      <td>0.028189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>139466</td>\n",
       "      <td>139467</td>\n",
       "      <td>3</td>\n",
       "      <td>Active Life</td>\n",
       "      <td>Bethel Park</td>\n",
       "      <td>Bethel Park, PA 15234</td>\n",
       "      <td>40.356896</td>\n",
       "      <td>-80.01591</td>\n",
       "      <td>Cool Springs Golf Center</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>business</td>\n",
       "      <td>15234</td>\n",
       "      <td>224</td>\n",
       "      <td>0.006258</td>\n",
       "      <td>35</td>\n",
       "      <td>22.828972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>139467</td>\n",
       "      <td>139468</td>\n",
       "      <td>4</td>\n",
       "      <td>Mini Golf</td>\n",
       "      <td>Bethel Park</td>\n",
       "      <td>Bethel Park, PA 15234</td>\n",
       "      <td>40.356896</td>\n",
       "      <td>-80.01591</td>\n",
       "      <td>Cool Springs Golf Center</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>business</td>\n",
       "      <td>15234</td>\n",
       "      <td>224</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>35</td>\n",
       "      <td>2575.869048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>139468</td>\n",
       "      <td>139469</td>\n",
       "      <td>5</td>\n",
       "      <td>Golf</td>\n",
       "      <td>Bethel Park</td>\n",
       "      <td>Bethel Park, PA 15234</td>\n",
       "      <td>40.356896</td>\n",
       "      <td>-80.01591</td>\n",
       "      <td>Cool Springs Golf Center</td>\n",
       "      <td>False</td>\n",
       "      <td>5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>business</td>\n",
       "      <td>15234</td>\n",
       "      <td>224</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>35</td>\n",
       "      <td>3091.042857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>139469</td>\n",
       "      <td>139470</td>\n",
       "      <td>6</td>\n",
       "      <td>Shopping</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>Pittsburgh, PA 15241</td>\n",
       "      <td>40.357620</td>\n",
       "      <td>-80.05998</td>\n",
       "      <td>Verizon Wireless</td>\n",
       "      <td>True</td>\n",
       "      <td>3</td>\n",
       "      <td>3.5</td>\n",
       "      <td>PA</td>\n",
       "      <td>business</td>\n",
       "      <td>15241</td>\n",
       "      <td>207</td>\n",
       "      <td>0.013149</td>\n",
       "      <td>18</td>\n",
       "      <td>12.675630</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1  X   categories         city  \\\n",
       "0      139465        139466  2    Nightlife   Dravosburg   \n",
       "1      139466        139467  3  Active Life  Bethel Park   \n",
       "2      139467        139468  4    Mini Golf  Bethel Park   \n",
       "3      139468        139469  5         Golf  Bethel Park   \n",
       "4      139469        139470  6     Shopping   Pittsburgh   \n",
       "\n",
       "             full_address   latitude  longitude                      name  \\\n",
       "0    Dravosburg, PA 15034  40.350519  -79.88693              Clancy's Pub   \n",
       "1   Bethel Park, PA 15234  40.356896  -80.01591  Cool Springs Golf Center   \n",
       "2   Bethel Park, PA 15234  40.356896  -80.01591  Cool Springs Golf Center   \n",
       "3   Bethel Park, PA 15234  40.356896  -80.01591  Cool Springs Golf Center   \n",
       "4    Pittsburgh, PA 15241  40.357620  -80.05998          Verizon Wireless   \n",
       "\n",
       "    open  review_count  stars state      type zipcode  cluster     E_div  \\\n",
       "0   True             4    3.5    PA  business   15034        0  0.047206   \n",
       "1  False             5    2.5    PA  business   15234      224  0.006258   \n",
       "2  False             5    2.5    PA  business   15234      224  0.000055   \n",
       "3  False             5    2.5    PA  business   15234      224  0.000046   \n",
       "4   True             3    3.5    PA  business   15241      207  0.013149   \n",
       "\n",
       "     EI           LQ  \n",
       "0  3006     0.028189  \n",
       "1    35    22.828972  \n",
       "2    35  2575.869048  \n",
       "3    35  3091.042857  \n",
       "4    18    12.675630  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Pittsburgh_500m_2.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The low activity of some businesses in their particular cluster caused their Location Quotient to be marked as inf because of the computing roundup. <br>\n",
    "The trick is with the LQ equation. Here you can see the equation again. <br>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$LQ_{ij}=\\frac{\\frac{E_{ij}}{E_i}}{\\frac{\\sum_i E_{ij}}{\\sum_i E_i}}$$\n",
    "Where   \n",
    "$E_{ij}$ is economic activity in subarea i, department j  \n",
    "$E_i$ is total economic activity in subarea i   \n",
    "$\\sum_i E_{ij}$ is economic activity of department j in the whole area    \n",
    "$\\sum_i E_i$ is total economic activity in the whole area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So when a business has low activity, the real value of nominator is of an order of $10^{-6}$.  That is small enough number that my computer decides that the value is so close to the zero and thus can be zero; causing division to produce infinity. This creates a problem to me since, in reality, those businesses are not the most popular, they are the least popular in the cluster.  <br>\n",
    "Therefore, I decided to replace all infinity values with the minimum value of Location Quotient. Yes, this will assign a higher value of LQ to those unpopular businesses, but in clusters that have more than four business each, such low visited businesses do not even come into future calculations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "i=0\n",
    "for t in df.LQ:\n",
    "    if t == np.inf:\n",
    "        df.LQ[i]=df.LQ.min()\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us see individual cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UUlUNchFxdkTcExHLI+JdXbT5dGX/HRFxXLvt/x4R6yLizg7tJ0TEdRFxX0T8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\nERERiSkFOREREZGY+j/BfJs6cGnD6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a4d5a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lqCluster=df[df.cluster ==1]\n",
    "plt.figure(figsize=(10, 10), dpi=100)\n",
    "df_scatter = plt.scatter(lqCluster['longitude'], lqCluster['latitude'], c='b', alpha=.5, s=lqCluster['LQ']*10)\n",
    "plt.title( 'LQ in cluster', fontsize=20)\n",
    "plt.xlabel('Longitude', fontsize=18)\n",
    "plt.ylabel('Latitude', fontsize =18)\n",
    "plt.xlim(lqCluster.longitude.min()-0.002,lqCluster.longitude.max()+0.002)\n",
    "plt.ylim(lqCluster.latitude.min()-0.002,lqCluster.latitude.max()+0.002)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining the buis. enviroment\n",
    "\n",
    "\n",
    "Now is time to determine which businesses are carrying economic activity in each cluster. <br>\n",
    "I also wish to see how those categories relate to the most common category of the cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df2=pd.DataFrame(df.cluster.unique())\n",
    "df2['BusNum']=0\n",
    "df2['topBusCat']=''\n",
    "df2['topCatNum']=0\n",
    "df2['LQmax']=0\n",
    "df2['cat1']=''\n",
    "df2['cat1num']=0\n",
    "df2['cat2']=''\n",
    "df2['cat2num']=0\n",
    "df2['cat3']=''\n",
    "df2['cat3num']=0\n",
    "df2 = df2.rename(columns={0: 'cluster'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, it is time to populate this table. <br>\n",
    "So, I am picking top business as the business that has the highest LQ in the cluster. Then, for other three categories I am counting business for each type of business, sorting them in descending order and put into the table the top 3. <br>\n",
    "Of course, in my cluster classification I have clusters that have less than four business together. In that case, all remaining business are sorted accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import operator\n",
    "for c in df.cluster.unique():\n",
    "    pom = df[df.cluster == c]\n",
    "    large = pom.LQ.max()\n",
    "    topBus = pom.name.ix[pom.LQ == large].values[0]\n",
    "    topBusCat = pom.categories.ix[(pom.name == topBus) & (pom.LQ == large)].values[0]\n",
    "    df2.topBusCat.loc[df2.cluster == c] = topBusCat\n",
    "    df2.LQmax.loc[df2.cluster == c]=large\n",
    "    bNum=pom.name.unique()\n",
    "    df2.BusNum.loc[df2.cluster == c]=len(bNum)\n",
    "    topCatStart=[]\n",
    "    topCat = getTopCategories(topBusCat, topCatStart, pom)\n",
    "    topCatNum =pom.name[pom.categories == topBusCat].count()\n",
    "    df2.topCatNum.loc[df2.cluster ==c]=topCatNum\n",
    "    cat = pom.categories.unique()\n",
    "    Rcat = cat[np.argwhere(np.in1d(cat,np.intersect1d(cat,topCat))==False)]\n",
    "    if len(Rcat)>0:\n",
    "        sortedBusR = SortingBusinessCategories(Rcat,pom)\n",
    "        first = sortedBusR[0]\n",
    "        df2.cat1.loc[df2.cluster == c] = first[0]\n",
    "        df2.cat1num.loc[df2.cluster == c] = first[1].astype(int)\n",
    "        topCate = getTopCategories(first[0],topCat,pom)\n",
    "        Rcat = cat[np.argwhere(np.in1d(cat,np.intersect1d(cat,topCate))==False)]\n",
    "        if len(Rcat)>0:\n",
    "            sortedBusR = SortingBusinessCategories(Rcat,pom)\n",
    "            second = sortedBusR[0]\n",
    "            df2.cat2.loc[df2.cluster == c] = second[0]\n",
    "            df2.cat2num.loc[df2.cluster == c] = second[1].astype(int)\n",
    "            topCateg = getTopCategories(second[0],topCate,pom)\n",
    "            Rcat = cat[np.argwhere(np.in1d(cat,np.intersect1d(cat,topCateg))==False)]\n",
    "            if len(Rcat)>0:\n",
    "                sortedBusR = SortingBusinessCategories(Rcat,pom)\n",
    "                third = sortedBusR[0]\n",
    "                df2.cat3.loc[df2.cluster == c] = third[0]\n",
    "                df2.cat3num.loc[df2.cluster == c] = third[1].astype(int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And resulting table looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>BusNum</th>\n",
       "      <th>topBusCat</th>\n",
       "      <th>topCatNum</th>\n",
       "      <th>LQmax</th>\n",
       "      <th>cat1</th>\n",
       "      <th>cat1num</th>\n",
       "      <th>cat2</th>\n",
       "      <th>cat2num</th>\n",
       "      <th>cat3</th>\n",
       "      <th>cat3num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>Hot Dogs</td>\n",
       "      <td>1</td>\n",
       "      <td>19.280457</td>\n",
       "      <td>Food</td>\n",
       "      <td>6</td>\n",
       "      <td>Thai</td>\n",
       "      <td>3</td>\n",
       "      <td>Shopping</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>224</td>\n",
       "      <td>3</td>\n",
       "      <td>Golf</td>\n",
       "      <td>2</td>\n",
       "      <td>3091.042857</td>\n",
       "      <td>Restaurants</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207</td>\n",
       "      <td>1</td>\n",
       "      <td>Internet Service Providers</td>\n",
       "      <td>1</td>\n",
       "      <td>1639.189394</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>Lounges</td>\n",
       "      <td>1</td>\n",
       "      <td>57.469588</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>234</td>\n",
       "      <td>3</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>1</td>\n",
       "      <td>12.990967</td>\n",
       "      <td>Bars</td>\n",
       "      <td>1</td>\n",
       "      <td>Event Planning &amp; Services</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  BusNum                   topBusCat  topCatNum        LQmax  \\\n",
       "0        0      28                    Hot Dogs          1    19.280457   \n",
       "1      224       3                        Golf          2  3091.042857   \n",
       "2      207       1  Internet Service Providers          1  1639.189394   \n",
       "3      157       1                     Lounges          1    57.469588   \n",
       "4      234       3                     Mexican          1    12.990967   \n",
       "\n",
       "          cat1  cat1num                       cat2  cat2num      cat3  cat3num  \n",
       "0         Food        6                       Thai        3  Shopping        2  \n",
       "1  Restaurants        1                                   0                  0  \n",
       "2                     0                                   0                  0  \n",
       "3                     0                                   0                  0  \n",
       "4         Bars        1  Event Planning & Services        1                  0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In next step I'll use:\n",
    "\n",
    "\n",
    "##Kendall tau rank correlation coefficient\n",
    "\n",
    "In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau ($\\tau$) coefficient, is a statistic used to measure the association between two measured quantities. A tau test is a non-parametric hypothesis test for statistical dependence based on the tau coefficient.    \n",
    "\n",
    "It is a measure of rank correlation: the similarity of the orderings of the data when ranked by each of the quantities.   \n",
    "\n",
    "Let $(x_1, y_1), (x_2, y_2)$, …, $(x_n, y_n)$ be a set of observations of the joint random variables X and Y respectively, such that all the values of $(x_i)$ and $(y_i)$ are unique. Any pair of observations $(x_i, y_i)$ and $(x_j, y_j)$ are said to be concordant if the ranks for both elements agree: that is, if both $x_i > x_j$ and $y_i > y_j$ or if both $x_i < x_j$ and $y_i < y_j$. They are said to be discordant, if $x_i > x_j$ and $y_i < y_j$ or if $x_i < x_j$ and $y_i > y_j$. If $x_i = x_j$ or $y_i = y_j$, the pair is neither concordant nor discordant.\n",
    "\n",
    "The Kendall $\\tau$ coefficient is defined as:\n",
    "\n",
    "$$\\tau = \\frac{(\\text{number of concordant pairs}) - (\\text{number of discordant pairs})}{\\frac{1}{2} n (n-1) }$$\n",
    "\n",
    "The coefficient range is $−1 ≤ \\tau ≤ 1$.\n",
    "\n",
    "-If the agreement between the two rankings is perfect (i.e., the two rankings are the same) the coefficient has value 1.   \n",
    "-If the disagreement between the two rankings is perfect (i.e., one ranking is the reverse of the other) the coefficient has value −1.   \n",
    "-If X and Y are independent, then we would expect the coefficient to be approximately zero.   \n",
    "\n",
    "To get a decent sample, only business categories that existed in more than 4 different clusters are taken into consideration. For each of them Kendall Tau coefficient is calculated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pizza nan nan\n",
      "Diners -0.387298334621 0.275092389408\n",
      "Italian 0.0 1.0\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import kendalltau\n",
    "topCategories = df2.topBusCat.unique()\n",
    "Tbus = {}\n",
    "for rc in topCategories:\n",
    "    Tbus.update({rc: df2.cluster[df2.topBusCat == rc].count()})\n",
    "sortedCat = sorted(Tbus.items(), key=operator.itemgetter(1), reverse=True)\n",
    "cou=0.\n",
    "for s in sortedCat:\n",
    "    if s[1]>4:\n",
    "        df3=df2[df2.topBusCat == s[0]]\n",
    "        result=df3.LQmax.tolist()\n",
    "        parameter = df3.cat1.tolist()\n",
    "        cou+=1.\n",
    "        KT = kendalltau(parameter, result)\n",
    "        sida=1.-(1.-KT[1])**cou #make Sidak correction.\n",
    "        if sida<0.1:\n",
    "            print s[0], KT[0], KT[1], 'Sidak correction:', sida\n",
    "        print s[0], KT[0], KT[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not a single one has positive KT correlation. Meaning each and every top business is in disagreement with second category business in cluster. <br>\n",
    "I am speculating, maybe other business drag customers away from the top business. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used multiple comparisons, so that needs to be corrected. I used a method called:  \n",
    "\n",
    "##Sidak correction\n",
    "\n",
    "It is a simple method to control the familywise error rate that is probabilistically exact when the individual tests are independent of each other but is conservative otherwise.  \n",
    "The test is used if the test statistics are independent of each other then testing each of m hypotheses at level  $$\\alpha_{SID} = 1-(1-\\alpha)^\\frac{1}{m}$$  is Sidak's multiple testing procedure.   \n",
    "This test is more powerful than Bonferroni, but the gain is small: for  $\\alpha_{SID}  = 0.05$  and $m= 10$ and $10^{12}$, Bonferroni vs Sidak give 0.005 and 5 $10^{-14}$ vs 0.005116 and $5.129 10^{-14}$, respectively. The main merit of the correction is that it is exact probabilistically when the tests are independent of each other. Bonferroni is an easier approximate way to calculate the Sidak correction.    \n",
    "\n",
    "The Šidák correction is derived by assuming that the individual tests are independent. Let the significance threshold for each test be $\\alpha_1$; then the probability that at least one of the tests is significant under this threshold is (1 - the probability that none of them is significant). Since it is assumed that they are independent, the probability that all of them are not significant is the product of the probabilities that each of them are not significant, or $1 - (1 - \\alpha_1)^n$. Our intention is for this probability to equal \\alpha, the significance level for the entire series of tests. By solving for $\\alpha_1$, we obtain $\\alpha_1 = 1 - (1 - \\alpha)^{1/n}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validation\n",
    "\n",
    "I'll try brute force validation, calculating KT between randomly selected second category business and randomly selected values of LQ of the top business. I'll select across all clusters. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0591868855927 0.408715533095\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "category = random.sample(df2.cat1,90)\n",
    "LQrandom = random.sample(df2.LQmax,90)\n",
    "KT = kendalltau(category, LQrandom)\n",
    "print  KT[0], KT[1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So I got KT close to 0 with quite nasty *p* value. I would say there is no correlation here. With such *p* this is still random. <br>\n",
    "In conclusion, seems that Top businesses of any cluster should fare better if they are alone. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def getTopCategories(newcat,listCat,pomDF):\n",
    "    busi= pomDF.name.ix[pomDF.categories == newcat].values\n",
    "    if len(busi)>0:\n",
    "        for b in busi:\n",
    "            rcat = pom.categories.ix[pom.name == b].values\n",
    "            if len(rcat)>0:\n",
    "                for t in rcat:\n",
    "                    if t in listCat:\n",
    "                        pass\n",
    "                    else:\n",
    "                        listCat.append(t)\n",
    "    return listCat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def SortingBusinessCategories(categ,pomDF):\n",
    "    busR = {}\n",
    "    for rc in categ:\n",
    "        rcat=rc.flatten().tolist()[0]\n",
    "        busR.update({rcat: pomDF.name[pomDF.categories == rcat].count()})\n",
    "    sortedBus = sorted(busR.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedBus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getCategoryStats(pomDF, categ):\n",
    "    RcatList= arrayToList(categ)\n",
    "    pom2 = pomDF.loc[pomDF['categories'].isin(RcatList)]\n",
    "    Large = pom2.LQ.max()\n",
    "    Bus = pom2.name.ix[pom2.LQ == Large].values[0]\n",
    "    BusCat = pom2.categories.ix[(pom2.name == Bus) & (pom2.LQ == Large)].values[0]\n",
    "    CatNum = pom2.name[pom2.categories == BusCat].count()\n",
    "    answer=[]\n",
    "    answer.append(Large)\n",
    "    answer.append(BusCat)\n",
    "    answer.append(CatNum)\n",
    "    return answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def arrayToList(array):\n",
    "    i=0\n",
    "    newlist=[]\n",
    "    for r in array:\n",
    "        if type(r)==np.ndarray:\n",
    "            st=r[0]\n",
    "            newlist.append(st)\n",
    "    return newlist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Performing analysis for some other business in the cluster\n",
    "\n",
    "However, I cannot answer the question where to put stand-alone business, not with just Yelp data. To answer that question, I would need an additional data source. <br>\n",
    "The question I can answer is can some business play well together. <br>\n",
    "So, now, I'll concentrate on business that cluster around top business. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll pick experimental category and run with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yeah\n"
     ]
    }
   ],
   "source": [
    "#print df.categories.unique()\n",
    "targetCategory ='Grocery'#'Cafes'#'Used'#'Pets'#'Comic Books'#\"Bookstores\" # \"Taxis\" #\"Rugs\"#\"Coffee & Tea\"#\n",
    "if targetCategory in df.categories.unique():\n",
    "    print 'yeah'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, so there is something connected with Coffee. <br>\n",
    "Next step let us see how many clusters have this category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n"
     ]
    }
   ],
   "source": [
    "pom=df[df['categories']== targetCategory]\n",
    "pom2=pom.cluster.unique().tolist()\n",
    "#print pom2\n",
    "clustersDF = df.loc[df['cluster'].isin(pom2)]\n",
    "targetCategoryClusters=clustersDF.cluster.unique()\n",
    "print len(targetCategoryClusters)\n",
    "#print clustersDF.cluster.unique()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Here we have only 41 cluster.  \n",
    "Next step is to determine the top four businesses in each category and get LQ of targeted category for each cluster.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df3=pd.DataFrame(df.cluster.unique())\n",
    "df3['BusNum']=0\n",
    "df3['topBusCat']=''\n",
    "df3['topCatNum']=0\n",
    "df3['LQmax']=0\n",
    "df3['cat1']=''\n",
    "df3['LQ2']=0\n",
    "df3['cat1num']=0\n",
    "df3['cat2']=''\n",
    "df3['LQ3']=0\n",
    "df3['cat2num']=0\n",
    "df3['cat3']=''\n",
    "df3['LQ4']=0\n",
    "df3['cat3num']=0\n",
    "df3['targetLQ']=0\n",
    "df3 = df3.rename(columns={0: 'cluster'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Lexa/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/Lexa/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/Lexa/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:36: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th>BusNum</th>\n",
       "      <th>topBusCat</th>\n",
       "      <th>topCatNum</th>\n",
       "      <th>LQmax</th>\n",
       "      <th>cat1</th>\n",
       "      <th>LQ2</th>\n",
       "      <th>cat1num</th>\n",
       "      <th>cat2</th>\n",
       "      <th>LQ3</th>\n",
       "      <th>cat2num</th>\n",
       "      <th>cat3</th>\n",
       "      <th>LQ4</th>\n",
       "      <th>cat3num</th>\n",
       "      <th>targetLQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>224</td>\n",
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       "      <td></td>\n",
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       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>207</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cluster  BusNum topBusCat  topCatNum  LQmax cat1  LQ2  cat1num cat2  LQ3  \\\n",
       "0        0       0                    0      0         0        0         0   \n",
       "1      224       0                    0      0         0        0         0   \n",
       "2      207       0                    0      0         0        0         0   \n",
       "3      157       0                    0      0         0        0         0   \n",
       "4      234       0                    0      0         0        0         0   \n",
       "\n",
       "   cat2num cat3  LQ4  cat3num  targetLQ  \n",
       "0        0         0        0         0  \n",
       "1        0         0        0         0  \n",
       "2        0         0        0         0  \n",
       "3        0         0        0         0  \n",
       "4        0         0        0         0  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import operator\n",
    "for c in clustersDF.cluster.unique():\n",
    "    pom = clustersDF[clustersDF.cluster == c]\n",
    "    large = pom.LQ.max()\n",
    "    topBus = pom.name.ix[pom.LQ == large].values[0]\n",
    "    topBusCat = pom.categories.ix[(pom.name == topBus) & (pom.LQ == large)].values[0]\n",
    "    df3.topBusCat.loc[df3.cluster == c] = topBusCat\n",
    "    df3.LQmax.loc[df3.cluster == c]=large\n",
    "    bNum=pom.name.unique()\n",
    "    df3.BusNum.loc[df3.cluster == c]=len(bNum)\n",
    "    topCatStart=[]\n",
    "    topCat = getTopCategories(topBusCat, topCatStart, pom)\n",
    "    topCatNum =pom.name[pom.categories == topBusCat].count()\n",
    "    df3.topCatNum.loc[df3.cluster ==c]=topCatNum\n",
    "    df3.targetLQ.loc[df3.cluster ==c]=pom.LQ[(pom.categories == targetCategory) & (pom.name != topBus)].max()\n",
    "    cat = pom.categories.unique()\n",
    "    Rcat = cat[np.argwhere(np.in1d(cat,np.intersect1d(cat,topCat))==False)]\n",
    "    if len(Rcat)>0:\n",
    "        ans=getCategoryStats(pom,Rcat)\n",
    "        df3.cat1.loc[df3.cluster == c] = ans[1]\n",
    "        df3.cat1num.loc[df3.cluster == c] = ans[2]\n",
    "        df3.LQ2[df3.cluster == c] = ans[0]\n",
    "        topCate = getTopCategories(ans[1], topCat, pom)\n",
    "        Rcat1 = cat[np.argwhere(np.in1d(cat,np.intersect1d(cat,topCate))==False)]\n",
    "        if len(Rcat1)>0:\n",
    "            ans2=getCategoryStats(pom,Rcat1)\n",
    "            df3.cat2.loc[df3.cluster == c] = ans2[1]\n",
    "            df3.cat2num.loc[df3.cluster == c] = ans2[2]\n",
    "            df3.LQ3[df3.cluster == c] = ans2[0]\n",
    "            topCateg = getTopCategories(ans2[1], topCate, pom)\n",
    "            Rcat2 = cat[np.argwhere(np.in1d(cat,np.intersect1d(cat,topCateg))==False)]\n",
    "            if len(Rcat2)>0:\n",
    "                ans3=getCategoryStats(pom,Rcat2)\n",
    "                df3.cat3.loc[df3.cluster == c] = ans3[1]\n",
    "                df3.cat3num.loc[df3.cluster == c] = ans3[2]\n",
    "                df3.LQ4[df3.cluster == c] = ans3[0]\n",
    "df3.head(5)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us determine is our category in one of the 4 four here. If it is, then we can proceed with the analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yeah\n"
     ]
    }
   ],
   "source": [
    "allCatIndf3=df3.topBusCat.unique().tolist()+df3.cat1.unique().tolist()+df3.cat2.unique().tolist()+df3.cat3.unique().tolist()\n",
    "uniqCatDF3= set(allCatIndf3)\n",
    "if targetCategory in df.categories.unique():\n",
    "    print 'yeah'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The goal is to find business that preform well. When business is in one off the four most popular in the cluster, it si more probable that I will find a positive influence in that cluster. Taking all clusters with targeted business in consideration will drown the signal with the noise. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Grocery', 'Thrift Stores', 'Middle Eastern']\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "from scipy.stats import kendalltau\n",
    "par=[]\n",
    "for c in df3.topBusCat.unique():\n",
    "    if c == targetCategory:\n",
    "        par.append(df3.topBusCat[df3['topBusCat']==c].tolist()[0])\n",
    "for c in df3.cat1.unique():\n",
    "    if c == targetCategory:\n",
    "        par.append(df3.topBusCat[df3['cat1']==c].tolist()[0])\n",
    "for c in df3.cat2.unique():\n",
    "    if c == targetCategory:\n",
    "        par.append(df3.topBusCat[df3['cat2']==c].tolist()[0])\n",
    "for c in df3.cat3.unique():\n",
    "    if c == targetCategory:\n",
    "        par.append(df3.topBusCat[df3['cat3']==c].tolist()[0])\n",
    "print par"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now with these categories, we'll find clusters where there are target category and one of the listed categories and tests with Spearman correlation how they influence coffee shops. \n",
    "\n",
    "#Spearman's rank correlation coefficient\n",
    "\n",
    "It is often denoted by the Greek letter $\\rho$ (rho) or as $r_s$, is a nonparametric measure of statistical dependence between two variables. It assesses how well the relationship between two variables can be described using a monotonic function. If there are no repeated data values, a perfect Spearman correlation of +1 or −1 occurs when each of the variables is a perfect monotone function of the other.\n",
    "\n",
    "Spearman's coefficient, like any correlation calculation, is appropriate for both continuous and discrete variables, including ordinal variables. Spearman's $\\rho$ and Kendall's $\\tau$ can be formulated as special cases of a more a general correlation coefficient.  \n",
    "\n",
    "The Spearman correlation coefficient is defined as the Pearson correlation coefficient between the ranked variables.For a sample of size n, the n raw scores $X_i$, $Y_i$ are converted to ranks $x_i$, $y_i$, and $\\rho$ is computed from:\n",
    "\n",
    " $$\\rho = {1- \\frac {6 \\sum d_i^2}{n(n^2 - 1)}}$$\n",
    "where $d_i = x_i - y_i$, is the difference between ranks.\n",
    "\n",
    "And for those who do not know:\n",
    "\n",
    "##Pearson product-moment correlation coefficient\n",
    "\n",
    "The Pearson product-moment correlation coefficient (sometimes referred to as the PPMCC or PCC or Pearson's r) is a measure of the linear correlation (dependence) between two variables X and Y, giving a value between +1 and −1 inclusive, where 1 is total positive correlation, 0 is no correlation, and −1 is total negative correlation. It is widely used in the sciences as a measure of the degree of linear dependence between two variables.   \n",
    "Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a \"product moment\", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.   \n",
    "Pearson's correlation coefficient when applied to a population is commonly represented by the Greek letter $\\rho$ (rho) and may be referred to as the population correlation coefficient or the population Pearson correlation coefficient. The formula is:\n",
    "\n",
    " $$\\rho_{X,Y}= \\frac{\\operatorname{cov}(X,Y)}{\\sigma_X \\sigma_Y} $$\n",
    "where:    \n",
    " $\\operatorname{cov}$  is the covariance    \n",
    " $\\sigma_X$  is the standard deviation of  X "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "resul=pd.DataFrame(range(100))\n",
    "resul['category']=''\n",
    "resul['Spearman']=0.\n",
    "resul['P']=0.\n",
    "resul['sidak']=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Lexa/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/Lexa/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/Users/Lexa/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grocery -> 1.0 p= 0.0 Sidak correction: 0.0\n",
      "Thrift Stores -> 0.828571428571 p= 0.0415626822157 Sidak correction: 0.119577474716\n",
      "Middle Eastern -> 0.885714285714 p= 0.0188454810496 Sidak correction: 0.0554776796939\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Lexa/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:20: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "from scipy.stats import spearmanr\n",
    "i=0\n",
    "for ca in par:\n",
    "    tarClustDF=clustersDF[clustersDF['categories']==ca]\n",
    "    pomList=tarClustDF.cluster.unique().tolist()\n",
    "    clustersDF2 = clustersDF.loc[clustersDF['cluster'].isin(pomList)]\n",
    "    if len(tarClustDF)>5:\n",
    "        para=[]\n",
    "        resu=[]\n",
    "        for c in pomList:\n",
    "            para.append(clustersDF2.LQ[(clustersDF2['categories']==ca) & (clustersDF2['cluster']==c) ].max())\n",
    "            resu.append(clustersDF2.LQ[(clustersDF2['categories']==targetCategory) & (clustersDF2['cluster']==c)].max())\n",
    "        cou=len(par)\n",
    "        spear=spearmanr(para,resu)\n",
    "        sida=1.-(1.-spear[1])**cou\n",
    "        resul.category[i]=ca\n",
    "        resul.Spearman[i]=spear[0]\n",
    "        resul.P[i]=spear[1]\n",
    "        resul.sidak[i]=sida\n",
    "        i+=1\n",
    "        print ca, '->', spear[0],'p=',spear[1],'Sidak correction:', sida  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result=resul[resul['sidak'] < 0.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Validation\n",
    "\n",
    "Using the same random pick of the categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.246428571429 0.375950515974\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "category = random.sample(df.LQ,15)\n",
    "LQrandom = random.sample(df.LQ, 15)\n",
    "spear = spearmanr(category, LQrandom)\n",
    "print  spear[0], spear[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shows there is no correlation between randomly chosen categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##let's make a map\n",
    "\n",
    "First we need to sort out which Spearman coefficient is significant, larger than 0.5 and then sort which one is negative and which one is positive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "negCat=result.category[result['Spearman']<-0.5].tolist()\n",
    "posCat=result.category[result['Spearman']>0.5].tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we have to get clusters that have a positive influence business and do not have negative influence business. <br>\n",
    "In case that one of the categories is empty, it is skipped. The rest of the clusters are treated as neutral. Meaning, there,  a personal performance of a business, together with unaccounted for factors will have the greatest influence. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "allClust=df.cluster.unique()\n",
    "goodList=[]\n",
    "neutralList=[]\n",
    "if len(posCat)>0:\n",
    "    goodf= df.loc[df['categories'].isin(posCat)]\n",
    "    good=goodf.cluster.unique()#.tolist()\n",
    "    withGood =allClust[np.argwhere(np.in1d(allClust,np.intersect1d(allClust,good))==True)]\n",
    "    goodList=arrayToList(withGood)\n",
    "\n",
    "if len(negCat)>0:\n",
    "    badf= df.loc[df['categories'].isin(negCat)]\n",
    "    bad=badf.cluster.unique()#.tolist()\n",
    "    withoutBad = allClust[np.argwhere(np.in1d(allClust,np.intersect1d(allClust,bad))==False)]\n",
    "    neutralList=arrayToList(withoutBad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get coordinates for the good and neutral clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "if (len(goodList)==0) & (len(neutralList)==0):\n",
    "    print 'No good places for business. Try put it as stand alone business.'\n",
    "if len(goodList)>0:\n",
    "    coordGood = pd.DataFrame(goodList)\n",
    "    coordGood = coordGood.rename(columns={0: 'cluster'})\n",
    "    coordGood['lati']=0\n",
    "    coordGood['longi']=0\n",
    "    coordGood['ratioNbus']=0\n",
    "    coordGood['LQmean']=0\n",
    "    coordGood['scale']=0\n",
    "if len(neutralList)>1:\n",
    "    coordNeutral = pd.DataFrame(neutralList)\n",
    "    coordNeutral = coordNeutral.rename(columns={0: 'cluster'})\n",
    "    coordNeutral['lati']=0\n",
    "    coordNeutral['longi']=0\n",
    "    coordNeutral['ratioNbus']=0\n",
    "    coordNeutral['LQmean']=0\n",
    "    coordNeutral['scale']=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from __future__ import division\n",
    "if len(goodList)>0:\n",
    "    for c in goodList:\n",
    "        pom4= df[df.cluster == c]\n",
    "        NumBus=len(pom4.name.unique())\n",
    "        NumGoodBus = pom4.name[pom4.categories == cat[0]].count()\n",
    "        ratio=NumGoodBus/NumBus\n",
    "        meanLQ = pom4.LQ[pom4.categories==cat[0]].mean()\n",
    "        lati =pom4.latitude.mean()\n",
    "        longi =pom4.longitude.mean()\n",
    "        scal = ratio*meanLQ\n",
    "        coordGood.lati.loc[coordGood.cluster == c] = lati\n",
    "        coordGood.longi.loc[coordGood.cluster == c] = longi\n",
    "        coordGood.ratioNbus.loc[coordGood.cluster == c] = ratio\n",
    "        coordGood.LQmean.loc[coordGood.cluster == c] = meanLQ\n",
    "        coordGood.scale.loc[coordGood.cluster == c] = scal\n",
    "if len(neutralList)>1:\n",
    "    for c in neutralList:\n",
    "        pom4= df[df.cluster == c]\n",
    "        NumBus=len(pom4.name.unique())\n",
    "        NumGoodBus = pom4.name[pom4.categories == cat[0]].count()\n",
    "        ratio=NumGoodBus/NumBus\n",
    "        meanLQ = pom4.LQ[pom4.categories==cat[0]].mean()\n",
    "        lati =pom4.latitude.mean()\n",
    "        longi =pom4.longitude.mean()\n",
    "        scal = ratio*meanLQ\n",
    "        coordNeutral.lati.loc[coordNeutral.cluster == c] = lati\n",
    "        coordNeutral.longi.loc[coordNeutral.cluster == c] = longi\n",
    "        coordNeutral.ratioNbus.loc[coordNeutral.cluster == c] = ratio\n",
    "        coordNeutral.LQmean.loc[coordNeutral.cluster == c] = meanLQ\n",
    "        coordNeutral.scale.loc[coordNeutral.cluster == c] = scal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the result. Let us first check the sanity of our coordinates. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[83, 51, 15, 130, 3, 79, 19, 5, 107, 43, 2, 1, 25, 70, 6, 74, 44, 69, 29, 38, 123, 31, 180, 27, 67, 26, 7, 42, 8, 88, 82, 239, 24, 238] []\n"
     ]
    }
   ],
   "source": [
    "print goodList, neutralList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def intersect(a,b):\n",
    "    return list(set(a) & set(b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uYwauz8u6HPhpi/XYNLY29a0+xqcX0o4GrgGuBl7UZPpXAlcCDwJPrUtrO/0A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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aaec250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(15, 3)\n",
    "if len(goodList)>0:\n",
    "    tornTupleG = coordGood.cluster.tolist()\n",
    "    latsG      = coordGood.lati.tolist()\n",
    "    lonsG      = coordGood.longi.tolist()\n",
    "    #scalesG    = coordGood.scale.tolist()\n",
    "\n",
    "    subplot(141)\n",
    "    title(\"Distribution of  good Latitudes\");\n",
    "    hist(latsG, bins=20);\n",
    "\n",
    "    subplot(142)\n",
    "    title(\"Distribution of good Longitudes\");\n",
    "    hist(lonsG, bins=20);\n",
    "\n",
    "    \n",
    "if len(neutralList)>0:\n",
    "    tornTupleN = coordNeutral.cluster.tolist()\n",
    "    latsN      = coordNeutral.lati.tolist()\n",
    "    lonsN      = coordNeutral.longi.tolist()\n",
    "    #scalesN    = coordNeutral.scale.tolist()\n",
    "\n",
    "    subplot(141)\n",
    "    title(\"Distribution of  neutral Latitudes\");\n",
    "    hist(latsN, bins=20);\n",
    "\n",
    "    subplot(142)\n",
    "    title(\"Distribution of neutral Longitudes\");\n",
    "    hist(lonsN, bins=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us try now plot on a map using Basemaps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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W5aTcUdoiV02LHVoMrEVI5W3P/pRNLSA00o/RSPuCqFQflhNTlkQARBQAAADb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Lg+hrYHkfsMK6c4ixYwm8cCJH41i1NMSvcOkf2SGkLxbZacz0EEs1abSiqrhszsfRMOdSudQJ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\nSwscUwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c4c7f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meanlat=df.latitude.mean()\n",
    "meanlong=df.longitude.mean()\n",
    "\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# setup Lambert Conformal basemap.\n",
    "# set resolution=None to skip processing of boundary datasets.\n",
    "m = Basemap(width=100000,height=100000,projection='lcc',\n",
    "            resolution='h',lat_0=meanlat,lon_0=meanlong)\n",
    "m.etopo()\n",
    "m.drawcountries()\n",
    "m.drawcoastlines() #(linewidth=0.5)\n",
    "m.drawmapboundary()\n",
    "\n",
    "figsize(15, 15)\n",
    "if len(goodList)>0:\n",
    "    for i in range(len(tornTupleG)):\n",
    "        m.plot(lonsG[i], latsG[i], 'o', color=\"Blue\", latlon=True)\n",
    "    \n",
    "if len(neutralList)>0:\n",
    "    for i in range(len(tornTupleG)):\n",
    "        m.plot(lonsG[i], latsG[i], 'o', color=\"Blue\", latlon=True)\n",
    "        \n",
    "        \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Resolution is terrible, so let us try something else, like Folium. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from IPython.display import HTML\n",
    "import folium\n",
    "def inline_map(map):\n",
    "    \"\"\"\n",
    "    Embeds the HTML source of the map directly into the IPython notebook.\n",
    "    \n",
    "    This method will not work if the map depends on any files (json data). Also this uses\n",
    "    the HTML5 srcdoc attribute, which may not be supported in all browsers.\n",
    "    \"\"\"\n",
    "    map._build_map()\n",
    "    return HTML('<iframe srcdoc=\"{srcdoc}\" style=\"width: 100%; height: 310px; border: none\"></iframe>'.format(srcdoc=map.HTML.replace('\"', '&quot;')))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe srcdoc=\"<!DOCTYPE html>\n",
       "<head>\n",
       "   <meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; />\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.css&quot; />\n",
       "   <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.3/leaflet.js&quot;></script>\n",
       "\n",
       "   <script src=&quot;//ajax.googleapis.com/ajax/libs/jquery/1.11.1/jquery.min.js&quot;></script>\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap.min.css&quot;>\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/css/bootstrap-theme.min.css&quot;>\n",
       "   <script src=&quot;//maxcdn.bootstrapcdn.com/bootstrap/3.2.0/js/bootstrap.min.js&quot;></script>\n",
       "\n",
       "   <link href=&quot;//maxcdn.bootstrapcdn.com/font-awesome/4.1.0/css/font-awesome.min.css&quot; rel=&quot;stylesheet&quot;>\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;//rawgit.com/lvoogdt/Leaflet.awesome-markers/2.0/develop/dist/leaflet.awesome-markers.css&quot;>\n",
       "   <script src=&quot;//rawgithub.com/lvoogdt/Leaflet.awesome-markers/2.0/develop/dist/leaflet.awesome-markers.js&quot;></script>\n",
       "\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/MarkerCluster.Default.css&quot;>\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/MarkerCluster.css&quot;>\n",
       "   <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/leaflet.markercluster-src.js&quot;></script>\n",
       "   <script src=&quot;//cdnjs.cloudflare.com/ajax/libs/leaflet.markercluster/0.4.0/leaflet.markercluster.js&quot;></script>\n",
       "\n",
       "   <link rel=&quot;stylesheet&quot; href=&quot;//birdage.github.io/Leaflet.awesome-markers/dist/leaflet.awesome.rotate.css&quot;>\n",
       "\n",
       "   \n",
       "   \n",
       "   \n",
       "   \n",
       "\n",
       "   <style>\n",
       "\n",
       "      html, body {\n",
       "        width: 100%;\n",
       "        height: 100%;\n",
       "        margin: 0;\n",
       "        padding: 0;\n",
       "      }\n",
       "\n",
       "      #map {\n",
       "        position:absolute;\n",
       "        top:0;\n",
       "        bottom:0;\n",
       "        right:0;\n",
       "        left:0;\n",
       "      }\n",
       "\n",
       "   </style>\n",
       "</head>\n",
       "\n",
       "<body>\n",
       "\n",
       "   <div class=&quot;folium-map&quot; id=&quot;folium_60cd54a1688a48038ee949e78ccef8ee&quot; style=&quot;width: 600px; height: 600px&quot;></div>\n",
       "\n",
       "   <script>\n",
       "\n",
       "      \n",
       "\n",
       "      var base_tile = L.tileLayer('http://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n",
       "          maxZoom: 18,\n",
       "          minZoom: 1,\n",
       "          attribution: 'Map data (c) <a href=&quot;http://openstreetmap.org&quot;>OpenStreetMap</a> contributors'\n",
       "      });\n",
       "\n",
       "      var baseLayer = {\n",
       "        &quot;Base Layer&quot;: base_tile\n",
       "      };\n",
       "\n",
       "      /*\n",
       "      addition of the wms layers\n",
       "      */\n",
       "\n",
       "      \n",
       "\n",
       "      /*\n",
       "      addition of the tile layers\n",
       "      */\n",
       "      \n",
       "\n",
       "      /*\n",
       "      list of layers to be added\n",
       "      */\n",
       "      var layer_list = {\n",
       "      \n",
       "      };\n",
       "\n",
       "      /*\n",
       "      Bounding box.\n",
       "      */\n",
       "      var southWest = L.latLng(-90, -180),\n",
       "          northEast = L.latLng(90, 180),\n",
       "          bounds = L.latLngBounds(southWest, northEast);\n",
       "\n",
       "      /*\n",
       "      Creates the map and adds the selected layers\n",
       "      */\n",
       "      var map = L.map('folium_60cd54a1688a48038ee949e78ccef8ee', {\n",
       "                                       center:[40.4373791634, -79.9738892809],\n",
       "                                       zoom: 10,\n",
       "                                       maxBounds: bounds,\n",
       "                                       layers: [base_tile]\n",
       "                                     });\n",
       "\n",
       "      L.control.layers(baseLayer, layer_list).addTo(map);\n",
       "\n",
       "      //cluster group\n",
       "      var clusteredmarkers = L.markerClusterGroup();\n",
       "      //section for adding clustered markers\n",
       "      \n",
       "      //add the clustered markers to the group anyway\n",
       "      map.addLayer(clusteredmarkers);\n",
       "\n",
       "      \n",
       "      var marker_1_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_1 = L.marker([40.3871204583, \n",
       "\t\t\t\t\t\t\t-80.0927539125],\n",
       "\t\t\t\t\t\t\t{'icon':marker_1_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_1.bindPopup(&quot;83 Reccomended&quot;);\n",
       "      marker_1._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_1)\n",
       "      \n",
       "      var marker_2_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_2 = L.marker([40.3928041626, \n",
       "\t\t\t\t\t\t\t-80.0633062194],\n",
       "\t\t\t\t\t\t\t{'icon':marker_2_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_2.bindPopup(&quot;51 Reccomended&quot;);\n",
       "      marker_2._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_2)\n",
       "      \n",
       "      var marker_3_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_3 = L.marker([40.4090248556, \n",
       "\t\t\t\t\t\t\t-79.9124038284],\n",
       "\t\t\t\t\t\t\t{'icon':marker_3_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_3.bindPopup(&quot;15 Reccomended&quot;);\n",
       "      marker_3._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_3)\n",
       "      \n",
       "      var marker_4_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_4 = L.marker([40.3736775, \n",
       "\t\t\t\t\t\t\t-79.8737336],\n",
       "\t\t\t\t\t\t\t{'icon':marker_4_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_4.bindPopup(&quot;130 Reccomended&quot;);\n",
       "      marker_4._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_4)\n",
       "      \n",
       "      var marker_5_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_5 = L.marker([40.4282721212, \n",
       "\t\t\t\t\t\t\t-79.9772387967],\n",
       "\t\t\t\t\t\t\t{'icon':marker_5_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_5.bindPopup(&quot;3 Reccomended&quot;);\n",
       "      marker_5._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_5)\n",
       "      \n",
       "      var marker_6_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_6 = L.marker([40.4962299154, \n",
       "\t\t\t\t\t\t\t-80.0571488115],\n",
       "\t\t\t\t\t\t\t{'icon':marker_6_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_6.bindPopup(&quot;79 Reccomended&quot;);\n",
       "      marker_6._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_6)\n",
       "      \n",
       "      var marker_7_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_7 = L.marker([40.4647364939, \n",
       "\t\t\t\t\t\t\t-79.9387083646],\n",
       "\t\t\t\t\t\t\t{'icon':marker_7_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_7.bindPopup(&quot;19 Reccomended&quot;);\n",
       "      marker_7._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_7)\n",
       "      \n",
       "      var marker_8_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_8 = L.marker([40.4522074235, \n",
       "\t\t\t\t\t\t\t-79.9424112214],\n",
       "\t\t\t\t\t\t\t{'icon':marker_8_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_8.bindPopup(&quot;5 Reccomended&quot;);\n",
       "      marker_8._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_8)\n",
       "      \n",
       "      var marker_9_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_9 = L.marker([40.4757350645, \n",
       "\t\t\t\t\t\t\t-79.9200319032],\n",
       "\t\t\t\t\t\t\t{'icon':marker_9_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_9.bindPopup(&quot;107 Reccomended&quot;);\n",
       "      marker_9._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_9)\n",
       "      \n",
       "      var marker_10_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_10 = L.marker([40.4479574984, \n",
       "\t\t\t\t\t\t\t-79.8980835956],\n",
       "\t\t\t\t\t\t\t{'icon':marker_10_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_10.bindPopup(&quot;43 Reccomended&quot;);\n",
       "      marker_10._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_10)\n",
       "      \n",
       "      var marker_11_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_11 = L.marker([40.4504276506, \n",
       "\t\t\t\t\t\t\t-79.9894613301],\n",
       "\t\t\t\t\t\t\t{'icon':marker_11_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_11.bindPopup(&quot;2 Reccomended&quot;);\n",
       "      marker_11._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_11)\n",
       "      \n",
       "      var marker_12_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_12 = L.marker([40.3679966586, \n",
       "\t\t\t\t\t\t\t-79.9823008595],\n",
       "\t\t\t\t\t\t\t{'icon':marker_12_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_12.bindPopup(&quot;1 Reccomended&quot;);\n",
       "      marker_12._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_12)\n",
       "      \n",
       "      var marker_13_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_13 = L.marker([40.4009006466, \n",
       "\t\t\t\t\t\t\t-80.0428120001],\n",
       "\t\t\t\t\t\t\t{'icon':marker_13_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_13.bindPopup(&quot;25 Reccomended&quot;);\n",
       "      marker_13._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_13)\n",
       "      \n",
       "      var marker_14_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_14 = L.marker([40.4103551429, \n",
       "\t\t\t\t\t\t\t-80.0248486429],\n",
       "\t\t\t\t\t\t\t{'icon':marker_14_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_14.bindPopup(&quot;70 Reccomended&quot;);\n",
       "      marker_14._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_14)\n",
       "      \n",
       "      var marker_15_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_15 = L.marker([40.4261719543, \n",
       "\t\t\t\t\t\t\t-79.8883285378],\n",
       "\t\t\t\t\t\t\t{'icon':marker_15_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_15.bindPopup(&quot;6 Reccomended&quot;);\n",
       "      marker_15._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_15)\n",
       "      \n",
       "      var marker_16_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_16 = L.marker([40.4224571, \n",
       "\t\t\t\t\t\t\t-80.0355884],\n",
       "\t\t\t\t\t\t\t{'icon':marker_16_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_16.bindPopup(&quot;74 Reccomended&quot;);\n",
       "      marker_16._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_16)\n",
       "      \n",
       "      var marker_17_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_17 = L.marker([40.3839272077, \n",
       "\t\t\t\t\t\t\t-80.0155321],\n",
       "\t\t\t\t\t\t\t{'icon':marker_17_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_17.bindPopup(&quot;44 Reccomended&quot;);\n",
       "      marker_17._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_17)\n",
       "      \n",
       "      var marker_18_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_18 = L.marker([40.394088629, \n",
       "\t\t\t\t\t\t\t-80.0207801894],\n",
       "\t\t\t\t\t\t\t{'icon':marker_18_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_18.bindPopup(&quot;69 Reccomended&quot;);\n",
       "      marker_18._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_18)\n",
       "      \n",
       "      var marker_19_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_19 = L.marker([40.3810571264, \n",
       "\t\t\t\t\t\t\t-80.0444537021],\n",
       "\t\t\t\t\t\t\t{'icon':marker_19_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_19.bindPopup(&quot;29 Reccomended&quot;);\n",
       "      marker_19._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_19)\n",
       "      \n",
       "      var marker_20_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_20 = L.marker([40.3547422069, \n",
       "\t\t\t\t\t\t\t-80.0508082735],\n",
       "\t\t\t\t\t\t\t{'icon':marker_20_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_20.bindPopup(&quot;38 Reccomended&quot;);\n",
       "      marker_20._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_20)\n",
       "      \n",
       "      var marker_21_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_21 = L.marker([40.3688393401, \n",
       "\t\t\t\t\t\t\t-80.0347883309],\n",
       "\t\t\t\t\t\t\t{'icon':marker_21_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_21.bindPopup(&quot;123 Reccomended&quot;);\n",
       "      marker_21._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_21)\n",
       "      \n",
       "      var marker_22_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_22 = L.marker([40.3625837779, \n",
       "\t\t\t\t\t\t\t-80.0277515401],\n",
       "\t\t\t\t\t\t\t{'icon':marker_22_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_22.bindPopup(&quot;31 Reccomended&quot;);\n",
       "      marker_22._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_22)\n",
       "      \n",
       "      var marker_23_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_23 = L.marker([40.364216, \n",
       "\t\t\t\t\t\t\t-80.02247],\n",
       "\t\t\t\t\t\t\t{'icon':marker_23_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_23.bindPopup(&quot;180 Reccomended&quot;);\n",
       "      marker_23._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_23)\n",
       "      \n",
       "      var marker_24_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_24 = L.marker([40.4880242159, \n",
       "\t\t\t\t\t\t\t-79.8874119874],\n",
       "\t\t\t\t\t\t\t{'icon':marker_24_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_24.bindPopup(&quot;27 Reccomended&quot;);\n",
       "      marker_24._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_24)\n",
       "      \n",
       "      var marker_25_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_25 = L.marker([40.4220687576, \n",
       "\t\t\t\t\t\t\t-79.9284184758],\n",
       "\t\t\t\t\t\t\t{'icon':marker_25_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_25.bindPopup(&quot;67 Reccomended&quot;);\n",
       "      marker_25._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_25)\n",
       "      \n",
       "      var marker_26_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_26 = L.marker([40.4261651289, \n",
       "\t\t\t\t\t\t\t-79.9408232414],\n",
       "\t\t\t\t\t\t\t{'icon':marker_26_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_26.bindPopup(&quot;26 Reccomended&quot;);\n",
       "      marker_26._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_26)\n",
       "      \n",
       "      var marker_27_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_27 = L.marker([40.4298255956, \n",
       "\t\t\t\t\t\t\t-80.0072244621],\n",
       "\t\t\t\t\t\t\t{'icon':marker_27_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_27.bindPopup(&quot;7 Reccomended&quot;);\n",
       "      marker_27._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_27)\n",
       "      \n",
       "      var marker_28_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_28 = L.marker([40.4895055089, \n",
       "\t\t\t\t\t\t\t-79.9019234436],\n",
       "\t\t\t\t\t\t\t{'icon':marker_28_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_28.bindPopup(&quot;42 Reccomended&quot;);\n",
       "      marker_28._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_28)\n",
       "      \n",
       "      var marker_29_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_29 = L.marker([40.4352985805, \n",
       "\t\t\t\t\t\t\t-79.9222729587],\n",
       "\t\t\t\t\t\t\t{'icon':marker_29_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_29.bindPopup(&quot;8 Reccomended&quot;);\n",
       "      marker_29._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_29)\n",
       "      \n",
       "      var marker_30_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_30 = L.marker([40.4416584625, \n",
       "\t\t\t\t\t\t\t-79.9205944625],\n",
       "\t\t\t\t\t\t\t{'icon':marker_30_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_30.bindPopup(&quot;88 Reccomended&quot;);\n",
       "      marker_30._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_30)\n",
       "      \n",
       "      var marker_31_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_31 = L.marker([40.3843838334, \n",
       "\t\t\t\t\t\t\t-79.9779480031],\n",
       "\t\t\t\t\t\t\t{'icon':marker_31_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_31.bindPopup(&quot;82 Reccomended&quot;);\n",
       "      marker_31._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_31)\n",
       "      \n",
       "      var marker_32_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_32 = L.marker([40.489263, \n",
       "\t\t\t\t\t\t\t-80.043466],\n",
       "\t\t\t\t\t\t\t{'icon':marker_32_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_32.bindPopup(&quot;239 Reccomended&quot;);\n",
       "      marker_32._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_32)\n",
       "      \n",
       "      var marker_33_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_33 = L.marker([40.4051093, \n",
       "\t\t\t\t\t\t\t-79.99071],\n",
       "\t\t\t\t\t\t\t{'icon':marker_33_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_33.bindPopup(&quot;24 Reccomended&quot;);\n",
       "      marker_33._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_33)\n",
       "      \n",
       "      var marker_34_icon = L.AwesomeMarkers.icon({ icon: 'ok-sign',markerColor: 'green',prefix: 'glyphicon',extraClasses: 'fa-rotate-0'});\n",
       "      var marker_34 = L.marker([40.3685013505, \n",
       "\t\t\t\t\t\t\t-80.0280508999],\n",
       "\t\t\t\t\t\t\t{'icon':marker_34_icon}\n",
       "\t\t\t\t\t\t\t);\n",
       "      marker_34.bindPopup(&quot;238 Reccomended&quot;);\n",
       "      marker_34._popup.options.maxWidth = 300;\n",
       "      map.addLayer(marker_34)\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "      \n",
       "\n",
       "   </script>\n",
       "\n",
       "</body>\" style=\"width: 100%; height: 310px; border: none\"></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meanlat=df.latitude.mean()\n",
    "meanlong=df.longitude.mean()\n",
    "\n",
    "map = folium.Map(width=600,height=600,location=[meanlat,meanlong], zoom_start=10)\n",
    "\n",
    "if len(goodList)>0:\n",
    "    for i in range(len(tornTupleG)):\n",
    "        map.simple_marker([latsG[i], lonsG[i]], popup=str(tornTupleG[i])+' Reccomended',marker_color='green',marker_icon='ok-sign')\n",
    "\n",
    "if len(neutralList)>0:\n",
    "    for i in range(len(tornTupleG)):\n",
    "        map.simple_marker([latsN[i], lonsN[i]], popup=str(tornTupleN[i])+' Neutral',marker_color='blue',marker_icon='ok-sign')    \n",
    "        \n",
    "inline_map(map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2 Leisure Centers\n",
      "['Skin Care' 'Hair Salons' 'Beauty & Spas' 'Pubs' 'Bars' 'Nightlife'\n",
      " 'Shopping' 'Photography Stores & Services' 'German' 'Restaurants'\n",
      " 'Chinese' 'Delis' 'Hotels & Travel' 'Bed & Breakfast'\n",
      " 'Event Planning & Services' 'Hotels' 'American (Traditional)' 'Pizza'\n",
      " 'Beer' ' Wine & Spirits' 'Food' nan 'Italian' 'Chicken Wings' 'Grocery'\n",
      " 'Barbers' 'Local Services' 'Dry Cleaning & Laundry' 'American (New)'\n",
      " 'Active Life' 'Leisure Centers' 'Hot Dogs' 'Nail Salons' 'Sandwiches'\n",
      " 'Kids Activities' 'Barbeque' 'Ice Cream & Frozen Yogurt'\n",
      " 'Arts & Entertainment' 'Professional Sports Teams' 'Print Media'\n",
      " 'Mass Media' 'Auto Repair' 'Automotive' 'Coffee & Tea' 'Seafood'\n",
      " 'Caterers' 'Tapas Bars' 'Cocktail Bars' 'Lounges' 'Ethnic Food'\n",
      " 'Specialty Food' 'Churches' 'Religious Organizations' 'Car Rental'\n",
      " 'Wholesale Stores' 'Cosmetics & Beauty Supply' 'Burgers' 'Fast Food'\n",
      " 'Mexican' \"Men's Clothing\" 'Fashion' 'Greek' 'Mediterranean'\n",
      " 'Hair Removal' 'Hair Stylists' 'Art Galleries' 'Steakhouses' 'Doctors'\n",
      " 'Optometrists' 'Ophthalmologists' 'Health & Medical' 'Eyewear & Opticians'\n",
      " 'Bakeries' 'Real Estate Services' 'Home Services' 'Real Estate'\n",
      " \"Women's Clothing\" 'Office Equipment' 'Performing Arts'\n",
      " 'Venues & Event Spaces' 'Airport Shuttles' 'Limos' 'Transportation'\n",
      " 'Community Service/Non-Profit' 'Sewing & Alterations' 'Flowers & Gifts'\n",
      " 'Florists' 'Luggage' 'Sushi Bars' 'Candy Stores' 'Korean' 'Midwives'\n",
      " 'Chiropractors' 'Home & Garden' 'Furniture Stores' 'Cafes' 'Shoe Repair'\n",
      " 'Gay Bars' 'Chocolatiers & Shops' 'Bagels' 'Massage' 'Opera & Ballet'\n",
      " 'Arts & Crafts' 'Souvenir Shops' 'Cards & Stationery'\n",
      " 'Landmarks & Historical Buildings' 'Public Services & Government'\n",
      " 'Train Stations' 'Furniture Reupholstery' 'Fabric Stores' 'Cheese Shops'\n",
      " 'Meat Shops' 'Cinema' 'Veterinarians' 'Pets' 'Salad' 'Pet Services'\n",
      " 'Pet Stores' 'Pet Boarding/Pet Sitting' 'Karaoke' 'Irish' 'Vietnamese'\n",
      " 'Department Stores' 'Thai' 'Colleges & Universities' 'Education' 'Tours'\n",
      " 'Breakfast & Brunch' 'Diners' 'Convenience Stores' 'Dive Bars'\n",
      " 'Outlet Stores' 'Music Venues' 'Banks & Credit Unions'\n",
      " 'Financial Services' 'Drugstores' 'Tires' 'Desserts' 'Breweries'\n",
      " 'Electricians' 'Movers' 'Music & DVDs' 'Books' ' Mags' ' Music & Video'\n",
      " 'Comic Books' 'Bookstores' 'Hospitals' 'Social Clubs' 'Sports Clubs'\n",
      " 'Day Spas' 'Property Management' 'Apartments' 'Shoe Stores' 'Gift Shops'\n",
      " 'Funeral Services & Cemeteries' 'Stadiums & Arenas' 'Polish' 'Gluten-Free'\n",
      " 'Dance Clubs' 'Museums' 'Oil Change Stations' 'Sports Bars'\n",
      " 'Animal Shelters' 'Pet Adoption' 'Dentists' 'Zoos' 'Jewelry'\n",
      " 'Public Transportation' 'Antiques' 'Caribbean' 'Seafood Markets'\n",
      " 'Tobacco Shops' 'Pakistani' 'Indian' 'Home Decor' 'Juice Bars & Smoothies'\n",
      " 'Vegetarian' 'Adult Entertainment' 'Cuban' 'Latin American'\n",
      " 'Middle Eastern' 'Soul Food' 'Newspapers & Magazines' 'Bowling' 'Peruvian'\n",
      " 'Asian Fusion' 'Filipino' 'Art Supplies' 'Car Share Services' 'Buffets'\n",
      " 'Parks' 'Toy Stores' 'Junk Removal & Hauling' 'Television Stations'\n",
      " 'Vegan' 'Jazz & Blues' 'Appliances' 'Health Markets' 'Yoga'\n",
      " 'Fitness & Instruction' 'Turkish' 'Tex-Mex' 'Donuts' 'Tattoo' 'Gyms'\n",
      " 'Trainers' 'Accessories' 'Rafting/Kayaking' 'Bike Rentals'\n",
      " 'Interior Design' 'Shipping Centers' 'Printing Services' 'Signmaking'\n",
      " 'Professional Services' 'Costumes' 'Wigs' 'Parking'\n",
      " 'Party & Event Planning' 'Skating Rinks' 'Taxis' 'Sporting Goods'\n",
      " 'Sports Wear' 'Local Flavor' 'Southern' 'Belgian' 'Gas & Service Stations'\n",
      " 'Wine Bars' 'French' 'Street Vendors' 'Food Delivery Services'\n",
      " 'Vinyl Records' 'Farmers Market' 'Food Trucks' 'Car Wash' 'Auto Detailing'\n",
      " 'Shaved Ice' 'Wineries' 'Accountants' 'Payroll Services' 'Tax Services'\n",
      " 'Cajun/Creole' 'Kosher' 'Gastropubs' 'Festivals' 'Thrift Stores' 'Bikes'\n",
      " 'Departments of Motor Vehicles' 'Nurseries & Gardening'\n",
      " 'Tapas/Small Plates' 'Soup' 'Post Offices' 'Special Education'\n",
      " 'Art Classes' 'Pilates' 'Butcher' 'Champagne Bars' 'Truck Rental'\n",
      " 'Self Storage' 'Dog Parks' 'Television Service Providers'\n",
      " 'Internet Service Providers' 'Acupuncture' 'Art Schools'\n",
      " 'Specialty Schools' 'Police Departments' 'Food Court' 'Eyelash Service'\n",
      " 'Comedy Clubs' 'Makeup Artists' 'Videos & Video Game Rental'\n",
      " 'General Dentistry' 'Cosmetic Dentists' 'Fruits & Veggies' 'Tea Rooms'\n",
      " 'Arcades' 'African' 'Dog Walkers' 'Bridal' 'Halal' 'Do-It-Yourself Food'\n",
      " 'Photographers' 'Videographers' 'Dim Sum' 'Yelp Events' 'Pet Groomers'\n",
      " 'Comfort Food' 'Japanese' 'Uzbek' \"Men's Hair Salons\"]\n"
     ]
    }
   ],
   "source": [
    "i=2\n",
    "hpom=df[df.cluster == i]\n",
    "large = hpom.LQ.max()\n",
    "topBus = hpom.name.ix[hpom.LQ == large].values[0]\n",
    "topBusCat = hpom.categories.ix[(hpom.name == topBus) & (hpom.LQ == large)].values[0]\n",
    "bus=hpom.categories.unique()\n",
    "print i, topBusCat\n",
    "print bus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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