{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analysis of the clusters\n", "\n", "So the time is here to do some analysis. \n", "Here dataset already has detected clusters and calculated Location Quotient. " ] }, { "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": 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 ='AZ' \", con)\n", "print \"data loaded\"" ] }, { "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.
\n", "The trick is with the LQ equation. Here you can see the equation again.
\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.
\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": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/Lexa/anaconda/lib/python2.7/site-packages/pandas/core/indexing.py:118: 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", " self._setitem_with_indexer(indexer, value)\n" ] } ], "source": [ "i=0\n", "for t in df.LQ:\n", " if t == np.inf:\n", " df.LQ.loc[i]=df.LQ.min()\n", " i=i+1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us see individual cluster." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAJ1CAYAAABdKx0tAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqVEGOUmSpEYZ5CRJkhplkJMkSWrU/wcnX3KBFekCKQAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "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.
\n", "I also wish to see how those categories relate to the most common category of the cluster. " ] }, { "cell_type": "code", "execution_count": 4, "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.
\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.
\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": 8, "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": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
clusterBusNumtopBusCattopCatNumLQmaxcat1cat1numcat2cat2numcat3cat3num
038637Beer Bar11.770652Shopping1691Mexican495Pizza303
10853Fireplace Services139.935685Restaurants271Shopping129Automotive88
28281Apartments1415.353912000
39191Golf1523.512327000
418995Ski Resorts121.027324Restaurants269Beauty & Spas94Home Services80
\n", "
" ], "text/plain": [ " cluster BusNum topBusCat topCatNum LQmax cat1 \\\n", "0 3 8637 Beer Bar 1 1.770652 Shopping \n", "1 0 853 Fireplace Services 1 39.935685 Restaurants \n", "2 828 1 Apartments 1 415.353912 \n", "3 919 1 Golf 1 523.512327 \n", "4 18 995 Ski Resorts 1 21.027324 Restaurants \n", "\n", " cat1num cat2 cat2num cat3 cat3num \n", "0 1691 Mexican 495 Pizza 303 \n", "1 271 Shopping 129 Automotive 88 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 269 Beauty & Spas 94 Home Services 80 " ] }, "execution_count": 9, "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 cluster are taken into consideration. For each of them Kendall Tau coefficient is calculated. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Golf -0.621069137264 1.8017368295e-09 Sidak correction: 1.80173687081e-09\n", "Horseback Riding -0.567961834247 0.00466260929476 Sidak correction: 0.00930347866408\n", "Heating & Air Conditioning/HVAC -1.0 6.30004526164e-07 Sidak correction: 1.89001238793e-06\n", "Parks -0.654653670708 0.00183760462999 Sidak correction: 0.00733018258473\n", "Hiking -0.625054110212 0.00467137749728 Sidak correction: 0.0231396868076\n", "Gas & Service Stations -0.781407802208 0.000820490338414 Sidak correction: 0.00491285500491\n", "Landscaping -0.727606875109 0.00340534893264 Sidak correction: 0.0235952955457\n", "American (Traditional) -0.73029674334 0.00612537970762 Sidak correction: 0.0595926702738\n", "Contractors -0.73029674334 0.00612537970762 Sidak correction: 0.0653530222482\n", "Electricians -0.845154254729 0.00151349686017 Sidak correction: 0.0180115380534\n", "Middle Schools & High Schools -0.797924998575 0.00570812323015 Sidak correction: 0.0770153503827\n", "Auto Repair -1.0 0.000532005687423 Sidak correction: 0.00900570679885\n", "Pool Cleaners -0.881917103688 0.00541053508968 Sidak correction: 0.0979445405817\n", "Pet Boarding/Pet Sitting -1.0 0.00161079542706 Sidak correction: 0.0317276549235\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.
\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": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.0362012719446 0.61333901534\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.
\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": 8, "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": 9, "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": 10, "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": 11, "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.
\n", "The question I can answer is can some business play well together.
\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": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "yeah\n" ] } ], "source": [ "#print df.categories.unique()\n", "targetCategory =\"Coffee & Tea\"#\"Bookstores\" # \"Taxis\" #\"Rugs\"#\n", "if targetCategory in df.categories.unique():\n", " print 'yeah'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, so there is something connected with Coffee.
\n", "Next step let us see how many clusters have this category." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "139\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": [ "Neat, over 100 clusters. \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": 31, "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": 32, "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": [ "
\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
clusterBusNumtopBusCattopCatNumLQmaxcat1LQ2cat1numcat2LQ3cat2numcat3LQ4cat3numtargetLQ
038637Beer Bar11.770652Religious Schools1.7706521Horse Racing1.7706521Hearing Aid Providers1.77065210.062581
10853Fireplace Services139.935685Fishing39.9356851Fireworks39.9356851Chimney Sweeps32.67465210.224396
2828000.0000000.00000000.00000000.00000000.000000
3919000.0000000.00000000.00000000.00000000.000000
418995Ski Resorts121.027324Bistros21.0273241Rugs21.0273241Cooking Classes21.02732410.147817
\n", "
" ], "text/plain": [ " cluster BusNum topBusCat topCatNum LQmax \\\n", "0 3 8637 Beer Bar 1 1.770652 \n", "1 0 853 Fireplace Services 1 39.935685 \n", "2 828 0 0 0.000000 \n", "3 919 0 0 0.000000 \n", "4 18 995 Ski Resorts 1 21.027324 \n", "\n", " cat1 LQ2 cat1num cat2 LQ3 cat2num \\\n", "0 Religious Schools 1.770652 1 Horse Racing 1.770652 1 \n", "1 Fishing 39.935685 1 Fireworks 39.935685 1 \n", "2 0.000000 0 0.000000 0 \n", "3 0.000000 0 0.000000 0 \n", "4 Bistros 21.027324 1 Rugs 21.027324 1 \n", "\n", " cat3 LQ4 cat3num targetLQ \n", "0 Hearing Aid Providers 1.770652 1 0.062581 \n", "1 Chimney Sweeps 32.674652 1 0.224396 \n", "2 0.000000 0 0.000000 \n", "3 0.000000 0 0.000000 \n", "4 Cooking Classes 21.027324 1 0.147817 " ] }, "execution_count": 32, "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": 46, "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": 73, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Gas & Service Stations', 'Gastropubs']\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": 100, "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": 102, "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": [ "Gas & Service Stations -> 0.541412272587 p= 0.00010216046362 Sidak correction: 0.00020431049048\n", "Gastropubs -> 0.928571428571 p= 0.00251947240379 Sidak correction: 0.0050325970664\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": 105, "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": 106, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0857142857143 0.761334126191\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": 119, "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.
\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": 108, "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": 109, "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": 110, "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": 65, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[] []\n" ] } ], "source": [ "print goodList, neutralList" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def intersect(a,b):\n", " return list(set(a) & set(b))" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb8AAADSCAYAAADaDs5CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "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": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAANTCAYAAABLuEecAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzaOVMWhXLwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "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": 116, "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(''.format(srcdoc=map.HTML.replace('\"', '"')))\n" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 120, "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": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "210 Skate Parks\n", "['Local Services' 'Dry Cleaning & Laundry' 'Optometrists'\n", " 'Health & Medical' 'Beauty & Spas' 'Nail Salons' 'Home Services'\n", " 'Real Estate' 'Apartments' 'Food' 'Grocery' 'Shipping Centers'\n", " 'Printing Services' 'Notaries' 'Chiropractors' 'Fast Food' 'Sandwiches'\n", " 'Restaurants' 'Sushi Bars' 'Japanese' 'Donuts' 'Web Design' 'Marketing'\n", " 'Graphic Design' 'Professional Services' 'Nightlife' 'Irish' 'Bars'\n", " 'Sports Bars' 'Libraries' 'Public Services & Government' 'Landscaping'\n", " 'Veterinarians' 'Pets' 'Pizza' 'Automotive' 'Gas & Service Stations'\n", " 'Plumbing' 'Active Life' 'Skate Parks' 'Swimming Pools' 'Parks'\n", " 'Fitness & Instruction' 'Fashion' 'Shopping' 'Hair Salons'\n", " \"Women's Clothing\" 'Day Spas' 'Laser Hair Removal' 'Hair Removal'\n", " 'Medical Spas' 'Real Estate Agents' 'Juice Bars & Smoothies' 'Gyms'\n", " 'Trainers' 'Lounges']\n" ] } ], "source": [ "i=210\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": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }